Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

Large-scale transcriptomic and genomic analyses reveal a novel functional gene SERPINB6 for chicken carcass traits

BACKGROUND

Carcass traits are crucial indicators of meat production efficiency. However, the molecular regulatory mechanisms associated with these traits remain unclear.

RESULTS

In this study, we conducted comprehensive transcriptomic and genomic analyses on 399 Tiannong partridge chickens to identify key genes and variants associated with carcass traits and to elucidate the underlying regulatory mechanisms. Based on association analyses with the elastic net (EN) model, we identified 12 candidate genes (AMY1A, AP3B2, CEBPG, EEF2, EIF4EBP1, FGFR1, FOXD3, GOLM1, LOC107052698, PABPC1, SERPINB6 and TBC1D16) for 4 carcass-related traits, namely live weight, dressed weight, eviscerated weight, and breast muscle weight. SERPINB6 was identified as the only overlapping gene by 3 analyses, EN model analysis, weighted gene co-expression network analysis and differential expression analysis. Cell-level experiments confirmed that SERPINB6 promotes the proliferation of chicken DF1 cells and primary myoblasts. Further expression genome-wide association study and association analysis indicated that rs317934171 is the critical site that enhances SERPINB6 expression. Furthermore, a dual-luciferase reporter assay proved that gga-miR-1615 targets the 3'UTR of SERPINB6.

CONCLUSIONS

Collectively, our findings reveal that SERPINB6 serves as a novel gene for chicken carcass traits by promoting fibroblast and myoblast proliferation. Additionally, the downstream variant rs317934171 regulates SERPINB6 expression. These results identify a new target gene and molecular marker for the molecular mechanisms of chicken carcass traits.

Co-expression network analysis and identification of core genes in the interaction between wheat and Puccinia striiformis f. sp. tritici

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns.

CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies.

Gene coexpression network analysis reveals perirenal adipose tissue as an important target of prenatal malnutrition in sheep

We have previously demonstrated that pre- and early postnatal malnutrition in sheep induced depot- and sex-specific changes in adipose morphological features, metabolic outcomes, and transcriptome in adulthood, with perirenal (PER) as the major target followed by subcutaneous (SUB) adipose tissue. We aimed to identify coexpressed and hub genes in SUB and PER to identify the underlying molecular mechanisms contributing to the early nutritional programming of adipose-related phenotypic outcomes. Transcriptomes of SUB and PER of male and female adult sheep with different pre- and early postnatal nutrition histories were used to construct networks of coexpressed genes likely to be functionally associated with pre- and early postnatal nutrition histories and phenotypic traits using weighted gene coexpression network analysis. The modules from PER showed enrichment of cell cycle regulation, gene expression, transmembrane transport, and metabolic processes associated with both sexes' prenatal nutrition. In SUB (only males), a module of enriched adenosine diphosphate metabolism and development correlated with prenatal nutrition. Sex-specific module enrichments were found in PER, such as chromatin modification in the male network but histone modification and mitochondria- and oxidative phosphorylation-related functions in the female network. These sex-specific modules correlated with prenatal nutrition and adipocyte size distribution patterns. Our results point to PER as a primary target of prenatal malnutrition compared to SUB, which played only a minor role. The prenatal programming of gene expression and cell cycle, potentially through epigenetic modifications, might be underlying mechanisms responsible for observed changes in PER expandability and adipocyte-size distribution patterns in adulthood in both sexes.

RNA-Seq Identified Putative Genes Conferring Photosynthesis and Root Development of Melon under Salt Stress

Melon is an important fruit crop of the Cucurbitaceae family that is being cultivated over a large area in China. Unfortunately, salt stress has crucial effects on crop plants and damages photosynthesis, membranal lipid components, and hormonal metabolism, which leads to metabolic imbalance and retarded growth. Herein, we performed RNA-seq analysis and a physiological parameter evaluation to assess the salt-induced stress impact on photosynthesis and root development activity in melon. The endogenous quantification analysis showed that the significant oxidative damage in the membranal system resulted in an increased ratio of non-bilayer/bilayer lipid (MGDG/DGDG), suggesting severe irregular stability in the photosynthetic membrane. Meanwhile, root development was slowed down by a superoxidized membrane system, and downregulated genes showed significant contributions to cell wall biosynthesis and IAA metabolism. The comparative transcriptomic analysis also exhibited that major DEGs were more common in the intrinsic membrane component, photosynthesis, and metabolism. These are all processes that are usually involved in negative responses. Further, the WGCN analysis revealed the involvement of two main network modules: the thylakoid membrane and proteins related to photosystem II. The qRT-PCR analysis exhibited that two key genes (MELO3C006053.2 and MELO3C023596.2) had significant variations in expression profiling at different time intervals of salt stress treatments (0, 6, 12, 24, and 48 h), which were also consistent with the RNA-seq results, denoting the significant accuracy of molecular dataset analysis. In summary, we performed an extensive molecular and metabolic investigation to check the salt-stress-induced physiological changes in melon and proposed that the PSII reaction centre may likely be the primary stress target.

Insight into increased risk of portal vein thrombosis in nonalcoholic fatty liver disease

Nonalcoholic fatty liver disease (NAFLD) is one of the leading chronic liver diseases with increased morbidity and mortality rates for extrahepatic diseases (including cardiovascular disease, portal vein thrombosis, etc.). There is an increased risk of thrombosis in both the portal and systemic circulation in patients with NAFLD, independent of traditional liver cirrhosis. However, increased portal pressure, the most critical factor, is frequently observed in NAFLD patients, predisposing them to portal vein thrombosis (PVT). It has been reported that there is an 8.5% incidence of PVT among patients with non-cirrhotic NAFLD in a prospective cohort study. Based on the prothrombotic status of NAFLD itself, patients combined with cirrhosis may accelerate the development of PVT and lead to a poor prognosis. Moreover, PVT has been shown to complicate the procedure and adversely affect the outcome during liver transplantation surgery. NAFLD is in a prothrombotic state, and its underlying mechanisms have not been fully understood so far. Particularly noteworthy is that gastroenterologists currently overlook the higher risk of PVT in NAFLD. We investigate the pathogenesis of NAFLD complicated with PVT from the perspective of primary, secondary, and tertiary hemostasis, and also summarize relevant studies in humans. Some treatment options that may affect NAFLD and its PVT are also explored to improve patient-oriented outcomes.

Chicken adaptive response to nutrient density: immune function change revealed by transcriptomic analysis of spleen

Feed accounts for the largest portion (65-70%) of poultry production costs. The feed formulation is generally improved to efficiently meet the nutritional needs of chickens by reducing the proportion of crude protein (CP) and metabolizable energy (ME) levels in the diet. Although many studies have investigated the production performance during dietary restriction, there is a lack of research on the mechanisms by which immune cell function is altered. This study examined the effects of ME and CP restriction in the chicken diet on serum immunoglobulins and expression of immune function genes in spleen. Changes in serum immunoglobulins and immune-related gene expression were analyzed in 216 YS-909 broilers fed with 9 different dietary treatments, including experimental treatment diets containing low, standard, and high levels of ME or CP in the diet. At 42 days of age, serum immunoglobulins and expression of spleen immune genes in 6 female chickens selected randomly from each dietary treatment (3×3 factorial arrangement) group were measured by enzyme-linked immunosorbent assay (ELISA) and transcriptomic analysis using RNA sequencing, respectively. The results showed that the IgM level in the low ME group chickens was significantly (p < 0.05) lower than that in other groups. In addition, immune-related genes, such as MX1, USP18, TLR4, IFNG and IL18 were significantly upregulated when the dietary nutrient density was reduced, which may put the body in an inflammatory state. This study provided general information on the molecular mechanism of the spleen immune response to variable nutrient density.

RNA Sequencing of Intestinal Enterocytes Pre- and Post-Roux-en-Y Gastric Bypass Reveals Alteration in Gene Expression Related to Enterocyte Differentiation, Restitution, and Obesity with Regulation by Schlafen 12

BACKGROUND

The intestinal lining renews itself in a programmed fashion that can be affected by adaptation to surgical procedures such as gastric bypass.

METHODS

To assess adaptive mechanisms in the human intestine after Roux-en-Y gastric bypass (RYGB), we biopsied proximal jejunum at the anastomotic site during surgery to establish a baseline and endoscopically re-biopsied the same area 6-9 months after bypass for comparison. Laser microdissection was performed on pre- and post-RYGB biopsies to isolate enterocytes for RNA sequencing.

RESULTS

RNA sequencing suggested significant decreases in gene expression associated with G2/M DNA damage checkpoint regulation of the cell cycle pathway, and significant increases in gene expression associated with the CDP-diacylglycerol biosynthesis pathway TCA cycle II pathway, and pyrimidine ribonucleotide salvage pathway after RYGB. Since Schlafen 12 (SLFN12) is reported to influence enterocytic differentiation, we stained mucosa for SLFN12 and observed increased SLFN12 immunoreactivity. We investigated SLFN12 overexpression in HIEC-6 and FHs 74 Int intestinal epithelial cells and observed similar increased expression of the following genes that were also increased after RYGB: HES2, CARD9, SLC19A2, FBXW7, STXBP4, SPARCL1, and UTS.

CONCLUSIONS

Our data suggest that RYGB promotes SLFN12 protein expression, cellular mechanism and replication pathways, and genes associated with differentiation and restitution (HES2, CARD9, SLC19A2), as well as obesity-related genes (FBXW7, STXBP4, SPARCL1, UTS).

Genetic architecture and selection of Anhui autochthonous pig population revealed by whole genome resequencing

The genetic resources among pigs in Anhui Province are diverse, but their value and potential have yet to be discovered. To illustrate the genetic diversity and population structure of the Anhui pigs population, we resequenced the genome of 150 pigs from six representative Anhui pigs populations and analyzed this data together with the sequencing data from 40 Asian wild boars and commercial pigs. Our results showed that Anhui pigs were divided into two distinct types based on ancestral descent: Wannan Spotted pig (WSP) and Wannan Black pig (WBP) origins from the same ancestor and the other four populations origins from another ancestor. We also identified several potential selective sweep regions associated with domestication characteristics among Anhui pigs, including reproduction-associated genes (CABS1, INSL6, MAP3K12, IGF1R, INSR, LIMK2, PATZ1, MAPK1), lipid- and meat-related genes (SNX19, MSTN, MC5R, PRKG1, CREBBP, ADCY9), and ear size genes (MSRB3 and SOX5). Therefore, these findings expand the catalogue and how these genetic differences among pigs and this newly generated data will be a valuable resource for future genetic studies and for improving genome-assisted breeding of pigs and other domesticated animals.

Using Machine Learning to Identify Biomarkers Affecting Fat Deposition in Pigs by Integrating Multisource Transcriptome Information

Fat deposition in pigs is not only closely related to pig production efficiency and pork quality but also an ideal model for human obesity. Transcriptome sequencing is widely used to study fat deposition. However, due to small sample sizes, high false positive rates, and poor consistency of results from different studies, new strategies are urgently needed. Machine learning, a new analysis method, can effectively fit complex data and accurately identify samples and genes. In this study, 36 samples of adipose tissue, muscle tissue, and liver tissue were collected from Songliao black pigs and Landrace pigs, and the mRNA of all the samples was sequenced. In addition, we collected transcriptome data for 64 samples in the GEO database from four different sources. After standardization and imputation of missing values in the data set comprising 100 samples, traditional differential expression analysis was carried out, and different numbers of expressed genes were selected as features for the training model of eight machine learning methods. In the 1000 replications of fourfold cross validation with 100 samples, AdaBoost performed best, with an average prediction accuracy greater than 93% and the highest mean area under the curve in predicting the high- and low-fat content groups among the eight ML methods. According to their performance-based ranks inferred by AdaBoost, 12 genes related to fat deposition were identified; among them, FASN and APOD were specifically expressed in adipose tissue, and APOA1 was specifically expressed in the liver, which could be important candidate biomarkers affecting fat deposition.

Comparative Transcriptome Analysis Identified Key Pathways and Genes Regulating Differentiated Stigma Color in Melon (Cucumis melo L.)

Stigma color is an important morphological trait in many flowering plants. Visual observations in different field experiments have shown that a green stigma in melons is more attractive to natural pollinators than a yellow one. In the current study, we evaluated the characterization of two contrasted melon lines (MR-1 with a green stigma and M4-7 with a yellow stigma). Endogenous quantification showed that the chlorophyll and carotenoid content in the MR-1 stigmas was higher compared to the M4-7 stigmas. The primary differences in the chloroplast ultrastructure at different developmental stages depicted that the stigmas of both melon lines were mainly enriched with granum, plastoglobulus, and starch grains. Further, comparative transcriptomic analysis was performed to identify the candidate pathways and genes regulating melon stigma color during key developmental stages (S1–S3). The obtained results indicated similar biological processes involved in the three stages, but major differences were observed in light reactions and chloroplast pathways. The weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) uncovered a “black” network module (655 out of 5302 genes), mainly corresponding to light reactions, light harvesting, the chlorophyll metabolic process, and the chlorophyll biosynthetic process, and exhibited a significant contribution to stigma color. Overall, the expression of five key genes of the chlorophyll synthesis pathway—CAO (MELO03C010624), CHLH (MELO03C007233), CRD (MELO03C026802), HEMA (MELO03C011113), POR (MELO03C016714)—were checked at different stages of stigma development in both melon lines using quantitative real time polymerase chain reaction (qRT-PCR). The results exhibited that the expression of these genes gradually increased during the stigma development of the MR-1 line but decreased in the M4-7 line at S2. In addition, the expression trends in different stages were the same as RNA-seq, indicating data accuracy. To sum up, our research reveals an in-depth molecular mechanism of stigma coloration and suggests that chlorophyll and related biological activity play an important role in differentiating melon stigma color.

Identification of key sex-specific pathways and genes in the subcutaneous adipose tissue from pigs using WGCNA method

Background

Adipose tissues (ATs), including visceral ATs (VATs) and subcutaneous ATs (SATs), are crucial for maintaining energy and metabolic homeostasis. SATs have been found to be closely related to obesity and obesity-induced metabolic disease. Some studies have shown a significant association between subcutaneous fat metabolism and sexes. However, the molecular mechanisms for this association are still unclear. Here, using the pig as a model, we investigated the systematic association between the subcutaneous fat metabolism and sexes, and identified some key sex-specific pathways and genes in the SATs from pigs.

Results

The results revealed that 134 differentially expressed genes (DEGs) were identified in female and male pigs from the obese group. A total of 17 coexpression modules were detected, of which six modules were significantly correlated with the sexes (P < 0.01). Among the significant modules, the greenyellow module (cor = 0.68, P < 9e-06) and green module (cor = 0.49, P < 0.003) were most significantly positively correlated with the male and female, respectively. Functional analysis showed that one GO term and four KEGG pathways were significantly enriched in the greenyellow module while six GO terms and six KEGG pathways were significantly enriched in the green module. Furthermore, a total of five and two key sex-specific genes were identified in the two modules, respectively. Two key sex-specific pathways (Ras-MAPK signaling pathway and type I interferon response) play an important role in the SATs of males and females, respectively.

Conclusions

The present study identified some key sex-specific pathways and genes in the SATs from pigs, which provided some new insights into the molecular mechanism of being involved in fat formation and immunoregulation between pigs of different sexes. These findings may be beneficial to breeding in the pig industry and obesity treatment in medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01054-w.

Gene co-expression networks contributing to reproductive development in Holstein-Friesian bull calves

Enhanced early life nutrition stimulates the functionality of the hypothalamic-pituitary-testicular (HPT) biochemical signalling axis, resulting in precocious reproductive development in bull calves. Additionally, there is evidence that peptides and hormones produced within adipose tissue depots are also central in mediating the effect of metabolic status with reproductive development. The objective of this study was to undertake gene co-expression analyses on transcriptional data of the HPT and adipose tissues derived from bull calves fed contrasting planes of nutrition up to 18 weeks of life. The relationship between networks of co-expressed genes in each tissue dataset with calf phenotypic data was also assessed using a Pearson correlation analysis. Phenotypic data were related to metabolic status (systemic concentrations of insulin, leptin, adiponectin and IGF-1) reproductive development (systemic concentrations of testosterone, FSH and LH) and markers of testicular development (seminiferous tubule diameter, seminiferous tubule lumen score, spermatogenic cells and Sertoli cells). In the hypothalamus, gene co-expression networks involved in biochemical signalling processes related to gonadotropin-releasing hormone (GnRH) secretion were positively associated (P < 0.05) with systemic concentrations of IGF-1 and insulin. Similarly, a network of gene transcripts involved in GnRH signalling in the anterior pituitary was positively associated (P < 0.05) with systemic concentrations of LH. In the testes and adipose tissues, networks of co-expressed genes implicated in cholesterol and fatty acid biosynthesis were positively associated (P < 0.05) with lumen score, Sertoli cell number, and stage of spermatogenesis. Additionally, gene co-expression networks significantly associated (P < 0.05) with both metabolic and reproductive trait data were found to be enriched (P < 0.05) for biological pathways related to energy production, cellular growth and proliferation, GnRH signalling and cholesterol biosynthesis across multiple tissues examined. Results from this study highlight networks of co-expressed genes directly associated with markers of enhanced metabolic status and subsequent earlier reproductive development. Furthermore, genes involved in biological processes mentioned above may hold potential for informing genomic selection breeding programmes for the selection of calves capable of displaying earlier reproductive development as a consequence of enhanced dietary intake.

Identification of Potential Candidate Genes From Co-Expression Module Analysis During Preadipocyte Differentiation in Landrace Pig

Preadipocyte differentiation plays an important role in lipid deposition and affects fattening efficiency in pigs. In the present study, preadipocytes isolated from the subcutaneous adipose tissue of three Landrace piglets were induced into mature adipocytes in vitro. Gene clusters associated with fat deposition were investigated using RNA sequencing data at four time points during preadipocyte differentiation. Twenty-seven co-expression modules were subsequently constructed using weighted gene co-expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed three modules (blue, magenta, and brown) as being the most critical during preadipocyte differentiation. Based on these data and our previous differentially expressed gene analysis, angiopoietin-like 4 (ANGPTL4) was identified as a key regulator of preadipocyte differentiation and lipid metabolism. After inhibition of ANGPTL4, the expression of adipogenesis-related genes was reduced, except for that of lipoprotein lipase (LPL), which was negatively regulated by ANGPTL4 during preadipocyte differentiation. Our findings provide a new perspective to understand the mechanism of fat deposition.

Emerging Roles of Non-Coding RNAs in the Feed Efficiency of Livestock Species

A global population of already more than seven billion people has led to an increased demand for food and water, and especially the demand for meat. Moreover, the cost of feed used in animal production has also increased dramatically, which requires animal breeders to find alternatives to reduce feed consumption. Understanding the biology underlying feed efficiency (FE) allows for a better selection of feed-efficient animals. Non-coding RNAs (ncRNAs), especially micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs), play important roles in the regulation of bio-logical processes and disease development. The functions of ncRNAs in the biology of FE have emerged as they participate in the regulation of many genes and pathways related to the major FE indicators, such as residual feed intake and feed conversion ratio. This review provides the state of the art studies related to the ncRNAs associated with FE in livestock species. The contribution of ncRNAs to FE in the liver, muscle, and adipose tissues were summarized. The research gap of the function of ncRNAs in key processes for improved FE, such as the nutrition, heat stress, and gut–brain axis, was examined. Finally, the potential uses of ncRNAs for the improvement of FE were discussed.

Gene Co-Expression Network Analysis Identifies Vitamin D-Associated Gene Modules in Adult Normal Rectal Epithelium Following Supplementation

Colorectal cancer (CRC) is a common, multifactorial disease. While observational studies have identified an association between lower vitamin D and higher CRC risk, supplementation trials have been inconclusive and the mechanisms by which vitamin D may modulate CRC risk are not well understood. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify modules present after vitamin D supplementation (when plasma vitamin D level was sufficient) which were absent before supplementation, and then to identify influential genes in those modules. The transcriptome from normal rectal mucosa biopsies of 49 individuals free from CRC were assessed before and after 12 weeks of 3200IU/day vitamin D (Fultium-D3) supplementation using paired-end total RNAseq. While the effects on expression patterns following vitamin D supplementation were subtle, WGCNA identified highly correlated genes forming gene modules. Four of the 17 modules identified in the post-vitamin D network were not preserved in the pre-vitamin D network, shedding new light on the biochemical impact of supplementation. These modules were enriched for GO terms related to the immune system, hormone metabolism, cell growth and RNA metabolism. Across the four treatment-associated modules, 51 hub genes were identified, with enrichment of 40 different transcription factor motifs in promoter regions of those genes, including VDR:RXR. Six of the hub genes were nominally differentially expressed in studies of vitamin D effects on adult normal mucosa organoids: LCN2, HLA-C, AIF1L, PTPRU, PDE4B and IFI6. By taking a gene-correlation network approach, we have described vitamin D induced changes to gene modules in normal human rectal epithelium in vivo, the target tissue from which CRC develops.

Transcriptomic Analysis of Fusarium oxysporum Stress-Induced Pathosystem and Screening of Fom-2 Interaction Factors in Contrasted Melon Plants

Fusarium wilt is one of the most destructive and less controllable diseases in melon, which is usually caused by fusarium oxysporum. In this study, transcriptome sequencing and Yeast Two-Hybrid (Y2H) methods were used for quantification of differentially expressed genes (DEGs) involved in fusarium oxysporum (f. sp. melonis race 1) stress-induced mechanisms in contrasted melon varieties (M4-45 "susceptible" and MR-1 "resistant"). The interaction factors of Fom-2 resistance genes were also explored in response to the plant-pathogen infection mechanism. Transcriptomic analysis exhibited total 1,904 new genes; however, candidate DEGs analysis revealed a total of 144 specific genes (50 upregulated and 94 downregulated) for M4-45 variety and 104 specific genes (71 upregulated and 33 downregulated) for MR-1 variety, respectively. The analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway depicted some candidate DEGs, including Phenylalanine metabolism, phenylpropane biosynthesis, plants-pathogen interaction, and signal transduction of plant hormones, which were mainly involved in disease resistance metabolic pathways. The weighted gene co-expression network analysis (WGCNA) analysis revealed a strong correlation module and exhibited the disease resistance-related genes encoding course proteins, transcription factors, protein kinase, benzene propane biosynthesis path, plants-pathogen interaction pathway, and glutathione S-transferase. Meanwhile, the resistance-related specific genes expression was relatively abundant in MR-1 compared to the M4-45, and cell wall-associated receptor kinases (MELO3C008452 and MELO3C008453), heat shock protein (Cucumis_melo_newGene_172), defensin-like protein (Cucumis_melo_newGene_5490), and disease resistance response protein (MELO3C016325), activator response protein (MELO3C021623), leucine-rich repeat receptor protein kinase (MELO3C024412), lactyl glutathione ligase (Cucumis_melo_newGene_36), and unknown protein (MELO3C007588) were persisted by exhibiting the upregulated expressions. At the transcription level, the interaction factors between the candidate genes in response to the fusarium oxysporum induced stress, and Y2H screening signified the main contribution of MYB transcription factors (MELO3C009678 and MELO3C014597), BZIP (MELO3C011839 and MELO3C019349), unknown proteins, and key enzymes in the ubiquitination process (4XM334FK014). The candidate genes were further verified in exogenously treated melon plants with f. oxysporum (Fom-2, Race 1), Abscisic acid (ABA), Methyl Jasmonite (MeJA), and Salicylic acid (SA), using the fluorescence quantitative polymerase chain reaction (qRT-PCR) analysis. The overall expression results indicated that the SA signal pathway is involved in effective regulation of the Fom-2 gene activity.

An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs

Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig's muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10^-4) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10^-5), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality.

Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects

Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.

Transcriptome Reveals Allele Contribution to Heterosis in Maize

Heterosis, which has greatly increased maize yields, is associated with gene expression patterns during key developmental stages that enhance hybrid phenotypes relative to parental phenotypes. Before heterosis can be more effectively used for crop improvement, hybrid maize developmental gene expression patterns must be better understood. Here, six maize hybrids, including the popular hybrid Zhengdan958 (ZC) from China, were studied. Maize hybrids created in-house were generated using an incomplete diallel cross (NCII)-based strategy from four elite inbred parental lines. Differential gene expression (DEG) profiles corresponding to three developmental stages revealed that hybrid partial expression patterns exhibited complementarity of expression of certain parental genes, with parental allelic expression patterns varying both qualitatively and quantitatively in hybrids. Single-parent expression (SPE) and parent-specific expression (PSE) types of qualitative variation were most prevalent, 43.73 and 41.07% of variation, respectively. Meanwhile, negative super-dominance (NSD) and positive super-dominance (PSD) types of quantitative variation were most prevalent, 31.06 and 24.30% of variation, respectively. During the early reproductive growth stage, the gene expression pattern differed markedly from other developmental stage patterns, with allelic expression patterns during seed development skewed toward low-value parental alleles in hybrid seeds exhibiting significant quantitative variation-associated superiority. Comparisons of qualitative gene expression variation rates between ZC and other hybrids revealed proportions of SPE-DEGs (41.36%) in ZC seed DEGs that significantly exceeded the average proportion of SPE-DEGs found in seeds of other hybrids (28.36%). Importantly, quantitative gene expression variation rate comparisons between ZC and hybrids, except for transgressive expression, revealed that the ZC rate exceeded the average rate for other hybrids, highlighting the importance of partial gene expression in heterosis. Moreover, enriched ZC DEGs exhibiting distinct tissue-specific expression patterns belonged to four biological pathways, including photosynthesis, plant hormone signal transduction, biology metabolism and biosynthesis. These results provide valuable technical insights for creating hybrids exhibiting strong heterosis.

Identification of key genes affecting porcine fat deposition based on co-expression network analysis of weighted genes

BACKGROUND

Fat deposition is an important economic consideration in pig production. The amount of fat deposition in pigs seriously affects production efficiency, quality, and reproductive performance, while also affecting consumers' choice of pork. Weighted gene co-expression network analysis (WGCNA) is effective in pig genetic studies. Therefore, this study aimed to identify modules that co-express genes associated with fat deposition in pigs (Songliao black and Landrace breeds) with extreme levels of backfat (high and low) and to identify the core genes in each of these modules.

RESULTS

We used RNA sequences generated in different pig tissues to construct a gene expression matrix consisting of 12,862 genes from 36 samples. Eleven co-expression modules were identified using WGCNA and the number of genes in these modules ranged from 39 to 3,363. Four co-expression modules were significantly correlated with backfat thickness. A total of 16 genes (RAD9A, IGF2R, SCAP, TCAP, SMYD1, PFKM, DGAT1, GPS2, IGF1, MAPK8, FABP, FABP5, LEPR, UCP3, APOF, and FASN) were associated with fat deposition.

CONCLUSIONS

RAD9A, TCAP, SMYD1, PFKM, GPS2, and APOF were the key genes in the four modules based on the degree of gene connectivity. Combining these results with those from differential gene analysis, SMYD1 and PFKM were proposed as strong candidate genes for body size traits. This study explored the key genes that regulate porcine fat deposition and lays the foundation for further research into the molecular regulatory mechanisms underlying porcine fat deposition.

FOSL2 Is Involved in the Regulation of Glycogen Content in Chicken Breast Muscle Tissue

The glycogen content in muscle of livestock and poultry animals affects the homeostasis of their body, growth performance, and meat quality after slaughter. FOS-like 2, AP-1 transcription factor subunit (FOSL2) was identified as a candidate gene related to muscle glycogen (MG) content in chicken in our previous study, but the role of FOSL2 in the regulation of MG content remains to be elucidated. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed on differentially expressed genes (DEGs) in breast muscle tissues from the high-MG-content (HMG) group and low-MG-content (LMG) group of Jingxing yellow chickens. Analysis of the 1,171 DEGs (LMG vs. HMG) identified, besides FOSL2, some additional genes related to MG metabolism pathway, namely PRKAG3, CEBPB, FOXO1, AMPK, and PIK3CB. Additionally, WGCNA revealed that FOSL2, CEBPB, MAP3K14, SLC2A14, PPP2CA, SLC38A2, PPP2R5E, and other genes related to the classical glycogen metabolism in the same coexpressed module are associated with MG content. Also, besides finding that FOSL2 expression is negatively correlated with MG content, a possible interaction between FOSL2 and CEBPB was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes) database. Furthermore, we investigated the effects of lentiviral overexpression of FOSL2 on the regulation of the glycogen content in vitro, and the result indicated that FOSL2 decreases the glycogen content in DF1 cells. Collectively, our results confirm that FOSL2 has a key role in the regulation of the MG content in chicken. This finding is helpful to understand the mechanism of MG metabolism regulation in chicken and provides a new perspective for the production of high-quality broiler and the development of a comprehensive nutritional control strategy.

An ABA-flavonoid relationship contributes to the differences in drought resistance between different sea buckthorn subspecies

Drought is the most severe abiotic stress and hinders the normal growth and development of plants. Sea buckthorn (Hippophae rhamnoides Linn.) is a typical drought-resistant tree species. In this study, the leaves of the H. rhamnoides ssp. sinensis ('FN') and H. rhamnoides ssp. mongolica ('XY') were selected during drought-recovery cycles for RNA sequencing, and physiological and biochemical analyses. The results revealed that drought stress significantly decreased leaf water potential, net photosynthetic rate and stomatal conductance in both sea buckthorn subspecies. Similarly, the contents of flavone, flavonol, isoflavone and flavanone significantly decreased under drought stress in 'XY'. Conversely, in 'FN', the flavone and abscisic acid (ABA) contents were significantly higher under drought stress and recovered after rehydration. Meanwhile, 4618 and 6100 differentially expressed genes (DEGs) were identified under drought stress in 'FN' and 'XY', respectively. In total, 5164 DEGs were observed in the comparison between 'FN' and 'XY' under drought stress. This was more than the 3821 and 3387 DEGs found when comparing the subspecies under control and rehydration conditions, respectively. These DEGs were mainly associated with carotenoid biosynthesis, flavonoid biosynthesis, photosynthesis and plant hormone signal transduction. Six hub DEGs (ABCG5, ABCG22, ABCG32, ABCG36, ABF2 and PYL4) were identified to respond to drought stress based on weighted gene co-expression network analysis and Basic Local Alignment Search Tool (BLAST) analysis using DroughtDB. These six DEGs were annotated to play roles in the ABA-dependent signaling pathway. Sixteen RNA sequencing results involving eight genes and similar expression patterns (12/16) were validated using quantitative real-time Polymerase Chain Reaction (PCR). The biochemical and molecular mechanisms underlying the regulation of drought responses by ABA and flavonoids in sea buckthorn were clarified. In this study, gene co-expression networks were constructed, and the results suggested that the mutual regulation of ABA and flavonoid signaling contributed to the difference in drought resistance between the different sea buckthorn subspecies.

Transcriptomics analysis of differentially expressed genes in subcutaneous and perirenal adipose tissue of sheep as affected by their pre- and early postnatal malnutrition histories

BACKGROUND

Early life malnutrition is known to target adipose tissue with varying impact depending on timing of the insult. This study aimed to identify differentially expressed genes in subcutaneous (SUB) and perirenal (PER) adipose tissue of 2.5-years old sheep to elucidate the biology underlying differential impacts of late gestation versus early postnatal malnutrition on functional development of adipose tissues. Adipose tissues were obtained from 37 adult sheep born as twins to dams fed either NORM (fulfilling energy and protein requirements), LOW (50% of NORM) or HIGH (110% of protein and 150% of energy requirements) diets in the last 6-weeks of gestation. From day 3 to 6 months of age, lambs were fed high-carbohydrate-high-fat (HCHF) or moderate low-fat (CONV) diets, and thereafter the same moderate low-fat diet.

RESULTS

The gene expression profile of SUB in the adult sheep was not affected by the pre- or early postnatal nutrition history. In PER, 993 and 186 differentially expressed genes (DEGs) were identified in LOW versus HIGH and NORM, respectively, but no DEG was found between HIGH and NORM. DEGs identified in the mismatched pre- and postnatal nutrition groups LOW-HCHF (101) and HIGH-HCHF (192) were largely downregulated compared to NORM-CONV. Out of 831 DEGs, 595 and 236 were up- and downregulated in HCHF versus CONV, respectively. The functional enrichment analyses revealed that transmembrane (ion) transport activities, motor activities related to cytoskeletal and spermatozoa function (microtubules and the cytoskeletal motor protein, dynein), and responsiveness to the (micro) environmental extracellular conditions, including endocrine and nervous stimuli were enriched in the DEGs of LOW versus HIGH and NORM. We confirmed that mismatched pre- and postnatal feeding was associated with long-term programming of adipose tissue remodeling and immunity-related pathways. In agreement with phenotypic measurements, early postnatal HCHF feeding targeted pathways involved in kidney cell differentiation, and mismatched LOW-HCHF sheep had specific impairments in cholesterol metabolism pathways.

CONCLUSIONS

Both pre- and postnatal malnutrition differentially programmed (patho-) physiological pathways with implications for adipose functional development associated with metabolic dysfunctions, and PER was a major target.

Identification of a Core Module for Bone Mineral Density through the Integration of a Co-expression Network and GWAS Data

The "omnigenic" model of the genetic architecture of complex traits proposed two categories of causal genes: core and peripheral. Core genes are hypothesized to directly regulate disease and may serve as therapeutic targets. Using a cell-type- and time-point-specific gene co-expression network for mineralizing osteoblasts, we identify a co-expression module enriched for genes implicated by bone mineral density (BMD) genome-wide association studies (GWASs), correlated with in vitro osteoblast mineralization and associated with skeletal phenotypes in human monogenic disease and mouse knockouts. Four genes from this module (B4GALNT3, CADM1, DOCK9, and GPR133) are located within the BMD GWAS loci with colocalizing expression quantitative trait loci (eQTL) and exhibit altered BMD in mouse knockouts, suggesting that they are causal genetic drivers of BMD in humans. Our network-based approach identifies a "core" module for BMD and provides a resource for expanding our understanding of the genetics of bone mass.

Positive selection signatures in Anqing six-end-white pig population based on reduced-representation genome sequencing data

Anqing six-end-white (AQ) pig performs well on resistance to coarse fodder and disease, reproduction and meat quality, offering high potential for exploitation. Environmental conditions and strict selections from local farmers have cultivated the AQ pig to be an outstanding and unique local pig breed. Thus we aim to detect genetic positive selection signatures within the AQ pig population to explore underlying genetic mechanisms. A relative extended haplotype homozygosity (REHH) test was performed in the population of 79 AQ pigs to seek evidence demonstrating that selective actions have left an imprint on the whole genome. In total, 430 500 REHH tests were performed on 53 067 core regions with average REHH tests of 8.11, average lengths of 11.50 kb and an overall length of 610.38 Mb which accounted for 26.94% of the whole genome. Finally, a total of 1819 core haplotypes (P < 0.01) and 586 candidate genes were obtained. These genes were mainly related to meat quality (MYOG, SNX19), resistance to disease (CRISPLD2, CD14) and reproduction traits (ERBB2, NRP2). A panel of genes within the 30 top significant REHH tests was mainly categorized to traits of meat quality and disease resistance. Among 13 KEGG pathways, MAPK, GnRH and Oxytocin signaling pathways, associated with the biological processes of crucial economic traits, were noteworthy. The excellent characteristics of the AQ pig benefited from the combination of natural and human factors. We provide a sketch map that shows the distribution of selection footprints on the whole genome of AQ pig and found potential genes for future studies.

Exploring chromatin conformation and gene co-expression through graph embedding

MOTIVATION

The relationship between gene co-expression and chromatin conformation is of great biological interest. Thanks to high-throughput chromosome conformation capture technologies (Hi-C), researchers are gaining insights on the tri-dimensional organization of the genome. Given the high complexity of Hi-C data and the difficult definition of gene co-expression networks, the development of proper computational tools to investigate such relationship is rapidly gaining the interest of researchers. One of the most fascinating questions in this context is how chromatin topology correlates with gene co-expression and which physical interaction patterns are most predictive of co-expression relationships.

RESULTS

To address these questions, we developed a computational framework for the prediction of co-expression networks from chromatin conformation data. We first define a gene chromatin interaction network where each gene is associated to its physical interaction profile; then, we apply two graph embedding techniques to extract a low-dimensional vector representation of each gene from the interaction network; finally, we train a classifier on gene embedding pairs to predict if they are co-expressed. Both graph embedding techniques outperform previous methods based on manually designed topological features, highlighting the need for more advanced strategies to encode chromatin information. We also establish that the most recent technique, based on random walks, is superior. Overall, our results demonstrate that chromatin conformation and gene regulation share a non-linear relationship and that gene topological embeddings encode relevant information, which could be used also for downstream analysis.

AVAILABILITY AND IMPLEMENTATION

The source code for the analysis is available at: https://github.com/marcovarrone/gene-expression-chromatin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A transcriptomic analysis reveals the adaptability of the growth and physiology of immature tassel to long-term soil water deficit in Zea mays L

Drought is a key threat to maize growth and yield. Understanding the mechanism of immature tassel (IT) response to long term drought is of paramount importance. Here, the maize inbred line PH6WC was tested under well-watered (CK) and two water deficit treatments (WD1 and WD2). The final IT length in the WD1 and WD2 treatments decreased by nearly 6.2% and 21.2% compared to the CK, respectively, and the average accumulation rate IT dry matter was 1.5-fold and 1.8-fold slower, respectively. Furthermore, RNA sequencing analysis was conducted on the IT sampled at 30 days after the WD treatments. In total, the cellular component in gene ontology (GO) analysis suggested that the differentially expressed genes were significantly enriched in three common terms (apoplast, plant-type cell wall, and anchored component of membrane) among the CK vs WD1, CK vs WD2, and WD1 vs WD2 comparisons. Next, a co-expression network analysis identified 44 modules that contained global expression genes. Finally, by combining the GO analysis with modules, nine genes involved in carbohydrate metabolism and the antioxidant system were screened out, and the six corresponding physiological parameters were all significantly increased under the WD treatments. These results showed that, although the IT length and dry matter decreased, the IT enhanced the adaptation to drought by regulating their own genetic and physiological changes.

Weighted gene co-expression network analysis reveals potential candidate genes affecting drip loss in pork

Drip loss is an essential evaluation indicator for pork quality. It is closely related to other meat quality indicators, including water-holding capacity, water loss rate and pH value at 45 min (pH₁ ) and 24 h post-mortem (pH₂ ), and is influenced by environmental and genetic factors and their interactions. We previously conducted differentially expressed gene analysis to identify candidate genes affecting drip loss using eight individuals with extremely high- and low-drip loss selected from 28 purebred Duroc pigs. Using 28 identical samples, in the present study, we performed weighted gene co-expression network analysis with drip loss and drip loss-related traits, including water-holding capacity, water loss rate, pH₁ and pH₂ . A total of 25 modules were identified, and five of them correlated with at least two drip loss or drip loss-related traits. After functional enrichment analysis of genes in the five modules, three modules were found to be critical, as their genes were significantly involved in amino acid metabolism, immune response and apoptosis, which have potential relationships with drip loss. Furthermore, we identified five candidate genes affecting drip loss in one critical module, AASS, BCKDHB, ALDH6A1, MUT and MCCC1, as they overlapped with differentially expressed genes detected in our previous study, exhibited protein-protein interactions and had potential biological functions in affecting drip loss according to the literature. The outcomes of the present study enhance our understanding of the molecular mechanisms underlying drip loss and will aid in improving the pork quality.

Muscle Transcriptome Analysis Reveals Potential Candidate Genes and Pathways Affecting Intramuscular Fat Content in Pigs

Intramuscular fat (IMF) content plays an essential role in meat quality. For identifying potential candidate genes and pathways regulating IMF content, the IMF content and the longissimus dorsi transcriptomes of 28 purebred Duroc pigs were measured. As a result, the transcriptome analysis of four high- and four low-IMF individuals revealed a total of 309 differentially expressed genes (DEGs) using edgeR and DESeq2 (p < 0.05, |log2(fold change)| ≥ 1). Functional enrichment analysis of the DEGs revealed 19 hub genes significantly enriched in the Gene Ontology (GO) terms and pathways (q < 0.05) related to lipid metabolism and fat cell differentiation. The weighted gene coexpression network analysis (WGCNA) of the 28 pigs identified the most relevant module with 43 hub genes. The combined results of DEGs, WGCNA, and protein-protein interactions revealed ADIPOQ, PPARG, LIPE, CIDEC, PLIN1, CIDEA, and FABP4 to be potential candidate genes affecting IMF. Furthermore, the regulation of lipolysis in adipocytes and the peroxisome proliferator-activated receptor (PPAR) signaling pathway were significantly enriched for both the DEGs and genes in the most relevant module. Some DEGs and pathways detected in our study play essential roles and are potential candidate genes and pathways that affect IMF content in pigs. This study provides crucial information for understanding the molecular mechanism of IMF content and would be helpful in improving pork quality.

Genetic background and diet affect brown adipose gene coexpression networks associated with metabolic phenotypes

Adipose is a dynamic endocrine organ that is critical for regulating metabolism and is highly responsive to nutritional environment. Brown adipose tissue is an exciting potential therapeutic target; however, there are no systematic studies of gene-by-environment interactions affecting function of this organ. We leveraged a weighted gene coexpression network analysis to identify transcriptional networks in brown adipose tissue from LG/J and SM/J inbred mice fed high- or low-fat diets and correlate these networks with metabolic phenotypes. We identified eight primary gene network modules associated with variation in obesity and diabetes-related traits. Four modules were enriched for metabolically relevant processes such as immune and cytokine response, cell division, peroxisome functions, and organic molecule metabolic processes. The relative expression of genes in these modules is highly dependent on both genetic background and dietary environment. Genes in the immune/cytokine response and cell division modules are particularly highly expressed in high fat-fed SM/J mice, which show unique brown adipose-dependent remission of diabetes. The interconnectivity of genes in these modules is also heavily dependent on diet and strain, with most genes showing both higher expression and coexpression under the same context. We highlight several genes of interest, Col28a1, Cyp26b1, Bmp8b, and Ngef, that have distinct expression patterns among strain-by-diet contexts and fall under metabolic quantitative trait loci previously mapped in an F16 generation of an advanced intercross between LG/J and SM/J. Each of these genes have some connection to obesity and diabetes-related traits, but have not been studied in brown adipose tissue. Our results provide important insights into the relationship between brown adipose and systemic metabolism by being the first gene-by-environment study of brown adipose transcriptional networks.

Co-Expression Network Analysis of Spleen Transcriptome in Rock Bream (Oplegnathus fasciatus) Naturally Infected with Rock Bream Iridovirus (RBIV)

Rock bream iridovirus (RBIV) is a notorious agent that causes high mortality in aquaculture of rock bream (Oplegnathus fasciatus). Despite severity of this virus, no transcriptomic studies on RBIV-infected rock bream that can provide fundamental information on protective mechanism against the virus have been reported so far. This study aimed to investigate physiological mechanisms between host and RBIV through transcriptomic changes in the spleen based on RNA-seq. Depending on infection intensity and sampling time point, fish were divided into five groups: uninfected healthy fish at week 0 as control (0C), heavy infected fish at week 0 (0H), heavy mixed RBIV and bacterial infected fish at week 0 (0MH), uninfected healthy fish at week 3 (3C), and light infected fish at week 3 (3L). We explored clusters from 35,861 genes with Fragments Per Kilo-base of exon per Million mapped fragments (FPKM) values of 0.01 or more through signed co-expression network analysis using WGCNA package. Nine of 22 modules were highly correlated with viral infection (|gene significance (GS) vs. module membership (MM) |> 0.5, p-value < 0.05). Expression patterns in selected modules were divided into two: heavy infected (0H and 0MH) and control and light-infected groups (0C, 3C, and 3L). In functional analysis, genes in two positive modules (5448 unigenes) were enriched in cell cycle, DNA replication, transcription, and translation, and increased glycolysis activity. Seven negative modules (3517 unigenes) built in this study showed significant decreases in the expression of genes in lymphocyte-mediated immune system, antigen presentation, and platelet activation, whereas there was significant increased expression of endogenous apoptosis-related genes. These changes lead to RBIV proliferation and failure of host defense, and suggests the importance of blood cells such as thrombocytes and B cells in rock bream in RBIV infection. Interestingly, a hub gene, pre-mRNA processing factor 19 (PRPF19) showing high connectivity (kME), and expression of this gene using qRT-PCR was increased in rock bream blood cells shortly after RBIV was added. It might be a potential biomarker for diagnosis and vaccine studies in rock bream against RBIV. This transcriptome approach and our findings provide new insight into the understanding of global rock bream-RBIV interactions including immune and pathogenesis mechanisms.

Metabolomic networks and pathways associated with feed efficiency and related-traits in Duroc and Landrace pigs

Improving feed efficiency (FE) is a major goal of pig breeding, reducing production costs and providing sustainability to the pig industry. Reliable predictors for FE could assist pig producers. We carried out untargeted blood metabolite profiling in uncastrated males from Danbred Duroc (n = 59) and Danbred Landrace (n = 50) pigs at the beginning and end of a FE testing phase to identify biomarkers and biological processes underlying FE and related traits. By applying linear modeling and clustering analyses coupled with WGCNA framework, we identified 102 and 73 relevant metabolites in Duroc and Landrace based on two sampling time points. Among them, choline and pyridoxamine were hub metabolites in Duroc in early testing phase, while, acetoacetate, cholesterol sulfate, xanthine, and deoxyuridine were identified in the end of testing. In Landrace, cholesterol sulfate, thiamine, L-methionine, chenodeoxycholate were identified at early testing phase, while, D-glutamate, pyridoxamine, deoxycytidine, and L-2-aminoadipate were found at the end of testing. Validation of these results in larger populations could establish FE prediction using metabolomics biomarkers. We conclude that it is possible to identify a link between blood metabolite profiles and FE. These results could lead to improved nutrient utilization, reduced production costs, and increased FE.

Comparison of Methods for Differential Co-expression Analysis for Disease Biomarker Prediction

In the recent past, a number of methods have been developed for analysis of biological data. Among these methods, gene co-expression networks have the ability to mine functionally related genes with similar co-expression patterns, because of which such networks have been most widely used. However, gene co-expression networks cannot identify genes, which undergo condition specific changes in their relationships with other genes. In contrast, differential co-expression analysis enables finding co-expressed genes exhibiting significant changes across disease conditions. In this paper, we present some significant outcomes of a comparative study of four co-expression network module detection techniques, namely, THD-Module Extractor, DiffCoEx, MODA, and WGCNA, which can perform differential co-expression analysis on both gene and miRNA expression data (microarray and RNA-seq) and discuss the applications to Alzheimer's disease and Parkinson's disease research. Our observations reveal that compared to other methods, THD-Module Extractor is the most effective in finding modules with higher functional relevance and biological significance.

Transcription Profiling of Monocyte-Derived Macrophages Infected In Vitro With Two Strains of Streptococcus agalactiae Reveals Candidate Pathways Affecting Subclinical Mastitis in Cattle

Macrophages are key cells of innate immune response and serve as the first line of defense against bacteria. Transcription profiling of bacteria-infected macrophages could provide important insights on the pathogenicity and host defense mechanisms during infection. We have examined transcription profiles of bovine monocyte-derived macrophages (bMDMs) isolated from the blood of 12 animals and infected in vitro with two strains of Streptococcus agalactiae. Illumina sequencing of RNA from 36 bMDMs cultures exposed in vitro to either one of two sequence types of S. agalactiae (ST103 or ST12) for 6 h and unchallenged controls was performed. Analyses of over 1,656 million high-quality paired-end sequence reads revealed 5,936 and 6,443 differentially expressed genes (p < 0.05) in bMDMs infected with ST103 and ST12, respectively, versus unchallenged controls. Moreover, 588 genes differentially expressed between bMDMs infected with ST103 versus ST12 were identified. Ingenuity pathway analysis of the differentially up-regulated genes in the bMDMs infected with ST103 revealed significant enrichment for granulocyte adhesion and diapedesis, while significant enrichment for the phagosome formation pathway was found among down-regulated genes. Moreover, Ingenuity pathway analysis of the differentially up-regulated genes in the bMDMs infected with ST12 showed significant enrichment for type 1/type 2 T helper cell activation, while the complement activation pathway was overrepresented in the down-regulated genes. Our study identified pathogen-induced regulation of key genes and pathways involved in the immune response of macrophages against infection but also likely involved in bacterial evasion of the host immune system. These results may contribute to better understanding of the mechanisms underlying subclinical infection such as bovine streptococcal mastitis.

Prediction of hub genes associated with intramuscular fat content in Nelore cattle

BACKGROUND

The aim of this study was to use transcriptome RNA-Seq data from longissimus thoracis muscle of uncastrated Nelore males to identify hub genes based on co-expression network obtained from differentially expressed genes (DEGs) associated with intramuscular fat content.

RESULTS

A total of 30 transcriptomics datasets (RNA-Seq) obtained from longissimus thoracis muscle were selected based on the phenotypic value of divergent intramuscular fat content: 15 with the highest intramuscular fat content (HIF) and 15 with the lowest intramuscular fat content (LIF). The transcriptomics datasets were aligned with a reference genome and 65 differentially expressed genes (DEGs) were identified, including 21 upregulated and 44 downregulated genes in HIF animals. The normalized count data from DEGs was then used for co-expression network construction. From the co-expression network, four modules were identified. The topological properties of the network were analyzed; those genes engaging in the most interactions (maximal clique centrality method) with other DEGs were predicted to be hub genes (PDE4D, KLHL30 and IL1RAP), which consequently may play a role in cellular and/or systemic lipid biology in Nelore cattle. Top modules screened from the gene co-expression network were identify. The two candidate modules had clear associated biological pathways related to fat development, cell adhesion, and muscle differentiation, immune system, among others. The hub genes belonged in top modules and were downregulated in HIF animals. PDE4D and IL1RAP have known effects on lipid metabolism and the immune system through the regulation of cAMP signaling. Given that cAMP is known to play a role in lipid systems, PDE4D and IL1RAP downregulation may contribute to increased levels of intracellular cAMP and thus may have effects on IF content differences in Nelore cattle. KLHL30 may have effects on muscle metabolism. Klhl protein families play a role in protein degradation. However, the downregulation of this gene and its role in lipid metabolism has not yet been clarified.

CONCLUSIONS

The results reported in this study indicate candidate genes and molecular mechanisms involved in IF content difference in Nelore cattle.

About the existence of common determinants of gene expression in the porcine liver and skeletal muscle

BACKGROUND

The comparison of expression QTL (eQTL) maps obtained in different tissues is an essential step to understand how gene expression is genetically regulated in a context-dependent manner. In the current work, we have compared the transcriptomic and eQTL profiles of two porcine tissues (skeletal muscle and liver) which typically show highly divergent expression profiles, in 103 Duroc pigs genotyped with the Porcine SNP60 BeadChip (Illumina) and with available microarray-based measurements of hepatic and muscle mRNA levels. Since structural variation could have effects on gene expression, we have also investigated the co-localization of cis-eQTLs with copy number variant regions (CNVR) segregating in this Duroc population.

RESULTS

The analysis of differential expresssion revealed the existence of 1204 and 1490 probes that were overexpressed and underexpressed in the gluteus medius muscle when compared to liver, respectively (|fold-change| > 1.5, q-value < 0.05). By performing genome scans in 103 Duroc pigs with available expression and genotypic data, we identified 76 and 28 genome-wide significant cis-eQTLs regulating gene expression in the gluteus medius muscle and liver, respectively. Twelve of these cis-eQTLs were shared by both tissues (i.e. 42.8% of the cis-eQTLs identified in the liver were replicated in the gluteus medius muscle). These results are consistent with previous studies performed in humans, where 50% of eQTLs were shared across tissues. Moreover, we have identified 41 CNVRs in a set of 350 pigs from the same Duroc population, which had been genotyped with the Porcine SNP60 BeadChip by using the PennCNV and GADA softwares, but only a small proportion of these CNVRs co-localized with the cis-eQTL signals.

CONCLUSION

Despite the fact that there are considerable differences in the gene expression patterns of the porcine liver and skeletal muscle, we have identified a substantial proportion of common cis-eQTLs regulating gene expression in both tissues. Several of these cis-eQTLs influence the mRNA levels of genes with important roles in meat (CTSF) and carcass quality (TAPT1), lipid metabolism (TMEM97) and obesity (MARC2), thus evidencing the practical importance of dissecting the genetic mechanisms involved in their expression.

Gene co-expression networks contributing to the expression of compensatory growth in metabolically active tissues in cattle

Compensatory growth (CG) is an accelerated growth phenomenon which occurs in animals upon re-alimentation following a period of dietary restriction. The objective of this study was to perform gene co-expression analysis on metabolic tissues of animals undergoing CG, in order to elucidate the molecular control governing this phenomenon. Thirty Holstein Friesian bulls were fed a restricted diet for 125 days, after which they received feed ad libitum. Following 55 days of ad libitum feeding all animals were slaughtered. RNAseq and gene co-expression analyses were performed on tissue samples collected at slaughter including liver, rumen papillae and jejunum epithelium tissues. A period of CG resulted in 15 networks of co-expressed genes. One network of genes, involved in proteasome core complex, signal transduction and protein synthesis was found to be similar across liver and jejunum tissue datasets (r = 0.68, P = 0.04). Results from this study also showed that a large portion of co-expressed genes had not previously been implicated in the expression of CG, thus this study identifies novel genes involved in controlling CG across tissues, with hub genes holding potential for use as biomarkers for the selection of animals with a greater propensity to display CG.

Identifying Hub Genes for Heat Tolerance in Water Buffalo (Bubalus bubalis) Using Transcriptome Data

Heat stress has a detrimental effect on the physiological and production performance of buffaloes. Elucidating the underlying mechanisms of heat stress is challenging, therefore identifying candidate genes is urgent and necessary. We evaluated the response of buffaloes (n = 30) to heat stress using the physiological parameters, ELISA indexes, and hematological parameters. We then performed mRNA and microRNA (miRNA) expression profiles analysis between heat tolerant (HT, n = 4) and non-heat tolerant (NHT, n = 4) buffaloes, as well as the specific modules, significant genes, and miRNAs related to the heat tolerance identified using the weighted gene co-expression network analysis (WGCNA). The results indicated that the buffaloes in HT had a significantly lower rectal temperature (RT) and respiratory rate (RR) and displayed a higher plasma heat shock protein (HSP70 and HSP90) and cortisol (COR) levels than those of NHT buffaloes. Differentially expressed analysis revealed a total of 753 differentially expressed genes (DEGs) and 16 differentially expressed miRNAs (DEmiRNAs) were identified between HT and NHT. Using the WGCNA analysis, these DEGs assigned into 5 modules, 4 of which were significantly correlation with the heat stress indexes. Interestingly, 158 DEGs associated with heat tolerance in the turquoise module were identified, 35 of which were found within the protein-protein interaction network. Several hub genes (IL18RAP, IL6R, CCR1, PPBP, IL1B, and IL1R1) were identified that significantly enriched in the Cytokine-cytokine receptor interaction. The findings may help further elucidate the underlying mechanisms of heat tolerance in buffaloes.

Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Background

Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows.

RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results

WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = − 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion

The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2553-z) contains supplementary material, which is available to authorized users.

An integrative transcriptome analysis indicates regulatory mRNA-miRNA networks for residual feed intake in Nelore cattle

Residual Feed Intake (RFI) is an economically relevant trait in beef cattle. Among the molecular regulatory mechanisms, microRNAs (miRNAs) are an important dimension in post-transcriptional regulation and have been associated with different biological pathways. Here, we performed differential miRNAs expression and weighted gene co-expression network analyses (WGCNA) to better understand the complex interactions between miRNAs and mRNAs expressed in bovine skeletal muscle and liver. MiRNA and mRNA expression data were obtained from Nelore steers that were genetically divergent for RFI (N = 10 [low RFI or feed efficient]; N = 10 [high RFI or feed inefficient]). Differentially expressed and hub miRNAs such as bta-miR-486, bta-miR-7, bta-miR15a, bta-miR-21, bta-miR 29, bta- miR-30b, bta-miR-106b, bta-miR-199a-3p, bta-miR-204, and bta-miR 296 may have a potential role in variation of RFI. Functional enrichment analysis of differentially expressed (DE) miRNA's target genes and miRNA-mRNA correlated modules revealed that insulin, lipid, immune system, oxidative stress and muscle development signaling pathways might potentially be involved in RFI in this population. Our study identified DE miRNAs, miRNA - mRNA regulatory networks and hub miRNAs related to RFI. These findings suggest a possible role of miRNAs in regulation of RFI, providing new insights into the potential molecular mechanisms that control feed efficiency in Nelore cattle.

Gene Co-expression Analysis Indicates Potential Pathways and Regulators of Beef Tenderness in Nellore Cattle

Beef tenderness, a complex trait affected by many factors, is economically important to beef quality, industry, and consumer’s palatability. In this study, RNA-Seq was used in network analysis to better understand the biological processes that lead to differences in beef tenderness. Skeletal muscle transcriptional profiles from 24 Nellore steers, selected by extreme estimated breeding values (EBVs) for shear force after 14 days of aging, were analyzed and 22 differentially expressed transcripts were identified. Among these were genes encoding ribosomal proteins, glutathione transporter ATP-binding cassette, sub-family C (CFTR/MRP), member 4 (ABCC4), and synaptotagmin IV (SYT4). Complementary co-expression analyses using Partial Correlation with Information Theory (PCIT), Phenotypic Impact Factor (PIF) and the Regulatory Impact Factor (RIF) methods identified candidate regulators and related pathways. The PCIT analysis identified ubiquitin specific peptidase 2 (USP2), growth factor receptor-bound protein 10 (GBR10), anoctamin 1 (ANO1), and transmembrane BAX inhibitor motif containing 4 (TMBIM4) as the most differentially hubbed (DH) transcripts. The transcripts that had a significant correlation with USP2, GBR10, ANO1, and TMBIM4 enriched for proteasome KEGG pathway. RIF analysis identified microRNAs as candidate regulators of variation in tenderness, including bta-mir-133a-2 and bta-mir-22. Both microRNAs have target genes present in the calcium signaling pathway and apoptosis. PIF analysis identified myoglobin (MB), enolase 3 (ENO3), and carbonic anhydrase 3 (CA3) as potentially having fundamental roles in tenderness. Pathways identified in our study impacted in beef tenderness included: calcium signaling, apoptosis, and proteolysis. These findings underscore some of the complex molecular mechanisms that control beef tenderness in Nellore cattle.

Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites

MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA–mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows’ diets with 5% linseed oil (LSO) (n = 6 cows) or 5% safflower oil (SFO) (n = 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor–beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (p = 0.03) correlated with the turquoise module in LSO treatment while protein yield (p = 0.003) and milk yield (p = 7 × 10⁻⁰⁴) correlated with the turquoise model, protein and milk yields and lactose percent (p < 0.05) correlated with the blue module and fat percent (p = 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (p < 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA–mRNA pairs: miR-183/RHBDD2 (p = 0.003), miR-484/EIF1AD (p = 0.011) and miR-130a/SBSPON (p = 0.004) with lowest p-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (p < 0.05) with 28, 31 and 5 miRNA–mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA–mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows’ diets with 5% LSO or 5% SFO.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co-expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co-expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co-expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC-related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.

Gene co-expression analysis for functional classification and gene-disease predictions

Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.

Adipocyte Long-Noncoding RNA Transcriptome Analysis of Obese Mice Identified Lnc-Leptin, Which Regulates Leptin

Obesity induces profound transcriptome changes in adipocytes, and recent evidence suggests that long-noncoding RNAs (lncRNAs) play key roles in this process. We performed a comprehensive transcriptome study by RNA sequencing in adipocytes isolated from interscapular brown, inguinal, and epididymal white adipose tissue in diet-induced obese mice. The analysis revealed a set of obesity-dysregulated lncRNAs, many of which exhibit dynamic changes in the fed versus fasted state, potentially serving as novel molecular markers of adipose energy status. Among the most prominent lncRNAs is Lnc-leptin, which is transcribed from an enhancer region upstream of leptin (Lep). Expression of Lnc-leptin is sensitive to insulin and closely correlates to Lep expression across diverse pathophysiological conditions. Functionally, induction of Lnc-leptin is essential for adipogenesis, and its presence is required for the maintenance of Lep expression in vitro and in vivo. Direct interaction was detected between DNA loci of Lnc-leptin and Lep in mature adipocytes, which diminished upon Lnc-leptin knockdown. Our study establishes Lnc-leptin as a new regulator of Lep.

A Combined PLS and Negative Binomial Regression Model for Inferring Association Networks from Next-Generation Sequencing Count Data

A major challenge of genomics data is to detect interactions displaying functional associations from large-scale observations. In this study, a new cPLS-algorithm combining partial least squares approach with negative binomial regression is suggested to reconstruct a genomic association network for high-dimensional next-generation sequencing count data. The suggested approach is applicable to the raw counts data, without requiring any further pre-processing steps. In the settings investigated, the cPLS-algorithm outperformed the two widely used comparative methods, graphical lasso, and weighted correlation network analysis. In addition, cPLS is able to estimate the full network for thousands of genes without major computational load. Finally, we demonstrate that cPLS is capable of finding biologically meaningful associations by analyzing an example data set from a previously published study to examine the molecular anatomy of the craniofacial development.

Transcription Factor Co-expression Networks of Adipose RNA-Seq Data Reveal Regulatory Mechanisms of Obesity

BACKGROUND

Transcription Factors (TFs) control actuation of genes in the genome and are key mediators of complex processes such as obesity. Master Regulators (MRs) are the genes at the top of a regulation hierarchy which regulate other genes.

OBJECTIVE

To elucidate clusters of highly co-expressed TFs (modules), involved pathways, highly inter-connected TFs (hub-TFs) and MRs leading to obesity and leanness, using porcine model for human obesity.

METHODS

We identified 817 expressed TFs in RNA-Sequencing dataset representing extreme degrees of obesity (DO; lean, obese). We built a single Weighted Transcription Factor Co-expression Network (WTFCN) and TF sub-networks (based on the DO). Hub-TFs and MRs (using iRegulon) were identi-fied in biologically relevant WTFCNs modules.

RESULTS

Single WTFCN detected the Red module significantly associated with DO (P < 0.03). This module was enriched for regulation processes in the immune system, e.g.: Immune system process (Padj = 2.50E-06) and metabolic lifestyle disorders, e.g. Circadian rhythm - mammal pathway (Padj = 2.33E-11). Detected MR, hub-TF SPI1 was involved in obesity, immunity and osteoporosis. Within the obese sub-network, the Red module suggested possible associations with immunity, e.g. TGF-beta signaling pathway (Padj = 1.73E-02) and osteoporosis, e.g. Osteoclast differentiation (Padj = 1.94E-02). Within the lean sub-network, the Magenta module displayed associations with type 2 diabetes, obesity and os-teoporosis e.g. Notch signaling pathway (Padj = 2.40E-03), osteoporosis e.g. hub-TF VDR (a prime candidate gene for osteoporosis).

CONCLUSION

Our results provide insights into the regulatory network of TFs and biologically relevant hub TFs in obesity.

Gene co-expression analysis identifies gene clusters associated with isotropic and polarized growth in Aspergillus fumigatus conidia

Aspergillus fumigatus is a saprophytic fungus that extensively produces conidia. These microscopic asexually reproductive structures are small enough to reach the lungs. Germination of conidia followed by hyphal growth inside human lungs is a key step in the establishment of infection in immunocompromised patients. RNA-Seq was used to analyze the transcriptome of dormant and germinating A. fumigatus conidia. Construction of a gene co-expression network revealed four gene clusters (modules) correlated with a growth phase (dormant, isotropic growth, polarized growth). Transcripts levels of genes encoding for secondary metabolites were high in dormant conidia. During isotropic growth, transcript levels of genes involved in cell wall modifications increased. Two modules encoding for growth and cell cycle/DNA processing were associated with polarized growth. In addition, the co-expression network was used to identify highly connected intermodular hub genes. These genes may have a pivotal role in the respective module and could therefore be compelling therapeutic targets. Generally, cell wall remodeling is an important process during isotropic and polarized growth, characterized by an increase of transcripts coding for hyphal growth and cell cycle/DNA processing when polarized growth is initiated.

Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle

BACKGROUND

The amount of intramuscular fat can influence the sensory characteristics and nutritional value of beef, thus the selection of animals with adequate fat deposition is important to the consumer. There is growing knowledge about the genes and pathways that control the biological processes involved in fat deposition in muscle. MicroRNAs (miRNAs) belong to a well-conserved class of non-coding small RNAs that modulate gene expression across a range of biological functions in animal development and physiology. The aim of this study was to identify differentially expressed (DE) miRNAs, regulatory candidate genes and co-expression networks related to intramuscular fat (IMF) deposition. To achieve this, we used mRNA and miRNA expression data from the Longissimus dorsi muscle of 30 Nelore steers with high (H) and low (L) genomic estimated breeding values (GEBV) for IMF deposition.

RESULTS

Differential miRNA expression analysis between animals with extreme GEBV values for IMF identified six DE miRNAs (FDR 10%). Functional annotation of the target genes for these microRNAs indicated that the PPARs signaling pathway is involved with IMF deposition. Candidate regulatory genes such as SDHAF4, FBXO17, ALDOA and PKM were identified by partial correlation with information theory (PCIT), phenotypic impact factor (PIF) and regulatory impact factor (RIF) co-expression approaches from integrated miRNA-mRNA expression data. Two DE miRNAs (FDR 10%), bta-miR-143 and bta-miR-146b, which were upregulated in the Low IMF group, were correlated with regulatory candidate genes, which were functionally enriched for fatty acid oxidation GO terms. Co-expression patterns obtained by weighted correlation network analysis (WGCNA), which showed possible interaction and regulation between mRNAs and miRNAs, identified several modules related to immune system function, protein metabolism, energy metabolism and glucose catabolism according to in silico analysis performed herein.

CONCLUSION

In this study, several genes and miRNAs were identified as candidate regulators of IMF by analyzing DE miRNAs using two different miRNA-mRNA co-expression network methods. This study contributes to the understanding of potential regulatory mechanisms of gene signaling networks involved in fat deposition processes measured in muscle. Glucose metabolism and inflammation processes were the main pathways found in silico to influence intramuscular fat deposition in beef cattle in the integrative mRNA-miRNA co-expression analysis.