Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

Integrative analysis of the shikonin metabolic network identifies new gene connections and reveals evolutionary insight into shikonin biosynthesis

Plant specialized 1,4-naphthoquinones present a remarkable case of convergent evolution. Species across multiple discrete orders of vascular plants produce diverse 1,4-naphthoquinones via one of several pathways using different metabolic precursors. Evolution of these pathways was preceded by events of metabolic innovation and many appear to share connections with biosynthesis of photosynthetic or respiratory quinones. Here, we sought to shed light on the metabolic connections linking shikonin biosynthesis with its precursor pathways and on the origins of shiknoin metabolic genes. Downregulation of Lithospermum erythrorhizon geranyl diphosphate synthase (LeGPPS), recently shown to have been recruited from a cytoplasmic farnesyl diphosphate synthase (FPPS), resulted in reduced shikonin production and a decrease in expression of mevalonic acid and phenylpropanoid pathway genes. Next, we used LeGPPS and other known shikonin pathway genes to build a coexpression network model for identifying new gene connections to shikonin metabolism. Integrative in silico analyses of network genes revealed candidates for biochemical steps in the shikonin pathway arising from Boraginales-specific gene family expansion. Multiple genes in the shikonin coexpression network were also discovered to have originated from duplication of ubiquinone pathway genes. Taken together, our study provides evidence for transcriptional crosstalk between shikonin biosynthesis and its precursor pathways, identifies several shikonin pathway gene candidates and their evolutionary histories, and establishes additional evolutionary links between shikonin and ubiquinone metabolism. Moreover, we demonstrate that global coexpression analysis using limited transcriptomic data obtained from targeted experiments is effective for identifying gene connections within a defined metabolic network.

Transcriptome and Coexpression Network Analyses Reveal Hub Genes in Chinese Cabbage (Brassica rapa L. ssp. pekinensis) During Different Stages of Plasmodiophora brassicae Infection

Clubroot, caused by the soil-borne protist Plasmodiophora brassicae, is one of the most destructive diseases of Chinese cabbage worldwide. However, the clubroot resistance mechanisms remain unclear. In this study, in both clubroot-resistant (DH40R) and clubroot-susceptible (DH199S) Chinese cabbage lines, the primary (root hair infection) and secondary (cortical infection) infection stages started 2 and 5 days after inoculation (dai), respectively. With the extension of the infection time, cortical infection was blocked and complete P. brassica resistance was observed in DH40R, while disease scales of 1, 2, and 3 were observed at 8, 13, and 22 dai in DH199S. Transcriptome analysis at 0, 2, 5, 8, 13, and 22 dai identified 5,750 relative DEGs (rDEGs) between DH40R and DH199S. The results indicated that genes associated with auxin, PR, disease resistance proteins, oxidative stress, and WRKY and MYB transcription factors were involved in clubroot resistance regulation. In addition, weighted gene coexpression network analysis (WGCNA) identified three of the modules whose functions were highly associated with clubroot-resistant, including ten hub genes related to clubroot resistance (ARF2, EDR1, LOX4, NHL3, NHL13, NAC29, two AOP1, EARLI 1, and POD56). These results provide valuable information for better understanding the molecular regulatory mechanism of Chinese cabbage clubroot resistance.

Characteristics of the AT-Hook Motif Containing Nuclear Localized (AHL) Genes in Carrot Provides Insight into Their Role in Plant Growth and Storage Root Development

The AT-hook motif containing nuclear localized (AHL) gene family, controlling various developmental processes, is conserved in land plants. They comprise Plant and Prokaryote Conserved (PPC) domain and one or two AT-hook motifs. DcAHLc1 has been proposed as a candidate gene governing the formation of the carrot storage root. We identified and in-silico characterized carrot AHL proteins, performed phylogenetic analyses, investigated their expression profiles and constructed gene coexpression networks. We found 47 AHL genes in carrot and grouped them into two clades, A and B, comprising 29 and 18 genes, respectively. Within Clade-A, we distinguished three subclades, one of them grouping noncanonical AHLs differing in their structure (two PPC domains) and/or cellular localization (not nucleus). Coexpression network analysis attributed AHLs expressed in carrot roots into four of the 72 clusters, some of them showing a large number of interactions. Determination of expression profiles of AHL genes in various tissues and samples provided basis to hypothesize on their possible roles in the development of the carrot storage root. We identified a group of rapidly evolving noncanonical AHLs, possibly differing functionally from typical AHLs, as suggested by their expression profiles and their predicted cellular localization. We pointed at several AHLs likely involved in the development of the carrot storage root.

Genome Sequence and Comparative Analysis of Colletotrichum gloeosporioides Isolated from Liriodendron Leaves

Colletotrichum gloeosporioides is a hemibiotrophic pathogen causing significant losses to economically important crops and forest trees, including Liriodendron. To explore the interaction between C. gloeosporioides and Liriodendron and to identify the candidate genes determining the pathogenesis, we sequenced and assembled the whole genome of C. gloeosporioides Lc1 (CgLc1) using PacBio and Illumina next generation sequencing and performed a comparative genomic analysis between CgLc1 and Cg01, the latter being a described endophytic species of the C. gloeosporioides complex. Gene structure prediction identified 15,744 protein-coding genes and 837 noncoding RNAs. Species-specific genes were characterized using an ortholog analysis followed by a pathway enrichment analysis, which showed that genes specific to CgLc1 were enriched for the arginine biosynthetic process. Furthermore, genome synteny analysis revealed that most of the protein-coding genes fell into collinear blocks. However, two clusters of polyketide synthase genes were identified to be specific for CgLc1, suggesting that they might have an important role in virulence control. Transcriptional regulators coexpressed with polyketide synthase genes were detected through a Weighted Correlation Network Analysis. Taken together, this work provides new insight into the virulence- and pathogenesis-associated genes present in C. gloeosporioides and its possible lifestyle.

Revisiting the complex architecture of ALS in Turkey: Expanding genotypes, shared phenotypes, molecular networks, and a public variant database

The last decade has proven that amyotrophic lateral sclerosis (ALS) is clinically and genetically heterogeneous, and that the genetic component in sporadic cases might be stronger than expected. This study investigates 1,200 patients to revisit ALS in the ethnically heterogeneous yet inbred Turkish population. Familial ALS (fALS) accounts for 20% of our cases. The rates of consanguinity are 30% in fALS and 23% in sporadic ALS (sALS). Major ALS genes explained the disease cause in only 35% of fALS, as compared with ~70% in Europe and North America. Whole exome sequencing resulted in a discovery rate of 42% (53/127). Whole genome analyses in 623 sALS cases and 142 population controls, sequenced within Project MinE, revealed well-established fALS gene variants, solidifying the concept of incomplete penetrance in ALS. Genome-wide association studies (GWAS) with whole genome sequencing data did not indicate a new risk locus. Coupling GWAS with a coexpression network of disease-associated candidates, points to a significant enrichment for cell cycle- and division-related genes. Within this network, literature text-mining highlights DECR1, ATL1, HDAC2, GEMIN4, and HNRNPA3 as important genes. Finally, information on ALS-related gene variants in the Turkish cohort sequenced within Project MinE was compiled in the GeNDAL variant browser (www.gendal.org).

Identification of key lncRNAs in the tumorigenesis of intraductal pancreatic mucinous neoplasm by coexpression network analysis

Intraductal papillary mucinous neoplasm (IPMN) is an intraepithelial precancerous lesion of pancreatic ductal adenocarcinoma (PDAC) that progresses from adenoma to carcinoma, and long noncoding RNAs (lncRNA) might be involved in the tumorigenesis. In this study, we obtained the expression profiles of more than 4000 lncRNAs by probe reannotation of a microarray dataset. As a correlation network‐based systems biology method, weighted gene coexpression network analysis (WGCNA) was used to find clusters of highly correlated lncRNAs in the tumorigenesis of IPMN, which covered four stepwise stages from normal main pancreatic duct to invasive IPMN. In the most relevant module (R² = −0.75 and P = 5E‐05), three hub lncRNAs were identified (HAND2‐AS1, CTD‐2033D15.2, and lncRNA‐TFG). HAND2‐AS1 and CTD‐2033D15.2 were negatively correlated with the tumorigenesis (P in one‐way ANOVA test = 1.45E‐07 and 1.39E‐0.5), while lncRNA‐TFG were positively correlated with the tumorigenesis (P = 3.99E‐08). The validation set reached consistent results (P = 2.66E‐03 in HAND2‐AS1, 1.47E‐04 in CTD‐2033D15.2 and 6.23E‐08 in lncRNA‐TFG). In functional enrichment analysis, the target genes of microRNAs targeting also these lncRNAs were overlapped in multiple biological processes, pathways and malignant diseases including pancreatic cancer. In survival analysis, patients with higher expression of HAND2‐AS1‐targeted and CTD‐2033D15.2‐targeted microRNAs showed a significantly poorer prognosis in PDAC, while high expression of lncRNA‐TFG‐targeted microRNAs demonstrated an obviously better prognosis (log‐rank P < .05). In conclusion, by coexpression network analysis of the lncRNA profiles, three key lncRNAs were identified in association with the tumorigenesis of IPMN, and those lncRNAs might act as early diagnostic biomarkers or therapeutic targets in pancreatic cancer.

Transcriptome Analysis Reveals Differences in Key Genes and Pathways Regulating Carbon and Nitrogen Metabolism in Cotton Genotypes under N Starvation and Resupply

Nitrogen (N) is the most important limiting factor for cotton production worldwide. Genotype-dependent ability to cope with N shortage has been only partially explored in cotton, and in this context, the comparison of molecular responses of cotton genotypes with different nitrogen use efficiency (NUE) is of particular interest to dissect the key molecular mechanisms underlying NUE. In this study, we employed Illumina RNA-Sequencing to determine the genotypic difference in transcriptome profile using two cotton genotypes differing in NUE (CCRI-69, N-efficient, and XLZ-30, N-inefficient) under N starvation and resupply treatments. The results showed that a large genetic variation existed in differentially expressed genes (DEGs) related to amino acid, carbon, and nitrogen metabolism between CCRI-69 and XLZ-30. Further analysis of metabolic changes in cotton genotypes under N resupply showed that nitrogen metabolism and aromatic amino acid metabolism pathways were mainly enriched in CCRI-69 by regulating carbon metabolism pathways such as starch and sucrose metabolism, glycolysis/gluconeogenesis, and pentose phosphate pathway. Additionally, we performed an expression network analysis of genes related to amino acid, carbon, and nitrogen metabolism. In total, 75 and 33 genes were identified as hub genes in shoots and roots of cotton genotypes, respectively. In summary, the identified hub genes may provide new insights into coordinating carbon and nitrogen metabolism and improving NUE in cotton.

Considering smoking status, coexpression network analysis of non-small cell lung cancer at different cancer stages, exhibits important genes and pathways

Non-small cell lung cancer (NSCLC) is the most common subtype of lung cancer among smokers, nonsmokers, women, and young individuals. Tobacco smoking and different stages of the NSCLC have important roles in cancer evolution and require different treatments. Existence of poorly effective therapeutic options for the NSCLC brings special attention to targeted therapies by considering genetic alterations. In this study, we used RNA-Seq data to compare expression levels of RefSeq genes and to find some genes with similar expression levels. We utilized the "Weighted Gene Co-expression Network Analysis" method for three different datasets to create coexpressed genetic modules having relations with the smoking status and different stages of the NSCLC. Our results indicate seven important genetic modules having important associations with the smoking status and cancer stages. Based on investigated genetic modules and their biological explanation, we then identified 13 newly candidate genes and 7 novel transcription factors in association with the NSCLC, the smoking status, and cancer stages. We then examined those results using other datasets and explained our results biologically to illustrate some important genes in relation with the smoking status and metastatic stage of the NSCLC that can bring some crucial information about cancer evolution. Our genetic findings also can be used as some therapeutic targets for different clinical conditions of the NSCLC.

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein-protein interaction (PPI) network analysis. After verification of TCGA's ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.

Identification of key candidate genes and pathways in hepatitis B virus-associated acute liver failure by bioinformatical analysis

Hepatitis B virus-associated acute liver failure (HBV-ALF) is a rare but life-threatening syndrome that carried a high morbidity and mortality. Our study aimed to explore the possible molecular mechanisms of HBV-ALF by means of bioinformatics analysis. In this study, genes expression microarray datasets of HBV-ALF from Gene Expression Omnibus were collected, and then we identified differentially expressed genes (DEGs) by the limma package in R. After functional enrichment analysis, we constructed the protein-protein interaction (PPI) network by the Search Tool for the Retrieval of Interacting Genes online database and weighted genes coexpression network by the WGCNA package in R. Subsequently, we picked out the hub genes among the DEGs. A total of 423 DEGs with 198 upregulated genes and 225 downregulated genes were identified between HBV-ALF and normal samples. The upregulated genes were mainly enriched in immune response, and the downregulated genes were mainly enriched in complement and coagulation cascades. Orosomucoid 1 (ORM1), orosomucoid 2 (ORM2), plasminogen (PLG), and aldehyde oxidase 1 (AOX1) were picked out as the hub genes that with a high degree in both PPI network and weighted genes coexpression network. The weighted genes coexpression network analysis found out 3 of the 5 modules that upregulated genes enriched in were closely related to immune system. The downregulated genes enriched in only one module, and the genes in this module majorly enriched in the complement and coagulation cascades pathway. In conclusion, 4 genes (ORM1, ORM2, PLG, and AOX1) with immune response and the complement and coagulation cascades pathway may take part in the pathogenesis of HBV-ALF, and these candidate genes and pathways could be therapeutic targets for HBV-ALF.

Gene Coexpression Networks in Human Brain Developmental Transcriptomes Implicate the Association of Long Noncoding RNAs with Intellectual Disability

The advent of next-generation sequencing for genetic diagnoses of complex developmental disorders, such as intellectual disability (ID), has facilitated the identification of hundreds of predisposing genetic variants. However, there still exists a vast gap in our knowledge of causal genetic factors for ID as evidenced by low diagnostic yield of genetic screening, in which identifiable genetic causes are not found for the majority of ID cases. Most methods of genetic screening focus on protein-coding genes; however, noncoding RNAs may outnumber protein-coding genes and play important roles in brain development. Long noncoding RNAs (lncRNAs) specifically have been shown to be enriched in the brain and have diverse roles in gene regulation at the transcriptional and posttranscriptional levels. LncRNAs are a vastly uncharacterized group of noncoding genes, which could function in brain development and harbor ID-predisposing genetic variants. We analyzed lncRNAs for coexpression with known ID genes and affected biological pathways within a weighted gene coexpression network derived from RNA-sequencing data spanning human brain development. Several ID-associated gene modules were found to be enriched for lncRNAs, known ID genes, and affected biological pathways. Utilizing a list of de novo and pathogenic copy number variants detected in ID probands, we identified lncRNAs overlapping these genetic structural variants. By integrating our results, we have made a prioritized list of potential ID-associated lncRNAs based on the developing brain gene coexpression network and genetic structural variants found in ID probands.