Systems genetics in "-omics" era: current and future development

Study of hub nodes of transcription factor-target gene regulatory network and immune mechanism for type 2 diabetes based on chip analysis of GEO database

Identification of novel therapeutic targets for type 2 diabetes is a key area of contemporary research. In this study, we screened differentially expressed genes in type 2 diabetes through the GEO database and sought to identify the key virulence factors for type 2 diabetes through a transcription factor regulatory network. Our findings may help identify new therapeutic targets for type 2 diabetes. Data pertaining to the humoral (whole blood) gene expression profile of diabetic patients were obtained from the NCBI's GEO Datasets database and gene sets with differential expression were identified. Subsequently, the TRED transcriptional regulatory element database was integrated to build a gene regulatory network for type 2 diabetes. Functional analysis (GO-Analysis) and Pathway-analysis of differentially expressed genes were performed using the DAVID database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, gene-disease correlation analysis was performed using the DAVID online annotation tool. A total of 236 pathogenic genes, four transcription factors related to the pathogenic genes, and 261 corresponding target genes were identified. A transcription factor-target gene regulatory network for type 2 diabetes was constructed. Most of the key factors of the transcription factor-target gene regulatory network for type 2 diabetes were found closely related to the immune metabolic system and the functions of cell proliferation and transformation.

Genetic basis of chicken plumage color in artificial population of complex epistasis

Chicken plumage color, the genetic basis of which is often affected by epistasis, has long interested scientists. In the current study, a population of complex epistasis was constructed by crossing dominant White Leghorn chickens with recessive white feather chickens. Through a genome-wide association study, we identified single nucleotide polymorphisms and genes significantly associated with white and colored plumage in hens at different developmental stages. Interestingly, white plumage in adulthood was associated with the recessive white feather gene (TYR), whereas white feathers at birth stage were associated with the dominant white feather gene (PMEL), indicating age-related roles for these genes. TYR was shown to exert an epistatic effect on PMEL in adult hens. Additionally, TYR had an epistatic effect on barred plumage, while barred plumage had an epistatic effect on black plumage. TYR had no epistatic effect on the yellow plumage. We confirmed that the barred plumage gene is CDKN2A, as reported in previous studies. Golgb1 and REEP3, which play important roles in the Golgi network and affect the formation of feather pigments, are important candidate genes for yellow plumage. The candidate genes for black plumage are CAMKK1 and IFT22. Further research is warranted to elucidate the molecular mechanisms underlying these traits.

Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence

BACKGROUND

Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample.

METHODS

LncRNA and protein-coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL).

RESULTS

At Bonferroni adj. p-value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune-related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively.

CONCLUSIONS

Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.

Variable selection and validation in multivariate modelling

MOTIVATION

Validation of variable selection and predictive performance is crucial in construction of robust multivariate models that generalize well, minimize overfitting and facilitate interpretation of results. Inappropriate variable selection leads instead to selection bias, thereby increasing the risk of model overfitting and false positive discoveries. Although several algorithms exist to identify a minimal set of most informative variables (i.e. the minimal-optimal problem), few can select all variables related to the research question (i.e. the all-relevant problem). Robust algorithms combining identification of both minimal-optimal and all-relevant variables with proper cross-validation are urgently needed.

RESULTS

We developed the MUVR algorithm to improve predictive performance and minimize overfitting and false positives in multivariate analysis. In the MUVR algorithm, minimal variable selection is achieved by performing recursive variable elimination in a repeated double cross-validation (rdCV) procedure. The algorithm supports partial least squares and random forest modelling, and simultaneously identifies minimal-optimal and all-relevant variable sets for regression, classification and multilevel analyses. Using three authentic omics datasets, MUVR yielded parsimonious models with minimal overfitting and improved model performance compared with state-of-the-art rdCV. Moreover, MUVR showed advantages over other variable selection algorithms, i.e. Boruta and VSURF, including simultaneous variable selection and validation scheme and wider applicability.

AVAILABILITY AND IMPLEMENTATION

Algorithms, data, scripts and tutorial are open source and available as an R package ('MUVR') at https://gitlab.com/CarlBrunius/MUVR.git.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomics-genomics data integration and expression quantitative trait loci analyses in oocyte donors and embryo recipients for improving invitro production of dairy cattle embryos

In this paper we first provide a brief review of main results from our previously published studies on genome-wide gene expression (transcriptomics) in donor and recipient cattle used in invitro production (IVP) of embryos and embryo transfer (ET). Then, we present novel results from applying integrative systems genomics and biological analyses where transcriptomics data are combined with genomic data in both donor and recipient cattle to map expression quantitative trait loci (eQTLs). The eQTLs are genetic markers that can regulate or control the expression of genes in the entire genome, via complex molecular mechanisms, and thus can act as a powerful tool for genomic and gene-assisted selection. We identified significant eQTLs potentially controlling the expression of 13 candidate genes for donor cow quality (IVP parameters; e.g. cyclin B1 (CCNB1), outer dense fiber of sperm tails 2 like (ODF2L)) and 19 candidate genes for recipient cows quality (endometrial receptivity; e.g. ER membrane protein complex subunit 9 (EMC9), mannosidase beta (MANBA), peptidase inhibitor 16 (PI16)). Annotation and colocation of detected eQTLs show that some of the eQTLs are in the same genomic regions previously reported as QTLs for reproduction-related traits. However, eQTLs and the candidate genes identified should be further validated in larger populations before implementation as genetic markers or used in genomic selection for improving IVP and ET performance.

Integrated analysis of microRNA and transcription factor reveals important regulators and regulatory motifs in adult B-cell acute lymphoblastic leukemia

B-cell acute lymphoblastic leukemia (B‑ALL) is an aggressive hematological malignancy and a leading cause of cancer-related mortality in children and young adults. The molecular mechanisms involved in the regulation of its gene expression has yet to be fully elucidated. In the present study, we performed large scale expression profiling of microRNA (miRNA) and transcription factor (TF) by Illumina deep‑sequencing and TF array technology, respectively, and identified 291 differentially expressed miRNAs and 201 differentially expressed TFs in adult B‑ALL samples relative to their controls. After integrating expression profile data with computational prediction of miRNA and TF targets from different databases, we construct a comprehensive miRNA‑TF regulatory network specifically for adult B‑ALL. Network function analysis revealed 25 significantly enriched pathways, four pathways are well‑known to be involved in B‑ALL, such as PI3K‑Akt signaling pathway, Jak‑STAT signaling pathway, Ras signaling pathway and cell cycle pathway. By analyzing the network topology, we identified 28 hub miRNAs and 19 hub TFs in the network, and found nine potential B‑ALL regulators among these hub nodes. We also constructed a Jak‑STAT signaling sub‑network for B‑ALL. Based on the sub‑network analysis and literature survey, we proposed a cellular model to discuss MYC/miR‑15a‑5p/FLT3 feed-forward loop (FFL) with Jak‑STAT signaling pathway in B‑ALL. These findings enhance our understanding of this disease at the molecular level, as well as provide putative therapeutic targets for B-ALL.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Molecular mechanisms underlying variations in lung function: a systems genetics analysis

BACKGROUND

Lung function measures reflect the physiological state of the lung, and are essential to the diagnosis of chronic obstructive pulmonary disease (COPD). The SpiroMeta-CHARGE consortium undertook the largest genome-wide association study (GWAS) so far (n=48,201) for forced expiratory volume in 1 s (FEV1) and the ratio of FEV1 to forced vital capacity (FEV1/FVC) in the general population. The lung expression quantitative trait loci (eQTLs) study mapped the genetic architecture of gene expression in lung tissue from 1111 individuals. We used a systems genetics approach to identify single nucleotide polymorphisms (SNPs) associated with lung function that act as eQTLs and change the level of expression of their target genes in lung tissue; termed eSNPs.

METHODS

The SpiroMeta-CHARGE GWAS results were integrated with lung eQTLs to map eSNPs and the genes and pathways underlying the associations in lung tissue. For comparison, a similar analysis was done in peripheral blood. The lung mRNA expression levels of the eSNP-regulated genes were tested for associations with lung function measures in 727 individuals. Additional analyses identified the pleiotropic effects of eSNPs from the published GWAS catalogue, and mapped enrichment in regulatory regions from the ENCODE project. Finally, the Connectivity Map database was used to identify potential therapeutics in silico that could reverse the COPD lung tissue gene signature.

FINDINGS

SNPs associated with lung function measures were more likely to be eQTLs and vice versa. The integration mapped the specific genes underlying the GWAS signals in lung tissue. The eSNP-regulated genes were enriched for developmental and inflammatory pathways; by comparison, SNPs associated with lung function that were eQTLs in blood, but not in lung, were only involved in inflammatory pathways. Lung function eSNPs were enriched for regulatory elements and were over-represented among genes showing differential expression during fetal lung development. An mRNA gene expression signature for COPD was identified in lung tissue and compared with the Connectivity Map. This in-silico drug repurposing approach suggested several compounds that reverse the COPD gene expression signature, including a nicotine receptor antagonist. These findings represent novel therapeutic pathways for COPD.

INTERPRETATION

The system genetics approach identified lung tissue genes driving the variation in lung function and susceptibility to COPD. The identification of these genes and the pathways in which they are enriched is essential to understand the pathophysiology of airway obstruction and to identify novel therapeutic targets and biomarkers for COPD, including drugs that reverse the COPD gene signature in silico.

FUNDING

The research reported in this article was not specifically funded by any agency. See Acknowledgments for a full list of funders of the lung eQTL study and the Spiro-Meta CHARGE GWAS.

van der Vaart A, Web BT, Bacanu SA, Kalsi G, Kendler KS, Miles MF, Dick D, Riley BP, Dumur C, Vladimirov VI. Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence

Alcohol consumption is known to lead to gene expression changes in the brain. After performing weighted gene co-expression network analyses (WGCNA) on genome-wide mRNA and microRNA (miRNA) expression in Nucleus Accumbens (NAc) of subjects with alcohol dependence (AD; N = 18) and of matched controls (N = 18), six mRNA and three miRNA modules significantly correlated with AD were identified (Bonferoni-adj. p≤ 0.05). Cell-type-specific transcriptome analyses revealed two of the mRNA modules to be enriched for neuronal specific marker genes and downregulated in AD, whereas the remaining four mRNA modules were enriched for astrocyte and microglial specific marker genes and upregulated in AD. Gene set enrichment analysis demonstrated that neuronal specific modules were enriched for genes involved in oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling. Glial-specific modules were predominantly enriched for genes involved in processes related to immune functions, i.e. cytokine signaling (all adj. p≤ 0.05). In mRNA and miRNA modules, 461 and 25 candidate hub genes were identified, respectively. In contrast to the expected biological functions of miRNAs, correlation analyses between mRNA and miRNA hub genes revealed a higher number of positive than negative correlations (χ2 test p≤ 0.0001). Integration of hub gene expression with genome-wide genotypic data resulted in 591 mRNA cis-eQTLs and 62 miRNA cis-eQTLs. mRNA cis-eQTLs were significantly enriched for AD diagnosis and AD symptom counts (adj. p = 0.014 and p = 0.024, respectively) in AD GWAS signals in a large, independent genetic sample from the Collaborative Study on Genetics of Alcohol (COGA). In conclusion, our study identified putative gene network hubs coordinating mRNA and miRNA co-expression changes in the NAc of AD subjects, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.

Personalized medicine approaches in epilepsy

Epilepsy affects 50 million persons worldwide, a third of whom continue to experience debilitating seizures despite optimum anti-epileptic drug (AED) treatment. Twelve-month remission from seizures is less likely in female patients, individuals aged 11-36 years and those with neurological insults and shorter time between first seizure and starting treatment. It has been found that the presence of multiple seizures prior to diagnosis is a risk factor for pharmacoresistance and is correlated with epilepsy type as well as intrinsic severity. The key role of neuroinflammation in the pathophysiology of resistant epilepsy is becoming clear. Our work in this area suggests that high-mobility group box 1 isoforms may be candidate biomarkers for treatment stratification and novel drug targets in epilepsy. Furthermore, transporter polymorphisms contributing to the intrinsic severity of epilepsy are providing robust neurobiological evidence on an emerging theory of drug resistance, which may also provide new insights into disease stratification. Some of the rare genetic epilepsies enable treatment stratification through testing for the causal mutation, for example SCN1A mutations in patients with Dravet's syndrome. Up to 50% of patients develop adverse reactions to AEDs which in turn affects tolerability and compliance. Immune-mediated hypersensitivity reactions to AED therapy, such as toxic epidermal necrolysis, are the most serious adverse reactions and have been associated with polymorphisms in the human leucocyte antigen (HLA) complex. Pharmacogenetic screening for HLA-B*15:02 in Asian populations can prevent carbamazepine-induced Stevens-Johnson syndrome. We have identified HLA-A*31:01 as a potential risk marker for all phenotypes of carbamazepine-induced hypersensitivity with applicability in European and other populations. In this review, we explore the currently available key stratification approaches to address the therapeutic challenges in epilepsy.

Roseomics: a blank slate

Recent technological advances have led to an explosion in the system-wide profiling of biological processes in the study of herpesvirus biology, herein referred to as '-omics'. In many cases these approaches have revealed novel virus-induced changes to host cell biology that can be targeted with new antiviral therapeutics. Despite these successes, -omics approaches are not widely applied in the study of roseoloviruses. Here we describe examples of how -omics studies have shaped our understanding of herpesvirus biology, and discuss how these approaches might be used to identify host and viral factors that mediate roseolovirus pathogenesis.

Engineering crassulacean acid metabolism to improve water-use efficiency

Climatic extremes threaten agricultural sustainability worldwide. One approach to increase plant water-use efficiency (WUE) is to introduce crassulacean acid metabolism (CAM) into C3 crops. Such a task requires comprehensive systems-level understanding of the enzymatic and regulatory pathways underpinning this temporal CO2 pump. Here we review the progress that has been made in achieving this goal. Given that CAM arose through multiple independent evolutionary origins, comparative transcriptomics and genomics of taxonomically diverse CAM species are being used to define the genetic 'parts list' required to operate the core CAM functional modules of nocturnal carboxylation, diurnal decarboxylation, and inverse stomatal regulation. Engineered CAM offers the potential to sustain plant productivity for food, feed, fiber, and biofuel production in hotter and drier climates.