A novel insight into neurological disorders through HDAC6 protein-protein interactions.

Due to its involvement in physiological and pathological processes, histone deacetylase 6 (HDAC6) is considered a promising pharmaceutical target for several neurological manifestations. However, the exact regulatory role of HDAC6 in the central nervous system (CNS) is still not fully understood. Hence, using a semi-automated literature screening technique, we systematically collected HDAC6-protein interactions that are experimentally validated and reported in the CNS. The resulting HDAC6 network encompassed 115 HDAC6-protein interactions divided over five subnetworks: (de)acetylation, phosphorylation, protein complexes, regulatory, and aggresome-autophagy subnetworks. In addition, 132 indirect interactions identified through HDAC6 inhibition were collected and categorized. Finally, to display the application of our HDAC6 network, we mapped transcriptomics data of Alzheimer's disease, Parkinson's disease, and Amyotrophic Lateral Sclerosis on the network and highlighted that in the case of Alzheimer's disease, alterations predominantly affect the HDAC6 phosphorylation subnetwork, whereas differential expression within the deacetylation subnetwork is observed across all three neurological disorders. In conclusion, the HDAC6 network created in the present study is a novel and valuable resource for the understanding of the HDAC6 regulatory mechanisms, thereby providing a framework for the integration and interpretation of omics data from neurological disorders and pharmacodynamic assessments.

Interactive neuroinflammation pathways and transcriptomics-based identification of drugs and chemical compounds for schizophrenia.

OBJECTIVES: Schizophrenia is a psychiatric disorder affecting 1% of the population. Accumulating evidence indicates that neuroinflammation is involved in the pathology of these disorders by altering neurodevelopmental processes and specifically affecting glutamatergic signalling and astrocytic functioning. The aim of this study was to curate interactive biological pathways involved in schizophrenia for the identification of novel pharmacological targets implementing pathway, gene ontology, and network analysis. METHODS: Neuroinflammatory pathways were created using PathVisio and published in WikiPathways. A transcriptomics dataset, originally created by Narla et al. was selected for data visualisation and analysis. Transcriptomics data was visualised within pathways and networks, extended with transcription factors, pathways, and drugs. Network hubs were determined based on degrees of connectivity. RESULTS: Glutamatergic, immune, and astrocytic signalling as well as extracellular matrix reorganisation were altered in schizophrenia while we did not find an effect on the complement system. Pharmacological agents that target the glutamate receptor subunits, inflammatory mediators, and metabolic enzymes were identified. CONCLUSIONS: New neuroinflammatory pathways incorporating the extracellular matrix, glutamatergic neurons, and astrocytes in the aetiology of schizophrenia were established. Transcriptomics based network analysis provided novel targets, including extra-synaptic glutamate receptors, glutamate transporters and extracellular matrix molecules that can be evaluated for therapeutic strategies.

WikiPathways 2024: next generation pathway database.

WikiPathways (wikipathways.org) is an open-source biological pathway database. Collaboration and open science are pivotal to the success of WikiPathways. Here we highlight the continuing efforts supporting WikiPathways, content growth and collaboration among pathway researchers. As an evolving database, there is a growing need for WikiPathways to address and overcome technical challenges. In this direction, WikiPathways has undergone major restructuring, enabling a renewed approach for sharing and curating pathway knowledge, thus providing stability for the future of community pathway curation. The website has been redesigned to improve and enhance user experience. This next generation of WikiPathways continues to support existing features while improving maintainability of the database and facilitating community input by providing new functionality and leveraging automation.

Exploring pathway interactions to detect molecular mechanisms of disease: 22q11.2 deletion syndrome.

BACKGROUND: 22q11.2 Deletion Syndrome (22q11DS) is a genetic disorder characterized by the deletion of adjacent genes at a location specified as q11.2 of chromosome 22, resulting in an array of clinical phenotypes including autistic spectrum disorder, schizophrenia, congenital heart defects, and immune deficiency. Many characteristics of the disorder are known, such as the phenotypic variability of the disease and the biological processes associated with it; however, the exact and systemic molecular mechanisms between the deleted area and its resulting clinical phenotypic expression, for example that of neuropsychiatric diseases, are not yet fully understood. RESULTS: Using previously published transcriptomics data (GEO:GSE59216), we constructed two datasets: one set compares 22q11DS patients experiencing neuropsychiatric diseases versus healthy controls, and the other set 22q11DS patients without neuropsychiatric diseases versus healthy controls. We modified and applied the pathway interaction method, originally proposed by Kelder et al. (2011), on a network created using the WikiPathways pathway repository and the STRING protein-protein interaction database. We identified genes and biological processes that were exclusively associated with the development of neuropsychiatric diseases among the 22q11DS patients. Compared with the 22q11DS patients without neuropsychiatric diseases, patients experiencing neuropsychiatric diseases showed significant overrepresentation of regulated genes involving the natural killer cell function and the PI3K/Akt signalling pathway, with affected genes being closely associated with downregulation of CRK like proto-oncogene adaptor protein. Both the pathway interaction and the pathway overrepresentation analysis observed the disruption of the same biological processes, even though the exact lists of genes collected by the two methods were different. CONCLUSIONS: Using the pathway interaction method, we were able to detect a molecular network that could possibly explain the development of neuropsychiatric diseases among the 22q11DS patients. This way, our method was able to complement the pathway overrepresentation analysis, by filling the knowledge gaps on how the affected pathways are linked to the original deletion on chromosome 22. We expect our pathway interaction method could be used for problems with similar contexts, where complex genetic mechanisms need to be identified to explain the resulting phenotypic plasticity.

Extending inherited metabolic disorder diagnostics with biomarker interaction visualizations.

BACKGROUND: Inherited Metabolic Disorders (IMDs) are rare diseases where one impaired protein leads to a cascade of changes in the adjacent chemical conversions. IMDs often present with non-specific symptoms, a lack of a clear genotype-phenotype correlation, and de novo mutations, complicating diagnosis. Furthermore, products of one metabolic conversion can be the substrate of another pathway obscuring biomarker identification and causing overlapping biomarkers for different disorders. Visualization of the connections between metabolic biomarkers and the enzymes involved might aid in the diagnostic process. The goal of this study was to provide a proof-of-concept framework for integrating knowledge of metabolic interactions with real-life patient data before scaling up this approach. This framework was tested on two groups of well-studied and related metabolic pathways (the urea cycle and pyrimidine de-novo synthesis). The lessons learned from our approach will help to scale up the framework and support the diagnosis of other less-understood IMDs. METHODS: Our framework integrates literature and expert knowledge into machine-readable pathway models, including relevant urine biomarkers and their interactions. The clinical data of 16 previously diagnosed patients with various pyrimidine and urea cycle disorders were visualized on the top 3 relevant pathways. Two expert laboratory scientists evaluated the resulting visualizations to derive a diagnosis. RESULTS: The proof-of-concept platform resulted in varying numbers of relevant biomarkers (five to 48), pathways, and pathway interactions for each patient. The two experts reached the same conclusions for all samples with our proposed framework as with the current metabolic diagnostic pipeline. For nine patient samples, the diagnosis was made without knowledge about clinical symptoms or sex. For the remaining seven cases, four interpretations pointed in the direction of a subset of disorders, while three cases were found to be undiagnosable with the available data. Diagnosing these patients would require additional testing besides biochemical analysis. CONCLUSION: The presented framework shows how metabolic interaction knowledge can be integrated with clinical data in one visualization, which can be relevant for future analysis of difficult patient cases and untargeted metabolomics data. Several challenges were identified during the development of this framework, which should be resolved before this approach can be scaled up and implemented to support the diagnosis of other (less understood) IMDs. The framework could be extended with other OMICS data (e.g. genomics, transcriptomics), and phenotypic data, as well as linked to other knowledge captured as Linked Open Data.

Tissue-specific pathway activities: A retrospective analysis in COVID-19 patients.

The ACE2 receptors essential for SARS-CoV-2 infections are expressed not only in the lung but also in many other tissues in the human body. To better understand the disease mechanisms and progression, it is essential to understand how the virus affects and alters molecular pathways in the different affected tissues. In this study, we mapped the proteomics data obtained from Nie X. et al. (2021) to the pathway models of the COVID-19 Disease Map project and WikiPathways. The differences in pathway activities between COVID-19 and non-COVID-19 patients were calculated using the Wilcoxon test. As a result, 46% (5,235) of the detected proteins were found to be present in at least one pathway. Only a few pathways were altered in multiple tissues. As an example, the Kinin-Kallikrein pathway, an important inflammation regulatory pathway, was found to be less active in the lung, spleen, testis, and thyroid. We can confirm previously reported changes in COVID-19 patients such as the change in cholesterol, linolenic acid, and arachidonic acid metabolism, complement, and coagulation pathways in most tissues. Of all the tissues, we found the thyroid to be the organ with the most changed pathways. In this tissue, lipid pathways, energy pathways, and many COVID-19 specific pathways such as RAS and bradykinin pathways, thrombosis, and anticoagulation have altered activities in COVID-19 patients. Concluding, our results highlight the systemic nature of COVID-19 and the effect on other tissues besides the lung.

A Simple Standard for Sharing Ontological Mappings (SSSOM).

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

A Community-Driven, Openly Accessible Molecular Pathway Integrating Knowledge on Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a highly aggressive malignancy mainly triggered by exposure to asbestos and characterized by complex biology. A significant body of knowledge has been generated over the decades by the research community which has improved our understanding of the disease toward prevention, diagnostic opportunities and new treatments. Omics technologies are opening for additional levels of information and hypotheses. Given the growing complexity and technological spread of biological knowledge in MPM, there is an increasing need for an integrating tool that may allow scientists to access the information and analyze data in a simple and interactive way. We envisioned that a platform to capture this widespread and fast-growing body of knowledge in a machine-readable and simple visual format together with tools for automated large-scale data analysis could be an important support for the work of the general scientist in MPM and for the community to share, critically discuss, distribute and eventually advance scientific results. Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the exploration and the analysis of omics data. The resource is publicly available in the WikiPathways database (Wikipathways : WP5087) and continues to be under development and curation by the community, enabling the scientists in MPM to actively participate in the prioritization of shared biological knowledge.

Providing Adverse Outcome Pathways from the AOP-Wiki in a Semantic Web Format to Increase Usability and Accessibility of the Content.

Introduction: The AOP-Wiki is the main platform for the development and storage of adverse outcome pathways (AOPs). These AOPs describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content, and to link it with chemical and biological resources. Together, this allows more detailed exploration, which can be automated. Materials and Methods: We converted the complete AOP-Wiki content into resource description framework (RDF) triples. We used >20 ontologies for the semantic annotation of property-object relations, including the Chemical Information Ontology, Dublin Core, and the AOP Ontology. Results: The resulting RDF contains >122,000 triples describing 158 unique properties of >15,000 unique subjects. Furthermore, >3500 link-outs were added to 12 chemical databases, and >7500 link-outs to 4 gene and protein databases. The AOP-Wiki RDF has been made available at https://aopwiki.rdf.bigcat-bioinformatics.org. Discussion: SPARQL queries can be used to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. Conclusion: Overall, the AOP-Wiki RDF allows new ways to explore the rapidly growing AOP knowledge and makes the integration of this database in automated workflows possible, making the AOP-Wiki more FAIR.

Understanding signaling and metabolic paths using semantified and harmonized information about biological interactions.

To grasp the complexity of biological processes, the biological knowledge is often translated into schematic diagrams of, for example, signalling and metabolic pathways. These pathway diagrams describe relevant connections between biological entities and incorporate domain knowledge in a visual format making it easier for humans to interpret. Still, these diagrams can be represented in machine readable formats, as done in the KEGG, Reactome, and WikiPathways databases. However, while humans are good at interpreting the message of the creators of diagrams, algorithms struggle when the diversity in drawing approaches increases. WikiPathways supports multiple drawing styles which need harmonizing to offer semantically enriched access. Particularly challenging, here, are the interactions between the biological entities that underlie the biological causality. These interactions provide information about the biological process (metabolic conversion, inhibition, etc.), the direction, and the participating entities. Availability of the interactions in a semantic and harmonized format is essential for searching the full network of biological interactions. We here study how the graphically-modelled biological knowledge in diagrams can be semantified and harmonized, and exemplify how the resulting data is used to programmatically answer biological questions. We find that we can translate graphically modelled knowledge to a sufficient degree into a semantic model and discuss some of the current limitations. We then use this to show that reproducible notebooks can be used to explore up- and downstream targets of MECP2 and to analyse the sphingolipid metabolism. Our results demonstrate that most of the graphical biological knowledge from WikiPathways is modelled into the semantic layer with the semantic information intact and connectivity information preserved. Being able to evaluate how biological elements affect each other is useful and allows, for example, the identification of up or downstream targets that will have a similar effect when modified.

The AOP-DB RDF: Applying FAIR Principles to the Semantic Integration of AOP Data Using the Research Description Framework.

Computational toxicology is central to the current transformation occurring in toxicology and chemical risk assessment. There is a need for more efficient use of existing data to characterize human toxicological response data for environmental chemicals in the US and Europe. The Adverse Outcome Pathway (AOP) framework helps to organize existing mechanistic information and contributes to what is currently being described as New Approach Methodologies (NAMs). AOP knowledge and data are currently submitted directly by users and stored in the AOP-Wiki (https://aopwiki.org/). Automatic and systematic parsing of AOP-Wiki data is challenging, so we have created the EPA Adverse Outcome Pathway Database. The AOP-DB, developed by the US EPA to assist in the biological and mechanistic characterization of AOP data, provides a broad, systems-level overview of the biological context of AOPs. Here we describe the recent semantic mapping efforts for the AOP-DB, and how this process facilitates the integration of AOP-DB data with other toxicologically relevant datasets through a use case example.

A systematically derived overview of the non-ubiquitous pathways and genes that define the molecular and genetic signature of the healthy trabecular meshwork.

PURPOSE: The trabecular meshwork (TM) is situated in the most frontal part of the eye and is thought to play an important role in the regulation of the eye pressure. However, this tissue is rather difficult to harvest for research. The purpose of this study is therefore to integrate the existing gene expression data of the healthy TM to increase sample size and identify its signature genes and pathways. This provides a robust reference for the study of molecular disease processes and supports the selection of candidate target genes for new treatments. METHODS: A systematic search identified microarray data of healthy TM tissue. After quality control, datasets of low quality and deviating samples were excluded. Remaining individuals were jointly normalized and integrated into one database. The average gene expression of each tested gene over all individuals was calculated. The 25% genes with the highest average expression were identified as the most active genes in the healthy TM and used as input for pathway and network analysis. Additionally, ubiquitous pathways and genes were identified and excluded from the results. Lastly, we identified genes which are likely to be TM-specific. RESULTS: The gene expression data of 44 individuals, obtained from 18 datasets, were jointly normalized. Ubiquitous genes (n = 688) and ubiquitous pathways (n = 73) were identified and excluded. Following, 1882 genes and 211 pathways were identified as the signature genes and pathways of the healthy TM. Pathway analysis revealed multiple molecular processes of which some were already known to be active in the TM, for example extracellular matrix and elastic fiber formation. Forty-six candidate TM-specific genes were identified. These consist mainly of pseudogenes or novel transcripts of which the function is unknown. CONCLUSIONS: In this comprehensive meta-analysis we identified non-ubiquitous genes and pathways that form the signature of the functioning of the healthy TM. Additionally, 46 candidate TM-specific genes were identified. This method can also be used for other tissues that are difficult to obtain for study.

Systems Biology in ELIXIR: modelling in the spotlight.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Impact of In Utero Folate Exposure on DNA Methylation and Its Potential Relevance for Later-Life Health-Evidence from Mouse Models Translated to Human Cohorts.

SCOPE: Persistent DNA methylation changes may mediate effects of early-life exposures on later-life health. Human lifespan is challenging for prospective studies, therefore data from longitudinal studies are limited. Projecting data from mouse models of early-life exposure to human studies offers a tool to address this challenge. METHODS AND RESULTS: C57BL/6J mice were fed low/normal folate diets before and during pregnancy and lactation. Genome-wide promoter methylation was measured in male offspring livers at 17.5 days gestation and 28 weeks. Eight promoters were concurrently hypermethylated by folate depletion in fetuses and adults (>1.10 fold-change; p < 0.05). Processes/pathways potentially influenced by global changes, and function of these eight genes, suggest neurocognitive effects. Human observational and randomized controlled trial data were interrogated for translation. Methylation at birth was inversely associated with maternal plasma folate in six genes (-1.15% to -0.16% per nmol L-1 ; p < 0.05), while maternal folic acid supplementation was associated with differential methylation of four genes in adulthood. Three CpGs were persistently hypermethylated with lower maternal folate (p = 0.04). CONCLUSION: Some persistent folate-induced methylation changes in mice are mirrored in humans. This demonstrates utility of mouse data in identifying human loci for interrogation as biomarkers of later-life health.

An interferon-related signature characterizes the whole blood transcriptome profile of insulin-resistant individuals-the CODAM study.

BACKGROUND: Worldwide, the prevalence of obesity and insulin resistance has grown dramatically. Gene expression profiling in blood represents a powerful means to explore disease pathogenesis, but the potential impact of inter-individual differences in a cell-type profile is not always taken into account. The objective of this project was to investigate the whole blood transcriptome profile of insulin-resistant as compared to insulin-sensitive individuals independent of inter-individual differences in white blood cell profile. RESULTS: We report a 3% higher relative amount of monocytes in the insulin-resistant individuals. Furthermore, independent of their white blood cell profile, insulin-resistant participants had (i) higher expression of interferon-stimulated genes and (ii) lower expression of genes involved in cellular differentiation and remodeling of the actin cytoskeleton. CONCLUSIONS: We present an approach to investigate the whole blood transcriptome of insulin-resistant individuals, independent of their DNA methylation-derived white blood cell profile. An interferon-related signature characterizes the whole blood transcriptome profile of the insulin-resistant individuals, independent of their white blood cell profile. The observed signature indicates increased systemic inflammation possibly due to an innate immune response and whole-body insulin resistance, which can be a cause or a consequence of insulin resistance. Altered gene expression in specific organs may be reflected in whole blood; hence, our results may reflect obesity and/or insulin resistance-related organ dysfunction in the insulin-resistant individuals.

Comparison of metabolic states using genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks.

Investigating the Molecular Processes behind the Cell-Specific Toxicity Response to Titanium Dioxide Nanobelts.

Some engineered nanomaterials incite toxicological effects, but the underlying molecular processes are understudied. The varied physicochemical properties cause different initial molecular interactions, complicating toxicological predictions. Gene expression data allow us to study the responses of genes and biological processes. Overrepresentation analysis identifies enriched biological processes using the experimental data but prompts broad results instead of detailed toxicological processes. We demonstrate a targeted filtering approach to compare public gene expression data for low and high exposure on three cell lines to titanium dioxide nanobelts. Our workflow finds cell and concentration-specific changes in affected pathways linked to four Gene Ontology terms (apoptosis, inflammation, DNA damage, and oxidative stress) to select pathways with a clear toxicity focus. We saw more differentially expressed genes at higher exposure, but our analysis identifies clear differences between the cell lines in affected processes. Colorectal adenocarcinoma cells showed resilience to both concentrations. Small airway epithelial cells displayed a cytotoxic response to the high concentration, but not as strongly as monocytic-like cells. The pathway-gene networks highlighted the gene overlap between altered toxicity-related pathways. The automated workflow is flexible and can focus on other biological processes by selecting other GO terms.

Assessing the Contribution of Relative Macrophage Frequencies to Subcutaneous Adipose Tissue.

Background: Macrophages play an important role in regulating adipose tissue function, while their frequencies in adipose tissue vary between individuals. Adipose tissue infiltration by high frequencies of macrophages has been linked to changes in adipokine levels and low-grade inflammation, frequently associated with the progression of obesity. The objective of this project was to assess the contribution of relative macrophage frequencies to the overall subcutaneous adipose tissue gene expression using publicly available datasets. Methods: Seven publicly available microarray gene expression datasets from human subcutaneous adipose tissue biopsies (n = 519) were used together with TissueDecoder to determine the adipose tissue cell-type composition of each sample. We divided the subjects in four groups based on their relative macrophage frequencies. Differential gene expression analysis between the high and low relative macrophage frequencies groups was performed, adjusting for sex and study. Finally, biological processes were identified using pathway enrichment and network analysis. Results: We observed lower frequencies of adipocytes and higher frequencies of adipose stem cells in individuals characterized by high macrophage frequencies. We additionally studied whether, within subcutaneous adipose tissue, interindividual differences in the relative frequencies of macrophages were reflected in transcriptional differences in metabolic and inflammatory pathways. Adipose tissue of individuals with high macrophage frequencies had a higher expression of genes involved in complement activation, chemotaxis, focal adhesion, and oxidative stress. Similarly, we observed a lower expression of genes involved in lipid metabolism, fatty acid synthesis, and oxidation and mitochondrial respiration. Conclusion: We present an approach that combines publicly available subcutaneous adipose tissue gene expression datasets with a deconvolution algorithm to calculate subcutaneous adipose tissue cell-type composition. The results showed the expected increased inflammation gene expression profile accompanied by decreased gene expression in pathways related to lipid metabolism and mitochondrial respiration in subcutaneous adipose tissue in individuals characterized by high macrophage frequencies. This approach demonstrates the hidden strength of reusing publicly available data to gain cell-type-specific insights into adipose tissue function.

Integrative multiomics analysis of human atherosclerosis reveals a serum response factor-driven network associated with intraplaque hemorrhage.

BACKGROUND: While single-omics analyses on human atherosclerotic plaque have been very useful to map stage- or disease-related differences in expression, they only partly capture the array of changes in this tissue and suffer from scale-intrinsic limitations. In order to better identify processes associated with intraplaque hemorrhage and plaque instability, we therefore combined multiple omics into an integrated model. METHODS: In this study, we compared protein and gene makeup of low- versus high-risk atherosclerotic lesion segments from carotid endarterectomy patients, as judged from the absence or presence of intraplaque hemorrhage, respectively. Transcriptomic, proteomic, and peptidomic data of this plaque cohort were aggregated and analyzed by DIABLO, an integrative multivariate classification and feature selection method. RESULTS: We identified a protein-gene associated multiomics model able to segregate stable, nonhemorrhaged from vulnerable, hemorrhaged lesions at high predictive performance (AUC >0.95). The dominant component of this model correlated with αSMA- PDGFRα+ fibroblast-like cell content (p = 2.4E-05) and Arg1+ macrophage content (p = 2.2E-04) and was driven by serum response factor (SRF), possibly in a megakaryoblastic leukemia-1/2 (MKL1/2) dependent manner. Gene set overrepresentation analysis on the selected key features of this model pointed to a clear cardiovascular disease signature, with overrepresentation of extracellular matrix synthesis and organization, focal adhesion, and cholesterol metabolism terms, suggestive of the model's relevance for the plaque vulnerability. Finally, we were able to corroborate the predictive power of the selected features in several independent mRNA and proteomic plaque cohorts. CONCLUSIONS: In conclusion, our integrative omics study has identified an intraplaque hemorrhage-associated cardiovascular signature that provides excellent stratification of low- from high-risk carotid artery plaques in several independent cohorts. Further study revealed suppression of an SRF-regulated disease network, controlling lesion stability, in vulnerable plaque, which can serve as a scaffold for the design of targeted intervention in plaque destabilization.