Functional Gene Networks Research Articles

Improper hydration induces global gene expression changes associated with renal development in infant mice.

BackgroundThe kidney is a major organ in which fluid balance and waste excretion is regulated. For the kidney to achieve maturity with functions, normal renal developmental processes need to occur. Comprehensive genetic programs underlying renal development during the prenatal period have been widely studied. However, postnatal renal development, from infancy to the juvenile period, has not been studied yet. Here, we investigated whether structural and functional kidney development was still ongoing in early life by analyzing the renal transcriptional networks of infant (4 weeks old) and juvenile (7 weeks old) mice. We further examined the effects of dehydration on kidney development to unravel the mechanistic bases underlying deteriorative impact of pediatric dehydration on renal development.Methods3-week-old infant mice that just finished weaning period were provided limited access to a water for fifteen minutes per day for one week (RES 1W) and four weeks (RES 4W) to induce dehydration while control group consumed water ad libitum with free access to the water bottle. Transcriptome analysis was conducted to understand physiological changes during postnatal renal development and dehydration.ResultsKidneys in 4-week- and 7-week-old mice showed significantly distinctive functional gene networks. Gene sets related to cell cycle regulators, fetal kidney patterning molecules, and immature basement membrane integrity were upregulated in infantile kidneys while heightened expressions of genes associated with ion transport and drug metabolism were observed in juvenile kidneys. Dehydration during infancy suppressed renal growth by interrupting the SHH signaling pathway, which targets cell cycle regulators. Importantly, it is likely that disruption of the developmental program ultimately led to a decline in gene expression associated with basement membrane integrity.ConclusionsAltogether, we demonstrate transcriptional events during renal development in infancy and show that the impacts of inadequate water intake in the early postnatal state heavily rely on the impairment of normal renal development. Here, we provide a meaningful perspective of renal development in infancy with a molecular and physiological explanation of why infants are more vulnerable to dehydration than adults. These results provide new insights into the molecular effects of dehydration on renal physiology and indicate that optimal nutritional interventions are necessary for pediatric renal development.Electronic supplementary materialThe online version of this article (doi:10.1186/s12263-016-0544-0) contains supplementary material, which is available to authorized users.

Infiltration-RNAseq: transcriptome profiling of Agrobacterium-mediated infiltration of transcription factors to discover gene function and expression networks in plants.

BackgroundTranscription factors (TFs) coordinate precise gene expression patterns that give rise to distinct phenotypic outputs. The identification of genes and transcriptional networks regulated by a TF often requires stable transformation and expression changes in plant cells. However, the production of stable transformants can be slow and laborious with no guarantee of success. Furthermore, transgenic plants overexpressing a TF of interest can present pleiotropic phenotypes and/or result in a high number of indirect gene expression changes. Therefore, fast, efficient, high-throughput methods for assaying TF function are needed.ResultsAgroinfiltration is a simple plant biology method that allows transient gene expression. It is a rapid and powerful tool for the functional characterisation of TF genes in planta. High throughput RNA sequencing is now a widely used method for analysing gene expression profiles (transcriptomes). By coupling TF agroinfiltration with RNA sequencing (named here as Infiltration-RNAseq), gene expression networks and gene function can be identified within a few weeks rather than many months. As a proof of concept, we agroinfiltrated Medicago truncatula leaves with M. truncatula LEGUME ANTHOCYANIN PRODUCITION 1 (MtLAP1), a MYB transcription factor involved in the regulation of the anthocyanin pathway, and assessed the resulting transcriptome. Leaves infiltrated with MtLAP1 turned red indicating the production of anthocyanin pigment. Consistent with this, genes encoding enzymes in the anthocyanin biosynthetic pathway, and known transcriptional activators and repressors of the anthocyanin biosynthetic pathway, were upregulated. A novel observation was the induction of a R3-MYB transcriptional repressor that likely provides transcriptional feedback inhibition to prevent the deleterious effects of excess anthocyanins on photosynthesis.ConclusionsInfiltration-RNAseq is a fast and convenient method for profiling TF-mediated gene expression changes. We utilised this method to identify TF-mediated transcriptional changes and TF target genes in M. truncatula and Nicotiana benthamiana. This included the identification of target genes of a TF not normally expressed in leaves, and targets of TFs from other plant species. Infiltration-RNAseq can be easily adapted to other plant species where agroinfiltration protocols have been optimised. The ability to identify downstream genes, including positive and negative transcriptional regulators, will result in a greater understanding of TF function.Electronic supplementary materialThe online version of this article (doi:10.1186/s13007-016-0141-7) contains supplementary material, which is available to authorized users.

Iron homeostasis in Arabidopsis thaliana: transcriptomic analyses reveal novel FIT-regulated genes, iron deficiency marker genes and functional gene networks.

BackgroundFIT (FER-LIKE IRON DEFICIENCY-INDUCED TRANSCRIPTION FACTOR) is the central regulator of iron uptake in Arabidopsis thaliana roots. We performed transcriptome analyses of six day-old seedlings and roots of six week-old plants using wild type, a fit knock-out mutant and a FIT over-expression line grown under iron-sufficient or iron-deficient conditions. We compared genes regulated in a FIT-dependent manner depending on the developmental stage of the plants. We assembled a high likelihood dataset which we used to perform co-expression and functional analysis of the most stably iron deficiency-induced genes.Results448 genes were found FIT-regulated. Out of these, 34 genes were robustly FIT-regulated in root and seedling samples and included 13 novel FIT-dependent genes. Three hundred thirty-one genes showed differential regulation in response to the presence and absence of FIT only in the root samples, while this was the case for 83 genes in the seedling samples. We assembled a virtual dataset of iron-regulated genes based on a total of 14 transcriptomic analyses of iron-deficient and iron-sufficient wild-type plants to pinpoint the best marker genes for iron deficiency and analyzed this dataset in depth. Co-expression analysis of this dataset revealed 13 distinct regulons part of which predominantly contained functionally related genes.ConclusionsWe could enlarge the list of FIT-dependent genes and discriminate between genes that are robustly FIT-regulated in roots and seedlings or only in one of those. FIT-regulated genes were mostly induced, few of them were repressed by FIT. With the analysis of a virtual dataset we could filter out and pinpoint new candidates among the most reliable marker genes for iron deficiency. Moreover, co-expression and functional analysis of this virtual dataset revealed iron deficiency-induced and functionally distinct regulons.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-016-0899-9) contains supplementary material, which is available to authorized users.

Multiscale Genomics to Model Functional Gene Networks Involved in Radiosensitivity

Multiscale Genomics to Model Functional Gene Networks Involved in Radiosensitivity

Long-Term Transcriptional Consequences of Drug Exposure as Cues to Understand Vulnerability to Relapse: Advances and Perspectives.

Quitting drug abuse represents a true challenge for addicted individuals because of the highly persistent vulnerability to relapse. Identifying long-lasting, drug-induced alterations in the brain-including at the transcriptome level-that underlie such vulnerability appears invaluable to improve relapse prevention. Despite substantial technological developments and research effort, however, the picture of drug-induced adaptations provided by high-throughput transcriptomics remains frustratingly partial, notably because of methodological issues. Major advances were made, however, regarding the time course and specificity of long-term transcriptional consequences of drug exposure as well as the recruitment of small, noncoding mRNAs (or miRNAs [microRNAs]) that were previously undetectable. Most importantly, high-throughput studies have benefited from systems biology approaches and shifted their interest toward regulations within functional gene networks rather than individual changes. Such network-based gene discovery approaches have proven informative to delineate the physiological processes, cellular signaling pathways, and neuronal populations altered by drug exposure. Provided the high-throughput effort will be pursued, together with the development of adapted bioinformatics tools, addiction transcriptomics should progressively integrate data across multiple scales (from epigenome to protein), allowing a better understanding of the genetics of drug abuse and opening novel therapeutic trails.

Gene expression of muscular and neuronal pathways is cooperatively dysregulated in patients with idiopathic achalasia.

Idiopathic achalasia is characterized by the absence of peristalsis secondary to loss of neurons in the myenteric plexus that hampers proper relaxation of the lower esophageal sphincter. Achalasia can be considered a multifactorial disorder as it occurs in related individuals and is associated with HLA class II genes, thereby suggesting genetic influence. We used microarray technology and advanced in-silico functional analyses to perform the first genome-wide expression profiling of mRNA in tissue samples from 12 achalasia and 5 control patients. It revealed 1,728 differentially expressed genes, of these, 837 (48.4%) were up-regulated in cases. In particular, genes participating to the smooth muscle contraction biological function were mostly up-regulated. Functional analysis revealed a significant enrichment of neuronal/muscular and neuronal/immunity processes. Upstream regulatory analysis of 180 genes involved in these processes suggested TLR4 and IL18 as critical key-players. Two functional gene networks were significantly over-represented: one involved in organ morphology, skeletal muscle system development and function, and neurological diseases, and the other participating in cell morphology, humoral immune response and cellular movement. These results highlight on pivotal genes that may play critical roles in neuronal/muscular and neuronal/immunity processes, and that may contribute to the onset and development of achalasia.

Endocrine effects of real-life mixtures of persistent organic pollutants (POP) in experimental models and wild fish

ABSTRACTA series of studies have assessed the occurrence, levels, and potential adverse effects of persistent organic pollutants (POP) in fish from Lake Mjøsa. In this lake, high levels of various POP were detected in biota. Fish from the nearby Lake Losna contain background levels of POP and served as reference (controls) in these studies. Significantly higher prevalence of mycobacteriosis and pathological changes were documented in burbot (Lota lota) from Mjøsa compared to burbot from Losna. Further, transcriptional profiling identified changes in gene expression in burbot from Mjøsa compared to burbot from Losna associated with drug metabolism enzymes and oxidative stress. POP extracted from burbot liver oil from the two lakes was used to expose zebrafish (Danio rerio) during two consecutive generations. During both generations, POP mixtures from both lakes increased the rate of mortality, induced earlier onset of puberty, and skewed sex ratio toward males. However, opposite effects on weight gain were found in exposure groups compared to controls during the two generations. Exposure to POP from both lakes was associated with suppression of ovarian follicle development. Analyses of genome-wide transcription profiling identified functional networks of genes associated with weight homeostasis, steroid hormone functions, and insulin signaling. In human cell studies using adrenocortical H295R and primary porcine theca and granulosa cells, exposure to lake extracts from both populations modulated steroid hormone production with significant difference from controls. The results suggest that POP from both lakes may possess the potential to induce endocrine disruption and may adversely affect health in wild fish.

Inferring Crohn's disease association from exome sequences by integrating biological knowledge.

BackgroundExome sequencing has been emerged as a primary method to identify detailed sequence variants associated with complex diseases including Crohn’s disease in the protein-coding regions of human genome. However, constructing an interpretable model for exome sequencing data is challenging because of the huge diversity of genomic variation. In addition, it has been known that utilizing biologically relevant information in a rigorous manner is essential for effectively extracting disease-associated information.ResultsIn this paper, we incorporate three different types of biological knowledge such as predicted pathogenicity, disease gene annotation, and functional interaction network of human genes, and integrate them with exome sequence data in non-negative matrix tri-factorization framework. Based on the proposed method, we successfully identified Crohn’s disease patients from exome sequencing data and achieved the area under the receiver operating characteristics curve (AUC) of 0.816, while other clustering methods not using biological information achieved the AUC of 0.786. Moreover, the disease association score derived from our method showed higher correlation with Crohn’s disease genes than other unrelated genes.ConclusionsAs a consequence, by integrating biological information across multiple levels such as variant, gene, and systems, our method could be useful for identifying disease susceptibility and its associated genes from exome sequencing data.

Phenome-based gene discovery provides information about Parkinson's disease drug targets.

BackgroundParkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs.MethodsWe propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery.ResultsIn a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.

PoplarGene: poplar gene network and resource for mining functional information for genes from woody plants.

Poplar is not only an important resource for the production of paper, timber and other wood-based products, but it has also emerged as an ideal model system for studying woody plants. To better understand the biological processes underlying various traits in poplar, e.g., wood development, a comprehensive functional gene interaction network is highly needed. Here, we constructed a genome-wide functional gene network for poplar (covering ~70% of the 41,335 poplar genes) and created the network web service PoplarGene, offering comprehensive functional interactions and extensive poplar gene functional annotations. PoplarGene incorporates two network-based gene prioritization algorithms, neighborhood-based prioritization and context-based prioritization, which can be used to perform gene prioritization in a complementary manner. Furthermore, the co-functional information in PoplarGene can be applied to other woody plant proteomes with high efficiency via orthology transfer. In addition to poplar gene sequences, the webserver also accepts Arabidopsis reference gene as input to guide the search for novel candidate functional genes in PoplarGene. We believe that PoplarGene (http://bioinformatics.caf.ac.cn/PoplarGene and http://124.127.201.25/PoplarGene) will greatly benefit the research community, facilitating studies of poplar and other woody plants.

Development of a synthetic gene network to modulate gene expression by mechanical forces.

The majority of (mammalian) cells in our body are sensitive to mechanical forces, but little work has been done to develop assays to monitor mechanosensor activity. Furthermore, it is currently impossible to use mechanosensor activity to drive gene expression. To address these needs, we developed the first mammalian mechanosensitive synthetic gene network to monitor endothelial cell shear stress levels and directly modulate expression of an atheroprotective transcription factor by shear stress. The technique is highly modular, easily scalable and allows graded control of gene expression by mechanical stimuli in hard-to-transfect mammalian cells. We call this new approach mechanosyngenetics. To insert the gene network into a high proportion of cells, a hybrid transfection procedure was developed that involves electroporation, plasmids replication in mammalian cells, mammalian antibiotic selection, a second electroporation and gene network activation. This procedure takes 1 week and yielded over 60% of cells with a functional gene network. To test gene network functionality, we developed a flow setup that exposes cells to linearly increasing shear stress along the length of the flow channel floor. Activation of the gene network varied logarithmically as a function of shear stress magnitude.

GIANT API: an application programming interface for functional genomics.

GIANT API provides biomedical researchers programmatic access to tissue-specific and global networks in humans and model organisms, and associated tools, which includes functional re-prioritization of existing genome-wide association study (GWAS) data. Using tissue-specific interaction networks, researchers are able to predict relationships between genes specific to a tissue or cell lineage, identify the changing roles of genes across tissues and uncover disease-gene associations. Additionally, GIANT API enables computational tools like NetWAS, which leverages tissue-specific networks for re-prioritization of GWAS results. The web services covered by the API include 144 tissue-specific functional gene networks in human, global functional networks for human and six common model organisms and the NetWAS method. GIANT API conforms to the REST architecture, which makes it stateless, cacheable and highly scalable. It can be used by a diverse range of clients including web browsers, command terminals, programming languages and standalone apps for data analysis and visualization. The API is freely available for use at http://giant-api.princeton.edu.

Cellular gene expression induced by parasite antigens and allergens in neonates from parasite-infected mothers.

Prenatal exposure to parasite antigens or allergens will influence the profile and strength of postnatal immune responses, such contact may tolerize and increase susceptibility to future infections or sensitize to environmental allergens. Exposure in utero to parasite antigens will distinctly alter cellular gene expression in newborns. Gene microarrays were applied to study gene expression in umbilical cord blood cell (UCBC) from parasite-exposed (Para-POS) and non-exposed (Para-NEG) neonates. UCBC were activated with antigens of helminth (Onchocerca volvulus), amoeba (Entamoeba histolytica) or allergens of mite (Dermatophagoides farinae). When UCBC from Para-POS and Para-NEG newborns were exposed to helminth antigens or allergens consistent differences occurred in the expression of genes encoding for MHC class I and II alleles, signal transducers of activation and transcription (STATs), cytokines, chemokines, immunoglobulin heavy and light chains, and molecules associated with immune regulation (SOCS, TLR, TGF), inflammation (TNF, CCR) and apoptosis (CASP). Expression of genes associated with innate immune responses were enhanced in Para-NEG, while in Para-POS, the expression of MHC class II and STAT genes was reduced. Within functional gene networks for cellular growth, proliferation and immune responses, Para-NEG neonates presented with significantly higher expression values than Para-POS. In Para-NEG newborns, the gene cluster and pathway analyses suggested that gene expression profiles may predispose for the development of immunological, hematological and dermatological disorders upon postnatal helminth parasite infection or allergen exposure. Thus, prenatal parasite contact will sensitize without generating aberrant inflammatory immune responses, and increased pro-inflammatory but decreased regulatory gene expression profiles will be present in those neonates lacking prenatal parasite antigen encounter.

Large-Scale Functional RNAi Screen in C. elegans Identifies TGF-β and Notch Signaling Pathways as Modifiers of CACNA1A.

Variants in CACNA1A that encodes the pore-forming α1-subunit of human voltage-gated Cav2.1 (P/Q-type) Ca2+ channels cause several autosomal-dominant neurologic disorders, including familial hemiplegic migraine type 1, episodic ataxia type 2, and spinocerebellar ataxia type 6. To identify modifiers of incoordination in movement disorders, we performed a large-scale functional RNAi screen, using the Caenorhabditis elegans strain CB55, which carries a truncating mutation in the unc-2 gene, the worm ortholog for the human CACNA1A. The screen was carried out by the feeding method in 96-well liquid culture format, using the ORFeome v1.1 feeding library, and time-lapse imaging of worms in liquid culture was used to assess changes in thrashing behavior. We looked for genes that, when silenced, either ameliorated the slow and uncoordinated phenotype of unc-2, or interacted to produce a more severe phenotype. Of the 350 putative hits from the primary screen, 37 genes consistently showed reproducible results. At least 75% of these are specifically expressed in the C. elegans neurons. Functional network analysis and gene ontology revealed overrepresentation of genes involved in development, growth, locomotion, signal transduction, and vesicle-mediated transport. We have expanded the functional network of genes involved in neurodegeneration leading to cerebellar ataxia related to unc-2/CACNA1A, further confirming the involvement of the transforming growth factor β pathway and adding a novel signaling cascade, the Notch pathway.

Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants.

BackgroundThe Nordic Red Cattle consisting of three different populations from Finland, Sweden and Denmark are under a joint breeding value estimation system. The long history of recording of production and health traits offers a great opportunity to study production traits and identify causal variants behind them. In this study, we used whole genome sequence level data from 4280 progeny tested Nordic Red Cattle bulls to scan the genome for loci affecting milk, fat and protein yields.ResultsUsing a genome-wise significance threshold, regions on Bos taurus chromosomes 5, 14, 23, 25 and 26 were associated with fat yield. Regions on chromosomes 5, 14, 16, 19, 20 and 25 were associated with milk yield and chromosomes 5, 14 and 25 had regions associated with protein yield. Significantly associated variations were found in 227 genes for fat yield, 72 genes for milk yield and 30 genes for protein yield. Ingenuity Pathway Analysis was used to identify networks connecting these genes displaying significant hits. When compared to previously mapped genomic regions associated with fertility, significantly associated variations were found in 5 genes common for fat yield and fertility, thus linking these two traits via biological networks.ConclusionThis is the first time when whole genome sequence data is utilized to study genomic regions affecting milk production in the Nordic Red Cattle population. Sequence level data offers the possibility to study quantitative traits in detail but still cannot unambiguously reveal which of the associated variations is causative. Linkage disequilibrium creates difficulties to pinpoint the causative genes and variations. One solution to overcome these difficulties is the identification of the functional gene networks and pathways to reveal important interacting genes as candidates for the observed effects. This information on target genomic regions may be exploited to improve genomic prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0363-8) contains supplementary material, which is available to authorized users.

Bovine Host Genetic Variation Influences Rumen Microbial Methane Production with Best Selection Criterion for Low Methane Emitting and Efficiently Feed Converting Hosts Based on Metagenomic Gene Abundance

Methane produced by methanogenic archaea in ruminants contributes significantly to anthropogenic greenhouse gas emissions. The host genetic link controlling microbial methane production is unknown and appropriate genetic selection strategies are not developed. We used sire progeny group differences to estimate the host genetic influence on rumen microbial methane production in a factorial experiment consisting of crossbred breed types and diets. Rumen metagenomic profiling was undertaken to investigate links between microbial genes and methane emissions or feed conversion efficiency. Sire progeny groups differed significantly in their methane emissions measured in respiration chambers. Ranking of the sire progeny groups based on methane emissions or relative archaeal abundance was consistent overall and within diet, suggesting that archaeal abundance in ruminal digesta is under host genetic control and can be used to genetically select animals without measuring methane directly. In the metagenomic analysis of rumen contents, we identified 3970 microbial genes of which 20 and 49 genes were significantly associated with methane emissions and feed conversion efficiency respectively. These explained 81% and 86% of the respective variation and were clustered in distinct functional gene networks. Methanogenesis genes (e.g. mcrA and fmdB) were associated with methane emissions, whilst host-microbiome cross talk genes (e.g. TSTA3 and FucI) were associated with feed conversion efficiency. These results strengthen the idea that the host animal controls its own microbiota to a significant extent and open up the implementation of effective breeding strategies using rumen microbial gene abundance as a predictor for difficult-to-measure traits on a large number of hosts. Generally, the results provide a proof of principle to use the relative abundance of microbial genes in the gastrointestinal tract of different species to predict their influence on traits e.g. human metabolism, health and behaviour, as well as to understand the genetic link between host and microbiome.

Current status and prospects of citrus genomics

Citrus is an economically important fruit tree with the largest amount of fruit production in the world. It provides important nutrition such as vitamin C and other health-promoting compounds including its unique flavonoids for human health. However, it is classified into the most difficult crops to develop new cultivars through conventional breeding approaches due to its long juvenility and some unique reproductive biological features such as gamete sterility, nucellar embryony, and high level of heterozygosity. Due to global warming and changes in consumer trends, establishing a systematic and efficient breeding programs is highly required for sustainable production of high quality fruits and diversification of cultivars. Recently, reference genome sequences of sweet orange and clementine mandarin have been released. Based on the reference whole-genome sequences, comparative genomics, reference-guided resequencing, and genotyping-by-sequencing for various citrus cultivars and crosses could be performed for the advance of functional genomics and development of traits-related molecular markers. In addition, a full understanding of gene function and gene co-expression networks can be provided through combined analysis of various transcriptome data. Analytic information on whole-genome and transcriptome will provide massive data on polymorphic molecular markers such as SNP, INDEL, and SSR, suggesting that it is possible to construct integrated maps and high-density genetic maps as well as physical maps. In the near future, integrated maps will be useful for map-based precise cloning of genes that are specific to citrus with major agronomic traits to facilitate rapid and efficient marker-assisted selection.

Origin and diversification of wings: Insights from a neopteran insect

Winged insects underwent an unparalleled evolutionary radiation, but mechanisms underlying the origin and diversification of wings in basal insects are sparsely known compared with more derived holometabolous insects. In the neopteran species Oncopeltus fasciatus, we manipulated wing specification genes and used RNA-seq to obtain both functional and genomic perspectives. Combined with previous studies, our results suggest the following key steps in wing origin and diversification. First, a set of dorsally derived outgrowths evolved along a number of body segments including the first thoracic segment (T1). Homeotic genes were subsequently co-opted to suppress growth of some dorsal flaps in the thorax and abdomen. In T1 this suppression was accomplished by Sex combs reduced, that when experimentally removed, results in an ectopic T1 flap similar to prothoracic winglets present in fossil hemipteroids and other early insects. Global gene-expression differences in ectopic T1 vs. T2/T3 wings suggest that the transition from flaps to wings required ventrally originating cells, homologous with those in ancestral arthropod gill flaps/epipods, to migrate dorsally and fuse with the dorsal flap tissue thereby bringing new functional gene networks; these presumably enabled the T2/T3 wing's increased size and functionality. Third, "fused" wings became both the wing blade and surrounding regions of the dorsal thorax cuticle, providing tissue for subsequent modifications including wing folding and the fit of folded wings. Finally, Ultrabithorax was co-opted to uncouple the morphology of T2 and T3 wings and to act as a general modifier of hindwings, which in turn governed the subsequent diversification of lineage-specific wing forms.

MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates.

Laboratory mouse, Mus musculus, is one of the most important animal tools in biomedical research. Functional characterization of the mouse genes, hence, has been a long-standing goal in mammalian and human genetics. Although large-scale knockout phenotyping is under progress by international collaborative efforts, a large portion of mouse genome is still poorly characterized for cellular functions and associations with disease phenotypes. A genome-scale functional network of mouse genes, MouseNet, was previously developed in context of MouseFunc competition, which allowed only limited input data for network inferences. Here, we present an improved mouse co-functional network, MouseNet v2 (available at http://www.inetbio.org/mousenet), which covers 17 714 genes (>88% of coding genome) with 788 080 links, along with a companion web server for network-assisted functional hypothesis generation. The network database has been substantially improved by large expansion of genomics data. For example, MouseNet v2 database contains 183 co-expression networks inferred from 8154 public microarray samples. We demonstrated that MouseNet v2 is predictive for mammalian phenotypes as well as human diseases, which suggests its usefulness in discovery of novel disease genes and dissection of disease pathways. Furthermore, MouseNet v2 database provides functional networks for eight other vertebrate models used in various research fields.

A novel approach identifies the first transcriptome networks in bats: a new genetic model for vocal communication.

BackgroundBats are able to employ an astonishingly complex vocal repertoire for navigating their environment and conveying social information. A handful of species also show evidence for vocal learning, an extremely rare ability shared only with humans and few other animals. However, despite their potential for the study of vocal communication, bats remain severely understudied at a molecular level. To address this fundamental gap we performed the first transcriptome profiling and genetic interrogation of molecular networks in the brain of a highly vocal bat species, Phyllostomus discolor.ResultsGene network analysis typically needs large sample sizes for correct clustering, this can be prohibitive where samples are limited, such as in this study. To overcome this, we developed a novel bioinformatics methodology for identifying robust co-expression gene networks using few samples (N=6). Using this approach, we identified tissue-specific functional gene networks from the bat PAG, a brain region fundamental for mammalian vocalisation. The most highly connected network identified represented a cluster of genes involved in glutamatergic synaptic transmission. Glutamatergic receptors play a significant role in vocalisation from the PAG, suggesting that this gene network may be mechanistically important for vocal-motor control in mammals.ConclusionWe have developed an innovative approach to cluster co-expressing gene networks and show that it is highly effective in detecting robust functional gene networks with limited sample sizes. Moreover, this work represents the first gene network analysis performed in a bat brain and establishes bats as a novel, tractable model system for understanding the genetics of vocal mammalian communication.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2068-1) contains supplementary material, which is available to authorized users.