Systems Genetics Approach Research Articles

Type V Collagen in Scar Tissue Regulates the Size of Scar after Heart Injury

Scar tissue size following myocardial infarction is an independent predictor of cardiovascular outcomes, yet little is known about factors regulating scar size. We demonstrate that collagen V, a minor constituent of heart scars, regulates the size of heart scars after ischemic injury. Depletion of collagen V led to a paradoxical increase in post-infarction scar size with worsening of heart function. A systems genetics approach across 100 in-bred strains of mice demonstrated that collagen V is a critical driver of postinjury heart function. We show that collagen V deficiency alters the mechanical properties of scar tissue, and altered reciprocal feedback between matrix and cells induces expression of mechanosensitive integrins that drive fibroblast activation and increase scar size. Cilengitide, an inhibitor of specific integrins, rescues the phenotype of increased post-injury scarring in collagen-V-deficient mice. These observations demonstrate that collagen V regulates scar size in an integrin-dependent manner.

Dissecting the Genetic Architecture of Cystatin C in Diversity Outbred Mice.

Plasma concentration of Cystatin C (CysC) level is a biomarker of glomerular filtration rate in the kidney. We use a Systems Genetics approach to investigate the genetic determinants of plasma CysC concentration. To do so we perform Quantitative Trait Loci (QTL) and expression QTL (eQTL) analysis of 120 Diversity Outbred (DO) female mice, 56 weeks of age. We performed network analysis of kidney gene expression to determine if the gene modules with common functions are associated with kidney biomarkers of chronic kidney diseases. Our data demonstrates that plasma concentrations and kidney mRNA levels of CysC are associated with genetic variation and are transcriptionally coregulated by immune genes. Specifically, Type-I interferon signaling genes are coexpressed with Cst3 mRNA levels and associated with CysC concentrations in plasma. Our findings demonstrate the complex control of CysC by genetic polymorphisms and inflammatory pathways.

Systems Genetics for Mechanistic Discovery in Heart Diseases.

Cardiovascular diseases are the leading cause of death worldwide. Complex diseases with highly heterogenous disease progression among patient populations, cardiovascular diseases feature multifactorial contributions from both genetic and environmental stressors. Despite significant effort utilizing multiple approaches from molecular biology to genome-wide association studies, the genetic landscape of cardiovascular diseases, particularly for the nonfamilial forms of heart failure, is still poorly understood. In the past decade, systems-level approaches based on omics technologies have become an important approach for the study of complex traits in large populations. These advances create opportunities to integrate genetic variation with other biological layers to identify and prioritize candidate genes, understand pathogenic pathways, and elucidate gene-gene and gene-environment interactions. In this review, we will highlight some of the recent progress made using systems genetics approaches to uncover novel mechanisms and molecular bases of cardiovascular pathophysiological manifestations. The key technology and data analysis platforms necessary to implement systems genetics will be described, and the current major challenges and future directions will also be discussed. For complex cardiovascular diseases, such as heart failure, systems genetics represents a powerful strategy to obtain mechanistic insights and to develop individualized diagnostic and therapeutic regiments, paving the way for precision cardiovascular medicine.

Integrating GWAS with bulk and single-cell RNA-sequencing reveals a role for LY86 in the anti-Candida host response.

Candida bloodstream infection, i.e. candidemia, is the most frequently encountered life-threatening fungal infection worldwide, with mortality rates up to almost 50%. In the majority of candidemia cases, Candida albicans is responsible. Worryingly, a global increase in the number of patients who are susceptible to infection (e.g. immunocompromised patients), has led to a rise in the incidence of candidemia in the last few decades. Therefore, a better understanding of the anti-Candida host response is essential to overcome this poor prognosis and to lower disease incidence. Here, we integrated genome-wide association studies with bulk and single-cell transcriptomic analyses of immune cells stimulated with Candida albicans to further our understanding of the anti-Candida host response. We show that differential expression analysis upon Candida stimulation in single-cell expression data can reveal the important cell types involved in the host response against Candida. This confirmed the known major role of monocytes, but more interestingly, also uncovered an important role for NK cells. Moreover, combining the power of bulk RNA-seq with the high resolution of single-cell RNA-seq data led to the identification of 27 Candida-response QTLs and revealed the cell types potentially involved herein. Integration of these response QTLs with a GWAS on candidemia susceptibility uncovered a potential new role for LY86 in candidemia susceptibility. Finally, experimental follow-up confirmed that LY86 knockdown results in reduced monocyte migration towards the chemokine MCP-1, thereby implying that this reduced migration may underlie the increased susceptibility to candidemia. Altogether, our integrative systems genetics approach identifies previously unknown mechanisms underlying the immune response to Candida infection.

Systems Genetics Approaches in Mouse Models of Group A Streptococcal Necrotizing Soft-Tissue Infections.

Mouse models are invaluable resources for studying the pathogenesis and preclinical evaluation of therapeutics and vaccines against many human pathogens. Infections caused by group A streptococcus (GAS, Streptococcus pyogenes) are heterogeneous ranging from mild pharyngitis to severe invasive necrotizing fasciitis, a subgroup of necrotizing soft-tissue infections (NSTIs). While several strains of mice including BALB/c, C3H/HeN, CBA/J, and C57BL/10 offered significant insights, the human specificity and the interindividual variations on susceptibility or resistance to GAS infections limit their ability to mirror responses as seen in humans. In this chapter, we discuss the advanced recombinant inbred (ARI) BXD mouse model that mimics the genetic diversity as seen in humans and underpins the feasibility to map multiple genes (genetic loci) modulating GAS NSTI. GAS produces a myriad of virulence factors, including superantigens (SAg). Superantigens are potent immune toxins that activate T cells by cross-linking T cell receptors with human leukocyte antigen class-II (HLA-II) molecules expressed on antigen-presenting cells. This leads to a pro-inflammatory cytokine storm and the subsequent multiple organ damage and shock. Inbred mice are innately refractive to SAg-mediated responses. In this chapter, we discuss the versatility of the HLA-II transgenic mouse model that allowed the biological validation of known genetic associations to GAS NSTI. The combined utility of ARI-BXD and HLA-II mice as complementary approaches that offer clinically translatable insights into pathomechanisms driven by complex traits and host genetic context and novel means to evaluate the in vivo efficiency of therapies to improve outcomes of GAS NSTI are also discussed.

Insight into plant cell wall degradation and pathogenesis of Ganoderma boninense via comparative genome analysis.

BackgroundG. boninense is a hemibiotrophic fungus that infects oil palms (Elaeis guineensis Jacq.) causing basal stem rot (BSR) disease and consequent massive economic losses to the oil palm industry. The pathogenicity of this white-rot fungus has been associated with cell wall degrading enzymes (CWDEs) released during saprophytic and necrotrophic stage of infection of the oil palm host. However, there is a lack of information available on the essentiality of CWDEs in wood-decaying process and pathogenesis of this oil palm pathogen especially at molecular and genome levels.MethodsIn this study, comparative genome analysis was carried out using the G. boninense NJ3 genome to identify and characterize carbohydrate-active enzyme (CAZymes) including CWDE in the fungal genome. Augustus pipeline was employed for gene identification in G. boninense NJ3 and the produced protein sequences were analyzed via dbCAN pipeline and PhiBase 4.5 database annotation for CAZymes and plant-host interaction (PHI) gene analysis, respectively. Comparison of CAZymes from G. boninense NJ3 was made against G. lucidum, a well-studied model Ganoderma sp. and five selected pathogenic fungi for CAZymes characterization. Functional annotation of PHI genes was carried out using Web Gene Ontology Annotation Plot (WEGO) and was used for selecting candidate PHI genes related to cell wall degradation of G. boninense NJ3.ResultsG. boninense was enriched with CAZymes and CWDEs in a similar fashion to G. lucidum that corroborate with the lignocellulolytic abilities of both closely-related fungal strains. The role of polysaccharide and cell wall degrading enzymes in the hemibiotrophic mode of infection of G. boninense was investigated by analyzing the fungal CAZymes with necrotrophic Armillaria solidipes, A. mellea, biotrophic Ustilago maydis, Melampsora larici-populina and hemibiotrophic Moniliophthora perniciosa. Profiles of the selected pathogenic fungi demonstrated that necrotizing pathogens including G. boninense NJ3 exhibited an extensive set of CAZymes as compared to the more CAZymes-limited biotrophic pathogens. Following PHI analysis, several candidate genes including polygalacturonase, endo β-1,3-xylanase, β-glucanase and laccase were identified as potential CWDEs that contribute to the plant host interaction and pathogenesis.DiscussionThis study employed bioinformatics tools for providing a greater understanding of the biological mechanisms underlying the production of CAZymes in G. boninense NJ3. Identification and profiling of the fungal polysaccharide- and lignocellulosic-degrading enzymes would further facilitate in elucidating the infection mechanisms through the production of CWDEs by G. boninense. Identification of CAZymes and CWDE-related PHI genes in G. boninense would serve as the basis for functional studies of genes associated with the fungal virulence and pathogenicity using systems biology and genetic engineering approaches.

A Cross-Species Systems Genetics Analysis Links APBB1IP as a Candidate for Schizophrenia and Prepulse Inhibition.

Background: Prepulse inhibition (PPI) of the startle response is a highly conserved form of sensorimotor gating, disruption of which is found in schizophrenia patients and their unaffected first-degree relatives. PPI can be measured in many species, and shows considerable phenotypic variation between and within rodent models. This makes PPI a useful endophenotype. Genome-wide association studies (GWAS) have been carried out to identify genetic variants underlying schizophrenia, and these suggest that schizophrenia is highly polygenic. GWAS have been unable to account for the high heritability of schizophrenia seen in family studies, partly because of the low power of GWAS due to multiple comparisons. By contrast, complementary mouse model linkage studies often have high statistical power to detect variants for behavioral traits but lower resolution, producing loci that include tens or hundreds of genes. To capitalize on the advantages of both GWAS and genetic mouse models, our study uses a cross-species approach to identify novel genes associated with PPI regulation, which thus may contribute to the PPI deficits seen in schizophrenia.Results: Using experimental data from the recombinant inbred (RI) mouse panel BXD, we identified two significant loci affecting PPI. These genomic regions contain genetic variants which influence PPI in mice and are therefore candidates that may be influencing aspects of schizophrenia in humans. We next investigated these regions in whole-genome data from the Psychiatric Genomics Consortium (PGC) schizophrenia GWAS and identify one novel candidate gene (ABPP1IP) that was significantly associated with PPI in mice and risk of schizophrenia in humans. A systems genetics approach demonstrates that APBB1IP coexpresses with several other genes related to schizophrenia in several brain regions. Gene coexpression and enrichment analysis shows clear links between APBB1IP and the immune system.Conclusion: The combination of human GWAS and mouse quantitative trait loci (QTL) from some of the largest study systems available has enabled us to identify a novel gene, APBB1IP, which influences schizophrenia in humans and PPI in mice.

A Multi-Modular System-Genetics (MMSG) Approach for Deep Representation Learning for Personalized Treatment of Cancer Using Sensitivity Analysis of Precision Drugs and Gene Expression Data

One-size-fits-all has never succeeded profoundly and this is the reason why personalization has become so important. Many fatal diseases face such problem due to intense and risky medication procedures with immense side effects. Personalized medication through precision medicine is the new big thing that will provide better treatment solution through analysis and artificial intelligence. In this work, we have introduced for the first time a multi-modular training approach (MMSG+DRRL/ERRL) for generalized representation for cancer drug prediction based on gene expression for personalized treatment through the use of different system-genetics information like subclinical medical information to determine their suitability. We have shown how different modular information can be used computationally to enhance prediction behavior using the genetic and subcategory information of cancer diseases. Subcategorization analysis can decode many useful information of the disease and can help in better treatment modeling. We have shown for the first time how we can use the subcategory information to extract sensitivity of cancer cell lines for different drugs, a procedure never tried before. We have utilized the best practices of the feature training for deep learning to determine the interacting genes for particular drugs.

An integrative systems genetic analysis of mammalian lipid metabolism

Summary: Novel diagnostic and prognostic biomarkers in the blood that can predict early fatty liver disease were identified, using a proteomic and lipidomic-wide systems genetic approach in 107 genetically distinct mouse strains. In addition, the novel lipid-regulatory protein, PSMD9, was identified as a regulator of lipid metabolism, with potential therapeutic implications.

One small step for mice, one giant leap for GWAS?

Genome-wide association studies (GWAS) have provided a wealth of information on potential disease-associated genes in the human population. In particular, several loci have been associated with type 2 diabetes (T2D). However, due to the complexity of the disease, it has been a challenge to unravel the exact effects of specific loci on T2D pathogenesis. In this issue of the JCI, Keller and colleagues developed a systems genetic approach to identify insulin secretion-associated genes in nondiabetic mice followed by tissue-level and functional phenotyping. Several of the loci identified were syntenic with human T2D-related loci, indicating that this approach may be feasible for discerning genetic variation in nondiabetic individuals that may lead to the development of T2D.

Systems genetics approach reveals cross-talk between bile acids and intestinal microbes.

Systems genetics approach reveals cross-talk between bile acids and intestinal microbes.

Relevance of Multi-Omics Studies in Cardiovascular Diseases.

Cardiovascular diseases are the leading cause of death around the world. Despite the larger number of genes and loci identified, the precise mechanisms by which these genes influence risk of cardiovascular disease is not well understood. Recent advances in the development and optimization of high-throughput technologies for the generation of “omics data” have provided a deeper understanding of the processes and dynamic interactions involved in human diseases. However, the integrative analysis of “omics” data is not straightforward and represents several logistic and computational challenges. In spite of these difficulties, several studies have successfully applied integrative genomics approaches for the investigation of novel mechanisms and plasma biomarkers involved in cardiovascular diseases. In this review, we summarized recent studies aimed to understand the molecular framework of these diseases using multi-omics data from mice and humans. We discuss examples of omics studies for cardiovascular diseases focused on the integration of genomics, epigenomics, transcriptomics, and proteomics. This review also describes current gaps in the study of complex diseases using systems genetics approaches as well as potential limitations and future directions of this emerging field.

Systems genetics approaches to map new genes in metabolism and aging

Systems genetics approaches to map new genes in metabolism and aging

CRAFTing a New Approach to Antiepileptic Drug Discovery.

Srivastava PK, van Eyll J, Godard P, Mazzuferi M, Delahaye-Duriez A, Steenwinckel JV, et al. A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target. Nat Commun. 2018;9(1):3561. doi:10.1038/s41467-018-06008-4.The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning (“Causal Reasoning Analytical Framework for Target discovery”-CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in 3 preclinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. The CRAFT is applicable to disease settings other than epilepsy.

Predicting and reducing hepatic lipotoxicity in non-alcoholic fatty liver disease.

Using a systems genetics approach, a new study identifies genetic variants and proteins associated with plasma and hepatic lipid abundance and hepatic lipotoxicity.

An integrative systems genetic analysis of mammalian lipid metabolism.

Dysregulation of lipid homeostasis is a precipitating event in the pathogenesis and progression of hepatosteatosis and metabolic syndrome. These conditions are highly prevalent in developed societies and currently have limited options for diagnostic and therapeutic intervention. Here, using a proteomic and lipidomic-wide systems genetic approach, we interrogated lipid regulatory networks in 107 genetically distinct mouse strains to reveal key insights into the control and network structure of mammalian lipid metabolism. These include the identification of plasma lipid signatures that predict pathological lipid abundance in the liver of mice and humans, defining subcellular localization and functionality of lipid-related proteins, and revealing functional protein and genetic variants that are predicted to modulate lipid abundance. Trans-omic analyses using these datasets facilitated the identification and validation of PSMD9as a previously unknown lipid regulatory protein. Collectively, our study serves as a rich resource for probing mammalian lipid metabolism and provides opportunities for the discovery of therapeutic agents and biomarkers in the setting of hepatic lipotoxicity.

Xylan in the Middle: Understanding Xylan Biosynthesis and Its Metabolic Dependencies Toward Improving Wood Fiber for Industrial Processing.

Lignocellulosic biomass, encompassing cellulose, lignin and hemicellulose in plant secondary cell walls (SCWs), is the most abundant source of renewable materials on earth. Currently, fast-growing woody dicots such as Eucalyptus and Populus trees are major lignocellulosic (wood fiber) feedstocks for bioproducts such as pulp, paper, cellulose, textiles, bioplastics and other biomaterials. Processing wood for these products entails separating the biomass into its three main components as efficiently as possible without compromising yield. Glucuronoxylan (xylan), the main hemicellulose present in the SCWs of hardwood trees carries chemical modifications that are associated with SCW composition and ultrastructure, and affect the recalcitrance of woody biomass to industrial processing. In this review we highlight the importance of xylan properties for industrial wood fiber processing and how gaining a greater understanding of xylan biosynthesis, specifically xylan modification, could yield novel biotechnology approaches to reduce recalcitrance or introduce novel processing traits. Altering xylan modification patterns has recently become a focus of plant SCW studies due to early findings that altered modification patterns can yield beneficial biomass processing traits. Additionally, it has been noted that plants with altered xylan composition display metabolic differences linked to changes in precursor usage. We explore the possibility of using systems biology and systems genetics approaches to gain insight into the coordination of SCW formation with other interdependent biological processes. Acetyl-CoA, s-adenosylmethionine and nucleotide sugars are precursors needed for xylan modification, however, the pathways which produce metabolic pools during different stages of fiber cell wall formation still have to be identified and their co-regulation during SCW formation elucidated. The crucial dependence on precursor metabolism provides an opportunity to alter xylan modification patterns through metabolic engineering of one or more of these interdependent pathways. The complexity of xylan biosynthesis and modification is currently a stumbling point, but it may provide new avenues for woody biomass engineering that are not possible for other biopolymers.

Exploring the involvement of Tac2 in the mouse hippocampal stress response through gene networking

Tachykinin 2 (Tac2) is expressed in a number of areas throughout the brain, including the hippocampus. However, knowledge about its function has been only well explored in the hypothalamus in the context of reproductive health. In this study, we identified and validated increased hippocampal Tac2 mRNA expression in response to chronic mild stress in mice. Expression quantitative trait locus (eQTL) analysis showed Tac2 is cis-regulated in the hippocampus. Using a systems genetics approach, we constructed a Tac2 co-expression network to better understand the relationship between Tac2 and the hippocampal stress response. Our network identified 69 total genes associated with Tac2, several of which encode major neuropeptides involved in hippocampal stress signaling as well as critical genes for producing neural plasticity, indicating that Tac2 is involved in these processes. Pathway analysis for the member of Tac2 gene network revealed a strong connection between Tac2 and neuroactive ligand-receptor interaction, calcium signaling pathway, as well as cardiac muscle contraction. In addition, we also identified 46 stress-related phenotypes, specifically fear conditioning response, that were significantly correlated with Tac2 expression. Our results provide evidence for Tac2 as a strong candidate gene who likely plays a role in hippocampal stress processing and neural plasticity.

Major Critical Periods in Developmental Pathogenomics of Endometriosis

In spite on numerous experimental and clinical data molecular mechanisms of endometriosis (EM) - the most common benign tumor of the female reproductive tract still remains obscure . The deciphering enigmas of EM gave a birth to a number of hypothesis . System genetics approach used in our studies of common diseases support the existence of special genetic program of EM operative in its development. It is taken for granted that EM results from abnormal differentiation of stem cells (SC). Two major sources of EM SC are considered : SC disseminated throughout peritoneum during female reproductive organs embryogenesis , SC from junction zone the uterine endometrium (2) [6]. According to our reviewed hypothesis [7] the genetic program of EM consists of several critical periods (CP) [8] corresponding to three crucial events in EM development with each of them corresponding to major genome reprogramming in EM cells.

Systems Genetics Approach to Biomarker Discovery: GPNMB and Heart Failure in Mice and Humans.

We describe a simple bioinformatics method for biomarker discovery that is based on the analysis of global transcript levels in a population of inbred mouse strains showing variation for disease-related traits. This method has advantages such as controlled environment and accessibility to heart and plasma tissue in the preclinical selection stage. We illustrate the approach by identifying candidate heart failure (HF) biomarkers by overlaying mouse transcriptome and clinical traits from 91 Hybrid Mouse Diversity Panel (HMDP) inbred strains and human HF transcriptome from the Myocardial Applied Genomics Network (MAGNet) consortium. We found that some of the top differentially expressed genes correlated with known human HF biomarkers, such as galectin-3 and tissue inhibitor of metalloproteinase 1. Using ELISA assays, we investigated one novel candidate, Glycoprotein NMB, in a mouse model of chronic β-adrenergic stimulation by isoproterenol (ISO) induced HF. We observed significantly lower GPNMB plasma levels in the ISO model compared to the control group (p-value = 0.007). In addition, we assessed GPNMB plasma levels among 389 HF cases and controls from the METabolic Syndrome In Men (METSIM) study. Lower levels of GPNMB were also observed in patients with HF from the METSIM study compared to non-HF controls (p-value < 0.0001). In summary, we have identified several candidate biomarkers for HF using the cardiac transcriptome data in a population of mice that may be directly relevant and applicable to human populations.