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Osteoporosis is a systemic bone disease characterized by low bone mineral density and bone microstructure damage, resulting in increased bone fragility and fracture risk. The present study aimed to identify key genes and functionally enriched pathways in osteoporotic patients. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to microarray datasets of blood samples of osteoporotic patients from the Sao Paulo Ageing & Health [SPAH] study (26 osteoporotic samples and 31 normal samples) to construct co-expression networks and identify hub gene. The results showed that HDGF, AP2M1, DNAJC6, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, IGKV3-7, IGKV3D-11, and IGKV1D-42 are genes which were associated with the disease status of osteoporosis. Differentially expressed genes are enriched in proteasomal protein catabolic process, ubiquitin ligase complex, and ubiquitin-like protein transferase activity. Functional enrichment analysis demonstrated that genes in the tan module were enriched in immune-related functions, indicating that the immune system plays a critical role in osteoporosis. Validation assay demonstrated that the HDGF, AP2M1, TMEM183B, and MFSD2B levels were decreased in osteoporosis samples compared with healthy controls, while the levels of IGKV1-5, IGKV1-8, and IGKV1D-42 were increased in osteoporosis samples compared with healthy controls. In conclusion, our data identified and validated the association of HDGF, AP2M1, TMEM183B, MFSD2B, IGKV1-5, IGKV1-8, and IGKV1D-42 with osteoporosis in elderly women. These results suggest that these transcripts have potential clinical significance and may help to explain the molecular mechanisms and biological functions of osteoporosis.

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Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

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This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between the expression of select expansin genes, and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.

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BACKGROUND: The solubilization of aluminum ions (Al3+) that results from soil acidity (pH < 5.5) is a limiting factor in oil palm yield. Al can be uptaken by the plant roots affecting DNA replication and cell division and triggering root morphological alterations, nutrient and water deprivation. In different oil palm-producing countries, oil palm is planted in acidic soils, representing a challenge for achieving high productivity. Several studies have reported the morphological, physiological, and biochemical oil palm mechanisms in response to Al-stress. However, the molecular mechanisms are just partially understood. RESULTS: Differential gene expression and network analysis of four contrasting oil palm genotypes (IRHO 7001, CTR 3-0-12, CR 10-0-2, and CD 19 - 12) exposed to Al-stress helped to identify a set of genes and modules involved in oil palm early response to the metal. Networks including the ABA-independent transcription factors DREB1F and NAC and the calcium sensor Calmodulin-like (CML) that could induce the expression of internal detoxifying enzymes GRXC1, PER15, ROMT, ZSS1, BBI, and HS1 against Al-stress were identified. Also, some gene networks pinpoint the role of secondary metabolites like polyphenols, sesquiterpenoids, and antimicrobial components in reducing oxidative stress in oil palm seedlings. STOP1 expression could be the first step of the induction of common Al-response genes as an external detoxification mechanism mediated by ABA-dependent pathways. CONCLUSIONS: Twelve hub genes were validated in this study, supporting the reliability of the experimental design and network analysis. Differential expression analysis and systems biology approaches provide a better understanding of the molecular network mechanisms of the response to aluminum stress in oil palm roots. These findings settled a basis for further functional characterization of candidate genes associated with Al-stress in oil palm.

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Introduction: Biological systems respond to environmental disturbances and a wide range of compounds through complex gene interaction networks. The enormous growth of experimental information obtained using large-scale genomic techniques such as microarrays and RNA sequencing led to the construction of a wide variety of gene co-expression networks in recent years. These networks allow the discovery of clusters of co-expressed genes that potentially work in the same process linking them to biological processes often of interest to industrial, medicinal, and academic research. Methods: In this study, we built the gene co-expression network of Ustilago maydis from the gene expression data of 168 samples belonging to 19 series, which correspond to the GPL3681 platform deposited in the NCBI using WGCNA software. This network was analyzed to identify clusters of co-expressed genes, gene hubs and Gene Ontology terms. Additionally, we identified relevant modules through a hypergeometric approach based on a predicted set of transcription factors and virulence genes. Results and Discussion: We identified 13 modules in the gene co-expression network of U. maydis. The TFs enriched in the modules of interest belong to the superfamilies of Nucleic acid-binding proteins, Winged helix DNA-binding, and Zn2/Cys6 DNA-binding. On the other hand, the modules enriched with virulence genes were classified into diseases related to corn smut, Invasive candidiasis, among others. Finally, a large number of hypothetical, a large number of hypothetical genes were identified as highly co-expressed with virulence genes, making them possible experimental targets.

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PURPOSE: Transcriptome analysis of pancreatic ductal adenocarcinoma (PDAC) has been useful to identify gene expression changes that sustain malignant phenotypes. Yet, most studies examined only tumor tissues and focused on protein-coding genes, leaving long non-coding RNAs (lncRNAs) largely underexplored. METHODS: We generated total RNA-Seq data from patient-matched tumor and nonmalignant pancreatic tissues and implemented a computational pipeline to survey known and novel lncRNAs. siRNA-mediated knockdown in tumor cell lines was performed to assess the contribution of PDAC-associated lncRNAs to malignant phenotypes. Gene co-expression network and functional enrichment analyses were used to assign deregulated lncRNAs to biological processes and molecular pathways. RESULTS: We detected 9,032 GENCODE lncRNAs as well as 523 unannotated lncRNAs, including transcripts significantly associated with patient outcome. Aberrant expression of a subset of novel and known lncRNAs was confirmed in patient samples and cell lines. siRNA-mediated knockdown of a subset of these lncRNAs (LINC01559, LINC01133, CCAT1, LINC00920 and UCA1) reduced cell proliferation, migration and invasion. Gene co-expression network analysis associated PDAC-deregulated lncRNAs with diverse biological processes, such as cell adhesion, protein glycosylation and DNA repair. Furthermore, UCA1 knockdown was shown to specifically deregulate co-expressed genes involved in DNA repair and to negatively impact DNA repair following damage induced by ionizing radiation. CONCLUSIONS: Our study expands the repertoire of lncRNAs deregulated in PDAC, thereby revealing novel candidate biomarkers for patient risk stratification. It also provides a roadmap for functional assays aimed to characterize novel mechanisms of action of lncRNAs in pancreatic cancer, which could be explored for therapeutic development.

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The genus Coffea (Rubiaceae) encompasses a group of perennial plant species, including a commodity crop from which seeds are roasted, ground, and infused to make one of the most appreciated beverages in the world. As an important tropical crop restricted to specific regions of the world, coffee production is highly susceptible to the effects of environmental instabilities (i.e., local year-to-year weather fluctuations and global climate change) and threatening pest pressures, not to mention an increasing quality rigor by consumers in industrialized countries. Specialized metabolites are substances that largely affect plant-environment interactions as well as how consumers experience agricultural products. Membrane transporters are key targets, albeit understudied, for understanding and tailoring the spatiotemporal distribution of specialized metabolites as they mediate and control molecular trafficking and substance accumulation. Therefore, we analyzed the transportome of C. canephora encoded within the 25,574 protein-coding genes annotated in the genome of this species and identified 1847 putative membrane transporters. Following, we mined 152 transcriptional profiles of C. canephora and C. arabica and performed a comprehensive co-expression analysis to identify transporters potentially involved in the accumulation of specialized metabolites associated with beverage quality and bioactivity attributes. In toto, this report points to an avenue of possibilities on Coffea genomic and transcriptomic data mining for genetic breeding strategies, which can lead to the development of new, resilient varieties for more sustainable coffee production systems.

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Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

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Schistosoma japonicum is a flatworm that causes schistosomiasis, a neglected tropical disease. S. japonicum RNA-Seq analyses has been previously reported in the literature on females and males obtained during sexual maturation from 14 to 28 days post-infection in mouse, resulting in the identification of protein-coding genes and pathways, whose expression levels were related to sexual development. However, this work did not include an analysis of long non-coding RNAs (lncRNAs). Here, we applied a pipeline to identify and annotate lncRNAs in 66 S. japonicum RNA-Seq publicly available libraries, from different life-cycle stages. We also performed co-expression analyses to find stage-specific lncRNAs possibly related to sexual maturation. We identified 12,291 S. japonicum expressed lncRNAs. Sequence similarity search and synteny conservation indicated that some 14% of S. japonicum intergenic lncRNAs have synteny conservation with S. mansoni intergenic lncRNAs. Co-expression analyses showed that lncRNAs and protein-coding genes in S. japonicum males and females have a dynamic co-expression throughout sexual maturation, showing differential expression between the sexes; the protein-coding genes were related to the nervous system development, lipid and drug metabolism, and overall parasite survival. Co-expression pattern suggests that lncRNAs possibly regulate these processes or are regulated by the same activation program as that of protein-coding genes.

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BACKGROUND: Inflorescence architecture is denoted by the spatial arrangement of various lateral branches and florets formed on them, which is shaped by a complex of regulators. Unveiling of the regulatory mechanisms underlying inflorescence architecture is pivotal for improving crop yield potential. Quinoa (Chenopodium quinoa Willd), a pseudo cereal originated from Andean region of South America, has been widely recognized as a functional super food due to its excellent nutritional elements. Increasing worldwide consumption of this crop urgently calls for its yield improvement. However, dissection of the regulatory networks underlying quinoa inflorescence patterning is lacking. RESULTS: In this study, we performed RNA-seq analysis on quinoa inflorescence samples collected from six developmental stages, yielding a total of 138.8 GB data. We screened 21,610 differentially expressed genes (DEGs) among all the stages through comparative analysis. Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to categorize the DEGs into ten different modules. Subsequently, we placed emphasis on investigating the modules associated with none branched and branched inflorescence samples. We manually refined the coexpression networks with stringent edge weight cutoffs, and generated core networks using transcription factors and key inflorescence architecture related genes as seed nodes. The core networks were visualized and analyzed by Cytoscape to obtain hub genes in each network. Our finding indicates that the specific occurrence of B3, TALE, WOX, LSH, LFY, GRAS, bHLH, EIL, DOF, G2-like and YABBY family members in early reproductive stage modules, and of TFL, ERF, bZIP, HD-ZIP, C2H2, LBD, NAC, C3H, Nin-like and FAR1 family members in late reproductive stage modules, as well as the several different MADS subfamily members identified in both stages may account for shaping quinoa inflorescence architecture. CONCLUSION: In this study we carried out comparative transcriptome analysis of six different stages quinoa inflorescences, and using WGCNA we obtained the most highly potential central hubs for shaping inflorescence. The data obtained from this study will enhance our understanding of the gene network regulating quinoa inflorescence architecture, as well will supply with valuable genetic resources for high-yield elite breeding in the future.

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Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic-uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e. expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria-enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte-bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte-bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

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Shiga toxin-producing Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some strains may cause hemolytic–uremic syndrome (HUS). In Brazil, these strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here, a system biology approach was used to investigate the differential transcriptomic and phenotypic responses of enterocyte-like Caco-2 cells to two STEC O113:H21 strains with similar virulence factor profiles (i.e., expressing stx2, ehxA, epeA, espA, iha, saa, sab, and subA): EH41 (Caco-2/EH41), isolated from a HUS patient in Australia, and Ec472/01 (Caco-2/Ec472), isolated from bovine feces in Brazil, during a 3 h period of bacteria–enterocyte interaction. Gene co-expression network analysis for Caco-2/EH41 revealed a quite abrupt pattern of topological variation along 3 h of enterocyte–bacteria interaction when compared with networks obtained for Caco-2/Ec472. Transcriptional module characterization revealed that EH41 induces inflammatory and apoptotic responses in Caco-2 cells just after the first hour of enterocyte–bacteria interaction, whereas the response to Ec472/01 is associated with cytoskeleton organization at the first hour, followed by the expression of immune response modulators. Scanning electron microscopy showed more intense microvilli destruction in Caco-2 cells exposed to EH41 when compared to those exposed to Ec472/01. Altogether, these results show that EH41 expresses virulence genes, inducing a distinctive host cell response, and is likely associated with severe pathogenicity.

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The biomass-degrading fungus Trichoderma reesei has been considered a model for cellulose degradation, and it is the primary source of the industrial enzymatic cocktails used in second-generation (2G) ethanol production. However, although various studies and advances have been conducted to understand the cellulolytic system and the transcriptional regulation of T. reesei, the whole set of genes related to lignocellulose degradation has not been completely elucidated. In this study, we inferred a weighted gene co-expression network analysis based on the transcriptome dataset of the T. reesei RUT-C30 strain aiming to identify new target genes involved in sugarcane bagasse breakdown. In total, ~70% of all the differentially expressed genes were found in 28 highly connected gene modules. Several cellulases, sugar transporters, and hypothetical proteins coding genes upregulated in bagasse were grouped into the same modules. Among them, a single module contained the most representative core of cellulolytic enzymes (cellobiohydrolase, endoglucanase, ß-glucosidase, and lytic polysaccharide monooxygenase). In addition, functional analysis using Gene Ontology (GO) revealed various classes of hydrolytic activity, cellulase activity, carbohydrate binding and cation:sugar symporter activity enriched in these modules. Several modules also showed GO enrichment for transcription factor activity, indicating the presence of transcriptional regulators along with the genes involved in cellulose breakdown and sugar transport as well as other genes encoding proteins with unknown functions. Highly connected genes (hubs) were also identified within each module, such as predicted transcription factors and genes encoding hypothetical proteins. In addition, various hubs contained at least one DNA binding site for the master activator Xyr1 according to our in silico analysis. The prediction of Xyr1 binding sites and the co-expression with genes encoding carbohydrate active enzymes and sugar transporters suggest a putative role of these hubs in bagasse cell wall deconstruction. Our results demonstrate a vast range of new promising targets that merit additional studies to improve the cellulolytic potential of T. reesei strains and to decrease the production costs of 2G ethanol.

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The goal of this research was to generate networks of co-expressed genes to explore the genomic responses of Rhizophora mangle L. populations to contrasting environments and to use gene network analysis to investigate their capacity for adaptation in the face of historical and future perturbations and climatic changes. RNA sequencing data were generated for R. mangle samples collected under field conditions from contrasting climate zones in the equatorial and subtropical regions of Brazil. A gene co-expression network was constructed using Pearson's correlation coefficient, showing correlations among 78,364 transcriptionally coordinated genes. Each region exhibited two distinct network profiles; genes correlated with the oxidative stress response showed higher relative expression levels in subtropical samples than in equatorial samples, whereas genes correlated with the hyperosmotic salinity response, heat response and UV response had higher expression levels in the equatorial samples than in the subtropical samples. In total, 992 clusters had enriched ontology terms, which suggests that R. mangle is under higher stress in the equatorial region than in the subtropical region. Increased heat may thus pose a substantial risk to species diversity at the center of its distribution range in the Americas. This study, which was performed using trees in natural field conditions, allowed us to associate the specific responses of genes previously described in controlled environments with their responses to the local habitat where the species occurs. The study reveals the effects of contrasting environments on gene expression in R. mangle, shedding light on the different abiotic variables that may contribute to the genetic divergence previously described for the species through the use of simple sequence repeats (SSRs). These effects may result from two fundamental processes in evolution, namely, phenotypic plasticity and natural selection.

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Cardiovascular disease (CVD) is a broad definition for diseases of the heart and blood vessels with high mortality and morbidity worldwide. Atherosclerosis and hypertension are the most common causes of CVD, and multiple factors confer the susceptibility. Some of the predisposing factors are modifiable such as diet, smoking, and exercise, whereas others, including age, sex, and individual's genetic variations contributing to the CVD composition traits, are non-modifiable. This latter group includes serum lipid traits. High serum lipid levels, specifically high levels of serum low-density lipoprotein cholesterol and triglycerides, are well-established key risk factors of atherosclerosis. This review will discuss genomics and systems biology approaches in the study of common dyslipidemias. The non-Mendelian forms of dyslipidemias are highly complex, and the molecular mechanisms underlying these polygenic lipid disorders are estimated to involve hundreds of genes. Interactions between the different genes and environmental factors also contribute to the clinical outcomes; however, very little is known about these interactions and their molecular mechanisms. To better address the complex genetic architecture and multiple properties leading to high serum lipid levels, networks and systems approach combining information at genomic, transcriptomics, methylomics, proteomics, metabolomics, and phenome level are being developed, with the ultimate goal to elucidate the cascade of dynamic changes leading to CVD in humans. (REV INVEST CLIN. 2018;70:217-23).

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The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

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CONTEXT: Paracoccidioides brasiliensis, a dimorphic fungus is the causative agent of paracoccidioidomycosis, a disease globally affecting millions of people. The haloacid dehalogenase (HAD) superfamily hydrolases enzyme in the fungi, in particular, is known to be responsible in the pathogenesis by adhering to the tissue. Hence, identification of novel drug targets is essential. AIMS: In-silico based identification of co-expressed genes along with HAD superfamily hydrolase in P. brasiliensis during the morphogenesis from mycelium to yeast to identify possible genes as drug targets. MATERIALS AND METHODS: In total, four datasets were retrieved from the NCBI-gene expression omnibus (GEO) database, each containing 4340 genes, followed by gene filtration expression of the data set. Further co-expression (CE) study was performed individually and then a combination these genes were visualized in the Cytoscape 2. 8.3. STATISTICAL ANALYSIS USED: Mean and standard deviation value of the HAD superfamily hydrolase gene was obtained from the expression data and this value was subsequently used for the CE calculation purpose by selecting specific correlation power and filtering threshold. RESULTS: The 23 genes that were thus obtained are common with respect to the HAD superfamily hydrolase gene. A significant network was selected from the Cytoscape network visualization that contains total 7 genes out of which 5 genes, which do not have significant protein hits, obtained from gene annotation of the expressed sequence tags by BLAST X. For all the protein PSI-BLAST was performed against human genome to find the homology. CONCLUSIONS: The gene co-expression network was obtained with respect to HAD superfamily dehalogenase gene in P. Brasiliensis.