A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.

Since genes do not function individually, the gene module is considered an important tool for interpreting gene expression profiles. In order to consider both functional similarity and expression similarity in module identification, GMIGAGO, a functional Gene Module Identification algorithm based on Genetic Algorithm and Gene Ontology, was proposed in this work. GMIGAGO is an overlapping gene module identification algorithm, which mainly includes two stages: In the first stage (initial identification of gene modules), Improved Partitioning Around Medoids Based on Genetic Algorithm (PAM-GA) is used for the initial clustering on gene expression profiling, and traditional gene co-expression modules can be obtained. Only similarity of expression levels is considered at this stage. In the second stage (optimization of functional similarity within gene modules), Genetic Algorithm for Functional Similarity Optimization (FSO-GA) is used to optimize gene modules based on gene ontology, and functional similarity within gene modules can be improved. Without loss of generality, we compared GMIGAGO with state-of-the-art gene module identification methods on six gene expression datasets, and GMIGAGO identified the gene modules with the highest functional similarity (much higher than state-of-the-art algorithms). GMIGAGO was applied in BRCA, THCA, HNSC, COVID-19, Stem, and Radiation datasets, and it identified some interesting modules which performed important biological functions. The hub genes in these modules could be used as potential targets for diseases or radiation protection. In summary, GMIGAGO has excellent performance in mining molecular mechanisms, and it can also identify potential biomarkers for individual precision therapy.

Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis.

Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.

Identification of hub genes associated with somatic cell score in dairy cow.

CONTEXT: Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). AIM: Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. METHODS: This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. KEY RESULTS: Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). CONCLUSION: Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. IMPLICATIONS: Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

An Arabidopsis expression predictor enables inference of transcriptional regulators for gene modules.

The regulation of gene expression by transcription factors (TFs) has been studied for a long time, but no model that can accurately predict transcriptome profiles based on TF activities currently exists. Here, we developed a computational approach, named EXPLICIT (Expression Prediction via Log-linear Combination of Transcription Factors), to construct a universal predictor for Arabidopsis to predict the expression of 29 182 non-TF genes using 1678 TFs. When applied to RNA-Seq samples from diverse tissues, EXPLICIT generated accurate predicted transcriptomes correlating well with actual expression, with an average correlation coefficient of 0.986. After recapitulating the quantitative relationships between TFs and their target genes, EXPLICIT enabled downstream inference of TF regulators for genes and gene modules functioning in diverse plant pathways, including those involved in suberin, flavonoid, glucosinolate metabolism, lateral root, xylem, secondary cell wall development or endoplasmic reticulum stress response. Our approach showed a better ability to recover the correct TF regulators when compared with existing plant tools, and provides an innovative way to study transcriptional regulation.

TPQCI: A topology potential-based method to quantify functional influence of copy number variations.

Copy number variation (CNV) is a major type of chromosomal structural variation that play important roles in many diseases including cancers. Due to genome instability, a large number of CNV events can be detected in diseases such as cancer. Therefore, it is important to identify the functionally important CNVs in diseases, which currently still poses a challenge in genomics. One of the critical steps to solve the problem is to define the influence of CNV. In this paper, we provide a topology potential based method, TPQCI, to quantify this kind of influence by integrating statistics, gene regulatory associations, and biological function information. We used this metric to detect functionally enriched genes on genomic segments with CNV in breast cancer and multiple myeloma and discovered biological functions influenced by CNV. Our results demonstrate that, by using our proposed TPQCI metric, we can detect disease-specific genes that are influenced by CNVs. Source codes of TPQCI are provided in Github (https://github.com/usos/TPQCI).

ModularBoost: an efficient network inference algorithm based on module decomposition.

BACKGROUND: Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. RESULTS: ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. CONCLUSIONS: As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

An analytical method for the identification of cell type-specific disease gene modules.

BACKGROUND: Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. METHODS: To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. RESULTS: Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. CONCLUSIONS: The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

The TuMYB46L-TuACO3 module regulates ethylene biosynthesis in einkorn wheat defense to powdery mildew.

Powdery mildew disease, elicited by the obligate fungal pathogen Blumeria graminis f.sp. tritici (Bgt), causes widespread yield losses in global wheat crop. However, the molecular mechanisms governing wheat defense to Bgt are still not well understood. Here we found that TuACO3, encoding the 1-aminocyclopropane-1-carboxylic acid (ACC) oxidase functioning in ethylene (ET) biosynthesis, was induced by Bgt infection of the einkorn wheat Triticum urartu, which was accompanied by increased ET content. Silencing TuACO3 decreased ET production and compromised wheat defense to Bgt, whereas both processes were enhanced in the transgenic wheat overexpressing TuACO3. TuMYB46L, phylogenetically related to Arabidopsis MYB transcription factor AtMYB46, was found to bind to the TuACO3 promoter region in yeast-one-hybrid and EMSA experiments. TuMYB46L expression decreased rapidly following Bgt infection. Silencing TuMYB46L promoted ET content and Bgt defense, but the reverse was observed when TuMYB46L was overexpressed. Hence, decreased expression of TuMYB46L permits elevated function of TuACO3 in ET biosynthesis in Bgt-infected wheat. The TuMYB46L-TuACO3 module regulates ET biosynthesis to promote einkorn wheat defense against Bgt. Furthermore, we found four chitinase genes acting downstream of the TuMYB46L-TuACO3 module. Collectively, our data shed a new light on the molecular mechanisms underlying wheat defense to Bgt.

Regulation of interferon stimulated gene expression levels at homeostasis.

Interferon stimulated genes (ISGs), a collection of genes important in the early innate immune response, are upregulated in response to stimulation by extracellular type I interferons. The regulation of ISGs has been extensively studied in cells exposed to significant interferon stimulation, but less is known about ISG regulation in homeostatic regimes in which extracellular interferon levels are low. Using a collection of pre-existing, publicly available microarray datasets, we investigated ISG regulation at homeostasis in CD4, pulmonary epithelial, fibroblast and macrophage cells. We used a linear regression model to predict ISG expression levels from regulator expression levels. Our results suggest significant regulation of ISG expression at homeostasis, both through the ISGF3 molecule and through IRF7 and IRF8 associated pathways. We find that roughly 50% of ISGs have expression levels significantly correlated with ISGF3 expression levels at homeostasis, supporting previous results suggesting that homeostatic IFN levels have broad functional consequences. We find that ISG expression levels varied in their correlation with ISGF3, with epithelial and macrophage cells showing more correlation than CD4 and fibroblast cells. Our analysis provides a novel approach for decomposing and quantifying ISG regulation.

Senescence-associated genes and non-coding RNAs function in pancreatic cancer progression.

Pancreatic cancer is a major cause of mortality with a poor diagnosis and prognosis that most often occurs in elderly patients. Few studies, however, focus on the interplay of age and pancreatic cancer at the transcriptional level. Here we evaluated the possible roles of age-dependent, differentially expressed genes (DEGs) in pancreatic cancer. These DEGs were used to construct a correlation network and clustered in six gene modules, among which two modules were highly correlated with patients' survival time. Integrating different datasets, including ATAC-Seq and ChIP-Seq, we performed multi-parallel analyses and identified eight age-dependent protein coding genes and two non-coding RNAs as potential candidates. These candidates, together with KLF5, a potent functional transcription factor in pancreatic cancer, are likely to be key elements linking cellular senescence and pancreatic cancer, providing insights on the balance between them, as well as on diagnosis and subsequent prognosis of pancreatic cancer.

Identification of novel key lncRNAs involved in periodontitis by weighted gene co-expression network analysis.

BACKGROUND AND OBJECTIVE: Periodontitis is a multifactorial disease that can lead to the progressive destruction of dental support tissue. However, the detailed mechanisms and specific biomarkers involved in periodontitis remain to be further studied. Recently, long non-coding RNAs (lncRNAs) have been found to play a more important role than other types of RNAs. In our study, we analysed the expression of lncRNAs in periodontitis by analysing GSE16134. MATERIAL AND METHODS: We identified highly correlated genes by analysing GSE16134 with weighted gene co-expression network analysis (WGCNA) and identified 50 hub lncRNAs that were dysregulated. Then, we used the Linear Models for Microarray Data (Limma) package to identify the hub lncRNAs that were differentially expressed (DElncRNAs). The ceRNA co-expression network data were obtained from several sites, including miRcode, and were used to assess the potential WGCNA function of hub DElncRNAs in periodontitis. Besides, we divided the samples into LBX2-AS1 high and low expression group by the expression level of LBX2-AS1 and calculated DEG by Limma package. Furthermore, we performed GO function, KEGG pathway and GSEA enrichment of DEGs. RESULTS: In the analysis, we identified 50 hub lncRNAs that may play important roles in periodontitis. Then, we used the Limma package to identify 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566). We elucidated the potential function of the hub DElncRNA LBX2-AS1 in periodontitis by constructing a co-expression network of lncRNA-miRNA-mRNA interactions. Totally, 573 DEGs (354 up- and 219 downregulated) in periodontitis samples were identified. DEGs were enriched in different GO terms and pathways, such as neutrophil degranulation, neutrophil activation, neutrophil activation involved in immune response, neutrophil-mediated immunity, antigen processing and presentation, JAK-STAT signalling pathway, natural killer cell-mediated cytotoxicity, EGFR tyrosine kinase inhibitor resistance, phosphatidylinositol signalling system and Vascular Endothelial Growth Factor (VEGF) signalling pathway. CONCLUSION: In our study, we found that 3 hub DElncRNAs (LINC00687, LBX2-AS1 and LINC01566) may be involved in the pathogenesis of periodontitis based on WGCNA and Limma analysis. Our study aimed to elucidate the mechanisms involved in periodontitis at the genetic and epigenetic levels by constructing a ceRNA network associated with lncRNA. Besides, identification DEGs of differential LBX2-AS1 and functional annotation showed that LBX2-AS1 might be associated with periodontitis.

Weighted gene coexpression network analysis identifies specific transcriptional modules and hub genes related to intramuscular fat traits in chicken breast muscle.

Intramuscular fat (IMF) traits are important factors that influence meat quality. However, the molecular regulatory mechanisms that underlie this trait in chickens are still poorly understood at the gene coexpression level. Here, we performed a weighted gene coexpression network analysis between IMF traits and transcriptome profile in breast muscle in the Chinese domestic Gushi chicken breed at 6, 14, 22, and 30 weeks. A total of 26 coexpressed gene modules were identified. Six modules, which included the dark gray, purple, cyan, pink, light cyan, and blue modules, showed a significant positive correlation (P < 0.05) with IMF traits. The strongest correlation was observed between the dark gray module and IMF content (r = 0.85; P = 4e-04) and between the blue module and different fatty acid content (r = 0.87~0.91; P = 5e-05~2e-04). Enrichment analysis showed that the enrichment of biological processes, such as fatty acid metabolic process, fat cell differentiation, acylglycerol metabolic process, and glycerolipid metabolism were significantly different in the six modules. In addition, the 32, 24, 4, 7, 6, and 25 hub genes were identified from the blue, pink, light cyan, cyan, dark gray, and purple modules, respectively. These hub genes are involved in multiple links to fatty acid metabolism, phospholipid metabolism, cholesterol metabolism, diverse cellular behaviors, and cell events. These results provide novel insights into the molecular regulatory mechanisms for IMF-related traits in chicken and may also help to uncover the formation mechanism for excellent meat quality traits in local breeds of Chinese chicken.

Genome-wide transcriptional adaptation to salt stress in Populus.

BACKGROUND: Adaptation to abiotic stresses is crucial for the survival of perennial plants in a natural environment. However, very little is known about the underlying mechanisms. Here, we adopted a liquid culture system to investigate plant adaptation to repeated salt stress in Populus trees. RESULTS: We first evaluated phenotypic responses and found that plants exhibit better stress tolerance after pre-treatment of salt stress. Time-course RNA sequencing (RNA-seq) was then performed to profile changes in gene expression over 12 h of salt treatments. Analysis of differentially expressed genes (DEGs) indicated that significant transcriptional reprogramming and adaptation to repeated salt treatment occurred. Clustering analysis identified two modules of co-expressed genes that were potentially critical for repeated salt stress adaptation, and one key module for salt stress response in general. Gene Ontology (GO) enrichment analysis identified pathways including hormone signaling, cell wall biosynthesis and modification, negative regulation of growth, and epigenetic regulation to be highly enriched in these gene modules. CONCLUSIONS: This study illustrates phenotypic and transcriptional adaptation of Populus trees to salt stress, revealing novel gene modules which are potentially critical for responding and adapting to salt stress.

MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization.

BACKGROUND: Prioritizing genes according to their associations with a cancer allows researchers to explore genes in more informed ways. By far, Gene-centric or network-centric gene prioritization methods are predominated. Genes and their protein products carry out cellular processes in the context of functional modules. Dysfunctional gene modules have been previously reported to have associations with cancer. However, gene module information has seldom been considered in cancer-related gene prioritization. RESULTS: In this study, we propose a novel method, MGOGP (Module and Gene Ontology-based Gene Prioritization), for cancer-related gene prioritization. Different from other methods, MGOGP ranks genes considering information of both individual genes and their affiliated modules, and utilize Gene Ontology (GO) based fuzzy measure value as well as known cancer-related genes as heuristics. The performance of the proposed method is comprehensively validated by using both breast cancer and prostate cancer datasets, and by comparison with other methods. Results show that MGOGP outperforms other methods, and successfully prioritizes more genes with literature confirmed evidence. CONCLUSIONS: This work will aid researchers in the understanding of the genetic architecture of complex diseases, and improve the accuracy of diagnosis and the effectiveness of therapy.

Weighted gene co-expression network analysis of expression data of monozygotic twins identifies specific modules and hub genes related to BMI.

BACKGROUND: The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. RESULTS: In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT, TLR9, PTGS2, HBD, and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI (r = 0.56, P = 0.04) and disease status (r = 0.56, P = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL, ASB9, NPPB, TBX2, IL17C, APOE, ABCG4, and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI (r = 0.56, P = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed. CONCLUSION: We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

CARMO: a comprehensive annotation platform for functional exploration of rice multi-omics data.

High-throughput technology is gradually becoming a powerful tool for routine research in rice. Interpretation of biological significance from the huge amount of data is a critical but non-trivial task, especially for rice, for which gene annotations rely heavily on sequence similarity rather than direct experimental evidence. Here we describe the annotation platform for comprehensive annotation of rice multi-omics data (CARMO), which provides multiple web-based analysis tools for in-depth data mining and visualization. The central idea involves systematic integration of 1819 samples from omics studies and diverse sources of functional evidence (15 401 terms), which are further organized into gene sets and higher-level gene modules. In this way, the high-throughput data may easily be compared across studies and platforms, and integration of multiple types of evidence allows biological interpretation from the level of gene functional modules with high confidence. In addition, the functions and pathways for thousands of genes lacking description or validation may be deduced based on concerted expression of genes within the constructed co-expression networks or gene modules. Overall, CARMO provides comprehensive annotations for transcriptomic datasets, epi-genomic modification sites, single nucleotide polymorphisms identified from genome re-sequencing, and the large gene lists derived from these omics studies. Well-organized results, as well as multiple tools for interactive visualization, are available through a user-friendly web interface. Finally, we illustrate how CARMO enables biological insights using four examples, demonstrating that CARMO is a highly useful resource for intensive data mining and hypothesis generation based on rice multi-omics data. CARMO is freely available online (http://bioinfo.sibs.ac.cn/carmo).

Identifying candidate drivers of drug response in heterogeneous cancer by mining high throughput genomics data.

BACKGROUND: With advances in technologies, huge amounts of multiple types of high-throughput genomics data are available. These data have tremendous potential to identify new and clinically valuable biomarkers to guide the diagnosis, assessment of prognosis, and treatment of complex diseases, such as cancer. Integrating, analyzing, and interpreting big and noisy genomics data to obtain biologically meaningful results, however, remains highly challenging. Mining genomics datasets by utilizing advanced computational methods can help to address these issues. RESULTS: To facilitate the identification of a short list of biologically meaningful genes as candidate drivers of anti-cancer drug resistance from an enormous amount of heterogeneous data, we employed statistical machine-learning techniques and integrated genomics datasets. We developed a computational method that integrates gene expression, somatic mutation, and copy number aberration data of sensitive and resistant tumors. In this method, an integrative method based on module network analysis is applied to identify potential driver genes. This is followed by cross-validation and a comparison of the results of sensitive and resistance groups to obtain the final list of candidate biomarkers. We applied this method to the ovarian cancer data from the cancer genome atlas. The final result contains biologically relevant genes, such as COL11A1, which has been reported as a cis-platinum resistant biomarker for epithelial ovarian carcinoma in several recent studies. CONCLUSIONS: The described method yields a short list of aberrant genes that also control the expression of their co-regulated genes. The results suggest that the unbiased data driven computational method can identify biologically relevant candidate biomarkers. It can be utilized in a wide range of applications that compare two conditions with highly heterogeneous datasets.

CCor: A whole genome network-based similarity measure between two genes.

Measuring the similarity between genes is often the starting point for building gene regulatory networks. Most similarity measures used in practice only consider pairwise information with a few also consider network structure. Although theoretical properties of pairwise measures are well understood in the statistics literature, little is known about their statistical properties of those similarity measures based on network structure. In this article, we consider a new whole genome network-based similarity measure, called CCor, that makes use of information of all the genes in the network. We derive a concentration inequality of CCor and compare it with the commonly used Pearson correlation coefficient for inferring network modules. Both theoretical analysis and real data example demonstrate the advantages of CCor over existing measures for inferring gene modules.

Integrated analysis of miRNAome and transcriptome reveals that microgravity induces the alterations of critical functional gene modules via the regulation of miRNAs in short-term space-flown C. elegans.

Microgravity, as a unique hazardous factor encountered in space, can induce a series of harmful effects on living organisms. The impact of microgravity on the pivotal functional gene modules stemming from gene enrichment analysis via the regulation of miRNAs is not fully illustrated. To explore the microgravity-induced alterations in critical functional gene modules via the regulation of miRNAs, in the present study, we proposed a novel bioinformatics algorithm for the integrated analysis of miRNAome and transcriptome from short-term space-flown C. elegans. The samples of C. elegans were exposed to two space conditions, namely spaceflight (SF) and spaceflight control (SC) onboard the International Space Station for 4 days. Additionally, the samples of ground control (GC) were included for comparative analysis. Using the present algorithm, we constructed regulatory networks of functional gene modules annotated from differentially expressed genes (DEGs) and their associated regulatory differentially expressed miRNAs (DEmiRNAs). The results showed that functional gene modules of molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathway were facilitated by 25 down-regulated DEmiRNAs (e.g., cel-miR-792, cel-miR-65, cel-miR-70, cel-lsy-6, cel-miR-796, etc.) in the SC vs. GC groups, whereas these modules were inhibited by 13 up-regulated DEmiRNAs (e.g., cel-miR-74, cel-miR-229, cel-miR-70, cel-miR-249, cel-miR-85, etc.) in the SF vs. GC groups. These findings indicated that microgravity could significantly alter gene expression patterns and their associated functional gene modules in short-term space-flown C. elegans. Additionally, we identified 34 miRNAs as post-transcriptional regulators that modulated these functional gene modules under microgravity conditions. Through the experimental verification, our results demonstrated that microgravity could induce the down-regulation of five critical functional gene modules (i.e., molting cycle, defense response, fatty acid metabolism, lysosome, and longevity regulating pathways) via the regulation of miRNAs in short-term space-flown C. elegans.

Transcriptional Responses of Different Brain Cell Types to Oxygen Decline.

Brain hypoxia is associated with a wide range of physiological and clinical conditions. Although oxygen is an essential constituent of maintaining brain functions, our understanding of how specific brain cell types globally respond and adapt to decreasing oxygen conditions is incomplete. In this study, we exposed mouse primary neurons, astrocytes, and microglia to normoxia and two hypoxic conditions and obtained genome-wide transcriptional profiles of the treated cells. Analysis of differentially expressed genes under conditions of reduced oxygen revealed a canonical hypoxic response shared among different brain cell types. In addition, we observed a higher sensitivity of neurons to oxygen decline, and dissected cell type-specific biological processes affected by hypoxia. Importantly, this study establishes novel gene modules associated with brain cells responding to oxygen deprivation and reveals a state of profound stress incurred by hypoxia.