SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies

NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

Insights into symbiotic interactions from metatranscriptome analysis of deep-sea mussel Gigantidas platifrons under long-term laboratory maintenance

Symbioses between invertebrates and chemosynthetic bacteria are of fundamental importance in deep-sea ecosystems, but the mechanisms that enable their symbiont associations are still largely undescribed, owing to the culturable difficulties of deep-sea lives. Bathymodiolinae mussels are remarkable in their ability to overcome decompression and can be maintained successfully for an extended period under atmospheric pressure, thus providing a model for investigating the molecular basis of symbiotic interactions. Herein, we conducted metatranscriptome sequencing and gene co-expression network analysis of Gigantidas platifrons under laboratory maintenance with gradual loss of symbionts. The results revealed that one-day short-term maintenance triggered global transcriptional perturbation in symbionts, but little gene expression changes in mussel hosts, which were mainly involved in responses to environmental changes. Long-term maintenance with depleted symbionts induced a metabolic shift in the mussel host. The most notable changes were the suppression of sterol biosynthesis and the complementary activation of terpenoid backbone synthesis in response to the reduction of bacteria-derived terpenoid sources. In addition, we detected the upregulation of host proteasomes responsible for amino acid deprivation caused by symbiont depletion. Additionally, a significant correlation between host microtubule motor activity and symbiont abundance was revealed, suggesting the possible function of microtubule-based intracellular trafficking in the nutritional interaction of symbiosis. Overall, by analyzing the dynamic transcriptomic changes during the loss of symbionts, our study highlights the nutritional importance of symbionts in supplementing terpenoid compounds and essential amino acids and provides insight into the molecular mechanisms and strategies underlying the symbiotic interactions in deep-sea ecosystems.

Genome-Wide Association Study for Udder Conformation Traits in Chinese Holstein Cattle

Udder conformation traits are one of the most economic traits in dairy cows, greatly affecting animal health, milk production, and producer profitability in the dairy industry. Genetic analysis of udder structure and scores have been developed in Holstein cattle. In our research, we conducted a genome-wide association study for five udder traits, including anterior udder attachment (AUA), central suspensory ligament (CSL), posterior udder attachment height (PUAH), posterior udder attachment width (PUAW), and udder depth (UD), in which the fixed and random model circulating probability unification (FarmCPU) model was applied for the association analysis. The heritability and the standard errors of these five udder traits ranged from 0.04 ± 0.00 to 0.49 ± 0.03. Phenotype data were measured from 1000 Holstein cows, and the GeneSeek Genomic Profiler (GGP) Bovine 100 K SNP chip was used to analyze genotypic data in Holstein cattle. For GWAS analysis, 984 individual cows and 84,407 single-nucleotide polymorphisms (SNPs) remained after quality control; a total of 18 SNPs were found at the GW significant threshold (p < 5.90 × 10−7). Many candidate genes were identified within 200kb upstream or downstream of the significant SNPs, which include MGST1, MGST2, MTUS1, PRKN, STXBP6, GRID2, E2F8, CDH11, FOXP1, SLF1, TMEM117, SBF2, GC, ADGRB3, and GCLC. Pathway analysis revealed that 58 Gene Ontology (GO) terms and 18 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched with adjusted p values, and these GO terms and the KEGG pathway analysis were associated with biological information, metabolism, hormonal growth, and development processes. These results could give valuable biological information for the genetic architecture of udder conformation traits in dairy Holstein cattle.

Application of high-throughput transcriptomics for mechanism-based biological read-across of short-chain carboxylic acid analogues of valproic acid

Chemical read-across is commonly evaluated without specific knowledge of the biological mechanisms leading to observed adverse outcomes in vivo. Integrating data that indicate shared modes of action in humans will strengthen read-across cases. Here we studied transcriptomic responses of primary human hepatocytes (PHH) to a large panel of carboxylic acids to include detailed mode-of-action data as a proof-of-concept for read-across in risk assessment. In rodents, some carboxylic acids, including valproic acid (VPA), are known to cause hepatic steatosis, whereas others do not. We investigated transcriptomics responses of PHHs exposed for 24 h to 18 structurally different VPA analogues in a concentration range to determine biological similarity in relation to in vivo steatotic potential. Using a targeted high-throughput screening assay, we assessed the differential expression of ~3,000 genes covering relevant biological pathways. Differentially expressed gene analysis revealed differences in potency of carboxylic acids, and expression patterns were highly similar for structurally similar compounds. Strong clustering occurred for steatosis-positive versus steatosis-negative carboxylic acids. To quantitatively define biological read-across, we combined pathway analysis and weighted gene co-expression network analysis. Active carboxylic acids displayed high similarity in gene network modulation. Importantly, free fatty acid synthesis modulation and stress pathway responses are affected by active car­boxylic acids, providing coherent mechanistic underpinning for our findings. Our work shows that transcriptomic analysis of cultured human hepatocytes can reinforce the prediction of liver injury outcome based on quantitative and mechanistic biological data and support its application in read-across.

Type 2 Diabetes Mellitus Mediation by the Disruptive Activity of Environmental Toxicants on Sex Hormone Receptors: In Silico Evaluation

This study investigates the disruptive activity of environmental toxicants on sex hormone receptors mediating type 2 diabetes mellitus (T2DM). Toxicokinetics, gene target prediction, molecular docking, molecular dynamics, and gene network analysis were applied in silico techniques. From the results, permethrin, perfluorooctanoic acid, dichlorodiphenyltrichloroethane, O-phenylphenol, bisphenol A, and diethylstilbestrol were the active toxic compounds that could modulate androgen (AR) and estrogen-α and -β receptors (ER) to induce T2DM. Early growth response 1 (EGR1), estrogen receptor 1 (ESR1), and tumour protein 63 (TP63) were the major transcription factors, while mitogen-activated protein kinases (MAPK) and cyclin-dependent kinases (CDK) were the major kinases upregulated by these toxicants via interactions with intermediary proteins such as PTEN, AKT1, NfKβ1, SMAD3 and others in the gene network analysis to mediate T2DM. These toxicants pose a major challenge to public health; hence, monitoring their manufacture, use, and disposal should be enforced. This would ensure reduced interaction between people and these toxic chemicals, thereby reducing the incidence and prevalence of T2DM.

Transcriptomic links to muscle mass loss and declines in cumulative muscle protein synthesis during short-term disuse in healthy younger humans

Muscle disuse leads to a rapid decline in muscle mass, with reduced muscle protein synthesis (MPS) considered the primary physiological mechanism. Here, we employed a systems biology approach to uncover molecular networks and key molecular candidates that quantitatively link to the degree of muscle atrophy and/or extent of decline in MPS during short-term disuse in humans. After consuming a bolus dose of deuterium oxide (D₂ O; 3 mL.kg^-1 ), eight healthy males (22 ± 2 years) underwent 4 days of unilateral lower-limb immobilization. Bilateral muscle biopsies were obtained post-intervention for RNA sequencing and D₂ O-derived measurement of MPS, with thigh lean mass quantified using dual-energy X-ray absorptiometry. Application of weighted gene co-expression network analysis identified 15 distinct gene clusters ("modules") with an expression profile regulated by disuse and/or quantitatively connected to disuse-induced muscle mass or MPS changes. Module scans for candidate targets established an experimentally tractable set of candidate regulatory molecules (242 hub genes, 31 transcriptional regulators) associated with disuse-induced maladaptation, many themselves potently tied to disuse-induced reductions in muscle mass and/or MPS and, therefore, strong physiologically relevant candidates. Notably, we implicate a putative role for muscle protein breakdown-related molecular networks in impairing MPS during short-term disuse, and further establish DEPTOR (a potent mTOR inhibitor) as a critical mechanistic candidate of disuse driven MPS suppression in humans. Overall, these findings offer a strong benchmark for accelerating mechanistic understanding of short-term muscle disuse atrophy that may help expedite development of therapeutic interventions.

Development of the Pre-gnathal Segments in the Milkweed Bug Oncopeltus fasciatus Suggests They Are Not Serial Homologs of Trunk Segments

The three anterior-most segments in arthropods contain the ganglia that make up the arthropod brain. These segments, the pre-gnathal segments (PGS), are known to exhibit many developmental differences to other segments, believed to reflect their divergent morphology. We have analyzed the expression and function of the genes involved in the conserved segment-polarity network, including genes from the Wnt and Hedgehog pathways, in the PGS, compared with the trunk segments, in the hemimetabolous insect Oncopeltus fasciatus. Gene function was tested by manipulating expression through RNA interference against components of the two pathways. We show that there are fundamental differences in the expression patterns of the segment polarity genes, in the timing of their expression and in the interactions among them in the process of pre-gnathal segment generation, relative to all other segments. We argue that given these differences, the PGS should not be considered serially homologous to trunk segments. This realization raises important questions about the differing evolutionary ancestry of different regions of the arthropod head.

Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis

Guar (Cyamopsis tetragonoloba (L.) Taub.) is an annual legume crop native to India and Pakistan. Seeds of the plant serve as a source of galactomannan polysaccharide (guar gum) used in the food industry as a stabilizer (E412) and as a gelling agent in oil and gas fracturing fluids. There were several attempts to introduce this crop to countries of more northern latitudes. However, guar is a plant of a short photoperiod, therefore, its introduction, for example, to Russia is complicated by a long day length during the growing season. Breeding of new guar varieties insensitive to photoperiod slowed down due to the lack of information on functional molecular markers, which, in turn, requires information on guar genome. Modern breeding strategies, e.g., genomic predictions, benefit from integration of multi-omics approaches such as transcriptome, proteome and metabolome assays. Here we present an attempt to use transcriptome-metabolome integration to understand the genetic determination of flowering time variation among guar plants that differ in their photoperiod sensitivity. This study was performed on nine early- and six delayed-flowering guar varieties with the goal to find a connection between 63 metabolites and 1,067 differentially expressed transcripts using Shiny GAM approach. For the key biomarker of flowering in guar myo-inositol we also evaluated the KEGG biochemical pathway maps available for Arabidopsis thaliana. We found that the phosphatidylinositol signaling pathway is initiated in guar plants that are ready for flowering through the activation of the phospholipase C (PLC) gene, resulting in an exponential increase in the amount of myo-inositol in its free form observed on GC-MS chromatograms. The signaling pathway is performed by suppression of myo-inositol phosphate kinases (phosphorylation) and alternative overexpression of phosphatases (dephosphorylation). Our study suggests that metabolome and transcriptome information taken together, provide valuable information about biomarkers that can be used as a tool for marker-assisted breeding, metabolomics and functional genomics of this important legume crop.

Prenatal maternal distress and immune cell epigenetic profiles at 3-months of age

BACKGROUND

Prenatal maternal distress predicts altered offspring immune outcomes, potentially via altered epigenetics. The role of different kinds of prenatal maternal distress on DNA methylation profiles is not understood.

METHODS

A sample of 117 women (APrON cohort) were followed from pregnancy to the postpartum period. Maternal distress (depressive symptoms, pregnancy-specific anxiety, stressful life events) were assessed mid-pregnancy, late-pregnancy, and 3-months postpartum. DNA methylation profiles were obtained from 3-month-old blood samples. Principal component analysis identified two epigenetic components, characterized as Immune Signaling and DNA Transcription through gene network analysis. Covariates were maternal demographics, pre-pregnancy body mass index, child sex, birth gestational age, and postpartum maternal distress. Penalized regression (LASSO) models were used.

RESULTS

Late-pregnancy stressful life events, b = 0.006, early-pregnancy depressive symptoms, b = 0.027, late-pregnancy depressive symptoms, b = 0.014, and pregnancy-specific anxiety during late pregnancy, b = -0.631, were predictive of the Immune Signaling component, suggesting that these aspects of maternal distress could affect methylation in offspring immune signaling pathways. Only early-pregnancy depressive symptoms was predictive of the DNA Transcription component, b = -0.0004, suggesting that this aspect of maternal distress is implicated in methylation of offspring DNA transcription pathways.

CONCLUSIONS

Exposure timing and kind of prenatal maternal distress could matter in the prediction of infant immune epigenetic profiles.

Antiviral activity of traditional medicinal plants from Ayurveda against SARS-CoV-2 infection

SARS-CoV-2 is the etiological agent of COVID-19 and responsible for more than 6 million cases globally, for which no vaccine or antiviral is available. Therefore, this study was planned to investigate the antiviral role of the active constituents against spike glycoprotein of SARS-CoV-2 as well as its host ACE2 receptor. Structure-based drug design approach has been used to elucidate the antiviral activity of active constituents present in traditional medicinal plants from Ayurveda. Further, parameters like drug-likeness, pharmacokinetics, and toxicity were determined to ensure the safety and efficacy of active constituents. Gene network analysis was performed to investigate the pathways altered during COVID-19. The prediction of drug–target interactions was performed to discover novel targets for active constituents. The results suggested that amarogentin, eufoliatorin, α-amyrin, caesalpinins, kutkin, β-sitosterol, and belladonnine are the top-ranked molecules have the highest affinity towards both the spike glycoprotein and ACE2. Most active constituents have passed the criteria of drug-likeness and demonstrated good pharmacokinetic profile with minimum predicted toxicity level. Gene network analysis confirmed that G-protein coupled receptor, protein kinase B signaling, protein secretion, peptidyl-serine phosphorylation, nuclear transport, apoptotic pathway, tumor necrosis factor, regulation of angiotensin level, positive regulation of ion transport, and membrane protein proteolysis were altered during COVID-19. The target prediction analysis revealed that most active constituents target the same pathways which are found to be altered during COVID-19. Collectively, our data encourages the use of active constituents as a potential therapy for COVID-19. However, further studies are ongoing to confirm its efficacy against disease.

Coexpression Analysis Reveals Dynamic Modules Regulating the Growth and Development of Cirri in the Rattans (Calamus simplicifolius and Daemonorops jenkinsiana)

Rattan is regarded as one of the major non-timber forest products, second only to wood and bamboo, worldwide. Although the published genomes of Calamus simplicifolius and Daemonorops jenkinsiana have facilitated genome-wide gene functional analyses, coexpression networks (CENs) provide more comprehensive and complete annotations of gene function at the transcriptome level. Thus, we analyzed the CENs of the two rattans, C. simplicifolius and D. jenkinsiana, by integrating the genome sequences and analyzing in-house transcriptome data from different development stages of their cirri using a well-developed strategy. A total of 3,504 and 3,027 functional modules were identified in C. simplicifolius and D. jenkinsiana, respectively, based on a combination of CENs, gene family classification, and function enrichment tools. These modules covered the major developmental processes, including photosynthesis, lignin biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis. Reference annotations were refined using CENs and functional modules. Moreover, we obtained novel insights into the regulation of cirrus growth and development in rattans. Furthermore, Rattan-NET (http://rattan.bamboogdb.org/), an online database with analysis tools for gene set enrichment analysis, module enrichment, network comparison analysis, and cis-element analysis, was constructed for the easy analysis of gene function and regulation modules involved in the growth and development of cirri in rattans.

Master Regulator Analysis of the SARS-CoV-2/Human Interactome

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.

The Phenotype Paradox: Lessons From Natural Transcriptome Evolution on How to Engineer Plants

Plants have evolved genome complexity through iterative rounds of single gene and whole genome duplication. This has led to substantial expansion in transcription factor numbers following preferential retention and subsequent functional divergence of these regulatory genes. Here we review how this simple evolutionary network rewiring process, regulatory gene duplication followed by functional divergence, can be used to inspire synthetic biology approaches that seek to develop novel phenotypic variation for future trait based breeding programs in plants.

Plasmacytoid DCs From Patients With Sjögren's Syndrome Are Transcriptionally Primed for Enhanced Pro-inflammatory Cytokine Production

Primary Sjögren's syndrome (pSS) is a systemic auto-immune disease typified by dryness of the mouth and eyes. A majority of patients with pSS have a type-I interferon (IFN)-signature, which is defined as the increased expression of IFN-induced genes in circulating immune cells and is associated with increased disease activity. As plasmacytoid dendritic cells (pDC) are the premier type-I IFN-producing cells and are present at the site of inflammation, they are thought to play a significant role in pSS pathogenesis. Considering the lack of data on pDC regulation and function in pSS patients, we here provided the first in-depth molecular characterization of pSS pDCs. In addition, a group of patients with non-Sjögren's sicca (nSS) was included; these poorly studied patients suffer from complaints similar to pSS patients, but are not diagnosed with Sjögren's syndrome. We isolated circulating pDCs from two independent cohorts of patients and controls (each n = 31) and performed RNA-sequencing, after which data-driven networks and modular analysis were used to identify robustly reproducible transcriptional “signatures” of differential and co-expressed genes. Four signatures were identified, including an IFN-induced gene signature and a ribosomal protein gene-signature, that indicated pDC activation. Comparison with a dataset of in vitro activated pDCs showed that pSS pDCs have higher expression of many genes also upregulated upon pDC activation. Corroborating this transcriptional profile, pSS pDCs produced higher levels of pro-inflammatory cytokines, including type-I IFN, upon in vitro stimulation with endosomal Toll-like receptor ligands. In this setting, cytokine production was associated with expression of hub-genes from the IFN-induced and ribosomal protein gene-signatures, indicating that the transcriptional profile of pSS pDCs underlies their enhanced cytokine production. In all transcriptional analyses, nSS patients formed an intermediate group in which some patients were molecularly similar to pSS patients. Furthermore, we used the identified transcriptional signatures to develop a discriminative classifier for molecular stratification of patients with sicca. Altogether, our data provide in-depth characterization of the aberrant regulation of pDCs from patients with nSS and pSS and substantiate their perceived role in the immunopathology of pSS, supporting studies that target pDCs, type-I IFNs, or IFN-signaling in pSS.

Weighted Fused Pathway Graphical Lasso for Joint Estimation of Multiple Gene Networks

Gene regulatory networks (GRNs) are often inferred based on Gaussian graphical models that could identify the conditional dependence among genes by estimating the corresponding precision matrix. Classical Gaussian graphical models are usually designed for single network estimation and ignore existing knowledge such as pathway information. Therefore, they can neither make use of the common information shared by multiple networks, nor can they utilize useful prior information to guide the estimation. In this paper, we propose a new weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple networks by incorporating prior knowledge derived from known pathways and gene interactions. Based on the assumption that two genes are less likely to be connected if they do not participate together in any pathways, a pathway-based constraint is considered in our model. Moreover, we introduce a weighted fused lasso penalty in our model to take into account prior gene interaction data and common information shared by multiple networks. Our model is optimized based on the alternating direction method of multipliers (ADMM). Experiments on synthetic data demonstrate that our method outperforms other five state-of-the-art graphical models. We then apply our model to two real datasets. Hub genes in our identified state-specific networks show some shared and specific patterns, which indicates the efficiency of our model in revealing the underlying mechanisms of complex diseases.

[The Myocardial Infarction Associated Variant in the MIR196A2 Gene and Presumable Signaling Pathways to Involve miR-196a2 in the Pathological Phenotype]

The heritable component of susceptibility to myocardial infraction (MI) remains unexplained, possibly due to the minor effects of genes, which are not obviously associated with the disease. These genes may be integrated in miRNA regulated networks associated with myocardial infarction. A systematic review of the literature led us to selecting rs2910164 (MIR146A), rs11614913 (MIR196A2), and rs3746444 (MIR499А) variants to study the association with the MI phenotype. In ethnic Russians, variant rs11614913*C (MIR196A2) was found to be associated with the risk of myocardial infraction (p = 0.023, OR = 1.74) for the first time; this association was validated in an independent cohort. The gene-gene interaction network for experimentally validated miR-196a2 target genes was built and analyzed. One of its four topological clusters contained the majority of miR-196a2 target genes associated with atherosclerosis, coronary artery disease or myocardial infarction and was enriched with the genes regulating the TGFβ and class I MHC signaling pathways, platelet activation/aggregation, and the cell cycle control. This analysis points towards the role of miR-196a2 in the pathological coronary phenotypes and opens up an avenue for further investigations.

Co-expression Gene Network Analysis and Functional Module Identification in Bamboo Growth and Development

Bamboo is one of the fastest-growing non-timber forest plants. Moso bamboo (Phyllostachys edulis) is the most economically valuable bamboo in Asia, especially in China. With the release of the whole-genome sequence of moso bamboo, there are increasing demands for refined annotation of bamboo genes. Recently, large amounts of bamboo transcriptome data have become available, including data on the multiple growth stages of tissues. It is now feasible for us to construct co-expression networks to improve bamboo gene annotation and reveal the relationships between gene expression and growth traits. We integrated the genome sequence of moso bamboo and 78 transcriptome data sets to build genome-wide global and conditional co-expression networks. We overlaid the gene expression results onto the network with multiple dimensions (different development stages). Through combining the co-expression network, module classification and function enrichment tools, we identified 1,896 functional modules related to bamboo development, which covered functions such as photosynthesis, hormone biosynthesis, signal transduction, and secondary cell wall biosynthesis. Furthermore, an online database (http://bioinformatics.cau.edu.cn/bamboo) was built for searching the moso bamboo co-expression network and module enrichment analysis. Our database also includes cis-element analysis, gene set enrichment analysis, and other tools. In summary, we integrated public and in-house bamboo transcriptome data sets and carried out co-expression network analysis and functional module identification. Through data mining, we have yielded some novel insights into the regulation of growth and development. Our established online database might be convenient for the bamboo research community to identify functional genes or modules with important traits.

The Pathogenesis-Related Maize Seed (PRms) Gene Plays a Role in Resistance to Aspergillus flavus Infection and Aflatoxin Contamination

Aspergillus flavus is an opportunistic plant pathogen that colonizes and produces the toxic and carcinogenic secondary metabolites, aflatoxins, in oil-rich crops such as maize (Zea mays ssp. mays L.). Pathogenesis-related (PR) proteins serve as an important defense mechanism against invading pathogens by conferring systemic acquired resistance in plants. Among these, production of the PR maize seed protein, ZmPRms (AC205274.3_FG001), has been speculated to be involved in resistance to infection by A. flavus and other pathogens. To better understand the relative contribution of ZmPRms to A. flavus resistance and aflatoxin production, a seed-specific RNA interference (RNAi)-based gene silencing approach was used to develop transgenic maize lines expressing hairpin RNAs to target ZmPRms. Downregulation of ZmPRms in transgenic kernels resulted in a ∼250-350% increase in A. flavus infection accompanied by a ∼4.5-7.5-fold higher accumulation of aflatoxins than control plants. Gene co-expression network analysis of RNA-seq data during the A. flavus-maize interaction identified ZmPRms as a network hub possibly responsible for regulating several downstream candidate genes associated with disease resistance and other biochemical functions. Expression analysis of these candidate genes in the ZmPRms-RNAi lines demonstrated downregulation (vs. control) of a majority of these ZmPRms-regulated genes during A. flavus infection. These results are consistent with a key role of ZmPRms in resistance to A. flavus infection and aflatoxin accumulation in maize kernels.

Identification of Linkages between EDCs in Personal Care Products and Breast Cancer through Data Integration Combined with Gene Network Analysis

Approximately 1000 chemicals have been reported to possibly have endocrine disrupting effects, some of which are used in consumer products, such as personal care products (PCPs) and cosmetics. We conducted data integration combined with gene network analysis to: (i) identify causal molecular mechanisms between endocrine disrupting chemicals (EDCs) used in PCPs and breast cancer; and (ii) screen candidate EDCs associated with breast cancer. Among EDCs used in PCPs, four EDCs having correlation with breast cancer were selected, and we curated 27 common interacting genes between those EDCs and breast cancer to perform the gene network analysis. Based on the gene network analysis, ESR1, TP53, NCOA1, AKT1, and BCL6 were found to be key genes to demonstrate the molecular mechanisms of EDCs in the development of breast cancer. Using GeneMANIA, we additionally predicted 20 genes which could interact with the 27 common genes. In total, 47 genes combining the common and predicted genes were functionally grouped with the gene ontology and KEGG pathway terms. With those genes, we finally screened candidate EDCs for their potential to increase breast cancer risk. This study highlights that our approach can provide insights to understand mechanisms of breast cancer and identify potential EDCs which are in association with breast cancer.

Nanoparticle Fullerene (C60) demonstrated stable binding with antibacterial potential towards probable targets of drug resistant Salmonella typhi - a computational perspective and in vitro investigation

Salmonella typhi, a Gram negative bacterium, has become multidrug resistant (MDR) to wide classes of antibacterials which necessitate an alarming precaution. This study focuses on the binding potential and therapeutic insight of Nano-Fullerene C60 towards virulent targets of Salmonella typhi by computational prediction and preliminary in vitro assays. The clinical isolates of Salmonella typhi were collected and antibiotic susceptibility profiles were assessed. The drug targets of pathogen were selected by rigorous literature survey and gene network analysis by various metabolic network resources. Based on this study, 20 targets were screened and the 3D structures of few drug targets were retrieved from PDB and others were computationally predicted. The structures of nanoleads such as Fullerene C60, ZnO and CuO were retrieved from drug databases. The binding potential of these nanoleads towards all selected targets were predicted by molecular docking. The best docked conformations were screened and concept was investigated by preliminary bioassays. This study revealed that most of the isolates of Salmonella typhi were found to be MDR (p < .05). The theoretical models of selected drug targets showed high stereochemical validity. The molecular docking studies suggested that Fullerene C60 showed better binding affinity towards the drug targets when compared to ZnO and CuO. The preliminary in vitro assays suggested that 100 μg/L Fullerene C60 posses significant inhibitory activities and absence of drug resistance to this nanoparticle. This study suggests that Fullerene C60 can be scaled up as probable lead molecules against the major drug targets of MDR Salmonella typhi.

Systematic review of blood transcriptome profiling in neuropsychiatric disorders: guidelines for biomarker discovery

INTRODUCTION

The utility of blood for genome-wide gene expression profiling and biomarker discovery has received much attention in patients diagnosed with major neuropsychiatric disorders. While numerous studies have been conducted, statistical rigor and clarity in terms of blood-based biomarker discovery, validation, and testing are needed.

METHODS

We conducted a systematic review of the literature to investigate methodological approaches and to assess the value of blood transcriptome profiling in research on mental disorders. We were particularly interested in statistical considerations related to machine learning, gene network analyses, and convergence across different disorders.

RESULTS

A total of 108 peripheral blood transcriptome studies across 15 disorders were surveyed: 25 studies used a variety of machine learning techniques to assess putative clinical viability of the candidate biomarkers; 11 leveraged a higher-order systems-level perspective to identify gene module-based biomarkers; and nine performed analyses across two or more neuropsychiatric phenotypes. Notably, ~50% of the surveyed studies included fewer than 50 samples (cases and controls), while ~75% included less than 100.

CONCLUSIONS

Detailed consideration of statistical analysis in the early stages of experimental planning is critical to ensure blood-based biomarker discovery and validation. Statistical guidelines are presented to enhance implementation and reproducibility of machine learning and gene network analyses across independent studies. Future studies capitalizing on larger sample sizes and emerging next-generation technologies set the stage for moving the field forwards. Copyright © 2016 John Wiley & Sons, Ltd.

Transcriptome analysis reveals regulatory networks underlying differential susceptibility to Botrytis cinerea in response to nitrogen availability in Solanum lycopersicum

Nitrogen (N) is one of the main limiting nutrients for plant growth and crop yield. It is well documented that changes in nitrate availability, the main N source found in agricultural soils, influences a myriad of developmental programs and processes including the plant defense response. Indeed, many agronomical reports indicate that the plant N nutritional status influences their ability to respond effectively when challenged by different pathogens. However, the molecular mechanisms involved in N-modulation of plant susceptibility to pathogens are poorly characterized. In this work, we show that Solanum lycopersicum defense response to the necrotrophic fungus Botrytis cinerea is affected by plant N availability, with higher susceptibility in nitrate-limiting conditions. Global gene expression responses of tomato against B. cinerea under contrasting nitrate conditions reveals that plant primary metabolism is affected by the fungal infection regardless of N regimes. This result suggests that differential susceptibility to pathogen attack under contrasting N conditions is not only explained by a metabolic alteration. We used a systems biology approach to identify the transcriptional regulatory network implicated in plant response to the fungus infection under contrasting nitrate conditions. Interestingly, hub genes in this network are known key transcription factors involved in ethylene and jasmonic acid signaling. This result positions these hormones as key integrators of nitrate and defense against B. cinerea in tomato plants. Our results provide insights into potential crosstalk mechanisms between necrotrophic defense response and N status in plants.