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Collaborative Systems Biology Projects for the Military Medical Community

Collaborative Systems Biology Projects for the Military Medical Community ABSTRACT Introduction: This pilot study was conducted to examine, for the first time, the ongoing systems biology research and development projects within the laboratories and centers of the U.S. Army Medical Research and Materiel Command (USAMRMC). The analysis has provided an understanding of the breadth of systems biology activities, resources, and collaborations across all USAMRMC subordinate laboratories. Methods: The Systems Biology Collaboration Center at USAMRMC issued a survey regarding systems biology research projects to the eight U.S.-based USAMRMC laboratories and centers in August 2016. This survey included a data call worksheet to gather self-identified project and programmatic information. The general topics focused on the investigators and their projects, on the project's research areas, on omics and other large data types being collected and stored, on the analytical or computational tools being used, and on identifying intramural (i.e., USAMRMC) and extramural collaborations. Results: Among seven of the eight laboratories, 62 unique systems biology studies were funded and active during the final quarter of fiscal year 2016. Of 29 preselected medical Research Task Areas, 20 were associated with these studies, some of which were applicable to two or more Research Task Areas. Overall, studies were categorized among six general types of objectives: biological mechanisms of disease, risk of/susceptibility to injury or disease, innate mechanisms of healing, diagnostic and prognostic biomarkers, and host/patient responses to vaccines, and therapeutic strategies including host responses to therapies. We identified eight types of omics studies and four types of study subjects. Studies were categorized on a scale of increasing complexity from single study subject/single omics technology studies (23/62) to studies integrating results across two study subject types and two or more omics technologies (13/62). Investigators at seven USAMRMC laboratories had collaborations with systems biology experts from 18 extramural organizations and three other USAMRMC laboratories. Collaborators from six USAMRMC laboratories and 58 extramural organizations were identified who provided additional research expertise to these systems biology studies. Conclusions: At the end of fiscal year 2016, USAMRMC laboratories self-reported 66 systems biology/computational biology studies (62 of which were unique) with 25 intramural and 81 extramural collaborators. Nearly two-thirds were led by or in collaboration with the U.S. Army Telemedicine and Advanced Technology Research Center/Department of Defense Biotechnology High-Performance Computing Software Applications Institute and U.S. Army Center for Environmental Health Research. The most common study objective addressed biological mechanisms of disease. The most common types of Research Task Areas addressed infectious diseases (viral and bacterial) and chemical agents (environmental toxicant exposures, and traditional and emerging chemical threats). More than 40% of the studies (27/62) involved collaborations between the reporting USAMRMC laboratory and one other organization. Nearly half of the studies (30/62) involved collaborations between the reporting USAMRMC laboratory and at least two other organizations. These survey results indicate that USAMRMC laboratories are compliant with data-centric policy and guidance documents whose goals are to prevent redundancy and promote collaborations by sharing data and leveraging capabilities. These results also serve as a foundation to make recommendations for future systems biology research efforts. INTRODUCTION Military medical research is a key driver for providing requirements-driven, evidence-based recommendations, and better medical care.1,2 The military medical research community's implicit mandate is for the continual improvement of medical care.3 Systems biology, a relatively new research concept, is becoming a rapidly adopted approach to increase our understanding of the mechanisms underlying disease processes and normal homeostasis, as well as being a foundation for precision or personalized medicine.4 As background, several definitions are presented here along with related concepts and data-centric policy drivers. Systems biology (also referred to as systems medicine or integrative medicine) integrates a variety of experimental, clinical, and human subject data, along with computational models,5 in which predictions are “the gold standard of scientific tests.”6 Some predictions focus on biomarker identification which, in turn, could lead to more effective therapies or even to preventive care. Often associated with systems biology are the genomics and other omics technologies that generate big data. An early definition of big data was “the amount of data just beyond technology's capability to store, manage, and process efficiently.”7 More recently, big data were defined by the four Vs: volume, variety, velocity, and value. This and other multi-V definitions are widely used to highlight the meaning and necessity of big data, especially in the case of biomedical data.8 The rapid rise of high through-put omics technologies and advances in computer capabilities have brought with them the recognition of requirements for more data storage, open data access, and data sharing. Accordingly, the Executive Branch and the Department of Defense (DoD) have sought to provide guidance on these issues9,–14 and to foster collaborative efforts.15,16 Collaborations have also been the focus in other research areas. For example, a set of key principles was recently drafted that is related to industry-university partnerships.17 These principles included sharing ideas, incentivizing collaborations, establishing clear agreements on the use of intellectual property, and leadership's role in building trust (confidence) among the partners.18 As with confidence-building measures of openness and transparency in developing policies and diplomatic actions,19 trustworthiness of research colleagues and their data are subjective variables that, along with data sharing and teamwork, form a foundation for building productive collaborations. Data integrity and, implicitly trust, have also been the subject of several DoD policies20,21 that were initiated by the White House.22 Although there are policies, metrics, and standards for data reproducibility or integrity, these have yet to be implemented, and they represent capability gaps in the area of data sharing. The present study is an initial effort to determine the scope of systems biology research within USAMRMC laboratories and the extent to which data sharing and collaborative efforts are occurring in these projects. This study also provides a gauge for assessing the degree of compliance with these policies and the related guidance. The main assessment tool used was a survey sent to the eight USAMRMC laboratories (August 8, 2016) entitled “USAMRMC Systems Biology Research Studies.” This survey was designed to provide the USAMRMC senior leadership and the Systems Biology Collaboration Center (SBCC) with an understanding of the breadth of on-going systems biology activities and resources across all USAMRMC subordinate laboratories. This information was intended to (1) demonstrate the scope of USAMRMC's system biology activities to stakeholders, (2) identify systems biology resources across USAMRMC that can facilitate collaborations for understanding and counteracting military medical threats, and (3) identify systems biology experts who can advise USAMRMC and others about opportunities for and the needs of systems biology investigators. The results of surveys such as this provide a foundation for recommendations on making programmatic and funding decisions by the senior leadership for future systems biology research efforts. METHODS Survey The SBCC created a survey entitled “USAMRMC Systems Biology Research Studies” that was sent to all U.S.-based USAMRMC laboratories. The laboratories included eight organizations: U.S. Army Telemedicine and Advanced Technology Research Center (TATRC)/DoD Biotechnology High-Performance Computing Software Applications Institute (BHSAI), U.S. Army Aeromedical Research Laboratory, U.S. Army Center for Environmental Health Research (USACEHR), U.S. Army Institute of Surgical Research, U.S. Army Medical Research Institute of Chemical Defense, U.S. Army Medical Research Institute of Infectious Diseases, U.S. Army Research Institute of Environmental Medicine, and Walter Reed Army Institute of Research. The survey consisted of two MS Excel worksheets of currently funded systems biology studies being conducted at each of the USAMRMC laboratories. Although there are different interpretations of “systems biology,” the following working definition served as a guide for the types of studies that should be included: any study that applies bioinformatics/computational biology tools to integrate, analyze, and model large volume biological (e.g., “omics”) data with experimental animal research data, human clinical and phenotypic data, and/or microbial phenotypic data. The “Systems Biology Studies” worksheet/tab contained the data fields (rows) to be completed for each systems biology research study being performed by investigators in the laboratory (Supplemental Table S1). The second (“instructions”) worksheet provided definitions and examples for the data fields (Supplemental Table S2). Although some programmatic information, such as funding source was sought (Supplemental Table S1, data field “PAD/JPC” [Army's Program Area Directorate/DoD's Joint Program Committee]), levels of funding were not requested. The amounts of data being generated and stored were also not requested but will be an item in future surveys (see Conclusions). Interpretation and Categorization of Responses Responses to the survey were evaluated for completeness and relevance to the instructions. Responses for many of the fields were optional. However, follow-up questions were asked to either clarify incomplete answers, or to clarify answers that were in conflict between two data fields, or to complete answers to fields that required a response. For responses to the required data field “Task Area Being Investigated” that did not follow the instructions, the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling fields” were examined to determine whether those entries could be used to unambiguously assign the Research Task Area. The SBCC provided the USAMRMC laboratories a list of 29 Research Task Areas in the survey instructions that included the Budget Activity 6.1 to 6.3 Army Budget Task Areas for 25 of the 29 Research Task Areas. The other four Research Task Areas to account for competencies that are not currently managed by Army Task Areas are “Pain” and “Neuromusculoskeletal Rehabilitation” (Defense Health Program [DHP] funded), “Biological Toxins,” and “Traditional and Emerging Chemical Threats” (Defense Threat Reduction Agency [DTRA] funded). For example, a “Project Title” that included the term human immunodeficiency virus would be identified only with the “Viral Diseases Research” Task Area. If those entries could not be used to unambiguously categorize the Research Task Areas, the laboratory point of contact was requested by the SBCC to update the data field. The submitted data sets were evaluated to look specifically for reports of the same collaborative study from two or more laboratories. Four such projects that were reported by separate laboratories were confirmed as the same overall collaborative studies, and the total number of unique collaborative studies was adjusted to eliminate this duplication. For reporting purposes, data fields that permitted open responses were reviewed to categorize the results. Responses to the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling” fields were reviewed to define common themes, and six common categories of Topics of Study Objectives were created. Responses to the “Type(s) of Omics in Study” were consolidated from 12 to 8 types of omics based on the molecule under investigation. For example, genomics and metagenomics (from environmental samples) that rely on DNA as the source material were combined as one omics category. Sources of Error and Caveats A number of different false-positive identifications were possible, including misinterpretation of a submitted response.23 Misidentifications or misunderstandings of the survey instructions by a submitter may have led to reporting a project that was not, in fact, related to systems biology. Also, the identified collaborator may have been an organization performing a service rather than acting in a collaborative manner. Further, proposed projects rather than active ones could have been reported. Due to the few responses received for “Publications,” this survey item was not analyzed presumably because most of the reported studies were only recently started. In contrast, some projects may have been overlooked (false negatives) as being a part of a systems biology approach. The reported laboratory omics instrumentation item in the survey (Supplemental Table S1) did not allow for other important instrumentation at that laboratory to be listed in Supplemental Table S6. Nonresponses (non-observations) also introduced a form of bias in surveys24 (Table I footnote). The categorization of Research Task Areas and the categorization of overall study objectives using other information provided in the responses reduced potential variable interpretations of the categories, yet introduced error related to interpreting the responses. TABLE I. Responses to Data Fields in the Systems Biology Studies Worksheet Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  t1 Answers of “N/A” and “None” were considered responses. Answers of “unknown” and “data generation in progress” were excluded (see Results). View Large TABLE I. Responses to Data Fields in the Systems Biology Studies Worksheet Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  t1 Answers of “N/A” and “None” were considered responses. Answers of “unknown” and “data generation in progress” were excluded (see Results). View Large RESULTS Participating USAMRMC Laboratories and Initial Results The percentage of responses answered for the survey's 18 data fields are listed in Table I. Response percentages ranged from 58% (“Additional Comments”) to several with 100%. The “% Answered” field considered a response of “Unknown” or a blank field to be negative replies, which represents a potential survey response bias (see Methods). Even so, most of the nonresponses occurred in data fields that would not apply to every study, and many of the nonresponses likely indicate that the data field did not apply (e.g., there was no “Additional Point of Contact”). Seven of the eight USAMRMC laboratories self-reported that 66 currently funded studies were systems biology efforts (Fig. 1, Supplemental Table S3). The eighth laboratory responded that no relevant studies were currently being conducted. Of these, 62 were unique studies, whereas four studies were collaborations between a few of these laboratories. Nearly two-thirds of the USAMRMC systems biology studies were conducted by investigators at USACEHR and BHSAI, and, notably, investigators at BHSAI conducted all the TATRC systems biology studies. FIGURE 1. View largeDownload slide Number of Systems Biology Studies Ongoing at the Laboratories in fiscal year 2016. FIGURE 1. View largeDownload slide Number of Systems Biology Studies Ongoing at the Laboratories in fiscal year 2016. Research Task Areas and Topics of Study Objectives As outlined in the Methods, the SBCC provided the USAMRMC laboratories a list of 29 Research Task Areas in the survey instructions. The list included the Army Budget Task Areas for 25 of the 29 Research Task Areas with four other Research Task Areas funded by either the DHP or the DTRA. It should be noted that 35% of the original responses (28 of 66) did not correspond to any item on the Research Task Area list. For most of these noncorresponding responses, Research Task Areas were chosen by the SBCC based on unambiguous descriptions provided in other data fields, whereas other Research Task Areas could only be assigned by recontacting the laboratory point of contact, as described in the Methods. The distribution of Research Task Areas among the USAMRMC laboratories conducting systems biology studies is shown in Figure 2. Twenty Research Task Areas were matched (Fig. 2) with each of the unique 62 self-reported studies (Supplemental Table S4). Although most studies were matched to only one Research Task Area, 12 studies were matched to two or more Research Task Areas. Seventeen of the 20 Research Task Areas being investigated by the systems biology studies correspond to USAMRMC Army medical research budget planning and programming tasks. Of the four Research Task Areas identified with DHP or DTRA support, only “Neuromusculoskeletal Rehabilitation” was not reported to be associated with any systems biology study. Studies investigating infectious disease and toxicant/chemical injury Research Task Areas accounted for more than half the USAMRMC's systems biology portfolio. FIGURE 2. View largeDownload slide Distribution of Research Task Areas among the USAMRMC Laboratories Conducting Systems Biology Studies. FIGURE 2. View largeDownload slide Distribution of Research Task Areas among the USAMRMC Laboratories Conducting Systems Biology Studies. In addition, the SBCC reviewed the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling” data fields for each study to classify the study according to six categories of study objectives (Supplemental Table S5): biological mechanisms of disease, risk of/susceptibility to injury or disease, innate mechanisms of healing, diagnostic and prognostic biomarkers, host/patient responses to vaccines, and therapeutic strategies including host responses to therapies. Study objectives were not mutually exclusive, and studies were assigned either one or two study objectives. Forty-one studies were assigned one study objective, and the remaining 21 studies were assigned two study objectives. Investigating “biological mechanisms of disease” was the most common study objective, identified with 38 separate studies. Investigating “innate mechanisms of healing,” such as immune responses to virus infection, was identified with 14 studies, of which six were also categorized to the “biological mechanisms of disease” study objective. Systems Biology Study Methods and Complexity of Studies The SBCC identified eight types of omics studies and seven type(s) of study subjects (i.e., animals, humans, microbes, three combination pairs, and “other”) (Table II). TABLE II. Systems Biology Study Methods    Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4     Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4  View Large TABLE II. Systems Biology Study Methods    Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4     Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4  View Large Furthermore, the 62 studies were defined on a scale of increasing complexity based on the application of omics analytical tools and the number of study subjects investigated. Seven studies did not include any omics research. Twenty-three studies applied one omics technology to one study subject. Five studies applied one omics technology to interrogate two study subject types, and 14 studies applied two or more omics technologies to one study subject. Thirteen studies applied two or more omics technologies to interrogate two study subject types (Supplemental Figure S1). Collaborating Organizations The numbers of intramural and extramural organizations collaborating with each of the USAMRMC laboratories in systems biology studies are shown in Figure 3. Notably, USACEHR had systems biology study research collaborations with 54 separate organizations—both intramural and extramural—in 2016. FIGURE 3. View largeDownload slide Distribution of organizations collaborating on USAMRMC System biology studies among USAMRMC Laboratories. FIGURE 3. View largeDownload slide Distribution of organizations collaborating on USAMRMC System biology studies among USAMRMC Laboratories. Seven of USAMRMC's intramural laboratories had collaborations with investigators identified as systems biology experts (Supplemental Figure S2) from 18 extramural organizations and three other USAMRMC laboratories. Four of USAMRMC's intramural laboratories (Supplemental Figure S3) had collaborations with researchers in 64 different organizations applying other types of research expertise. Of these, 58 were extramural organizations and six were other USAMRMC laboratories. DoD labs and organizations collaborating with those in the USAMRMC included the Air Force Research Laboratory, Armed Forces Health Surveillance Center, Edgewood Chemical Biological Center, Millennium Cohort Study, Natick Soldier Systems Center, Naval Health Research Center, U.S. Army Public Health Center, and U.S. Special Operations Command. The distribution of USAMRMC systems biology studies by number of collaborating organizations per study is shown in Figure 4. The majority of systems biology studies involved collaborations with one or two other organizations. Five studies had no collaborators (data not shown), whereas two studies involved six or more collaborating organizations. Among the 54 systems biology studies with collaborators, five had intramural collaborators only, 17 had both intramural and extramural collaborators, and 32 had extramural collaborators only. Responses from four other studies did not specify numbers or locations of collaborators (data not shown). FIGURE 4. View largeDownload slide Distribution of USAMRMC System biology studies according to those with only extramural collaborators, only intramural collaborators, and a combination of intramural and extramural collaborators. FIGURE 4. View largeDownload slide Distribution of USAMRMC System biology studies according to those with only extramural collaborators, only intramural collaborators, and a combination of intramural and extramural collaborators. Collaborating Principal Investigators The numbers of intramural and extramural principal investigators collaborating on USAMRMC Systems Biology studies were collected for each USAMRMC laboratory (Fig. 5A). The numbers of extramural principal investigators with systems biology expertise or expertise in other areas of study were also collected (Fig. 5B). FIGURE 5. View largeDownload slide Distribution of Principal Investigators collaborating on USAMRMC systems biology studies by institutional type and technical expertise. (A) System Biology Collaborating Investigators at USAMRMC labs. (B) Technical contributions Extramural Investigators. FIGURE 5. View largeDownload slide Distribution of Principal Investigators collaborating on USAMRMC systems biology studies by institutional type and technical expertise. (A) System Biology Collaborating Investigators at USAMRMC labs. (B) Technical contributions Extramural Investigators. Principal investigators were counted for each laboratory with which they partnered. Intramural and extramural systems biology collaborators were counted separately (Fig. 5A). From each USAMRMC laboratory, extramural principal investigators providing systems biology expertise were counted separately from those investigators providing other technical and scientific contributions to the collaboration (Fig. 5B). If the survey response did not indicate name(s) of investigator(s) at a collaborating organization, the organization was assumed to have one principal investigator. Collectively, these data indicate 25 intramural and 81 extramural investigators were collaborating on USAMRMC systems biology studies. Types of Omics Instrumentation and Technical Expertise The list of methods used in these systems biology studies by four of the seven USAMRMC laboratories conducting systems biology studies includes sequencing applications, transcriptome analyses, bioassays, and mass spectroscopy (Supplemental Table S6). As mentioned in the Methods caveats section, laboratory instrumentation that was not currently used on a given systems biology study may not have been identified in the responses, thus making this list only a partial reflection of a laboratory's entire total technical capability. Data Storage Plans for Each USAMRMC Laboratory The main objective in DoD's “Plan to Establish Public Access to the Results of Federally Funded Research”14 is to make publically available the reports in peer-reviewed scholarly publications and the accompanying digital data sets that support the conclusions of the DoD-funded research. Large volumes of data are planned to be stored in decentralized public repositories, e.g., the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). In contrast, the plan assigns the responsibility to the Defense Technical Information Center and its Unified Research and Engineering Database25 to be the central data catalog/locator to consolidate the metadata which includes links to the digital data and related documents. Local domain experts will have oversight responsibilities ensuring that smaller data sets are stored either locally or in other repositories “in formats that comply with DoD's implementation of OMB Memorandum M13-13.”10 Responses to “Data Storage” survey items are summarized by location of a storage resource in Supplemental Figure S4. The vast majority of USAMRMC systems biology studies store or plan to store data on their own networks and servers, though significant numbers of studies store data with partners' networks and servers and online data repositories. CONCLUSIONS At the end of fiscal year 2016, USAMRMC laboratories self-reported 62 unique systems biology studies with ∼80 extramural collaborators. Nearly two-thirds of all the studies were led by or in collaboration with BHSAI (TATRC) and USACEHR. The most common study objective addressed biological mechanisms of disease. The most commonly studied types of military-relevant biomedical research task areas were infectious diseases (viral and bacterial) and chemical agents (environmental toxicant exposures and traditional and emerging chemical threats). Studies varied widely in their complexity. Most of the single omics studies were conducted using a single organism (37% of total), whereas integrated analyses of multiple omics types were reported for most of the studies investigating two subjects (21% of total). More than 40% of the studies (27/62) involved collaborations between a USAMRMC laboratory and one other organization, whereas nearly half of the studies (30/62) involved collaborations between the USAMRMC laboratory and at least two other organizations. Given the relatively large numbers of system biology collaborations, these survey results indicate that these laboratories are compliant with data-centric policies and guidance. However, federal and military research data storage policies and guidance continue to evolve, and research studies that generate large volumes of heterogeneous data types that need to be rapidly analyzed will be significantly impacted by these policies. Future directions for the SBCC include conducting periodic surveys to monitor and project trends in the systems biology arena, and requesting USAMRMC labs to provide additional survey items that should help to enhance our understanding of our systems biology activities and capabilities. Moreover, future surveys should also help in filling knowledge gaps, tracking results (publications and other knowledge products, etc.), identifying nonfinancial barriers to conducting this research, preventing redundancies, promoting collaborations, and providing senior leadership with readiness and situational awareness information. It is anticipated that the results of this and other surveys will provide a foundation for making recommendations toward evidence-based programmatic decisions by the senior leadership for future systems biology research efforts. ACKNOWLEDGMENTS This work was supported by core Army funds. Constructive comments from the two reviewers were appreciated. Supplemental Figures and Tables are located at: https://sysbiocube-abcc.ncifcrf.gov/utils/documents/USAMRMC-SysBio-collaborations-2016.pdf. REFERENCES 1. Rasmussen TE, Reilly PA, Baer DG Why military medical research? Mil Med  2014; 179: 1– 2. Google Scholar CrossRef Search ADS PubMed  2. 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Office of the President of the United States National Security Strategy of the United States.  Available at https://obamawhitehouse.archives.gov/sites/default/files/docs/2015_national_security_strategy_2.pdf, May 2010; accessed February 3, 2017. 17. Cesa MC Building strategic industry-university partnerships. Chem Eng News  2016; 94: 38. 18. Brand A, Allen L, Altman M, Hlava M, Scott J Beyond authorship: attribution, contribution, collaboration, and credit. Learned Publishing  2015; 28: 151– 5. Google Scholar CrossRef Search ADS   19. Lebeda FJ Deterrence of biological and chemical warfare: a review of policy options. Mil Med  1997; 162: 156– 61. Google Scholar PubMed  20. Office of Science and Technology Policy Scientific Integrity.  Available at https://obamawhitehouse.archives.gov/the-press-office/memorandum-heads-executive-departments-and-agencies-3-9-09, December 17, 2010; accessed February 6, 2017. 21. Department of Defense Instruction 3200.20: Scientific and Engineering Integrity. Under Secretary of Defense for Acquisition, Technology, and Logistics.  Available at http://www.dtic.mil/whs/directives/corres/pdf/320020p.pdf, July 26, 2012; accessed February 1, 2017. 22. Office of the President of the United States Press Release. Scientific Integrity.  March 9, 2009. Available at https://obamawhitehouse.archives.gov/blog/2016/12/19/scientific-integrity-policies-update; accessed February 6, 2017. 23. Salinsky M, Parko K, Rutecki P, Boudreau E, Storzbach D Attributing seizures to TBI: Validation of a brief patient questionnaire. Epilepsy Behav  2016; 57: 141– 4. Google Scholar CrossRef Search ADS PubMed  24. Groves RM, Heeringa SG Responsive design for household surveys: tools for actively controlling survey errors and costs. J R Stat Soc Ser A Stat Soc  2006; 169: 439– 57. Google Scholar CrossRef Search ADS   25. Department of Defense Manual 3200.14 Principles and operational parameters of the DoD Scientific and Technical Information Program (STIP): general processes, Vol. 1 , 2014. Available at www.dtic.mil/whs/directives/corres/pdf/320014vol1_2014.pdf; accessed February 1, 2017. Reprint & Copyright © Association of Military Surgeons of the U.S. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Military Medicine Oxford University Press

Collaborative Systems Biology Projects for the Military Medical Community

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Publisher
Oxford University Press
Copyright
Reprint & Copyright © Association of Military Surgeons of the U.S.
ISSN
0026-4075
eISSN
1930-613X
DOI
10.7205/MILMED-D-16-00446
pmid
28885940
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See Article on Publisher Site

Abstract

ABSTRACT Introduction: This pilot study was conducted to examine, for the first time, the ongoing systems biology research and development projects within the laboratories and centers of the U.S. Army Medical Research and Materiel Command (USAMRMC). The analysis has provided an understanding of the breadth of systems biology activities, resources, and collaborations across all USAMRMC subordinate laboratories. Methods: The Systems Biology Collaboration Center at USAMRMC issued a survey regarding systems biology research projects to the eight U.S.-based USAMRMC laboratories and centers in August 2016. This survey included a data call worksheet to gather self-identified project and programmatic information. The general topics focused on the investigators and their projects, on the project's research areas, on omics and other large data types being collected and stored, on the analytical or computational tools being used, and on identifying intramural (i.e., USAMRMC) and extramural collaborations. Results: Among seven of the eight laboratories, 62 unique systems biology studies were funded and active during the final quarter of fiscal year 2016. Of 29 preselected medical Research Task Areas, 20 were associated with these studies, some of which were applicable to two or more Research Task Areas. Overall, studies were categorized among six general types of objectives: biological mechanisms of disease, risk of/susceptibility to injury or disease, innate mechanisms of healing, diagnostic and prognostic biomarkers, and host/patient responses to vaccines, and therapeutic strategies including host responses to therapies. We identified eight types of omics studies and four types of study subjects. Studies were categorized on a scale of increasing complexity from single study subject/single omics technology studies (23/62) to studies integrating results across two study subject types and two or more omics technologies (13/62). Investigators at seven USAMRMC laboratories had collaborations with systems biology experts from 18 extramural organizations and three other USAMRMC laboratories. Collaborators from six USAMRMC laboratories and 58 extramural organizations were identified who provided additional research expertise to these systems biology studies. Conclusions: At the end of fiscal year 2016, USAMRMC laboratories self-reported 66 systems biology/computational biology studies (62 of which were unique) with 25 intramural and 81 extramural collaborators. Nearly two-thirds were led by or in collaboration with the U.S. Army Telemedicine and Advanced Technology Research Center/Department of Defense Biotechnology High-Performance Computing Software Applications Institute and U.S. Army Center for Environmental Health Research. The most common study objective addressed biological mechanisms of disease. The most common types of Research Task Areas addressed infectious diseases (viral and bacterial) and chemical agents (environmental toxicant exposures, and traditional and emerging chemical threats). More than 40% of the studies (27/62) involved collaborations between the reporting USAMRMC laboratory and one other organization. Nearly half of the studies (30/62) involved collaborations between the reporting USAMRMC laboratory and at least two other organizations. These survey results indicate that USAMRMC laboratories are compliant with data-centric policy and guidance documents whose goals are to prevent redundancy and promote collaborations by sharing data and leveraging capabilities. These results also serve as a foundation to make recommendations for future systems biology research efforts. INTRODUCTION Military medical research is a key driver for providing requirements-driven, evidence-based recommendations, and better medical care.1,2 The military medical research community's implicit mandate is for the continual improvement of medical care.3 Systems biology, a relatively new research concept, is becoming a rapidly adopted approach to increase our understanding of the mechanisms underlying disease processes and normal homeostasis, as well as being a foundation for precision or personalized medicine.4 As background, several definitions are presented here along with related concepts and data-centric policy drivers. Systems biology (also referred to as systems medicine or integrative medicine) integrates a variety of experimental, clinical, and human subject data, along with computational models,5 in which predictions are “the gold standard of scientific tests.”6 Some predictions focus on biomarker identification which, in turn, could lead to more effective therapies or even to preventive care. Often associated with systems biology are the genomics and other omics technologies that generate big data. An early definition of big data was “the amount of data just beyond technology's capability to store, manage, and process efficiently.”7 More recently, big data were defined by the four Vs: volume, variety, velocity, and value. This and other multi-V definitions are widely used to highlight the meaning and necessity of big data, especially in the case of biomedical data.8 The rapid rise of high through-put omics technologies and advances in computer capabilities have brought with them the recognition of requirements for more data storage, open data access, and data sharing. Accordingly, the Executive Branch and the Department of Defense (DoD) have sought to provide guidance on these issues9,–14 and to foster collaborative efforts.15,16 Collaborations have also been the focus in other research areas. For example, a set of key principles was recently drafted that is related to industry-university partnerships.17 These principles included sharing ideas, incentivizing collaborations, establishing clear agreements on the use of intellectual property, and leadership's role in building trust (confidence) among the partners.18 As with confidence-building measures of openness and transparency in developing policies and diplomatic actions,19 trustworthiness of research colleagues and their data are subjective variables that, along with data sharing and teamwork, form a foundation for building productive collaborations. Data integrity and, implicitly trust, have also been the subject of several DoD policies20,21 that were initiated by the White House.22 Although there are policies, metrics, and standards for data reproducibility or integrity, these have yet to be implemented, and they represent capability gaps in the area of data sharing. The present study is an initial effort to determine the scope of systems biology research within USAMRMC laboratories and the extent to which data sharing and collaborative efforts are occurring in these projects. This study also provides a gauge for assessing the degree of compliance with these policies and the related guidance. The main assessment tool used was a survey sent to the eight USAMRMC laboratories (August 8, 2016) entitled “USAMRMC Systems Biology Research Studies.” This survey was designed to provide the USAMRMC senior leadership and the Systems Biology Collaboration Center (SBCC) with an understanding of the breadth of on-going systems biology activities and resources across all USAMRMC subordinate laboratories. This information was intended to (1) demonstrate the scope of USAMRMC's system biology activities to stakeholders, (2) identify systems biology resources across USAMRMC that can facilitate collaborations for understanding and counteracting military medical threats, and (3) identify systems biology experts who can advise USAMRMC and others about opportunities for and the needs of systems biology investigators. The results of surveys such as this provide a foundation for recommendations on making programmatic and funding decisions by the senior leadership for future systems biology research efforts. METHODS Survey The SBCC created a survey entitled “USAMRMC Systems Biology Research Studies” that was sent to all U.S.-based USAMRMC laboratories. The laboratories included eight organizations: U.S. Army Telemedicine and Advanced Technology Research Center (TATRC)/DoD Biotechnology High-Performance Computing Software Applications Institute (BHSAI), U.S. Army Aeromedical Research Laboratory, U.S. Army Center for Environmental Health Research (USACEHR), U.S. Army Institute of Surgical Research, U.S. Army Medical Research Institute of Chemical Defense, U.S. Army Medical Research Institute of Infectious Diseases, U.S. Army Research Institute of Environmental Medicine, and Walter Reed Army Institute of Research. The survey consisted of two MS Excel worksheets of currently funded systems biology studies being conducted at each of the USAMRMC laboratories. Although there are different interpretations of “systems biology,” the following working definition served as a guide for the types of studies that should be included: any study that applies bioinformatics/computational biology tools to integrate, analyze, and model large volume biological (e.g., “omics”) data with experimental animal research data, human clinical and phenotypic data, and/or microbial phenotypic data. The “Systems Biology Studies” worksheet/tab contained the data fields (rows) to be completed for each systems biology research study being performed by investigators in the laboratory (Supplemental Table S1). The second (“instructions”) worksheet provided definitions and examples for the data fields (Supplemental Table S2). Although some programmatic information, such as funding source was sought (Supplemental Table S1, data field “PAD/JPC” [Army's Program Area Directorate/DoD's Joint Program Committee]), levels of funding were not requested. The amounts of data being generated and stored were also not requested but will be an item in future surveys (see Conclusions). Interpretation and Categorization of Responses Responses to the survey were evaluated for completeness and relevance to the instructions. Responses for many of the fields were optional. However, follow-up questions were asked to either clarify incomplete answers, or to clarify answers that were in conflict between two data fields, or to complete answers to fields that required a response. For responses to the required data field “Task Area Being Investigated” that did not follow the instructions, the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling fields” were examined to determine whether those entries could be used to unambiguously assign the Research Task Area. The SBCC provided the USAMRMC laboratories a list of 29 Research Task Areas in the survey instructions that included the Budget Activity 6.1 to 6.3 Army Budget Task Areas for 25 of the 29 Research Task Areas. The other four Research Task Areas to account for competencies that are not currently managed by Army Task Areas are “Pain” and “Neuromusculoskeletal Rehabilitation” (Defense Health Program [DHP] funded), “Biological Toxins,” and “Traditional and Emerging Chemical Threats” (Defense Threat Reduction Agency [DTRA] funded). For example, a “Project Title” that included the term human immunodeficiency virus would be identified only with the “Viral Diseases Research” Task Area. If those entries could not be used to unambiguously categorize the Research Task Areas, the laboratory point of contact was requested by the SBCC to update the data field. The submitted data sets were evaluated to look specifically for reports of the same collaborative study from two or more laboratories. Four such projects that were reported by separate laboratories were confirmed as the same overall collaborative studies, and the total number of unique collaborative studies was adjusted to eliminate this duplication. For reporting purposes, data fields that permitted open responses were reviewed to categorize the results. Responses to the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling” fields were reviewed to define common themes, and six common categories of Topics of Study Objectives were created. Responses to the “Type(s) of Omics in Study” were consolidated from 12 to 8 types of omics based on the molecule under investigation. For example, genomics and metagenomics (from environmental samples) that rely on DNA as the source material were combined as one omics category. Sources of Error and Caveats A number of different false-positive identifications were possible, including misinterpretation of a submitted response.23 Misidentifications or misunderstandings of the survey instructions by a submitter may have led to reporting a project that was not, in fact, related to systems biology. Also, the identified collaborator may have been an organization performing a service rather than acting in a collaborative manner. Further, proposed projects rather than active ones could have been reported. Due to the few responses received for “Publications,” this survey item was not analyzed presumably because most of the reported studies were only recently started. In contrast, some projects may have been overlooked (false negatives) as being a part of a systems biology approach. The reported laboratory omics instrumentation item in the survey (Supplemental Table S1) did not allow for other important instrumentation at that laboratory to be listed in Supplemental Table S6. Nonresponses (non-observations) also introduced a form of bias in surveys24 (Table I footnote). The categorization of Research Task Areas and the categorization of overall study objectives using other information provided in the responses reduced potential variable interpretations of the categories, yet introduced error related to interpreting the responses. TABLE I. Responses to Data Fields in the Systems Biology Studies Worksheet Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  t1 Answers of “N/A” and “None” were considered responses. Answers of “unknown” and “data generation in progress” were excluded (see Results). View Large TABLE I. Responses to Data Fields in the Systems Biology Studies Worksheet Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  Data Field  % Answered  Project Title  100  Principal Investigator  100  Additional Point of Contact  68  PAD/JPC  100  Laboratory  100  Health Condition/Injury/Illness   95  Task Area Being Investigated (e.g., Parasitic Diseases or Extremity Trauma)  100  Type(s) of Organism(s) from Which Data Are Derived: Human, Animal, Microbe, Other?  100  Type(s) of Omics in Study   91  Omics Scientific Equipment in USAMRMC Laboratory Used in the Study   89  Purpose of Computational Modeling  100  Method to Integrate Animal/Human Clinical Data with Omics Data   92  Other High Volume Data Types   64  Data Storage Plan  100  Systems Biology/Computational Biology Expert Collaborators (Affiliations)   89  Other Study Collaborators (Affiliations)   83  Publications from the Systems Biology Study   82  Additional Comments   58  t1 Answers of “N/A” and “None” were considered responses. Answers of “unknown” and “data generation in progress” were excluded (see Results). View Large RESULTS Participating USAMRMC Laboratories and Initial Results The percentage of responses answered for the survey's 18 data fields are listed in Table I. Response percentages ranged from 58% (“Additional Comments”) to several with 100%. The “% Answered” field considered a response of “Unknown” or a blank field to be negative replies, which represents a potential survey response bias (see Methods). Even so, most of the nonresponses occurred in data fields that would not apply to every study, and many of the nonresponses likely indicate that the data field did not apply (e.g., there was no “Additional Point of Contact”). Seven of the eight USAMRMC laboratories self-reported that 66 currently funded studies were systems biology efforts (Fig. 1, Supplemental Table S3). The eighth laboratory responded that no relevant studies were currently being conducted. Of these, 62 were unique studies, whereas four studies were collaborations between a few of these laboratories. Nearly two-thirds of the USAMRMC systems biology studies were conducted by investigators at USACEHR and BHSAI, and, notably, investigators at BHSAI conducted all the TATRC systems biology studies. FIGURE 1. View largeDownload slide Number of Systems Biology Studies Ongoing at the Laboratories in fiscal year 2016. FIGURE 1. View largeDownload slide Number of Systems Biology Studies Ongoing at the Laboratories in fiscal year 2016. Research Task Areas and Topics of Study Objectives As outlined in the Methods, the SBCC provided the USAMRMC laboratories a list of 29 Research Task Areas in the survey instructions. The list included the Army Budget Task Areas for 25 of the 29 Research Task Areas with four other Research Task Areas funded by either the DHP or the DTRA. It should be noted that 35% of the original responses (28 of 66) did not correspond to any item on the Research Task Area list. For most of these noncorresponding responses, Research Task Areas were chosen by the SBCC based on unambiguous descriptions provided in other data fields, whereas other Research Task Areas could only be assigned by recontacting the laboratory point of contact, as described in the Methods. The distribution of Research Task Areas among the USAMRMC laboratories conducting systems biology studies is shown in Figure 2. Twenty Research Task Areas were matched (Fig. 2) with each of the unique 62 self-reported studies (Supplemental Table S4). Although most studies were matched to only one Research Task Area, 12 studies were matched to two or more Research Task Areas. Seventeen of the 20 Research Task Areas being investigated by the systems biology studies correspond to USAMRMC Army medical research budget planning and programming tasks. Of the four Research Task Areas identified with DHP or DTRA support, only “Neuromusculoskeletal Rehabilitation” was not reported to be associated with any systems biology study. Studies investigating infectious disease and toxicant/chemical injury Research Task Areas accounted for more than half the USAMRMC's systems biology portfolio. FIGURE 2. View largeDownload slide Distribution of Research Task Areas among the USAMRMC Laboratories Conducting Systems Biology Studies. FIGURE 2. View largeDownload slide Distribution of Research Task Areas among the USAMRMC Laboratories Conducting Systems Biology Studies. In addition, the SBCC reviewed the “Project Title,” “Health Condition/Injury/Illness,” and “Purpose of Computational Modeling” data fields for each study to classify the study according to six categories of study objectives (Supplemental Table S5): biological mechanisms of disease, risk of/susceptibility to injury or disease, innate mechanisms of healing, diagnostic and prognostic biomarkers, host/patient responses to vaccines, and therapeutic strategies including host responses to therapies. Study objectives were not mutually exclusive, and studies were assigned either one or two study objectives. Forty-one studies were assigned one study objective, and the remaining 21 studies were assigned two study objectives. Investigating “biological mechanisms of disease” was the most common study objective, identified with 38 separate studies. Investigating “innate mechanisms of healing,” such as immune responses to virus infection, was identified with 14 studies, of which six were also categorized to the “biological mechanisms of disease” study objective. Systems Biology Study Methods and Complexity of Studies The SBCC identified eight types of omics studies and seven type(s) of study subjects (i.e., animals, humans, microbes, three combination pairs, and “other”) (Table II). TABLE II. Systems Biology Study Methods    Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4     Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4  View Large TABLE II. Systems Biology Study Methods    Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4     Number of Studies  Use of Omics Technologies   Transcriptomics, microRNA-omics, and Metatranscriptomics  37   Metabolomics  17   Proteomics  16   Epigenomics (e.g., DNA methylation)  13   Genomics and Metagenomics  12   Immunomics and Systems Serology  11   Microbiomics  6   Lipidomics  2  Study Subjects   Animals  17   Human  16   Human and Animal  12   Microbe  6   Animal and Microbe  6   Human and Microbe  4   Other (e.g., Eukaryotic cells)  4  View Large Furthermore, the 62 studies were defined on a scale of increasing complexity based on the application of omics analytical tools and the number of study subjects investigated. Seven studies did not include any omics research. Twenty-three studies applied one omics technology to one study subject. Five studies applied one omics technology to interrogate two study subject types, and 14 studies applied two or more omics technologies to one study subject. Thirteen studies applied two or more omics technologies to interrogate two study subject types (Supplemental Figure S1). Collaborating Organizations The numbers of intramural and extramural organizations collaborating with each of the USAMRMC laboratories in systems biology studies are shown in Figure 3. Notably, USACEHR had systems biology study research collaborations with 54 separate organizations—both intramural and extramural—in 2016. FIGURE 3. View largeDownload slide Distribution of organizations collaborating on USAMRMC System biology studies among USAMRMC Laboratories. FIGURE 3. View largeDownload slide Distribution of organizations collaborating on USAMRMC System biology studies among USAMRMC Laboratories. Seven of USAMRMC's intramural laboratories had collaborations with investigators identified as systems biology experts (Supplemental Figure S2) from 18 extramural organizations and three other USAMRMC laboratories. Four of USAMRMC's intramural laboratories (Supplemental Figure S3) had collaborations with researchers in 64 different organizations applying other types of research expertise. Of these, 58 were extramural organizations and six were other USAMRMC laboratories. DoD labs and organizations collaborating with those in the USAMRMC included the Air Force Research Laboratory, Armed Forces Health Surveillance Center, Edgewood Chemical Biological Center, Millennium Cohort Study, Natick Soldier Systems Center, Naval Health Research Center, U.S. Army Public Health Center, and U.S. Special Operations Command. The distribution of USAMRMC systems biology studies by number of collaborating organizations per study is shown in Figure 4. The majority of systems biology studies involved collaborations with one or two other organizations. Five studies had no collaborators (data not shown), whereas two studies involved six or more collaborating organizations. Among the 54 systems biology studies with collaborators, five had intramural collaborators only, 17 had both intramural and extramural collaborators, and 32 had extramural collaborators only. Responses from four other studies did not specify numbers or locations of collaborators (data not shown). FIGURE 4. View largeDownload slide Distribution of USAMRMC System biology studies according to those with only extramural collaborators, only intramural collaborators, and a combination of intramural and extramural collaborators. FIGURE 4. View largeDownload slide Distribution of USAMRMC System biology studies according to those with only extramural collaborators, only intramural collaborators, and a combination of intramural and extramural collaborators. Collaborating Principal Investigators The numbers of intramural and extramural principal investigators collaborating on USAMRMC Systems Biology studies were collected for each USAMRMC laboratory (Fig. 5A). The numbers of extramural principal investigators with systems biology expertise or expertise in other areas of study were also collected (Fig. 5B). FIGURE 5. View largeDownload slide Distribution of Principal Investigators collaborating on USAMRMC systems biology studies by institutional type and technical expertise. (A) System Biology Collaborating Investigators at USAMRMC labs. (B) Technical contributions Extramural Investigators. FIGURE 5. View largeDownload slide Distribution of Principal Investigators collaborating on USAMRMC systems biology studies by institutional type and technical expertise. (A) System Biology Collaborating Investigators at USAMRMC labs. (B) Technical contributions Extramural Investigators. Principal investigators were counted for each laboratory with which they partnered. Intramural and extramural systems biology collaborators were counted separately (Fig. 5A). From each USAMRMC laboratory, extramural principal investigators providing systems biology expertise were counted separately from those investigators providing other technical and scientific contributions to the collaboration (Fig. 5B). If the survey response did not indicate name(s) of investigator(s) at a collaborating organization, the organization was assumed to have one principal investigator. Collectively, these data indicate 25 intramural and 81 extramural investigators were collaborating on USAMRMC systems biology studies. Types of Omics Instrumentation and Technical Expertise The list of methods used in these systems biology studies by four of the seven USAMRMC laboratories conducting systems biology studies includes sequencing applications, transcriptome analyses, bioassays, and mass spectroscopy (Supplemental Table S6). As mentioned in the Methods caveats section, laboratory instrumentation that was not currently used on a given systems biology study may not have been identified in the responses, thus making this list only a partial reflection of a laboratory's entire total technical capability. Data Storage Plans for Each USAMRMC Laboratory The main objective in DoD's “Plan to Establish Public Access to the Results of Federally Funded Research”14 is to make publically available the reports in peer-reviewed scholarly publications and the accompanying digital data sets that support the conclusions of the DoD-funded research. Large volumes of data are planned to be stored in decentralized public repositories, e.g., the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/). In contrast, the plan assigns the responsibility to the Defense Technical Information Center and its Unified Research and Engineering Database25 to be the central data catalog/locator to consolidate the metadata which includes links to the digital data and related documents. Local domain experts will have oversight responsibilities ensuring that smaller data sets are stored either locally or in other repositories “in formats that comply with DoD's implementation of OMB Memorandum M13-13.”10 Responses to “Data Storage” survey items are summarized by location of a storage resource in Supplemental Figure S4. The vast majority of USAMRMC systems biology studies store or plan to store data on their own networks and servers, though significant numbers of studies store data with partners' networks and servers and online data repositories. CONCLUSIONS At the end of fiscal year 2016, USAMRMC laboratories self-reported 62 unique systems biology studies with ∼80 extramural collaborators. Nearly two-thirds of all the studies were led by or in collaboration with BHSAI (TATRC) and USACEHR. The most common study objective addressed biological mechanisms of disease. The most commonly studied types of military-relevant biomedical research task areas were infectious diseases (viral and bacterial) and chemical agents (environmental toxicant exposures and traditional and emerging chemical threats). Studies varied widely in their complexity. Most of the single omics studies were conducted using a single organism (37% of total), whereas integrated analyses of multiple omics types were reported for most of the studies investigating two subjects (21% of total). More than 40% of the studies (27/62) involved collaborations between a USAMRMC laboratory and one other organization, whereas nearly half of the studies (30/62) involved collaborations between the USAMRMC laboratory and at least two other organizations. Given the relatively large numbers of system biology collaborations, these survey results indicate that these laboratories are compliant with data-centric policies and guidance. However, federal and military research data storage policies and guidance continue to evolve, and research studies that generate large volumes of heterogeneous data types that need to be rapidly analyzed will be significantly impacted by these policies. Future directions for the SBCC include conducting periodic surveys to monitor and project trends in the systems biology arena, and requesting USAMRMC labs to provide additional survey items that should help to enhance our understanding of our systems biology activities and capabilities. 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Journal

Military MedicineOxford University Press

Published: Sep 1, 2017

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