Experimental competition induces immediate and lasting effects on the neurogenome in free-living female birds

Periods of social instability can elicit adaptive phenotypic plasticity to promote success in future competition. However, the underlying molecular mechanisms have primarily been studied in captive and laboratory-reared animals, leaving uncertainty as to how natural competition among free-living animals affects gene activity. Here, we experimentally generated social competition among wild, cavity-nesting female birds (tree swallows, Tachycineta bicolor). After territorial settlement, we reduced the availability of key breeding resources (i.e., nest boxes), generating heightened competition; within 24 h we reversed the manipulation, causing aggressive interactions to subside. We sampled females during the peak of competition and 48 h after it ended, along with date-matched controls. We measured transcriptomic and epigenomic responses to competition in two socially relevant brain regions (hypothalamus and ventromedial telencephalon). Gene network analyses suggest that processes related to energy mobilization and aggression (e.g., dopamine synthesis) were up-regulated during competition, the latter of which persisted 2 d after competition had ended. Cellular maintenance processes were also down-regulated after competition. Competition additionally altered methylation patterns, particularly in pathways related to hormonal signaling, suggesting those genes were transcriptionally poised to respond to future competition. Thus, experimental competition among free-living animals shifts gene expression in ways that may facilitate the demands of competition at the expense of self-maintenance. Further, some of these effects persisted after competition ended, demonstrating the potential for epigenetic biological embedding of the social environment in ways that may prime individuals for success in future social instability.

Gene regulatory networks exhibit several kinds of memory: quantification of memory in biological and random transcriptional networks

Gene regulatory networks (GRNs) process important information in developmental biology and biomedicine. A key knowledge gap concerns how their responses change over time. Hypothesizing long-term changes of dynamics induced by transient prior events, we created a computational framework for defining and identifying diverse types of memory in candidate GRNs. We show that GRNs from a wide range of model systems are predicted to possess several types of memory, including Pavlovian conditioning. Associative memory offers an alternative strategy for the biomedical use of powerful drugs with undesirable side effects, and a novel approach to understanding the variability and time-dependent changes of drug action. We find evidence of natural selection favoring GRN memory. Vertebrate GRNs overall exhibit more memory than invertebrate GRNs, and memory is most prevalent in differentiated metazoan cell networks compared with undifferentiated cells. Timed stimuli are a powerful alternative for biomedical control of complex in vivo dynamics without genomic editing or transgenes.

•

Gene regulatory networks' dynamics are modified by transient stimuli

•

GRNs have several different types of memory, including associative conditioning

•

Evolution favored GRN memory, and differentiated cells have the most memory capacity

•

Training GRNs offers a novel biomedical strategy not dependent on genetic rewiring

Critical Comparison of MaxCal and Other Stochastic Modeling Approaches in Analysis of Gene Networks

Learning the underlying details of a gene network with feedback is critical in designing new synthetic circuits. Yet, quantitative characterization of these circuits remains limited. This is due to the fact that experiments can only measure partial information from which the details of the circuit must be inferred. One potentially useful avenue is to harness hidden information from single-cell stochastic gene expression time trajectories measured for long periods of time—recorded at frequent intervals—over multiple cells. This raises the feasibility vs. accuracy dilemma while deciding between different models of mining these stochastic trajectories. We demonstrate that inference based on the Maximum Caliber (MaxCal) principle is the method of choice by critically evaluating its computational efficiency and accuracy against two other typical modeling approaches: (i) a detailed model (DM) with explicit consideration of multiple molecules including protein-promoter interaction, and (ii) a coarse-grain model (CGM) using Hill type functions to model feedback. MaxCal provides a reasonably accurate model while being significantly more computationally efficient than DM and CGM. Furthermore, MaxCal requires minimal assumptions since it is a top-down approach and allows systematic model improvement by including constraints of higher order, in contrast to traditional bottom-up approaches that require more parameters or ad hoc assumptions. Thus, based on efficiency, accuracy, and ability to build minimal models, we propose MaxCal as a superior alternative to traditional approaches (DM, CGM) when inferring underlying details of gene circuits with feedback from limited data.

Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in Pisum sativum

Transition to flowering is an important stage of plant development. Many regulatory modules that control floral transition are conservative across plants. This process is best studied for the model plant Arabidopsis thaliana. The homologues of Arabidopsis genes responsible for the flowering initiation in legumes have been identified, and available data on their expression provide a good basis for gene network modeling. In this study, we developed several dynamical models of a gene network controlling transition to flowering in pea (Pisum sativum) using two different approaches. We used differential equations for modeling a previously proposed gene regulation scheme of floral initiation in pea and tested possible alternative hypothesis about some regulations. As the second approach, we applied neural networks to infer interactions between genes in the network directly from gene expression data. All models were verified on previously published experimental data on the dynamic expression of the main genes in the wild type and in three mutant genotypes. Based on modeling results, we made conclusions about the functionality of the previously proposed interactions in the gene network and about the influence of different growing conditions on the network architecture. It was shown that regulation of the PIM, FTa1, and FTc genes in pea does not correspond to the previously proposed hypotheses. The modeling suggests that short- and long-day growing conditions are characterized by different gene network architectures. Overall, the results obtained can be used to plan new experiments and create more accurate models to study the flowering initiation in pea and, in a broader context, in legumes.

Multi-Omics Data Analysis Uncovers Molecular Networks and Gene Regulators for Metabolic Biomarkers

The insulin-like growth factors (IGFs)/insulin resistance (IR) axis is the major metabolic hormonal pathway mediating the biologic mechanism of several complex human diseases, including type 2 diabetes (T2DM) and cancers. The genomewide association study (GWAS)-based approach has neither fully characterized the phenotype variation nor provided a comprehensive understanding of the regulatory biologic mechanisms. We applied systematic genomics to integrate our previous GWAS data for IGF-I and IR with multi-omics datasets, e.g., whole-blood expression quantitative loci, molecular pathways, and gene network, to capture the full range of genetic functionalities associated with IGF-I/IR and key drivers (KDs) in gene-regulatory networks. We identified both shared (e.g., T2DM, lipid metabolism, and estimated glomerular filtration signaling) and IR-specific (e.g., mechanistic target of rapamycin, phosphoinositide 3-kinases, and erb-b2 receptor tyrosine kinase 4 signaling) molecular biologic processes of IGF-I/IR axis regulation. Next, by using tissue-specific gene–gene interaction networks, we identified both well-established (e.g., IRS1 and IGF1R) and novel (e.g., AKT1, HRAS, and JAK1) KDs in the IGF-I/IR-associated subnetworks. Our results, if validated in additional genomic studies, may provide robust, comprehensive insights into the mechanisms of IGF-I/IR regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for the associated diseases, e.g., T2DM and cancers.

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

With the rapid advancement of single-cell RNA-sequencing (scRNA-seq) technology, many data-preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene network reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on Human Cell Atlas bone marrow data with respect to their impacts on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data-preprocessing steps due to oversmoothing of the raw data. We proposed a model-agnostic noise-regularization method that can effectively eliminate the correlation artifacts. The noise-regularized gene-gene correlations were further used to reconstruct a gene co-expression network and successfully revealed several known immune cell modules.

Exome Chip Analyses and Genetic Risk for IgA Nephropathy among Han Chinese

BACKGROUND AND OBJECTIVES

IgA nephropathy is the most common form of primary GN worldwide. The evidence of geographic and ethnic differences, as well as familial aggregation of the disease, supports a strong genetic contribution to IgA nephropathy. Evidence for genetic factors in IgA nephropathy comes also from genome-wide association patient-control studies. However, few studies have systematically evaluated the contribution of coding variation in IgA nephropathy.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

We performed a two-stage exome chip-based association study in 13,242 samples, including 3363 patients with IgA nephropathy and 9879 healthy controls of Han Chinese ancestry. Common variant functional annotation, gene-based low-frequency variants analysis, differential mRNA expression, and gene network integration were also explored.

RESULTS

We identified three non-HLA gene regions (FBXL21, CCR6, and STAT3) and one HLA gene region (GABBR1) with suggestive significance (Pmeta <5×10-5) in single-variant associations. These novel non-HLA variants were annotated as expression-associated single-nucleotide polymorphisms and were located in enhancer regions enriched in histone marks H3K4me1 in primary B cells. Gene-based low-frequency variants analysis suggests CFB as another potential susceptibility gene. Further combined expression and network integration suggested that the five novel susceptibility genes, TGFBI, CCR6, STAT3, GABBR1, and CFB, were involved in IgA nephropathy.

CONCLUSIONS

Five novel gene regions with suggestive significance for IgA nephropathy were identified and shed new light for further mechanism investigation.

Single-cell analysis of nonhuman primate preimplantation development in comparison to humans and mice

BACKGROUND

Genetic programs underlying preimplantation development and early lineage segregation are highly conserved across mammals. It has been suggested that nonhuman primates would be better model organisms for human embryogenesis, but a limited number of studies have investigated the monkey preimplantation development. In this study, we collect single cells from cynomolgus monkey preimplantation embryos for transcriptome profiling and compare with single-cell RNA-seq data derived from human and mouse embryos.

RESULTS

By weighted gene-coexpression network analysis, we found that cynomolgus gene networks have greater conservation with human embryos including a greater number of conserved hub genes than that of mouse embryos. Consistently, we found that early ICM/TE lineage-segregating genes in monkeys exhibit greater similarity with human when compared to mouse, so are the genes in signaling pathways such as LRP1 and TCF7 involving in WNT pathway. Last, we tested the role of one conserved pre-EGA hub gene, SIN3A, using a morpholino knockdown of maternal RNA transcripts in monkey embryos followed by single-cell RNA-seq. We found that SIN3A knockdown disrupts the gene-silencing program during the embryonic genome activation transition and results in developmental delay of cynomolgus embryos.

CONCLUSION

Taken together, our study provided new insight into evolutionarily conserved and divergent transcriptome dynamics during mammalian preimplantation development.

Quantifying the Stability of Coupled Genetic and Epigenetic Switches With Variational Methods

The Waddington landscape provides an intuitive metaphor to view development as a ball rolling down the hill, with distinct phenotypes as basins and differentiation pathways as valleys. Since, at a molecular level, cell differentiation arises from interactions among the genes, a mathematical definition for the Waddington landscape can, in principle, be obtained by studying the gene regulatory networks. For eukaryotes, gene regulation is inextricably and intimately linked to histone modifications. However, the impact of such modifications on both landscape topography and stability of attractor states is not fully understood. In this work, we introduced a minimal kinetic model for gene regulation that combines the impact of both histone modifications and transcription factors. We further developed an approximation scheme based on variational principles to solve the corresponding master equation in a second quantized framework. By analyzing the steady-state solutions at various parameter regimes, we found that histone modification kinetics can significantly alter the behavior of a genetic network, resulting in qualitative changes in gene expression profiles. The emerging epigenetic landscape captures the delicate interplay between transcription factors and histone modifications in driving cell-fate decisions.

Descriptive and functional characterization of epidermal growth factor‑like domain 8 in mouse cortical thymic epithelial cells by integrated analysis of gene expression signatures and networks

Epidermal growth factor-like domain 8 (EGFL8), a newly identified member of the EGFL family, and plays negative regulatory roles in mouse thymic epithelial cells (TECs) and thymocytes. However, the role of EGFL8 in these cells remains poorly understood. In the present study, in order to characterize the function of EGFL8, genome-wide expression profiles in EGFL8-overexpressing or -silenced mouse cortical TECs (cTECs) were analyzed. Microarray analysis revealed that 458 genes exhibited a >2-fold change in expression levels in the EGFL8-overexpressing vs. the EGFL8-silenced cTECs. Several genes involved in a number of cellular processes, such as the cell cycle, proliferation, growth, migration and differentiation, as well as in apoptosis, reactive oxygen species generation, chemotaxis and immune responses, were differentially expressed in the EGFL8-overexpressing or -silenced cTECs. WST-1 analysis revealed that that the overexpression of EGFL8 inhibited cTEC proliferation. To investigate the underlying mechanisms of EGFL8 in the regulation of cTEC function, genes related to essential cellular functions were selected. Reverse transcription-polymerase chain reaction analysis revealed that EGFL8 knockdown upregulated the expression of cluster differentiation 74 (CD74), Fas ligand (FasL), C-X-C motif chemokine ligand 5 (CXCL5), CXCL10, CXCL16, C-C motif chemokine ligand 20 (CCL20), vascular endothelial growth factor-A (VEGF-A), interferon regulatory factor 7 (Irf7), insulin-like growth factor binding protein-4 (IGFBP-4), thrombospondin 1 (Thbs1) and nuclear factor κB subunit 2 (NF-κB2) genes, and downregulated the expression of angiopoietin-like 1 (Angptl1), and neuropilin-1 (Nrp1) genes. Additionally, EGFL8 silencing enhanced the expression of anti-apoptotic molecules, such as B-cell lymphoma-2 (Bcl-2) and Bcl-extra large (Bcl-xL), and that of cell cycle-regulating molecules, such as cyclin-dependent kinase 1 (CDK1), CDK4, CDK6 and cyclin D1. Moreover, gene network analysis revealed that EGFL8 exerted negative effects on VEGF-A gene expression. Hence, the altered expression of several genes associated with EGFL8 expression in cTECs highlights the important physiological processes in which EGFL8 is involved, and provides insight into its biological functions.

Adaptation and co-adaptation of skin pigmentation and vitamin D genes in native Americans

We carried out an exhaustive review regarding human skin color variation and how much it may be related to vitamin D metabolism and other photosensitive molecules. We discuss evolutionary contexts that modulate this variability and hypotheses postulated to explain them; for example, a small amount of melanin in the skin facilitates vitamin D production, making it advantageous to have fair skin in an environment with little radiation incidence. In contrast, more melanin protects folate from degradation in an environment with a high incidence of radiation. Some Native American populations have a skin color at odds with what would be expected for the amount of radiation in the environment in which they live, a finding challenging the so-called "vitamin D-folate hypothesis." Since food is also a source of vitamin D, dietary habits should also be considered. Here we argue that a gene network approach provides tools to explain this phenomenon since it indicates potential alleles co-evolving in a compensatory way. We identified alleles of the vitamin D metabolism and pigmentation pathways segregated together, but in different proportions, in agriculturalists and hunter-gatherers. Finally, we highlight how an evolutionary approach can be useful to understand current topics of medical interest.

Identification of Novel Genomic Associations and Gene Candidates for Grain Starch Content in Sorghum

Starch accumulated in the endosperm of cereal grains as reserve energy for germination serves as a staple in human and animal nutrition. Unraveling genetic control for starch metabolism is important for breeding grains with high starch content. In this study, we used a sorghum association panel with 389 individuals and 141,557 single nucleotide polymorphisms (SNPs) to fit linear mixed models (LMM) for identifying genomic regions and potential candidate genes associated with starch content. Three associated genomic regions, one in chromosome (chr) 1 and two novel associations in chr-8, were identified using combination of LMM and Bayesian sparse LMM. All significant SNPs were located within protein coding genes, with SNPs ∼ 52 Mb of chr-8 encoding a Casperian strip membrane protein (CASP)-like protein (Sobic.008G111500) and a heat shock protein (HSP) 90 (Sobic.008G111600) that were highly expressed in reproductive tissues including within the embryo and endosperm. The HSP90 is a potential hub gene with gene network of 75 high-confidence first interactors that is enriched for five biochemical pathways including protein processing. The first interactors of HSP90 also showed high transcript abundance in reproductive tissues. The candidates of this study are likely involved in intricate metabolic pathways and represent candidate gene targets for source-sink activities and drought and heat stress tolerance during grain filling.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia

MOTIVATION

Annotating genetic variants from summary statistics of genome-wide association studies (GWAS) is crucial for predicting risk genes of various disorders. The multimarker analysis of genomic annotation (MAGMA) is one of the most popular tools for this purpose, where MAGMA aggregates signals of single nucleotide polymorphisms (SNPs) to their nearby genes. In biology, SNPs may also affect genes that are far away in the genome, thus missed by MAGMA. Although different upgrades of MAGMA have been proposed to extend gene-wise variant annotations with more information (e.g. Hi-C or eQTL), the regulatory relationships among genes and the tissue specificity of signals have not been taken into account.

RESULTS

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks. When applied to schizophrenia (SCZ), nMAGMA is able to detect more risk genes (217% more than MAGMA and 57% more than H-MAGMA) that are involved in SCZ compared with MAGMA and H-MAGMA, and more of nMAGMA results can be validated with known SCZ risk genes. Some disease-related functions (e.g. the ATPase pathway in Cortex) are also uncovered in nMAGMA but not in MAGMA or H-MAGMA. Moreover, nMAGMA provides tissue-specific risk signals, which are useful for understanding disorders with multitissue origins.

Tau Pathology Drives Dementia Risk-Associated Gene Networks toward Chronic Inflammatory States and Immunosuppression

To understand how neural-immune-associated genes and pathways contribute to neurodegenerative disease pathophysiology, we performed a systematic functional genomic analysis in purified microglia and bulk tissue from mouse and human AD, FTD, and PSP. We uncover a complex temporal trajectory of microglial-immune pathways involving the type 1 interferon response associated with tau pathology in the early stages, followed by later signatures of partial immune suppression and, subsequently, the type 2 interferon response. We find that genetic risk for dementias shows disease-specific patterns of pathway enrichment. We identify drivers of two gene co-expression modules conserved from mouse to human, representing competing arms of microglial-immune activation (NAct) and suppression (NSupp) in neurodegeneration. We validate our findings by using chemogenetics, experimental perturbation data, and single-cell sequencing in post-mortem brains. Our results refine the understanding of stage- and disease-specific microglial responses, implicate microglial viral defense pathways in dementia pathophysiology, and highlight therapeutic windows.

Rexach et al. use transcriptional network analysis to define dynamic microglial transitions across neurodegeneration, discovering that three dementias with tau pathology involve dysregulated microglial viral and antiviral pathways. Bio-informatics coupled with experimental validation identifies regulatory drivers, implicating double-stranded RNA and interferon-response genes as drivers of early immune suppression in disease.

Gene regulation could be attributed to TCF3 and other key transcription factors in the muscle of pubertal heifers

Puberty is a whole-body event, driven by the hypothalamic integration of peripheral signals such as leptin or IGF-1. In the process of puberty, reproductive development is simultaneous to growth, including muscle growth. To enhance our understanding of muscle function related to puberty, we performed transcriptome analyses of muscle samples from six pre- and six post-pubertal Brahman heifers (Bos indicus). Our aims were to perform differential expression analyses and co-expression analyses to derive a regulatory gene network associate with puberty. As a result, we identified 431 differentially expressed (DEx) transcripts (genes and non-coding RNAs) when comparing pre- to post-pubertal average gene expression. The DEx transcripts were compared with all expressed transcripts in our samples (over 14,000 transcripts) for functional enrichment analyses. The DEx transcripts were associated with "extracellular region," "inflammatory response" and "hormone activity" (adjusted p < .05). Inflammatory response for muscle regeneration is a necessary aspect of muscle growth, which is accelerated during puberty. The term "hormone activity" may signal genes that respond to progesterone signalling in the muscle, as the presence of this hormone is an important difference between pre- and post-pubertal heifers in our experimental design. The DEx transcript with the highest average expression difference was a mitochondrial gene, ENSBTAG00000043574 that might be another important link between energy metabolism and puberty. In the derived co-expression gene network, we identified six hub genes: CDC5L, MYC, TCF3, RUNX2, ATF2 and CREB1. In the same network, 48 key regulators of DEx transcripts were identified, using a regulatory impact factor metric. The hub gene TCF3 was also a key regulator. The majority of the key regulators (22 genes) are members of the zinc finger family, which has been implicated in bovine puberty in other tissues. In conclusion, we described how puberty may affect muscle gene expression in cattle.

Differential Defense Responses of Upland and Lowland Switchgrass Cultivars to a Cereal Aphid Pest

Yellow sugarcane aphid (YSA) (Sipha flava, Forbes) is a damaging pest on many grasses. Switchgrass (Panicum virgatum L.), a perennial C4 grass, has been selected as a bioenergy feedstock because of its perceived resilience to abiotic and biotic stresses. Aphid infestation on switchgrass has the potential to reduce the yields and biomass quantity. Here, the global defense response of switchgrass cultivars Summer and Kanlow to YSA feeding was analyzed by RNA-seq and metabolite analysis at 5, 10, and 15 days after infestation. Genes upregulated by infestation were more common in both cultivars compared to downregulated genes. In total, a higher number of differentially expressed genes (DEGs) were found in the YSA susceptible cultivar (Summer), and fewer DEGs were observed in the YSA resistant cultivar (Kanlow). Interestingly, no downregulated genes were found in common between each time point or between the two switchgrass cultivars. Gene co-expression analysis revealed upregulated genes in Kanlow were associated with functions such as flavonoid, oxidation-response to chemical, or wax composition. Downregulated genes for the cultivar Summer were found in co-expression modules with gene functions related to plant defense mechanisms or cell wall composition. Global analysis of defense networks of the two cultivars uncovered differential mechanisms associated with resistance or susceptibility of switchgrass in response to YSA infestation. Several gene co-expression modules and transcription factors correlated with these differential defense responses. Overall, the YSA-resistant Kanlow plants have an enhanced defense even under aphid uninfested conditions.

Genetic Signatures of Evolution of the Pluripotency Gene Regulating Network across Mammals

Mammalian pluripotent stem cells (PSCs) have distinct molecular and biological characteristics among species, but to date we lack a comprehensive understanding of regulatory network evolution in mammals. Here, we carried out a comparative genetic analysis of 134 genes constituting the pluripotency gene regulatory network across 48 mammalian species covering all the major taxonomic groups. We report that mammalian genes in the pluripotency regulatory network show a remarkably high degree of evolutionary stasis, suggesting the conservation of fundamental biological process of mammalian PSCs across species. Nevertheless, despite the overall conservation of the regulatory network, we discovered rapid evolution of the downstream targets of the core regulatory elements and specific amino acid residues that have undergone positive selection. Our data indicate development of lineage-specific pluripotency regulating networks that may explain observed variations in some characteristics of mammalian PSCs. We further revealed that positively selected genes could be associated with species' unique adaptive characteristics that were not dedicated to regulation of PSCs. These results provide important insight into the evolution of the pluripotency gene regulatory network underlying variations in characteristics of mammalian PSCs.

Single-step genome-wide association studies (GWAS) and post-GWAS analyses to identify genomic regions and candidate genes for milk yield in Brazilian Girolando cattle

Milk production is economically important to the Brazilian agribusiness, and the majority of the country's milk production derives from Girolando (Gir × Holstein) cows. This study aimed to identify quantitative trait loci (QTL) and candidate genes associated with 305-d milk yield (305MY) in Girolando cattle. In addition, we investigated the SNP-specific variances for Holstein and Gir breeds of origin within the sequence of candidate genes. A single-step genomic BLUP procedure was used to identify QTL associated with 305MY, and the most likely candidate genes were identified through follow-up analyses. Genomic breeding values specific for Holstein and Gir were estimated in the Girolando animals using a model that uses breed-specific partial relationship matrices, which were converted to breed of origin SNP effects. Differences between breed of origin were evaluated by comparing estimated SNP variances between breeds. From 10 genome regions explaining most additive genetic variance for 305MY in Girolando cattle, 7 candidate genes were identified on chromosomes 1, 4, 6, and 26. Within the sequence of these 7 candidate genes, Gir breed of origin SNP alleles showed the highest genetic variance. These results indicated QTL regions that could be further explored in genomic selection panels and which may also help in understanding the gene mechanisms involved in milk production in the Girolando breed.

Jasmonate induced alternative splicing responses in Arabidopsis

Jasmonate is an essential phytohormone regulating plant growth, development, and defense. Alternative splicing (AS) in jasmonate ZIM-domain (JAZ) repressors is well-characterized and plays an important role in jasmonate signaling regulation. However, it is unknown whether other genes in the jasmonate signaling pathway are regulated by AS. We explore the potential for AS regulation in three Arabidopsis genotypes (WT, jaz2, jaz7) in response to methyl jasmonate (MeJA) treatment with respect to: (a) differential AS, (b) differential miRNA targeted AS, and (c) AS isoforms with novel functions. AS events identified from transcriptomic data were validated with proteomic data. Protein interaction networks identified two genes, SKIP and ALY4 whose products have both DNA- and RNA-binding affinities, as potential key regulators mediating jasmonate signaling and AS regulation. We observed cases where AS alone, or AS and transcriptional regulation together, can influence gene expression in response to MeJA. Twenty-one genes contain predicted miRNA target sites subjected to AS, which implies that AS is coupled to miRNA regulation. We identified 30 cases where alternatively spliced isoforms may have novel functions. For example, AS of bHLH160 generates an isoform without a basic domain, which may convert it from an activator to a repressor. Our study identified potential key regulators in AS regulation of jasmonate signaling pathway. These findings highlight the importance of AS regulation in the jasmonate signaling pathway, both alone and in collaboration with other regulators.

SIGNIFICANCE STATEMENT

By exploring alternative splicing, we demonstrate its regulation in the jasmonate signaling pathway alone or in collaboration with other posttranscriptional regulations such as nonsense and microRNA-mediated decay. A signal transduction network model for alternative splicing in jasmonate signaling pathway was generated, contributing to our understanding for this important, prevalent, but relatively unexplored regulatory mechanism in plants.

Identification and Functional Annotation of Genes Related to Horses' Performance: From GWAS to Post-GWAS

Simple Summary

It is assumed that the athletic performance of horses is influenced by a large number of genes; however, to date, not many genomic studies have been performed to identify candidate genes. In this study we performed a systematic review of genome-wide association studies followed by functional analyses aiming to identify the most candidate genes for horse performance. We were successful in identifying 669 candidate genes, from which we built biological process networks. Regulatory elements (transcription factors, TFs) of these genes were identified and used to build a gene–TF network. Genes and TFs presented in this study are suggested to play a role in the studied traits through biological processes related with exercise performance, for example, positive regulation of glucose metabolism, regulation of vascular endothelial growth factor production, skeletal system development, cellular response to fatty acids and cellular response to lipids. In general, this study may provide insights into the genetic architecture underlying horse performance in different breeds around the world.

Abstract

Integration of genomic data with gene network analysis can be a relevant strategy for unraveling genetic mechanisms. It can be used to explore shared biological processes between genes, as well as highlighting transcription factors (TFs) related to phenotypes of interest. Unlike other species, gene–TF network analyses have not yet been well applied to horse traits. We aimed to (1) identify candidate genes associated with horse performance via systematic review, and (2) build biological processes and gene–TF networks from the identified genes aiming to highlight the most candidate genes for horse performance. Our systematic review considered peer-reviewed articles using 20 combinations of keywords. Nine articles were selected and placed into groups for functional analysis via gene networks. A total of 669 candidate genes were identified. From that, gene networks of biological processes from each group were constructed, highlighting processes associated with horse performance (e.g., regulation of systemic arterial blood pressure by vasopressin and regulation of actin polymerization and depolymerization). Transcription factors associated with candidate genes were also identified. Based on their biological processes and evidence from the literature, we identified the main TFs related to horse performance traits, which allowed us to construct a gene–TF network highlighting TFs and the most candidate genes for horse performance.

The pulse of the tree is under genetic control: eucalyptus as a case study

The pulse of the tree (diurnal cycle of stem radius fluctuations) has been widely studied as a way of analyzing tree responses to the environment, including the phenotypic plasticity of tree-water relationships in particular. However, the genetic basis of this daily phenotype and its interplay with the environment remain largely unexplored. We characterized the genetic and environmental determinants of this response, by monitoring daily stem radius fluctuation (dSRF) on 210 trees from a Eucalyptus urophylla × E. grandis full-sib family over 2 years. The dSRF signal was broken down into hydraulic capacitance, assessed as the daily amplitude of shrinkage (DA), and net growth, estimated as the change in maximum radius between two consecutive days (ΔR). The environmental determinants of these two traits were clearly different: DA was positively correlated with atmospheric variables relating to water demand, while ΔR was associated with soil water content. The heritability for these two traits ranged from low to moderate over time, revealing a time-dependent or environment-dependent complex genetic determinism. We identified 686 and 384 daily quantitative trait loci (QTL) representing 32 and 31 QTL regions for DA and ΔR, respectively. The identification of gene networks underlying the 27 major genomics regions for both traits generated additional hypotheses concerning the biological mechanisms involved in response to water demand and supply. This study highlights that environmentally induced changes in daily stem radius fluctuation are genetically controlled in trees and suggests that these daily responses integrated over time shape the genetic architecture of mature traits.

Common Gene Modules Identified for Chicken Adiposity by Network Construction and Comparison

Excessive fat deposition can cause chicken health problem, and affect production efficiency by causing great economic losses to the industry. However, the molecular underpinnings of the complex adiposity trait remain elusive. In the current study, we constructed and compared the gene co-expression networks on four transcriptome profiling datasets, from two chicken lines under divergent selection for abdominal fat contents, in an attempt to dissect network compositions underlying adipose tissue growth and development. After functional enrichment analysis, nine network modules important to adipogenesis were discovered to be involved in lipid metabolism, PPAR and insulin signaling pathways, and contained hub genes related to adipogenesis, cell cycle, inflammation, and protein synthesis. Moreover, after additional functional annotation and network module comparisons, common sub-modules of similar functionality for chicken fat deposition were identified for different chicken lines, apart from modules specific to each chicken line. We further validated the lysosome pathway, and found TFEB and its downstream target genes showed similar expression patterns along with chicken preadipocyte differentiation. Our findings could provide novel insights into the genetic basis of complex adiposity traits, as well as human obesity and related metabolic diseases.

Microarray Profile of Long Noncoding RNA and Messenger RNA Expression in a Model of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by a deficiency in cognitive skills. Although long noncoding RNAs (lncRNAs) have been proposed as associated with AD, the aberrant lncRNAs expression and the co-expression of lncRNAs-mRNAs network in AD remains unclear. Therefore, in this study, lncRNA microarray was performed on the brain of APP/PS1 mice at different age, widely used as an AD mouse model, and on age-matched wide-type controls. Our results identified a total of 3306 lncRNAs and 2458 mRNAs as aberrantly expressed among AD mice at different age and their age-matched control. Gene Ontology and pathway analysis of the AD-related lncRNAs and mRNAs indicated that neuroinflammation-related and synaptic transmission signaling pathways represented the main enriched pathways. An lncRNA-mRNA-miRNA network between the differentially expressed transcripts was constructed. Moreover, an mRNA-miRNA network between both significantly dysregulated and highly conserved genes was also constructed, and among this network, the IGF1, P2RX7, TSPO, SERPINE1, EGFR, HMOX1, and NFE212 genes were predicted to play a role in the development of AD. In conclusion, this study illustrated the prognostic value of lncRNAs and mRNAs associated to AD pathology by microarray analysis and might provide potential novel biomarkers in the diagnosis and treatment of AD.

Weighted Gene Correlation Network Meta-Analysis Reveals Functional Candidate Genes Associated with High- and Sub-Fertile Reproductive Performance in Beef Cattle

Improved reproductive efficiency could lead to economic benefits for the beef industry, once the intensive selection pressure has led to a decreased fertility. However, several factors limit our understanding of fertility traits, including genetic differences between populations and statistical limitations. In the present study, the RNA-sequencing data from uterine samples of high-fertile (HF) and sub-fertile (SF) animals was integrated using co-expression network meta-analysis, weighted gene correlation network analysis, identification of upstream regulators, variant calling, and network topology approaches. Using this pipeline, top hub-genes harboring fixed variants (HF × SF) were identified in differentially co-expressed gene modules (DcoExp). The functional prioritization analysis identified the genes with highest potential to be key-regulators of the DcoExp modules between HF and SF animals. Consequently, 32 functional candidate genes (10 upstream regulators and 22 top hub-genes of DcoExp modules) were identified. These genes were associated with the regulation of relevant biological processes for fertility, such as embryonic development, germ cell proliferation, and ovarian hormone regulation. Additionally, 100 candidate variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs)) were identified within those genes. In the long-term, the results obtained here may help to reduce the frequency of subfertility in beef herds, reducing the associated economic losses caused by this condition.

Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies

Developmental genes can be coopted to generate evolutionary novelties by changing their spatial regulation. However, developmental genes seldom act independently, but rather work in a gene regulatory network (GRN). How is it possible to recruit a single gene from a whole GRN? What are the properties that allow parallel cooptions of the same genes during evolution? Here, we show that a novel engrailed gene expression underlies a novel wing color pattern in flies. We show that cooption is facilitated 1) because of GRN flexibility over development and 2) because every single gene of the GRN has its own functional time window. We suggest these two temporal properties could explain why the same gene can be independently recruited several times during evolution.

Organisms have evolved endless morphological, physiological, and behavioral novel traits during the course of evolution. Novel traits were proposed to evolve mainly by orchestration of preexisting genes. Over the past two decades, biologists have shown that cooption of gene regulatory networks (GRNs) indeed underlies numerous evolutionary novelties. However, very little is known about the actual GRN properties that allow such redeployment. Here we have investigated the generation and evolution of the complex wing pattern of the fly Samoaia leonensis. We show that the transcription factor Engrailed is recruited independently from the other players of the anterior–posterior specification network to generate a new wing pattern. We argue that partial cooption is made possible because 1) the anterior–posterior specification GRN is flexible over time in the developing wing and 2) this flexibility results from the fact that every single gene of the GRN possesses its own functional time window. We propose that the temporal flexibility of a GRN is a general prerequisite for its possible cooption during the course of evolution.