DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila

Decoding the connection between lncRNA and obesity: Perspective from humans and Drosophila

Background

Obesity is a burgeoning global health problem with an escalating prevalence and severe implications for public health. New evidence indicates that long non-coding RNAs (lncRNAs) may play a pivotal role in regulating adipose tissue function and energy homeostasis across various species. However, the molecular mechanisms underlying obesity remain elusive.

Scope of review

This review discusses obesity and fat metabolism in general, highlighting the emerging importance of lncRNAs in modulating adipogenesis. It describes the regulatory networks, latest tools, techniques, and approaches to enhance our understanding of obesity and its lncRNA-mediated epigenetic regulation in humans and Drosophila.

Major conclusions

This review analyses large datasets of human and Drosophila lncRNAs from published databases and literature with experimental evidence supporting lncRNAs role in fat metabolism. It concludes that lncRNAs play a crucial role in obesity-related metabolism. Cross-species comparisons highlight the relevance of Drosophila findings to human obesity, emphasizing their potential role in adipose tissue biology. Furthermore, it discusses how recent technological advancements and multi-omics data integration enhance our capacity to characterize lncRNAs and their function. Additionally, this review briefly touches upon innovative methodologies like experimental evolution and advanced sequencing technologies for identifying novel genes and lncRNA regulators in Drosophila, which can potentially contribute to obesity research.

Functional exploration of copy number alterations in a Drosophila model of triple-negative breast cancer

Accounting for 10-20% of breast cancer cases, triple-negative breast cancer (TNBC) is associated with a disproportionate number of breast cancer deaths. One challenge in studying TNBC is its genomic profile: with the exception of TP53 loss, most breast cancer tumors are characterized by a high number of copy number alterations (CNAs), making modeling the disease in whole animals challenging. We computationally analyzed 186 CNA regions previously identified in breast cancer tumors to rank genes within each region by likelihood of acting as a tumor driver. We then used a Drosophila p53-Myc TNBC model to identify 48 genes as functional drivers. To demonstrate the utility of this functional database, we established six 3-hit models; altering candidate genes led to increased aspects of transformation as well as resistance to the chemotherapeutic drug fluorouracil. Our work provides a functional database of CNA-associated TNBC drivers, and a template for an integrated computational/whole-animal approach to identify functional drivers of transformation and drug resistance within CNAs in other tumor types.

Phylogenomics of the Ecdysteroid Kinase-like (EcKL) Gene Family in Insects Highlights Roles in Both Steroid Hormone Metabolism and Detoxification

The evolutionary dynamics of large gene families can offer important insights into the functions of their individual members. While the ecdysteroid kinase-like (EcKL) gene family has previously been linked to the metabolism of both steroid molting hormones and xenobiotic toxins, the functions of nearly all EcKL genes are unknown, and there is little information on their evolution across all insects. Here, we perform comprehensive phylogenetic analyses on a manually annotated set of EcKL genes from 140 insect genomes, revealing the gene family is comprised of at least 13 subfamilies that differ in retention and stability. Our results show the only two genes known to encode ecdysteroid kinases belong to different subfamilies and therefore ecdysteroid metabolism functions must be spread throughout the EcKL family. We provide comparative phylogenomic evidence that EcKLs are involved in detoxification across insects, with positive associations between family size and dietary chemical complexity, and we also find similar evidence for the cytochrome P450 and glutathione S-transferase gene families. Unexpectedly, we find that the size of the clade containing a known ecdysteroid kinase is positively associated with host plant taxonomic diversity in Lepidoptera, possibly suggesting multiple functional shifts between hormone and xenobiotic metabolism. Our evolutionary analyses provide hypotheses of function and a robust framework for future experimental studies of the EcKL gene family. They also open promising new avenues for exploring the genomic basis of dietary adaptation in insects, including the classically studied coevolution of butterflies with their host plants.

The identification of regulatory ceRNA network involved in Drosophila Toll immune responses

Non-coding RNAs play important roles in the innate immunity of Drosophila, with various lncRNAs and miRNAs identified to maintain Drosophila innate immune homeostasis by regulating protein functions. However, it remains unclear whether interactions between lncRNAs and miRNAs give rise to a ceRNA network. In our previous study, we observed the highest differential expression levels of lncRNA-CR11538, lncRNA-CR33942, and lncRNA-CR46018 in wild-type flies after Gram-positive bacterial infection, prompting us to investigate their role in the regulation of Drosophila Toll immune response through RNA-seq analysis. Herein, our comprehensive bioinformatics analysis revealed that lncRNA-CR11538, lncRNA-CR33942, and lncRNA-CR46018 are involved in defense mechanisms and stimulus response. Moreover, lncRNA-CR11538 and lncRNA-CR46018 can also participate in the metabolic recovery processes following Gram-positive bacterial infection. Subsequently, we employed GSEA screening and RT-qPCR to identify seven miRNAs (miR-957, miR-1015, miR-982, miR-993, miR-1007, miR-193, and miR-978) that may be regulated by these three lncRNAs. Furthermore, we predicted the potential target genes in the Toll signaling pathway for these miRNAs and their interaction with the three lncRNAs using TargetScan and miRanda software and preliminary verification. As a result, we established a potential ceRNA regulatory network for Toll immune responses in Drosophila, comprising three lncRNAs and seven miRNAs. This study provides evidence of a ceRNA regulatory network in Drosophila Toll immune responses and offers novel insights into understanding the regulatory networks involved in the innate immunity of other animals.

Finding information about uncharacterized Drosophila melanogaster genes

Genes that have been identified in the genome but remain uncharacterized with regards to function offer an opportunity to uncover novel biological information. Novelty is exciting but can also be a barrier. If nothing is known, how does one start planning and executing experiments? Here, we provide a recommended information-mining workflow and a corresponding guide to accessing information about uncharacterized Drosophila melanogaster genes, such as those assigned only a systematic coding gene identifier. The available information can provide insights into where and when the gene is expressed, what the function of the gene might be, whether there are similar genes in other species, whether there are known relationships to other genes, and whether any other features have already been determined. In addition, available information about relevant reagents can inspire and facilitate experimental studies. Altogether, mining available information can help prioritize genes for further study, as well as provide starting points for experimental assays and other analyses.

A multi-layered network model identifies Akt1 as a common modulator of neurodegeneration

The accumulation of misfolded and aggregated proteins is a hallmark of neurodegenerative proteinopathies. Although multiple genetic loci have been associated with specific neurodegenerative diseases (NDs), molecular mechanisms that may have a broader relevance for most or all proteinopathies remain poorly resolved. In this study, we developed a multi-layered network expansion (MLnet) model to predict protein modifiers that are common to a group of diseases and, therefore, may have broader pathophysiological relevance for that group. When applied to the four NDs Alzheimer's disease (AD), Huntington's disease, and spinocerebellar ataxia types 1 and 3, we predicted multiple members of the insulin pathway, including PDK1, Akt1, InR, and sgg (GSK-3β), as common modifiers. We validated these modifiers with the help of four Drosophila ND models. Further evaluation of Akt1 in human cell-based ND models revealed that activation of Akt1 signaling by the small molecule SC79 increased cell viability in all models. Moreover, treatment of AD model mice with SC79 enhanced their long-term memory and ameliorated dysregulated anxiety levels, which are commonly affected in AD patients. These findings validate MLnet as a valuable tool to uncover molecular pathways and proteins involved in the pathophysiology of entire disease groups and identify potential therapeutic targets that have relevance across disease boundaries. MLnet can be used for any group of diseases and is available as a web tool at http://ssbio.cau.ac.kr/software/mlnet.

From network analysis to experimental validation: identification of regulators of non-muscle myosin II contractility using the folded-gastrulation signaling pathway

The morphogenetic process of apical constriction, which relies on non-muscle myosin II (NMII) generated constriction of apical domains of epithelial cells, is key to the development of complex cellular patterns. Apical constriction occurs in almost all multicellular organisms, but one of the most well-characterized systems is the Folded-gastrulation (Fog)-induced apical constriction that occurs in Drosophila. The binding of Fog to its cognizant receptors Mist/Smog results in a signaling cascade that leads to the activation of NMII-generated contractility. Despite our knowledge of key molecular players involved in Fog signaling, we sought to explore whether other proteins have an undiscovered role in its regulation. We developed a computational method to predict unidentified candidate NMII regulators using a network of pairwise protein-protein interactions called an interactome. We first constructed a Drosophila interactome of over 500,000 protein-protein interactions from several databases that curate high-throughput experiments. Next, we implemented several graph-based algorithms that predicted 14 proteins potentially involved in Fog signaling. To test these candidates, we used RNAi depletion in combination with a cellular contractility assay in Drosophila S2R + cells, which respond to Fog by contracting in a stereotypical manner. Of the candidates we screened using this assay, two proteins, the serine/threonine phosphatase Flapwing and the putative guanylate kinase CG11811 were demonstrated to inhibit cellular contractility when depleted, suggestive of their roles as novel regulators of the Fog pathway.

Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration

All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions. Mutations of candidate genes implicated by our results affected night sleep, and gene expression profiles largely met predicted gene-gene interactions.

The Drosophila gene encoding JIG protein (CG14850) is critical for CrebA nuclear trafficking during development

Coordination of mitochondrial and nuclear processes is key to the cellular health; however, very little is known about the molecular mechanisms regulating nuclear-mitochondrial crosstalk. Here, we report a novel molecular mechanism controlling the shuttling of CREB (cAMP response element-binding protein) protein complex between mitochondria and nucleoplasm. We show that a previously unknown protein, herein termed as Jig, functions as a tissue-specific and developmental timing-specific coregulator in the CREB pathway. Our results demonstrate that Jig shuttles between mitochondria and nucleoplasm, interacts with CrebA protein and controls its delivery to the nucleus, thus triggering CREB-dependent transcription in nuclear chromatin and mitochondria. Ablating the expression of Jig prevents CrebA from localizing to the nucleoplasm, affecting mitochondrial functioning and morphology and leads to Drosophila developmental arrest at the early third instar larval stage. Together, these results implicate Jig as an essential mediator of nuclear and mitochondrial processes. We also found that Jig belongs to a family of nine similar proteins, each of which has its own tissue- and time-specific expression profile. Thus, our results are the first to describe the molecular mechanism regulating nuclear and mitochondrial processes in a tissue- and time-specific manner.

On the identification of potential novel therapeutic targets for spinocerebellar ataxia type 1 (SCA1) neurodegenerative disease using EvoPPI3

EvoPPI (http://evoppi.i3s.up.pt), a meta-database for protein-protein interactions (PPI), has been upgraded (EvoPPI3) to accept new types of data, namely, PPI from patients, cell lines, and animal models, as well as data from gene modifier experiments, for nine neurodegenerative polyglutamine (polyQ) diseases caused by an abnormal expansion of the polyQ tract. The integration of the different types of data allows users to easily compare them, as here shown for Ataxin-1, the polyQ protein involved in spinocerebellar ataxia type 1 (SCA1) disease. Using all available datasets and the data here obtained for Drosophila melanogaster wt and exp Ataxin-1 mutants (also available at EvoPPI3), we show that, in humans, the Ataxin-1 network is much larger than previously thought (380 interactors), with at least 909 interactors. The functional profiling of the newly identified interactors is similar to the ones already reported in the main PPI databases. 16 out of 909 interactors are putative novel SCA1 therapeutic targets, and all but one are already being studied in the context of this disease. The 16 proteins are mainly involved in binding and catalytic activity (mainly kinase activity), functional features already thought to be important in the SCA1 disease.

Next-generation large-scale binary protein interaction network for Drosophila melanogaster

Generating reference maps of interactome networks illuminates genetic studies by providing a protein-centric approach to finding new components of existing pathways, complexes, and processes. We apply state-of-the-art methods to identify binary protein-protein interactions (PPIs) for Drosophila melanogaster. Four all-by-all yeast two-hybrid (Y2H) screens of > 10,000 Drosophila proteins result in the 'FlyBi' dataset of 8723 PPIs among 2939 proteins. Testing subsets of data from FlyBi and previous PPI studies using an orthogonal assay allows for normalization of data quality; subsequent integration of FlyBi and previous data results in an expanded binary Drosophila reference interaction network, DroRI, comprising 17,232 interactions among 6511 proteins. We use FlyBi data to generate an autophagy network, then validate in vivo using autophagy-related assays. The deformed wings (dwg) gene encodes a protein that is both a regulator and a target of autophagy. Altogether, these resources provide a foundation for building new hypotheses regarding protein networks and function.

Tyrosyl-tRNA synthetase has a noncanonical function in actin bundling

Dominant mutations in tyrosyl-tRNA synthetase (YARS1) and six other tRNA ligases cause Charcot-Marie-Tooth peripheral neuropathy (CMT). Loss of aminoacylation is not required for their pathogenicity, suggesting a gain-of-function disease mechanism. By an unbiased genetic screen in Drosophila, we link YARS1 dysfunction to actin cytoskeleton organization. Biochemical studies uncover yet unknown actin-bundling property of YARS1 to be enhanced by a CMT mutation, leading to actin disorganization in the Drosophila nervous system, human SH-SY5Y neuroblastoma cells, and patient-derived fibroblasts. Genetic modulation of F-actin organization improves hallmark electrophysiological and morphological features in neurons of flies expressing CMT-causing YARS1 mutations. Similar beneficial effects are observed in flies expressing a neuropathy-causing glycyl-tRNA synthetase. Hence, in this work, we show that YARS1 is an evolutionary-conserved F-actin organizer which links the actin cytoskeleton to tRNA-synthetase-induced neurodegeneration.

Exhaustive Cross-Linking Search with Protein Feedback

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/β mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

Genetic architecture of natural variation of cardiac performance from flies to humans

Deciphering the genetic architecture of human cardiac disorders is of fundamental importance but their underlying complexity is a major hurdle. We investigated the natural variation of cardiac performance in the sequenced inbred lines of the Drosophila Genetic Reference Panel (DGRP). Genome-wide associations studies (GWAS) identified genetic networks associated with natural variation of cardiac traits which were used to gain insights as to the molecular and cellular processes affected. Non-coding variants that we identified were used to map potential regulatory non-coding regions, which in turn were employed to predict transcription factors (TFs) binding sites. Cognate TFs, many of which themselves bear polymorphisms associated with variations of cardiac performance, were also validated by heart-specific knockdown. Additionally, we showed that the natural variations associated with variability in cardiac performance affect a set of genes overlapping those associated with average traits but through different variants in the same genes. Furthermore, we showed that phenotypic variability was also associated with natural variation of gene regulatory networks. More importantly, we documented correlations between genes associated with cardiac phenotypes in both flies and humans, which supports a conserved genetic architecture regulating adult cardiac function from arthropods to mammals. Specifically, roles for PAX9 and EGR2 in the regulation of the cardiac rhythm were established in both models, illustrating that the characteristics of natural variations in cardiac function identified in Drosophila can accelerate discovery in humans.

Dynamic Bayesian Network Learning to Infer Sparse Models From Time Series Gene Expression Data

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN, along with the documentation and tutorials.

Photoperiod-Dependent Expression of MicroRNA in Drosophila

Like many other insects in temperate regions, Drosophila melanogaster exploits the photoperiod shortening that occurs during the autumn as an important cue to trigger a seasonal response. Flies survive the winter by entering a state of reproductive arrest (diapause), which drives the relocation of resources from reproduction to survival. Here, we profiled the expression of microRNA (miRNA) in long and short photoperiods and identified seven differentially expressed miRNAs (dme-mir-2b, dme-mir-11, dme-mir-34, dme-mir-274, dme-mir-184, dme-mir-184*, and dme-mir-285). Misexpression of dme-mir-2b, dme-mir-184, and dme-mir-274 in pigment-dispersing, factor-expressing neurons largely disrupted the normal photoperiodic response, suggesting that these miRNAs play functional roles in photoperiodic timing. We also analyzed the targets of photoperiodic miRNA by both computational predication and by Argonaute-1-mediated immunoprecipitation of long- and short-day RNA samples. Together with global transcriptome profiling, our results expand existing data on other Drosophila species, identifying genes and pathways that are differentially regulated in different photoperiods and reproductive status. Our data suggest that post-transcriptional regulation by miRNA is an important facet of photoperiodic timing.

NAADP-binding proteins find their identity

Nicotinic acid adenine dinucleotide phosphate (NAADP) is a second messenger that releases Ca²⁺ from endosomes and lysosomes by activating ion channels called two-pore channels (TPCs). However, no NAADP-binding site has been identified on TPCs. Rather, NAADP activates TPCs indirectly by engaging NAADP-binding proteins (NAADP-BPs) that form part of the TPC complex. After a decade of searching, two different NAADP-BPs were recently identified: Jupiter microtubule associated homolog 2 (JPT2) and like-Sm protein 12 (LSM12). These discoveries bridge the gap between NAADP generation and NAADP activation of TPCs, providing new opportunity to understand and manipulate the NAADP-signaling pathway. The unmasking of these NAADP-BPs will catalyze future studies to define the molecular choreography of NAADP action.

Using FlyBase: A Database of Drosophila Genes and Genetics

Since 1992, FlyBase has provided a freely available online database of information about the model organism Drosophila melanogaster. Data in FlyBase is curated manually from research papers as well as computationally from a variety of relevant sources, to serve as an information hub that enables and accelerates research discovery. This chapter aims to give users new to the database an overview of the layout and types of data available, as well as introducing some tools with which to access the data. More experienced users will find useful information about recent improvements and descriptions to enable more efficient navigation of the database.

Variation in Pleiotropic Hub Gene Expression Is Associated with Interspecific Differences in Head Shape and Eye Size in Drosophila

Revealing the mechanisms underlying the breathtaking morphological diversity observed in nature is a major challenge in Biology. It has been established that recurrent mutations in hotspot genes cause the repeated evolution of morphological traits, such as body pigmentation or the gain and loss of structures. To date, however, it remains elusive whether hotspot genes contribute to natural variation in the size and shape of organs. As natural variation in head morphology is pervasive in Drosophila, we studied the molecular and developmental basis of differences in compound eye size and head shape in two closely related Drosophila species. We show differences in the progression of retinal differentiation between species and we applied comparative transcriptomics and chromatin accessibility data to identify the GATA transcription factor Pannier (Pnr) as central factor associated with these differences. Although the genetic manipulation of Pnr affected multiple aspects of dorsal head development, the effect of natural variation is restricted to a subset of the phenotypic space. We present data suggesting that this developmental constraint is caused by the coevolution of expression of pnr and its cofactor u-shaped (ush). We propose that natural variation in expression or function of highly connected developmental regulators with pleiotropic functions is a major driver for morphological evolution and we discuss implications on gene regulatory network evolution. In comparison to previous findings, our data strongly suggest that evolutionary hotspots are not the only contributors to the repeated evolution of eye size and head shape in Drosophila.

Bow-tie architecture of gene regulatory networks in species of varying complexity

The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures.

Transcription Factors Drive Opposite Relationships between Gene Age and Tissue Specificity in Male and Female Drosophila Gonads

Evolutionarily young genes are usually preferentially expressed in the testis across species. Although it is known that older genes are generally more broadly expressed than younger genes, the properties that shaped this pattern are unknown. Older genes may gain expression across other tissues uniformly, or faster in certain tissues than others. Using Drosophila gene expression data, we confirmed previous findings that younger genes are disproportionately testis biased and older genes are disproportionately ovary biased. We found that the relationship between gene age and expression is stronger in the ovary than any other tissue and weakest in testis. We performed ATAC-seq on Drosophila testis and found that although genes of all ages are more likely to have open promoter chromatin in testis than in ovary, promoter chromatin alone does not explain the ovary bias of older genes. Instead, we found that upstream transcription factor (TF) expression is highly predictive of gene expression in ovary but not in testis. In the ovary, TF expression is more predictive of gene expression than open promoter chromatin, whereas testis gene expression is similarly influenced by both TF expression and open promoter chromatin. We propose that the testis is uniquely able to express younger genes controlled by relatively few TFs, whereas older genes with more TF partners are broadly expressed with peak expression most likely in the ovary. The testis allows widespread baseline expression that is relatively unresponsive to regulatory changes, whereas the ovary transcriptome is more responsive to trans-regulation and has a higher ceiling for gene expression.

Tissue-specific modulation of gene expression in response to lowered insulin signalling in Drosophila

Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of >3600 transcripts and >3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis, which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms.

Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila

The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted a genome wide association study (GWAS) using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. Egfr, a highly pleiotropic developmental gene, was among the most significant additive variants identified. We functionally validated 13 of the additive candidate genes’ (Adgf-A/Adgf-A2/CG32181, bru1, CadN, flapper (CG11073), CG15236, flippy (CG9766), CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo), and introduce a novel approach to whole gene significance screens: PEGASUS_flies. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. We propose a model that suggests genetic modifiers of wing and muscle morphology, nervous system development and function, BMP signaling, sexually dimorphic neural wiring, and gene regulation are all important for the observed differences flight performance in a natural population. Additionally, these results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects.

Insect flight is a widely recognizable phenotype of many winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster, are a genetically tractable model, flight performance is a highly integrative phenotype, and therefore challenging to identify comprehensively which genetic modifiers contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational approaches, we identified additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks that contribute to variation in flight performance. More generally, we demonstrate the benefits of employing multiple methodologies to elucidate the genetic architecture of complex traits. Many variants and genes mapped to regions of the genome that affect neurodevelopment, wing and muscle development, and regulation of gene expression. We also introduce PEGASUS_flies, a Drosophila-adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the gene’s distribution of individual variant P-values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.

The Transcriptome in Transition: Global Gene Expression Profiles of Young Adult Fruit Flies Depend More Strongly on Developmental Than Adult Diet

Developmental diet is known to exert long-term effects on adult phenotypes in many animal species as well as disease risk in humans, purportedly mediated through long-term changes in gene expression. However, there are few studies linking developmental diet to adult gene expression. Here, we use a full-factorial design to address how three different larval and adult diets interact to affect gene expression in 1-day-old adult fruit flies (Drosophila melanogaster) of both sexes. We found that the largest contributor to transcriptional variation in young adult flies is larval, and not adult diet, particularly in females. We further characterized gene expression variation by applying weighted gene correlation network analysis (WGCNA) to identify modules of co-expressed genes. In adult female flies, the caloric content of the larval diet associated with two strongly negatively correlated modules, one of which was highly enriched for reproduction-related processes. This suggests that gene expression in young adult female flies is in large part related to investment into reproduction-related processes, and that the level of expression is affected by dietary conditions during development. In males, most modules had expression patterns independent of developmental or adult diet. However, the modules that did correlate with larval and/or adult dietary regimes related primarily to nutrient sensing and metabolic functions, and contained genes highly expressed in the gut and fat body. The gut and fat body are among the most important nutrient sensing tissues, and are also the only tissues known to avoid histolysis during pupation. This suggests that correlations between larval diet and gene expression in male flies may be mediated by the carry-over of these tissues into young adulthood. Our results show that developmental diet can have profound effects on gene expression in early life and warrant future research into how they correlate with actual fitness related traits in early adulthood.

The Genomic Architecture of Adaptation to Larval Malnutrition Points to a Trade-off with Adult Starvation Resistance in Drosophila

Periods of nutrient shortage impose strong selection on animal populations. Experimental studies of genetic adaptation to nutrient shortage largely focus on resistance to acute starvation at adult stage; it is not clear how conclusions drawn from these studies extrapolate to other forms of nutritional stress. We studied the genomic signature of adaptation to chronic juvenile malnutrition in six populations of Drosophila melanogaster evolved for 150 generations on an extremely nutrient-poor larval diet. Comparison with control populations evolved on standard food revealed repeatable genomic differentiation between the two set of population, involving >3,000 candidate SNPs forming >100 independently evolving clusters. The candidate genomic regions were enriched in genes implicated in hormone, carbohydrate, and lipid metabolism, including some with known effects on fitness-related life-history traits. Rather than being close to fixation, a substantial fraction of candidate SNPs segregated at intermediate allele frequencies in all malnutrition-adapted populations. This, together with patterns of among-population variation in allele frequencies and estimates of Tajima’s D, suggests that the poor diet results in balancing selection on some genomic regions. Our candidate genes for tolerance to larval malnutrition showed a high overlap with genes previously implicated in acute starvation resistance. However, adaptation to larval malnutrition in our study was associated with reduced tolerance to acute adult starvation. Thus, rather than reflecting synergy, the shared genomic architecture appears to mediate an evolutionary trade-off between tolerances to these two forms of nutritional stress.

Predicted Drosophila Interactome Resource and web tool for functional interpretation of differentially expressed genes

Drosophila melanogaster is a well-established model organism that is widely used in genetic studies. This species enjoys the availability of a wide range of research tools, well-annotated reference databases and highly similar gene circuitry to other insects. To facilitate molecular mechanism studies in Drosophila, we present the Predicted Drosophila Interactome Resource (PDIR), a database of high-quality predicted functional gene interactions. These interactions were inferred from evidence in 10 public databases providing information for functional gene interactions from diverse perspectives. The current version of PDIR includes 102 835 putative functional associations with balanced sensitivity and specificity, which are expected to cover 22.56% of all Drosophila protein interactions. This set of functional interactions is a good reference for hypothesis formulation in molecular mechanism studies. At the same time, these interactions also serve as a high-quality reference interactome for gene set linkage analysis (GSLA), which is a web tool for the interpretation of the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that the PDIR/GSLA system was able to produce a more comprehensive and concise interpretation of the collective functional impact of multiple simultaneously changed genes compared with the widely used gene set annotation tools, including PANTHER and David. PDIR and its associated GSLA service can be accessed at http://drosophila.biomedtzc.cn.

Deciphering cell-cell interactions and communication from gene expression

Cell-cell interactions orchestrate organismal development, homeostasis and single-cell functions. When cells do not properly interact or improperly decode molecular messages, disease ensues. Thus, the identification and quantification of intercellular signalling pathways has become a common analysis performed across diverse disciplines. The expansion of protein-protein interaction databases and recent advances in RNA sequencing technologies have enabled routine analyses of intercellular signalling from gene expression measurements of bulk and single-cell data sets. In particular, ligand-receptor pairs can be used to infer intercellular communication from the coordinated expression of their cognate genes. In this Review, we highlight discoveries enabled by analyses of cell-cell interactions from transcriptomic data and review the methods and tools used in this context.

Internetwork connectivity of molecular networks across species of life

Molecular interactions are studied as independent networks in systems biology. However, molecular networks do not exist independently of each other. In a network of networks approach (called multiplex), we study the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. We find that TRN and PPI are non-randomly coupled across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene and protein-protein interactions in TRN and PPI, respectively, also non-randomly overlap. These design principles are conserved across the five eukaryotic species. Robustness of the TRN-PPI multiplex is dependent on this coupling. Functionally important genes and proteins, such as essential, disease-related and those interacting with pathogen proteins, are preferentially situated in important parts of the human multiplex with highly overlapping interactions. We unveil the multiplex architecture of TRN and PPI. Multiplex architecture may thus define a general framework for studying molecular networks. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

A Comprehensive Drosophila melanogaster Transcription Factor Interactome

Combinatorial interactions among transcription factors (TFs) play essential roles in generating gene expression specificity and diversity in metazoans. Using yeast 2-hybrid (Y2H) assays on nearly all sequence-specific Drosophila TFs, we identified 1,983 protein-protein interactions (PPIs), more than doubling the number of currently known PPIs among Drosophila TFs. For quality assessment, we validated a subset of our interactions using MITOMI and bimolecular fluorescence complementation assays. We combined our interactome with prior PPI data to generate an integrated Drosophila TF-TF binary interaction network. Our analysis of ChIP-seq data, integrating PPI and gene expression information, uncovered different modes by which interacting TFs are recruited to DNA. We further demonstrate the utility of our Drosophila interactome in shedding light on human TF-TF interactions. This study reveals how TFs interact to bind regulatory elements in vivo and serves as a resource of Drosophila TF-TF binary PPIs for understanding tissue-specific gene regulation.

Combinatorial regulation by transcription factors (TFs) is one mechanism for achieving condition and tissue-specific gene regulation. Shokri et al. mapped TF-TF interactions between most Drosophila TFs, reporting a comprehensive TF-TF network integrated with previously known interactions. They used this network to discern distinct TF-DNA binding modes.

modEnrichr: a suite of gene set enrichment analysis tools for model organisms

High-throughput experiments produce increasingly large datasets that are difficult to analyze and integrate. While most data integration approaches focus on aligning metadata, data integration can be achieved by abstracting experimental results into gene sets. Such gene sets can be made available for reuse through gene set enrichment analysis tools such as Enrichr. Enrichr currently only supports gene sets compiled from human and mouse, limiting accessibility for investigators that study other model organisms. modEnrichr is an expansion of Enrichr for four model organisms: fish, fly, worm and yeast. The gene set libraries within FishEnrichr, FlyEnrichr, WormEnrichr and YeastEnrichr are created from the Gene Ontology, mRNA expression profiles, GeneRIF, pathway databases, protein domain databases and other organism-specific resources. Additionally, libraries were created by predicting gene function from RNA-seq co-expression data processed uniformly from the gene expression omnibus for each organism. The modEnrichr suite of tools provides the ability to convert gene lists across species using an ortholog conversion tool that automatically detects the species. For complex analyses, modEnrichr provides API access that enables submitting batch queries. In summary, modEnrichr leverages existing model organism databases and other resources to facilitate comprehensive hypothesis generation through data integration.

Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features

Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein-protein interactions, evolutionary conservation and functional annotations. Employing cross-validation, we obtained an excellent prediction performance. The best model achieved in D. melanogaster a ROC-AUC of 0.90, a PR-AUC of 0.30 and a F1 score of 0.34. Our approach considerably outperformed a benchmark method in which only features derived from the protein sequences were used (P < 0.001). Investigating which features contributed to this success, we found all categories of features, most prominently network topological, functional and sequence-based features. To evaluate our approach we performed the same workflow for essential gene prediction in human and achieved an ROC-AUC = 0.97, PR-AUC = 0.73, and F1 = 0.64. In summary, this study shows that using our well-elaborated assembly of features covering a broad range of intrinsic and extrinsic gene and protein features enabled intelligent systems to predict well the essentiality of genes in an organism.

Meta-analysis of honey bee neurogenomic response links Deformed wing virus type A to precocious behavioral maturation

Crop pollination by the western honey bee Apis mellifera is vital to agriculture but threatened by alarmingly high levels of colony mortality, especially in Europe and North America. Colony loss is due, in part, to the high viral loads of Deformed wing virus (DWV), transmitted by the ectoparasitic mite Varroa destructor, especially throughout the overwintering period of a honey bee colony. Covert DWV infection is commonplace and has been causally linked to precocious foraging, which itself has been linked to colony loss. Taking advantage of four brain transcriptome studies that unexpectedly revealed evidence of covert DWV-A infection, we set out to explore whether this effect is due to DWV-A mimicking naturally occurring changes in brain gene expression that are associated with behavioral maturation. Consistent with this hypothesis, we found that brain gene expression profiles of DWV-A infected bees resembled those of foragers, even in individuals that were much younger than typical foragers. In addition, brain transcriptional regulatory network analysis revealed a positive association between DWV-A infection and transcription factors previously associated with honey bee foraging behavior. Surprisingly, single-cell RNA-Sequencing implicated glia, not neurons, in this effect; there are relatively few glial cells in the insect brain and they are rarely associated with behavioral plasticity. Covert DWV-A infection also has been linked to impaired learning, which together with precocious foraging can lead to increased occurrence of infected bees from one colony mistakenly entering another colony, especially under crowded modern apiary conditions. These findings provide new insights into the mechanisms by which DWV-A affects honey bee health and colony survival.

Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs

Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.

Candidate gene screen for potential interaction partners and regulatory targets of the Hox gene labial in the spider Parasteatoda tepidariorum

The Hox gene labial (lab) governs the formation of the tritocerebral head segment in insects and spiders. However, the morphology that results from lab action is very different in the two groups. In insects, the tritocerebral segment (intercalary segment) is reduced and lacks appendages, whereas in spiders the corresponding segment (pedipalpal segment) is a proper segment including a pair of appendages (pedipalps). It is likely that this difference between lab action in insects and spiders is mediated by regulatory targets or interacting partners of lab. However, only a few such genes are known in insects and none in spiders. We have conducted a candidate gene screen in the spider Parasteatoda tepidariorum using as candidates Drosophila melanogaster genes known to (potentially) interact with lab or to be expressed in the intercalary segment. We have studied 75 P. tepidariorum genes (including previously published and duplicated genes). Only 3 of these (proboscipedia-A (pb-A) and two paralogs of extradenticle (exd)) showed differential expression between leg and pedipalp. The low success rate points to a weakness of the candidate gene approach when it is applied to lineage specific organs. The spider pedipalp has no counterpart in insects, and therefore relying on insect data apparently cannot identify larger numbers of factors implicated in its specification and formation. We argue that in these cases a de novo approach to gene discovery might be superior to the candidate gene approach.

RNAInter in 2020: RNA interactome repository with increased coverage and annotation

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

Ancestral reconstruction of protein interaction networks

The molecular and cellular basis of novelty is an active area of research in evolutionary biology. Until very recently, the vast majority of cellular phenomena were so difficult to sample that cross-species studies of biochemistry were rare and comparative analysis at the level of biochemical systems was almost impossible. Recent advances in systems biology are changing what is possible, however, and comparative phylogenetic methods that can handle this new data are wanted. Here, we introduce the term "phylogenetic latent variable models" (PLVMs, pronounced "plums") for a class of models that has recently been used to infer the evolution of cellular states from systems-level molecular data, and develop a new parameterization and fitting strategy that is useful for comparative inference of biochemical networks. We deploy this new framework to infer the ancestral states and evolutionary dynamics of protein-interaction networks by analyzing >16,000 predominantly metazoan co-fractionation and affinity-purification mass spectrometry experiments. Based on these data, we estimate ancestral interactions across unikonts, broadly recovering protein complexes involved in translation, transcription, proteostasis, transport, and membrane trafficking. Using these results, we predict an ancient core of the Commander complex made up of CCDC22, CCDC93, C16orf62, and DSCR3, with more recent additions of COMMD-containing proteins in tetrapods. We also use simulations to develop model fitting strategies and discuss future model developments.

Insights into the Functions of LncRNAs in Drosophila

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs longer than 200 nucleotides (nt). LncRNAs have high spatiotemporal specificity, and secondary structures have been preserved throughout evolution. They have been implicated in a range of biological processes and diseases and are emerging as key regulators of gene expression at the epigenetic, transcriptional, and post-transcriptional levels. Comparative analyses of lncRNA functions among multiple organisms have suggested that some of their mechanisms seem to be conserved. Transcriptome studies have found that some Drosophila lncRNAs have highly specific expression patterns in embryos, nerves, and gonads. In vivo studies of lncRNAs have revealed that dysregulated expression of lncRNAs in Drosophila may result in impaired embryo development, impaired neurological and gonadal functions, and poor stress resistance. In this review, we summarize the epigenetic, transcriptional, and post-transcriptional mechanisms of lncRNAs and mainly focus on recent insights into the transcriptome studies and biological functions of lncRNAs in Drosophila.

Mitochondrial genomic variation drives differential nuclear gene expression in discrete regions of Drosophila gene and protein interaction networks

BACKGROUND

Mitochondria perform many key roles in their eukaryotic hosts, from integrating signaling pathways through to modulating whole organism phenotypes. The > 1 billion years of nuclear and mitochondrial gene co-evolution has necessitated coordinated expression of gene products from both genomes that maintain mitochondrial, and more generally, eukaryotic cellular function. How mitochondrial DNA (mtDNA) variation modifies host fitness has proved a challenging question but has profound implications for evolutionary and medical genetics. In Drosophila, we have previously shown that recently diverged mtDNA haplotypes within-species can have more impact on organismal phenotypes than older, deeply diverged haplotypes from different species. Here, we tested the effects of mtDNA haplotype variation on gene expression in Drosophila under standardized conditions. Using the Drosophila Genetic Reference Panel (DGRP), we constructed a panel of mitonuclear genotypes that consists of factorial variation in nuclear and mtDNA genomes, with mtDNAs originating in D. melanogaster (2x haplotypes) and D. simulans (2x haplotypes).

RESULTS

We show that mtDNA haplotype variation unequivocally alters nuclear gene expression in both females and males, and mitonuclear interactions are pervasive modifying factors for gene expression. There was appreciable overlap between the sexes for mtDNA-sensitive genes, and considerable transcriptional variation attributed to particular mtDNA contrasts. These genes are generally found in low-connectivity gene co-expression networks, occur in gene clusters along chromosomes, are often flanked by non-coding RNA, and are under-represented among housekeeping genes. Finally, we identify the giant (gt) transcription factor motif as a putative regulatory sequence associated with mtDNA-sensitive genes.

CONCLUSIONS

There are predictive conditions for nuclear genes that are influenced by mtDNA variation.

Adaptation and Conservation throughout the Drosophila melanogaster Life-Cycle

Previous studies of the evolution of genes expressed at different life-cycle stages of Drosophila melanogaster have not been able to disentangle adaptive from nonadaptive substitutions when using nonsynonymous sites. Here, we overcome this limitation by combining whole-genome polymorphism data from D. melanogaster and divergence data between D. melanogaster and Drosophila yakuba. For the set of genes expressed at different life-cycle stages of D. melanogaster, as reported in modENCODE, we estimate the ratio of substitutions relative to polymorphism between nonsynonymous and synonymous sites (α) and then α is discomposed into the ratio of adaptive (ω_a) and nonadaptive (ω_na) substitutions to synonymous substitutions.

We find that the genes expressed in mid- and late-embryonic development are the most conserved, whereas those expressed in early development and postembryonic stages are the least conserved. Importantly, we found that low conservation in early development is due to high rates of nonadaptive substitutions (high ω_na), whereas in postembryonic stages it is due, instead, to high rates of adaptive substitutions (high ω_a).

By using estimates of different genomic features (codon bias, average intron length, exon number, recombination rate, among others), we also find that genes expressed in mid- and late-embryonic development show the most complex architecture: they are larger, have more exons, more transcripts, and longer introns. In addition, these genes are broadly expressed among all stages. We suggest that all these genomic features are related to the conservation of mid- and late-embryonic development. Globally, our study supports the hourglass pattern of conservation and adaptation over the life-cycle.

Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data

Model organism and human databases are rich with information about genetic and physical interactions. These data can be used to interpret and guide the analysis of results from new studies and develop new hypotheses. Here, we report the development of the Molecular Interaction Search Tool (MIST; http://fgrtools.hms.harvard.edu/MIST/). The MIST database integrates biological interaction data from yeast, nematode, fly, zebrafish, frog, rat and mouse model systems, as well as human. For individual or short gene lists, the MIST user interface can be used to identify interacting partners based on protein–protein and genetic interaction (GI) data from the species of interest as well as inferred interactions, known as interologs, and to view a corresponding network. The data, interologs and search tools at MIST are also useful for analyzing ‘omics datasets. In addition to describing the integrated database, we also demonstrate how MIST can be used to identify an appropriate cut-off value that balances false positive and negative discovery, and present use-cases for additional types of analysis. Altogether, the MIST database and search tools support visualization and navigation of existing protein and GI data, as well as comparison of new and existing data.

Identification of genes underlying phenotypic plasticity of wing size via insulin signaling pathway by network-based analysis in Sogatella furcifera

Background

Phenotypic plasticity is a common and highly adaptive phenomenon where the same genotype produces different phenotypes in response to environmental cues. Sogatella furcifera, a migratory pest of rice exhibits wing dimorphism, is a model insect for studying phenotypic plasticity of wing size. The Insullin-PI3K-Akt-FOXO signaling pathway plays a crucial role in the manipulation of wing size in the migratory insects. However, the regulatory mechanism via the pathway involved in wing dimorphism are still unexplored.

Results

Accompanied by special alternative splicing, genes involved in muscle contraction and energy metabolism were highly expressed in the wing hinges of macropters, demonstrating their adaptation for energy-demanding long-distance flights. Based on FOXO ChIP-Seq analysis, a total of 1259 putative target genes were observed in the wing hinges, including wing morph development, flight muscle and energy metabolism genes. An integrated gene interaction network was built by combining four heterogeneous datasets, and the IIS-PI3K-Akt-FOXO pathway was clustered in a divided functional module. In total, 45 genes in the module directly interacting with the IIS-PI3K-Akt-FOXO pathway showed differential expression levels between the two wing hinges, thus are regarded as potential Insulin pathway mediated wing dimorphism related genes (IWDRGs). Of the 45 IWDRGs, 5 were selected for verification by gene knockdown experiments, and played significant roles in the insect wing size regulation.

Conclusions

We provided valuable insights on the genetic basis of wing dimorphism, and also demonstrated that network analysis is a powerful approach to identify new genes regulating wing dimorphic development via insulin signaling pathway in the migratory insect.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5793-z) contains supplementary material, which is available to authorized users.

The histone methyltransferase G9a regulates tolerance to oxidative stress-induced energy consumption

Stress responses are crucial processes that require activation of genetic programs that protect from the stressor. Stress responses are also energy consuming and can thus be deleterious to the organism. The mechanisms coordinating energy consumption during stress response in multicellular organisms are not well understood. Here, we show that loss of the epigenetic regulator G9a in Drosophila causes a shift in the transcriptional and metabolic responses to oxidative stress (OS) that leads to decreased survival time upon feeding the xenobiotic paraquat. During OS exposure, G9a mutants show overactivation of stress response genes, rapid depletion of glycogen, and inability to access lipid energy stores. The OS survival deficiency of G9a mutants can be rescued by a high-sugar diet. Control flies also show improved OS survival when fed a high-sugar diet, suggesting that energy availability is generally a limiting factor for OS tolerance. Directly limiting access to glycogen stores by knocking down glycogen phosphorylase recapitulates the OS-induced survival defects of G9a mutants. We propose that G9a mutants are sensitive to stress because they experience a net reduction in available energy due to (1) rapid glycogen use, (2) an inability to access lipid energy stores, and (3) an overinduced transcriptional response to stress that further exacerbates energy demands. This suggests that G9a acts as a critical regulatory hub between the transcriptional and metabolic responses to OS. Our findings, together with recent studies that established a role for G9a in hypoxia resistance in cancer cell lines, suggest that G9a is of wide importance in controlling the cellular and organismal response to multiple types of stress.

Stress responses require proper activation of genetic programs to protect the organism from the stressor. However, the mechanisms controlling energy consumption during stress responses are not well understood. Here, we investigate the role of epigenetic modifier G9a in regulating metabolism and gene transcription during oxidative stress responses in Drosophila. Flies lacking G9a show a shift in the metabolic and transcriptional responses to oxidative stress, leading to decreased stress tolerance despite intact oxidative stress defense mechanisms. During oxidative stress exposure, G9a mutants show overactivation of stress response and many other genes, rapid depletion of glycogen energy stores, and an inability to access lipid energy stores. The increased susceptibility of G9a mutant flies to oxidative stress can be rescued simply by providing extra sugar. This suggests that G9a mutants are sensitive to stress because of reduced access to immediately available energy. Wild-type flies also become more tolerant to oxidative stress when they are fed extra sugar, whereas blocking energy access by genetically reducing a key metabolic enzyme leads to oxidative stress sensitivity. Though the genetic response to oxidative stress has long been appreciated, our study emphasizes the importance of energy metabolism for stress tolerance and identifies the histone methyltransferase G9a as an important player regulating both.

Pleiotropy Modulates the Efficacy of Selection in Drosophila melanogaster

Pleiotropy is the well-established idea that a single mutation affects multiple phenotypes. If a mutation has opposite effects on fitness when expressed in different contexts, then genetic conflict arises. Pleiotropic conflict is expected to reduce the efficacy of selection by limiting the fixation of beneficial mutations through adaptation, and the removal of deleterious mutations through purifying selection. Although this has been widely discussed, in particular in the context of a putative "gender load," it has yet to be systematically quantified. In this work, we empirically estimate to which extent different pleiotropic regimes impede the efficacy of selection in Drosophila melanogaster. We use whole-genome polymorphism data from a single African population and divergence data from D. simulans to estimate the fraction of adaptive fixations (α), the rate of adaptation (ωA), and the direction of selection (DoS). After controlling for confounding covariates, we find that the different pleiotropic regimes have a relatively small, but significant, effect on selection efficacy. Specifically, our results suggest that pleiotropic sexual antagonism may restrict the efficacy of selection, but that this conflict can be resolved by limiting the expression of genes to the sex where they are beneficial. Intermediate levels of pleiotropy across tissues and life stages can also lead to maladaptation in D. melanogaster, due to inefficient purifying selection combined with low frequency of mutations that confer a selective advantage. Thus, our study highlights the need to consider the efficacy of selection in the context of antagonistic pleiotropy, and of genetic conflict in general.

Genomic changes associated with adaptation to arid environments in cactophilic Drosophila species

BACKGROUND

Insights into the genetic capacities of species to adapt to future climate change can be gained by using comparative genomic and transcriptomic data to reconstruct the genetic changes associated with such adaptations in the past. Here we investigate the genetic changes associated with adaptation to arid environments, specifically climatic extremes and new cactus hosts, through such an analysis of five repleta group Drosophila species.

RESULTS

We find disproportionately high rates of gene gains in internal branches in the species' phylogeny where cactus use and subsequently cactus specialisation and high heat and desiccation tolerance evolved. The terminal branch leading to the most heat and desiccation resistant species, Drosophila aldrichi, also shows disproportionately high rates of both gene gains and positive selection. Several Gene Ontology terms related to metabolism were enriched in gene gain events in lineages where cactus use was evolving, while some regulatory and developmental genes were strongly selected in the Drosophila aldrichi branch. Transcriptomic analysis of flies subjected to sublethal heat shocks showed many more downregulation responses to the stress in a heat sensitive versus heat resistant species, confirming the existence of widespread regulatory as well as structural changes in the species' differing adaptations. Gene Ontology terms related to metabolism were enriched in the differentially expressed genes in the resistant species while terms related to stress response were over-represented in the sensitive one.

CONCLUSION

Adaptations to new cactus hosts and hot desiccating environments were associated with periods of accelerated evolutionary change in diverse biochemistries. The hundreds of genes involved suggest adaptations of this sort would be difficult to achieve in the timeframes projected for anthropogenic climate change.

Transcriptional dynamics of microRNAs and their targets during Drosophila neurogenesis

During Drosophila melanogaster embryogenesis, tight regulation of gene expression in time and space is required for the orderly emergence of specific cell types. While the general importance of microRNAs in regulating eukaryotic gene expression has been well-established, their role in early neurogenesis remains to be addressed. In this survey, we investigate the transcriptional dynamics of microRNAs and their target transcripts during neurogenesis of Drosophila melanogaster. To this end, we use the recently developed DIV-MARIS protocol, a method for enriching specific cell types from the Drosophila embryo in vivo, to sequence cell type-specific transcriptomes. We generate dedicated small and total RNA-seq libraries for neuroblasts, neurons and glia cells at early (6-8 h after egg laying (AEL)) and late (18-22 h AEL) stage. This allows us to directly compare these transcriptomes and investigate the potential functional roles of individual microRNAs with spatiotemporal resolution genome-wide, which is beyond the capabilities of existing in situ hybridization methods. Overall, we identify 74 microRNAs that are significantly differentially expressed between the three cell types and the two developmental stages. In all cell types, predicted target genes of down-regulated microRNAs show a significant enrichment of Gene Ontology terms related to neurogenesis. We also investigate how microRNAs regulate the transcriptome by targeting transcription factors and find many candidate microRNAs with putative roles in neurogenesis. Our survey highlights the roles of microRNAs as regulators of differentiation and glioneurognesis in the fruit fly and provides distinct starting points for dedicated functional follow-up studies.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Cyclin G and the Polycomb Repressive complexes PRC1 and PR-DUB cooperate for developmental stability

In Drosophila, ubiquitous expression of a short Cyclin G isoform generates extreme developmental noise estimated by fluctuating asymmetry (FA), providing a model to tackle developmental stability. This transcriptional cyclin interacts with chromatin regulators of the Enhancer of Trithorax and Polycomb (ETP) and Polycomb families. This led us to investigate the importance of these interactions in developmental stability. Deregulation of Cyclin G highlights an organ intrinsic control of developmental noise, linked to the ETP-interacting domain, and enhanced by mutations in genes encoding members of the Polycomb Repressive complexes PRC1 and PR-DUB. Deep-sequencing of wing imaginal discs deregulating CycG reveals that high developmental noise correlates with up-regulation of genes involved in translation and down-regulation of genes involved in energy production. Most Cyclin G direct transcriptional targets are also direct targets of PRC1 and RNAPolII in the developing wing. Altogether, our results suggest that Cyclin G, PRC1 and PR-DUB cooperate for developmental stability.