Integrative Analysis of Transcriptomic Profiles and Physiological Responses Provide New Insights into Drought Stress Tolerance in Oil Palm (Elaeis guineensis Jacq.).

Oil palm (Elaeis guineensis Jacq.) is a highly productive crop economically significant for food, cosmetics, and biofuels. Abiotic stresses such as low water availability, salt accumulation, and high temperatures severely impact oil palm growth, physiology, and yield by restricting water flux among soil, plants, and the environment. While drought stress's physiological and biochemical effects on oil palm have been extensively studied, the molecular mechanisms underlying drought stress tolerance remain unclear. Under water deficit conditions, this study investigates two commercial E. guineensis cultivars, IRHO 7001 and IRHO 2501. Water deficit adversely affected the physiology of both cultivars, with IRHO 2501 being more severely impacted. After several days of water deficit, there was a 40% reduction in photosynthetic rate (A) for IRHO 7001 and a 58% decrease in IRHO 2501. Further into the drought conditions, there was a 75% reduction in A for IRHO 7001 and a 91% drop in IRHO 2501. Both cultivars reacted to the drought stress conditions by closing stomata and reducing the transpiration rate. Despite these differences, no significant variations were observed between the cultivars in stomatal conductance, transpiration, or instantaneous leaf-level water use efficiency. This indicates that IRHO 7001 is more tolerant to drought stress than IRHO 2501. A differential gene expression and network analysis was conducted to elucidate the differential responses of the cultivars. The DESeq2 algorithm identified 502 differentially expressed genes (DEGs). The gene coexpression network for IRHO 7001 comprised 274 DEGs and 46 predicted HUB genes, whereas IRHO 2501's network included 249 DEGs and 3 HUB genes. RT-qPCR validation of 15 DEGs confirmed the RNA-Seq data. The transcriptomic profiles and gene coexpression network analysis revealed a set of DEGs and HUB genes associated with regulatory and transcriptional functions. Notably, the zinc finger protein ZAT11 and linoleate 13S-lipoxygenase 2-1 (LOX2.1) were overexpressed in IRHO 2501 but under-expressed in IRHO 7001. Additionally, phytohormone crosstalk was identified as a central component in the response and adaptation of oil palm to drought stress.

Coordinated inflammation and immune response transcriptional regulation in breast cancer molecular subtypes.

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.

Inferring Interaction Networks from Transcriptomic Data: Methods and Applications.

Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.

Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.

BACKGROUND: Recent studies uncovered pervasive transcription and translation of thousands of noncanonical open reading frames (nORFs) outside of annotated genes. The contribution of nORFs to cellular phenotypes is difficult to infer using conventional approaches because nORFs tend to be short, of recent de novo origins, and lowly expressed. Here we develop a dedicated coexpression analysis framework that accounts for low expression to investigate the transcriptional regulation, evolution, and potential cellular roles of nORFs in Saccharomyces cerevisiae. RESULTS: Our results reveal that nORFs tend to be preferentially coexpressed with genes involved in cellular transport or homeostasis but rarely with genes involved in RNA processing. Mechanistically, we discover that young de novo nORFs located downstream of conserved genes tend to leverage their neighbors' promoters through transcription readthrough, resulting in high coexpression and high expression levels. Transcriptional piggybacking also influences the coexpression profiles of young de novo nORFs located upstream of genes, but to a lesser extent and without detectable impact on expression levels. Transcriptional piggybacking influences, but does not determine, the transcription profiles of de novo nORFs emerging nearby genes. About 40% of nORFs are not strongly coexpressed with any gene but are transcriptionally regulated nonetheless and tend to form entirely new transcription modules. We offer a web browser interface ( https://carvunislab.csb.pitt.edu/shiny/coexpression/ ) to efficiently query, visualize, and download our coexpression inferences. CONCLUSIONS: Our results suggest that nORF transcription is highly regulated. Our coexpression dataset serves as an unprecedented resource for unraveling how nORFs integrate into cellular networks, contribute to cellular phenotypes, and evolve.

Comparative transcriptomic analysis provides insights into the genetic networks regulating oil differential production in oil crops.

BACKGROUND: Plants differ more than threefold in seed oil contents (SOCs). Soybean (Glycine max), cotton (Gossypium hirsutum), rapeseed (Brassica napus), and sesame (Sesamum indicum) are four important oil crops with markedly different SOCs and fatty acid compositions. RESULTS: Compared to grain crops like maize and rice, expanded acyl-lipid metabolism genes and relatively higher expression levels of genes involved in seed oil synthesis (SOS) in the oil crops contributed to the oil accumulation in seeds. Here, we conducted comparative transcriptomics on oil crops with two different SOC materials. In common, DIHYDROLIPOAMIDE DEHYDROGENASE, STEAROYL-ACYL CARRIER PROTEIN DESATURASE, PHOSPHOLIPID:DIACYLGLYCEROL ACYLTRANSFERASE, and oil-body protein genes were both differentially expressed between the high- and low-oil materials of each crop. By comparing functional components of SOS networks, we found that the strong correlations between genes in "glycolysis/gluconeogenesis" and "fatty acid synthesis" were conserved in both grain and oil crops, with PYRUVATE KINASE being the common factor affecting starch and lipid accumulation. Network alignment also found a conserved clique among oil crops affecting seed oil accumulation, which has been validated in Arabidopsis. Differently, secondary and protein metabolism affected oil synthesis to different degrees in different crops, and high SOC was due to less competition of the same precursors. The comparison of Arabidopsis mutants and wild type showed that CINNAMYL ALCOHOL DEHYDROGENASE 9, the conserved regulator we identified, was a factor resulting in different relative contents of lignins to oil in seeds. The interconnection of lipids and proteins was common but in different ways among crops, which partly led to differential oil production. CONCLUSIONS: This study goes beyond the observations made in studies of individual species to provide new insights into which genes and networks may be fundamental to seed oil accumulation from a multispecies perspective.

A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing.

BACKGROUND: miR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-wide association studies have implicated miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing. METHODS: Using RNA sequencing data from postmortem prefrontal cortex (N = 522), we identified a coexpression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of coexpression prediction and associated them with functional magnetic resonance imaging activation in healthy volunteers (n1 = 214; n2 = 136; n3 = 2075; n4 = 1800) and with short-term treatment response in patients with schizophrenia (N = 427). RESULTS: In 4652 human participants, we found that 1) schizophrenia risk genes were coexpressed in a biologically validated set enriched for miR-137 targets; 2) increased expression of miR-137 target risk genes was mediated by low prefrontal miR-137 expression; 3) alleles that predict greater gene set coexpression were associated with greater prefrontal activation during emotion processing in 3 independent healthy cohorts (n1, n2, n3) in interaction with age (n4); and 4) these alleles predicted less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia. CONCLUSIONS: The functional translation of miR-137 target gene expression linked with schizophrenia involves the neural substrates of emotion processing.

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

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

Metabolomics and transcriptomics provide insights into the molecular mechanisms of anthocyanin accumulation in the seed coat of differently colored mung bean (Vigna radiata L.).

Black mung bean is rich in anthocyanin, however, the accumulation and the molecular mechanism of anthocyanin synthesis in black mung bean are unclear. In this study, anthocyanin metabolomics and transcriptomics on the seed coats of two different colors of mung bean were performed to clarify the composition of anthocyanins, and identify transcription factors involved in regulating anthocyanin biosynthesis. In the mature stage, 23 kinds of anthocyanin compounds were identified. All anthocyanin components contents were significantly higher in seed coat of black mung bean compare with green mung bean. Transcriptome analysis suggested that most of the structural genes for anthocyanin biosynthesis and some potential regulatory genes were significantly differentially expressed. WGCNA suggested VrMYB90 was an important regulatory gene in anthocyanin biosynthesis. Arabidopsis thaliana overexpressing VrMYB90 showed significant accumulation of anthocyanins. PAL, 4CL, DFR, F3'5'H, LDOX, F3'H and UFGT were up-regulated in 35S:VrMYB90 Arabidopsis thaliana. These findings provide valuable information for understanding the synthesis mechanism of anthocyanins in black mung bean seed coats.

Parthenocarpy-related genes induced by naphthalene acetic acid in oil palm interspecific O × G [Elaeis oleifera (Kunth) Cortés × Elaeis guineensis Jacq.] hybrids.

Parthenocarpy is the development without fertilization of seedless fruits. In the oil palm industry, the development of parthenocarpic fruits is considered an attractive option to increase palm oil production. Previous studies have shown the application of synthetic auxins in Elaeis guineensis, and interspecific O×G hybrids (Elaeis oleifera (Kunth) Cortés × E. guineensis Jacq.) induces parthenocarpy. The aim of this study was to identify the molecular mechanism through transcriptomics and biology system approach to responding to how the application of NAA induces parthenocarpic fruits in oil palm O×G hybrids. The transcriptome changes were studied in three phenological stages (PS) of the inflorescences: i) PS 603, pre-anthesis III, ii) PS 607, anthesis, and iii) PS 700, fertilized female flower. Each PS was treated with NAA, Pollen, and control (any application). The expression profile was studied at three separate times: five minutes (T0), 24 hours (T1), and 48 h post-treatment (T2). The RNA sequencing (RNA seq) approach was used with 27 oil palm O×G hybrids for a total of 81 raw samples. RNA-Seq showed around 445,920 genes. Numerous differentially expressed genes (DEGs) were involved in pollination, flowering, seed development, hormone biosynthesis, and signal transduction. The expression of the most relevant transcription factors (TF) families was variable and dependent on the stage and time post-treatment. In general, NAA treatment expressed differentially more genes than Pollen. Indeed, the gene co-expression network of Pollen was built with fewer nodes than the NAA treatment. The transcriptional profiles of Auxin-responsive protein and Gibberellin-regulated genes involved in parthenocarpy phenomena agreed with those previously reported in other species. The expression of 13 DEGs was validated by RT-qPCR analysis. This detailed knowledge about the molecular mechanisms involved in parthenocarpy could be used to facilitate the future development of genome editing techniques that enable the production of parthenocarpic O×G hybrid cultivars without growth regulator application.

The rubber tree kinome: Genome-wide characterization and insights into coexpression patterns associated with abiotic stress responses.

The protein kinase (PK) superfamily constitutes one of the largest and most conserved protein families in eukaryotic genomes, comprising core components of signaling pathways in cell regulation. Despite its remarkable relevance, only a few kinase families have been studied in Hevea brasiliensis. A comprehensive characterization and global expression analysis of the PK superfamily, however, is currently lacking. In this study, with the aim of providing novel inferences about the mechanisms associated with the stress response developed by PKs and retained throughout evolution, we identified and characterized the entire set of PKs, also known as the kinome, present in the Hevea genome. Different RNA-sequencing datasets were employed to identify tissue-specific expression patterns and potential correspondences between different rubber tree genotypes. In addition, coexpression networks under several abiotic stress conditions, such as cold, drought and latex overexploitation, were employed to elucidate associations between families and tissues/stresses. A total of 1,809 PK genes were identified using the current reference genome assembly at the scaffold level, and 1,379 PK genes were identified using the latest chromosome-level assembly and combined into a single set of 2,842 PKs. These proteins were further classified into 20 different groups and 122 families, exhibiting high compositional similarities among family members and with two phylogenetically close species Manihot esculenta and Ricinus communis. Through the joint investigation of tandemly duplicated kinases, transposable elements, gene expression patterns, and coexpression events, we provided insights into the understanding of the cell regulation mechanisms in response to several conditions, which can often lead to a significant reduction in rubber yield.

Leaf physiology variations are modulated by natural variations that underlie stomatal morphology in Populus.

Stomata are essential for photosynthesis and abiotic stress tolerance. Here, we used multiomics approaches to dissect the genetic architecture and adaptive mechanisms that underlie stomatal morphology in Populus tomentosa juvenile natural population (303 accessions). We detected 46 candidate genes and 15 epistatic gene-pairs, associated with 5 stomatal morphologies and 18 leaf development and photosynthesis traits, through genome-wide association studies. Expression quantitative trait locus mapping revealed that stomata-associated gene loci were significantly associated with the expression of leaf-related genes; selective sweep analysis uncovered significant differentiation in the allele frequencies of genes that underlie stomatal variations. An allelic regulatory network operating under drought stress and adequate precipitation conditions, with three key regulators (DUF538, TRA2 and AbFH2) and eight interacting genes, was identified that might regulate leaf physiology via modulation of stomatal shape and density. Validation of candidate gene variations in drought-tolerant and F1 hybrid populations of P. tomentosa showed that the DUF538, TRA2 and AbFH2 loci cause functional stabilisation of spatiotemporal regulatory, whose favourable alleles can be faithfully transmitted to offspring. This study provides insights concerning leaf physiology and stress tolerance via the regulation of stomatal determination in perennial plants.

Genome-wide characterization of the common bean kinome: Catalog and insights into expression patterns and genetic organization.

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinome of common bean (Phaseolus vulgaris) has not been profiled yet. Here, we identified and characterised the complete set of kinases of common bean, performing an in-depth investigation with phylogenetic analyses and measurements of gene distribution, structural organization, protein properties, and expression patterns over a large set of RNA-Sequencing data. Being composed of 1,203 PKs distributed across all P. vulgaris chromosomes, this set represents 3.25% of all predicted proteins for the species. These PKs could be classified into 20 groups and 119 subfamilies, with a more pronounced abundance of subfamilies belonging to the receptor-like kinase (RLK)-Pelle group. In addition to provide a vast and rich reservoir of data, our study supplied insights into the compositional similarities between PK subfamilies, their evolutionary divergences, highly variable functional profile, structural diversity, and expression patterns, modeled with coexpression networks for investigating putative interactions associated with stress response.

Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells.

The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicated regulators were identified in the TRN and with multiple alignments of the TFs' target gene sequences. Six transcription factors (E2F3, FHL2, ETS1, KAT6B, TWIST1, and RUNX2) were identified in the GRN as essential regulators of gene expression in non-small-cell lung cancer (NSCLC) and related to the lung tumoral process. Our findings indicate that RUNX2 could be an important regulator of the lung cancer GRN through the formation of coregulatory complexes with other TFs related to the establishment and progression of lung cancer. Therefore, RUNX2 could become an essential biomarker for developing diagnostic tools and specific treatments against tumoral diseases in the lung after the experimental validation of its regulatory function.

Approaches in Gene Coexpression Analysis in Eukaryotes.

Gene coexpression analysis constitutes a widely used practice for gene partner identification and gene function prediction, consisting of many intricate procedures. The analysis begins with the collection of primary transcriptomic data and their preprocessing, continues with the calculation of the similarity between genes based on their expression values in the selected sample dataset and results in the construction and visualisation of a gene coexpression network (GCN) and its evaluation using biological term enrichment analysis. As gene coexpression analysis has been studied extensively, we present most parts of the methodology in a clear manner and the reasoning behind the selection of some of the techniques. In this review, we offer a comprehensive and comprehensible account of the steps required for performing a complete gene coexpression analysis in eukaryotic organisms. We comment on the use of RNA-Seq vs. microarrays, as well as the best practices for GCN construction. Furthermore, we recount the most popular webtools and standalone applications performing gene coexpression analysis, with details on their methods, features and outputs.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis.

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Construction of the coexpression network involved in the pathogenesis of thyroid eye disease via bioinformatics analysis.

BACKGROUND: Thyroid eye disease (TED) is the most common orbital pathology that occurs in up to 50% of patients with Graves' disease. Herein, we aimed at discovering the possible hub genes and pathways involved in TED based on bioinformatical approaches. RESULTS: The GSE105149 and GSE58331 datasets were downloaded from the Gene Expression Omnibus (GEO) database and merged for identifying TED-associated modules by weighted gene coexpression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. EdgeR was run to screen differentially expressed genes (DEGs). Transcription factor (TF), microRNA (miR) and drug prediction analyses were performed using ToppGene suite. Function enrichment analysis was used to investigate the biological function of genes. Protein-protein interaction (PPI) analysis was performed based on the intersection between the list of genes obtained by WGCNA, lmQCM and DEGs, and hub genes were identified using the MCODE plugin. Based on the overlap of 497 genes retrieved from the different approaches, a robust TED coexpression network was constructed and 11 genes (ATP6V1A, PTGES3, PSMD12, PSMA4, METAP2, DNAJA1, PSMA1, UBQLN1, CCT2, VBP1 and NAA50) were identified as hub genes. Key TFs regulating genes in the TED-associated coexpression network, including NFRKB, ZNF711, ZNF407 and MORC2, and miRs including hsa-miR-144, hsa-miR-3662, hsa-miR-12136 and hsa-miR-3646, were identified. Genes in the coexpression network were enriched in the biological processes including proteasomal protein catabolic process and proteasome-mediated ubiquitin-dependent protein catabolic process and the pathways of endocytosis and ubiquitin-mediated proteolysis. Drugs perturbing genes in the coexpression network were also predicted and included enzyme inhibitors, chlorodiphenyl and finasteride. CONCLUSIONS: For the first time, TED-associated coexpression network was constructed and key genes and their functions, as well as TFs, miRs and drugs, were predicted. The results of the present work may be relevant in the treatment and diagnosis of TED and may boost molecular studies regarding TED.

Integrative Analysis of Genes Involved in the Global Response to Potato Wart Formation.

Synchytrium endobioticum, the causal agent of potato wart disease, poses a major threat to commercial potato production. Understanding the roles of transcriptionally regulated genes following pathogen infection is necessary for understanding the system-level host response to pathogen. Although some understanding of defense mechanisms against S. endobioticum infection has been gained for incompatible interactions, the genes and signaling pathways involved in the compatible interaction remain unclear. Based on the collection of wart diseased tubers of a susceptible cultivar, we performed phenotypic and dual RNA-Seq analyses of wart lesions in seven stages of disease progression. We totally detected 5,052 differentially expressed genes (DEGs) by comparing the different stages of infection to uninfected controls. The tendency toward differential gene expression was active rather than suppressed under attack by the pathogen. The number of DEGs step-up along with the development of the disease and the first, third and seventh of the disease stages showed substantially increase of DEGs in comparison of the previous stage. The important functional groups identified via Gene ontology (GO) and KEGG enrichment were those responsible for plant-pathogen interaction, fatty acid elongation and phenylpropanoid biosynthesis. Gene coexpression networks, composed of 17 distinct gene modules that contained between 25 and 813 genes, revealed high interconnectivity of the induced response and led to the identification of a number of hub genes enriched at different stages of infection. These results provide a comprehensive perspective on the global response of potato to S. endobioticum infection and identify a potential transcriptional regulatory network underlying this susceptible response, which contribute to a better understanding of the potato-S. endobioticum pathosystem.

Network Analysis Reveals Different Cellulose Degradation Strategies Across Trichoderma harzianum Strains Associated With XYR1 and CRE1.

Trichoderma harzianum, whose gene expression is tightly controlled by the transcription factors (TFs) XYR1 and CRE1, is a potential candidate for hydrolytic enzyme production. Here, we performed a network analysis of T. harzianum IOC-3844 and T. harzianum CBMAI-0179 to explore how the regulation of these TFs varies between these strains. In addition, we explored the evolutionary relationships of XYR1 and CRE1 protein sequences among Trichoderma spp. The results of the T. harzianum strains were compared with those of Trichoderma atroviride CBMAI-0020, a mycoparasitic species. Although transcripts encoding carbohydrate-active enzymes (CAZymes), TFs, transporters, and proteins with unknown functions were coexpressed with cre1 or xyr1, other proteins indirectly related to cellulose degradation were identified. The enriched GO terms describing the transcripts of these groups differed across all strains, and several metabolic pathways with high similarity between both regulators but strain-specific differences were identified. In addition, the CRE1 and XYR1 subnetworks presented different topology profiles in each strain, likely indicating differences in the influences of these regulators according to the fungi. The hubs of the cre1 and xyr1 groups included transcripts not yet characterized or described as being related to cellulose degradation. The first-neighbor analyses confirmed the results of the profile of the coexpressed transcripts in cre1 and xyr1. The analyses of the shortest paths revealed that CAZymes upregulated under cellulose degradation conditions are most closely related to both regulators, and new targets between such signaling pathways were discovered. Although the evaluated T. harzianum strains are phylogenetically close and their amino acid sequences related to XYR1 and CRE1 are very similar, the set of transcripts related to xyr1 and cre1 differed, suggesting that each T. harzianum strain used a specific regulation strategy for cellulose degradation. More interestingly, our findings may suggest that XYR1 and CRE1 indirectly regulate genes encoding proteins related to cellulose degradation in the evaluated T. harzianum strains. An improved understanding of the basic biology of fungi during the cellulose degradation process can contribute to the use of their enzymes in several biotechnological applications and pave the way for further studies on the differences across strains of the same species.

Protection against severe infant lower respiratory tract infections by immune training: Mechanistic studies.

BACKGROUND: Results from recent clinical studies suggest potential efficacy of immune training (IT)-based approaches for protection against severe lower respiratory tract infections in infants, but underlying mechanisms are unclear. OBJECTIVE: We used systems-level analyses to elucidate IT mechanisms in infants in a clinical trial setting. METHODS: Pre- and posttreatment peripheral blood mononuclear cells from a placebo-controlled trial in which winter treatment with the IT agent OM85 reduced infant respiratory infection frequency and/or duration were stimulated for 24 hours with the virus/bacteria mimics polyinosinic:polycytidylic acid/lipopolysaccharide. Transcriptomic profiling via RNA sequencing, pathway and upstream regulator analyses, and systems-level gene coexpression network analyses were used sequentially to elucidate and compare responses in treatment and placebo groups. RESULTS: In contrast to subtle changes in antivirus-associated polyinosinic:polycytidylic acid response profiles, the bacterial lipopolysaccharide-triggered gene coexpression network responses exhibited OM85 treatment-associated upregulation of IFN signaling. This was accompanied by network rewiring resulting in increased coordination of TLR4 expression with IFN pathway-associated genes (especially master regulator IRF7); segregation of TNF and IFN-γ (which potentially synergize to exaggerate inflammatory sequelae) into separate expression modules; and reduced size/complexity of the main proinflammatory network module (containing, eg, IL-1,IL-6, and CCL3). Finally, we observed a reduced capacity for lipopolysaccharide-induced inflammatory cytokine (eg, IL-6 and TNF) production in the OM85 group. CONCLUSION: These changes are consistent with treatment-induced enhancement of bacterial pathogen detection/clearance capabilities concomitant with enhanced capacity to regulate ensuing inflammatory response intensity and duration. We posit that IT agents exemplified by OM85 potentially protect against severe lower respiratory tract infections in infants principally by effects on innate immune responses targeting the bacterial components of the mixed respiratory viral/bacterial infections that are characteristic of this age group.

Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data.

BACKGROUND: Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques. FINDINGS: We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein-protein interaction networks assayed in different cell lines. CONCLUSIONS: These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques.