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Handedness and language are two well-studied examples of asymmetrical brain function in humans. Approximately 90% of humans exhibit a right-hand preference, and the vast majority shows left-hemisphere dominance for language function. Although genetic models of human handedness and language have been proposed, the actual gene expression differences between cerebral hemispheres in humans remain to be fully defined. In the present study, gene expression profiles were examined in both hemispheres of three cortical regions involved in handedness and language in humans and their homologues in rhesus macaques: ventrolateral prefrontal cortex, posterior superior temporal cortex (STC), and primary motor cortex. Although the overall pattern of gene expression was very similar between hemispheres in both humans and macaques, weighted gene correlation network analysis revealed gene co-expression modules associated with hemisphere, which are different among the three cortical regions examined. Notably, a receptor-enriched gene module in STC was particularly associated with hemisphere and showed different expression levels between hemispheres only in humans.

The beef cattle industry has experienced a shift driven by a market demand for healthier meat, cost efficiency and environmental sustainability in recent years. Consequently, there has been a growing focus on the fatty acids content and functions of meat in cattle breeding programmes. Besides, a deeper understanding of the biological mechanisms influencing the expression of different phenotypes related to fatty acid profiles is crucial. In this study, we aimed to identify Single‐Nucleotide Variants (SNV) and Insertion/Deletion (InDels) DNA variants in candidate genes related to fatty acid profiles described in genomic, transcriptomic and proteomic studies conducted in beef cattle breeds. Utilizing whole‐genome re‐sequencing data from Brazilian locally adapted bovine breeds, namely Caracu and Pantaneiro, we identified SNVs and InDels associated with 23,947 genes. From these, we identified 318 candidate genes related to fatty acid profiles that contain variants. Subsequently, we select only genes with SNVs and InDels in their promoter, 5′ UTR and coding region. Through the gene–biological process network, approximately 19 genes were highlighted. Furthermore, considering the studied trait and a literature review, we selected the main transcription factors (TF). Functional analysis via gene–TF network allowed us to identify the 30 most likely candidate genes for meat fatty acid profile in cattle. LIPE, MFSD2A and SREBF1 genes were highlighted in networks due to their biological importance. Further dissection of these genes revealed 15 new variants found in promoter regions of Caracu and Pantaneiro sequences. The gene networks facilitated a better functional understanding of genes and TF, enabling the identification of variants potentially related to the expression of candidate genes for meat fatty acid profiles in cattle. [ABSTRACT FROM AUTHOR]

Summary: Our study utilized genome‐wide association studies (GWAS) to link nucleotide variants to traits in Populus trichocarpa, a species with rapid linkage disequilibrium decay. The aim was to overcome the challenge of interpreting statistical associations at individual loci without sufficient biological context, which often leads to reliance solely on gene annotations from unrelated model organisms.We employed an integrative approach that included GWAS targeting multiple traits using three individual techniques for lignocellulose phenotyping, expression quantitative trait loci (eQTL) analysis to construct transcriptional regulatory networks around each candidate locus and co‐expression analysis to provide biological context for these networks, using lignocellulose biosynthesis in Populus trichocarpa as a case study.The research identified three candidate genes potentially involved in lignocellulose formation, including one previously recognized gene (Potri.005G116800/VND1, a critical regulator of secondary cell wall formation) and two genes (Potri.012G130000/AtSAP9 and Potri.004G202900/BIC1) with newly identified putative roles in lignocellulose biosynthesis.Our integrative approach offers a framework for providing biological context to loci associated with trait variation, facilitating the discovery of new genes and regulatory networks. [ABSTRACT FROM AUTHOR]

Synthetic peptides have a wide range of clinical effects. Of particular interest are peptides based on adrenocorticotropic hormone (ACTH) both as already used and as potential drugs for preventing consequences of cerebral ischemia. However, it is necessary to study influence of the peptide on the brain cells under normal physiological conditions, including understanding the risks of their use. Here, we used high-throughput RNA sequencing (RNA-Seq) to identify differentially expressed genes (DEGs) in the brain frontal cortex of rat receiving intraperitoneal administration of ACTH-like peptides ACTH(4-7)PGP (Semax) and ACTH(6-9)PGP, or saline. We identified 258 and 228 DEGs, respectively, with the fold change > 1.5 and Padj < 0.05 at 22.5 h after the first administration of Semax and ACTH(6-9)PGP. Metabolic pathways, characterizing both common and specific effects of the peptides on the transcriptome were identified. Both peptides predominantly caused decrease in expression of the genes associated with the immune system. At the same time, when comparing the effects of ACTH(6-9)PGP relative to Semax, DEGs were identified that characterized the main differences in the effects of the peptides. These genes were mostly downregulated and associated with neurosignaling systems and regulation of ion channels, thus characterizing differences in the effects of the peptides. Our data show how differences in the structure of ACTH derivatives are associated with the changes in the brain cell transcriptome following exposure to these related peptides. Furthermore, our results demonstrate that when studying influence of regulatory peptides on transcriptome under pathological conditions, it is necessary to take into account their actions under normal physiological conditions. [ABSTRACT FROM AUTHOR]

Endometriosis (EM) is a gynecological condition known by the manifestation of endometrium alike soft tissue external to the usual place affecting up to 10% of all womenfolk in the reproductively active stage. However, the pathological process of endometriosis is not identified fully. The study aims to investigate the genes associated with the progression of endometriosis and its pathways using bioinformatics tools and techniques. The gene expression profile of three sets was retrieved, and bioinformatics data analysis was carried out for the microarray samples using GEO, DAVID, and STICH. Differently expressed genes (DEGs) refer to genes that exhibit significant changes in their expression levels between different conditions or groups, such as between different cell types, treatments, disease states, or developmental stages. DEG was determined based on a significant cutoff resulting in 298 unique elements based on the GEO Venn diagram map. DAVID (database for annotation, visualization, and integrated discovery) helps understand the biological significance of the data by identifying overrepresented biological terms, pathways, and functional annotations among a set of genes or proteins of interest. DAVID analysis revealed positively and negatively associated genes and followed by target proteins. DAVID is helpful for getting results of molecular mechanisms and pathways associated with DEGs. The gene expression studies showed that the m-RNA expression of all the genes was upregulated in the PA1 cell line. The present study identified five genes (COMT, CYP19A1, GALT, LTA, and STAR) from 298 unique DEGs using microarray data analysis, and 5 protein targets were also identified that were linked with EM. The study concludes that this information may provide a bridging gap in understanding the progression of endometriosis. [ABSTRACT FROM AUTHOR]

Background: Soybean establishes a mutualistic interaction with nitrogen-fixing rhizobacteria, acquiring most of its nitrogen requirements through symbiotic nitrogen fixation. This crop is susceptible to water deficit; evidence suggests that its nodulation status—whether it is nodulated or not—can influence how it responds to water deficit. The translational control step of gene expression has proven relevant in plants subjected to water deficit. Results: Here, we analyzed soybean roots' differential responses to water deficit at transcriptional, translational, and mixed (transcriptional + translational) levels. Thus, the transcriptome and translatome of four combined-treated soybean roots were analyzed. We found hormone metabolism-related genes among the differentially expressed genes (DEGs) at the translatome level in nodulated and water-restricted plants. Also, weighted gene co-expression network analysis followed by differential expression analysis identified gene modules associated with nodulation and water deficit conditions. Protein-protein interaction network analysis was performed for subsets of mixed DEGs of the modules associated with the plant responses to nodulation, water deficit, or their combination. Conclusions: Our research reveals that the stand-out processes and pathways in the before-mentioned plant responses partially differ; terms related to glutathione metabolism and hormone signal transduction (2 C protein phosphatases) were associated with the response to water deficit, terms related to transmembrane transport, response to abscisic acid, pigment metabolic process were associated with the response to nodulation plus water deficit. Still, two processes were common: galactose metabolism and branched-chain amino acid catabolism. A comprehensive analysis of these processes could lead to identifying new sources of tolerance to drought in soybean. [ABSTRACT FROM AUTHOR]

Introduction: A hexanucleotide repeat expansion (HRE) intronic to chromosome 9 open reading frame 72 (C9orf72) is recognized as the most common genetic cause of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and ALS-FTD. Identifying genes that show similar regional co-expression patterns to C9orf72 may help identify novel gene targets and biological mechanisms that mediate selective vulnerability to ALS and FTD pathogenesis. Methods: We leveraged mRNA expression data in healthy brain from the Allen Human Brain Atlas to evaluate C9orf72 co-expression patterns. To do this, we correlated average C9orf72 expression values in 51 regions across different anatomical divisions (cortex, subcortex, and cerebellum) with average gene expression values for 15,633 protein-coding genes, including 54 genes known to be associated with ALS, FTD, or ALS-FTD. We then performed imaging transcriptomic analyses to evaluate whether the identified C9orf72 coexpressed genes correlated with patterns of cortical thickness in symptomatic C9orf72 pathogenic HRE carriers (n = 19) compared to controls (n = 23). Lastly, we explored whether genes with significant C9orf72 imaging transcriptomic correlations (i.e., "C9orf72 imaging transcriptomic network") were enriched in specific cell populations in the brain and enriched for specific biological and molecular pathways. Results: A total of 2,120 genes showed an anatomical distribution of gene expression in the brain similar to C9orf72 and significantly correlated with patterns of cortical thickness in C9orf72 HRE carriers. This C9orf72 imaging transcriptomic network was differentially expressed in cell populations previously implicated in ALS and FTD, including layer 5b cells, cholinergic neurons in the spinal cord and brainstem and medium spiny neurons of the striatum, and was enriched for biological and molecular pathways associated with protein ubiquitination, autophagy, cellular response to DNA damage, endoplasmic reticulum to Golgi vesicle-mediated transport, among others. Conclusion: Considered together, we identified a network of C9orf72 associated genes that may influence selective regional and cell-type-specific vulnerabilities in ALS/FTD. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus (HPV)-induced cervical cancer progresses through a series of steps. Despite our limited understanding of the mechanisms driving this progression, identifying the key genes involved could significantly improve early detection and treatment. Materials and Methods: Two gene expression profiles of GSE9750 and GSE6791, which included cervical cancer HPV-positive and -negative samples, were evaluated using the R limma package with established cut-off criteria of P value < 0.05 and | fold change| ≥ 1. KEGG pathway enrichment was performed to identify potential pathways. Weighted gene co-expression network analysis (WGCNA) was used to discover co-expressed gene modules and trait–module connections. Results: Considering the defined criteria, 115 differentially expressed genes (DEGs) were identified. The DEG’s KEGG pathway enrichment analysis revealed enrichment in highly relevant pathways to the HPV infection, including cell cycle, viral carcinogenesis, autophagy-animal, Epstein-Barr virus infection, human T-cell leukemia virus 1 infection, and microRNAs in cancer. WGCNA results in 13 co-expression modules, and the magenta module is identified with significant relations to HPV, cervical cancer stage, and metastasis traits. The survival analysis identified BEX1 and CDC45 as potential prognostic factors in HPV-associated cervical cancer. Conclusion: The innovation of our work lies in identifying essential genes associated with the multi-step process of cervical carcinogenesis. In fact, the current study has the potential to give a distinct viewpoint on the molecular pathways linked to cervical cancer. Considering the potential importance of the hub genes, we recommend conducting in-depth wet lab research to determine their impact on the biological mechanisms of cervical cancer.

Diabetes cell replacement therapy has the potential to be transformed by human pluripotent stem cell-derived β cells (SC-β cells). However, the precise identity of SC-β cells in relationship to primary fetal and adult β-cells remains unclear. Here, we used single-cell sequencing datasets to characterize the transcriptional identity of islets from in vitro differentiation, fetal islets, and adult islets. Our analysis revealed that SC-β cells share a core β-cell transcriptional identity with human adult and fetal β-cells, however SC-β cells possess a unique transcriptional profile characterized by the persistent expression and activation of progenitor and neural-biased gene networks. These networks are present in SC-β cells, irrespective of the derivation protocol used. Notably, fetal β-cells also exhibit this neural signature at the transcriptional level. Our findings offer insights into the transcriptional identity of SC-β cells and underscore the need for further investigation of the role of neural transcriptional networks in their development. [ABSTRACT FROM AUTHOR]

During future space missions, astronauts will be exposed to cosmic radiation and microgravity (μG), which are known to be health risk factors. To examine the differentially expressed genes (DEG) and their prevalent biological processes and pathways as a response to these two risk factors simultaneously, 1BR-hTERT human fibroblast cells were cultured under 1 gravity (1G) or simulated μG for 48 h in total and collected at 0 (sham irradiated), 3 or 24 h after 1 Gy of X-ray or Carbon-ion (C-ion) irradiation. A three-dimensional clinostat was used for the simulation of μG and the simultaneous radiation exposure of the samples. The RNA-seq method was used to produce lists of differentially expressed genes between different environmental conditions. Over-representation analyses were performed and the enriched biological pathways and targeting transcription factors were identified. Comparing sham-irradiated cells under simulated μG and 1G conditions, terms related to response to oxygen levels and muscle contraction were identified. After irradiation with X-rays or C-ions under 1G, identified DEGs were found to be involved in DNA damage repair, signal transduction by p53 class mediator, cell cycle arrest and apoptosis pathways. The same enriched pathways emerged when cells were irradiated under simulated μG condition. Nevertheless, the combined effect attenuated the transcriptional response to irradiation which may pose a subtle risk in space flights. [ABSTRACT FROM AUTHOR]

Behavioural variation is essential for animals to adapt to different social and environmental conditions. The Kinship Theory of Intragenomic Conflict (KTIC) predicts that parent‐specific alleles can support different behavioural strategies to maximize allele fitness. Previous studies, including in honey bees (Apis mellifera), supported predictions of the KTIC for parent‐specific alleles to promote selfish behaviour. Here, we test the KTIC prediction that for altruism‐promoting genes (i.e. those that promote behaviours that support the reproductive fitness of kin), the allele with the higher altruism optimum should be selected to be expressed while the other is silenced. In honey bee colonies, workers act altruistically when tending to the queen by performing a 'retinue' behaviour, distributing the queen's mandibular pheromone (QMP) throughout the hive. Workers exposed to QMP do not activate their ovaries, ensuring they care for the queen's brood instead of competing to lay unfertilized eggs. Due to the haplodiploid genetics of honey bees, the KTIC predicts that response to QMP is favoured by the maternal genome. We report evidence for parent‐of‐origin effects on the retinue response behaviour, ovarian development and gene expression in brains of worker honey bees exposed to QMP, consistent with the KTIC. Additionally, we show enrichment for genes with parent‐of‐origin expression bias within gene regulatory networks associated with variation in bees' response to QMP. Our study demonstrates that intragenomic conflict can shape diverse social behaviours and influence expression patterns of single genes as well as gene networks. [ABSTRACT FROM AUTHOR]

Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissuespecific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD. [ABSTRACT FROM AUTHOR]

BackgroundIntracerebral hemorrhage (ICH) has a high morbidity and mortality. The peripheral immune system and cross-talk between peripheral blood and brain have been implicated in the ICH immune response. Thus, we delineated the gene networks associated with human ICH in the peripheral blood transcriptome. We also compared the differentially expressed genes in blood following ICH to a prior human study of perihematomal brain tissue.MethodsWe performed peripheral blood whole-transcriptome analysis of ICH and matched vascular risk factor control subjects (n = 66). Gene co-expression network analysis identified groups of co-expressed genes (modules) associated with ICH and their most interconnected genes (hubs). Mixed-effects regression identified differentially expressed genes in ICH compared to controls.ResultsOf seven ICH-associated modules, six were enriched with cell-specific genes: one neutrophil module, one neutrophil plus monocyte module, one T cell module, one Natural Killer cell module, and two erythroblast modules. The neutrophil/monocyte modules were enriched in inflammatory/immune pathways; the T cell module in T cell receptor signaling genes; and the Natural Killer cell module in genes regulating alternative splicing, epigenetic, and post-translational modifications. One erythroblast module was enriched in autophagy pathways implicated in experimental ICH, and NRF2 signaling implicated in hematoma clearance. Many hub genes or module members, such as IARS, mTOR, S1PR1, LCK, FYN, SKAP1, ITK, AMBRA1, NLRC4, IL6R, IL17RA, GAB2, MXD1, PIK3CD, NUMB, MAPK14, DDX24, EVL, TDP1, ATG3, WDFY3, GSK3B, STAT3, STX3, CSF3R, PIP4K2A, ANXA3, DGAT2, LRP10, FLOT2, ANK1, CR1, SLC4A1, and DYSF, have been implicated in neuroinflammation, cell death, transcriptional regulation, and some as experimental ICH therapeutic targets. Gene-level analysis revealed 1225 genes (FDR p  |1.2|) have altered expression in ICH in peripheral blood. There was significant overlap of the 1225 genes with dysregulated genes in human perihematomal brain tissue (p = 7 × 10-3). Overlapping genes were enriched for neutrophil-specific genes (p = 6.4 × 10-08) involved in interleukin, neuroinflammation, apoptosis, and PPAR signaling.ConclusionsThis study delineates key processes underlying ICH pathophysiology, complements experimental ICH findings, and the hub genes significantly expand the list of novel ICH therapeutic targets. The overlap between blood and brain gene responses underscores the importance of examining blood-brain interactions in human ICH.

MicroRNAs (miRNAs) are small noncoding RNAs that act as negative regulators of gene expression at the post-transcriptional level, promoting mRNA degradation or translation repression. Despite the well-described presence of miRNAs in various human tissues, there is still a lack of information about the relationship between miRNAs and the translation regulation in human embryonic stem cells (hESCs) during cardiomyogenesis. Here, we investigate RNA-seq data from hESCs, focusing on distinct stages of cardiomyogenesis and searching for polysome-bound miRNAs that could be involved in translational regulation. We identify miR-6087 as a differentially expressed miRNA at latest steps of cardiomyocyte differentiation. We analyzed the coexpression pattern between the differentially expressed mRNAs and miR-6087, evaluating whether they are predicted targets of the miRNA. We arranged the genes into an interaction network and identified BLM, RFC4, RFC3, and CCNA2 as key genes of the network. A post hoc analysis of the key genes suggests that miR-6087 could act as a regulator of the cell cycle in hESC during cardiomyogenesis. [ABSTRACT FROM AUTHOR]

Glyprolines are Gly-Pro (GP)- or Pro-Gly (PG)-containing biogenic peptides. These peptides can act as neutrophil chemoattractants, or atheroprotective, anticoagulant, and neuroprotective agents. The Pro-Gly-Pro (PGP) tripeptide is an active factor of resistance to the biodegradation of peptide drugs. The synthetic Semax peptide, which includes Met-Glu-His-Phe (MEHF) fragments of adrenocorticotropic hormone and the C-terminal tripeptide PGP, serves as a neuroprotective drug for the treatment of ischemic stroke. Previously, we revealed that Semax mostly prevented the disruption of the gene expression pattern 24 h after a transient middle cerebral artery occlusion (tMCAO) in a rat brain model. The genes of this pattern were grouped into an inflammatory cluster (IC) and a neurotransmitter cluster (NC). Here, using real-time RT-PCR, the effect of other PGP-containing peptides, PGP and Pro-Gly-Pro-Leu (PGPL), on the expression of a number of genes in the IC and NC was studied 24 h after tMCAO. Both the PGP and PGPL peptides showed Semax-unlike effects, predominantly without changing gene expression 24 h after tMCAO. Moreover, there were IC genes (iL1b, iL6, and Socs3) for PGP, as well as IC (iL6, Ccl3, Socs3, and Fos) and NC genes (Cplx2, Neurod6, and Ptk2b) for PGPL, that significantly changed in expression levels after peptide administration compared to Semax treatment under tMCAO conditions. Furthermore, gene enrichment analysis was carried out, and a regulatory gene network was constructed. Thus, the spectra of the common and unique effects of the PGP, PGPL, and Semax peptides under ischemia–reperfusion were distinguished. [ABSTRACT FROM AUTHOR]

The ecological success of social Hymenoptera (ants, bees, wasps) depends on the division of labour between the queen and workers. Each caste exhibits highly specialized morphology, behaviour, and life‐history traits, such as lifespan and fecundity. Despite strong defences against alien intruders, insect societies are vulnerable to social parasites, such as workerless inquilines or slave‐making ants. Here, we investigate whether gene expression varies in parallel ways between lifestyles (slave‐making versus host ants) across five independent origins of ant slavery in the "Formicoxenus‐group" of the ant tribe Crematogastrini. As caste differences are often less pronounced in slave‐making ants than in nonparasitic ants, we also compare caste‐specific gene expression patterns between lifestyles. We demonstrate a substantial overlap in expression differences between queens and workers across taxa, irrespective of lifestyle. Caste affects the transcriptomes much more profoundly than lifestyle, as indicated by 37 times more genes being linked to caste than to lifestyle and by multiple caste‐associated modules of coexpressed genes with strong connectivity. However, several genes and one gene module are linked to slave‐making across the independent origins of this parasitic lifestyle, pointing to some evolutionary convergence. Finally, we do not find evidence for an interaction between caste and lifestyle, indicating that caste differences in gene expression remain consistent even when species switch to a parasitic lifestyle. Our findings strongly support the existence of a core set of genes whose expression is linked to the queen and worker caste in this ant taxon, as proposed by the "genetic toolkit" hypothesis. [ABSTRACT FROM AUTHOR]

Background: Banana fruits are rich in various high-value metabolites and play a key role in the human diet. Of these components, carotenoids have attracted considerable attention due to their physiological role and human health care functions. However, the accumulation patterns of carotenoids and genome-wide analysis of gene expression during banana fruit development have not been comprehensively evaluated. Results: In the present study, an integrative analysis of metabolites and transcriptome profiles in banana fruit with three different development stages was performed. A total of 11 carotenoid compounds were identified, and most of these compounds showed markedly higher abundances in mature green and/or mature fruit than in young fruit. Results were linked to the high expression of carotenoid synthesis and regulatory genes in the middle and late stages of fruit development. Co-expression network analysis revealed that 79 differentially expressed transcription factor genes may be responsible for the regulation of LCYB (lycopene β-cyclase), a key enzyme catalyzing the biosynthesis of α- and β-carotene. Conclusions: Collectively, the study provided new insights into the understanding of dynamic changes in carotenoid content and gene expression level during banana fruit development. [ABSTRACT FROM AUTHOR]

Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field. [ABSTRACT FROM AUTHOR]

Abstract Rapid environmental changes impact the global distribution and abundance of species, highlighting the urgency to understand and predict how populations will respond. The analysis of differentially expressed genes has elucidated areas of the genome involved in adaptive divergence to past and present environmental change. Such studies however have been hampered by large numbers of differentially expressed genes and limited knowledge of how these genes work in conjunction with each other. Recent methods (broadly termed “pathway analyses”) have emerged that aim to group genes that behave in a coordinated fashion to a factor of interest. These methods aid in functional annotation and uncovering biological pathways, thereby collapsing complex datasets into more manageable units, providing more nuanced understandings of both the organism‐level effects of modified gene expression, and the targets of adaptive divergence. Here, we reanalyze a dataset that investigated temperature‐induced changes in gene expression in marine‐adapted and freshwater‐adapted threespine stickleback (Gasterosteus aculeatus), using Weighted Gene Co‐expression Network Analysis (WGCNA) with PANTHER Gene Ontology (GO)‐Slim overrepresentation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Six modules exhibited a conserved response and six a divergent response between marine and freshwater stickleback when acclimated to 7°C or 22°C. One divergent module showed freshwater‐specific response to temperature, and the remaining divergent modules showed differences in height of reaction norms. PPARAa, a transcription factor that regulates fatty acid metabolism and has been implicated in adaptive divergence, was located in a module that had higher expression at 7°C and in freshwater stickleback. This updated methodology revealed patterns that were not found in the original publication. Although such methods hold promise toward predicting population response to environmental stressors, many limitations remain, particularly with regard to module expression representation, database resources, and cross‐database integration.

Abstract Background Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body’s response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke. Methods Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene’s significance in the network was assessed according to the differences in their network’s pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm – support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model. Results A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies:

Abstract Background Clinical data identified an association between the use of HMG-CoA reductase inhibitors (statins) and incident diabetes in patients with underlying diabetes risk factors such as obesity, hypertension and dyslipidemia. The molecular mechanisms however are unknown. Methods An observational cross-sectional study included 910 severely obese patients, mean (SD) body mass index (BMI) 46.7 (8.7), treated with or without statins (ABOS cohort: a biological atlas of severe obesity). Data and sample collection took place in France between 2006 and 2016. Transcriptomic signatures of statin treatment in human liver obtained from genome-wide transcriptomic profiling of five different statin drugs using microarrays were correlated to clinico-biological phenotypes and also assigned to biological pathways and mechanisms. Patients from the non-statin-users group were matched to patients in the statin users group by propensity score analysis to minimize confounding effects from age, gender, parental familial history of diabetes, BMI, waist circumference, systolic and diastolic blood pressure and use of anti-hypertensive drugs as pre-specified covariates. Results We determined the hepatic, statin-related gene signature from genome-wide transcriptomic profiling in severely obese patients with varying degrees of glucose tolerance and cardio-metabolic comorbidities. One hundred and fifty seven patients on statin treatment in the matched cohort showed higher diabetes prevalence (OR = 2.67; 95%CI, 1.60–4.45; P = 0.0002) and impairment of glucose homeostasis. This phenotype was associated with molecular signatures of increased hepatic de novo lipogenesis (DNL) via activation of sterol regulatory element-binding protein 1 (SREBP1) and concomitant upregulation of the expression of key genes in both fatty acid and triglyceride metabolism. Conclusions A DNL gene activation profile in response to statins is associated with insulin resistance and the diabetic status of the patients. Identified molecular signatures thus suggest that statin treatment increases the risk for diabetes in humans at least in part via induction of DNL. Trial registration NCT01129297. Registered May 242,010 (retrospectively registered).

Kin selection may act differently on genes inherited from parents (matrigenes and patrigenes), resulting in intragenomic conflict. This conflict can be observed as differential expression of matrigenes and patrigenes, or parent‐specific gene expression (PSGE). In honey bees (Apis mellifera), intragenomic conflict is hypothesized to occur in multiple social contexts. Previously, we found that patrigene‐biased expression in reproductive tissues was associated with increased reproductive potential in worker honey bees, consistent with the prediction that patrigenes are selected to promote selfish behaviour in this context. Here, we examined brain gene expression patterns to determine if PSGE is also found in other tissues. As before, the number of transcripts showing patrigene expression bias was significantly greater in the brains of reproductive vs. sterile workers, while the number of matrigene‐biased transcripts was not significantly different. Twelve transcripts out of the 374 showing PSGE in either tissue showed PSGE in both brain and reproductive tissues; this overlap was significantly greater than expected by chance. However, the majority of transcripts show PSGE only in one tissue, suggesting the epigenetic mechanisms mediating PSGE exhibit plasticity between tissues. There was no significant overlap between transcripts that showed PSGE and transcripts that were significantly differentially expressed. Weighted gene correlation network analysis identified modules which were significantly enriched in both types of transcripts, suggesting that these genes may influence each other through gene networks. Our results provide further support for the kin selection theory of intragenomic conflict, and provide valuable insights into the mechanisms which may mediate this process. [ABSTRACT FROM AUTHOR]

Background: Analysis of gene expression based on primary experimental databases is a powerful systematic biological tool that can be implemented to facilitate the understanding of therapy prognosis and potential molecular interactions. Such an approach was adopted in the current research to explore key genes and functional relationships rising during clinical Rheumatoid Arthritis (RA) therapy by Methotrexate (MTX). Methods: The whole gene expression data GSE45867 from synovial biopsies of RA patients before and after MTX therapy was obtained from the Gene Expression Omnibus (GEO) database in The National Center for Biotechnology Information (NCBI). Gene clusters and biological characterizations were identified by Gene set enrichment analysis (GSEA) tool while co-expression genes and key molecular pathways of interaction were confirmed via Gene Multiple Association Network Integration Algorithm (GeneMANIA), The Search Tool for The Retrieval of Interacting Genes (STRING) and Reactome bioinformatics tools. Results: Data showed that MTX-treated RA patients had differentially expressed genes that are involved in MAPK kinase activation and Toll-like receptor pathways. Two distinct networks were discovered by the STRING database; a cluster of genes including STON2, ARRB1, CNKSR2, DLG3, FAT3, VANGL2 and another including NFIX, JUN, MAP3K8, APAF1, NAIP. Analysis of differentially expressed genes initiated by MTX treatment revealed significant gene clusters and networks that are involved in important inflammatory pathways. Conclusion: Such pathways and networks might serve as prognostic biomarkers for RA and can be further developed as predictive gene signatures for MTX activity and adverse effects. [ABSTRACT FROM AUTHOR]

Over a decade ago, Lèbre (2009) proposed an inference method, G1DBN, to learn the structure of gene regulatory networks (GRNs) from high dimensional, sparse time-series gene expression data. Their approach is based on concept of low-order conditional independence graphs that they extend to dynamic Bayesian networks (DBNs). They present results to demonstrate that their method yields better structural accuracy compared to the related Lasso and Shrinkage methods, particularly where the data is sparse, that is, the number of time measurements n is much smaller than the number of genes p. This paper challenges these claims using a careful experimental analysis, to show that the GRNs reverse engineered from time-series data using the G1DBN approach are less accurate than claimed by Lèbre (2009). We also show that the Lasso method yields higher structural accuracy for graphs learned from the simulated data, compared to the G1DBN method, particularly when the data is sparse ( n < < p $n{< }{< }p$). The Lasso method is also better than G1DBN at identifying the transcription factors (TFs) involved in the cell cycle of Saccharomyces cerevisiae. [ABSTRACT FROM AUTHOR]

Most of our knowledge of the biological basis of major depressive disorder (MDD) is derived from studies of chronic stress models in rodents. While these models capture certain aspects of the behavioral and neuroendocrine features of MDD, the extent to which they reproduce the molecular pathology of the human syndrome remains unknown. We systematically compared transcriptional signatures in two brain regions implicated in depression—medial prefrontal cortex and nucleus accumbens—of humans with MDD and of 3 chronic stress models in mice: chronic variable stress, adult social isolation, and chronic social defeat stress. We used differential expression analysis combined with weighted gene coexpression network analysis to create interspecies gene networks and assess the capacity of each stress paradigm to recapitulate the transcriptional organization of gene networks in human MDD. Our results show significant overlap between transcriptional alterations in medial prefrontal cortex and nucleus accumbens in human MDD and the 3 mouse chronic stress models, with each of the chronic stress paradigms capturing distinct aspects of MDD abnormalities. Chronic variable stress and adult social isolation better reproduce differentially expressed genes, while chronic social defeat stress and adult social isolation better reproduce gene networks characteristic of human MDD. We also identified several gene networks and their constituent genes that are most significantly associated with human MDD and mouse stress models. This study demonstrates the ability of 3 chronic stress models in mice to recapitulate distinct aspects of the broad molecular pathology of human MDD, with no one mouse model apparently better than another. [ABSTRACT FROM AUTHOR]

Rapid environmental changes impact the global distribution and abundance of species, highlighting the urgency to understand and predict how populations will respond. The analysis of differentially expressed genes has elucidated areas of the genome involved in adaptive divergence to past and present environmental change. Such studies however have been hampered by large numbers of differentially expressed genes and limited knowledge of how these genes work in conjunction with each other. Recent methods (broadly termed "pathway analyses") have emerged that aim to group genes that behave in a coordinated fashion to a factor of interest. These methods aid in functional annotation and uncovering biological pathways, thereby collapsing complex datasets into more manageable units, providing more nuanced understandings of both the organism‐level effects of modified gene expression, and the targets of adaptive divergence. Here, we reanalyze a dataset that investigated temperature‐induced changes in gene expression in marine‐adapted and freshwater‐adapted threespine stickleback (Gasterosteus aculeatus), using Weighted Gene Co‐expression Network Analysis (WGCNA) with PANTHER Gene Ontology (GO)‐Slim overrepresentation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Six modules exhibited a conserved response and six a divergent response between marine and freshwater stickleback when acclimated to 7°C or 22°C. One divergent module showed freshwater‐specific response to temperature, and the remaining divergent modules showed differences in height of reaction norms. PPARAa, a transcription factor that regulates fatty acid metabolism and has been implicated in adaptive divergence, was located in a module that had higher expression at 7°C and in freshwater stickleback. This updated methodology revealed patterns that were not found in the original publication. Although such methods hold promise toward predicting population response to environmental stressors, many limitations remain, particularly with regard to module expression representation, database resources, and cross‐database integration. [ABSTRACT FROM AUTHOR]

Improvement of feed efficiency (FE) is key for Sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc; n = 13) and Danbred Landrace (Landrace; n = 28), in relation to FE for identifying potential biomarkers. RNA-seq data on the 41 pigs was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We also compared the results with genome regulation in human exercise data, hypothesizing that increased FE mimics processes triggered in exercised muscle. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1 , TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found five significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly related to mitochondria, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, the most interesting genes included the mitochondrial genes: PPIF, MRPL35, NDUFS4 and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4 , and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of module genes identified MTR, electron transport chain and DNA repair as enriched pathways. The network analysis revealed the mitochondrial gene group NDUF as key network hub genes, showing their potential as biomarkers. Results show that genes related to human exercise were enriched in identified FCR related genes. We conclude that mitochondrial activity is a key driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs. [ABSTRACT FROM AUTHOR]

Vertebrate ear progenitors are induced by fibroblast growth factor signalling, however the molecular mechanisms leading to the coordinate activation of downstream targets are yet to be discovered. The ear, like other sensory placodes, arises from the pre-placodal region at the border of the neural plate. Using a multiplex NanoString approach, we determined the response of these progenitors to FGF signalling by examining the changes of more than 200 transcripts that define the otic and other placodes, neural crest and neural plate territories. This analysis identifies new direct and indirect FGF targets during otic induction. Investigating changes in histone marks by ChIP-seq reveals that FGF exposure of pre-placodal cells leads to rapid deposition of active chromatin marks H3K27ac near FGF-response genes, while H3K27ac is depleted in the vicinity of non-otic genes. Genomic regions that gain H3K27ac act as cis-regulatory elements controlling otic gene expression in time and space and define a unique transcription factor signature likely to control their activity. Finally, we show that in response to FGF signalling the transcription factor dimer AP1 recruits the histone acetyl transferase p300 to selected otic enhancers. Thus, during ear induction FGF signalling modifies the chromatin landscape to promote enhancer activation and chromatin accessibility. • New direct and indirect FGF targets during otic-epibranchial progenitor induction. • Identification and in vivo activity of enhancers controlling gene expression in otic-epibranchial progenitors. • Enhancer analysis reveals complex regulation of the transcription factor Foxi3. • FGF signalling enhances H3K27 acetylation at enhancers associated to otic-epibranchial specific genes. • Downstream of FGF signalling AP1 recruits p300 to otic-epibranchial enhancers. [ABSTRACT FROM AUTHOR]

Mining cancer-omics databases deepens our understanding of cancer biology and can lead to potential breakthroughs in cancer treatment. Here, we propose an integrative analytical approach to reveal across-cancer expression patterns and identify potential clinical impacts for genes of interest from five representative public databases. Using ribonucleotide reductase (RR), a key enzyme in DNA synthesis and cancer-therapeutic targeting, as an example, we characterized the mRNA expression profiles and inter-component associations of three RR subunit genes and assess their differing pathological and prognostic significance across over 30-types of cancers and their related subtypes. Findings were validated by immunohistochemistry with clinical tissue samples (n = 211) collected from multiple cancer centers in China and with clinical follow-up. Underlying mechanisms were further explored and discussed using co-expression gene network analyses. This framework represents a simple, efficient, accurate, and comprehensive approach for cancer-omics resource analysis and underlines the necessity to separate the tumors by their histological or pathological subtypes during the clinical evaluation of molecular biomarkers. [ABSTRACT FROM AUTHOR]

Gene regulatory network reconstruction is an essential task of genomics in order to further our understanding of how genes interact dynamically with each other. The most readily available data, however, are from steady-state observations. These data are not as informative about the relational dynamics between genes as knockout or over-expression experiments, which attempt to control the expression of individual genes. We develop a new framework for network inference using samples from the equilibrium distribution of a vector autoregressive (VAR) time-series model which can be applied to steady-state gene expression data. We explore the theoretical aspects of our method and apply the method to synthetic gene expression data generated using GeneNetWeaver. [ABSTRACT FROM AUTHOR]

Alcohol use disorder (AUD) is a complex psychiatric disorder with strong genetic and environmental risk factors. We studied the molecular perturbations underlying risky drinking behavior by measuring transcriptome changes across the neurocircuitry of addiction in a genetic mouse model of binge drinking. Sixteen generations of selective breeding for high blood alcohol levels after a binge drinking session produced global changes in brain gene expression in alcohol-naïve High Drinking in the Dark (HDID-1) mice. Using gene expression profiles to generate circuit-level hypotheses, we developed a systems approach that integrated regulation of gene coexpression networks across multiple brain regions, neuron-specific transcriptional signatures, and knowledgebase analytics. Whole-cell, voltage-clamp recordings from nucleus accumbens shell neurons projecting to the ventral tegmental area showed differential ethanol-induced plasticity in HDID-1 and control mice and provided support for one of the hypotheses. There were similarities in gene networks between HDID-1 mouse brains and postmortem brains of human alcoholics, suggesting that some gene expression patterns associated with high alcohol consumption are conserved across species. This study demonstrated the value of gene networks for data integration across biological modalities and species to study mechanisms of disease. [ABSTRACT FROM AUTHOR]

Abstract Stochastic reaction network modelling is widely utilized to describe the probabilistic dynamics of biochemical systems in general, and gene interaction networks in particular. The statistical analysis of the response of these systems to perturbation inputs is typically dependent on specific perturbation models. Motivated by reporter gene systems, widely utilized in biology to monitor gene activity in individual cells, we address the analysis of reaction networks with state-affine rates in presence of an input process. We develop a generalization of the so-called moment equations that precisely accounts for the first- and second-order moments of arbitrary inputs without the need for a model of the input process, as well as spectral relationships between the network input and state. We then apply these results to develop a method for the reconstruction of the autocovariance function of gene activity from reporter gene population-snapshot data, a crucial step toward the investigation of gene regulation, and demonstrate its performance on a simulated case study. [ABSTRACT FROM AUTHOR]

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Summary: In plants, there can be a trade‐off between resource allocations to growth vs defense. Here, we use partial correlation analysis of gene expression to make inferences about the nature of this interaction. We studied segregating progenies of Interior spruce subject to weevil attack. In a controlled experiment, we measured pre‐attack plant growth and post‐attack damage with several morphological measures, and profiled transcriptomes of 188 progeny. We used partial correlations of individual transcripts (expressed sequence tags, ESTs) with pairs of growth/defense traits to identify important nodes and edges in the inferred underlying gene network, for example, those pairs of growth/defense traits with high mutual correlation with a single EST transcript. We give a method to identify such ESTs. A terpenoid ABC transporter gene showed strongest correlations (P = 0.019); its transcript represented a hub within the compact 166‐member gene–gene interaction network (P = 0.004) of the negative genetic correlations between growth and subsequent pest attack. A small 21‐member interaction network (P = 0.004) represented the uncovered positive correlations. Our study demonstrates partial correlation analysis identifies important gene networks underlying growth and susceptibility to the weevil in spruce. In particular, we found transcripts that strongly modify the trade‐off between growth and defense, and allow identification of networks more central to the trade‐off. [ABSTRACT FROM AUTHOR]

Temperate maize was domesticated from its tropical ancestor, teosinte. Whereas temperate maize is an autonomous day-neutral plant, teosinte is an obligate short-day plant that requires uninterrupted long nights to induce flowering. Leaf-derived florigenic signals trigger reproductive growth in both teosinte and temperate maize. To study the genetic mechanisms underlying floral inductive pathways in maize and teosinte, mRNA and small RNA genome-wide expression analyses were conducted on leaf tissue from plants that were induced or not induced to flower. Transcriptome profiles reveal common differentially expressed genes during floral induction, but a comparison of candidate flowering time genes indicates that photoperiod and autonomous pathways act independently. Expression differences in teosinte are consistent with the current paradigm for photoperiod-induced flowering, where changes in circadian clock output trigger florigen production. Conversely, differentially expressed genes in temperate maize link carbon partitioning and flowering, but also show altered expression of circadian clock genes that are distinct from those altered upon photoperiodic induction in teosinte. Altered miRNA399 levels in both teosinte and maize suggest a novel common connection between flowering and phosphorus perception. These findings provide insights into the molecular mechanisms underlying a strengthened autonomous pathway that enabled maize growth throughout temperate regions. [ABSTRACT FROM AUTHOR]

Analyses of the levels of mRNAs encoding IFIT1, IFI16, RIG-1, MDA5, CXCL10, LGP2, PUM1, LSD1, STING, and IFNß in cell lines from which the gene encoding LGP2, LSD1, PML, HDAC4, IFI16, PUM1, STING, MDA5, IRF3, or HDAC 1 had been knocked out, as well as the ability of these cell lines to support the replication of HSV-1, revealed the following: (i) Cell lines lacking the gene encoding LGP2, PML, or HDAC4 (cluster 1) exhibited increased levels of expression of partially overlapping gene networks. Concurrently, these cell lines produced from 5 fold to 12 fold lower yields of HSV-1 than the parental cells. (ii) Cell lines lacking the genes encoding STING, LSD1, MDA5, IRF3, or HDAC 1 (cluster 2) exhibited decreased levels of mRNAs of partially overlapping gene networks. Concurrently, these cell lines produced virus yields that did not differ from those produced by the parental cell line. The genes up-regulated in cell lines forming cluster 1, overlapped in part with genes down-regulated in cluster 2. The key conclusions are that gene knockouts and subsequent selection for growth causes changes in expression of multiple genes, and hence the phenotype of the cell lines cannot be ascribed to a single gene; the patterns of gene expression may be shared by multiple knockouts; and the enhanced immunity to viral replication by cluster 1 knockout cell lines but not by cluster 2 cell lines suggests that in parental cells, the expression of innate resistance to infection is specifically repressed. [ABSTRACT FROM AUTHOR]

We previously found that, in a mouse model of Chagas cardiomyopathy, 18% of the 9390 quantified unigenes were significantly regulated by Trypanosoma cruzi infection. However, treatment with bone marrow-derived mononuclear cells (MNCs) resulted in 84% transcriptomic recovery. We have applied new algorithms to reanalyze these datasets with respect to specific pathways [Chagas disease (CHAGAS), cardiac muscle contraction (CMC) and chemokine signaling (CCS)]. In addition to the levels of expression of individual genes we also calculated gene expression variability and coordination of expression of each gene with all others. These additional measures revealed changes in the control of transcript abundances and gene networking in CHAGAS and restoration following MNC treatment, not accessible using the conventional approach limited to the average expression levels. Moreover, our weighted pathway regulation analysis incorporated the contributions of all affected genes, eliminating the arbitrary cut-off criteria of fold-change and/or p-value for significantly regulated genes. The new analyses revealed that T. cruzi infection had large transcriptomic consequences for the CMC pathway and triggered a huge cytokine signaling. Remarkably, MNC therapy not only restored normal expression levels of numerous genes, but it also recovered most of the CHAGAS, CMC and CCS fabrics that were altered by the infection. [ABSTRACT FROM AUTHOR]

Background: The ratio of sugars to organic acids, two of the major metabolites in fleshy fruits, has been considered the most important contributor to fruit sweetness. Although accumulation of sugars and acids have been extensively studied, whether plants evolve a mechanism to maintain, sense or respond to the fruit sugar/acid ratio remains a mystery. In a prior study, we used an integrated systems biology tool to identify a group of 39 acid-associated genes from the fruit transcriptomes in four sweet orange varieties (Citrus sinensis L. Osbeck) with varying fruit acidity, Succari (acidless), Bingtang (low acid), and Newhall and Xinhui (normal acid). Results: We reanalyzed the prior sweet orange fruit transcriptome data, leading to the identification of 72 genes highly correlated with the fruit sugar/acid ratio. The majority of these sugar/acid ratio-related genes are predicted to be involved in regulatory functions such as transport, signaling and transcription or encode enzymes involved in metabolism. Surprisingly, only three of these sugar/acid ratio-correlated genes are weakly correlated with sugar level and none of them overlaps with the acid-associated genes. Weighted Gene Coexpression Network Analysis (WGCNA) has revealed that these genes belong to four modules, Blue, Grey, Brown and Turquoise, with the former two modules being unique to the sugar/acid ratio control. Conclusion: Our results indicate that orange fruits contain a possible mechanistically distinct class of genes that may potentially be involved in maintaining fruit sugar/acid ratios and/or responding to the cellular sugar/acid ratio status. Therefore, our analysis of orange transcriptomes provides an intriguing insight into the potentially novel genetic or molecular mechanisms controlling the sugar/acid ratio in fruits. [ABSTRACT FROM AUTHOR]

An important problem in computational biology is the identification of potential points of intervention that can lead to modified network behavior in a genetic regulatory network. We consider the problem of deducing the effect of individual genes on the behavior of the network in a statistical framework. In this article, we make use of biological information from the literature to develop a Bayesian network and introduce a method to estimate parameters of this network using data that are relevant to the biological phenomena under study. Then, we give a novel approach to select significant nodes in the network using a decision-theoretic approach. The proposed method is applied to the analysis of the mitogen-activated protein kinase pathway in the plant defense response to pathogens. Results from applying the method to experimental data show that the proposed approach is effective in selecting genes that play crucial roles in the biological phenomenon being studied. [ABSTRACT FROM AUTHOR]

To understand genes, pathways, and networks related to puberty, we characterized the transcriptome of two tissues: the pituitary gland and ovaries. Samples were harvested from pre- and postpubertal Brahman heifers (same age group). Brahman heifers (Bos indicus) are older at puberty compared with Bos taurus, a productivity issue. With RNA sequencing, we identified differentially expressed (DEx) genes and important transcription factors (TF) and predicted coexpression networks. The number of DEx genes detected in the pituitary gland was 284 (P < 0.05), and VWC2L was the most DEx gene (fold change = 4.12, P = 0.01). The gene VWC2L promotes bone mineralization through transforming growth factor-β (TGFβ) signaling. Further studies of the link between bone mineralization and puberty could target VWC2L. In ovaries, 3,871 genes were DEx (P < 0.05). Four highly DEx genes were noteworthy for their function: SLC6A13 (a γ-aminobutyric acid [GABA] transporter), OXT (oxytocin), and NPY (neuropeptide Y) and its receptor NPY2R. These genes had higher ovarian expression in postpubertal heifers. The GABA and its receptors and transporters were expressed in the ovaries of many mammals, suggesting a role for this pathway beyond the brain. The OXT pathway has been known to influence the timing of puberty in rats, via modulation of GnRH. The effects of NPY at the hypothalamus, pituitary gland, and ovaries have been documented. Neuropeptide Y and its receptors are known factors in the release of GnRH, similar to OXT and GABA, although their roles in ovarian tissue are less clear. Pathways previously related to puberty such as TGFβ signaling (P = 6.71 × 10-5), Wnt signaling (P = 4.1 × 10-2), and peroxisome proliferator-activated receptor (PPAR) signaling (P = 4.84 × 10-2) were enriched in our data set. Seven genes were identified as key TF in both tissues: HIC2, ZIC4, ZNF219, ZSCAN26, LHX1, OLIG1, and a novel gene. An ovarian subnetwork created with TF and significant ovarian DEx genes revealed five zinc fingers as regulators: ZNF507, ZNF12, ZNF512, ZNF184, and ZNF432. Recent work of hypothalamic gene expression also pointed to zinc fingers as TF for bovine puberty. Although some zinc fingers may be ubiquitously expressed, the identification of DEx genes in common across tissues points to key regulators of puberty. The hypothalamus and pituitary gland had eight DEx genes in common. The hypothalamus and ovaries had 89 DEx genes in common. The pituitary gland and ovaries had 48 DEx genes in common. Our study confirmed the complexity of puberty and suggested further investigation on genes that code zinc fingers. [ABSTRACT FROM AUTHOR]

To better understand the impact of microarray preprocessing normalization techniques on the analysis of biological pathways in the prediction of chronic fatigue (CF) following radiation therapy, this study has compared the list of predictive genes found using the Robust Multiarray Averaging (RMA) and the Affymetrix MAS5 method, with the list that is obtained working with raw data (without any preprocessing). First, we modeled the spiked-in data set where differentially expressed genes were known and spiked-in at different known concentrations, showing that the precisions established by different gene ranking methods were higher than working with raw data. The results obtained from the spiked-in experiment were extrapolated to the CF data set to run learning and blind validation. RMA and MAS5 provided different sets of discriminatory genes that have a higher predictive accuracy in the learning phase, but lower predictive accuracy during the blind validation phase, suggesting that the genetic signatures generated using both preprocessing techniques cannot be generalizable. The pathways found using the raw data set better described what is a priori known for the CF disease. Besides, RMA produced more reliable pathways than MAS5. Understanding the strengths of these two preprocessing techniques in phenotype prediction is critical for precision medicine. Particularly, this article concludes that biological pathways might be better unraveled working with raw expression data. Moreover, the interpretation of the predictive gene profiles generated by RMA and MAS5 should be done with caution. This is an important conclusion with a high translational impact that should be confirmed in other disease data sets. [ABSTRACT FROM AUTHOR]

Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavour that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes.