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Influenza represents a serious threat to public health with thousands of deaths each year. A deeper understanding of the host-pathogen interactions is urgently needed to evaluate individual and population risks for severe influenza disease and to identify new therapeutic targets. Here, we review recent progress in large scale omics technologies, systems genetics as well as new mathematical and computational developments that are now in place to apply a systems biology approach for a comprehensive description of the multidimensional host response to influenza infection. In addition, we describe how results from experimental animal models can be translated to humans, and we discuss some of the future challenges ahead.

SummaryMetabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the EUMODIC EMPReSS protocols across a large panel of isogenic but diverse strains of mice (BXD type) to study the genetic control of metabolism. We generated and analyzed 140 classical phenotypes and deposited these in an open-access web service for systems genetics (www.genenetwork.org). Heritability, influence of sex, and genetic modifiers of traits were examined singly and jointly by using quantitative-trait locus (QTL) and expression QTL-mapping methods. Traits and networks were linked to loci encompassing both known variants and novel candidate genes, including alkaline phosphatase (ALPL), here linked to hypophosphatasia. The assembled and curated phenotypes provide key resources and exemplars that can be used to dissect complex metabolic traits and disorders.

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various similar to omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various similar to omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function. (C) 2014 Elsevier B.V. All rights reserved.

Background The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. Results We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. Conclusions Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1097-7) contains supplementary material, which is available to authorized users.

How lifespan and body weight vary as a function of diet and genetic differences is not well understood. Here we quantify the impact of differences in diet on lifespan in a genetically diverse family of female mice, split into matched isogenic cohorts fed a low-fat chow diet (CD, n = 663) or a high-fat diet (HFD, n = 685). We further generate key metabolic data in a parallel cohort euthanized at four time points. HFD feeding shortens lifespan by 12%: equivalent to a decade in humans. Initial body weight and early weight gains account for longevity differences of roughly 4-6 days per gram. At 500 days, animals on a HFD typically gain four times as much weight as control, but variation in weight gain does not correlate with lifespan. Classic serum metabolites, often regarded as health biomarkers, are not necessarily strong predictors of longevity. Our data indicate that responses to a HFD are substantially modulated by gene-by-environment interactions, highlighting the importance of genetic variation in making accurate individualized dietary recommendations., Roy et al. quantify the impact of a high-fat diet across genetically diverse strains of mice, revealing a generally negative effect on lifespan but also a wide variability.

A growing body of evidence suggests that changes in gene regulatory DNA are the major genetic determinants of variation in complex phenotypes; however, the underlying genes as well as cascades of intermediate molecular events remain in most cases elusive. This work has therefore centered on elucidating the genetic architecture of complex molecular phenotypes (i.e., protein-DNA interactions, chromatin structure, and gene expression) in different model systems in order to study the molecular consequences of regulatory DNA variation. The main contributions are: i.) a genome-wide map of gene expression-associated regulatory loci in Drosophila melanogaster; ii.) novel regulatory components in the murine adipogenic gene regulatory network; iii.) an algorithm to identify biased genetic associations with epigenomic datasets; and iv.) novel insights into the intermediatemolecular consequences of regulatory DNA variation within and between humans.

Background RNA splicing is a key post-transcriptional mechanism that generates protein diversity and contributes to the fine-tuning of gene expression, which may facilitate adaptation to environmental challenges. Here, we employ a systems approach to study alternative splicing changes upon enteric infection in females from classical Drosophila melanogaster strains as well as 38 inbred lines. Results We find that infection leads to extensive differences in isoform ratios, which results in a more diverse transcriptome with longer 5 ' untranslated regions (5 ' UTRs). We establish a role for genetic variation in mediating inter-individual splicing differences, with local splicing quantitative trait loci (local-sQTLs) being preferentially located at the 5 ' end of transcripts and directly upstream of splice donor sites. Moreover, local-sQTLs are more numerous in the infected state, indicating that acute stress unmasks a substantial number of silent genetic variants. We observe a general increase in intron retention concentrated at the 5 ' end of transcripts across multiple strains, whose prevalence scales with the degree of pathogen virulence. The length, GC content, and RNA polymerase II occupancy of these introns with increased retention suggest that they have exon-like characteristics. We further uncover that retained intron sequences are enriched for the Lark/RBM4 RNA binding motif. Interestingly, we find that lark is induced by infection in wild-type flies, its overexpression and knockdown alter survival, and tissue-specific overexpression mimics infection-induced intron retention. Conclusion Our collective findings point to pervasive and consistent RNA splicing changes, partly mediated by Lark/RBM4, as being an important aspect of the gut response to infection.

The challenge of precision medicine is to model complex interactions among DNA variants, phenotypes, development, environments, and treatments. We address this challenge by expanding the BXD family of mice to 140 fully isogenic strains, creating a uniquely powerful model for precision medicine. This family segregates for 6 million common DNA variants—a level that exceeds many human populations. Because each member can be replicated, heritable traits can be mapped with high power and precision. Current BXD phenomes are unsurpassed in coverage and include much omics data and thousands of quantitative traits. The BXDs can be extended by a single-generation cross to as many as 19,460 isogenic F1 progeny, and this extended BXD family is an effective platform for testing causal modeling and for predictive validation. The BXDs are a unique core resource for the field of experimental precision medicine.

SummaryBone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. Genome-wide association studies (GWAS) for BMD have identified dozens of associations; yet, the genes responsible for most associations remain elusive. Here, we used a bone co-expression network to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed. By mapping genes implicated by BMD GWAS onto a bone co-expression network, we predicted and inferred the function of causal genes for 30 of 64 GWAS loci. We experimentally confirmed that two of the genes predicted to be causal, SPTBN1 and MARK3, are potentially responsible for the effects of GWAS loci on chromosomes 2p16.2 and 14q32.32, respectively. This approach provides a roadmap for the dissection of additional BMD GWAS associations. Furthermore, it should be applicable to GWAS data for a wide range of diseases.

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Work on Drosophila melanogaster paved the way to our current understanding of modern genetics. Since then, this model organism has contributed greatly to various fields such as neurobiology, development, and immunology. The discovery and analysis of the various pathways of the Drosophila immune response led the way to the in-depth characterization of the gut immune response. Furthermore, a genome-wide association study on a population of Drosophila identified resistance to infection by the entomopathogenic bacteria Pseudomonas entomophila (P.e.) as a complex phenotype, with highly resistant and susceptible lines, and highlighted genes and pathways mediating inter-individual differences. However, the molecular mechanisms mediating the resistance to enteric infection remain largely uncharacterized. This study uses the same Drosophila population to analyze what are the molecular mechanisms mediating the resistance to enteric infection. We analyzed the genetic determinants of gene expression variation among the most resistant and susceptible lines in response to infection using expression quantitative trait loci (eQTL) analysis. We also characterized the mode of action of these eQTLs into cis- and trans-acting. Furthermore, we identified the gene nutcracker (ntc) as the only differentially-expressed gene between resistance classes. We then demonstrate that loss of function ntc mutants are significantly more susceptible to infection and then identify a single nucleotide polymorphism (SNP) located in a transcription factor binding site (TFBS) which affects the binding affinity of the transcription factor Broad leading to allele-specific expression variation of the gene ntc. If the transcriptomic response to infection has been thoroughly characterized, it is not the case for the proteomic response. We thus sought to characterize for the first time the proteomic response of the Drosophila to enteric infection. We performed mass spectrometry and RNA sequencing on dissected guts from flies infected by two Gram-negative bacteria, Erwinia carotovora carotovora 15 (Ecc15) or Pseudomonas entomophila, 4h and 16h after infection. We found that a large portion of the measurable proteome (12%) varies after infection and that protein changes are strongly time- and infection-dependent. We showed the relatively poor correlation between gene expression and protein abundance in the Drosophila enteric immune response. Finally, we performed a screen of several proteins that were identified in our proteomics work but had previously not been found when assessing the gene expression response to infection using loss-of-function mutants and identifying 7 genes that modulate overall susceptibility to P.e. infection. In summary, this work analyze the genetic determinants of gene expression variation in the DGRP population upon infection and characterize them according to their mode of action. Furthermore, we describe how a non-coding variant lowers resistance to infection by modulating ntc gene expression through altered Broad repressor binding. Finally, it provides the first comprehensive characterization of the Drosophila gut proteome upon infection by Gram-negative bacteria which can be the basis of future proteomic analysis of the enteric immune response

Background Phenotypic plasticity allows organisms to respond rapidly to changing environmental circumstances, and understanding its genomic basis can yield insights regarding the underlying genes and genetic networks affecting complex phenotypes. Female Drosophila melanogaster undergo dramatic physiological changes mediated by seminal fluid components transferred upon mating, including decreased longevity. Their physiological and behavioral effects have been well characterized, but little is known about resulting changes in regulation of gene expression or the extent to which mating-induced changes in gene expression are the same as those occurring during aging. Results We assessed genome-wide mRNA, microRNA, and three common histone modifications implicated in gene activation for young and aged virgin and mated female D. melanogaster in a factorial design. We identified phenotypically plastic transcripts and epigenetic modifications associated with mating and aging. We used these data to derive phenotypically plastic regulatory networks associated with mating of young flies, and aging of virgin and mated flies. Many of the mRNAs, microRNAs and epigenetic modifications associated with mating of young flies also occur with age in virgin flies, which may reflect mating-induced accelerated aging. We functionally tested the plastic regulatory networks by overexpressing environmentally sensitive microRNAs. Overexpression resulted in altered expression of ~70% of candidate target genes, and in all cases affected oviposition. Conclusions Our results implicate microRNAs as mediators of phenotypic plasticity associated with mating and provide a comprehensive documentation of the genomic and epigenomic changes that accompany mating- and aging-induced physiological changes in female D. melanogaster. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-927) contains supplementary material, which is available to authorized users.