Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

Dramatic genome-wide reprogramming of mRNA in hypometabolic muscle

In response to seasonal droughts, the green striped burrowing frog Cyclorana alboguttata enters a reversible hypometabolic state called aestivation where heart rate and oxygen consumption can be reduced despite warm (>25C°) ambient temperatures. With a view to understanding molecular mechanisms we profiled aestivating versus control gastrocnemius muscle using mRNA sequencing. This indicated an extensive metabolic reprogramming, with nearly a quarter of the entire transcriptome (3996 of 16,960 mRNA) exhibiting a nominal >2-fold change. Consistent with a physiological adaptation to spare carbohydrate reserves, carbohydrate catabolism was systemically downregulated. A 630-fold downregulation of ENO3 encoding the enolase enzyme was most striking. The 590 frog orthologs of mRNA encoding the mitoproteome were, viewed as a population, significantly downregulated during aestivation, although not to the same extent as mRNA encoding carbohydrate catabolism. Prominent examples include members of the TCA cycle (IDH2), electron transport chain (NDUFA6), the ATP synthase complex (ATP5F1B) and ADP/ATP intracellular transport (SLC25A4). Moreover, mRNA derived from the mt genome itself (e.g. mt-ND1) were also downregulated. Most prominent among the upregulated mRNA are those encoding aspects of regulated proteolysis including the proteosome (e.g. PSME4L), peptidases (USP25), atrogins (FBXO32) and ubiquitination (VCP). Finally, we note the ∼5-fold upregulation of the mRNA EIFG3 that encodes part of the EIF4F complex. This possesses global control of protein synthesis. Given protein synthesis is repressed in aestivating frogs this indicates the skeletal musculature is poised for accelerated translation of mRNA upon emergence, supporting a strategy to rapidly restore function when the summer rains come.

An association weight matrix identified biological pathways associated with bull fertility traits in a multi-breed population

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix-partial correlation information theory (AWM-PCIT) approach. Beyond a simple list of candidate genes, AWM-PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility-associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower-density genotypes. Sequence-level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes-KDM5C, LRRK2, and PME-was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Transcription factors and miRNA act as contrary regulators of gene expression in the testis and epididymis of Bos indicus animals

Spermatogenesis is highly conserved among mammalians, but its gene expression and regulatory profile are not entirely known. As transcription factors (TFs) and miRNAs are crucial for gene expression regulation, identifying genes negatively regulated by miRNAs and positively regulated by TFs in the testis and epididymis can provide a deeper understanding of gene expression and regulatory patterns. To do this, we used expression data coming from RNA-Seq and miRNA-Seq experiments made with biopsies from testicular parenchyma, head of the epididymis, and tail of the epididymis of four Brahman bulls. We identified miRNA differentially expressed (DE) by comparing the three distinct tissues. A co-expression analysis combined with a regulatory impact factor approach identified miRNAs and TFs with regulatory impact over gene expression regulation in the Bos indicus tissues studied. We identified 116 DE miRNAs, 206 miRNAs and 237 TFs with a significant regulatory impact on mRNA patterns in the tissues' comparisons. bta-miR-196b was the only DE miRNA for all tissue comparisons and it may be a regulator of spermatogenesis through its links with adipogenesis and insulin biosynthesis. DE genes and TFs involved in contrary regulations between the epididymis head and testis parenchyma were associated with spermatogenesis, sexual reproduction, and sperm motility. Our results provide possible mechanisms, governed by the contrary effect of miRNA and TF, leading to the differential expression between the studied tissues. We have demonstrated that our predictions of miRNAs and TFs co-regulations over target DE genes can retrieve known regulatory mechanisms and predict new ones that merit further validation.

An across breed, diet and tissue analysis reveals the transcription factor NR1H3 as a key mediator of residual feed intake in beef cattle

BACKGROUND

Provision of feed is a major determinant of overall profitability in beef production systems, accounting for up to 75% of the variable costs. Thus, improving cattle feed efficiency, by way of determining the underlying genomic control and subsequently selecting for feed efficient cattle, provides a method through which feed input costs may be reduced. The objective of this study was to undertake gene co-expression network analysis using RNA-Sequence data generated from Longissimus dorsi and liver tissue samples collected from steers of two contrasting breeds (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI), across two consecutive distinct dietary phases (zero-grazed grass and high-concentrate). Categories including differentially expressed genes (DEGs) based on the contrasts of RFI phenotype, breed and dietary source, as well as key transcription factors and proteins secreted in plasma were utilised as nodes of the gene co-expression network.

RESULTS

Of the 2,929 DEGs within the network analysis, 1,604 were reported to have statistically significant correlations (≥ 0.80), resulting in a total of 43,876 significant connections between genes. Pathway analysis of clusters of co-expressed genes revealed enrichment of processes related to lipid metabolism (fatty acid biosynthesis, fatty acid β-oxidation, cholesterol biosynthesis), immune function, (complement cascade, coagulation system, acute phase response signalling), and energy production (oxidative phosphorylation, mitochondrial L-carnitine shuttle pathway) based on genes related to RFI, breed and dietary source contrasts.

CONCLUSIONS

Although similar biological processes were evident across the three factors examined, no one gene node was evident across RFI, breed and diet contrasts in both liver and muscle tissues. However within the liver tissue, the IRX4, NR1H3, HOXA13 and ZNF648 gene nodes, which all encode transcription factors displayed significant connections across the RFI, diet and breed comparisons, indicating a role for these transcription factors towards the RFI phenotype irrespective of diet and breed. Moreover, the NR1H3 gene encodes a protein secreted into plasma from the hepatocytes of the liver, highlighting the potential for this gene to be explored as a robust biomarker for the RFI trait in beef cattle.

Expression network analysis of bovine skin infested with Rhipicephalus australis identifies pro-inflammatory genes contributing to tick susceptibility

The skin is the primary feeding site of ticks that infest livestock animals such as cattle. The highly specialised functions of skin at the molecular level may be a factor contributing to variation in susceptibility to tick infestation; but these remain to be well defined. The aim of this study was to investigate the bovine skin transcriptomic profiles of tick-naïve and tick-infested cattle and to uncover the gene expression networks that influence contrasting phenotypes of host resistance to ticks. RNA-Seq data was obtained from skin of Brangus cattle with high (n = 5) and low (n = 6) host resistance at 0 and 12 weeks following artificial tick challenge with Rhipicephalus australis larvae. No differentially expressed genes were detected pre-infestation between high and low resistance groups, but at 12-weeks there were 229 differentially expressed genes (DEGs; FDR < 0.05), of which 212 were the target of at least 1866 transcription factors (TFs) expressed in skin. Regulatory impact factor (RIF) analysis identified 158 significant TFs (P < 0.05) of which GRHL3, and DTX1 were also DEGs in the experiment. Gene term enrichment showed the significant TFs and DEGs were enriched in processes related to immune response and biological pathways related to host response to infectious diseases. Interferon Type 1-stimulated genes, including MX2, ISG15, MX1, OAS2 were upregulated in low host resistance steers after repeated tick challenge, suggesting dysregulated wound healing and chronic inflammatory skin processes contributing to host susceptibility to ticks. The present study provides an assessment of the bovine skin transcriptome before and after repeated tick challenge and shows that the up-regulation of pro-inflammatory genes is a prominent feature in the skin of tick-susceptible animals. In addition, the identification of transcription factors with high regulatory impact provides insights into the potentially meaningful gene-gene interactions involved in the variation of phenotypes of bovine host resistance to ticks.

Molecular mechanisms of microbiome modulation by the eukaryotic secondary metabolite azelaic acid

Photosynthetic eukaryotes, such as microalgae and plants, foster fundamentally important relationships with their microbiome based on the reciprocal exchange of chemical currencies. Among these, the dicarboxylate metabolite azelaic acid (Aze) appears to play an important, but heterogeneous, role in modulating these microbiomes, as it is used as a carbon source for some heterotrophs but is toxic to others. However, the ability of Aze to promote or inhibit growth, as well as its uptake and assimilation mechanisms into bacterial cells are mostly unknown. Here, we use transcriptomics, transcriptional factor coexpression networks, uptake experiments, and metabolomics to unravel the uptake, catabolism, and toxicity of Aze on two microalgal-associated bacteria, Phycobacter and Alteromonas, whose growth is promoted or inhibited by Aze, respectively. We identify the first putative Aze transporter in bacteria, a 'C4-TRAP transporter', and show that Aze is assimilated through fatty acid degradation, with further catabolism occurring through the glyoxylate and butanoate metabolism pathways when used as a carbon source. Phycobacter took up Aze at an initial uptake rate of 3.8×10-9 nmol/cell/hr and utilized it as a carbon source in concentrations ranging from 10 μM to 1 mM, suggesting a broad range of acclimation to Aze availability. For growth-impeded bacteria, we infer that Aze inhibits the ribosome and/or protein synthesis and that a suite of efflux pumps is utilized to shuttle Aze outside the cytoplasm. We demonstrate that seawater amended with Aze becomes enriched in bacterial families that can catabolize Aze, which appears to be a different mechanism from that in soil, where modulation by the host plant is required. This study enhances our understanding of carbon cycling in the oceans and how microscale chemical interactions can structure marine microbial populations. In addition, our findings unravel the role of a key chemical currency in the modulation of eukaryote-microbiome interactions across diverse ecosystems.

Genome-wide family prediction unveils molecular mechanisms underlying the regulation of agronomic traits in Urochloa ruziziensis

Tropical forage grasses, particularly those belonging to the Urochloa genus, play a crucial role in cattle production and serve as the main food source for animals in tropical and subtropical regions. The majority of these species are apomictic and tetraploid, highlighting the significance of U. ruziziensis, a sexual diploid species that can be tetraploidized for use in interspecific crosses with apomictic species. As a means to support breeding programs, our study investigates the feasibility of genome-wide family prediction in U. ruziziensis families to predict agronomic traits. Fifty half-sibling families were assessed for green matter yield, dry matter yield, regrowth capacity, leaf dry matter, and stem dry matter across different clippings established in contrasting seasons with varying available water capacity. Genotyping was performed using a genotyping-by-sequencing approach based on DNA samples from family pools. In addition to conventional genomic prediction methods, machine learning and feature selection algorithms were employed to reduce the necessary number of markers for prediction and enhance predictive accuracy across phenotypes. To explore the regulation of agronomic traits, our study evaluated the significance of selected markers for prediction using a tree-based approach, potentially linking these regions to quantitative trait loci (QTLs). In a multiomic approach, genes from the species transcriptome were mapped and correlated to those markers. A gene coexpression network was modeled with gene expression estimates from a diverse set of U. ruziziensis genotypes, enabling a comprehensive investigation of molecular mechanisms associated with these regions. The heritabilities of the evaluated traits ranged from 0.44 to 0.92. A total of 28,106 filtered SNPs were used to predict phenotypic measurements, achieving a mean predictive ability of 0.762. By employing feature selection techniques, we could reduce the dimensionality of SNP datasets, revealing potential genotype-phenotype associations. The functional annotation of genes near these markers revealed associations with auxin transport and biosynthesis of lignin, flavonol, and folic acid. Further exploration with the gene coexpression network uncovered associations with DNA metabolism, stress response, and circadian rhythm. These genes and regions represent important targets for expanding our understanding of the metabolic regulation of agronomic traits and offer valuable insights applicable to species breeding. Our work represents an innovative contribution to molecular breeding techniques for tropical forages, presenting a viable marker-assisted breeding approach and identifying target regions for future molecular studies on these agronomic traits.

Haploid androgenetic development of bovine embryos reveals imbalanced WNT signaling and impaired cell fate differentiation†

Haploid embryos have contributed significantly to our understanding of the role of parental genomes in development and can be applied to important biotechnology for human and animal species. However, development to the blastocyst stage is severely hindered in bovine haploid androgenetic embryos (hAE). To further our understanding of such developmental arrest, we performed a comprehensive comparison of the transcriptomic profile of morula-stage embryos, which were validated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) of transcripts associated with differentiation in haploid and biparental embryos. Among numerous disturbances, results showed that pluripotency pathways, especially the wingless-related integration site (WNT) signaling, were particularly unbalanced in hAE. Moreover, transcript levels of KLF4, NANOG, POU5F1, SOX2, CDX2, CTNNBL1, AXIN2, and GSK3B were noticeably altered in hAE, suggesting disturbance of pluripotency and canonical WNT pathways. To evaluate the role of WNT on hAE competence, we exposed early Day-5 morula stage embryos to the GSK3B inhibitor CHIR99021. Although no alterations were observed in pluripotency and WNT-related transcripts, exposure to CHIR99021 improved their ability to reach the blastocysts stage, confirming the importance of the WNT pathway in the developmental outcome of bovine hAE.

On Graphical Models and Convex Geometry

A mixture-model of beta distributions framework is introduced to identify significant correlations among P features when P is large. The method relies on theorems in convex geometry, which are used to show how to control the error rate of edge detection in graphical models. The proposed 'betaMix' method does not require any assumptions about the network structure, nor does it assume that the network is sparse. The results hold for a wide class of data-generating distributions that include light-tailed and heavy-tailed spherically symmetric distributions. The results are robust for sufficiently large sample sizes and hold for non-elliptically-symmetric distributions.

Integrated Analysis of the Effects of Cecal Microbiota and Serum Metabolome on Market Weights of Chinese Native Chickens

The gut microbiota plays an important role in the physiological activities of the host and affects the formation of important economic traits in livestock farming. The effects of cecal microbiota on chicken weights were investigated using the Guizhou yellow chicken as a model. Experimental cohorts from chickens with high- (HC, n = 16) and low-market-weights (LC, n = 16) were collected. Microbial 16S rRNA gene sequencing and non-targeted serum metabolome data were integrated to explore the effect and metabolic mechanism of cecal microbiota on market weight. The genera Lachnoclostridium, Alistipes, Negativibacillus, Sellimonas, and Ruminococcus torques were enriched in the HC group, while Phascolarctobacterium was enriched in the LC group (p < 0.05). Metabolomic analysis determined that pantothenic acid (vitamin B5), luvangetin (2H-1-benzopyran-6-acrylic acid), and menadione (vitamin K3) were significantly higher in HC serum, while beclomethasone dipropionate (a glucocorticoid) and chlorophene (2-benzyl-4-chlorophenol) were present at higher levels in the LC group. The microbes enriched in HC were significantly positively correlated with metabolites, including pantothenic acid and menadione, and negatively correlated with beclomethasone dipropionate and chlorophene. These results indicated that specific cecal bacteria in Guizhou yellow chickens alter the host metabolism and growth performance. This study provides a reference for revealing the mechanism of cecal microbe actions that affect chicken body weight.

Hematological Normality, Serum Biochemistry, and Acute Phase Proteins in Healthy Beef Calves in the Brazilian Savannah

The Brazilian savannah region, characterized by high average temperatures, well-defined rainy and dry seasons, soil with low productive potential, and high pressure for parasitic diseases, is home to the highest percentage of the beef herd, which is the world's largest commercial beef producer. Therefore, breeds that present rusticity combined with productivity are the focus of research in cattle breeding in the region. Considering their geographic particularities and their effects on the animals' blood parameters, the objective was to study the behavior of hematological variables, serum biochemistry, and acute phase proteins in beef calves at weaning age. Jugular blood samples were collected in a single day from 30 weaning calves (about eight months old and 200 kg of body weight) from the following breeds: Nellore, Senepol, Nellore × Aberdeen Angus cross, Nellore × Senepol cross, and Nellore × Aberdeen Angus × Senepol cross. Hematological data were obtained using an automatic cell counter, serum biochemical measurements were obtained using commercial kits, and the electrophoretogram was obtained using the SDS-page technique. In general, the results were consistent with data already published in similar situations regarding health status, age, and level of metabolic activity. However, differences observed between groups can be explained by differences observed in other concurrent variables like temperament. The pure zebu breed was more reactive than pure taurine. While crossbreds showed intermediate values, and parasitic infestation, the pure taurine breed with higher parasite infestation, while the zebu breed had lower values, which produced effects in some laboratory tests, and generated differences between breeds. In conclusion, the reference intervals available for healthy animals can be routinely used without interference from the geographic region for animals produced without nutritional failures as long as changes are recorded in pathological, infectious, metabolic, or nutritional deficiency situations. However, it is suggested that a study covering a larger number of herds may demonstrate a greater geographic effect on the studied variables.

Gut barrier-microbiota imbalances in early life lead to higher sensitivity to inflammation in a murine model of C-section delivery

BACKGROUND

Most interactions between the host and its microbiota occur at the gut barrier, and primary colonizers are essential in the gut barrier maturation in the early life. The mother-offspring transmission of microorganisms is the most important factor influencing microbial colonization in mammals, and C-section delivery (CSD) is an important disruptive factor of this transfer. Recently, the deregulation of symbiotic host-microbe interactions in early life has been shown to alter the maturation of the immune system, predisposing the host to gut barrier dysfunction and inflammation. The main goal of this study is to decipher the role of the early-life gut microbiota-barrier alterations and its links with later-life risks of intestinal inflammation in a murine model of CSD.

RESULTS

The higher sensitivity to chemically induced inflammation in CSD mice is related to excessive exposure to a too diverse microbiota too early in life. This early microbial stimulus has short-term consequences on the host homeostasis. It switches the pup's immune response to an inflammatory context and alters the epithelium structure and the mucus-producing cells, disrupting gut homeostasis. This presence of a too diverse microbiota in the very early life involves a disproportionate short-chain fatty acids ratio and an excessive antigen exposure across the vulnerable gut barrier in the first days of life, before the gut closure. Besides, as shown by microbiota transfer experiments, the microbiota is causal in the high sensitivity of CSD mice to chemical-induced colitis and in most of the phenotypical parameters found altered in early life. Finally, supplementation with lactobacilli, the main bacterial group impacted by CSD in mice, reverts the higher sensitivity to inflammation in ex-germ-free mice colonized by CSD pups' microbiota.

CONCLUSIONS

Early-life gut microbiota-host crosstalk alterations related to CSD could be the linchpin behind the phenotypic effects that lead to increased susceptibility to an induced inflammation later in life in mice. Video Abstract.

Integrative profiling of gene expression and chromatin accessibility elucidates specific transcriptional networks in porcine neutrophils

Neutrophils are vital components of the immune system for limiting the invasion and proliferation of pathogens in the body. Surprisingly, the functional annotation of porcine neutrophils is still limited. The transcriptomic and epigenetic assessment of porcine neutrophils from healthy pigs was performed by bulk RNA sequencing and transposase accessible chromatin sequencing (ATAC-seq). First, we sequenced and compared the transcriptome of porcine neutrophils with eight other immune cell transcriptomes to identify a neutrophil-enriched gene list within a detected neutrophil co-expression module. Second, we used ATAC-seq analysis to report for the first time the genome-wide chromatin accessible regions of porcine neutrophils. A combined analysis using both transcriptomic and chromatin accessibility data further defined the neutrophil co-expression network controlled by transcription factors likely important for neutrophil lineage commitment and function. We identified chromatin accessible regions around promoters of neutrophil-specific genes that were predicted to be bound by neutrophil-specific transcription factors. Additionally, published DNA methylation data from porcine immune cells including neutrophils were used to link low DNA methylation patterns to accessible chromatin regions and genes with highly enriched expression in porcine neutrophils. In summary, our data provides the first integrative analysis of the accessible chromatin regions and transcriptional status of porcine neutrophils, contributing to the Functional Annotation of Animal Genomes (FAANG) project, and demonstrates the utility of chromatin accessible regions to identify and enrich our understanding of transcriptional networks in a cell type such as neutrophils.

miRNA expression profiles of peripheral white blood cells from beef heifers with varying reproductive potential

Reproductive performance is the most critical factor affecting production efficiency in the cow-calf industry. Heifers with low reproductive efficiency may fail to become pregnant during the breeding season or maintain a pregnancy. The cause of reproductive failure often remains unknown, and the non-pregnant heifers are not identified until several weeks after the breeding season. Therefore, improving heifer fertility utilizing genomic information has become increasingly important. One approach is using microRNAs (miRNA) in the maternal blood that play an important role in regulating the target genes underlying pregnancy success and thereby in selecting reproductively efficient heifers. Therefore, the current study hypothesized that miRNA expression profiles from peripheral white blood cells (PWBC) at weaning could predict the future reproductive outcome of beef heifers. To this end, we measured the miRNA profiles using small RNA-sequencing in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 7) or subfertile (SFH, n = 7). In addition to differentially expressed miRNAs (DEMIs), their target genes were predicted from TargetScan. The PWBC gene expression from the same heifers were retrieved and co-expression networks were constructed between DEMIs and their target genes. We identified 16 differentially expressed miRNAs between the groups (p-value ≤0.05 and absolute (log2 fold change ≥0.05)). Interestingly, based on a strong negative correlation identified from miRNA-gene network analysis with PCIT (partial correlation and information theory), we identified miRNA-target genes in the SFH group. Additionally, TargetScan predictions and differential expression analysis identified bta-miR-1839 with ESR1 , bta-miR-92b with KLF4 and KAT2B, bta-miR-2419-5p with LILRA4, bta-miR-1260b with UBE2E1, SKAP2 and CLEC4D, and bta-let-7a-5p with GATM, MXD1 as miRNA-gene targets. The miRNA-target gene pairs in the FH group are over-represented for MAPK, ErbB, HIF-1, FoxO, p53, mTOR, T-cell receptor, insulin and GnRH signaling pathways, while those in the SFH group include cell cycle, p53 signaling pathway and apoptosis. Some miRNAs, miRNA-target genes and regulated pathways identified in this study have a potential role in fertility; other targets are identified as novel and need to be validated in a bigger cohort that could help to predict the future reproductive outcomes of beef heifers.

The coral microbiome: towards an understanding of the molecular mechanisms of coral-microbiota interactions

Corals live in a complex, multipartite symbiosis with diverse microbes across kingdoms, some of which are implicated in vital functions, such as those related to resilience against climate change. However, knowledge gaps and technical challenges limit our understanding of the nature and functional significance of complex symbiotic relationships within corals. Here, we provide an overview of the complexity of the coral microbiome focusing on taxonomic diversity and functions of well-studied and cryptic microbes. Mining the coral literature indicate that while corals collectively harbour a third of all marine bacterial phyla, known bacterial symbionts and antagonists of corals represent a minute fraction of this diversity and that these taxa cluster into select genera, suggesting selective evolutionary mechanisms enabled these bacteria to gain a niche within the holobiont. Recent advances in coral microbiome research aimed at leveraging microbiome manipulation to increase coral's fitness to help mitigate heat stress-related mortality are discussed. Then, insights into the potential mechanisms through which microbiota can communicate with and modify host responses are examined by describing known recognition patterns, potential microbially derived coral epigenome effector proteins and coral gene regulation. Finally, the power of omics tools used to study corals are highlighted with emphasis on an integrated host-microbiota multiomics framework to understand the underlying mechanisms during symbiosis and climate change-driven dysbiosis.

Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Capsicum annuum L.)

Gene co-expression networks are powerful tools to understand functional interactions between genes. However, large co-expression networks are difficult to interpret and do not guarantee that the relations found will be true for different genotypes. Statistically verified time expression profiles give information about significant changes in expressions through time, and genes with highly correlated time expression profiles, which are annotated in the same biological process, are likely to be functionally connected. A method to obtain robust networks of functionally related genes will be useful to understand the complexity of the transcriptome, leading to biologically relevant insights. We present an algorithm to construct gene functional networks for genes annotated in a given biological process or other aspects of interest. We assume that there are genome-wide time expression profiles for a set of representative genotypes of the species of interest. The method is based on the correlation of time expression profiles, bound by a set of thresholds that assure both, a given false discovery rate, and the discard of correlation outliers. The novelty of the method consists in that a gene expression relation must be repeatedly found in a given set of independent genotypes to be considered valid. This automatically discards relations particular to specific genotypes, assuring a network robustness, which can be set a priori. Additionally, we present an algorithm to find transcription factors candidates for regulating hub genes within a network. The algorithms are demonstrated with data from a large experiment studying gene expression during the development of the fruit in a diverse set of chili pepper genotypes. The algorithm is implemented and demonstrated in a new version of the publicly available R package "Salsa" (version 1.0).

mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development.

Identifying miRNA-mRNA regulatory networks on extreme n-6/n-3 polyunsaturated fatty acid ratio expression profiles in porcine skeletal muscle

Omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) are essential fatty acids with antagonistic inflammatory functions that play vital roles in metabolic health and immune response. Current commercial swine diets tend to over-supplement with n-6 PUFAs, which may increase the likelihood of developing inflammatory diseases and affect the overall well-being of the animals. However, it is still poorly understood how n-6/n-3 PUFA ratios affect the porcine transcriptome expression and how messenger RNAs (mRNAs) and microRNAs (miRNAs) might regulate biological processes related to PUFA metabolism. On account of this, we selected a total of 20 Iberian × Duroc crossbred pigs with extreme values for n-6/n-3 FA ratio (10 high vs 10 low), and longissimus dorsi muscle samples were used to identify differentially expressed mRNAs and miRNAs. The observed differentially expressed mRNAs were associated to biological pathways related to muscle growth and immunomodulation, while the differentially expressed microRNAs (ssc-miR-30a-3p, ssc-miR-30e-3p, ssc-miR-15b and ssc-miR-7142-3p) were correlated to adipogenesis and immunity. Relevant miRNA-to-mRNA regulatory networks were also predicted (i.e., mir15b to ARRDC3; mir-7142-3p to METTL21C), and linked to lipolysis, obesity, myogenesis, and protein degradation. The n-6/n-3 PUFA ratio differences in pig skeletal muscle revealed genes, miRNAs and enriched pathways involved in lipid metabolism, cell proliferation and inflammation.

Multi-breed genomic predictions and functional variants for fertility of tropical bulls

Worldwide, most beef breeding herds are naturally mated. As such, the ability to identify and select fertile bulls is critically important for both productivity and genetic improvement. Here, we collected ten fertility-related phenotypes for 6,063 bulls from six tropically adapted breeds. Phenotypes were comprised of four bull conformation traits and six traits directly related to the quality of the bull's semen. We also generated high-density DNA genotypes for all the animals. In total, 680,758 single nucleotide polymorphism (SNP) genotypes were analyzed. The genomic correlation of the same trait observed in different breeds was positive for scrotal circumference and sheath score on most breed comparisons, but close to zero for the percentage of normal sperm, suggesting a divergent genetic background for this trait. We confirmed the importance of a breed being present in the reference population to the generation of accurate genomic estimated breeding values (GEBV) in an across-breed validation scenario. Average GEBV accuracies varied from 0.19 to 0.44 when the breed was not included in the reference population. The range improved to 0.28 to 0.59 when the breed was in the reference population. Variants associated with the gene HDAC4, six genes from the spermatogenesis-associated (SPATA) family of proteins, and 29 transcription factors were identified as candidate genes. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems. This study also improves our understanding of the molecular basis of male fertility in mammals.

Structural variation and eQTL analysis in two experimental populations of chickens divergently selected for feather-pecking behavior

Feather pecking (FP) is a damaging nonaggressive behavior in laying hens with a heritable component. Its occurrence has been linked to the immune system, the circadian clock, and foraging behavior. Furthermore, dysregulation of miRNA biogenesis, disturbance of the gamma-aminobutyric acid (GABAergic) system, as well as neurodevelopmental deficiencies are currently under debate as factors influencing the propensity for FP behavior. Past studies, which focused on the dissection of the genetic factors involved in FP, relied on single nucleotide polymorphisms (SNPs) and short insertions and deletions < 50 bp (InDels). These variant classes only represent a certain fraction of the genetic variation of an organism. Hence, we reanalyzed whole-genome sequencing data from two experimental populations, which have been divergently selected for FP behavior for over more than 15 generations, performed variant calling for structural variants (SVs) as well as tandem repeats (TRs), and jointly analyzed the data with SNPs and InDels. Genotype imputation and subsequent genome-wide association studies, in combination with expression quantitative trait loci analysis, led to the discovery of multiple variants influencing the GABAergic system. These include a significantly associated TR downstream of the GABA receptor subunit beta-3 (GABRB3) gene, two microRNAs targeting several GABA receptor genes, and dystrophin (DMD), a direct regulator of GABA receptor clustering. Furthermore, we found the transcription factor ETV1 to be associated with the differential expression of 23 genes, which points toward a role of ETV1, together with SMAD4 and KLF14, in the disturbed neurodevelopment of high-feather pecking chickens.

Identification of transcriptional regulatory variants in pig duodenum, liver, and muscle tissues

BACKGROUND

In humans and livestock species, genome-wide association studies (GWAS) have been applied to study the association between variants distributed across the genome and a phenotype of interest. To discover genetic polymorphisms affecting the duodenum, liver, and muscle transcriptomes of 300 pigs from 3 different breeds (Duroc, Landrace, and Large White), we performed expression GWAS between 25,315,878 polymorphisms and the expression of 13,891 genes in duodenum, 12,748 genes in liver, and 11,617 genes in muscle.

RESULTS

More than 9.68 × 1011 association tests were performed, yielding 14,096,080 significantly associated variants, which were grouped in 26,414 expression quantitative trait locus (eQTL) regions. Over 56% of the variants were within 1 Mb of their associated gene. In addition to the 100-kb region upstream of the transcription start site, we identified the importance of the 100-kb region downstream of the 3'UTR for gene regulation, as most of the cis-regulatory variants were located within these 2 regions. We also observed 39,874 hotspot regulatory polymorphisms associated with the expression of 10 or more genes that could modify the protein structure or the expression of a regulator gene. In addition, 2 motifs (5'-GATCCNGYGTTGCYG-3' and a poly(A) sequence) were enriched across the 3 tissues within the neighboring sequences of the most significant single-nucleotide polymorphisms in each cis-eQTL region.

CONCLUSIONS

The 14 million significant associations obtained in this study are publicly available and have enabled the identification of expression-associated cis-, trans-, and hotspot regulatory variants within and across tissues, thus shedding light on the molecular mechanisms of regulatory variations that shape end-trait phenotypes.

Feature learning and network structure from noisy node activity data

In the studies of network structures, much attention has been devoted to developing approaches to reconstruct networks and predict missing links when edge-related information is given. However, such approaches are not applicable when we are only given noisy node activity data with missing values. This work presents an unsupervised learning framework to learn node vectors and construct networks from such node activity data. First, we design a scheme to generate random node sequences from node context sets, which are generated from node activity data. Then, a three-layer neural network is adopted training the node sequences to obtain node vectors, which allow us to construct networks and capture nodes with synergistic roles. Furthermore, we present an entropy-based approach to select the most meaningful neighbors for each node in the resulting network. Finally, the effectiveness of the method is validated through both synthetic and real data.

Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

BACKGROUND

Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques.

OBJECTIVE

This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways.

METHODS

The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medicine perspectives and the coupling of ML techniques. Each abstract and full-text study was individually evaluated, and the relevant studies were finally included in the review for discussion once they met the inclusion and exclusion criteria.

RESULTS

We identified 109 eligible publications from primary studies representing this systematic review. The data coalesced into two themes: application of network science to identify disease modules and promising biomarkers in ALS, along with network-based ML approaches. Conclusion This systematic review gives an overview of the network medicine approaches and implementations of network-based ML algorithms in ALS to determine new disease genes, and identify critical pathways and therapeutic target discovery for personalized treatment.

Evaluation of Fusarium Head Blight Resistance Effects by Haplotype-Based Genome-Wide Association Study in Winter Wheat Lines Derived by Marker Backcrossing Approach

Fusarium head blight (FHB) of wheat caused by Fusarium species is a destructive disease, causing grain yield and quality losses. Developing FHB-resistant cultivars is crucial to minimize the extent of the disease. The first objective of this study was incorporation of Fhb1 from a resistant donor into five Polish wheat breeding lines with good agronomical traits and different origins. We also performed a haplotype-based GWAS to identify chromosome regions in derived wheat families associated with Fusarium head blight resistance. As a result of marker-assisted backcrossing (MABC), five wheat combinations were obtained. Fungal inoculation and disease assessment were conducted for two years, 2019 and 2020. In 2019 the average phenotypic response of type II resistance was 2.2, whereas in 2020 it was 2.1. A haploblock-based GWAS performed on 10 phenotypic traits (related to type of resistance, year of experiment and FHB index) revealed nine marker-trait associations (MTA), among which six belong to chromosome 2D, two to 3B and one to 7D. Phenotypic variation (R²) explained by the identified haplotypes in haploblocks ranged from 6% to 49%. Additionally, an association weight matrix (AWM) was created, giving the partial correlation-information theory (PCIT) pipeline of 171 edges and 19 nodes. The resultant data and high level of explained phenotypic variance of MTA create the opportunity for data utilization in MAS.

The mechanism of lncRNAs in the crosstalk between epithelial-mesenchymal transition and tumor microenvironment for early colon adenocarcinoma based on molecular subtyping

A large number of colon adenocarcinoma (COAD) patients are already advanced when diagnosed. In this study, we aimed to further understand the mechanism of tumor development in early COAD by focusing on epithelial-mesenchymal transition (EMT) and long non-coding RNAs (lncRNAs). Expression profiles of early COAD patients were obtained from public databases. EMT-related lncRNAs were used as a basis for constructing molecular subtypes through unsupervised consensus clustering. Genomic features, pathways and tumor microenvironment (TME) were compared between two subtypes. LncATLAS database was applied to analyze the relation between lncRNAs and transcription factors (TFs). First order partial correlation analysis was conducted to identify key EMT-related lncRNAs.C1 and C2 subtypes with distinct prognosis were constructed. Oncogenic pathways such as EMT, KRAS signaling, JAK-STAT signaling, and TGF-β signaling were significantly enriched in C2 subtype. Higher immune infiltration and expression of immune checkpoints were also observed in C2 subtype, suggesting the key EMT-related lncRNAs may play a critical role in the modulation of TME. In addition, JAK-STAT signaling pathway was obviously enriched in upregulated TFs in C2 subtype, which indicated a link between key lncRNAs and JAK-STAT signaling that may regulate TME. The study further expanded the research on the role of EMT-related lncRNAs in the early COAD. The six identified EMT-related lncRNAs could serve as biomarkers for early screening COAD.

Fine-tuning genomic and pedigree inbreeding rates in equine population with a deep and reliable stud book: the case of the Pura Raza Española horse

BACKGROUND

Estimating inbreeding, which is omnipresent and inevitable in livestock populations, is a primary goal for management and animal breeding especially for those interested in mitigating the negative consequences of inbreeding. Inbreeding coefficients have been historically estimated by using pedigree information; however, over the last decade, genome-base inbreeding coefficients have come to the forefront in this field. The Pura Raza Española (PRE) horse is an autochthonous Spanish horse breed which has been recognised since 1912. The total PRE population (344,718 horses) was used to estimate Classical (F), Ballou's ancestral, Kalinowski's ancestral, Kalinowski's new and the ancestral history coefficient values. In addition, genotypic data from a selected population of 805 PRE individuals was used to determine the individual inbreeding coefficient using SNP-by-SNP-based techniques (methods of moments -F_HOM-, the diagonal elements of the genomic -F_G-, and hybrid matrixes -F_H-) and ROH measures (F_RZ). The analyse of both pedigree and genomic based inbreeding coefficients in a large and robust population such as the PRE horse, with proven parenteral information for the last 40 years and a high degree of completeness (over 90% for the last 70 years) will allow us to understand PRE genetic variability better and the correlations between the estimations will give the data greater reliability.

RESULTS

The mean values of the pedigree-based inbreeding coefficients ranged from 0.01 (F for the last 3 generations -F3-) to 0.44 (ancestral history coefficient) and the mean values of genomic-based inbreeding coefficients varied from 0.05 (F_RZ for three generations, F_H and F_HOM) to 0.11 (F_RZ for nine generations). Significant correlations were also found between pedigree and genomic inbreeding values, which ranged between 0.58 (F3 with F_HOM) and 0.79 (F with F_RZ). In addition, the correlations between F_RZ estimated for the last 20 generations and the pedigree-based inbreeding highlight the fact that fewer generations of genomic data are required when comparing total inbreeding values, and the opposite when ancient values are calculated.

CONCLUSIONS

Ultimately, our results show that it is still useful to work with a deep and reliable pedigree in pedigree-based genetic studies with very large effective population sizes. Obtaining a satisfactory parameter will always be desirable, but the approximation obtained with a robust pedigree will allow us to work more efficiently and economically than with massive genotyping.

Differential gene co-expression network analyses reveal novel molecules associated with transcriptional dysregulation of key biological processes in osteoarthritis knee cartilage

OBJECTIVES

To compare co-expression networks of normal and osteoarthritis knee cartilage to uncover molecules associated with the transcriptional misregulation compromising biological processes (BPs) critical for cartilage homeostasis.

DESIGN

Normal and osteoarthritis human knee cartilage RNA-seq GSE114007 dataset was obtained from the Gene Expression Omnibus database. Partial Correlation and Information Theory (PCIT) algorithm was used to build co-expression networks containing all nodes connecting to at least one differentially expressed gene (DEG) in normal and osteoarthritis networks. Hub and hub centrality genes were used to perform functional enrichment analysis. Enriched BPs known to be associated with both healthy and diseased cartilage were compared in depth.

RESULTS

Differential co-expression network analyses allowed the identification of DDX43 and USP42 as exclusively co-expressed with DEGs in normal and osteoarthritis networks, respectively. The top hub and hub centrality genes of these networks were HIST1H3A and SNHG12 (normal) and TAF9B and OTUD1 (osteoarthritis). Enrichment analysis revealed several shared BPs between the contrasting groups, which are well-known in osteoarthritis pathogenesis. Protein-protein interaction network analysis for these BPs showed a global down-regulation of transcription factors in osteoarthritis. Specific transcription factors were identified as pleiotropic mediators in articular cartilage maintenance since they take part in several BPs. In addition, chromatin organisation and modification proteins were found relevant for osteoarthritis development.

CONCLUSION

Differential gene co-expression analysis allowed the identification of novel and high priority therapeutic candidate genes that may drive modifications in the transcriptional "status" of cartilage in osteoarthritis.

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Simple Summary

Female reproductive failure is still a challenge for the beef industry. Several biological processes that underlie fertility-related traits, such as the establishment of pregnancy and embryo survival, are still unclear. Increased availability of transcriptomic data has allowed a deep investigation of the potential mechanisms involved in fertility. This study investigated candidate gene biomarkers predictive of pregnancy status and underlying fertility-related networks. To this end, we integrated gene expression profiles through supervised machine learning and gene network modeling. We identified nine biologically relevant endometrial gene biomarkers that could discriminate against pregnancy status in cows. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. This study outlined key pathways involved with pregnancy success and provided predictive candidate biomarkers for pregnancy outcome in cows.

Abstract

Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.

Conservative significance testing of tripartite statistical relations in multivariate neural data

An important goal in systems neuroscience is to understand the structure of neuronal interactions, frequently approached by studying functional relations between recorded neuronal signals. Commonly used pairwise measures (e.g., correlation coefficient) offer limited insight, neither addressing the specificity of estimated neuronal interactions nor potential synergistic coupling between neuronal signals. Tripartite measures, such as partial correlation, variance partitioning, and partial information decomposition, address these questions by disentangling functional relations into interpretable information atoms (unique, redundant, and synergistic). Here, we apply these tripartite measures to simulated neuronal recordings to investigate their sensitivity to noise. We find that the considered measures are mostly accurate and specific for signals with noiseless sources but experience significant bias for noisy sources.We show that permutation testing of such measures results in high false positive rates even for small noise fractions and large data sizes. We present a conservative null hypothesis for significance testing of tripartite measures, which significantly decreases false positive rate at a tolerable expense of increasing false negative rate. We hope our study raises awareness about the potential pitfalls of significance testing and of interpretation of functional relations, offering both conceptual and practical advice.

eQTL analysis of laying hens divergently selected for feather pecking identifies KLF14 as a potential key regulator for this behavioral disorder

Feather pecking in chickens is a damaging behavior, seriously impacting animal welfare and leading to economic losses. Feather pecking is a complex trait, which is partly under genetic control. Different hypotheses have been proposed to explain the etiology of feather pecking and notably, several studies have identified similarities between feather pecking and human mental disorders such as obsessive-compulsive disorder and schizophrenia. This study uses transcriptomic and phenotypic data from 167 chickens to map expression quantitative trait loci and to identify regulatory genes with a significant effect on this behavioral disorder using an association weight matrix approach. From 70 of the analyzed differentially expressed genes, 11,790 genome wide significantly associated variants were detected, of which 23 showed multiple associations (≥15). These were located in proximity to a number of genes, which are transcription regulators involved in chromatin binding, nucleic acid metabolism, protein translation and putative regulatory RNAs. The association weight matrix identified 36 genes and the two transcription factors: SP6 (synonym: KLF14) and ENSGALG00000042129 (synonym: CHTOP) as the most significant, with an enrichment of KLF14 binding sites being detectable in 40 differentially expressed genes. This indicates that differential expression between animals showing high and low levels of feather pecking was significantly associated with a genetic variant in proximity to KLF14. This multiallelic variant was located 652 bp downstream of KLF14 and is a deletion of 1-3 bp. We propose that a deletion downstream of the transcription factor KLF14 has a negative impact on the level of T cells in the developing brain of high feather pecking chickens, which leads to developmental and behavioral abnormalities. The lack of CD4 T cells and gamma-Aminobutyric acid (GABA) receptors are important factors for the increased propensity of laying hens to perform feather pecking. As such, KLF14 is a clear candidate regulator for the expression of genes involved in the pathogenic development. By further elucidating the regulatory pathways involved in feather pecking we hope to take significant steps forward in explaining and understanding other mental disorders, not just in chickens.

scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms

Single-cell RNA-sequencing (scRNA-seq) is a recent high-throughput technique that can measure gene expression, reveal cell heterogeneity, rare and complex cell populations, and discover cell types and their relationships. The analysis of scRNA-seq data is challenging because of transcripts sparsity, replication noise, and outlier cell populations. A gene coexpression network (GCN) analysis effectively deciphers phenotypic differences in specific states by describing gene–gene pairwise relationships. The underlying gene modules with different coexpression patterns partially bridge the gap between genotype and phenotype. This study presents a new framework called scGENA (single-cell gene coexpression network analysis) for GCN analysis based on scRNA-seq data. Although there are several methods for scRNA-seq data analysis, we aim to build an integrative pipeline for several purposes that cover primary data preprocessing, including data exploration, quality control, normalization, imputation, and dimensionality reduction of clustering as downstream of GCN analysis. To demonstrate this integrated workflow, an scRNA-seq dataset of the human diabetic pancreas with 1600 cells and 39,851 genes was implemented to perform all these processes in practice. As a result, scGENA is demonstrated to uncover interesting gene modules behind complex diseases, which reveal biological mechanisms. scGENA provides a state-of-the-art method for gene coexpression analysis for scRNA-seq data.

Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers

Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified TGM2, TMEM51, TAC3, NDRG4, and PDGFB as more connected in the NP heifers’ network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene–metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with EXOSC2, TRNAUIAP, and SNX12. In the NP group, α-ketoglutarate-SMG8 and putrescine-HSD17B13 were positively correlated, whereas a-ketoglutarate-ALAS2 and tryptophan-MTMR1 were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines.

Identification of fatty acid metabolism-related lncRNAs in the prognosis and immune microenvironment of colon adenocarcinoma

BACKGROUND

Cancer metabolism is largely altered compared to normal cells. This study aims to explore critical metabolism pathways in colon adenocarcinoma (COAD), and reveal the possible mechanism of their role in cancer progression.

METHODS

Expression data and sequencing data of COAD samples were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The expression profiles between tumor and normal samples were compared to identify differential metabolism pathways through single sample gene set enrichment analysis.

RESULTS

Fatty acid synthesis was identified as a key metabolism pathway in COAD. Based on fatty acid-related lncRNAs, two molecular subtypes (C1 and C2) were defined. C2 subtype with worse prognosis had higher immune infiltration and higher expression of immune checkpoints. Five transcription factors (TFs) including FOS, JUN, HIF1A, STAT3 and STAT2 were highly expressed in C2 subtype. Five fatty acid-related lncRNAs were identified to be biomarkers for predicting COAD prognosis. Finally, further experients showed that knockdown of lncRNA PAXIP1-AS1 decreased the triglyceride content and the fatty acid synthase and acetyl-CoA carboxylase 1 expressions, which suggested that lncRNA PAXIP1-AS1 plays an important role in fatty acid metabolism of COAD.

CONCLUSIONS

This study demonstrated that fatty acid synthesis was greatly altered in COAD. Fatty acid-related lncRNAs were speculated to be involved in cancer progression through associating with TFs. The five screened TFs may serve as new drug targets for treating COAD.

LncRNA-FAM66C Was Identified as a Key Regulator for Modulating Tumor Microenvironment and Hypoxia-Related Pathways in Glioblastoma

Although the role of hypoxia has been greatly explored and unveiled in glioblastoma (GBM), the mechanism of hypoxia-related long non-coding (lnc) RNAs has not been clearly understood. This study aims to reveal the crosstalk among hypoxia-related lncRNAs, tumor microenvironment (TME), and tumorigenesis for GBM. Gene expression profiles of GBM patients were used as a basis for identifying hypoxia-related lncRNAs. Unsupervised consensus clustering was conducted for classifying samples into different molecular subtypes. Gene set enrichment analysis (GSEA) was performed to analyze the enrichment of a series of genes or gene signatures. Three molecular subtypes were constructed based on eight identified hypoxia-related lncRNAs. Oncogenic pathways, such as epithelial mesenchymal transition (EMT), tumor necrosis factor-α (TNF-α) signaling, angiogenesis, hypoxia, P53 signaling, and glycolysis pathways, were significantly enriched in C1 subtype with poor overall survival. C1 subtype showed high immune infiltration and high expression of immune checkpoints. Furthermore, we identified 10 transcription factors (TFs) that were highly correlated with lncRNA-FAM66C. Three key lncRNAs (ADAMTS9-AS2, LINC00968, and LUCAT1) were screened as prognostic biomarkers for GBM. This study shed light on the important role of hypoxia-related lncRNAs for TME modulation and tumorigenesis in GBM. The eight identified hypoxia-related lncRNAs, especially FAM66C may serve as key regulators involving in hypoxia-related pathways.

EEF1A1 transcription cofactor gene polymorphism is associated with muscle gene expression and residual feed intake in Nelore cattle

Cis-acting effects of noncoding variants on gene expression and regulatory molecules constitute a significant factor for phenotypic variation in complex traits. To provide new insights into the impacts of single-nucleotide polymorphisms (SNPs) on transcription factors (TFs) and transcription cofactors (TcoF) coding genes, we carried out a multi-omic analysis to identify cis-regulatory effects of SNPs on these genes' expression in muscle and describe their association with feed efficiency-related traits in Nelore cattle. As a result, we identified one SNP, the rs137256008C > T, predicted to impact the EEF1A1 gene expression (β = 3.02; P-value = 3.51E-03) and the residual feed intake trait (β = - 3.47; P-value = 0.02). This SNP was predicted to modify transcription factor sites and overlaps with several QTL for feed efficiency traits. In addition, co-expression network analyses showed that animals containing the T allele of the rs137256008 SNP may be triggering changes in the gene network. Therefore, our analyses reinforce and contribute to a better understanding of the biological mechanisms underlying gene expression control of feed efficiency traits in bovines. The cis-regulatory SNP can be used as biomarker for feed efficiency in Nelore cattle.

Modeling microRNA-driven post-transcriptional regulation using exon-intron split analysis in pigs

The contribution of microRNAs (miRNAs) to mRNA post-transcriptional regulation has often been explored by the post hoc selection of downregulated genes and determining whether they harbor binding sites for miRNAs of interest. This approach, however, does not discriminate whether these mRNAs are also downregulated at the transcriptional level. Here, we have characterized the transcriptional and post-transcriptional changes in mRNA expression in two porcine tissues: gluteus medius muscle of fasted and fed Duroc gilts and adipose tissue of lean and obese Duroc-Göttingen minipigs. Exon-intron split analysis of RNA-seq data allowed us to identify downregulated mRNAs with high post-transcriptional signals in fed or obese states, and we assessed whether they harbor binding sites for upregulated miRNAs in any of these two physiological states. We found 26 downregulated mRNAs with high post-transcriptional signals in the muscle of fed gilts and 21 of these were predicted targets of miRNAs upregulated in fed pigs. For adipose tissue, 44 downregulated mRNAs in obese minipigs displayed high post-transcriptional signals, and 25 of these were predicted targets of miRNAs upregulated in the obese state. These results suggest that the contribution of miRNAs to mRNA repression is more prominent in the skeletal muscle system. Finally, we identified several genes that may play relevant roles in the energy homeostasis of the pig skeletal muscle (DKK2 and PDK4) and adipose (SESN3 and ESRRG) tissues. By differentiating transcriptional from post-transcriptional changes in mRNA expression, exon-intron split analysis provides a valuable view of the regulation of gene expression, complementary to canonical differential expression analyses.

Competing endogenous RNA-networks reveal key regulatory microRNAs involved in the response of Atlantic salmon to a novel orthomyxovirus

One of the most intriguing discoveries of the genomic era is that only a small fraction of the genome is dedicated to protein coding. The remaining fraction of the genome contains, amongst other elements, a number of non-coding transcripts that regulate the transcription of protein coding genes. Here we used transcriptome sequencing data to explore these gene regulatory networks using RNA derived from gill tissue of Atlantic salmon (Salmo salar) infected with Pilchard orthomyxovirus (POMV), but showing no clinical signs of disease. We examined fish sampled early during the challenge trial (8-12 days after infection) to uncover potential biomarkers of early infection and innate immunity, and fish sampled late during the challenge trial (19 dpi) to elucidate potential markers of resistance to POMV. We analysed total RNA-sequencing data to find differentially expressed messenger RNAs (mRNA) and identify new long-noncoding RNAs (lncRNAs). We also evaluated small RNA sequencing data to find differentially transcribed microRNAs (miRNAs) and explore their role in gene regulatory networks. Whole-genome expression data (both coding and non-coding transcripts) were used to explore the crosstalk between RNA molecules by constructing competing endogenous RNA networks (ceRNA). The teleost specific miR-462/miR-731 cluster was strongly induced in POMV infected fish and deemed a potential biomarker of early infection. Gene networks also identified a selenoprotein (selja), downregulated in fish sampled late during the challenge, which may be associated to viral clearance and the return to homeostasis after infection. This study provides the basis for further investigations using molecular tools to overexpress or inhibit miRNAs to confirm the functional impact of the interactions presented here on gene expression and their potential application at commercial level.

Leveraging transcriptome and epigenome landscapes to infer regulatory networks during the onset of sexual maturation

Background

Despite sexual development being ubiquitous to vertebrates, the molecular mechanisms underpinning this fundamental transition remain largely undocumented in many organisms. We designed a time course experiment that successfully sampled the period when Atlantic salmon commence their trajectory towards sexual maturation.

Results

Through deep RNA sequencing, we discovered key genes and pathways associated with maturation in the pituitary-ovarian axis. Analyzing DNA methylomes revealed a bias towards hypermethylation in ovary that implicated maturation-related genes. Co-analysis of DNA methylome and gene expression changes revealed chromatin remodeling genes and key transcription factors were both significantly hypermethylated and upregulated in the ovary during the onset of maturation. We also observed changes in chromatin state landscapes that were strongly correlated with fundamental remodeling of gene expression in liver. Finally, a multiomic integrated analysis revealed regulatory networks and identified hub genes including TRIM25 gene (encoding the estrogen-responsive finger protein) as a putative key regulator in the pituitary that underwent a 60-fold change in connectivity during the transition to maturation.

Conclusion

The study successfully documented transcriptome and epigenome changes that involved key genes and pathways acting in the pituitary – ovarian axis. Using a Systems Biology approach, we identified hub genes and their associated networks deemed crucial for onset of maturation. The results provide a comprehensive view of the spatiotemporal changes involved in a complex trait and opens the door to future efforts aiming to manipulate puberty in an economically important aquaculture species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08514-8.

Insights into the Transcriptional Reprogramming in Tomato Response to PSTVd Variants Using Network Approaches

Viroids are the smallest pathogens of angiosperms, consisting of non-coding RNAs that cause severe diseases in agronomic crops. Symptoms associated with viroid infection are linked to developmental alterations due to genetic regulation. To understand the global mechanisms of host viroid response, we implemented network approaches to identify master transcription regulators and their differentially expressed targets in tomato infected with mild and severe variants of PSTVd. Our approach integrates root and leaf transcriptomic data, gene regulatory network analysis, and identification of affected biological processes. Our results reveal that specific bHLH, MYB, and ERF transcription factors regulate genes involved in molecular mechanisms underlying critical signaling pathways. Functional enrichment of regulons shows that bHLH-MTRs are linked to metabolism and plant defense, while MYB-MTRs are involved in signaling and hormone-related processes. Strikingly, a member of the bHLH-TF family has a specific potential role as a microprotein involved in the post-translational regulation of hormone signaling events. We found that ERF-MTRs are characteristic of severe symptoms, while ZNF-TF, tf3a-TF, BZIP-TFs, and NAC-TF act as unique MTRs. Altogether, our results lay a foundation for further research on the PSTVd and host genome interaction, providing evidence for identifying potential key genes that influence symptom development in tomato plants.

Transcriptome Profile Reveals Genetic and Metabolic Mechanisms Related to Essential Fatty Acid Content of Intramuscular Longissimus thoracis in Nellore Cattle

Beef is a source of essential fatty acids (EFA), linoleic (LA) and alpha-linolenic (ALA) acids, which protect against inflammatory and cardiovascular diseases in humans. However, the intramuscular EFA profile in cattle is a complex and polygenic trait. Thus, this study aimed to identify potential regulatory genes of the essential fatty acid profile in Longissimus thoracis of Nellore cattle finished in feedlot. Forty-four young bulls clustered in four groups of fifteen animals with extreme values for each FA were evaluated through differentially expressed genes (DEG) analysis and two co-expression methodologies (WGCNA and PCIT). We highlight the ECHS1, IVD, ASB5, and ERLIN1 genes and the TF NFIA, indicated in both FA. Moreover, we associate the NFYA, NFYB, PPARG, FASN, and FADS2 genes with LA, and the RORA and ELOVL5 genes with ALA. Furthermore, the functional enrichment analysis points out several terms related to FA metabolism. These findings contribute to our understanding of the genetic mechanisms underlying the beef EFA profile in Nellore cattle finished in feedlot.

A global [Formula: see text] gene co-expression network constructed from hundreds of experimental conditions with missing values

BACKGROUND

Gene co-expression networks (GCNs) can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze co-expression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes.

RESULTS

In this paper, we establish a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The resulting network, which we name AgGCN1.0, is robust to random removal of conditions and has similar characteristics to small-world and scale-free networks. Analysis of network sub-graphs revealed that the core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes.

CONCLUSION

Analysis of the network reveals that both the architecture of the core sub-network and the network communities are based on gene function, supporting the power of the proposed method for GCN construction. Application of network science methodology reveals that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

Prediction of Metabolic Profiles from Transcriptomics Data in Human Cancer Cell Lines

The Metabolome and Transcriptome are mutually communicating within cancer cells, and this interplay is translated into the existence of quantifiable correlation structures between gene expression and metabolite abundance levels. Studying these correlations could provide a novel venue of understanding cancer and the discovery of novel biomarkers and pharmacological strategies, as well as laying the foundation for the prediction of metabolite quantities by leveraging information from the more widespread transcriptomics data. In the current paper, we investigate the correlation between gene expression and metabolite levels in the Cancer Cell Line Encyclopedia dataset, building a direct correlation network between the two molecular ensembles. We show that a metabolite/transcript correlation network can be used to predict metabolite levels in different samples and datasets, such as the NCI-60 cancer cell line dataset, both on a sample-by-sample basis and in differential contrasts. We also show that metabolite levels can be predicted in principle on any sample and dataset for which transcriptomics data are available, such as the Cancer Genome Atlas (TCGA).

A Co-Association Network Analysis Reveals Putative Regulators for Health-Related Traits in Pigs

In recent years, the increase in awareness of antimicrobial resistance together with the societal demand of healthier meat products have driven attention to health-related traits in livestock production. Previous studies have reported medium to high heritabilities for these traits and described genomic regions associated with them. Despite its genetic component, health- and immunity-related traits are complex and its study by association analysis with genomic markers may be missing some information. To analyse multiple phenotypes and gene-by-gene interactions, systems biology approaches, such as the association weight matrix (AWM), allows combining genome wide association study results with network inference algorithms. The present study aimed to identify gene networks, key regulators and candidate genes associated to immunocompetence in pigs by integrating multiple health-related traits, enriched for innate immune phenotypes, using the AWM approach. The co-association network analysis unveiled a network comprised of 3,636 nodes (genes) and 451,407 edges (interactions), including a total of 246 regulators. From these, five genes (ARNT2, BRMS1L, MED12L, SUPT3H and TRIM25) were selected as key regulators as they were associated with the maximum number of genes with the minimum overlapping (1,827 genes in total). The five regulators were involved in pathways related to immunity such as lymphocyte differentiation and activation, platelet activation and degranulation, megakaryocyte differentiation, FcγR-mediated phagocytosis and response to nitric oxide, among others, but also in immunometabolism. Furthermore, we identified genes co-associated with the key regulators previously reported as candidate genes (e.g., ANGPT1, CD4, CD36, DOCK1, PDE4B, PRKCE, PTPRC and SH2B3) for immunity traits in humans and pigs, but also new candidate ones (e.g., ACSL3, CXADR, HBB, MMP12, PTPN6, WLS) that were not previously described. The co-association analysis revealed new regulators associated with health-related traits in pigs. This approach also identified gene-by-gene interactions and candidate genes involved in pathways related to cell fate and metabolic and immune functions. Our results shed new light in the regulatory mechanisms involved in pig immunity and reinforce the use of the pig as biomedical model.

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

Background

The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.

Results

To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h²; proportion of phenotypic variance explained by the host genetics) and microbiability (m²; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m² (0.275) compared to h² (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.

Conclusions

Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00138-9.

Chromatin accessibility and regulatory vocabulary across indicine cattle tissues

BACKGROUND

Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions across different tissues and developmental stages is led by large international collaborative efforts mostly focusing on model organisms, such as ENCODE. Recently, the Functional Annotation of Animal Genomes (FAANG) has been established to unravel the regulatory elements in non-model organisms, including cattle. Now, we can transition from prediction to validation by experimentally identifying the regulatory elements in tropical indicine cattle. The identification of regulatory elements, their annotation and comparison with the taurine counterpart, holds high promise to link regulatory regions to adaptability traits and improve animal productivity and welfare.

RESULTS

We generate open chromatin profiles for liver, muscle, and hypothalamus of indicine cattle through ATAC-seq. Using robust methods for motif discovery, motif enrichment and transcription factor binding sites, we identify potential master regulators of the epigenomic profile in these three tissues, namely HNF4, MEF2, and SOX factors, respectively. Integration with transcriptomic data allows us to confirm some of their target genes. Finally, by comparing our results with Bos taurus data we identify potential indicine-specific open chromatin regions and overlaps with indicine selective sweeps.

CONCLUSIONS

Our findings provide insights into the identification and analysis of regulatory elements in non-model organisms, the evolution of regulatory elements within two cattle subspecies as well as having an immediate impact on the animal genetics community in particular for a relevant productive species such as tropical cattle.

Inferring Functional Epigenetic Modules by Integrative Analysis of Multiple Heterogeneous Networks

Gene expression and methylation are critical biological processes for cells, and how to integrate these heterogeneous data has been extensively investigated, which is the foundation for revealing the underlying patterns of cancers. The vast majority of the current algorithms fuse gene methylation and expression into a network, failing to fully explore the relations and heterogeneity of them. To resolve these problems, in this study we define the epigenetic modules as a gene set whose members are co-methylated and co-expressed. To address the heterogeneity of data, we construct gene co-expression and co-methylation networks, respectively. In this case, the epigenetic module is characterized as a common module in multiple networks. Then, a non-negative matrix factorization-based algorithm that jointly clusters the co-expression and co-methylation networks is proposed for discovering the epigenetic modules (called Ep-jNMF). Ep-jNMF is more accurate than the baselines on the artificial data. Moreover, Ep-jNMF identifies more biologically meaningful modules. And the modules can predict the subtypes of cancers. These results indicate that Ep-jNMF is efficient for the integration of expression and methylation data.