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Drought and heat stress substantially impact plant growth and productivity. When subjected to drought or heat stress, plants exhibit reduction in growth resulting in yield losses. The occurrence of these two stresses together intensifies their negative effects. Unraveling the molecular changes in response to combined abiotic stress is essential to breed climate-resilient crops. In this study, transcriptome profiles were compared between stress-tolerant (Otis), and stress-sensitive (Golden Promise) barley genotypes subjected to drought, heat, and combined heat and drought stress for five days during heading stage. The major differences that emerged from the transcriptome analysis were the overall number of differentially expressed genes was relatively higher in Golden Promise (GP) compared to Otis. The differential expression of more than 900 transcription factors in GP and Otis may aid this transcriptional reprogramming in response to abiotic stress. Secondly, combined heat and water deficit stress results in a unique and massive transcriptomic response that cannot be predicted from individual stress responses. Enrichment analyses of gene ontology terms revealed unique and stress type-specific adjustments of gene expression. Weighted Gene Co-expression Network Analysis identified genes associated with RNA metabolism and Hsp70 chaperone components as hub genes that can be useful for engineering tolerance to multiple abiotic stresses. Comparison of the transcriptomes of unstressed Otis and GP plants identified several genes associated with biosynthesis of antioxidants and osmolytes were higher in the former that maybe providing innate tolerance capabilities to effectively combat hostile conditions. Lines with different repertoire of innate tolerance mechanisms can be effectively leveraged in breeding programs for developing climate-resilient barley varieties with superior end-use traits.

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: Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human. Results: In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery. Conclusions: We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered. [ABSTRACT FROM AUTHOR]

Stress response helps microorganisms survive extreme environmental conditions and host immunity, making them more virulent or drug resistant. Although both reductionist approaches investigating specific genes and systems approaches analyzing individual stress conditions are being used, less is known about gene networks involved in multiple stress responses. Here, using a systems biology approach, we mined hundreds of transcriptomic data sets for key genes and pathways involved in the tolerance of the model microorganism Escherichia coli to multiple stressors. Specifically, we investigated the E. coli K-12 MG1655 transcriptome under five stresses: heat, cold, oxidative stress, nitrosative stress, and antibiotic treatment. Overlaps of transcriptional changes between studies of each stress factor and between different stressors were determined: energy-requiring metabolic pathways, transport, and motility are typically downregulated to conserve energy, while genes related to survival, bona fide stress response, biofilm formation, and DNA repair are mainly upregulated. The transcription of 15 genes with uncharacterized functions is higher in response to multiple stressors, which suggests they may play pivotal roles in stress response. In conclusion, using rank normalization of transcriptomic data, we identified a set of E. coli stress response genes and pathways, which could be potential targets to overcome antibiotic tolerance or multidrug resistance. [ABSTRACT FROM AUTHOR]

Abstract Aim Neuroblastoma is a heterogeneous childhood cancer derived from the neural crest. The dual cell identities of neuroblastoma include Mesenchymal (MES) and Adrenergic (ADRN). These identities are conferred by a small set of tightly-regulated transcription factors (TFs) binding super enhancers, collectively forming core regulatory circuitries (CRCs). The purpose of this study was to gain a deep understanding of the role of MES and ADRN TFs in neuroblastoma and other cancers as potential indicators of disease prognosis, progression, and relapse. Methods To that end, we first investigated the expression and mutational profile of MES and ADRN TFs in neuroblastoma. Moreover, we established their correlation with neuroblastoma risk groups and overall survival while establishing their extended networks with long non-coding RNAs (lncRNAs). Furthermore, we analysed the pan-cancer expression and mutational profile of these TFs and their correlation with patient survival and finally their network connectivity, using a panel of bioinformatic tools including GEPIA2, human pathology atlas, TIMER2, Omicsnet, and Cytoscape. Results We show the association of multiple MES and ADRN TFs with neuroblastoma risk groups and overall survival and find significantly higher expression of various MES and ADRN TFs compared to normal tissues and their association with overall survival and disease-free survival in multiple cancers. Moreover, we report the strong correlation of the expression of these TFs with the infiltration of stromal and immune cells in the tumour microenvironment and with stemness and metastasis-related genes. Furthermore, we reveal extended pan-cancer networks comprising these TFs that influence the tumour microenvironment and metastasis and may be useful indicators of cancer prognosis and patient survival. Conclusion Our meta-analysis shows the significance of MES and ADRN TFs as indicators of patient prognosis and the putative utility of these TFs as potential novel biomarkers.

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.

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.

Cell size is controlled to be within a specific range to support physiological function. To control their size, cells use diverse mechanisms ranging from ‘sizers’, in which differences in cell size are compensated for in a single cell division cycle, to ‘adders’, in which a constant amount of cell growth occurs in each cell cycle. This diversity raises the question why a particular cell would implement one rather than another mechanism? To address this question, we performed a series of simulations evolving cell size control networks. The size control mechanism that evolved was influenced by both cell cycle structure and specific selection pressures. Moreover, evolved networks recapitulated known size control properties of naturally occurring networks. If the mechanism is based on a G1 size control and an S/G2/M timer, as found for budding yeast and some human cells, adders likely evolve. But, if the G1 phase is significantly longer than the S/G2/M phase, as is often the case in mammalian cells in vivo, sizers become more likely. Sizers also evolve when the cell cycle structure is inverted so that G1 is a timer, while S/G2/M performs size control, as is the case for the fission yeast S. pombe. For some size control networks, cell size consistently decreases in each cycle until a burst of cell cycle inhibitor drives an extended G1 phase much like the cell division cycle of the green algae Chlamydomonas. That these size control networks evolved such self-organized criticality shows how the evolution of complex systems can drive the emergence of critical processes.

This review presents the applications of artificial intelligence for the study of the mechanisms of diabetes development and generation of new technologies of its prevention, monitoring and treatment. In recent years, a huge amount of molecular data has been accumulated, revealing the pathogenic mechanisms of diabetes and its complications. Data mining and text mining open up new possibilities for processing this information. Analysis of gene networks makes it possible to identify molecular interactions that are important for the development of diabetes and its complications, as well as to identify new targeted molecules. Based on the big data analysis and machine learning, new platforms have been created for prediction and screening of diabetes, diabetic retinopathy, chronic kidney disease, and cardiovascular disease. Machine learning algorithms are applied for personalized prediction of glucose trends, in the closed-loop insulin delivery systems and decision support systems for lifestyle modification and diabetes treatment. The use of artificial intelligence for the analysis of large databases, registers, and real-world evidence studies seems to be promising. The introduction of artificial intelligence systems is in line with global trends in modern medicine, including the transition to digital and distant technologies, personification of treatment, high-precision forecasting and patient-centered care. There is an urgent need for further research in this field, with an assessment of the clinical effectiveness and economic feasibility.

Congenital Zika syndrome (CZS) comprises a set of clinical manifestations that can be presented by neonates born to mothers infected by the Zika virus (ZIKV). CZS-associated phenotypes include neurological, skeletal, and systemic alterations and long-term developmental sequelae. One of the most frequently reported clinical conditions is microcephaly characterized by a reduction in head circumference and cognitive complications. Nevertheless, the associations among the diverse signaling pathways underlying CZS phenotypes remain to be elucidated. To shed light on CZS, we have extensively reviewed the morphological anomalies resulting from ZIKV infection, as well as genes and proteins of interest obtained from the published literature. With this list of genes or proteins, we performed computational analyses to explore the cellular processes, molecular mechanisms, and molecular pathways related to ZIKV infection. Therefore, in this review, we comprehensively describe the morphological abnormalities caused by congenital ZIKV infection and, through the analysis noted above, propose common molecular pathways altered by ZIKV that could explain both central nervous system and craniofacial skeletal alterations. • Congenital Zika syndrome mainly causes central nervous system and skeletal anomalies. • The effects of ZIKV on cell proliferation and migration surpass its impact on cell death. • ZIKV primarily affects immunity, ossification, and neurogenesis molecular pathways. • Molecular interactions indicate shared pathways between osteogenesis and neurogenesis. [ABSTRACT FROM AUTHOR]

Drosophila mechanoreceptors (bristles), represented by macro- and microchetae, are located on the insect body in an orderly manner and are the result of a deterministic conversion of ectodermal cells of imaginal discs into progenitor neural cells with the following differentiation of derivatives of these cells into components of the mechanoreceptor, which consists of two surface cuticle structures, a bristle with a socket and two underlying neural components: neuron and a glial cell. The morphogenesis of mechanoreceptors occurs through three successive stages: (1) segregation from the mass of ectodermal cells of domains that are potentially competent to the neural pathway of development (proneural clusters (PC)); (2) separation of the parental cell of mechanoreceptor (PCM) in the proneural cluster, and (3) three asymmetric divisions of PCM and its descendant cells with the specialization of daughter cells of the last generation into components of a definitive sensory organ. The formation of the bristle pattern is ordered in space and time. The spatial determination is due to the positioning of the parental cells, and the temporal determination is associated with two synchronization events: the completion of separation of PCM for all mechanoreceptors by the first to tenth hour after the formation of the puparium and the time limit for their entry into the first asymmetric mitosis. Our reconstruction and analysis of the molecular genetic system that provides the listed events of morphogenesis of a single mechanoreceptor (and the bristle pattern as a whole) revealed its hierarchical organization. The elements of the system are grouped into three modules corresponding to the stages of morphogenesis of the sensory organ: the gene networks "Neurogenesis: prepattern," "Neurogenesis: determination," and "Neurogenesis: asymmetric division." The functioning of the system consistently limits the number of cells that are competent for neural development, firstly to dozens at the level of clusters, and then to a single parental cell within the cluster. The main attribute and connecting link of networks is the complex of proneural achaete-scute (AS-C) genes, the functioning of which at the stage of the separation of PCM is controlled by the central regulatory circuit (CRC). The analysis of the functioning of CRC revealed two phases of its activity that are differing in the time of action and the composition of elements. The cardinal difference of the second phase is the change in the content of the Phyl protein, which is responsible for the degradation of the proneural ASC proteins. This review briefly describes the main stages of mechanoreceptor morphogenesis, the composition and interrelationships of the gene networks supporting them, and also considers the inter- and intracellular mechanisms of PCM segregation. [ABSTRACT FROM AUTHOR]

Although my engagement with human genetics emerged gradually, and sometimes serendipitously, it has held me spellbound for decades. Without my teachers, students, postdocs, colleagues, and collaborators, I would not be writing this review of my scientific adventures. Early gene and disease mapping was a satisfying puzzle-solving exercise, but building biological insight was my main goal. The project trajectory was hugely influenced by the evolutionarily conserved nature of the implicated genes and by the pace of progress in genetic technologies. The rich detail of clinical observations, particularly in eye disease, makes humans an excellent model, especially when complemented by the use of multiple other animal species for experimental validation. The contributions of collaborators and rivals also influenced our approach. We are very fortunate to work in this era of unprecedented progress in genetics and genomics. [ABSTRACT FROM AUTHOR]

People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity. [ABSTRACT FROM AUTHOR]

In the human brain, long non-coding RNAs (lncRNAs) are widely expressed in an exquisitely temporally and spatially regulated manner, thus suggesting their contribution to normal brain development and their probable involvement in the molecular pathology of neurodevelopmental disorders (NDD). Bypassing the classic protein-centric conception of disease mechanisms, some studies have been conducted to identify and characterize the putative roles of non-coding sequences in the genetic pathogenesis and diagnosis of complex diseases. However, their involvement in NDD, and more specifically in intellectual disability (ID), is still poorly documented and only a few genomic alterations affecting the lncRNAs function and/or expression have been causally linked to the disease endophenotype. Considering that a significant fraction of patients still lacks a genetic or molecular explanation, we expect that a deeper investigation of the non-coding genome will unravel novel pathogenic mechanisms, opening new translational opportunities. Here, we present evidence of the possible involvement of many lncRNAs in the etiology of different forms of ID and NDD, grouping the candidate disease-genes in the most frequently affected cellular processes in which ID-risk genes were previously collected. We also illustrate new approaches for the identification and prioritization of NDD-risk lncRNAs, together with the current strategies to exploit them in diagnosis. [ABSTRACT FROM AUTHOR]

The Coronavirus disease 2019 (COVID-19) pandemic disproportionately affects immunocompetent and immunocompromised individuals, with the latter group being more vulnerable to severe disease and death. However, the differential pathogenesis of SARS-CoV-2 in the context of a specific immunological niche remains unknown. Similarly, systematic analysis of disease pathology in various extrapulmonary organs in immunocompetent and immunocompromised hosts during SARS-CoV-2 infection is not fully understood. We used a hamster model of SARS-CoV-2 infection, which recapitulates the pathophysiology of patients with mild-to-moderate COVID-19, to determine the dynamics of SARS-CoV-2 replication and histopathology at organ-level niches and map how COVID-19 symptoms vary in different immune contexts. Hamsters were intranasally infected with low (LD) or high (HD) inoculums of SARS-CoV-2, and the kinetics of disease pathology and viral load in multiple organs, antibody response, inflammatory cytokine expression, and genome-wide lung transcriptome by RNAseq analysis were determined and compared against corresponding responses from chemically induced immunocompromised hamsters. We observed transient body weight loss proportional to the SARS-CoV-2 infectious dose in immunocompetent hamsters. The kinetics of viral replication and peak viral loads were similar between LD and HD groups, although the latter developed more severe disease pathology in organs. Both groups generated a robust serum antibody response. In contrast, infected immunocompromised animals showed more prolonged body weight loss and mounted an inadequate SARS-CoV-2-neutralizing antibody response. The live virus was detected in the pulmonary and extrapulmonary organs for extended periods. These hamsters also had persistent inflammation with severe bronchiolar-alveolar hyperplasia/metaplasia. Consistent with the differential disease presentation, distinct changes in inflammation and immune cell response pathways and network gene expression were seen in the lungs of SARS-CoV-2-infected immunocompetent and immunocompromised animals. [ABSTRACT FROM AUTHOR]

The complexity of the traumatic brain injury (TBI) pathology, particularly concussive injury, is a serious obstacle for diagnosis, treatment, and long-term prognosis. Here we utilize modern systems biology in a rodent model of concussive injury to gain a thorough view of the impact of TBI on fundamental aspects of gene regulation, which have the potential to drive or alter the course of the TBI pathology. TBI perturbed epigenomic programming, transcriptional activities (expression level and alternative splicing), and the organization of genes in networks centered around genes such as Anax2, Ogn, and Fmod. Transcriptomic signatures in the hippocampus are involved in neuronal signaling, metabolism, inflammation, and blood function, and they overlap with those in leukocytes from peripheral blood. The homology between genomic signatures from blood and brain elicited by TBI provides proof of concept information for development of biomarkers of TBI based on composite genomic patterns. By intersecting with human genome-wide association studies, many TBI signature genes and network regulators identified in our rodent model were causally associated with brain disorders with relevant link to TBI. The overall results show that concussive brain injury reprograms genes which could lead to predisposition to neurological and psychiatric disorders, and that genomic information from peripheral leukocytes has the potential to predict TBI pathogenesis in the brain.

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

Diseases were initially thought to be the consequence of a single gene mutation. Advances in DNA sequencing tools and our understanding of gene behavior have revealed that complex diseases, such as cancer, are the product of genes cooperating with each other and with their environment in orchestrated communication networks. Seeing that the function of individual genes is still used to analyze cancer, the shift to using functionally interacting groups of genes as a new unit of study holds promise for demystifying cancer. The literature search focused on three types of cancer, namely breast, lung, and prostate, but arguments from other cancers were also included. The aim was to prove that multigene analyses can accurately predict and prognosticate cancer risk, subtype cancer for more personalized and effective treatments, and discover anti-cancer therapies. Computational intelligence is being harnessed to analyze this type of data and is proving indispensable to scientific progress. In the future, comprehensive profiling of all kinds of patient data (e.g. serum molecules, environmental exposures) can be used to build universal networks that should help us elucidate the molecular mechanisms underlying diseases and provide appropriate preventive measures, ensuring lifelong health and longevity. [ABSTRACT FROM AUTHOR]

Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, as well as better understanding of the biological mechanisms. Selecting and classifying node features over genome‐scale networks has become increasingly important in genomic biology and genomic medicine. Existing methods have some critical drawbacks. The first is they do not allow flexible modeling of different subtypes of selected nodes. The second is they ignore nodes with missing values, very likely to increase bias in estimation. To address these limitations, we propose a general modeling framework for Bayesian node classification (BNC) with missing values. A new prior model is developed for the class indicators incorporating the network structure. For posterior computation, we resort to the Swendsen‐Wang algorithm for efficiently updating class indicators. BNC can naturally handle missing values in the Bayesian modeling framework, which improves the node classification accuracy and reduces the bias in estimating gene effects. We demonstrate the advantages of our methods via extensive simulation studies and the analysis of the cutaneous melanoma dataset from The Cancer Genome Atlas. [ABSTRACT FROM AUTHOR]

Background: There is increased prevalence of epilepsy in patients with Alzheimer's disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders. Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation. Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies. Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD. [ABSTRACT FROM AUTHOR]

The colonic epithelium is the site of production and transport of many vasoactive metabolites and neurotransmitters that can modulate the immune system, affect cellular metabolism, and subsequently regulate blood pressure. As an important interface between the microbiome and its host, the colon can contribute to the development of hypertension. In this critical review, we highlight the role of colonic inflammation and microbial metabolites on the gut brain axis in the pathology of hypertension, with special emphasis on the interaction between tumor necrosis factor α (TNFα) and short chain fatty acid (SCFA) metabolites. Here, we review the current literature and identify novel pathways in the colonic epithelium related to hypertension. A network analysis on transcriptome data previously generated in spontaneously hypertensive rats (SHR) and Wistar-Kyoto (WKY) rats reveals differences in several pathways associated with inflammation involving TNFα (NF-κB and STAT Expression Targets) as well as oxidative stress. We also identify down-regulation of networks associated with gastrointestinal function, cardiovascular function, enteric nervous system function, and cholinergic and adrenergic transmission. The analysis also uncovered transcriptome responses related to glycolysis, butyrate oxidation, and mitochondrial function, in addition to gut neuropeptides that serve as modulators of blood pressure and metabolic function. We present a model for the role of TNFα in regulating bacterial metabolite transport and neuropeptide signaling in the gastrointestinal system, highlighting the complexity of host–microbiota interactions in hypertension. [ABSTRACT FROM AUTHOR]

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

Abstract Background Almost all elderly dogs develop myxomatous mitral valve disease by the end of their life, but the cavalier King Charles spaniel (CKCS) has a heightened susceptibility, frequently resulting in death at a young age and suggesting that there is a genetic component to the condition in this breed. Transcriptional profiling can reveal the impact of genetic variation through differences in gene expression levels. The aim of this study was to determine whether expression patterns were different in mitral valves showing myxomatous degeneration from CKCS dogs compared to valves from non-CKCS dogs. Results Gene expression patterns in three groups of canine valves resulted in distinct separation of normal valves, diseased valves from CKCS and diseased valves from other breeds; the latter were more similar to the normal valves than were the valves from CKCS. Gene expression patterns in diseased valves from CKCS dogs were quite different from those in the valves from other dogs, both affected and normal. Patterns in all diseased valves (from CKCS and other breeds) were also somewhat different from normal non-diseased samples. Analysis of differentially expressed genes showed enrichment in GO terms relating to cardiac development and function and to calcium signalling canonical pathway in the genes down-regulated in the diseased valves from CKCS, compared to normal valves and to diseased valves from other breeds. F2 (prothrombin) (CKCS diseased valves compared to normal) and MEF2C pathway activation (CKCS diseased valves compared to non-CKCS diseased valves) had the strongest association with the gene changes. A large number of genes that were differentially expressed in the CKCS diseased valves compared with normal valves and diseased valves from other breeds were associated with cardiomyocytes including CASQ2, TNNI3 and RYR2. Conclusion Transcriptomic profiling identified gene expression changes in CKCS diseased valves that were not present in age and disease severity-matched non-CKCS valves. These genes are associated with cardiomyocytes, coagulation and extra-cellular matrix remodelling. Identification of genes that vary in the CKCS will allow exploration of genetic variation to understand the aetiology of the disease in this breed, and ultimately development of breeding strategies to eliminate this disease from the breed.

Abstract Background Sulfur is a major component of biological molecules and thus an essential element for plants. Deficiency of sulfate, the main source of sulfur in soils, negatively influences plant growth and crop yield. The effect of sulfate deficiency on plants has been well characterized at the physiological, transcriptomic and metabolomic levels in Arabidopsis thaliana and a limited number of crop plants. However, we still lack a thorough understanding of the molecular mechanisms and regulatory networks underlying sulfate deficiency in most plants. In this work we analyzed the impact of sulfate starvation on the transcriptome of tomato plants to identify regulatory networks and key transcriptional regulators at a temporal and organ scale. Results Sulfate starvation reduces the growth of roots and leaves which is accompanied by major changes in the organ transcriptome, with the response being temporally earlier in roots than leaves. Comparative analysis showed that a major part of the Arabidopsis and tomato transcriptomic response to sulfate starvation is conserved between these plants and allowed for the identification of processes specifically regulated in tomato at the transcript level, including the control of internal phosphate levels. Integrative gene network analysis uncovered key transcription factors controlling the temporal expression of genes involved in sulfate assimilation, as well as cell cycle, cell division and photosynthesis during sulfate starvation in tomato roots and leaves. Interestingly, one of these transcription factors presents a high identity with SULFUR LIMITATION1, a central component of the sulfate starvation response in Arabidopsis. Conclusions Together, our results provide the first comprehensive catalog of sulfate-responsive genes in tomato, as well as novel regulatory targets for future functional analyses in tomato and other crops.

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.

The BayesNetBP package has been developed for probabilistic reasoning and visualization in Bayesian networks with nodes that are purely discrete, continuous or mixed (discrete and continuous). Probabilistic reasoning enables a user to absorb information into a Bayesian network and make queries about how the probabilities within the network change in light of new information. The package was developed in the R programming language and is freely available from the Comprehensive R Archive Network. A shiny app with Cytoscape widgets provides an interactive interface for evidence absorption, queries, and visualizations.

Among the reasons of primary lymphedema development, a certain role belongs to genetic factors. The specific molecular products participate in remodeling of blood and lymphatic vascular networks. Vascular endothelial growth factors (VEGFs) are key regulators of endothelial functions of the cells, which are responsible for lympho- and vasculogenesis. Moreover, matrix metalloproteinases (ММР) may act as regulators of both lymphangiogenesis, and angiogenesis. Since the regulatory regions of VEGFA gene, as well as of ММР genes are polymorphic, one may suggest, that their different expression level, determined by these polymorphisms, could be associated with development of swellings typical for lymphedema.We have analyzed gene polymorphisms in two regulatory regions of vascular endothelial growth factor-A VEGF-A (rs 699947 and rs 3025039), and matrix metalloproteinase genes MMP2 (rs 2438650), MMP3 (rs 3025058), MMP9 (rs 3918242), and their combinations in the patients with primary lymphedema.A group of patients with primary lymphedema included 72 subjects (55 women and 17 men) at the age of 18 to 81 years. Control group included 526 inhabitants of Novosibirsk (153 men, 373 women) without chronic diseases, comparable for age with lymphedema patients. We have performed typing of regulatory regions in VEGF (rs 699947, rs 3025039), ММР2 (rs 2438650), ММР3 (rs 3025058), ММР9 genes (rs 3918242). Fifteen complex genotypes have been revealed that were positively associated with disease. Analysis of the gene network topology has outlined the main intergenic interactions upon primary lymphedema development. MMP2 -1306 CC, MMP9 -1562CC and VEGF +936CC arrange the basic knots in the gene network (53% of total interactions). A number of significantly different complex genotypes was revealed at patients with primary lymphedema with normal body mass index (BMI < 25) and obesity (BMI < 30). Hence, frequency of complex genotype VEGF +936 CC: MMP3 -1171 5А6А:MMP9 -1562 CC in the patients with obesity is increased more 5.5-fold compared to the patients with normal BMI.The data obtained may presume a certain value of the analyzed gene polymorphisms in pathogenesis of primary lymphedema. Topological analysis of gene networks allows to study the structural and functional organization of gene-gene interactions for development of approaches to individyal preventive maintenance and therapy of the disease.

Background: There is increased prevalence of epilepsy in patients with Alzheimer’s disease (AD). Although shared pathological and clinical features have been identified, the underlying pathophysiology and cause-effect relationships are poorly understood. We aimed to identify commonly dysregulated groups of genes between these two disorders.Methods: Using publicly available transcriptomic data from hippocampal tissue of patients with temporal lobe epilepsy (TLE), late onset AD and non-AD controls, we constructed gene coexpression networks representing all three states. We then employed network preservation statistics to compare the density and connectivity-based preservation of functional gene modules between TLE, AD and controls and used the difference in significance scores as a surrogate quantifier of module preservation.Results: The majority (>90%) of functional gene modules were highly preserved between all coexpression networks, however several modules identified in the TLE network showed various degrees of preservation in the AD network compared to that of control. Of note, two synaptic signalling-associated modules and two metabolic modules showed substantial gain of preservation, while myelination and immune system-associated modules showed significant loss of preservation. The genes SCN3B and EPHA4 were identified as central regulatory hubs of the highly preserved synaptic signalling-associated module. GABRB3 and SCN2A were identified as central regulatory hubs of a smaller neurogenesis-associated module, which was enriched for multiple epileptic activity and seizure-related human phenotype ontologies.Conclusion: We conclude that these hubs and their downstream signalling pathways are common modulators of synaptic activity in the setting of AD and TLE, and may play a critical role in epileptogenesis in AD.

Advances in single-cell technologies have led to the discovery and characterization of new brain cell types, which in turn lead to a better understanding of the pathogenesis of Alzheimer's disease (AD). Here, we present a detailed analysis of single-nucleus (sn)RNA-seq data for three stages of AD from middle temporal gyrus and compare it with snRNA-seq data from the prefrontal cortices from individuals with alcohol use disorder (AUD). We observed a significant decrease in both inhibitory and excitatory neurons, in general agreement with previous reports. We observed several cell-type-specific gene expressions and pathway dysregulations that delineate AD stages. Endothelial and vascular leptomeningeal cells showed the greatest degree of gene expression changes. Cell-type-specific evidence of neurodegeneration was seen in multiple neuronal cell types particularly in somatostatin and Layer 5 extratelencephalic neurons, among others. Evidence of inflammatory responses was seen in non-neuronal cells, particularly in intermediate and advanced AD. We observed common perturbations in AD and AUD, particularly in pathways, like transcription, translation, apoptosis, autophagy, calcium signaling, neuroinflammation, and phosphorylation, that imply shared transcriptional pathogenic mechanisms and support the role of excessive alcohol intake in AD progression. Major AUD gene markers form and perturb a network of genes significantly associated with intermediate and advanced AD. Master regulator analysis from AUD gene markers revealed significant correlation with advanced AD of transcription factors that have implications in intellectual disability, neuroinflammation, and other neurodegenerative conditions, further suggesting a shared nexus of transcriptional changes between AD and AUD., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Joshi et al.)

Dendritic cells (DCs) can be used for therapeutic vaccination against cancer. The success of this therapy depends on efficient tumor-antigen presentation to cytotoxic T lymphocytes (CTLs) and the induction of durable CTL responses by the DCs. Therefore, simulation of such a biological system by computational modeling is appealing because it can improve our understanding of the molecular mechanisms underlying CTL induction by DCs and help identify new strategies to improve therapeutic DC vaccination for cancer. Here, we developed a multi-level model accounting for the life cycle of DCs during anti-cancer immunotherapy. Specifically, the model is composed of three parts representing different stages of DC immunotherapy – the spreading and bio-distribution of intravenously injected DCs in human organs, the biochemical reactions regulating the DCs’ maturation and activation, and DC-mediated activation of CTLs. We calibrated the model using quantitative experimental data that account for the activation of key molecular circuits within DCs, the bio-distribution of DCs in the body, and the interaction between DCs and T cells. We showed how such a data-driven model can be exploited in combination with sensitivity analysis and model simulations to identify targets for enhancing anti-cancer DC vaccination. Since other previous works show how modeling improves therapy schedules and DC dosage, we here focused on the molecular optimization of the therapy. In line with this, we simulated the effect in DC vaccination of the concerted modulation of combined intracellular regulatory processes and proposed several possibilities that can enhance DC-mediated immunogenicity. Taken together, we present a comprehensive time-resolved multi-level model for studying DC vaccination in melanoma. Although the model is not intended for personalized patient therapy, it could be used as a tool for identifying molecular targets for optimizing DC-based therapy for cancer, which ultimately should be tested in in vitro and in vivo experiments.

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]

Genetic mechanisms underlying abnormal early neural development in toddlers with Autism Spectrum Disorder (ASD) remain uncertain due to the impossibility of direct brain gene expression measurement during critical periods of early development. Recent findings from a multi-tissue study demonstrated high expression of many of the same gene networks between blood and brain tissues, in particular with cell cycle functions. We explored relationships between blood gene expression and total brain volume (TBV) in 142 ASD and control male toddlers. In control toddlers, TBV variation significantly correlated with cell cycle and protein folding gene networks, potentially impacting neuron number and synapse development. In ASD toddlers, their correlations with brain size were lost as a result of considerable changes in network organization, while cell adhesion gene networks significantly correlated with TBV variation. Cell cycle networks detected in blood are highly preserved in the human brain and are upregulated during prenatal states of development. Overall, alterations were more pronounced in bigger brains. We identified 23 candidate genes for brain maldevelopment linked to 32 genes frequently mutated in ASD. The integrated network includes genes that are dysregulated in leukocyte and/or postmortem brain tissue of ASD subjects and belong to signaling pathways regulating cell cycle G1/S and G2/M phase transition. Finally, analyses of the CHD8 subnetwork and altered transcript levels from an independent study of CHD8 suppression further confirmed the central role of genes regulating neurogenesis and cell adhesion processes in ASD brain maldevelopment.

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.

Neurological diseases, such as MS, AD, PD and HD, are a major health concern of the elderly population, but still therapeutic options are limited. Recent advances in genomic sequencing and bioinformatics, present an opportunity to understand mechanisms of these diseases for identification of therapeutic targets. Several studies have shown association of immune dysfunction with immune system mediated neurological disease, MS, as well as neurodegenerative diseases (AD, PD and HD). However, similarities and differences in role of the immune system, immune pathways and immune cell types in these diseases remains unknown. In this study, immune cell type signature genes in gene networks associated with neurological diseases, MS, AD, PD and HD was investigated using meta-analysis and bioinformatics methods. Application of Weighted Gene Co-expression Network Analysis (WGCNA) on publicly available gene expression datasets (microarray and RNA-seq) revealed a ModArray_04 module (microarray) or ModRNAseq_06 module (RNA-seq), significantly associated with MS, AD, PD and HD. Hypergeometric enrichment test revealed significant enrichment of immune cell type genes in these neurological disease modules. This study demonstrates that immune system mediated neurological disease, MS and neurodegenerative diseases (AD, PD and HD), share a common gene network characterized by immune cell type signature genes (microglia, monocytes and macrophages) and are probable targets for therapeutic intervention. In summary, this work shows a connection between MS, a disease where the role of the immune system and inflammation is established, and neurodegenerative diseases (AD, PD and HD) where the role of inflammation is still a hypothesis.

OmicInt is an R software package developed for a user-friendly and in-depth exploration of significantly changed genes, gene expression patterns, and the associated epigenetic features as well as the related miRNA environment. In addition, OmicInt offers single cell RNA-seq and proteomics data integration to elucidate specific expression profiles. To achieve this, OmicInt builds on a novel scoring function capturing expression and pathology associations. The developed scoring function together with the implemented Gaussian mixture modelling pipline helps to explore genes and the linked interactome networks. The machine learning pipeline was designed to make the analyses straightforward for the non-experts so that researchers could take advantage of advanced analytics for their data evaluation. Additional functionalities, such as protein type and cellular location classification, provide useful assessments of the key interactors. The introduced package can aid in studying specific gene networks, understanding cellular perturbation events, and exploring interactions that might not be easily detectable otherwise. Thus, this robust set of bioinformatics tools can be very beneficial in drug discovery and target evaluation. OmicInt is designed to be freely accessible to involve a larger bioinformatics community and continuously improve the developed algorithmic methods.

Although the inclusion of polyunsaturated fatty acids (PUFAs) in ruminants’ diets appears to be a well-documented strategy to enrich milk with PUFAs, several gene networks that regulate milk synthesis and mammary gland homeostasis could be impaired. The objective of this literature review is to assess the effects of nutritional strategies focused on enriching milk with PUFAs on gene networks regulating mammary gland function and lipogenesis, as well as the impact of feed additives and bioactive compounds with prominent antioxidant potential on immune-oxidative transcriptional profiling, as a part of mammary gland homeostasis and health. The findings support the conclusion that PUFAs’ inclusion in ruminants’ diets more strongly downregulate the stearoyl-CoA desaturase (SCD) gene compared to other key genes involved in de novo fatty acid synthesis in the mammary gland. Additionally, it was revealed that seed oils rich in linoleic and linolenic acids have no such strong impact on networks that regulate lipogenic homeostasis compared to marine oils rich in eicosapentaenoic and docosahexaenoic acids. Furthermore, ample evidence supports that cows and sheep are more prone to the suppression of lipogenesis pathways compared to goats under the impact of dietary marine PUFAs. On the other hand, the inclusion of feed additives and bioactive compounds with prominent antioxidant potential in ruminants’ diets can strengthen mammary gland immune-oxidative status. Considering that PUFA’s high propensity to oxidation can induce a cascade of pro-oxidant incidences, the simultaneous supplementation of antioxidant compounds and especially polyphenols may alleviate any side effects caused by PUFA overload in the mammary gland. In conclusion, future studies should deeply investigate the effects of PUFAs on mammary gland gene networks in an effort to holistically understand their impact on both milk fat depression syndrome and homeostatic disturbance.

In the post-genomic era, our understanding of the molecular regulators of physiologic and pathologic processes in pregnancy is expanding at the whole-genome level. Longitudinal changes in the known protein-coding transcriptome during normal pregnancy, which we recently reported (Gomez-Lopez et al., 2019), have improved our definition of the major operant networks, yet pregnancy-related functions of the non-coding RNA transcriptome remain poorly understood. A key finding of the ENCODE (Encyclopedia of DNA Elements) Consortium, the successor of the Human Genome Project, was that the human genome contains approximately 60,000 genes, the majority of which do not encode proteins. The total transcriptional output of non-protein-coding RNA genes, collectively referred to as the non-coding transcriptome, is comprised mainly of long non-coding RNA (lncRNA) transcripts (Derrien et al., 2012). Although the ncRNA transcriptome eclipses its protein-coding counterpart in abundance, it has until recently lacked a comprehensive, unbiased, genome-scale characterization over the timecourse of normal human pregnancy. Here, we annotated, characterized, and selectively validated the longitudinal changes in the non-coding transcriptome of maternal whole blood during normal pregnancy to term. We identified nine long non-coding RNAs (lncRNAs), including long intergenic non-coding RNAs (lincRNAs) as well as lncRNAs antisense to or otherwise in the immediate vicinity of protein-coding genes, that were differentially expressed with advancing gestation in normal pregnancy: AL355711, BC039551 (expressed mainly in the placenta), JHDM1D-AS1, A2M-AS1, MANEA-AS1, NR_034004, LINC00649, LINC00861, and LINC01094. By cross-referencing our dataset against major public pseudogene catalogs, we also identified six transcribed pseudogenes that were differentially expressed over time during normal pregnancy in maternal blood: UBBP4, FOXO3B, two Makorin (MKRN) pseudogenes (MKRN9P and LOC441455), PSME2P2, and YBX3P1. We also identified three non-coding RNAs belonging to other classes that were modulated during gestation: the microRNA MIR4439, the small nucleolar RNA (snoRNA) SNORD41, and the small Cajal-body specific ncRNA SCARNA2. The expression profiles of most hits were broadly suggestive of functions in pregnancy. These time-dependent changes of the non-coding transcriptome during normal pregnancy, which may confer specific regulatory impacts on their protein-coding gene targets, will facilitate a deeper molecular understanding of pregnancy and lncRNA-mediated molecular pathways at the maternal-fetal interface and of how these pathways impact maternal and fetal health.

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.

We and many others have noted the advantages of using heterogeneous (HS) animals to map genes and gene networks associated with both behavioral and non-behavioral phenotypes. Importantly, genetically complex Mus musculus crosses provide substantially increased resolution to examine old and new relationships between gene expression and behavior. Here we report on data obtained from two HS populations: the HS/NPT derived from eight inbred laboratory mouse strains and the HS-CC derived from the eight collaborative cross inbred mouse strains that includes three wild-derived strains. Our work has focused on the genes and gene networks associated with risk for excessive ethanol consumption, individual variation in ethanol consumption and the consequences, including escalation, of long-term ethanol consumption. Background data on the development of HS mice is provided, including advantages for the detection of expression quantitative trait loci. Examples are also provided of using HS animals to probe the genes associated with ethanol preference and binge ethanol consumption.

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. [ABSTRACT FROM AUTHOR]

In the post-genomic era, our understanding of the molecular regulators of physiologic and pathologic processes in pregnancy is expanding at the whole-genome level. Longitudinal changes in the known protein-coding transcriptome during normal pregnancy, which we recently reported (Gomez-Lopez et al., 2019), have improved our definition of the major operant networks, yet pregnancy-related functions of the non-coding RNA transcriptome remain poorly understood. A key finding of the ENCODE (Encyclopedia of DNA Elements) Consortium, the successor of the Human Genome Project, was that the human genome contains approximately 60,000 genes, the majority of which do not encode proteins. The total transcriptional output of non-protein-coding RNA genes, collectively referred to as the non-coding transcriptome, is comprised mainly of long non-coding RNA (lncRNA) transcripts (Derrien et al., 2012). Although the ncRNA transcriptome eclipses its protein-coding counterpart in abundance, it has until recently lacked a comprehensive, unbiased, genome-scale characterization over the timecourse of normal human pregnancy. Here, we annotated, characterized, and selectively validated the longitudinal changes in the non-coding transcriptome of maternal whole blood during normal pregnancy to term. We identified nine long non-coding RNAs (lncRNAs), including long intergenic non-coding RNAs (lincRNAs) as well as lncRNAs antisense to or otherwise in the immediate vicinity of protein-coding genes, that were differentially expressed with advancing gestation in normal pregnancy: AL355711 , BC039551 (expressed mainly in the placenta), JHDM1D-AS1 , A2M-AS1 , MANEA-AS1 , NR_034004 , LINC00649, LINC00861 , and LINC01094. By cross-referencing our dataset against major public pseudogene catalogs, we also identified six transcribed pseudogenes that were differentially expressed over time during normal pregnancy in maternal blood: UBBP4 , FOXO3B , two Makorin (MKRN) pseudogenes (MKRN9P and LOC441455), PSME2P2 , and YBX3P1. We also identified three non-coding RNAs belonging to other classes that were modulated during gestation: the microRNA MIR4439, the small nucleolar RNA (snoRNA) SNORD41 , and the small Cajal-body specific ncRNA SCARNA2. The expression profiles of most hits were broadly suggestive of functions in pregnancy. These time-dependent changes of the non-coding transcriptome during normal pregnancy, which may confer specific regulatory impacts on their protein-coding gene targets, will facilitate a deeper molecular understanding of pregnancy and lncRNA-mediated molecular pathways at the maternal-fetal interface and of how these pathways impact maternal and fetal health. [ABSTRACT FROM AUTHOR]