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The Communicator’s Guide to Research, Analysis, and Evaluation

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This Guide is presented by the IPR Measurement Commission

The  Communicator’s Guide to Research, Analysis, and Evaluation  was created to help public relations leaders understand how they can apply data, research, and analytics to uncover insights that inform strategic decision making, improve communication performance, and deliver meaningful business contributions.

A five-step cyclical process based on the core components of communication research, analysis, and evaluation serves as the cornerstone of this report.

This Guide also underscores why research, analysis, and evaluation are critical in communication. Additionally, the Guide features examples and applications, a research and evaluation cadence reporting table, an outline of commtech tools for enterprises, and the top 10 “must-reads” on evaluation.

CONTRIBUTORS Mark Weiner (Primary Author),  Cognito Insights Marcia DiStaso, Ph.D., APR, University of Florida Pauline Draper-Watts, 20/20 Insights & Consulting Christof Ehrhart, University of Leipzig Alexis Fitzsimmons, University of Florida John Gilfeather, John Gilfeather & Associates Mohammad Hamid, Radian Partners Rob Jekielek, The Harris Poll Fraser Likely,  University of Ottawa & Fraser Likely PR/Comm Performance Management Jim Macnamara, Ph.D., University of Technology Sydney Tina McCorkindale, Ph.D., APR, Institute for Public Relations Chelsea Mirkin, Cision Insights Chris Monteiro, CM Consulting LLC Don Stacks, Ph.D., University of Miami

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In This Article Expand or collapse the "in this article" section Content Analysis

Introduction.

• The Centrality of Content Analysis to Programmatic Communication Research
• Measurement
• Sampling Traditional and Digital Media
• Sampling from Databases
• Reliability Sampling Studies
• Reliability and Validity
• Automated Textual Analysis (Computer Assisted Content Analysis)

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Content Analysis by Brendan Watson , Stephen Lacy LAST REVIEWED: 27 April 2017 LAST MODIFIED: 27 April 2017 DOI: 10.1093/obo/9780199756841-0175

Content analysis is a quantitative method that uses human coders to apply a set of valid measurement rules to reduce manifest features of content to numeric data in order to make replicable, generalizable inferences about that content. Because the method is applied to human artifacts, it has generic advantages that apply whether doing quantitative content analysis or qualitative textual or rhetorical analysis. For example, analyzing communication content is an unobtrusive research activity that is unaffected by self-report biases. However, it is critical to differentiate content analysis as a distinct, quantitative, social-scientific method using human coders from other methods of analyzing content: this is done in order to call attention to the method’s unique strengths and weaknesses. A weakness of content analysis is that assigning content to numeric categories loses some of the richness of human communication. A strength of content analysis is that it reduces complex communication phenomenon to numeric data, allowing researchers to study broader phenomenon than would be possible via methods that rely on close reading. Furthermore, probabilistic sampling allows researchers to draw inferences about a given communication phenomenon without observing all cases and processes. Reliability testing also helps ensure that results have greater precision and are replicable. Although content analysis developed out of the US scholarly community building on code breaking during the Second World War, it is now used around the world. However, most of the available texts in non-English languages are translations from texts originally written in English. The following sections provide references that give scholars, both novices and those who are experienced in using content analysis, a strong foundation in the method, especially as it applies to studying media content. The references focus on content analysis applied to theory, units of measurement, sampling, and reliability. They also suggest core texts and journals that are good outlets for content analysis scholarship. Compared to other methods based on measuring implicit attitudes (e.g., survey research), content analysis has been the subject of much less methodological research aimed at improving the method itself. So the following discussion also calls attention to those areas where more empirical research may help advance the method, providing young and experienced scholars alike an opportunity to make their own contributions to the method and improve measurement.

Berelson 1952 is the first quantitative content analysis text, and since then a handful of additional texts have been written for communication scholars. However, it was not until 2004 that a second edition appeared for any of the texts. Almost two decades after Berelson 1952 , Holsti 1969 appeared as an alternative. Currently, there are three texts in print, and two of them are in their third edition— Krippendorff 2013 ; Neuendorf 2017 ; and Riffe, et al. 2014 . Although these texts are stylistically varied, they tend to be consistent (with a few differences) in the recommendations for best practices and the standards they advocate. All of these texts provide an overview of the techniques and processes of content analysis, covering topics such as research design, protocol development, coding schemes, data analysis, as well as issues of validity and reliability. The three texts currently in print have more detail and discuss methodological issues to a greater degree than earlier text. Therefore, texts with more recent publication dates will provide more up-to-date standards on the conducting and reporting of content analysis. Krippendorff and Bock 2009 is a collection of articles, which is the only currently available content analysis reader. Most general communication research texts contain chapters about content analysis as an important data-generation technique. Although these may be worthwhile introductions and summaries of content analysis, scholars conducting a content analysis should read at least one of the more recent texts before conducting a quantitative content analysis.

Berelson, Bernard. 1952. Content analysis in communication research . New York: Free Press.

The first content analysis text. Much of 21st-century methodology is based on the theoretical foundations in this book. At the time of writing, the method was empirically underexplored to the point that one chapter title, “Technical Problems,” covered the areas of validity, reliability, sampling, and analysis.

Holsti, Ole R. 1969. Content analysis for the social sciences and humanities . Reading, MA: Addison-Wesley.

During the late 1950s and 1960s, content analysis began to be used in fields other than communication. This text aimed to serve scholars in a range of relevant social science and humanities fields by using a variety of examples. The chapters’ titles became the structure for future texts.

Krippendorff, Klaus. 2013. Content analysis: An introduction to its methodology . 3d ed. Los Angeles: SAGE.

This text contains the most detailed explication of Krippendorff’s alpha, a commonly used reliability coefficient. Alpha was first introduced in the initial edition. In addition, this text is the most mathematical of the texts.

Krippendorff, Klaus, and Mary A. Bock, eds. 2009. The content analysis reader . Thousand Oaks, CA: SAGE.

This is a collection of fifty-two published articles that cover the history of the process, discuss methodology, and provide important examples of content analysis studies that cover a number of social science fields, media (textual and visual), and approaches.

Neuendorf, Kimberly A. 2017. The content analysis guidebook . 2d ed. Thousand Oaks, CA: SAGE.

As with the other two texts currently in print, this one fully covers both the theory and methodology of content analysis and comes with a website and description of additional resources for students and content analysts.

Riffe, Daniel, Stephen Lacy, and Frederick G. Fico. 2014. Analyzing media messages: Using quantitative content analysis in research . 3d ed. New York: Routledge.

This text covers the application of content analysis to a range of media using examples from mediated communication studies. It provides the steps necessary to conduct a content analysis of textual and visual media.

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Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability

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Matthew Lombard, Jennifer Snyder-Duch, Cheryl Campanella Bracken, Content Analysis in Mass Communication: Assessment and Reporting of Intercoder Reliability, Human Communication Research , Volume 28, Issue 4, October 2002, Pages 587–604, https://doi.org/10.1111/j.1468-2958.2002.tb00826.x

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As a method specifically intended for the study of messages, content analysis is fundamental to mass communication research. Intercoder reliability, more specifically termed intercoder agreement, is a measure of the extent to which independent judges make the same coding decisions in evaluating the characteristics of messages, and is at the heart of this method. Yet there are few standard and accessible guidelines available regarding the appropriate procedures to use to assess and report intercoder reliability, or software tools to calculate it. As a result, it seems likely that there is little consistency in how this critical element of content analysis is assessed and reported in published mass communication studies. Following a review of relevant concepts, indices, and tools, a content analysis of 200 studies utilizing content analysis published in the communication literature between 1994 and 1998 is used to characterize practices in the field. The results demonstrate that mass communication researchers often fail to assess (or at least report) intercoder reliability and often rely on percent agreement, an overly liberal index. Based on the review and these results, concrete guidelines are offered regarding procedures for assessment and reporting of this important aspect of content analysis.

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• Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding).  In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis, other interesting articles.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work  and use statistical analysis to find differences over time or between candidates.

In addition, content analysis can be used to make qualitative inferences by analyzing the meaning and semantic relationship of words and concepts.

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy,   inequality or  laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analyzing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions, leading to various types of research bias and cognitive bias .

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

• The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
• The inclusion and exclusion criteria (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g. aged 30-40 ,  lawyer , parent ) or more conceptual (e.g. trustworthy , corrupt , conservative , family oriented ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti and Diction , which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

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Guide to Communication Research Methodologies: Quantitative, Qualitative and Rhetorical Research

Overview of Communication

Communication research methods, quantitative research, qualitative research, rhetorical research, mixed methodology.

Students interested in earning a graduate degree in communication should have at least some interest in understanding communication theories and/or conducting communication research. As students advance from undergraduate to graduate programs, an interesting change takes place — the student is no longer just a repository for knowledge. Rather, the student is expected to learn while also creating knowledge. This new knowledge is largely generated through the development and completion of research in communication studies. Before exploring the different methodologies used to conduct communication research, it is important to have a foundational understanding of the field of communication.

Defining communication is much harder than it sounds. Indeed, scholars have argued about the topic for years, typically differing on the following topics:

• Breadth : How many behaviors and actions should or should not be considered communication.
• Intentionality : Whether the definition includes an intention to communicate.
• Success : Whether someone was able to effectively communicate a message, or merely attempted to without it being received or understood.

However, most definitions discuss five main components, which include: sender, receiver, context/environment, medium, and message. Broadly speaking, communication research examines these components, asking questions about each of them and seeking to answer those questions.

As students seek to answer their own questions, they follow an approach similar to most other researchers. This approach proceeds in five steps: conceptualize, plan and design, implement a methodology, analyze and interpret, reconceptualize.

• Conceptualize : In the conceptualization process, students develop their area of interest and determine if their specific questions and hypotheses are worth investigating. If the research has already been completed, or there is no practical reason to research the topic, students may need to find a different research topic.
• Plan and Design : During planning and design students will select their methods of evaluation and decide how they plan to define their variables in a measurable way.
• Implement a Methodology : When implementing a methodology, students collect the data and information they require. They may, for example, have decided to conduct a survey study. This is the step when they would use their survey to collect data. If students chose to conduct a rhetorical criticism, this is when they would analyze their text.
• Analyze and Interpret : As students analyze and interpret their data or evidence, they transform the raw findings into meaningful insights. If they chose to conduct interviews, this would be the point in the process where they would evaluate the results of the interviews to find meaning as it relates to the communication phenomena of interest.
• Reconceptualize : During reconceptualization, students ask how their findings speak to a larger body of research — studies related to theirs that have already been completed and research they should execute in the future to continue answering new questions.

This final step is crucial, and speaks to an important tenet of communication research: All research contributes to a better overall understanding of communication and moves the field forward by enabling the development of new theories.

In the field of communication, there are three main research methodologies: quantitative, qualitative, and rhetorical. As communication students progress in their careers, they will likely find themselves using one of these far more often than the others.

Quantitative research seeks to establish knowledge through the use of numbers and measurement. Within the overarching area of quantitative research, there are a variety of different methodologies. The most commonly used methodologies are experiments, surveys, content analysis, and meta-analysis. To better understand these research methods, you can explore the following examples:

Experiments : Experiments are an empirical form of research that enable the researcher to study communication in a controlled environment. For example, a researcher might know that there are typical responses people use when they are interrupted during a conversation. However, it might be unknown as to how frequency of interruption provokes those different responses (e.g., do communicators use different responses when interrupted once every 10 minutes versus once per minute?). An experiment would allow a researcher to create these two environments to test a hypothesis or answer a specific research question. As you can imagine, it would be very time consuming — and probably impossible — to view this and measure it in the real world. For that reason, an experiment would be perfect for this research inquiry.

Surveys : Surveys are often used to collect information from large groups of people using scales that have been tested for validity and reliability. A researcher might be curious about how a supervisor sharing personal information with his or her subordinate affects way the subordinate perceives his or her supervisor. The researcher could create a survey where respondents answer questions about a) the information their supervisors self-disclose and b) their perceptions of their supervisors. The data collected about these two variables could offer interesting insights about this communication. As you would guess, an experiment would not work in this case because the researcher needs to assess a real relationship and they need insight into the mind of the respondent.

Content Analysis : Content analysis is used to count the number of occurrences of a phenomenon within a source of media (e.g., books, magazines, commercials, movies, etc.). For example, a researcher might be interested in finding out if people of certain races are underrepresented on television. They might explore this area of research by counting the number of times people of different races appear in prime time television and comparing that to the actual proportions in society.

Meta-Analysis : In this technique, a researcher takes a collection of quantitative studies and analyzes the data as a whole to get a better understanding of a communication phenomenon. For example, a researcher might be curious about how video games affect aggression. This researcher might find that many studies have been done on the topic, sometimes with conflicting results. In their meta-analysis, they could analyze the existing statistics as a whole to get a better understanding of the relationship between the two variables.

Qualitative research is interested in exploring subjects’ perceptions and understandings as they relate to communication. Imagine two researchers who want to understand student perceptions of the basic communication course at a university. The first researcher, a quantitative researcher, might measure absences to understand student perception. The second researcher, a qualitative researcher, might interview students to find out what they like and dislike about a course. The former is based on hard numbers, while the latter is based on human experience and perception.

Qualitative researchers employ a variety of different methodologies. Some of the most popular are interviews, focus groups, and participant observation. To better understand these research methods, you can explore the following examples:

Interviews : This typically consists of a researcher having a discussion with a participant based on questions developed by the researcher. For example, a researcher might be interested in how parents exert power over the lives of their children while the children are away at college. The researcher could spend time having conversations with college students about this topic, transcribe the conversations and then seek to find themes across the different discussions.

Focus Groups : A researcher using this method gathers a group of people with intimate knowledge of a communication phenomenon. For example, if a researcher wanted to understand the experience of couples who are childless by choice, he or she might choose to run a series of focus groups. This format is helpful because it allows participants to build on one another’s experiences, remembering information they may otherwise have forgotten. Focus groups also tend to produce useful information at a higher rate than interviews. That said, some issues are too sensitive for focus groups and lend themselves better to interviews.

Participant Observation : As the name indicates, this method involves the researcher watching participants in their natural environment. In some cases, the participants may not know they are being studied, as the researcher fully immerses his or herself as a member of the environment. To illustrate participant observation, imagine a researcher curious about how humor is used in healthcare. This researcher might immerse his or herself in a long-term care facility to observe how humor is used by healthcare workers interacting with patients.

Rhetorical research (or rhetorical criticism) is a form of textual analysis wherein the researcher systematically analyzes, interprets, and critiques the persuasive power of messages within a text. This takes on many forms, but all of them involve similar steps: selecting a text, choosing a rhetorical method, analyzing the text, and writing the criticism.

To illustrate, a researcher could be interested in how mass media portrays “good degrees” to prospective college students. To understand this communication, a rhetorical researcher could take 30 articles on the topic from the last year and write a rhetorical essay about the criteria used and the core message argued by the media.

Likewise, a researcher could be interested in how women in management roles are portrayed in television. They could select a group of popular shows and analyze that as the text. This might result in a rhetorical essay about the behaviors displayed by these women and what the text says about women in management roles.

As a final example, one might be interested in how persuasion is used by the president during the White House Correspondent’s Dinner. A researcher could select several recent presidents and write a rhetorical essay about their speeches and how they employed persuasion during their delivery.

Taking a mixed methods approach results in a research study that uses two or more techniques discussed above. Often, researchers will pair two methods together in the same study examining the same phenomenon. Other times, researchers will use qualitative methods to develop quantitative research, such as a researcher who uses a focus group to discuss the validity of a survey before it is finalized.

The benefit of mixed methods is that it offers a richer picture of a communication phenomenon by gathering data and information in multiple ways. If we explore some of the earlier examples, we can see how mixed methods might result in a better understanding of the communication being studied.

Example 1 : In surveys, we discussed a researcher interested in understanding how a supervisor sharing personal information with his or her subordinate affects the way the subordinate perceives his or her supervisor. While a survey could give us some insight into this communication, we could also add interviews with subordinates. Exploring their experiences intimately could give us a better understanding of how they navigate self-disclosure in a relationship based on power differences.

Example 2 : In content analysis, we discussed measuring representation of different races during prime time television. While we can count the appearances of members of different races and compare that to the composition of the general population, that doesn’t tell us anything about their portrayal. Adding rhetorical criticism, we could talk about how underrepresented groups are portrayed in either a positive or negative light, supporting or defying commonly held stereotypes.

Example 3 : In interviews, we saw a researcher who explored how power could be exerted by parents over their college-age children who are away at school. After determining the tactics used by parents, this interview study could have a phase two. In this phase, the researcher could develop scales to measure each tactic and then use those scales to understand how the tactics affect other communication constructs. One could argue, for example, that student anxiety would increase as a parent exerts greater power over that student. A researcher could conduct a hierarchical regression to see how each power tactic effects the levels of stress experienced by a student.

As you can see, each methodology has its own merits, and they often work well together. As students advance in their study of communication, it is worthwhile to learn various research methods. This allows them to study their interests in greater depth and breadth. Ultimately, they will be able to assemble stronger research studies and answer their questions about communication more effectively.

Note : For more information about research in the field of communication, check out our Guide to Communication Research and Scholarship .

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Content Analysis in the Research Field of Corporate Communication

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• First Online: 25 September 2022

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Content analyses in corporate communication can reveal organizational phenomena that are otherwise hard to obtain. Research themes are manifold and range from corporate social responsibility (CSR) and corporate reputation to stakeholder relations and crisis responses as well as corporate culture and employee commitment. Content analyses are able to assess concepts such as the vagueness of annual reports or the courage in speeches of chief executive officers (CEOs). Research designs employing content analysis follow qualitative, standardized manual, dictionary and machine-learning approaches, partly combined with surveys of stakeholder groups or interviews with corporate actors.

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Qualitative Content Analysis: Theoretical Background and Procedures

The Role of Corporate Communication in the Digital Age: An Era of Change for the Communication Profession

Qualitative Text Analysis: A Systematic Approach

• Organizational Communication
• Corporate Social Responsibility

1 Introduction

Corporate communication is an interdisciplinary concept that is approached from marketing, public relations (PR), organizational communication, and linguistic perspectives. In marketing, the role of corporate communication for loyal relationships with stakeholders is central. In PR, it is the managing of dialogic relations with an organization’s publics. For organizational communication, the social co-creation of the process of organizing is in focus (Mazzei 2014 ). In linguistics, business communication addresses the pragmatic dimension of language, often taking an (inter-)cultural perspective (Fuoli 2018 ). Regarding marketing and PR, corporate communication is often regarded as strategic communication (Zerfass et al. 2018 ). This contribution will largely focus on content analyses from a corporate communication perspective.

One central capacity of corporate communication is supporting to build intangible resources that reduce transaction costs for organizations and are key for an organization’s long-term competitive advantage (Barney 1991 , 2001 ). These intangible resources include concepts such as knowledge, trust, loyalty, reputation, responsibility, or identity (Cornelissen 2013 ; Fuoli 2018 ; Mazzei 2014 ). One major theme in corporate communication research is the role of corporate communication for explaining stakeholder attitudes and behavior, according to Zerfass and Viertmann’s ( 2017 ) meta study of research into corporate communication. Beyond the capacity of building intangible resources, corporate communication also enables operations, adjusts strategy, and ensures flexibility of firms (Zerfass and Viertmann 2017 ). That is, corporate communication supports strategic alignment, market positioning, innovation, or organizational change. These themes can become research topics in content analyses of corporate communication material. 

As organizations require monetary and human resources from their environment as well as seek sales markets, organizations also acquire social support, i.e., legitimacy from their environment (Palazzo and Scherer 2006 ; Suddaby et al. 2017 ). In this institutional perspective, organizations employ strategic communication to pursue their goals and to manage their legitimacy (Suchman 1995 ). Against this background, corporate social responsibility (CSR) has become a focus in corporate communication research. CSR is often conceptualized as a company’s capacity to conform to business, legal, ethical, and philanthropic standards (Carroll 1991 , 2016 ). Operating profitably (business) and obeying the law (legal) comprise rather essential requirements, while to do what is just and fair (ethical) and to be a good citizen (philanthropic) is less obligatory but desired by society (Carroll 1991 ). Research in CSR studies has focused on perception, impact and promotion; image and reputation; performance; and generally the rhetoric of organizations (Ellerup Nielsen and Thomsen 2018 ). In CSR research, content analysis is used to assess the performance (Gunawan and Abadi 2017 ) and the credibility of CSR reports (Lock and Seele 2016 ), for instance.

Content analyses have gained popularity in corporate communication as well as CSR research since the availability of computer-aided text analysis (CATA) (Duriau et al. 2007 ; Short et al. 2010 ), a label used in organizational research. Cornelissen ( 2013 ) claims that most research into corporate communication uses surveys, e.g., for stakeholder evaluations of company reputation, while content analyses are often part in case studies alongside interviews and observations. Yet, content analyses are indispensable to identify “who says what,” in the terms of Lasswell ( 1948 ), and thus represent a classical method for analyzing corporate documents. Content analysis of annual reports “can be of real usefulness for understanding some issues of corporate strategy,” argues Bowman ( 1984 , p. 70), because it can not only measure complex organizational constructs, including corporate culture, risk affinity, or CSR. Content analysis can also “show relationships [between constructs] which are otherwise difficult to obtain and which can be tested for validity” (ibid., p. 61). Similarly, Duriau et al. ( 2007 , p. 6) emphasize that content analyses can reliably access “values, intentions, attitudes, and cognitions” that have manifested in corporate messages. Hence, content analyses are used in organizational studies to reveal attitudinal or cognitive aspects of organizations and organizing. In comparison to responsive methods such as surveys or interviews, Harris ( 2001 , p. 195) suggests that content analyses serve as a “reality check” of managerial decision making.

The remainder of the article aims at providing an overview about the diversity of research themes and designs of content analyses in corporate communication.

2 Frequent Research Themes

To describe frequent research themes, I refer to two meta studies: Duriau et al. ( 2007 ) and Zerfass and Viertmann ( 2017 ). Duriau et al. ( 2007 ) conduct a meta study of content analyses in the field of organization studies between 1980 and 2005. Their analysis suggests that research into corporate communication differs regarding studies of corporate communication and studies using corporate communication material for researching corporate phenomena. They identify two major research themes that most frequently apply content analyses: (a) strategic management issues that address topics such as impression management, corporate reputation, or strategy reformulation and (b) the issue of managerial cognition involving corporate values and culture, sensemaking, blame attribution, or managerial attention in crises (Duriau et al. 2007 ).

Zerfass and Viertmann ( 2017 , p. 69) analyze publications from the fields of “corporate communication, organizational communication, public relations, marketing, and strategic management,” independent from the application of content analyses. They identify twelve central constructs of tangible and intangible outcomes of corporate communication that are studied, i.e., relationships, trust, legitimacy, thought leadership, innovation potential, crisis resilience, reputation, brands, corporate culture, publicity, customer preferences, and employee commitment (Zerfass and Viertmann 2017 ). Beyond surveying stakeholder groups, for example for assessing corporate reputation (Wartick 2016 ), some of these concepts can on principle be measured by analyzing the content of corporate communication material and user-generated content.

The following examples provide an impression of the variety of themes studied in corporate communication and may serve as starting point for further investigation into a specific area of interest. Interactivity dimensions of corporate websites are analyzed using content analysis (Ha and James 1998 ), addressing stakeholder relationships. In crisis communication research, content analysis is conducted to understand which  crisis response strategies are used in corporate messages and how news coverage as well as users respond, for instance on social media (Holladay 2010 ). Combining document analysis and interviews, Huang-Horowitz and Evans ( 2020 ) reveal how small companies communicate their organizational identity to gain legitimacy. Regarding leadership, content analyses can reveal the degree of courage expressed by executives and related news coverage (Harris 2001 ). Li et al. ( 2018 ) regard innovation potential as one dimension of corporate culture, along with integrity, quality, respect, and teamwork. They measure corporate culture using a machine learning (ML) approach on a corpus of earnings calls, in which public companies discuss their financial results addressing the investor and analyst communities. The sentiment of user-generated online product reviews indicates customer preferences (Jo and Oh 2011 ; Tirunillai and Tellis 2014 ). Concerning employee commitment, Bujaki et al. ( 2018 ) reveal impression management strategies of accounting firms addressing diversity‐sensitive employees. Regarding internal communication, Darics (2020) analyses instant message conversations between employees and shows that instant messages intend to achieve complex communication goals, including fostering informality and building team identity.

The themes of CSR messages are analysed for various industries in CSR reports (Landrum and Ohsowski  2018 ) or on social media platforms like Instagram (Kwon and Lee 2021 ). Moreover, Lock and Seele ( 2016 ) quantitatively analyze the credibility of CSR reports by measuring truth of statements, accuracy, completeness, standards used, and sincerity, and reveal that CSR reports can be considered as mediocrely credible. Hoffmann et al. ( 2018 ) discursively analyze Facebook’s CEO speech revealing it surrounds self - identity, constructs user identity and the relationship between Facebook and its users. As a final example, VanDyke and Tedesco ( 2016 ) analyze responsibility frames in green advertising over time, indicating that a habitat protection issue changes into energy efficiency.

3 Frequent Research Designs

Regarding research designs, corporate communication can represent the independent, dependent, or mediating variable. Regarding the independent variable, corporate communication messages represent an antecedent to explain attitudinal outcomes (trust and reputation in customers) as well as operational outcomes (e.g., economic results, stock market performance, speed of news product releases) (see Duriau et al. 2007 ; Zerfass and Viertmann 2017 ). Here, content analyses are used to evaluate corporate content material—but also content generated by customers or followers. Moreover, research into corporate communication addresses the relation between symbolic communication, which can be assessed with content analyses, and substantive corporate action (Seiffert et al. 2011 ), often comparing the content of CSR communication and action (Jong and van der Meer 2017 ; Perez-Batres et al. 2012 ; Schons and Steinmeier 2016 ; Wickert et al. 2016 ). Concerning the dependent variable, corporate communication content is regarded as a manifestation of internal processes such as managerial sensemaking or cognition. In this case, content analysis is used to deduce on such internal processes (see Duriau et al. 2007 ). —One central limitation for the deduction is intentional bias in corporate communication for specific stakeholder groups. For instance, annual reports include a bias toward the positive (Rutherford 2005 ) or dramatize ideas (Jameson 2000 ). Methodological responses to this challenge include using multiple data sources and richer databases, triangulation, and sophisticated methods that provide more accurate measurements (Duriau et al. 2007 ).—Corporate communication messages can also be conceptualized as a mediating variable between internal processes and organizational outcomes. For instance, Porcu et al. ( 2016 ) regard internal corporate communication as a mediator between corporate culture and operational outcomes, however, use a survey for data collection.

Methodologically, research designs employing content analysis follow qualitative, standardized manual, quantitative-computational approaches, or combinations thereof. Which design to follow depends on the availability of data sources for a research question at hand and the production contexts of the specific material to be analyzed (Steenkamp and Northcott 2007 ). For instance, studies into corporate communication addressing journalists as stakeholder group often compare corporate messages and news coverage using quantitative content analysis (e.g., Jonkman et al. 2020 ; Lischka et al. 2017 ; Nijkrake et al. 2015 ). Qualitative approaches aim at revealing organizational narratives, for instance regarding corporate responsibility (Haack et al. 2012 ), strategy change (Lischka 2019c ), and legitimacy (van Leeuwen and Wodak 1999 ).

According to Duriau et al. ( 2007 ), primary data sources of corporate communication content analyses are annual reports, followed by proxy statements, trade magazines, publicly available corporate documents, mission statements, internal company documents, and notes from interviews or answers to open-ended survey questions. Moreover, news coverage (e.g., Seiffert et al. 2011 ; Strycharz et al. 2017 ), CSR reports (e.g., Lock and Seele 2016 ), CEO speech (e.g., Beelitz and Merkl-Davies 2012 ; Hoffmann et al. 2018 ), social media communication and engagement (e.g., Abitbol and Lee 2017 ; Choy and Wu 2018 ; Kim et al. 2014 ; Macnamara and Zerfass 2012 ), corporate blogs (e.g., Catalano 2007 ; Colton and Poploski 2018 ), advertising (e.g., VanDyke and Tedesco 2016 ), and text messages (Darics 2020 ) represent data sources. Researchers from linguistics often build a corpus based on one corporate material genre from multiple organizations, for instance, a corpus of annual reports (Fuoli 2018 ; Rutherford 2005 ) or CRS reports (Yu and Bondi 2017 ). Researchers from other disciplines may also create corpora but without labelling their approach as a corpus approach (e.g., Seiffert et al. 2011 ).

For computational analyses, researchers have developed dictionaries, for instance, a finance- and accounting-specific dictionary in English (Loughran and McDonald 2011 , 2015 ) and German (Bannier et al. 2019 ), for environmental sustainability (Deng et al. 2017 ), and for vagueness in corporate communication (Guo et al. 2017 ). Also more general dictionaries such as Linguistic Inquiry and Word Count (LIWC) are applied as in Merkl‐Davies et al. ( 2011 ) and Lee et al. ( 2020 ).

There is a variety of methodological trends regarding content analyses of corporate communication. Research combines content analysis with other data collection methods, applies machine learning (ML) and (deep) natural language processing (NLP) techniques, and extends data capacity, contexts, and materiality. The following list provides recent exemplary studies for trends in computational methods, design, sampling, and material, with methods of computational content analysis representing a comparatively large evolving field.

ML and (deep) NLP

NLP is a computational method for analyzing naturally occurring human language by building statistical models of language, which has been applied in linguistics (Manning and Schütze 1999 ). With ML, algorithms are developed that should improve through training data and can be combined with human coding in supervised or semi-supervised settings. In deep ML, artificial neural networks are used for training (Deng and Liu 2018 ). Deep NLP can therefore use “both sentence structure and context of the text to provide a deeper understanding of the language” (Lee et al. 2020 ).

Combining human coding and ML (Park et al. 2019 ),

Applying semi-supervised ML (van Zoonen and van der Meer 2016 )

Applying topic modeling, which is unsupervised as it uses statistical associations of words in a text to generate topics without dictionaries or interpretive rules (Hannigan et al. 2019 ; Jaworksa and Nanda 2016 ; Kobayashi et al. 2018 ; Schmiedel et al. 2018 )

Specific dictionary development for corporate communication issues (Deng et al. 2017 ; Guo et al. 2017 )

Comparing deep NLP (IBM Watson Explorer) with dictionary approaches and human coding to detect the level of charisma in leadership speeches (Lee et al. 2020 )

Triangulation: Combining content analyses with surveys (Dudenhausen et al. 2020 ), combining qualitative and quantitative approaches (Jaworksa and Nanda 2016 )

Comparative designs: Comparative approaches within Western countries (Köhler and Zerfass 2019 ; Yu and Bondi 2019 ; Yuan 2019 ), and beyond, such as in Asia (Bondi and Yu 2015 ) and in Americana (Loureiro and Gomes 2016 )

Non-Western context: CSR communication in India (Jain and Moya 2016 ), in restrictive systems such as China (Zhang et al. 2017 ) and Russia (Sorokin et al. 2019 )

Visuality: Analyzing visual rhetoric in corporate reports (Goransson and Fagerholm 2018 ; Greenwood et al. 2018 ; Ruggiero 2020 ) and multimodal (textual and visual) content analysis, for instance to account for the multimodality of corporate websites (Höllerer et al. 2019 )

5 Research Desiderata

The trend on employing large collections of texts combined with ML, such as applying topic modelling algorithms, requires advances in methodological standards, for instance regarding procedures such as structural topic models (Roberts et al. 2019 ), validity comparisons across content analysis methods (van Atteveldt et al. 2021 ), and quality criteria for automated content analyses (Laugwitz 2021 ). With the ability to analyze extensive data sets, complex research designs may become better attainable. For instance, the various agents and processes that constitute organizational legitimacy as proposed in Bitektine and Haack ( 2015 ) may be tackled. In doing so, qualitative approaches, for instance to understand the dynamics of corporate narratives as in Jaworksa and Nanda ( 2016 ), can be fruitfully combined with computational analyses.

Regarding research objects, Zerfass and Viertmann ( 2017 ) suggest that the capacity of corporate communication should be assessed across various types and sizes of organizations (e.g., start-ups, small-and-medium enterprises, large corporations, non-profit organizations), across stakeholder groups (e.g., customers, employees, investors, and journalists), in various situational contexts (e.g., product launches, crises, and mergers), and industries. While organizations in any industry can become objects of analysis for corporate communication research, scholars in the field of communication and journalism studies may be especially interested in communication of organizations involved in public communication such a media organizations (Bachmann 2016 ; Lischka 2019b ; Siegert and Hangartner 2017 ) or social media platforms (Gillespie 2010 ; Iosifidis and Nicoli 2020 ; Lischka 2019a ). Against the background of globally acting organizations having the power, and sometimes the obligation, to assume political roles on a global scale (Scherer and Palazzo 2011 ), future research should focus on such global corporations to understand how they communicate their political stances and roles. There is additional need for comparative studies and, in particular, analyses of non-Western countries.

Moreover, the interaction of communication by multiple organizations can deliver relevant insights. Suchman ( 1995 , p. 592) argues, orchestrated communication by a group of companies, such as social media platforms and search engines, can become a powerful “collective evangelism” when occupying an issue. From an institutional perspective, analyzing potentially orchestrated communication of globally acting organizations can show how new institutions in societies are negotiated.

Lastly, there has been a normative turn in management research towards the “grand” challenges of global societies, including poverty, good economic growth, health disparities, climate change, and sustainability (United Nations n.d. ). Against the background that organizations should build value for societies, management researchers wish to contribute to how organizations can help to address and solve these grand problems (George et al. 2016 ). Corporate communication researchers, especially those focusing on CSR, are uniquely positioned to addressing grand challenges from a corporate communication perspective. Content analyses using material from companies as well as produced by various stakeholder groups can reveal links between communication and corporate goals as well as societal challenges on a broader scale.

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Lischka, J.A. (2023). Content Analysis in the Research Field of Corporate Communication. In: Oehmer-Pedrazzi, F., Kessler, S.H., Humprecht, E., Sommer, K., Castro, L. (eds) Standardisierte Inhaltsanalyse in der Kommunikationswissenschaft – Standardized Content Analysis in Communication Research. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-36179-2_30

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This exceptional collection--a compilation of meta-analyses related to issues in interpersonal communication--provides an expansive review of existing interpersonal communication research. Incorporating a wide variety of topics related to interpersonal communication, including couples and safe sex, parent-child communication, argumentativeness, and self-disclosure, the contributions in this volume also examine such basic issues as reciprocity, constructivism, social support in interpersonal communication, as well as gender, conflict, and marital and organizational issues. With contributions organized into five sections, this volume: *sets the stage for independent meta-analyses; *provides an overview of individual characteristics in interpersonal communication and the meta-analyses reflecting this theme; *explores the dyadic and interactional approaches to interpersonal communication; and *examines the impact of the meta-analyses on the understanding of interpersonal communication. As a resource for interpersonal communication researchers at all levels, this volume establishes a solid foundation from which to launch the next generation of study and research.

TABLE OF CONTENTS

Part | 2  pages, part i: interpersonal communication research and meta-analysis, chapter 1 | 10  pages, meta-analysis and interpersonal communication: function and applicability, chapter 2 | 30  pages, meta-analysis in context: a proto-theory of interpersonal communication, part ii: individual issues in interpersonal communication, chapter 3 | 14  pages, an overview of individual processes in interpersonal communication, chapter 4 | 14  pages, sex differences in self-esteem: a meta-analytic assessment, chapter 5 | 16  pages, comparing the production of power in language on the basis of sex, chapter 6 | 20  pages, social skills and communication, part iii: dyadic issues in interpersonal communication, chapter 7 | 14  pages, an overview of dyadic processes in interpersonal communication, chapter 8 | 20  pages, sexual orientation of the parent: the impact on the child, chapter 9 | 24  pages, similarity and attraction, chapter 10 | 18  pages, self-disclosure research: knowledge through meta-analysis, chapter 11 | 24  pages, the effects of situation on the use or suppression of possible compliance-gaining appeals, part iv: interactional issues in interpersonal communication, chapter 12 | 14  pages, an overview of interactional processes in interpersonal communication, chapter 13 | 20  pages, a synthesis and extension of constructivist comforting research, chapter 14 | 16  pages, divorce: how spouses seek social support, chapter 15 | 18  pages, couples negotiating safer sex behaviors: a meta-analysis of the impact of conversation and gender, chapter 16 | 34  pages, argumentativeness and its effect on verbal aggressiveness: a meta-analytic review, chapter 17 | 30  pages, sexual coercion and resistance, chapter 18 | 24  pages, a meta-analytic interpretation of intimate and nonintimate interpersonal conflict, part v: meta-analysis and interpersonal communication theory generation, chapter 19 | 18  pages, an analysis of textbooks in interpersonal communication: how accurate are the representations, chapter 20 | 18  pages, how does meta-analysis represent our knowledge of interpersonal communication, chapter 21 | 16  pages, better living through science: reflections on the future of interpersonal communication, chapter 22 | 24  pages, the state of the art of interpersonal communication research: are we addressing socially significant issues.

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Insights into research activities of senior dental students in the Middle East: A multicenter preliminary study

• Mohammad S. Alrashdan 1 , 2 ,
• Abubaker Qutieshat 3 , 4 ,
• Mohamed El-Kishawi 5 ,
• Abdulghani Alarabi 6 ,
• Lina Khasawneh 7 &
• Sausan Al Kawas 1  

BMC Medical Education volume  24 , Article number:  967 ( 2024 ) Cite this article

Metrics details

Despite the increasing recognition of the importance of research in undergraduate dental education, limited studies have explored the nature of undergraduate research activities in dental schools in the Middle East region. This study aimed to evaluate the research experience of final year dental students from three dental schools in the Middle East.

A descriptive, cross-sectional study was conducted among final-year dental students from three institutions, namely Jordan University of Science and Technology, University of Sharjah (UAE), and Oman Dental College. Participants were asked about the nature and scope of their research projects, the processes involved in the research, and their perceived benefits of engaging in research.

A total of 369 respondents completed the questionnaire.  Cross-sectional studies represented the most common research type  (50.4%), with public health (29.3%) and dental education (27.9%) being the predominant domains. More than half of research proposals were developed via discussions with instructors (55.0%), and literature reviews primarily utilized PubMed (70.2%) and Google Scholar (68.5%). Regarding statistical analysis, it was usually carried out with instructor’s assistance (45.2%) or using specialized software (45.5%). The students typically concluded their projects with a manuscript (58.4%), finding the discussion section most challenging to write (42.0%). The research activity was considered highly beneficial, especially in terms of teamwork and communication skills, as well as data interpretation skills, with 74.1% of students reporting a positive impact on their research perspectives.

Conclusions

The research experience was generally positive among surveyed dental students. However, there is a need for more diversity in research domains, especially in qualitative studies, greater focus on guiding students in research activities s, especially in manuscript writing and publication. The outcomes of this study could provide valuable insights for dental schools seeking to improve their undergraduate research activities.

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Introduction

The importance of research training for undergraduate dental students cannot be overstressed and many reports have thoroughly discussed the necessity of incorporating research components in the dental curricula [ 1 , 2 , 3 , 4 ]. A structured research training is crucial to ensure that dental graduates will adhere to evidence-based practices and policies in their future career and are able to critically appraise the overwhelming amount of dental and relevant medical literature so that only rigorous scientific outcomes are adopted. Furthermore, a sound research background is imperative for dental graduates to overcome some of the reported barriers to scientific evidence uptake. This includes the lack of familiarity or uncertain applicability and the lack of agreement with available evidence [ 5 ]. There is even evidence that engagement in research activities can improve the academic achievements of students [ 6 ]. Importantly, many accreditation bodies around the globe require a distinct research component with clear learning outcomes to be present in the curriculum of the dental schools [ 1 ].

Research projects and courses have become fundamental elements of modern biomedical education worldwide. The integration of research training in biomedical academic programs has evolved over the years, reflecting the growing recognition of research as a cornerstone of evidence-based practice [ 7 ]. Notwithstanding the numerous opportunities presented by the inclusion of research training in biomedical programs, it poses significant challenges such as limited resources, varying levels of student preparedness, and the need for faculty development in research mentorship [ 8 , 9 ]. Addressing these challenges is essential to maximize the benefits of research training and to ensure that all students can engage meaningfully in research activities.

While there are different models for incorporating research training into biomedical programs, including dentistry, almost all models share the common goals of equipping students with basic research skills and techniques, critical thinking training and undertaking research projects either as an elective or a summer training course, or more commonly as a compulsory course required for graduation [ 2 , 4 , 10 ].

Dental colleges in the Middle East region are not an exception and most of these colleges are continuously striving to update their curricula to improve the undergraduate research component and cultivate a research-oriented academic teaching environment. Despite these efforts, there remains a significant gap in our understanding of the nature and scope of student-led research in these institutions, the challenges they face, and the perceived benefits of their research experiences. Furthermore, a common approach in most studies in this domain is to confine data collection to a single center from a single country, which in turn limits the value of the outcomes. Therefore, it is of utmost importance to conduct studies with representative samples and preferably multiple institutions in order to address the existing knowledge gaps, to provide valuable insights that can inform future curricular improvements and to support the development of more effective research training programs in dental education across the region. Accordingly, this study was designed and conducted to elucidate some of these knowledge gaps.

The faculty of dentistry at Jordan University of Science and Technology (JUST) is the biggest in Jordan and adopts a five-year bachelor’s program in dental surgery (BDS). The faculty is home to more than 1600 undergraduate and 75 postgraduate students. The college of dental medicine at the University of Sharjah (UoS) is also the biggest in the UAE, with both undergraduate and postgraduate programs, local and international accreditation and follows a (1 + 5) program structure, whereby students need to finish a foundation year and then qualify for the five-year BDS program. Furthermore, the UoS dental college applies an integrated stream-based curriculum. Finally, Oman Dental College (ODC) is the sole dental school in Oman and represents an independent college that does not belong to a university body.

The aim of this study was to evaluate the research experience of final year dental students from three major dental schools in the Middle East, namely JUST from Jordan, UoS from the UAE, and ODC from Oman. Furthermore, the hypothesis of this study was that research activities conducted at dental schools has no perceived benefit for final year dental students.

The rationale for selecting these three dental schools stems from the diversity in the dental curriculum and program structure as well as the fact that final year BDS students are required to conduct a research project as a prerequisite for graduation in the three schools. Furthermore, the authors from these dental schools have a strong scholarly record and have been collaborating in a variety of academic and research activities.

Materials and methods

The current study is a population-based descriptive cross-sectional observational study. The study was conducted using an online self-administered questionnaire and targeted final-year dental students at three dental schools in the Middle East region: JUST from Jordan, UoS from the UAE, and ODC from Oman. The study took place in the period from January to June 2023.

For inclusion in the study, participants should have been final-year dental students at the three participating schools, have finished their research project and agreed to participate. Exclusion criteria included any students not in their final year, those who have not conducted or finished their research projects and those who refused to participate.

The study was approved by the institutional review board of JUST (Reference: 724–2022), the research ethics committee of the UoS (Reference: REC-22-02-22-3) as well as ODC (Reference: ODC-MA-2022-166). The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 11 ]. The checklist is available as a supplementary file.

Sample size determination was based on previous studies with a similar design and was further confirmed with a statistical formula. A close look at the relevant literature reveals that such studies were either targeting a single dental or medical school or multiple schools and the sample size generally ranged from 158 to 360 [ 4 , 8 , 9 , 10 , 12 ]. Furthermore, to confirm the sample size, the following 2-step formula for finite population sample size calculation was used [ 13 ]:

Wherein Z is the confidence level at 95% =1.96, P is the population proportion = 0.5, and E is the margin of error = 0.05. Based on this formula, the resultant initial sample size was 384.

Wherein n is the initial sample size = 384, N is the total population size (total number of final year dental students in the 3 schools) = 443. Based on this formula, the adjusted sample size was 206.

An online, self-administered questionnaire comprising 13 questions was designed to assess the research experience of final year dental students in the participating schools. The questionnaire was initially prepared by the first three authors and was then reviewed and approved by the other authors. The questionnaire was developed following an extensive review of relevant literature to identify the most critical aspects of research projects conducted at the dental or medical schools and the most common challenges experienced by students with regards to research project design, research components, attributes, analysis, interpretation, drafting, writing, and presentation of the final outcomes.

The questionnaire was then pretested for both face and content validity. Face validity was assessed by a pilot study that evaluated clarity, validity, and comprehensiveness in a small cohort of 30 students. Content validity was assessed by the authors, who are all experienced academics with remarkable research profiles and experience in supervising undergraduate and postgraduate research projects. The authors critically evaluated each item and made the necessary changes whenever required. Furthermore, Cronbach’s alpha was used to assess the internal consistency/ reliability of the questionnaire and the correlation between the questionnaire items was found to be 0.79. Thereafter, online invitations along with the questionnaire were sent out to a total of 443 students, 280 from JUST, 96 from UoS and 67 from ODC, which represented the total number of final year students at the three schools. A first reminder was sent 2 weeks later, and a second reminder was sent after another 2 weeks.

In addition to basic demographic details, the questionnaire comprised questions related to the type of study conducted, the scope of the research project, whether the research project was proposed by the students or the instructors or both, the literature review part of the project, the statistical analysis performed, the final presentation of the project, the writing up of the resultant manuscript if applicable, the perceived benefits of the research project and finally suggestions to improve the research component for future students.

The outcomes of the study were the students’ research experience in terms of research design, literature review, data collection, analysis, interpretation and presentation, students’ perceived benefits from research, students’ perspective towards research in their future career and students’ suggestions to improve their research experience.

The exposures were the educational and clinical experience of students, research supervision by mentors and faculty members, and participation in extracurricular activities, while the predictors were the academic performance of students, previous research experience and self-motivation.

The collected responses were entered into a Microsoft Excel spreadsheet and analyzed using SPSS Statistics software, version 20.0 (SPSS Inc., Chicago, IL, USA). Descriptive data were presented as frequencies and percentages. For this study, only descriptive statistics were carried out as the aim was not to compare and contrast the three schools but rather to provide an overview of the research activities at the participating dental schools.

The heatmap generated to represent the answers for question 11 (perceived benefits of the research activity) was created using Python programming language (Python 3.11) and the pandas, seaborn, and matplotlib libraries. The heatmap was customized to highlight the count and percentage of responses in each component, with the highest values shown in red and the lowest values shown in blue.

Potentially eligible participants in this study were all final year dental students at the three dental schools (443 students, 280 from JUST, 96 from UoS and 67 from ODC). All potentially eligible participants were confirmed to be eligible and were invited to participate in the study.

The total number of participants included in the study, i.e. the total number of students who completed the questionnaire and whose responses were analyzed, was 369 (223 from JUST, 80 from UoS and 66 from ODC). The overall response rate was 83.3% (79.6% from JUST, 83.3% from UoS and 98.5% from ODC).

The highest proportion of participants were from JUST ( n  = 223, 60.4%), followed by UoS ( n  = 80, 21.7%), and then ODC ( n  = 66, 17.9%). The majority of the participants were females ( n  = 296, 80.4%), while males represented a smaller proportion ( n  = 73, 19.6%). It is noteworthy that these proportions reflect the size of the cohorts in each college.

With regards to the type of study, half of final-year dental students in the 3 colleges participated in observational cross-sectional studies (i.e., population-based studies) ( n  = 186, 50.4%), while literature review projects were the second most common type ( n  = 83, 22.5%), followed by experimental studies ( n  = 55, 14.9%). Longitudinal studies randomized controlled trials, and other types of studies (e.g., qualitative studies, case reports) were less common, with ( n  = 5, 1.4%), ( n  = 10, 2.7%), and ( n  = 30, 8.1%) participation rates, respectively. Distribution of study types within each college is shown Fig.  1 .

Distribution in percent of study types within each college. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The most common scope of research projects among final-year dental students was in public health/health services ( n  = 108, 29.3%) followed by dental education/attitudes of students or faculty ( n  = 103, 27.9%) (Fig.  2 ). Biomaterials/dental materials ( n  = 62, 16.8%) and restorative dentistry ( n  = 41, 11.1%) were also popular research areas. Oral diagnostic sciences (oral medicine/oral pathology/oral radiology) ( n  = 28, 7.6%), oral surgery ( n  = 12, 3.2%) and other research areas ( n  = 15, 4.1%) were less common among the participants. Thirty-two students (8.7%) were engaged in more than one research project.

Percentages of the scope of research projects among final-year dental students. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The majority of research projects were proposed through a discussion and agreement between the students and the instructor (55.0%). Instructors proposed the topic for 36.6% of the research projects, while students proposed the topic for the remaining 8.4% of the projects.

Most dental students (79.1%) performed the literature review for their research projects using internet search engines. Material provided by the instructor was used for the literature review by 15.5% of the students, while 5.4% of the students did not perform a literature review. More than half of the students ( n  = 191, 51.7%) used multiple search engines in their literature search. The most popular search engines for literature review among dental students were PubMed (70.2% of cases) and Google Scholar (68.5% of cases). Scopus was used by 12.8% of students, while other search engines were used by 15.6% of students.

The majority of dental students ( n  = 276, 74.8%) did not utilize the university library to gain access to the required material for their research. In contrast, 93 students (25.2%) reported using the university library for this purpose.

Dental students performed statistical analysis in their projects primarily by receiving help from the instructor ( n  = 167, 45.2%) or using specialized software ( n  = 168, 45.5%). A smaller percentage of students ( n  = 34, 9.4%) consulted a professional statistician for assistance with statistical analysis. at the end of the research project, 58.4% of students ( n  = 215) presented their work in the form of a manuscript or scientific paper. Other methods of presenting the work included PowerPoint presentations ( n  = 80, 21.7%) and discussions with the instructor ( n  = 74, 19.8%).

For those students who prepared a manuscript at the conclusion of their project, the most difficult part of the writing-up was the discussion section ( n  = 155, 42.0%), followed by the methodology section ( n  = 120, 32.5%), a finding that was common across the three colleges. Fewer students found the introduction ( n  = 13, 3.6%) and conclusion ( n  = 10, 2.7%) sections to be challenging. Additionally, 71 students (19.2%) were not sure which part of the manuscript was the most difficult to prepare (Fig.  3 ).

Percentages of the most difficult part reported by dental students during the writing-up of their projects. JUST: Jordan University of Science and Technology, UOS: University of Sharjah, ODC: Oman Dental College

The dental students’ perceived benefits from the research activity were evaluated across seven components, including literature review skills, research design skills, data collection and interpretation, manuscript writing, publication, teamwork and effective communication, and engagement in continuing professional development.

The majority of students found the research activity to be beneficial or highly beneficial in most of the areas, with the highest ratings observed in teamwork and effective communication, where 33.5% rated it as beneficial and 32.7% rated it as highly beneficial. Similarly, in the area of data collection and interpretation, 33.0% rated it as beneficial and 27.5% rated it as highly beneficial. In the areas of literature review skills and research design skills, 28.6% and 34.0% of students rated the research activity as beneficial, while 25.3% and 22.7% rated it as highly beneficial, respectively. Students also perceived the research activity to be helpful for the manuscript writing, with 27.9% rating it as beneficial and 19.2% rating it as highly beneficial.

When it comes to publication, students’ perceptions were more variable, with 22.0% rating it as beneficial and 11.3% rating it as highly beneficial. A notable 29.9% rated it as neutral, and 17.9% reported no benefit. Finally, in terms of engaging in continuing professional development, 26.8% of students rated the research activity as beneficial and 26.2% rated it as highly beneficial (Fig.  4 ).

Heatmap of the dental students’ perceived benefits from the research activity

The research course’s impact on students’ perspectives towards being engaged in research activities or pursuing a research career after graduation was predominantly positive, wherein 274 students (74.1%) reported a positive impact on their research perspectives. However, 79 students (21.5%) felt that the course had no impact on their outlook towards research engagement or a research career. A small percentage of students ( n  = 16, 4.4%) indicated that the course had a negative impact on their perspective towards research activities or a research career after graduation.

Finally, when students were asked about their suggestions to improve research activities, they indicated the need for more training and orientation ( n  = 127, 34.6%) as well as to allow more time for students to finish their research projects ( n  = 87, 23.6%). Participation in competitions and more generous funding were believed to be less important factors to improve students` research experience ( n  = 78, 21.2% and n  = 63, 17.1%, respectively). Other factors such as external collaborations and engagement in research groups were even less important from the students` perspective (Fig.  5 ).

Precentages of dental students’ suggestions to improve research activities at their colleges

To the best of our knowledge, this report is the first to provide a comprehensive overview of the research experience of dental students from three leading dental colleges in the Middle East region, which is home to more than 50 dental schools according to the latest SCImago Institutions Ranking ® ( https://www.scimagoir.com ). The reasonable sample size and different curricular structure across the participating colleges enhanced the value of our findings not only for dental colleges in the Middle East, but also to any dental college seeking to improve and update its undergraduate research activities. However, it is noteworthy that since the study has included only three dental schools, the generalizability of the current findings would be limited, and the outcomes are preliminary in nature.

Cross-sectional (epidemiological) studies and literature reviews represented the most common types of research among our cohort of students, which can be attributed to the feasibility, shorter time and low cost required to conduct such research projects. On the contrary, longitudinal studies and randomized trials, both known to be time consuming and meticulous, were the least common types. These findings concur with previous reports, which demonstrated that epidemiological studies are popular among undergraduate research projects [ 4 , 10 ]. In a retrospective study, Nalliah et al. also demonstrated a remarkable increase in epidemiological research concurrent with a decline in the clinical research in dental students` projects over a period of 4 years [ 4 ]. However, literature reviews, whether systematic or scoping, were not as common in some dental schools as in our cohort. For instance, a report from Sweden showed that literature reviews accounted for less than 10% of total dental students` projects [ 14 ]. Overall, qualitative research was seldom performed among our cohort, which is in agreement with a general trend in dental research that has been linked to the low level of competence and experience of dental educators to train students in qualitative research, as this requires special training in social research [ 15 , 16 ].

In terms of the research topics, public health research, research in dental education and attitudinal research were the most prevalent among our respondents. In agreement with our results, research in health care appears common in dental students` projects [ 12 ]. In general, these research domains may reflect the underlying interests of the faculty supervisors, who, in our case, were actively engaged in the selection of the research topic for more than 90% of the projects. Other areas of research, such as clinical dentistry and basic dental research are also widely reported [ 4 , 10 , 14 , 17 ].

The selection of a research domain is a critical step in undergraduate research projects, and a systematic approach in identifying research gaps and selecting appropriate research topics is indispensable and should always be given an utmost attention by supervisors [ 18 ].

More than half of the projects in the current report were reasonably selected based on a discussion between the students and the supervisor, whereas 36% were selected by the supervisors. Otuyemi et al. reported that about half of undergraduate research topics in a Nigerian dental school were selected by students themselves, however, a significant proportion of these projects (20%) were subsequently modified by supervisors [ 19 ]. The autonomy in selecting the research topic was discussed in a Swedish report, which suggested that such approach can enhance the learning experience of students, their motivation and creativity [ 20 ]. Flexibility in selecting the research topic as well as the faculty supervisor, whenever feasible, should be offered to students in order to improve their research experience and gain better outcomes [ 12 ].

Pubmed and Google Scholar were the most widely used search engines for performing a literature review. This finding is consistent with recent reviews which classify these two search systems as the most commonly used ones in biomedical research despite some critical limitations [ 21 , 22 ]. It is noteworthy that students should be competent in critical appraisal of available literature to perform the literature review efficiently. Interestingly, only 25% of students used their respective university library`s access to the search engines, which means that most students retrieved only open access publications for their literature reviews, a finding that requires attention from faculty mentors to guide students to utilize the available library services to widen their accessibility to available literature.

Statistical analysis has classically been viewed as a perceived obstacle for undergraduate students to undertake research in general [ 23 , 24 ] and recent literature has highlighted the crucial need of biomedical students to develop necessary competencies in biostatistics during their studies [ 25 ]. One obvious advantage of conducting research in our cohort is that 45.5% of students used a specialized software to analyze their data, which means that they did have at least an overview of how data are processed and analyzed to reach their final results and inferences. Unfortunately, the remaining 54.5% of students were, partially or completely, dependent on the supervisor or a professional statistician for data analysis. It is noteworthy that the research projects were appropriately tailored to the undergraduate level, focusing on fundamental statistical analysis methods. Therefore, consulting a professional statistician for more complex analyses was done only if indicated, which explains the small percentage of students who consulted a professional statistician.

Over half of participating students (58.4%) prepared a manuscript at the end of their research projects and for these students, the discussion section was identified as the most challenging to prepare, followed by the methodology section. These findings can be explained by the students’ lack of knowledge and experience related to conducting and writing-up scientific research. The same was reported by Habib et al. who found dental students’ research knowledge to be less than that of medical students [ 26 ]. The skills of critical thinking and scientific writing are believed to be of paramount importance to biomedical students and several strategies have been proposed to enhance these skills especially for both English and non-English speaking students [ 27 , 28 , 29 ].

Dental students in the current study reported positive attitude towards research and found the research activity to be beneficial in several aspects of their education, with the most significant benefits in the areas of teamwork, effective communication, data collection and interpretation, literature review skills, and research design skills. Similar findings were reported by previous studies with most of participating students reporting a positive impact of their research experience [ 4 , 10 , 12 , 30 ]. Furthermore, 74% of students found that their research experience had a positive impact on their perspectives towards engagement in research in the future. This particular finding may be promising in resolving a general lack of interest in research by dental students, as shown in a previous report from one of the participating colleges in this study (JUST), which demonstrated that only 2% of students may consider a research career in the future [ 31 ].

Notably, only 11.3% of our students perceived their research experience as being highly beneficial with regards to publication. Students` attitudes towards publishing their research appear inconsistent in literature and ranges from highly positive rates in developed countries [ 4 ] to relatively low rates in developing countries [ 8 , 32 , 33 ]. This can be attributed to lack of motivation and poor training in scientific writing skills, a finding that has prompted researchers to propose strategies to tackle such a gap as mentioned in the previous section.

Finally, key suggestions by the students to improve the research experience were the provision of more training and orientation, more time to conduct the research, as well as participation in competitions and more funding opportunities. These findings are generally in agreement with previous studies which demonstrated that dental students perceived these factors as potential barriers to improving their research experience [ 8 , 10 , 17 , 30 , 34 ].

A major limitation of the current study is the inclusion of only three dental schools from the Middle East which my limit the generalizability and validity of the findings. Furthermore, the cross-sectional nature of the study would not allow definitive conclusions to be drawn as students’ perspectives were not evaluated before and after the research project. Potential confounders in the study include the socioeconomic status of the students, the teaching environment, previous research experience, and self-motivation. Moreover, potential sources of bias include variations in the available resources and funding to students’ projects and variations in the quality of supervision provided. Another potential source of bias is the non-response bias whereby students with low academic performance or those who were not motivated might not respond to the questionnaire. This potential source of bias was managed by sending multiple reminders to students and aiming for the highest response rate and largest sample size possible.

In conclusion, the current study evaluated the key aspects of dental students’ research experience at three dental colleges in the Middle East. While there were several perceived benefits, some aspects need further reinforcement and revision including the paucity of qualitative and clinical research, the need for more rigorous supervision from mentors with focus on scientific writing skills and research presentation opportunities. Within the limitations of the current study, these outcomes can help in designing future larger scale studies and provide valuable guidance for dental colleges to foster the research component in their curricula. Further studies with larger and more representative samples are required to validate these findings and to explore other relevant elements in undergraduate dental research activities.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to acknowledge final year dental students at the three participating colleges for their time completing the questionnaire.

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M.A.: Conceptualization, data curation, project administration; supervision, validation, writing - original draft; writing - review and editing. A.Q: Conceptualization, data curation, project administration; writing - review and editing. M.E: Conceptualization, data curation, project administration; validation, writing - original draft; writing - review and editing. A.A.: data curation, writing - original draft; writing - review and editing. L.K.: Conceptualization, data curation, validation, writing - original draft; writing - review and editing. S.A: Conceptualization, writing - review and editing.

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Alrashdan, M.S., Qutieshat, A., El-Kishawi, M. et al. Insights into research activities of senior dental students in the Middle East: A multicenter preliminary study. BMC Med Educ 24 , 967 (2024). https://doi.org/10.1186/s12909-024-05955-5

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• Published: 05 September 2024

Exploring the structural landscape of DNA maintenance proteins

• Kenneth Bødkter Schou   ORCID: orcid.org/0000-0002-7178-7133 1 , 2 , 3 ,
• Samuel Mandacaru 2 ,
• Muhammad Tahir 2 ,
• Nikola Tom 4 ,
• Ann-Sofie Nilsson 3 ,
• Jens S. Andersen   ORCID: orcid.org/0000-0002-6091-140X 2 ,
• Matteo Tiberti   ORCID: orcid.org/0000-0003-2529-3594 5 ,
• Elena Papaleo   ORCID: orcid.org/0000-0002-7376-5894 5 , 6 &
• Jiri Bartek   ORCID: orcid.org/0000-0003-2013-7525 1 , 3  

Nature Communications volume  15 , Article number:  7748 ( 2024 ) Cite this article

Metrics details

• Protein sequence analyses
• Structural biology

Evolutionary annotation of genome maintenance (GM) proteins has conventionally been established by remote relationships within protein sequence databases. However, often no significant relationship can be established. Highly sensitive approaches to attain remote homologies based on iterative profile-to-profile methods have been developed. Still, these methods have not been systematically applied in the evolutionary annotation of GM proteins. Here, by applying profile-to-profile models, we systematically survey the repertoire of GM proteins from bacteria to man. We identify multiple GM protein candidates and annotate domains in numerous established GM proteins, among other PARP, OB-fold, Macro, TUDOR, SAP, BRCT, KU, MYB (SANT), and nuclease domains. We experimentally validate OB-fold and MIS18 (Yippee) domains in SPIDR and FAM72 protein families, respectively. Our results indicate that, surprisingly, despite the immense interest and long-term research efforts, the repertoire of genome stability caretakers is still not fully appreciated.

Introduction

Genomes of all cellular lifeforms are plagued by the threat of DNA damaging insults, mutations, or copying errors. To counteract the potentially deleterious consequences of such insults, organisms have evolved systems that safeguard genetic information. Past studies have unearthed a plethora of proteins categorized structurally and functionally into several independent DNA repair networks. In humans alone, some >500 caretakers are directly or indirectly involved in GM, and almost as many are engaged in the mitotic and chromosome segregation processes. DNA damage response (DDR) proteins, however, are constructed from combinations of much fewer evolutionary conserved modules, that is by shuffling and recombining a limited repertoire of conserved domain precursors. Hence, the identification and categorization of proteins bearing such conserved structural entities can provide tangible insight into hitherto unknown protein functions. Previous evolutionary annotation methods have been highly successful in uncovering unknown relationships between DNA repair systems 1 , 2 . However, such profile-to-sequence comparative analysis often fails to identify any significant homology, revealing a limitation of this approach. This is certainly the case for the most intriguing group of DDR proteins in humans, namely those for which no orthologs have yet been studied. Both sensitivity and specificity of the homology searches can be dramatically improved by comparing sequence profiles through iterative profile-to-profile algorithms such as the hidden Markov model (HMM)-based iterative profile-HMM searches 3 , 4 . These methods compare the profiles of both query and target by exploiting databases of HMMs (such as the protein family (PFAM) database) in which protein profile HMMs rather than sequences are compiled. Profile HMMs are superior to simple sequence profiles since in addition to the amino acid frequencies identified in a multiple sequence alignment, they include the position-specific probabilities for inserts and deletions along the alignment. Among these, profile-HMMs have emerged as powerful tools in decoding the structural and functional landscape of genome maintenance proteins. For example, the elucidation of the S,T-Q phosphopeptide-binding BRCT domain, initially discovered in breast cancer susceptibility protein BRCA1 5 and later identified in many other proteins almost exclusively functioning in DNA damage response pathways 6 , has been greatly facilitated by the advent of profile-to-sequence and later profile-HMM- based computational database surveys, enabling reliable detection of subtle sequence homologies indicative of shared structural motifs 7 , 8 , 9 , 10 . Similarly, the OB fold domain, known for its role in nucleic acid binding and recognition, has recently witnessed a surge in profile-HMM applications 11 , 12 , 13 , 14 , 15 , 16 , 17 . Profile-HMMs, with their ability to capture remote homologies, have proven indispensable in accurately identifying OB-fold-containing proteins, offering valuable insights into their evolutionary relationships and functional implications. Previously, the systematic application of profile-HMMs has emerged as an efficient tool for identifying DNA repair protein structures in bacteria and metazoa. These studies individually surveyed nuclease and OB fold-bearing protein sequences, effectively revealing family members within DNA repair pathways 18 , 19 , 20 .

By leveraging the evolutionary information encoded in sequences, profile-HMMs have played a pivotal role in advancing our understanding of DNA repair mechanisms, offering a precise and efficient method for the computational annotation of protein structures associated with this vital cellular process. Although profile-HMM methods have been in existence for nearly two decades 21 , it is only in recent times that their complete potential, robustness, and accuracy in large-scale protein structural assessment through the AlphaFold methods have become evident 22 . Other recent methods for structural prediction and biomolecule interactions using deep learning, such as convolutional and graph neural networks 23 , 24 , have been developed. However, the latest version of AlphaFold, AlphaFold 3 25 , allowing for the highest degree of precision achieved so far in predicting protein structures and protein-biomolecule interactions. To our knowledge, however, the repertoire of GM proteins has so far not been the subject of systematic comparative analysis across domains and species using these state-of-the-art computational profile-HMM strategies. Hence, given the efficacy of the computational methods in dissecting the protein structures and functions, we resorted to an in-depth sequence analysis of all known and putative players in the DNA maintenance networks. Here, we report the results of such an analysis and discuss several previously undetected conserved domains that we have uncovered in the present study.

Overview of the genome maintenance architectural landscape analyses

A key goal of comparative sequence studies is the annotation of conserved domains as well as the discovery of structural and evolutionary relationships between cataloged domains. To understand the variety of GM protein structures across eukaryotes, we set out to answer two questions concerning GM architectures and their evolution. First, given the adeptness of profile-HMM methods in detecting remote homologies, can they be applied systematically to uncover unknown structures and relationships of in the human GM proteome? Second, despite the seemingly past eminent functional and evolutionary characterization of existing GM proteins, can we identify additional players in the human DNA damage responses? We, therefore, examined the structural characteristics and relationships among GM proteins across species by methodically evaluating their sequence homologies with protein profiles in the PFAM database through sensitive hidden Markov-based profile-to-profile (profile-HMM) searches. This information was then used to explore possible remote and undiscovered relationships within the human proteome. The result of this survey is summarized in Fig.  1 and -supplementary Fig.  1 and discussed in additional detail in the following sections. Collectively, we uncovered 113 unknown evolutionary conserved protein families, 59 predicted structures in established GM proteins, and 54 structures in uncharacterized GM candidate proteins.

a , In step 1 unique GM proteins were compiled by three different approaches. Flow diagram summarizing data collection and subsequent sequence searches. GO terms for GM proteins were compiled in the Amigo database 105 using four search terms across species yielding a total of 28,663 GO terms. Of these, 3635 are unique GM proteins from the seven selected organisms namely H. sapiens , D. melanogaster , C. elegans , A. thaliana , S. cerevisiae , S. pombe , and E. coli (K12) . GM physical interactors were retrieved from the IID database yielding a total of 975,877 interactors across species. Among these are 4618 interactors not previously implicated in GM. Of these, only 441 interactor pairs include one established DNA repair protein and one protein not previously linked to the DDR. Among these, 51 interactors were identified as recurrent contaminants in the CRAPome database yielding a final list of 390 unique GM interactors not previously implicated in the DDR. GM gene co-expressed genes were retrieved from the CEMiTool identifying a total of 36,410 GM co-expressed genes. Among these CEMiTool identifies 3523 overlapping co-expressed genes between two different tissues, which upon filtering for housekeeping genes and registered Crapome entries are reduced to 2820 co-expressed gene pairs (of which one gene per pair is an established GM gene). In Step 2 the compiled list of 4395 unique GM proteins were used as search queries in profile-HMM searches. These searches yielded a total of 108 hitherto unknown human domains in established and candidate GM proteins. These were used as seeds for reciprocal profile-HMM searches resulting in 108 validated candidate domains. Finally, the valid candidates were assessed by AlphaFold2 3D structural modeling to structurally validate the predicted evolutionary relationships across protein families. b Summary of identified classes of protein domains in the human proteome. c Validation of profile-HMM methods. Three methods were tested for their efficiency in detecting homologous protein domains in the protein databank (PDB). The three protein domains used as seeds were human RPA1 OB_2, the human BARD1 BRCT, and human MYB (SANT) domains. d, e Examples of predicted 3D structures of two identified candidate domains as judged by AlphaFold modeling. Predicted domains were superimposed with closest paralog domains in Pymol as indicated. f Probability plots of profile-HMM remote homology searches using either the predicted KU core domain of M1AP or the predicted BRCT domain of SMARCC1 as sequence queries. g – k Summary of examples of DNA repair candidates identified in the computational survey shown in red. g Summarizes DNA repair protein domain classes. h – k Four examples of identified DNA repair candidates as judged by their predicted protein domains. Red nodes represent candidates (at the time of analysis). The length of nodes from the center corresponds to the sequence homology of the signature domain family profile. l – p Summary of mitotic candidates identified in the computational survey shown in blue. l Summarizes mitotic protein domain classes. m – p Four examples of identified mitotic candidates as judged by their predicted protein domains. Blue nodes represent candidates (at the time of analysis). The length of nodes from the center corresponds to the sequence homology of the signature domain family profile. Source data are provided as a Source Data file.

The sequences and structures of the main catalytic domains of many GM proteins such as polymerases, helicases, and other ATPases have been characterized in detail previously and are readily recognizable due to the conservation of diagnostic motifs. Consequently, the analysis did not considerably expand these protein superfamilies. Most protein modules identified in our survey belong to either DNA binding domain families or to protein adapters i.e., protein-protein binding interfaces. Characteristically, binding domains are imperfect and show much less sequence conservation than enzymes, which is likely why many binding domains have remained undetected up to this point.

Adapter domain discovery

Noteworthy examples of the previously unknown (by the time of this analysis) DNA binding domain family members are seven MYB (SANT) domain proteins, nine TUDOR domain proteins, six OB-fold proteins, four Mis18 (yippee) domain proteins, 19 SAP domain proteins, three WSD domain proteins, and two KU70 or KU86 beta-barrel domain proteins (Fig.  1b and Supplementary Fig.  1a, b ). These domains occur in both well-described GM proteins as well as in uncharacterized proteins (Supplementary Fig.  1a, b ). Among established GM proteins and candidates, we identified adapter or DNA-binding domains in 62 proteins. Examples of these are four UBA ubiquitin-binding, five NNCH, three SFI1, two BRCT (Supplementary Data  1 ), two BAH, and one POLO box domains (Fig.  1b–p and Supplementary Fig.  1a, b ). We also uncovered members of six protein families tied to mitotic functions. (Fig.  1l–p and Supplementary Fig.  2d ). Four of the latter families have kinetochore ontologies, i.e., the protein members are enriched at kinetochore complexes required for microtubule attachment to centromeres during mitotic chromosomal segregation (Fig.  1l–p and Supplementary Fig.  2d ). We also expand our previous discovery of the kinetochore NDC80 (NUF) calponin (NNCH) subfamily domains 26 by identifying five additional members: CEP44, HAUS3, HAUS6, HAUS7, and TEDC1 (Fig.  1o and Supplementary Fig.  2a–c ). As with other members of the NNCH family, the N-terminal NNCC domain is adjoined to a C-terminal region of disparate arrays of heptad repeats (coiled-coils) (Supplementary Fig.  2a ) as predicted by weighted and unweighted matrices 27 indicating that these proteins are members of an evolutionary conserved family of bimodular coiled-coil proteins.

The majority of DNA repair proteins bear catalytic domains and DNA binding domains (Fig.  1g ). A noteworthy exception to this tendency is the multiprotein Integrator (Int) complex involved in the promotion of DNA repair and G2 to M checkpoint through crosstalk with the multiprotein complex sensor involved in sensing ssDNA (SOSS) and in small nuclear RNAs (snRNA) transmission 28 . In addition, our analysis extends the structural features of these Integrator complex subunits including the INTS2 and INTS10 that currently have no known conserved structures. We find that INTS2 and INTS10 are comprised almost entirely of disparate arrays of TPRs units (Supplementary Fig.  3a–c ). We collectively designate these alpha-helical repeats in the Int proteins alpha solenoid repeats (Supplementary Fig.  3a ). Interestingly, our analysis also revealed that the Int complex member INTS14 is a thus far unknown paralog of the NHEJ repair proteins KU70 (KU86). INTS14 bears, besides an N-terminal vWA Von Willebrand factor type A domain - as do the INTS6 and INTS13 subunits (Supplementary Fig.  3a ) - a predicted high confidence KU_core domain adjoined to a KU_C C-terminal domain (Supplementary Fig.  3a, d ). Supporting our structural prediction of INTS14 by sequence, recently using crystallography the structure of INTS14 in complex with INTS13 was shown to adopt a KU70 (KU86) complex-like structure 29 . Surprisingly, this overall architecture resembling the KU70 (KU86) proteins was also identified for the M1AP protein (Fig.  1d, f , Supplementary Fig. 3a, d , and Supplementary Data  3 ), suggesting that humans bear four KU paralogs (Fig.  1i ). The M1AP structural homology to the Ku70-Ku86 DNA repair complex proteins suggests nucleic acid affinity in DNA repair. M1AP was previously shown to be almost exclusively expressed in testis and having functions in male germ cell development and in meiosis 30 indicating that M1AP might be implicated in meiotic recombination events. Indeed, an M1AP gene co-expression analysis of testis mRNA using the GTEx RNA seq data showed that M1AP is strongly co-regulated with a network of DNA repair and DDR-related genes (Supplementary Fig. 3e, f ), suggesting that M1AP may be involved in DNA repair processes.

Predicted family members of the poly(ADP-ribose)-related enzymatic processes

While catalytic domains are generally well conserved, making it less likely to identify proteins in this category, we have now nevertheless uncovered several human enzymes, including six nucleases, three poly(ADP-ribose) polymerases (PARPs), four Poly (ADP-ribose) glycohydrolase (PARG)-like proteins, and three metallo-beta-lactamases (Figs.  1 b, 1g–k and Supplementary Fig. 1a, b ). Because the gene products of two of the identified PARPs, namely TEX15 and TASOR, are widely expressed putative GM players in thus far meiotic recombination or chromatin remodeling processes 31 , 32 , respectively, and their PARP domains share overall sequence and predicted structure (Fig.  2d, f ), this raises the possibility that TEX15 and TASOR, as well as the uncharacterized TASOR2 (FAM208B), could function as PARPs in the DDR analogous to e.g., PARP-1. Interestingly, recently using cryo-electron microscopy, a cryptic PARP domain was identified in TASOR. This domain is catalytically inactive and dispensable for assembly and chromatin targeting but is critical for epigenetic regulation of target histone H3K9me3 33 . This PARP domain corresponds to the first PARP domain in TASOR uncovered in our sequence analysis, whereas the second PARP domain that we predict is unknown (Fig.  2c ). Since TASOR and TASOR2 are paralogs and share high overall sequence homology, we assessed whether TASOR2 might share PARP domain properties with the cryptic PARP domain of TASOR. Indeed, an inspection of the primary structure of the TASOR2 PARP domain indicated that several residues essential for the catalytic function, are lost in the PARP domain of TASOR2 as with TASOR Fig.  2a, b . Hence, the PARP domains of TASOR and TASOR2 might resemble the cryptic catalytic dead PARP domain of PARP13, as previously shown for TASOR. In support of this notion, the 3D model of TASOR2 predicted by ColabFold indicated that its PARP domain adopts a closed structural loop comparable to the PARP13 catalytic domain Fig.  2e .

a Diagram of the active site residues of the PARP1-like catalytic domain. Similar to PARP13, TASOR and TASOR2 have lost essential residues required for catalytic activity. b MSA of selected PARP domain sequences including those of TEX15, TASOR, and TASOR2. Conserved residues are shown in orange as assessed by Clustal W with modifications. Predicted secondary structures are shown above the MSA. Boxes indicate alpha-helices, and arrows indicate beta-sheets. c Schematic domain architectures of selected human PARPs including the three PARP candidates TEX15, TASOR, and TASOR2. Phylogenetic trees were calculated from MSA average distances using approximately the maximum-likelihood method in the IQ-Tree v.2.050 program. d Predicted PARP domains of TEX15 and TASOR2 as assessed by AlphaFold. PARP domains of TEX15 and TASOR2 (orange) were superimposed with the PARP domain of PARP1 (white) in Pymol. e Superimposed PARP domains of either TASOR2 and PARP1 (left) or TASOR2 and PARP13 (right). Conserved residues are indicated. f Probability plots of profile-HMM searches using the predicted TEX15 PARP domains (top) or the predicted AKAP11 Macro domain (bottom) as queries. g 3D structures of AKAP family member Macro domains as predicted by AlphaFold. AKAP Macro domains (yellow/orange nuances) are superimposed with the Macro domain in human PARG (white). h PARG domain relationships to human AKAP family proteins. The C-termini of SPHKAP, AKAP3, AKAP4, and AKAP11 show remote homology to the C-terminal portion of PARG comprising the PAR-binding Macro domain. Source data are provided as a Source Data file.

The four hitherto unknown PARG domains identified with high confidence (Fig.  2f) in the putative A-kinase anchor proteins AKAP3, AKAP4, SPHKAP, and AKAP11, all show high sequence conservation and predicted 3D structure to the C-terminal half of the PARG catalytic domain (Fig.  2g, h and Supplementary Fig.  6a ). The remainder N-terminal portion in these AKAPs appears to have been lost during evolution. Because the predicted PARG domain residues in these AKAPs show high conservation compared to residues in PARG that are configured in close contact with its bound poly(ADP-ribose) (PAR) moiety (Supplementary Fig.  6b ), it is conceivable that the AKAP’s PARG domain evolved specifically to bind PAR-modified proteins. Indeed, a closer examination of the PARG homologies between identified AKAPs (and the PARG catalytic domain) revealed that this portion corresponds to the PARG macro domain (Fig.  2g ).

DNA-binding domains

The class of protein domains of which we identify most candidates is the DNA-binding domain. Three categories of DNA-binding domains are overrepresented among the candidates. These are the MYB (SANT), SAP, and OB-fold domains (Fig.  1b and Supplementary Fig. 4 ). Our analysis expands the superfamily of MYB (SANT) domain-containing proteins with six members, namely TIMELESS, SMARCC1, SMARCC2, CRAMP1, LOC100506514, and LOC107985532 (Supplementary Fig.  4a, b) . The outermost C-terminal region comprising the previously unknown MYB (SANT) domains in TIMELESS (Supplementary Fig. 4a, d ) is required for proficient circadian clock rhythm regulation in fruit flies 33 , suggesting an important biological function of these two MYB (SANT) domains in TIMELESS.

We also predict 19 SAP candidate domains that all appear to adopt similar 3D structures (Supplementary Fig.  4e ). Several of the predicted SAP domains were previously annotated as distinct domain classes such as the LEM domains in e.g., LETMD1, LETM2, LETM1, the ARMET domain in MANF and CDNF (Supplementary Fig. 4e ), as well as the NCD1 domain in NAB1 and NAB2. These domains were previously shown to individually adopt a three alpha-helix topology characteristic of the SAP domain 34 , but their mutual evolutionary relationship to the SAP domain was unknown. Our remote homology searches also revealed an until now unknown SAP domain in human PARP1 (233-287) (Fig.  2c ). Previously, the region encompassing the predicted SAP domain was designated the human PARP1 domain C 35 , 36 . Further inspection of domain C indicated, however, that it might consist of two subdomains i.e., a SAP domain comprising the N-terminal half and a C-terminal zinc ribbon fold comprising the third zinc-binding domain of PARP1 37 .

Among the DNA-binding domains, the Oligonucleotide or Oligosaccharide-binding (OB)-fold domain is one of the most common domains in DNA repair proteins 38 . We identified previously undetected, distinct versions of the OB-fold domain in the families of repair proteins (Fig.  1b, j and Supplementary Fig.  1a, b ). In each case, the amino acid signature typical of OB-fold domains is modified and not easily recognizable, but all show a remote relationship to the OB-fold domains seen in bacterial proteins. Significant similarity to OB-fold domains can be demonstrated for these domains for all known DNA-binding OB-fold-containing proteins as well as in six human proteins (at the time of this analysis), namely SHLD2 (RINN2 or FAM35A), HROB (C17ORF53), SPIDR, POLE2, SPATA22, and TDRD3 (Fig.  1j and Supplementary Fig.  1 and Supplementary Data  2 ). Structural characterization of the OB-fold domains and their role in the DDR of human SHLD2, HROB, SPIDR, and SPATA22 was recently reported 15 , 16 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , whereas SPIDR and TDRD3 remain structurally uncharacterized. Recently, the SPIDR protein was identified in a DDR protein complex with BLM helicase that functions in the repairing of DNA double-strand breaks (DSBs) through the non-homologous end joining (NHEJ) pathway 47 . While the structure of SPIDR and its DNA-binding properties are currently unknown, our sequence analysis predicts three OB folds in the SPIDR C-terminus with high confidence (Fig.  3a–e ). These OB folds appeared to adopt a C-terminal tandem arrangement including a zinc-ribbon in the outermost C-terminal OB-fold previously shown for other DNA repair proteins such as RPA1, RADX, MEIOB, and SHLD2 38 . Subsequent searches using the overall structure comprising these three SPIDR OB folds as a seed against protein tertiary structure databases using Foldseek 48 matched the three C-terminal OB-folds in S. cerevisiae RPA1 as the top-ranking significant match (Fig.  3d ). AlphaFold2 structural modeling of SPIDR full-length structure predicted a fourth central globular domain adjoined to the N-terminal disordered region (Fig.  3e ). Our remote homology sequence searches detect this domain with only low significance (E = 0.015) and hence the nature of this domain remains inconclusive. We therefore cautiously designate this fourth domain an inferred OB-fold (Fig.  3e ). Interestingly, we noticed that a subset of the OB-fold-bearing proteins shows striking similar overall structural architecture i.e., they bear an N-terminal intrinsically disordered portion adjoined to a C-terminus bearing one or more OB-fold domains (Fig.  3e ), indicating that these proteins might be evolutionarily and functionally closely related. This organization is reminiscent of the SHLD2 (RINN2), HROB, and SPATA22 all bind ssDNA through their OB-fold domains leading us to hypothesize that SPIDR may bind ssDNA. To test this notion, we tested a FLAG-tagged version of SPIDR for its ability to bind Biotin-labeled double and single-stranded DNA (dsDNA or ssDNA). Indeed, streptavidin pulldown assays of Biotin-ssDNA avidly retrieved FLAG-SPIDR expressed in HEK293T cells whereas FLAG-SPIDR showed much reduced binding efficiency to Biotin-dsDNA (Fig.  3h ). The OB-fold region of SPIDR is responsible for the binding to DNA as full-length FLAG-SPIDR and a FLAG-3XOB fragment but not FLAG-SPIDR lacking its OB-fold domains could be pulled down with Biotin-ssDNA (Fig.  3e, f ). Furthermore, based on alignment with disruptive mutations in the RPA1 DBD-C OB fold (C505A and C508A) that result in a marked reduction in ssDNA-binding affinity 49 , specific point mutations within the SPIDR OB3 domain (C817A and C820A) reduced its interaction with ssDNA markedly (Fig.  3i, j ). In addition, using the recently developed AlphaFold 3 method for modeling protein binding to diverse biomolecules, we assessed the predicted propensity of the OB3 domain to bind DNA. Interestingly, when using a random DNA sequence as a query, a stretch of ssDNA, but not the corresponding RNA sequence, was predicted to bind the SPIDR OB3 domain with high significance (Fig.  3k ), further supporting the notion that the OB-folds of SPIDR bind ssDNA. Recently causal polymorphisms in SPIDR were implicated in primary ovarian insufficiency (POI) with patient cells showing chromosomal instability 50 , 51 . Both pathogenic SPIDR variants are nonsense mutations (R272*, W280*) and lie near the inferred OB-folds (Fig.  3e ). Hence these variants are predicted to delete most of the OB-fold containing portion in SPIDR. To test whether such disease truncations will affect the ability of SPIDR to bind to chromatin, we expressed FLAG-tagged full length, the W280*-fragment (SPIDR 1-280), or a C-terminal portion only containing OB folds (623-915) of SPIDR in U2OS cells and isolated chromatin fractions. Indeed, Full-length SPIDR and the OB-fold C-terminus but not the W280* fragment associated with chromatin (Fig.  3g ), suggesting that the nonsense mutations seen in SPIDR in POI patients disrupt the SPIDER protein’s DNA-binding capabilities.

a MSA of selected OB fold domain sequences including the outermost C-terminal OB fold of SPIDR. Conserved residues shown in blue were calculated using the Clustal W algorithm with modifications. Predicted secondary structures are shown above the MSA. Arrows indicate beta-sheets, and boxes indicate alpha-helices. b Probability plots of profile-HMM remote homology searches using either the human RPA1_OB4 domain (forward) or the predicted SPIDR OB3 domain (reciprocal) as sequence queries. c The three predicted OB folds in the SPIDR C-terminus. Here, AlphaFold predicted models are superimposed with the solved structure of RPA1_4OB fold DBD-D (PDB: 4GOP), shown in white. Short unstructured coils have been stripped off the SPIDR OB folds for clarity. d The three tandem OB-fold domains in the SPIDR C-terminus resemble that of other OB fold-containing proteins. Searching the overall predicted structure comprising these three SPIDR OB folds against protein structure databases using Foldseek ( https://github.com/steineggerlab/foldseek ) identifies S. cerevisiae RPA1 as the closest significant match. e Schematic illustration of the bimodular family of IDP and OB-fold family DNA repair proteins. Two causative mutations identified in primary ovarian insufficiency (POI) patients are shown in SPIDR (red). f OB folds of SPIDR binds ssDNA. Cell extracts from HEK293T cells expressing indicated fragments of FLAG-SPIDR were incubated with biotinylated ssDNA and subjected to streptavidin pulldown using streptavidin resin. g Chromatin fractionation of HEK293T cells expressing either FLAG-SPIDR full length, truncated FLAG-SPIDR fragment corresponding to SPIDR containing disease mutation W280*. h FLAG-SPIDR binds ssDNA but not dsDNA. Cell extracts from HEK293T cells expressing FLAG-SPIDR were incubated with either biotinylated ssDNA or dsDNA and biotinylated DNA purified using a streptavidin (strep) resin. i Point mutations introduced into OB-fold domains of SPIDR. j Biotin-ssDNA pulldown analysis of cell extract from HEK293T cells expressing either GFP-SPIDR wildtype or a mutated version with the indicated amino acid substitutions. WCE = sample processing control. k , AlphaFold3 modeling of SPIDR OB3 domain in complex with either DNA or RNA. Immunoblots are representative results of two individual experiments (X = 2). Source data are provided as a Source Data file.

The FAM72 family proteins are MIS18 (Yippee) paralogs that oligomerize and bind RPA proteins

We identified the DNA binding domain MIS18 (yippee) in four human FAM72A paralogs (FAM72A-D) (Fig.  4a–c ). The MIS18 (yippee) domain is found in the kinetochore proteins MIS18a, MIS18b, and yeast homolog MIS18 52 as well as the poorly understood Yippee zinc-binding and DNA-binding proteins YPEL1-5. Using AlphaFold2, we found that the predicted tertiary structure of FAM72A-D resembles the solved 3D structure of human MIS18A (PDB: 5HJ0), indicating that the FAM72A-D homologies by sequence to the MIS18 (Yippee) family are genuine (Fig.  4d ). Because all four FAM72A paralogs are predicted to adopt almost the same 3D structures (Fig. 4d ) and display high protein sequence identity ( ≥98%), this raises the possibility that they all function in the same molecular pathway(s) in the cell. Indeed, a network deconvolution analysis of RNA-seq data derived from a broad array of human tissues 53 to define the transcriptional signatures of FAM72A-D co-expressed genes revealed that all four paralogs share highly similar gene co-expression signatures (Fig.  4f and Supplementary Fig.  5c ). Interestingly, among the top co-expressed genes across FAM72 family members are the FAM72A-D genes themselves (Fig. 4g ), suggesting that FAM72A-D proteins are tightly co-regulated. The Yippee-like domain of S. pombe MIS18 was recently shown to possess an intrinsic propensity to oligomerize 54 , raising the possibility that the FAM72A-D gene co-regulation reflects their mutual propensity to oligomerize. To test this possibility, we used the AlphaFold multimer suite 55 to assess whether FAM72 species oligomerize. Indeed, this analysis predicted that FAM72A can homodimerize and heterodimerize with all other FAM72 species (Fig.  4e ). These mutual interactions between FAM72 family members were also supported by GFP pulldown assays using cell extracts of HEK293T cells co-expressing GFP-FAM72A with either FLAG-tagged FAM72B, FAM72C, or FAM72D (Supplementary Fig.  5b ), indicating that all FAM72 family members might partake in multimer complexes in analogous molecular processes in the cell. Our FAM72 family gene co-expression analysis also revealed that, besides the mutual FAM72 gene family co-expression profiles (Supplementary Fig.  5c ), FAM72 family genes are co-regulated with genes with genome maintenance ontologies such as DNA biosynthesis processes, DNA replication, and DNA repair as well as mitosis and meiosis (Fig.  4f and Supplementary Fig. 5c ). FAM72A was recently shown to interact with and inhibit base excision repair (BER) uracil-DNA glycosylase UNG in antibody diversification 56 , 57 , 58 . Because we found that FAM72A’s human paralogs are analogous in structure, form dimer or multimer complexes, and share transcriptional network, we asked whether other FAM72A paralogs might function in the same molecular processes as FAM72A? Indeed, as an example, our FLAG pulldown analysis of FLAG-FAM72B followed by mass spectrometry of FLAG immunocomplexes revealed that FAM72B was also found to bind avidly to UNG (Fig.  4h and Supplementary Fig. 5a ) as previously shown for FAM72A, suggesting that these paralogs may participate in the same pathway. Surprisingly, these pulldown assays also revealed that RPA single-stranded DNA binding protein complex members (RPA1-3) were among the most abundant proteins to co-elute with FLAG-FAM72B (Fig.  4h ). Subsequent FLAG pulldown of high salt cell extracts from HEK293T cells expressing FLAG-FAM72B and immunoblot supported the interaction with RPA1 (Fig.  4i ). Hence, Fam72B, and by extension other FAM72 family members, besides binding to UNG, also reside in complexes with RPA proteins, likely to function in DNA repair. To test this emerging possibility in more detail, we asked whether FAM72B might recruit to DNA during replication in the S phase as has been shown previously for RPA proteins 56 , 59 . Indeed, FLAG-FAM72B expressing cells released from a thymidine cell cycle block in late G1 showed gradually increased retention of FLAG-FAM72B on chromatin as cells traversed into the S phase as judged by immunoblotting of resolved proteins from the chromatin fraction (Fig.  4j ). The chromatin accumulation of FAM72B in S phase coincides with RPA2 phosphorylation (Fig.  4j ), supporting the notion that FAM72B and RPA proteins function in the same pathway. Because RPA proteins are well known to recruit to DNA upon various DNA insults due to the formation of naked ssDNA, we also tested if FAM72B accumulated on chromatin after short-term treatment with camptothecin (CPT), which potently induces replication-dependent DSBs. FAM72B chromatin binding increases after CPT treatment (Fig.  4k ), suggesting that FAM72B recruits to DNA following genotoxic insults in vertebrate cells. Finally, to further understand the functional relationship between FAM72 proteins and RPA, we examined the proficiency of RPA2 phosphorylation after replication stress in cells depleted for FAM72 proteins using FAM72 siRNA. Interestingly, FAM72 silencing reduced the phosphorylation of RPA2 after both 1.5 and 3 h of CPT exposure (Supplementary Fig. 5d ), indicating that FAM72 proteins play an active role in the regulation of RPA following replication stress.

a MSA of human MIS18 domain sequences. Conserved residues shown in brown were assessed using the Clustal W algorithm. Predicted secondary structures are shown above the MSA. Arrows indicate beta-sheets. b Probability plots of profile-HMM remote homology searches using either the MIS18 domain of MIS18a (forward search) or the predicted MIS18 domain of FAM72B (reciprocal search) as sequence queries. c Family of human MIS18 family proteins. Phylogenetic trees were calculated from MSA average distances using the percentage identity (PID) algorithm. d Tertiary structures of FAM72 family proteins as predicted by AlphaFold. Predicted domains were superimposed in PyMol. e FAM72A complexes with either FAM72B, FAM72C, or FAM72D as predicted by ColabFold 106 . f Gene co-expression GO enrichment analysis result of the co-expression signature profile shown in ( g ). Combined FAM72A-D gene co-expression signature. The human FAM72 family co-expressed genes are ranked according to Pearson correlation coefficients (PCC) as shown. h Volcano blot showing top interactors of FLAG-FAM72B as assessed by mass spectrometry. i FLAG-FAM72B immunoprecipitation and subsequent immunoblot of eluted immunocomplexes. Proteins were probed with the indicated antibodies. WCE = sample processing control. j Immunoblot of FLAG-FAM72B-expressing U2OS cells chromatin fractions after a thymidine block. Cells were either left untreated or treated for 24 hours with thymidine followed by extensive washing, release in growth medium, and harvested at the indicated time points. The resolved proteins were probed with the indicated antibodies. Sol = soluble fraction, Chromatin = chromatin enriched fraction. k Immunoblot of chromatin fractions from FLAG-FAM72B-expressing cells after exposure to CPT for 1.5 and 3 h. Proteins were probed with the indicated antibodies. Immunoblots are representative results of two individual experiments (X = 2). Source data are provided as a Source Data file.

Nuclease domains

Most nuclease superfamilies have been widely studied. Accordingly, we recapitulated the previous annotation of all distinct nuclease branches such as the phosphodiesterase superfamily, 5’->3’ FLAP nuclease superfamily, 3’->5’nucleases, the bacterial endonuclease IV and V, RecB, and UvrC families. Among the many annotated DNA nucleases expressed in humans, we uncovered eight putative nucleases candidates, including: an ERCC4 (XPF) type nuclease C1ORF146 (recently designated SPO16 in mouse) (Fig.  5a, c ), two DDE superfamily endonucleases GVQW3 (FLJ37770) and C21ORF140 (FAM243A), as well as Metallo β-lactamase domains in the three proteins MAP1A, MAP1B, and MAP1S (Supplementary Fig.  6a ). Of the eight putative nucleases candidates retrieved from the human proteome (Fig.  1b and Supplementary Fig.  1a, b ), we decided to validate the predicted C1ORF146 ERCC4 (XPF) nuclease in more detail. We chose C1ORF146 because the ERCC4 (XPF) family comprises endonucleases belonging to the flap nuclease family invariably implicated in DNA repair processes such as nucleotide excision repair (NER), DNA interstrand cross-link repair (ICL) repair, and in resolving branched DNAs structures 57 . Recently SPO16 in mice was found to bind its ERCC4 (XPF) nuclease paralog SHOC1 to function in meiotic recombination 58 , raising the possibility that the SPO16-SHOC1 complex operates in a similar manner as known ERCC4 paralog heterodimers (e.g. ERCC1-XPF,FANCM-FAAP24, and MUS81-EME1). Hence, although their mutual binding interface was not conclusively mapped, it is possible that SPO16-SHOC1, and by extension, the predicted C1ORF146-SHOC1 complex in humans, might form a physical heterodimer complex analogous to related family members. To test this idea in more detail and validate whether they form a dimeric complex, we mapped the predicted C1ORF146-SHOC1 binding site using AlphaFold multimer-based complex structure 22 . Interestingly, we found that C1ORF146 and SHOC1 are predicted to reside in a high confidence heterodimeric nuclease complex (Fig. 5b ) as judged by the low Predicted Aligned Error (PAE) for the interacting regions (Fig.  5f ). This complex is much like the solved structure of the homologous yeast SPO16-ZIP2 complex (Fig.  5b ) and other ERCC4 (XPF) nuclease dimers, suggesting that C1ORF146-SHOC1might have related DNA repair functions. Further inspection of the contact sites suggested that C1ORF146 interacts with SHOC1 through many of the conserved residues in its two outermost C-terminal alpha-helix extensions (Fig. 5c, d ) like its homologous complex counterpart in yeast (Fig.  5e ). Curiously, many of these signature contact sites in C1ORF146 are also conserved in other human ERCC4 paralogs (Fig.  5C ) raising the question whether C1ORF146 might, in principle, have the propensity to bind other ERCC4 paralogs. We therefore modeled C1ORF146 in complex with each of the SHOC1 paralogs in humans. Despite C1ORF146 appearing to prefer SHOC1 relative to other ERCC4 nucleases (Supplementary Fig.  7e ), AlphaFold Multimer predicts three additional high-confidence ERCC4 complexes i.e., between C1ORF146 and XPF, MUS81, or FAAP24 (Supplementary Fig.  7b–d ). These results suggest that ERCC4 family members can, in principle, intermix e.g., as the cell's adaptive response to situations where one component is lost. The prediction that ERCC4-type heterodimers do not oligomerize or multimerize (Supplementary Fig.  7e ), however, supports the idea that these heterodimers evolved with distinct functions in DNA repair. We also tested the complex formation between ERCC4-type nucleases and non-related nuclease domains (but with an overall similar 3D structure) and found that none of the unrelated nuclease families were predicted to form complexes with ERCC4-type nucleases.

a Family of human ERCC4/XLF nucleases. Phylogenetic trees were calculated from MSA average distances using the percentage identity (PID) algorithm. b ColabFold 106 prediction of C1ORF146 in complex with SHOC1 and yeast SPO16-ZIP2 complex. c MSA of human ERCC4 domain sequences. Conserved residues shown in green were calculated using the Clustal W algorithm. Predicted secondary structures are shown above the MSA. Boxes indicate alpha-helices and arrows indicates beta-sheets. C1ORF146 residues predicted to make contacts to SHOC1 are highlighted in red. Many of these residues are conserved across ERCC4 nucleases (green) d Contact sites (red) between the outermost C-terminus of C1ORF146 (green) and SHOC1 (white). e Contacts (red) between the SPO16 outermost C-terminus (green) and ZIP2 (white). Chromatin retention of GFP-C1ORF146 in RPE1 cells. f The predicted alignment error (PAE) plot of the human C1ORF146-SHOC1 ERCC4 nuclease complex as shown in panel ( b ). g Immunoblot of chromatin fractions from GFP-C1ORF146-expressing U2OS cells either untreated or treated with DNA damaging agents. The resolved proteins were probed with the indicated antibodies. h Immunoblot of chromatin fractions from GFP-C1ORF146-expressing U2OS cells released from a thymidine block. Cells were either left untreated or treated for 24 hours with thymidine followed by extensive washing, release in growth medium and harvested at the indicated time points. The resolved proteins were probed with the indicated antibodies. Immunoblots are representative results of three individual experiments (X = 3). i Immunofluorescence microscopy images of U2OS cells expressing GFP-C1ORF146 either untreated or treated with cisplatin for 6 hours. After fixing the cells in PFA cells were permeabilized, BSA-blacked stained with the indicated antibody. The micrographs are representative of two individual experiments (X = 2). Source data are provided as a Source Data file. Scale bar in i 10 mm.

In agreement with the reported function of C1ORF146 in meiotic recombination, publicly available RNA-seq data suggests that C1ORF146 mRNA is primarily expressed in testis 58 concomitant with its role in meiosis. We noticed, however, that C1ORF146 mRNA also appears to be expressed at modest levels in other organs in humans such as the brain, pancreas, skin, blood, and retina ( https://www.proteinatlas.org/ENSG00000203910-C1orf146/tissue ). This opens the idea that C1ORF146 might have functions other than that in meiotic recombination. Virtually all ERCC4 (XPF) family nucleases are well-described DNA repair proteins and most members function in DNA interstrand cross-link (ICL) repair 57 suggesting that C1ORF146 might share similar functions in DNA repair. To explore this possibility, we expressed human green fluorescent protein (GFP)-tagged C1ORF146 in U2OS cells to test whether C1ORF146 could recruit to sites of DNA damage after treatment with various DNA-damaging drugs. Interestingly, treatment of cells with HU, H 2 O 2 , or cisplatin led to a marked chromatin retention of GFP-C1ORF146 (Fig.  5g ), indicating that C1ORF146 might recruit to damaged DNA to function in the DDRs. Supporting this idea, immunofluorescence microscopy analysis of GFP-C1ORF146 expressed in U2OS and RPE1 cells found that in a subset of cells, C1ORF146 forms discrete nuclear foci after treatment with cisplatin (Fig.  5i ), suggesting that C1ORF146 has functions in DNA repair. This function might rely on active replication fork progression as C1ORF146 recruitment to chromatin is confined to S-phase cells (Fig.  5h ).

In this study, we utilized a combination of systematic profile-HMM-based remote homology searches and the deep learning technology of AlphaFold to explore genome maintenance protein structures across species, with the aim of discovering previously unknown domains in the human proteome. Indeed, we identified unknown evolutionary conserved modules in GM protein structures and in uncharacterized proteins indicating that many facets of human GM remain unappreciated. We identify multiple DNA binding domain candidates such as OB-fold, SAP, MYB (SANT), and KU as well as adapter domains such as TUDOR, BRCT, NNCH, POLO box, MIS18, and UBA domains, supporting the idea that such protein domains tend to exhibit low evolutionary conservation, likely to enable variability in their binding affinities. Among the proteins functioning in mitosis, we expand our previous discovery of the NNCH family proteins with CEP44, HAUS3, HAUS6, HAUS7, and TEDC1. Interestingly, all are associated with the network of centrosomal proteins adding further credence to the notion that the NNCH family evolved to function in mitotic and ciliary processes 26 , 60 , 61 , 62 , 63 . The divergent SAP domain is most represented among all the domains in this study, which could be due to its unique and less conserved nature compared to other protein domains. In case of the OB-fold domain our study revealed (at the time of the analysis) six unknown OB-fold-bearing human proteins. Since then, structural characterization of OB-fold domains in CXORF57, SHLD2 (FAM35A), HROB (C17ORF53), and SPATA22 have been reported 15 , 16 , 17 , 39 , 41 , 45 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , while SPIDR remains structurally uncharacterized. Notably, we predict SPIDR to have three OB folds in its C-terminus, resembling other ssDNA repair proteins. Indeed, our experimental evidence supports SPIDR’s binding to ssDNA through its OB-fold region and point mutations affecting this domain markedly reduce ssDNA interaction. Interestingly, causal SPIDR variants implicated in primary ovarian insufficiency (POI) are predicted to disrupt OB-fold regions, affecting chromatin association. In agreement with these reports, we find that truncating mutations mimicking POI disrupts SPIDR’s chromatin affinity, supporting that POI is caused by loss of SPIDR’s DNA-binding properties and in turn its DNA repair activity. Curiously, HROB (C17ORF53), among other DNA repair proteins, was recently shown to be also mutated in POI 74 , further supporting the notion that POI is, at least in part, a DNA repair deficiency syndrome.

Another putative predicted DNA binding module, the MIS18 (Yippee) domain family of FAM72A-D, we also found to accumulate on chromatin during DNA replication and in response to genotoxic insults, suggesting a role in DNA damage response in addition to its reported roles in recombination class switch 75 , 76 . Further investigation demonstrated that this function of the FAM72 family members might be aided through multimer complexes, with FAM72A-D capable of homodimerization and heterodimerization. The role of FAM72 members on chromatin might be facilitated by additional DNA repair factors as FAM72B was identified to bind avidly to UNG in addition to RPA subunits involved in DNA repair. FAM72A was previously also shown to bind 77 and suppress UNG to inhibit base excision repair during CSR in human B cells 75 , 76 . Hence, since RPA is known to coordinate and reside in complex with UNG during CSR 78 , 79 and pre-replicative repair of mutagenic uracil in ssDNA 80 , FAM72B might suppress BER by acting on either UNG or RPA. Our present functional validation experiments with cells depleted of FAM72B revealed induced phosphorylation of RPA1 on serine residues 4 and 8, consistent with the functional link between FAM72B and RPA in avoiding or dealing with replication stress, a condition implicated in diverse pathologies including cancer 81 .

More surprising was the identification of eight hitherto unknown domains of the PARP and PARG families considering the past year’s intense scrutiny of PARPs in cancer therapy 82 . PARylation is a protein modification that is mediated by poly(ADP-ribose) polymerases (PARPs). Many PARPs have functions in GM processes particularly PARP-1 and PARP-2, the founding members of the PARP family, have been widely studied and have well-established functions in DNA integrity surveillance. The identification of an N-terminal PARP domain in TEX15 already known to function in the DDR 31 , suggests that additional PARPs partake in the chromatin remodeling after DNA damage. Recently, the PARP domain of TASOR was experimentally determined and subsequently demonstrated to be catalytically inactive. It was further found to be dispensable for assembly and chromatin targeting of TASOR but critical for its epigenetic regulation of repetitive genomic targets. Hence, TASOR was concluded to be a multifunctional pseudo-PARP that directs HUSH assembly and epigenetic regulation of repetitive genomic targets 33 . As such, the TASOR PARP domain recapitulates the non-catalytical pseudo-PARP function of ZAP (PARP13) by binding, albeit with low affinity, to RNA 83 , 84 . Indeed, the HUSH complex was recently demonstrated to collaborate with the NEXT complex of the RNA exosome pathway in the mechanisms of transcriptional and post-transcriptional control to limit the genotoxic activity of transposable element (TE) RNA 85 . Since we found that the predicted PARP domain in TASOR2 resembles that in TASOR, including the degenerate active site with its evolutionary loss of residues involved in NAD+ binding (otherwise conserved in active PARPs) 33 , we predict that TASOR2 might exert pseudo-PARP functions similar to TASOR e.g., bind RNA species.

We also identify thus far unknown HEAT or ARM repeats and TPRs in the lntegrator complex subunits. While well-defined at the functional level, the structure of the Int complex and its subunits had remained elusive until very recently. Recent cryo-electron microscopy analyzes of the human Int core complex, however, revealed that its subunits consist mostly of alpha-helical arrays adopting alpha-solenoid structures where short patches within each integrator subunit, INTS1, 3, 4, 7, and 8, could be evolutionarily annotated to known sequence repeat subfamilies 28 , 86 . We extend the structural features of these Integrator complex subunits to include the INTS2 and INTS10 subunits that are comprised of disparate arrays of TPRs units (Supplementary Fig.  3a, b ). Thus, the Int complex represents the third network in the overall human DNA repair system, besides the ATM or ATR kinase signaling network and the Fanconi anemia pathway, to be composed of aggregates of alpha-solenoid proteins 87 , 88 , 89 , 90 .

In conclusion, our study showcases the robust potential of computational methods in advancing our understanding of complex biological systems and demonstrates the power of combining these methods with experimental approaches for discoveries, thereby providing a roadmap for the identification of domains. Given the disease associations of mutations or deregulation of the genome caretakers 81 , 91 , 92 , the approach that we illustrate in this work can also inspire the identification and exploration of potential therapeutic targets.

Data collection

We compiled all annotated DDR and mitotic proteins from species for which primary evidence for protein DNA maintenance functions, through large-scale screens or low throughput studies, were available. These were extracted and filtered from commonly used databases 93 (Online Methods) and comprised four pooled gene ontology (GO) terms “DNA repair” (GO: 0006281), “DNA damage” (GO: 0006974), “cytokinesis”(GO: 0000910) and “mitotic cell cycle” (GO: 0000278) from each of the following species: E. coli, S. cerevisiae, S. pombe, A. thaliana, C. elegans, D. melanogaster , and H. sapiens . This strategy yielded a total of 3635 unique genome maintenance gene products. Next, to assess putative DDR proteins not accounted for by ontologies, for each species we examined all high-confidence interactions in two large interaction networks i.e., physical protein-protein interactions and co-expressed genes for associations. DNA repair genes or proteins interaction data sets that comprised all physical protein-protein interactions from the Integrated Interactions Database (IID) 94 , that were observed in at least two independent studies (390 GM interactors) and gene co-expression profiles (2820 GM co-expressed genes). In total, sequences from 6845 gene products, known or likely to be involved in GM processes were analyzed using the outlined profile-to-profile search scheme (Fig.  1a ). Each protein sequence was used in subsequent forward or reciprocal profile-HMM searches and the compiled list of GM candidate domain structures were validated using AlphaFold2 22 (Supplementary. Fig.  1a, b ).

Computational retrieval of DDR protein interactors

In order to identify potential gene pair candidates, we explored a state-of-the-art protein-protein interaction database, along with correlated expression patterns in gene expression data. We used the Integrated Interactions Database (IID) 94 . IID is a protein-protein interaction database that integrates data from different sources and includes pairs of interacting proteins that were either identified through experiments, inferred by orthology, or predicted. As we were interested in potential candidates involved in DNA repair, we searched IID for pairs of proteins of which one was known to be involved in such a process while the other wasn’t. To isolate these protein pairs of interest, we used Gene Ontology (GO) to annotate the protein pairs, focusing in particular on two GO terms: GO:0006281 (“DNA repair”) and GO:0006974 (“cellular response to DNA damage stimulus”). We integrated the information available in IID by associating the GO terms for each of the two genes whose proteins take part in every interaction, taken from the GO Consortium annotation for the human genome (generated on May 22, 2018; GO version 2018-05-14). We further elaborated this annotation by adding either one of both of our GO terms of interest (GO:0006281 or GO:0006974) when at least one of their respective child terms was present. We filtered the IID database, keeping those protein pairs for which GO:006281 was present in the annotation of one of the two, but neither GO:0006281 nor GO:0006974 were present in the annotation of the other. We further filtered the resulting database, aiming at keeping only those entries that i) had at least a piece of experimental evidence and ii) had at least two associated publications containing experimental evidence of the interaction to limit the number of potential false positives. Finally, we further filtered the dataset to exclude uninteresting proteins. We considered, for each protein pair, the one of the two that wasn’t annotated with GO:0006281, and filtered out the pair if the respective gene was present in a housekeeping gene list 95 . We similarly filtered the interaction list considering a list of protein contaminants frequently found in proteomics experiments, taken from the Crapome database 96 .

Analysis of gene co-expression profiles

For the initial GM gene compilation, we analyzed gene expression profiles from a panel of tissues of healthy individuals obtained from the Genotype-Tissue Expression (GTEx) database, aiming at identifying protein whose expression profiles were correlated, as proteins that are expressed with similar patterns are more likely to be interacting. We used the Co-expression Modules Identification Tool (CEMiTool) 97 to identify expression modules, i.e., groups of correlated genes, and considered each gene pair found in each of the identified modules as potentially interacting. Gene pairs were then processed by filtering them by a number of tissues they were found in, annotating them, filtered them by GOs of interest, and further filtered following the same protocol as the one we used for IID. We finally kept those pairs only present in at least two different tissues, yielding a total of 2820 pairs. Other than CEMiTool, the filtering and database manipulation was performed using Python. GOA terms annotation and ontology exploration were performed using the Biopython or goatools packages. Database processing was performed using the pandas package. All the software packages used are freely available. Scripts and data are available on our GitHub repository ( https://github.com/ELELAB/DDR-candidates ).

Focused gene co-expression analysis of specific genes i.e. FAM72 family and M1AP genes, were performed using the Gene Expression Profiling Interactive Analysis 2021 (GEPIA2) resource based on deconvolution analysis of the normal samples from The Cancer Genome Atlas (TCGA) and GTEx databases.

Profile-to-sequence and profile-HMM searches

For each protein, full-length FASTA sequences were extracted from the UniProt database and used to build MSAs by means of multiple re-iterative HHblits 21 or PSI-BLAST 98 searches with an E-value cutoff for MSA generation of E = 0.01. pBLAST and Iterative searches PSI-BLAST were performed at The National Center for Biotechnology Information (NCBI) http://blast.ncbi.nlm.nih.gov/Blast.cgi ) and MPI bioinformatics toolkit ( http://toolkit.tuebingen.mpg.de 1 , respectively. BLAST and PSI-BLAST searches were performed in the non-redundant (NR) protein sequence database at National Center for Biotechnology Information (NCBI). Default settings were utilized in the searches. To avoid spurious results or statistical bias, only regions devoid of coiled-coils as judged by heptad repeat occurrence and regions masked for compositional complexity were used in the searches. Upon detection of such spurious regions, the MSA generation was performed again with query sequences devoid of coiled-coil or disordered regions. We tested various methods available either as web servers or stand-alone programs, such as Compass 99 , COACH 100 , Jackhmmer 3 , or HHpred 4 , and found HHpred and Jackhmmer to display the best performances (Fig.  1c ). Hence, these methods were chosen for all subsequent HMM profile searches. HHpred searches were performed against the PFAMA database ( http://pfam.sanger.ac.uk ). To validate the matches, identified proteins in the first step were used as seeds in reciprocal profile-to-profile database searches performed as above. Only matches that could be recapitulated significantly in this second reciprocal step were regarded as positive hits. Finally, to further validate the robustness of the reciprocal GM candidate profile-HMM searches, we reassessed GM candidate searches by manually inspecting MSAs for corrupted regions using the filter options for HHblits query MSAs provided by the HH-suite package. Briefly, the initial HHblits query MSA was reduced to include a diverse set of over 30 sequences spanning various species, employing the hhfilter (-diff) option. This option preserves sequences with non-homologous segments, which often exhibit the greatest dissimilarity to the query sequence. Subsequently, the filtered MSA was further refined by removing inserts and non-homologous extensions using the “remove all insert” (-r) option, resulting in a master-slave alignment optimized for identifying non-homologous regions. After visually examining the filtered and trimmed MSA, any problematic regions, such as those containing non-homologous or short sequences, were eliminated. The resulting curated MSA was then utilized to construct an HMM for subsequent profile-HMM searches. Notably, this process consistently led to the recovery of the initially identified GM candidate structure families.

The scripts for the filtering procedure of GM protein MSAs are deposited at the https://github.com/ELELAB/DDR-candidates . Finally, we made use of the recent advances in protein structure prediction by machine learning as implemented in AlphaFold2 22 and validated the compiled list of GM candidates by protein 3D modeling.

Multiple sequence alignments were built by the MAFFT program ( http://myhits.isb-sib.ch/cgi-bin/mafft ) 101 (and the resulting alignment edited in Jalview ( http://www.jalview.org/ ). The consensus of the alignment was calculated and colored according to the Clustalx color scheme. Secondary structure information and structural alignment were predicted using HHpred. The AlphaFold2 software 6 was employed for homology modeling of 3D structures. Resulting 3D model coordinates were analyzed in Pymol and Discovery Studio 3.5 Visualizer. Coiled-coil propensities of target sequences were predicted as high propensity heptad repeats in Coils ( http://embnet.vital-it.ch/software/COILS_form.html ) 8 using both weighted and unweighted search algorithms. Identified coiled-coil regions were further validated based on the occurrence of alpha-helical content  (>50% helical) using the HNN server.

Protein complex prediction with ColabFold and AlphaFold3

To validate remote homology matches, reciprocal 3D models of candidate homologies, we utilized a modified version of AlphaFold2 on Colab notebook using the ColabFold v1.5.5 software package available at https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/AlphaFold2.ipynb for protein complexes with fewer than 1400 residues60,6,37. The MMseqs2 search engine46 was used to build the MSAs used for profile-HMM searches and subsequent evo blocks. No PDB templates were employed. From AlphaFold runs, AlphaPickle was used to derive the anticipated alignment score ( https://zenodo.org/record/5708709#.Y3OOpHbMKUk ). As shown in the figures, all structural predictions have low PAE scores for the interacting regions, indicating a high degree of certainty in the relative positions of the subunits within the complexes. All sequences used for structure prediction have at least 500 homologs in sequence databases that are currently available. Prediction of the complex between human SPIDR OB3 domain (residue 776-866) and DNA was performed in AlphaFold 3 at https://golgi.sandbox.google.com/ . The DNA sequence used was 5’-GGGATTTTCAGTTTGATTGACACC-3’ and the RNA control sequence used was 5’-GGGAUUUUCAGUUUGAUUGACACC-3’. Modeling was performed with the presence of Zn 2+ .

Phylogenetic analysis

Phylogenetic trees were obtained using phylogenetic analyzes using the maximum-likelihood method in the FastTree 2.1 program and IQ-Tree v.2.050 to estimate phylogenetic trees. In our analyzes, we utilized molecular evolution models determined through ModelFinder51 integrated into IQ-Tree. This approach identified an optimal partitioning scheme and the best model for each partition. To evaluate the support for the resulting topology, we conducted 1000 ultrafast bootstrap replicates52. The maximum-likelihood method from both the FastTree 2.1 and IQ-Tree v.2.050 programs produced essentially the same trees.

For immunoblot analysis, the following primary antibodies were used (dilutions in parenthesis): Mouse anti-FLAG (F-1804, 1:500) from Sigma, rabbit anti-FLAG (F7425, 1:1000) from Sigma, rabbit anti-GFP (sc-8334, 1:500) from Santa Cruz, mouse anti-GFP (sc-9996, 1:500) from Santa Cruz, rabbit anti-RPA1 (ab79398, 1:500) from Abcam, rabbit anti-Phospho-RPA32 (Ser4, Ser8) (A300-245A, 1:500) from Bethyl Laboratories, mouse anti-RPA32 (ab2175, 1:500) from Abcam, rabbit anti-GAPDH (2118, 1:2,000) from Cell Signal, and rabbit anti-histone H3 (ab1791, 1:4000) from Abcam.

PCR, cloning procedures, and plasmids

Plasmids encoding full-length and truncated versions of FLAG-SPIDR or full-length GFP-SPIDR were generated by PCR with relevant primers and human SPIDR plasmid as a template (Origene) followed by cloning into pFLAG-CMV2 (Sigma) or EGFP-C1 (Clontech) by standard procedures. Plasmid encoding human FLAG-tagged C1ORF146 was purchased from Origene Technologies (RC215196). Plasmid encoding full-length GFP-C1ORF146 was generated by PCR with relevant primers and human plasmid as a template (RC215196) followed by cloning into EGFP-C1 (Clontech). Mutagenesis of GFP-SPIDR by C817A and C820A substitutions was performed by a two-step PCR protocol as described previously with relevant primers 102 . Briefly, the point mutations were introduced by first performing two parallel PCR reactions using mutant primers and the same flanking primers as used to generate the WT GFP-SPIDR. First PCRs was performed using the primers

Forward: 5’-AAAAAGAATTCAATGCCCCGCGGCAGCCGC-3’ (EcoRI) with reverse: 5’-AAAAAGTGACCACCCGGGAGGCGTCCCCAGCGGAAAAGGCGCCTC-3’ and forward 5’-AAAAAGAGGCGCCTTTTCCGCTGGGGACGCCTCCCGGGTGGTCAC-3’ with reverse 5’-AAAAAGTCGACCTAGTGTTCTGCAGAGGC-3’ (SalI). Finally, the second step was performed by PCR of the flanking forward and reverse primers mixed with the PCR products of the first PCR step. The resulting PCR fragment was digested with the respective restriction enzymes and cloned into the EGFP-C1 vector (Clonetech).

Cell culture and transfections

HEK293T and U2OS cells were grown at 37 C in Dulbecco’s modified Eagle’s medium (DMEM, Gibco) with 10% heat inactivated fetal bovine serum (FBS, Gibco) and penicillin-streptomycin (Gibco) using 5% CO2 and 95% humidity. The RPE1 cells (laboratory stock, derived from the immortalized hTERT RPE1 cell line, ATCC CRL-4000) were grown in 45% DMEM and 45% F-12 (Ham; Sigma) with 10% FBS and penicillin-streptomycin; cultures were passaged every 3-4 days. For plasmid transfection of HEK293T cells, 8 mg DNA was transfected into cells in a 15 cm dish using Fugene 6 and incubated for 24 hours.

For FAM72B protein depletion, U2OS cells were transfected with siRNA two times over two days and incubated additionally two days. Cells were grown to approximately 80% confluence in 9.6 cm2 petri dishes before first transfection; 5-6 hours after transfection the medium was changed. Two FAM72B-specific siRNAs were used, both purchased at Eurofins MWG: Operon: siFAM72B-1 (5’- CCA GGC AGU UUA UGA UAU U-3’) and siFAM72B-2 (5’-CAG CAU GAU GUU AGA UAA A-3’). All transfections with siRNA (final concentration 250 nM) were carried out using DharmaFECT Duo. siCONTROL (Dharmacon) was used as a control siRNA 6 hours after transfection fresh growth medium was added. The cells were subjected to double transfection with an interval of 24 hours. In Biotin-DNA pull-down assays, Dynabeads T1 from Life Technologies underwent two washes in PBS buffer before being bound to biotinylated DNA substrates at room temperature for 30 minutes. Subsequently, the beads underwent two additional washes in PBS buffer, followed by two washes in binding buffer (composed of 80 mM Tris, pH 7.5, 100 mM KCl, 5 mM MgCl2, 2 mM DTT, and 100 mg/ml BSA in RNase and DNase-free water). Next, 1ul of beads carrying 4 picomoles of bound DNA was resuspended in the binding buffer. Approximately 500 femtomoles of purified protein were introduced to the mixture, which was then rotated at room temperature for 30 minutes. The supernatant was discarded, and the beads were boiled in 2X sample buffer for 5 minutes. Subsequently, captures were subjected to analysis through immunoblotting. The single-stranded DNA oligo employed for pull-down assays was a poly dT50, while the double-stranded DNA was created through annealing 5’-Biotin-GGATGATGAC TCTTCTGGTCCGGATGGTAGTTAAGTGTTGAG-3’ with its complimentary oligo.

Immunofluorescence microscopy

IFM analysis of GFP-C1ORF146-expressing RPE1 cells was carried out as follows. Cells grown on glass coverslips were washed once in ice-cold PBS, fixed with 4% paraformaldehyde (PFA) solution, permeabilized with permeabilization buffer (PBS with 0.1% (v/v) Triton-X100 and 1% (w/v) bovine serum albumin (BSA) and subjected to IFM as described previously 103 . Imaging was done using a Zeiss Observer Z1 microscope. Images were processed for publication using Adobe Photoshop CS4 version 11.0.

Immunoprecipitation and immunoblotting

HEK293T cells were transfected the day before immunoprecipitation (IP). Cells were harvested in ice-cold EBC buffer (140 mM NaCl, 10 mM Tris-HCl, 0.5% NP-40 and protease inhibitor cocktail (Roche)). For FLAG IP experiments, cleared cell extracts were incubated 1 h with 20 μl anti-FLAG (M2) conjugated magnetic beads (Sigma) under constant rotation (4 C). The subsequent IP was performed with 10 μl Anti-FLAG (M2) conjugated magnetic beads for 1 h, and Immunocomplexes were washed five times in EBC buffer before elution with 1x FLAG peptide (Sigma). Eluted FLAG-proteins complexes were purified further by micropore filter centrifugation.

Analysis by SDS-PAGE and immunoblotting with relevant antibodies was performed using the Novex system from Invitrogen and by following the protocol supplied by the vendor. Blots were incubated in primary antibodies at appropriate dilutions, incubated with relevant horse radish peroxidase-conjugated secondary antibodies. Images were processed in Adobe Photoshop CS6.

For immunoblot analysis, the following primary antibodies were used (dilutions in parenthesis): rabbit anti- RPA2 pSer33 (A300-246A, Bethyl (1:500)), rabbit anti-RPA1 (Ab79398, Abcam) (1:500), rabbit anti-FLAG (1:1000) from Invitrogen, mouse anti-FLAG-tag (F1804 (Clone M2), Sigma Aldrich (1:500)), mouse anti-GFP (11814460001, Roche (1:500), rabbit anti-GFP sc-9996 (Clone B2), Santa Cruz (1:1,500)), anti-Histone H3 (#9715), Cell Signal (1:2000). Secondary antibodies used for immunoblotting: horseradish peroxidase-conjugated goat anti-mouse (P0447, 1:4,000) or swine anti-rabbit (P0399, 1:4,000) from Dako. For IFM analysis, the following primary antibodies were used: Phospho-RAD17 (Ser656) from ThermoFisher (711717). Alexa Fluor 350-conjugated donkey anti-mouse (A-10035) or donkey anti-rabbit (A-10039); Alexa Fluor 488-conjugated donkey anti-mouse (A-21202), donkey anti-rabbit (A-21206) or donkey anti-goat (A-11055), Alexa Fluor 568-conjugated donkey anti-mouse (A-10037), donkey anti-rabbit (A-10042) or donkey anti-goat (A-11057).

Mass spectrometry

To understand the potential function of the FAM72 paralogs in GM, we searched for FAM72B-interacting proteins as an example using the FLAG affinity purification method on cell extracts of cells expressing the FLAG-FAM72B (Supplemental Fig. 5a). The FLAG-purified immunocomplexes were eluted with FLAG peptides and prepared with the Protein Aggregation Capture (PAC) method 104 followed by Trypsin and Lys-C-digestion. Peptides were subsequently analyzed by mass spectrometry. Mass spectrometry raw data were subsequently processed in MaxQuant and was further analyzed in Perseus. The experiment was done in two replicas (X = 2) using a control and a FLAG-FAM72B pulldown per experiment (four samples in total). The specific details of the mass spectrometry analysis were as follows. The nLC-nESI MS/MS analysis was performed in an Easy1200 chromatographic system coupled to a Exploris 480 mass spectrometer (Thermo). About 1 μg of the tryptic digest was applied into an analytical column (New Objective x 75 μm internal diameter x 1.9 μm particle size ReproSil-Pur 120 C18-AQ, DR. MAISCH). Mobile phase A (0.1% v/v formic acid in water) and mobile phase B (0.1% v/v formic acid in acetonitrile) were used in a separation gradient from 2 to 40 % B for 120 minutes. The spray voltage was adjusted to static in the nanoelectrospray source, with no auxiliary gas flow and capillary temperature are also static. The lens voltage was set to 50 V. MS1 spectra were acquired in the profile mode in the Orbitrap analyzer (m/z 350 to 1500) with a resolution of 60,000 FWHM and Automatic Gain Control % (AGC) set to 300. Up to 12 precursor ions per MS1 spectrum were selected for fragmentation with higher-energy collisional dissociation (HCD) with normalized collision energy (NCE) of 30. The isolation window was set to 1.2 m/z and the dynamic exclusion configured to 60 s. MS2 spectra were acquired in the Orbitrap at a resolution of 30,000 FWHM; AGC (%) was set to 200, intensity threshold of 10,000 counts. Singly charged and unassigned ions were not subjected to fragmentation. Data were obtained using Xcalibur software (version 4.4.16.14).

Bioinformatic analysis of proteomic data

The mass spectrometry data was searched using MaxQuant version 2.0.3.0 which includes the search andromeda integrated with default parameters, against human FASTA file downloaded from UniProt (uniprotkb_human_proteome_AND_reviewed_t_2023_06_18). Briefly, the parameters used for the search included carbamidomethylation of cysteines as a fixed modification and the variable modifications, methionine oxidation, and N-terminal acetylation. The criteria for peptide acceptance were peptide PSM FDR 0.01, protein FDR 0.01, 10 ppm for peptide tolerance, peptide minimum length 7 amino acids, peptide maximum length 45, site decoy fraction 0.01, minimum peptide 1, minimum razor + unique peptides 1, minimum unique peptide 0, minimum score for unmodified 0, minimum delta score for modified peptides 6, main search maximum combinations. Digestion mode was specific for trypsin with maximum 2 missed cleavages. The MaxQuant output data was further analyzed in Perseus version 2.0.10.0 using the LFQ intensities filtered for contaminants, reversed peptides, and proteins only identified by site. Log2 transformed LFQ intensities with imputed missing values were subjected to two-sample t-test followed by volcano plot visualization.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

Proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with PXD identifier: PXD043273 . The following structures from the PDB database have been used: 6BZG (yeast SPO16-ZIP2 complex), 1JEY (human Ku heterodimer bound to DNA), 3FA2 (human BARD1 Tandem BRCT Domains), 7KK2 (human PARP1 catalytic domain), 6UEJ (human PARP13 bound to RNA), 4B1G (human PARG catalytic domain), 6I52 (yeast RPA bound to ssDNA), 7SFZ (human Mis18a-yippee domain), 6G70 (murine Prpf39), 2LLK (human DMTF1 MYB (SANT) domain), and 1JJR (SAP domain of human KU70). Validation reports for each structure are provided for the respective PDB entries at https://www.rcsb.org/ . Datasets for gene co-expression analysis are available http://gepia2.cancer-pku.cn/#dataset and the Cancer Genome Atlas (TCGA) https://www.cancer.gov/ccg/research/genome-sequencing/tcga . For the initial data compilation and filtering, the house keeping list was retrieved from https://doi.org/10.1016/j.tig.2013.05.010 95 . Bulk raw output data can be found in the Figshare repository ( https://doi.org/10.6084/m9.figshare.26014669.v1 , https://doi.org/10.6084/m9.figshare.26014621.v1 , https://doi.org/10.6084/m9.figshare.26015185.v1 , https://doi.org/10.6084/m9.figshare.26014609.v1 , https://doi.org/10.6084/m9.figshare.26014858.v1 , https://doi.org/10.6084/m9.figshare.26014900.v1 , https://doi.org/10.6084/m9.figshare.26015095.v1 , https://doi.org/10.6084/m9.figshare.26015680.v1 , https://doi.org/10.6084/m9.figshare.26015332.v1 ).  Source data are provided with this paper.

Code availability

The scripts, input files and output files are available at GitHub: https://github.com/ELELAB/DDR-candidates .

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Acknowledgements

This work was funded by the following grants from: the Danish Cancer Society (R322-A17482), the Swedish Cancer Fonden (nr. 170176), the Swedish Research Council (VR-MH 201446602-117891-30), the Novo Nordisk Foundation (NNF 20OC0060590), Danish Foundation for Independent Research (DFF 1026-00241B), the Danish National Research Foundation (project CARD, DNRF 125) and Carlsberg Foundation Distinguished Fellowship (CF18-0314).

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Kenneth Bødkter Schou, Samuel Mandacaru, Muhammad Tahir & Jens S. Andersen

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K.B.S. and J.B. designed and interpreted experiments. K.B.S., S.M., M.Tahir., and A-S.N. conducted experiments. K.B.S., M.Tiberti. N.T., E.P. designed and carried out bioinformatics analysis. K.B.S. wrote, and J.B. and J.S.A. modified the manuscript. J.B. directed the project. All authors commented on and approved the manuscript for submission.

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Schou, K.B., Mandacaru, S., Tahir, M. et al. Exploring the structural landscape of DNA maintenance proteins. Nat Commun 15 , 7748 (2024). https://doi.org/10.1038/s41467-024-49983-7

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Aurora Borealis Can Light Up The Sky And Shut Down The Grid

During a thunderstorm, you can feel rain pouring, see lightning flashing and hear wind howling. Unlike these phenomena, the Aurora Borealis cannot be heard or felt because it occurs through the earth’s magnetic field’s invisible influence, noticeable only if you seek it out.

And while the northern lights may look mesmerizing, these silent solar storms flickering across the night sky can wreak havoc on the power grid and communication networks.

A team of researchers in the Department of Electrical and Computer Engineering at Texas A&M University, including Dr. Jonathan Snodgrass, a senior research engineer, and Dr. Thomas Overbye, a professor and the project’s principal investigator, are using real and synthetic grid models to research recommendations for power grid operators about how to lessen the impact of major solar storms on the grid.

“Think about it like an experiment in high school science class when you take a magnet and little iron shavings and line up the shavings with the magnet,” Snodgrass said. “The magnetic shavings are the lights we see, and the magnet is the magnetic field. When the magnetic field starts to wobble around from a solar storm, we get the Northern Lights. It’s interesting because most people wouldn’t think about it. After all, it has no other effect except on communication and power systems.”

Solar storms create a mostly direct current (DC) in the electric grid. Normally, the grid uses alternating current (AC) to produce and distribute power. When DC is on top of AC, it can cause issues with transformers, causing parts of the electric grid to trip. Depending on the category of the solar storm (G1-G5), the power system is affected, sometimes to a very large degree, pushing the Northern Lights farther south. Typically, a G1 is weak and a G5 is severe.

This year, a mild G5 geomagnetic disturbance (GMD) caused the Aurora Borealis lights to appear in Texas, visible from College Station.

“This was one of the biggest solar storms we’ve had in 20 years,” Snodgrass said. “But it wasn’t Superstorm Sandy-level. Our research focuses on answering the question: how bad could it be if we had a superstorm GMD event? How much damage would it do if we had a Carrington-level storm?”

Dr. Overbye and his team are using anecdotal evidence from the Carrington Event to reverse engineer how intense the storm was and use the data to prepare the grid for future superstorms. The 1859 Carrington Event is the largest recorded GMD event in history, named after English astronomer Richard Carrington, that greatly affected the telegraph system before the power grid was around.

“There were cases of people getting shocked by telegraph keys,” Snodgrass said. “They even disconnected their voltaic batteries that powered the telegraph system and used the current induced by the solar storm in the air to send messages for hours.”

A solar storm hit Quebec 130 years later in 1989 and caused a blackout for eight hours.

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“You don’t have a great certainty as to how bad it will be until it gets a little closer,” Snodgrass said. “A CME first hits the satellites in Earth’s orbit, which have sensors that can predict whether it will be a G1-5. We then have about a few hours to a day’s notice. This is why our research is so important because there needs to be a plan in place if, and when, we have a big superstorm, a category 5 or beyond.”

In the last 20 years, there have been large CMEs, but none of them have hit Earth. In 2003, there was a much bigger CME that passed right by Earth that might have been as big as the Carrington Event.

“It got people thinking, what if it had hit us?” Snodgrass said. “Between the ’89 storm and the 2003 near miss, it spurred a lot of this research. Dr. Overbye is one of the pioneers in this field of modeling geomagnetic storms and their impact on the electric grid.”

The team has a magnetometer network on Texas A&M System land they use to conduct experiments and record data, far away from metal and power lines. A magnetometer is like a very advanced compass, but instead of pointing north, it records fluctuations in the Earth’s magnetic field.

“There’s been a massive resurgence of research in the power grid,” Snodgrass said. “A&M is so well positioned to conduct it because we have some of the best researchers in the world here.”

The geomagnetic disturbance research team also includes electrical and computer engineering faculty Dr. Kate Davis, associate professor, and Dr. Adam Burchfield, assistant professor. Additionally, the Texas A&M researchers are working on this joint project with Pacific Northwest National Labs and the Electric Power Research Institute.

This article by Katie Satterlee originally appeared on the College of Engineering website .

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