A systematic approach to multifactorial cardiovascular disease: causal analysis

Vitamin E and cardiovascular diseases: an interest to public health?

Cardiovascular diseases (CVD) are the leading cause of death worldwide. From this perspective, the role of vitamin E and its metabolites in preventing CVD has been studied, being supported by the findings that low vitamin E concentrations are associated with an increased risk of cardiovascular events. Despite this, no studies have analysed the co-existence of vitamin E deficiency (VED) and CVD on the basis of population studies. Facing that, this study summarises information on the relationship between vitamin E status and CVD, providing a basis for understanding the determining and protective factors for its development. VED may be a public health problem since it has been observed to vary from 0·6% to 55·5% worldwide, with higher percentages in Asia and Europe, where CVD mortality rates stand out. Intervention studies with α-tocopherol supplementation do not confirm cardioprotective action of vitamin E, which may reflect that α-tocopherol alone does not provide cardiovascular protection to individuals, but the consumption of all isomers found in food. Considering that low concentrations of α-tocopherol can lead to a higher susceptibility to diseases involving oxidative stress in the population, in addition to the high and growing prevalence of CVD and VED, it is essential to investigate or reinterpret the mechanisms of action of vitamin E and its metabolites in the cardiovascular process to better understand the co-existence of CVD and VED. It is also important to implement public health policies and programmes aimed at promoting the consumption of natural food sources of vitamin E and healthy fats.

Acetylation in pathogenesis: Revealing emerging mechanisms and therapeutic prospects

Protein acetylation modifications play a central and pivotal role in a myriad of biological processes, spanning cellular metabolism, proliferation, differentiation, apoptosis, and beyond, by effectively reshaping protein structure and function. The metabolic state of cells is intricately connected to epigenetic modifications, which in turn influence chromatin status and gene expression patterns. Notably, pathological alterations in protein acetylation modifications are frequently observed in diseases such as metabolic syndrome, cardiovascular disorders, and cancer. Such abnormalities can result in altered protein properties and loss of function, which are closely associated with developing and progressing related diseases. In recent years, the advancement of precision medicine has highlighted the potential value of protein acetylation in disease diagnosis, treatment, and prevention. This review includes provocative and thought-provoking papers outlining recent breakthroughs in acetylation modifications as they relate to cardiovascular disease, mitochondrial metabolic regulation, liver health, neurological health, obesity, diabetes, and cancer. Additionally, it covers the molecular mechanisms and research challenges in understanding the role of acetylation in disease regulation. By summarizing novel targets and prognostic markers for the treatment of related diseases, we aim to contribute to the field. Furthermore, we discuss current hot topics in acetylation research related to health regulation, including N4-acetylcytidine and liquid-liquid phase separation. The primary objective of this review is to provide insights into the functional diversity and underlying mechanisms by which acetylation regulates proteins in disease contexts.

Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Association between Non-Dietary Cardiovascular Health and Expenditures Related to Acute Coronary Syndrome in the US between 2008-2018

Background: Acute Coronary Syndrome (ACS) causes the most deaths in the United States and accounts for the highest amount of healthcare spending. Cardiovascular Health (CVH) metrics have been widely used in primary prevention, but their benefits in secondary prevention on total healthcare expenditures related to ACS are largely unknown. This study aims to quantify the potential significance of ideal CVH scores as a tool in secondary cardiovascular disease prevention. Methods: In a cross-sectional analytical study, ten years of Medical Expenditure Panel Survey (MEPS) data from 2008 to 2018 were pooled, comparing ACS to non-ACS subgroups, utilizing a Two-part model with log link and gamma distribution, since our sample had both positive and zero costs. Conditional on positive expenditure, healthcare expenditure amounts were measured as a function of ACS status, socio-demographics, and CVH while controlling for relevant covariates. Finally, interactions of ACS with CVH metrics and other key variables were included to allow for variations in the effect of these variables on the two subgroups. Results: Improvements in CVH scores tended to reduce annual expenditures to a greater degree percentage-wise among ACS subjects compared to non-ACS groups, even though subjects with an ACS diagnosis tended to have approximately twice as big expenditures as similar subjects without an ACS diagnosis. Meanwhile, the financial impact of an ACS event on total expenditure would be approximately $88,500 ([95% CI, $70,200-106,900; p < 0.001]), and a unit improvement in CVH management score would generate savings of approximately $4160 ([95% CI, $5390-2950; p < 0.001]) in total health expenditures. Conclusions: Effective secondary preventive measures through targeted behavioral endeavors and improved health factors, especially the normalization of hypertension, diabetes mellitus, body mass index, and smoking cessation, have the potential to reduce medical spending for ACS subgroups.

New Genetically Determined Markers of the Functional State of the Cardiovascular System

Nowadays, cardiovascular diseases (CVDs) occupy a leading position in population mortality. Since it is known that the development of cardiovascular pathologies is determined mainly by the human genetic burden, an urgent task of primary prevention of CVDs is to assess the contribution of gene polymorphism to the formation of cardiovascular risk. The material for the study was the blood of volunteers aged 21 to 102 years. Polymorphisms were determined by real-time PCR. Multichannel volumetric sphygmography was performed to analyze the functional state of the vascular wall. The study revealed that the rs5742904 polymorphism of the ApoB gene was found to be absent in the studied groups of long-livers and descendants of long-livers. Results indicated that the carriage of the heterozygous variant of the MMP9 polymorphism is associated with a favorable prognosis for cardiovascular system functioning. A tendency towards an increase in the rate of biological age acceleration among subgroups with AA and GG genotypes of the MMP9 gene and a negative value of biological age acceleration among heterozygous carriers of this polymorphism allele were found. The conducted studies make it possible to identify new associations of the studied polymorphisms with the functional state of the cardiovascular system, which is of great clinical importance and requires further study.

Next-Generation Sequencing in the Assessment of the Transcriptomic Landscape of DNA Damage Repair Genes in Abdominal Aortic Aneurysm, Chronic Venous Disease and Lower Extremity Artery Disease

Vascular diseases are one of the most common causes of death and morbidity. Lower extremity artery disease (LEAD), abdominal aortic aneurysm (AAA) and chronic venous disease (CVD) belong to this group of conditions and exhibit various presentations and courses; thus, there is an urgent need for revealing new biomarkers for monitoring and potential treatment. Next-generation sequencing of mRNA allows rapid and detailed transcriptome analysis, allowing us to pinpoint the most pronounced differences between the mRNA expression profiles of vascular disease patients. Comparison of expression data of 519 DNA-repair-related genes obtained from mRNA next-generation sequencing revealed significant transcriptomic marks characterizing AAA, CVD and LEAD. Statistical, gene set enrichment analysis (GSEA), gene ontology (GO) and literature analyses were applied and highlighted many DNA repair and accompanying processes, such as cohesin functions, oxidative stress, homologous recombination, ubiquitin turnover, chromatin remodelling and DNA double-strand break repair. Surprisingly, obtained data suggest the contribution of genes engaged in the regulatory function of DNA repair as a key component that could be used to distinguish between analyzed conditions. DNA repair-related genes depicted in the presented study as dysregulated in AAA, CVD and LEAD could be utilized in the design of new biomarkers or therapies associated with these diseases.

Molecular evidence of herbal formula: a network-based analysis of Si-Wu decoction

INTRODUCTION

Emerging network pharmacology (NP) combines phytochemical information with bioinformatics tools allowing herbal formulae to be illustrated holistically in the context of phytochemical basis and therapeutic mechanisms.

OBJECTIVE

This study attempted to explore the holistic molecular evidence of herbal formula Si-Wu decoction (SWD) by using the method of NP.

MATERIAL AND METHOD

Databases of traditional medicines combined with PubChem, SciFinder, SEA, STRING, and KEGG were employed to gather information for establishing the "compound similarity" (CS) network and the "target-(pathway)-target" (TPT) network. Gephi software was applied to visualise the networks, with further module-based and node-based network topological analysis. Moreover, the approved drugs and shortest path analysis were used to validate the TPT network.

RESULTS

The CS network presented the phytochemical profile of SWD, including the major compound groups of iridoid glycosides, glycosides, phthalide lactones, phenylpropanoids, and monoterpenoids. Furthermore, the topological analysis of TPT network depicted the holistic property of SWD in interpretable neuroendocrine immunomodulation (NIM) perspective, and the node degree analysis indicated a closer connection of SWD with endocrine or metabolism system. Moreover, by combing the analysis of the CS network and TPT network, potential active ingredients could be primarily identified.

CONCLUSION

The phytochemical profile and molecular target profile, which might pave the way for an understanding of SWD in modern science and provide a reference for relevant quality research and evaluation, were demonstrated by network analysis. Moreover, the methods could be further applied to discover the phytochemical or biomolecular evidence with distinct advantages in dealing with the tremendous separated information.

G6PD activity contributes to the regulation of histone acetylation and gene expression in smooth muscle cells and to the pathogenesis of vascular diseases

We aimed to determine 1) the mechanism(s) that enables glucose-6-phosphate dehydrogenase (G6PD) to regulate serum response factor (SRF)- and myocardin (MYOCD)-driven smooth muscle cell (SMC)-restricted gene expression, a process that aids in the differentiation of SMCs, and 2) whether G6PD-mediated metabolic reprogramming contributes to the pathogenesis of vascular diseases in metabolic syndrome (MetS). Inhibition of G6PD activity increased (>30%) expression of SMC-restricted genes and concurrently decreased (40%) the growth of human and rat SMCs ex vivo. Expression of SMC-restricted genes decreased (>100-fold) across successive passages in primary cultures of SMCs isolated from mouse aorta. G6PD inhibition increased Myh11 (47%) while decreasing (>50%) Sca-1, a stem cell marker, in cells passaged seven times. Similarly, CRISPR-Cas9-mediated expression of the loss-of-function Mediterranean variant of G6PD (S188F; G6PDS188F) in rats promoted transcription of SMC-restricted genes. G6PD knockdown or inhibition decreased (48.5%) histone deacetylase (HDAC) activity, enriched (by 3-fold) H3K27ac on the Myocd promoter, and increased Myocd and Myh11 expression. Interestingly, G6PD activity was significantly higher in aortas from JCR rats with MetS than control Sprague-Dawley (SD) rats. Treating JCR rats with epiandrosterone (30 mg/kg/day), a G6PD inhibitor, increased expression of SMC-restricted genes, suppressed Serpine1 and Epha4, and reduced blood pressure. Moreover, feeding SD control (littermates) and G6PDS188F rats a high-fat diet for 4 mo increased Serpine1 and Epha4 expression and mean arterial pressure in SD but not G6PDS188F rats. Our findings demonstrate that G6PD downregulates transcription of SMC-restricted genes through HDAC-dependent deacetylation and potentially augments the severity of vascular diseases associated with MetS.NEW & NOTEWORTHY This study gives detailed mechanistic insight about the regulation of smooth muscle cell (SMC) phenotype by metabolic reprogramming and glucose-6-phosphate dehydrogenase (G6PD) in diabetes and metabolic syndrome. We demonstrate that G6PD controls the chromatin modifications by regulating histone deacetylase (HDAC) activity, which deacetylates histone 3-lysine 9 and 27. Notably, inhibition of G6PD decreases HDAC activity and enriches H3K27ac on myocardin gene promoter to enhance the expression of SMC-restricted genes. Also, we demonstrate for the first time that G6PD inhibitor treatment accentuates metabolic and transcriptomic reprogramming to reduce neointimal formation in coronary artery and large artery elastance in metabolic syndrome rats.

Interactions of antithrombotic herbal medicines with Western cardiovascular drugs

Thrombotic events act as a critical factor that interferes with Cardiovascular Diseases (CVDs), and antithrombotic herbal medicine is a long-standing controversial issue. Although a dispute is involved in their clinical application, all parties unanimously agree that herbal products have been widely used in folk medicine, and their interactions with conventional drugs are of high concern. This study aims to investigate how antithrombotic herbal medicines interact with Western cardiovascular drugs on the molecular level by taking an example of the most frequently used herbal pair, Danshen-Chuanxiong (DS-CX), and to discover more scientific evidence on their potential herb-drug interactions. Network pharmacology (NP), as an analytical approach of a complex system, is used to visualize and compare target profiles of DS-CX and Western cardiovascular drugs, which can be applied to predict common herb-drug targets and to construct a solid context for discussing herb-drug interactions. These interactions are further validated by in vitro assays, while in vivo zebrafish model employed for evaluating an overall pharmacological efficacy of herbal pairs in specific combination ratios. The study finds that DS could react directly to the Western cardiovascular drug targets relevant to antithrombotic pathways (i.e., thrombin, coagulation factor Xa and cyclooxygenase-1), whereas CX could not react directly and can synergistically affect antithrombotic effects with DS in specific combination ratios. Moreover, it is indicated that DS-CX may generate wide biological functions by a complicated mechanism of "neuro-immune-metabolism/endocrine" (NIM), which can further cause multiple direct and indirect interactions with Western cardiovascular drugs. From the clinical perspective, herb-drug interactions should be given high attention, especially when multiple herbs are used simultaneously.

Shared Causal Paths underlying Alzheimer's dementia and Type 2 Diabetes

Although Alzheimer's disease (AD) is a central nervous system disease and type 2 diabetes MELLITUS (T2DM) is a metabolic disorder, an increasing number of genetic epidemiological studies show clear link between AD and T2DM. The current approach to uncovering the shared pathways between AD and T2DM involves association analysis; however such analyses lack power to discover the mechanisms of the diseases. As an alternative, we developed novel causal inference methods for genetic studies of AD and T2DM and pipelines for systematic multi-omic casual analysis to infer multilevel omics causal networks for the discovery of common paths from genetic variants to AD and T2DM. The proposed pipelines were applied to 448 individuals from the ROSMAP Project. We identified 13 shared causal genes, 16 shared causal pathways between AD and T2DM, and 754 gene expression and 101 gene methylation nodes that were connected to both AD and T2DM in multi-omics causal networks.

An update on clonality: what smooth muscle cell type makes up the atherosclerotic plaque?

Almost 50 years ago, Earl Benditt and his son John described the clonality of the atherosclerotic plaque. This led Benditt to propose that the atherosclerotic lesion was a smooth muscle neoplasm, similar to the leiomyomata seen in the uterus of most women. Although the observation of clonality has been confirmed many times, interest in the idea that atherosclerosis might be a form of neoplasia waned because of the clinical success of treatments for hyperlipemia and because animal models have made great progress in understanding how lipid accumulates in the plaque and may lead to plaque rupture. Four advances have made it important to reconsider Benditt's observations. First, we now know that clonality is a property of normal tissue development. Second, this is even true in the vessel wall, where we now know that formation of clonal patches in that wall is part of the development of smooth muscle cells that make up the tunica media of arteries. Third, we know that the intima, the "soil" for development of the human atherosclerotic lesion, develops before the fatty lesions appear. Fourth, while the cells comprising this intima have been called "smooth muscle cells", we do not have a clear definition of cell type nor do we know if the initial accumulation is clonal. As a result, Benditt's hypothesis needs to be revisited in terms of changes in how we define smooth muscle cells and the quite distinct developmental origins of the cells that comprise the muscular coats of all arterial walls. Finally, since clonality of the lesions is real, the obvious questions are do these human tumors precede the development of atherosclerosis, how do the clones develop, what cell type gives rise to the clones, and in what ways do the clones provide the soil for development and natural history of atherosclerosis?

Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts

BACKGROUND

One goal of personalized medicine is leveraging the emerging tools of data science to guide medical decision-making. Achieving this using disparate data sources is most daunting for polygenic traits. To this end, we employed random forests (RFs) and neural networks (NNs) for predictive modeling of coronary artery calcium (CAC), which is an intermediate endo-phenotype of coronary artery disease (CAD).

METHODS

Model inputs were derived from advanced cases in the ClinSeq®; discovery cohort (n=16) and the FHS replication cohort (n=36) from 89 th -99 th CAC score percentile range, and age-matched controls (ClinSeq®; n=16, FHS n=36) with no detectable CAC (all subjects were Caucasian males). These inputs included clinical variables and genotypes of 56 single nucleotide polymorphisms (SNPs) ranked highest in terms of their nominal correlation with the advanced CAC state in the discovery cohort. Predictive performance was assessed by computing the areas under receiver operating characteristic curves (ROC-AUC).

RESULTS

RF models trained and tested with clinical variables generated ROC-AUC values of 0.69 and 0.61 in the discovery and replication cohorts, respectively. In contrast, in both cohorts, the set of SNPs derived from the discovery cohort were highly predictive (ROC-AUC ≥0.85) with no significant change in predictive performance upon integration of clinical and genotype variables. Using the 21 SNPs that produced optimal predictive performance in both cohorts, we developed NN models trained with ClinSeq®; data and tested with FHS data and obtained high predictive accuracy (ROC-AUC=0.80-0.85) with several topologies. Several CAD and "vascular aging" related biological processes were enriched in the network of genes constructed from the predictive SNPs.

CONCLUSIONS

We identified a molecular network predictive of advanced coronary calcium using genotype data from ClinSeq®; and FHS cohorts. Our results illustrate that machine learning tools, which utilize complex interactions between disease predictors intrinsic to the pathogenesis of polygenic disorders, hold promise for deriving predictive disease models and networks.

EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS

Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.

Navigating care for Bedouin patients with diabetes

The Bedouin Arab population in the southern Negev region of Israel has faced health problems as a result of transitioning rapidly from a nomadic agricultural lifestyle to a more modern urban lifestyle. Like many populations around the world, the Bedouins have changed their diets and become more sedentary and this has led to a high rate of diabetes. In this case report, we examine how diabetes has affected the life of an influential man in the Bedouin community and the significance this case has in the greater context of a global rise in chronic disease.

Personalized Medicine for Sepsis

Sepsis is a complex syndrome triggered by infection and characterized by systemic deregulation of immune and inflammatory pathways. It is a major cause of death worldwide and results in the widespread use of antibiotics and substantial health care costs. In a vicious circle, sepsis treatment promotes the emergence of highly virulent and resistant pathogens and devastating nosocomial infections. Sepsis is a heterogeneous disease affecting many people worldwide. Because individual patients have different inflammatory responses and unique profiles of immune activation against pathogens, the most effective way to advance the treatment of sepsis is probably through a tailored approach. The advent of high-throughput technologies and the remarkable progress in the field of bioinformatics has allowed the subclassification of many pathological conditions. This has potential to provide better understanding of life-threatening infections in people. The study of host factors, however, needs to be integrated with studies on bacterial signaling in both symbiotic and pathogenic bacteria. Sepsis is certainly the sum of multiple host-microbial interactions and the metagenome should be extensively investigated. Personalized medicine is probably the only strategy able to deconstruct and reassemble our knowledge about sepsis, and its use should allow us to understand and manipulate sepsis as a wide, interconnected phenomenon with myriad variables and peculiarities. In this study, the recent advances in this area, the major challenges that remain, and the reasons why the septic patient should be approached as a superorganism are discussed.

The center for causal discovery of biomedical knowledge from big data

The Big Data to Knowledge (BD2K) Center for Causal Discovery is developing and disseminating an integrated set of open source tools that support causal modeling and discovery of biomedical knowledge from large and complex biomedical datasets. The Center integrates teams of biomedical and data scientists focused on the refinement of existing and the development of new constraint-based and Bayesian algorithms based on causal Bayesian networks, the optimization of software for efficient operation in a supercomputing environment, and the testing of algorithms and software developed using real data from 3 representative driving biomedical projects: cancer driver mutations, lung disease, and the functional connectome of the human brain. Associated training activities provide both biomedical and data scientists with the knowledge and skills needed to apply and extend these tools. Collaborative activities with the BD2K Consortium further advance causal discovery tools and integrate tools and resources developed by other centers.

Implementation and utilization of genetic testing in personalized medicine

Clinical genetic testing began over 30 years ago with the availability of mutation detection for sickle cell disease diagnosis. Since then, the field has dramatically transformed to include gene sequencing, high-throughput targeted genotyping, prenatal mutation detection, preimplantation genetic diagnosis, population-based carrier screening, and now genome-wide analyses using microarrays and next-generation sequencing. Despite these significant advances in molecular technologies and testing capabilities, clinical genetics laboratories historically have been centered on mutation detection for Mendelian disorders. However, the ongoing identification of deoxyribonucleic acid (DNA) sequence variants associated with common diseases prompted the availability of testing for personal disease risk estimation, and created commercial opportunities for direct-to-consumer genetic testing companies that assay these variants. This germline genetic risk, in conjunction with other clinical, family, and demographic variables, are the key components of the personalized medicine paradigm, which aims to apply personal genomic and other relevant data into a patient’s clinical assessment to more precisely guide medical management. However, genetic testing for disease risk estimation is an ongoing topic of debate, largely due to inconsistencies in the results, concerns over clinical validity and utility, and the variable mode of delivery when returning genetic results to patients in the absence of traditional counseling. A related class of genetic testing with analogous issues of clinical utility and acceptance is pharmacogenetic testing, which interrogates sequence variants implicated in interindividual drug response variability. Although clinical pharmacogenetic testing has not previously been widely adopted, advances in rapid turnaround time genetic testing technology and the recent implementation of preemptive genotyping programs at selected medical centers suggest that personalized medicine through pharmacogenetics is now a reality. This review aims to summarize the current state of implementing genetic testing for personalized medicine, with an emphasis on clinical pharmacogenetic testing.

Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data

Posttraumatic stress disorder (PTSD) and other deployment-related outcomes originate from a complex interplay between constellations of changes in DNA, environmental traumatic exposures, and other biological risk factors. These factors affect not only individual genes or bio-molecules but also the entire biological networks that in turn increase or decrease the risk of illness or affect illness severity. This review focuses on recent developments in the field of systems biology which use multidimensional data to discover biological networks affected by combat exposure and post-deployment disease states. By integrating large-scale, high-dimensional molecular, physiological, clinical, and behavioral data, the molecular networks that directly respond to perturbations that can lead to PTSD can be identified and causally associated with PTSD, providing a path to identify key drivers. Reprogrammed neural progenitor cells from fibroblasts from PTSD patients could be established as an in vitro assay for high throughput screening of approved drugs to determine which drugs reverse the abnormal expression of the pathogenic biomarkers or neuronal properties.

Systems genetics approaches to understand complex traits

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It uses a range of experimental and statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein or metabolite levels, in populations that vary for traits of interest. Systems genetics studies have provided the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases. Given the urgent need to understand how the thousands of loci that have been identified in genome-wide association studies contribute to disease susceptibility, systems genetics is likely to become an increasingly important approach to understanding both biology and disease.

Text mining for systems biology

Scientific communication in biomedicine is, by and large, still text based. Text mining technologies for the automated extraction of useful biomedical information from unstructured text that can be directly used for systems biology modelling have been substantially improved over the past few years. In this review, we underline the importance of named entity recognition and relationship extraction as fundamental approaches that are relevant to systems biology. Furthermore, we emphasize the role of publicly organized scientific benchmarking challenges that reflect the current status of text-mining technology and are important in moving the entire field forward. Given further interdisciplinary development of systems biology-orientated ontologies and training corpora, we expect a steadily increasing impact of text-mining technology on systems biology in the future.