Comparative proteomic analysis across the developmental stages of the Eimeria tenella.

Eimeria tenella is the main pathogen responsible for coccidiosis in chickens. The life cycle of E. tenella is, arguably, the least complex of all Coccidia, with only one host. However, it presents different developmental stages, either in the environment or in the host and either intracellular or extracellular. Its signaling and metabolic pathways change with its different developmental stages. Until now, little is known about the developmental regulation and transformation mechanisms of its life cycle. In this study, protein profiles from the five developmental stages, including unsporulated oocysts (USO), partially sporulated (7 h) oocysts (SO7h), sporulated oocysts (SO), sporozoites (S) and second-generation merozoites (M2), were harvested using the label-free quantitative proteomics approach. Then the differentially expressed proteins (DEPs) for these stages were identified. A total of 314, 432, 689, and 665 DEPs were identified from the comparison of SO7h vs USO, SO vs SO7h, S vs SO, and M2 vs S, respectively. By conducting weighted gene coexpression network analysis (WGCNA), six modules were dissected. Proteins in blue and brown modules were calculated to be significantly positively correlated with the E. tenella developmental stages of sporozoites (S) and second-generation merozoites (M2), respectively. In addition, hub proteins with high intra-module degree were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyses revealed that hub proteins in blue modules were involved in electron transport chain and oxidative phosphorylation. Hub proteins in the brown module were involved in RNA splicing. These findings provide new clues and ideas to enhance our fundamental understanding of the molecular mechanisms underlying parasite development.

Coexpression network and trans-activation analyses of maize reproductive phasiRNA loci.

The anther-enriched phased, small interfering RNAs (phasiRNAs) play vital roles in sustaining male fertility in grass species. Their long non-coding precursors are synthesized by RNA polymerase II and are likely regulated by transcription factors (TFs). A few putative transcriptional regulators of the 21- or 24-nucleotide phasiRNA loci (referred to as 21- or 24-PHAS loci) have been identified in maize (Zea mays), but whether any of the individual TFs or TF combinations suffice to activate any PHAS locus is unclear. Here, we identified the temporal gene coexpression networks (modules) associated with maize anther development, including two modules highly enriched for the 21- or 24-PHAS loci. Comparisons of these coexpression modules and gene sets dysregulated in several reported male sterile TF mutants provided insights into TF timing with regard to phasiRNA biogenesis, including antagonistic roles for OUTER CELL LAYER4 and MALE STERILE23. Trans-activation assays in maize protoplasts of individual TFs using bulk-protoplast RNA-sequencing showed that two of the TFs coexpressed with 21-PHAS loci could activate several 21-nucleotide phasiRNA pathway genes but not transcription of 21-PHAS loci. Screens for combinatorial activities of these TFs and, separately, the recently reported putative transcriptional regulators of 24-PHAS loci using single-cell (protoplast) RNA-sequencing, did not detect reproducible activation of either 21-PHAS or 24-PHAS loci. Collectively, our results suggest that the endogenous transcriptional machineries and/or chromatin states in the anthers are necessary to activate reproductive PHAS loci.

Metabolomic and transcriptomic analyses of peach leaves and fruits in response to pruning.

BACKGROUND: Pruning is an important cultivation management option that has important effects on peach yield and quality. However, the effects of pruning on the overall genetic and metabolic changes in peach leaves and fruits are poorly understood. RESULTS: The transcriptomic and metabolomic profiles of leaves and fruits from trees subjected to pruning and unpruning treatments were measured. A total of 20,633 genes and 622 metabolites were detected. Compared with those in the control, 1,127 differentially expressed genes (DEGs) and 77 differentially expressed metabolites (DEMs) were identified in leaves from pruned and unpruned trees (pdLvsupdL), whereas 423 DEGs and 29 DEMs were identified in fruits from the pairwise comparison pdFvsupdF. The content of three auxin analogues was upregulated in the leaves of pruned trees, the content of all flavonoids detected in the leaves decreased, and the expression of almost all genes involved in the flavonoid biosynthesis pathway decreased. The phenolic acid and amino acid metabolites detected in fruits from pruned trees were downregulated, and all terpenoids were upregulated. The correlation analysis revealed that DEGs and DEMs in leaves were enriched in tryptophan metabolism, auxin signal transduction, and flavonoid biosynthesis. DEGs and DEMs in fruits were enriched in flavonoid and phenylpropanoid biosynthesis, as well as L-glutamic acid biosynthesis. CONCLUSIONS: Pruning has different effects on the leaves and fruits of peach trees, affecting mainly the secondary metabolism and hormone signalling pathways in leaves and amino acid biosynthesis in fruits.

In Silico Approach to Molecular Profiling of the Transition from Ovarian Epithelial Cells to Low-Grade Serous Ovarian Tumors for Targeted Therapeutic Insights.

This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.

Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

Profiling of the gene expression and alternative splicing landscapes of Eucalyptus grandis.

Eucalyptus is a widely planted hardwood tree species due to its fast growth, superior wood properties and adaptability. However, the post-transcriptional regulatory mechanisms controlling tissue development and stress responses in Eucalyptus remain poorly understood. In this study, we performed a comprehensive analysis of the gene expression profile and the alternative splicing (AS) landscape of E. grandis using strand-specific RNA-Seq, which encompassed 201 libraries including different organs, developmental stages, and environmental stresses. We identified 10 416 genes (33.49%) that underwent AS, and numerous differentially expressed and/or differential AS genes involved in critical biological processes, such as primary-to-secondary growth transition of stems, adventitious root formation, aging and responses to phosphorus- or boron-deficiency. Co-expression analysis of AS events and gene expression patterns highlighted the potential upstream regulatory role of AS events in multiple processes. Additionally, we highlighted the lignin biosynthetic pathway to showcase the potential regulatory functions of AS events in the KNAT3 and IRL3 genes within this pathway. Our high-quality expression atlas and AS landscape serve as valuable resources for unravelling the genetic control of woody plant development, long-term adaptation, and understanding transcriptional diversity in Eucalyptus. Researchers can conveniently access these resources through the interactive ePlant browser (https://bar.utoronto.ca/eplant_eucalyptus).

Genome-wide identification and coexpression network analysis of heat shock protein superfamily in Apolygus lucorum.

Heat shock proteins (Hsp) function as crucial molecular chaperones, playing pivotal roles in insects' response to stress stimuli. Apolygus lucorum, known for its broad spectrum of host plants and significant crop damage potential, presents a compelling subject for understanding stress response mechanisms. Hsp is important for A. lucorum to tolerate temperature and insecticide stress and may be involved in the formation of resistance to the interactive effects of temperature and insecticide. Here, we employed comprehensive genomic approaches to identify Hsp superfamily members in its genome. In total, we identified 42 Hsp genes, including 3 Hsp90, 16 Hsp70, 13 Hsp60, and 10 Hsp20. Notably, we conducted motif analysis and gene structures for Hsp members, which suggested the same families are relatively conserved. Furthermore, leveraging the weighted gene coexpression network analysis, we observed diverse expression patterns of different Hsp types across various tissues, with certain Hsp70 showing tissue-specific bias. Noteworthy among the highly expressed Hsp genes was testis-specific, which may serve as a pivotal hub gene regulating the gene network. Our findings shed light on the molecular evolutionary dynamics and temperature stress response mechanisms of Hsp genes in A. lucorum, offering insights into its adaptive strategies and potential targets for pest management.

Constructing local cell-specific networks from single-cell data.

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Expression divergence of expansin genes drive the heteroblasty in Ceratopteris chingii.

BACKGROUND: Sterile-fertile heteroblasty is a common phenomenon observed in ferns, where the leaf shape of a fern sporophyll, responsible for sporangium production, differs from that of a regular trophophyll. However, due to the large size and complexity of most fern genomes, the molecular mechanisms that regulate the formation of these functionally different heteroblasty have remained elusive. To shed light on these mechanisms, we generated a full-length transcriptome of Ceratopteris chingii with PacBio Iso-Seq from five tissue samples. By integrating Illumina-based sequencing short reads, we identified the genes exhibiting the most significant differential expression between sporophylls and trophophylls. RESULTS: The long reads were assembled, resulting in a total of 24,024 gene models. The differential expressed genes between heteroblasty primarily involved reproduction and cell wall composition, with a particular focus on expansin genes. Reconstructing the phylogeny of expansin genes across 19 plant species, ranging from green algae to seed plants, we identified four ortholog groups for expansins. The observed high expression of expansin genes in the young sporophylls of C. chingii emphasizes their role in the development of heteroblastic leaves. Through gene coexpression analysis, we identified highly divergent expressions of expansin genes both within and between species. CONCLUSIONS: The specific regulatory interactions and accompanying expression patterns of expansin genes are associated with variations in leaf shapes between sporophylls and trophophylls.

Evolution of isoform-level gene expression patterns across tissues during lotus species divergence.

Both gene duplication and alternative splicing (AS) drive the functional diversity of gene products in plants, yet the relative contributions of the two key mechanisms to the evolution of gene function are largely unclear. Here, we studied AS in two closely related lotus plants, Nelumbo lutea and Nelumbo nucifera, and the outgroup Arabidopsis thaliana, for both single-copy and duplicated genes. We show that most splicing events evolved rapidly between orthologs and that the origin of lineage-specific splice variants or isoforms contributed to gene functional changes during species divergence within Nelumbo. Single-copy genes contain more isoforms, have more AS events conserved across species, and show more complex tissue-dependent expression patterns than their duplicated counterparts. This suggests that expression divergence through isoforms is a mechanism to extend the expression breadth of genes with low copy numbers. As compared to isoforms of local, small-scale duplicates, isoforms of whole-genome duplicates are less conserved and display a less conserved tissue bias, pointing towards their contribution to subfunctionalization. Through comparative analysis of isoform expression networks, we identified orthologous genes of which the expression of at least some of their isoforms displays a conserved tissue bias across species, indicating a strong selection pressure for maintaining a stable expression pattern of these isoforms. Overall, our study shows that both AS and gene duplication contributed to the diversity of gene function during the evolution of lotus.

Gene coexpression network during ontogeny in the yellow fever mosquito, Aedes aegypti.

BACKGROUND: The behaviors and ontogeny of Aedes aegypti are closely related to the spread of diseases caused by dengue (DENV), chikungunya (CHIKV), Zika (ZIKV), and yellow fever (YFV) viruses. During the life cycle, Ae. aegypti undergoes drastic morphological, metabolic, and functional changes triggered by gene regulation and other molecular mechanisms. Some essential regulatory factors that regulate insect ontogeny have been revealed in other species, but their roles are still poorly investigated in the mosquito. RESULTS: Our study identified 6 gene modules and their intramodular hub genes that were highly associated with the ontogeny of Ae. aegypti in the constructed network. Those modules were found to be enriched in functional roles related to cuticle development, ATP generation, digestion, immunity, pupation control, lectins, and spermatogenesis. Additionally, digestion-related pathways were activated in the larvae and adult females but suppressed in the pupae. The integrated protein‒protein network also identified cilium-related genes. In addition, we verified that the 6 intramodular hub genes encoding proteins such as EcKinase regulating larval molt were only expressed in the larval stage. Quantitative RT‒PCR of the intramodular hub genes gave similar results as the RNA-Seq expression profile, and most hub genes were ontogeny-specifically expressed. CONCLUSIONS: The constructed gene coexpression network provides a useful resource for network-based data mining to identify candidate genes for functional studies. Ultimately, these findings will be key in identifying potential molecular targets for disease control.

The Toggle Switch Model for Gene Expression Change during the Prenatal-to-Postnatal Transition in Mammals.

The prenatal-to-postnatal transition is a pivotal process in the life cycle whereby an organism shifts from responding to intrauterine cues to undergoing extrauterine stresses with many physiological adaptations. However, the molecular basis underlying the evolutionarily conserved physiological adaptations remains elusive. Here, we analyze the transcriptomes of seven organs across developmental time points from five mammalian species by constructing computational coexpression networks and report a developmental shift of gene expression at the perinatal stage. The low-to-high and high-to-low expressed genes tightly coalesce in the functional categories and gene regulatory pathways that implicate the physiological adaptions during the prenatal-to-postnatal transition, including lipid metabolism, circadian rhythm, immune response, cell cycle, and cell division. The low-to-high and high-to-low expressed genes around the perinatal stage tend to form the mutually inhibitory toggle switch gene pairs linking the gene regulatory networks in response to the environmental changes. We thus propose the toggle switch model for the developmental shift of gene expression as a mechanic framework to investigate how the physiological adaptations occur during the prenatal-to-postnatal transition.

Unknown to Known: Advancing Knowledge of Coral Gene Function.

Given the catastrophic changes befalling coral reefs, understanding coral gene function is essential to advance reef conservation. This has proved challenging due to the paucity of genomic data and genetic tools available for corals. Recently, CRISPR/Cas9 gene editing was applied to these species; however, a major bottleneck is the identification and prioritization of candidate genes for manipulation. This issue is exacerbated by the many unknown ('dark') coral genes that may play key roles in the stress response. We review the use of gene coexpression networks that incorporate both known and unknown genes to identify targets for reverse genetic analysis. This approach also provides a framework for the annotation of dark genes in established interaction networks to improve our fundamental knowledge of coral gene function.

Optimized combination methods for exploring and verifying disease-resistant transcription factors in melon.

A large amount of omics data and number of bioinformatics tools has been produced. However, the methods for further exploring omics data are simple, in particular, to mine key regulatory genes, which are a priority concern in biological systems, and most of the specific functions are still unknown. First, raw data of two genotypes of melon (susceptible and resistant) were obtained by transcriptome analysis. Second, 391 transcription factors (TFs) were identified from the plant transcription factor database and cucurbit genomics database. Then, functional enrichment analysis indicated that these genes were mainly annotated in the process of transcription regulation. Third, 243 and 230 module-specific TFs were screened by weighted gene coexpression network analysis and short time series expression miner, respectively. Several TF genes, such as WRKYs and bHLHs, were regarded as key regulatory genes according to the values of significantly different modules. The coexpression network showed that these TF genes were significant correlated with resistance (R) genes, such as DRP2, RGA3, DRP1 and NB-ARC. Fourth, cis-acting element analysis illustrated that these R genes may bind to WRKY and bHLH. Finally, the expression of WRKY genes was verified by quantitative reverse transcription PCR (RT-qPCR). Phylogenetic analysis was carried out to further confirm that these TFs may play a critical role in Curcurbitaceae disease resistance. This study provides a new optimized combination strategy to explore the functions of TFs in a wide spectrum of biological processes. This strategy may also effectively predict potential relationships in the interactions of essential genes.

Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis.

Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19.

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

LncRNA analysis of mAb producing CHO clones reveals marker and engineering potential.

Long non-coding RNAs (lncRNAs) are a potential new cell line engineering tool for improvement of yield and stability of CHO cells. In this study, we performed RNA sequencing of mAb producer CHO clones to study the lncRNA and protein coding transcriptome in relation to productivity. First, a robust linear model was used to identify genes correlating to productivity. To unravel specific patterns in expression of these genes, we employed weighted gene coexpression analysis (WGCNA) to find coexpressed modules, looking both for lncRNAs and coding genes. There was little overlap in the genes associated with productivity between the two products studied, possibly due to the difference in absolute range of productivity between the two mAbs. Therefore, we focused on the product with higher productivity and stronger candidate lncRNAs. To evaluate their potential as engineering targets, these candidate lncRNAs were transiently overexpressed or deleted by stable CRISPR Cas9 knock out both in a high and a low productivity subclone. We found that the thus achieved expression level of the identified lncRNAs, as confirmed by qPCR, does correlate well to productivity, so that they represent good markers that may be used for early clone selection. Additionally, we found that the deletion of one tested lncRNA region decreased viable cell density (VCD), prolonged culture time and increased cell size, final titer and specific productivity per cell. These results demonstrate the feasibility and usefulness of engineering lncRNA expression in production cell lines.

A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.

It has been increasingly appealing to evaluate whether expression levels of two genes in a gene coexpression network are still dependent given samples' clinical information, in which the conditional independence test plays an essential role. For enhanced robustness regarding model assumptions, we propose a class of double-robust tests for evaluating the dependence of bivariate outcomes after controlling for known clinical information. Although the proposed test relies on the marginal density functions of bivariate outcomes given clinical information, the test remains valid as long as one of the density functions is correctly specified. Because of the closed-form variance formula, the proposed test procedure enjoys computational efficiency without requiring a resampling procedure or tuning parameters. We acknowledge the need to infer the conditional independence network with high-dimensional gene expressions, and further develop a procedure for multiple testing by controlling the false discovery rate. Numerical results show that our method accurately controls both the type-I error and false discovery rate, and it provides certain levels of robustness regarding model misspecification. We apply the method to a gastric cancer study with gene expression data to understand the associations between genes belonging to the transforming growth factor ß signaling pathway given cancer-stage information.

Identification of functional pathways and potential genes associated with interferon signaling during human adenovirus type 7 infection by weighted gene coexpression network analysis.

Human adenovirus type 7 (HAdV-7) can cause severe pneumonia and complications in children. However, the mechanism of pathogenesis and the genes involved remain largely unknown. We collected HAdV-7-infected and mock-infected A549 cells at 24, 48, and 72 hours postinfection (hpi) for RNA sequencing (RNA-Seq) and identified potential genes and functional pathways associated with HAdV-7 infection using weighted gene coexpression network analysis (WGCNA). Based on bioinformatics analysis, 12 coexpression modules were constructed by WGCNA, with the blue, tan, and brown modules significantly positively correlated with adenovirus infection at 24, 48, and 72 hpi, respectively. Functional enrichment analysis indicated that the blue module was mainly enriched in DNA replication and viral processes, the tan module was largely enriched in metabolic pathways and regulation of superoxide radical removal, and the brown module was predominantly enriched in regulation of cell death. qPCR was used to determine transcript abundance of some identified hub genes, and the results were consistent with those from RNA-Seq. Comprehensively analyzing hub genes and differentially expressed genes in the GSE68004 dataset, we identified SOCS3, OASL, ISG15, and IFIT1 as potential candidate genes for use as biomarkers or drug targets in HAdV-7 infection. We propose a multi-target inhibition of the interferon signaling mechanism to explain the association of HAdV-7 infection with the severity of clinical consequences. This study has allowed us to construct a framework of coexpression gene modules in A549 cells infected with HAdV-7, thus providing a basis for identifying potential genes and pathways involved in adenovirus infection and for investigating the pathogenesis of adenovirus-associated diseases.

Genome-Resolved Metagenomics and Metatranscriptomics Reveal Insights into the Ecology and Metabolism of Anaerobic Microbial Communities in PCB-Contaminated Sediments.

Growth of organohalide-respiring bacteria such as Dehalococcoides mccartyi on halogenated organics (e.g., polychlorinated biphenyls (PCBs)) at contaminated sites or in enrichment culture requires interaction and support from other microbial community members. To evaluate naturally occurring interactions between Dehalococcoides and key supporting microorganisms (e.g., production of H2, acetate, and corrinoids) in PCB-contaminated sediments, metagenomic and metatranscriptomic sequencing was conducted on DNA and RNA extracted from sediment microcosms, showing evidence of both Dehalococcoides growth and PCB dechlorination. Using a genome-resolved approach, 160 metagenome-assembled genomes (MAGs), including three Dehalococcoides MAGs, were recovered. A novel reductive dehalogenase gene, distantly related to the chlorophenol dehalogenase gene cprA (pairwise amino acid identity: 23.75%), was significantly expressed. Using MAG gene expression data, 112 MAGs were assigned functional roles (e.g., corrinoid producers, acetate/H2 producers, etc.). A network coexpression analysis of all 160 MAGs revealed correlations between 39 MAGs and the Dehalococcoides MAGs. The network analysis also showed that MAGs assigned with functional roles that support Dehalococcoides growth (e.g., corrinoid assembly, and production of intermediates required for corrinoid synthesis) displayed significant coexpression correlations with Dehalococcoides MAGs. This work demonstrates the power of genome-resolved metagenomic and metatranscriptomic analyses, which unify taxonomy and function, in investigating the ecology of dehalogenating microbial communities.