Integrative Gene Network Research Articles

IGF signaling pathway in bone and cartilage development, homeostasis, and disease.

The skeleton plays a fundamental role in the maintenance of organ function and daily activities. The insulin-like growth factor (IGF) family is a group of polypeptide substances with a pronounced role in osteoblast differentiation, bone development, and metabolism. Disturbance of the IGFs and the IGF signaling pathway is inextricably linked with assorted developmental defects, growth irregularities, and jeopardized skeletal structure. Recent findings have illustrated the significance of the action of the IGF signaling pathway via growth factors and receptors and its interactions with dissimilar signaling pathways (Wnt/β-catenin, BMP, TGF-β, and Hh/PTH signaling pathways) in promoting the growth, survival, and differentiation of osteoblasts. IGF signaling also exhibits profound influences on cartilage and bone development and skeletal homeostasis via versatile cell-cell interactions in an autocrine, paracrine, and endocrine manner systemically and locally. Our review summarizes the role and regulatory function as well as a potentially integrated gene network of the IGF signaling pathway with other signaling pathways in bone and cartilage development and skeletal homeostasis, which in turn provides an enlightening insight into visualizing bright molecular targets to be eligible for designing effective drugs to handle bone diseases and maladies, such as osteoporosis, osteoarthritis, and dwarfism.

A comprehensive integrated gene network construction to explore the essential role of Notch 1 in lung adenocarcinoma (LUAD)

The heterogeneous biological landscape of non-small cell lung cancer (NSCLC) is largely attributed to the activation of Notch signalling pathway. Among the Notch family transmembrane proteins, neurogenic locus notch homolog protein1 (NOTCH1) is a putative oncogene in NSCLC which activates the pathway as negative prognostic factor. This study aims to explore integrated network approach in lung adenocarcinoma (LUAD) especially linked to the notch pathway and its receptors. Our gene set enrichment analysis reveals the key Notch pathway genes are predominantly down regulated in LUAD. There were 675 genes with a total of 6517 functional interactions and 6 densely connected clusters of 38 miRNAs, 84 transcription factors with 156 edges identified through network construction. Here we report five key genes namely NOTCH1, CDH1, ERBB2, GAPDH and COL1A1 significantly enriched in Notch pathway which are further validated through the KM plot, box plots, stage plots and TIMER analysis. In addition, the NOTCH1 receptor is strongly linked to the immune checkpoint inhibitor CD274 (PD-L1) and can be considered as prognostic marker and tumour suppressor gene in LUAD which surely provide the basis for early diagnosis and futuristic immunotherapeutic targets for LUAD. Communicated by Ramaswamy H. Sarma

Differential Gene Expression Profiles and Pathways Highlight the Role of Osteoimmunology in Neurofibromatosis Type 1-Related Dystrophic Scoliosis With Osteopenia.

Microarray approach and integrated gene network analysis. To explore the differential genetic expression profile, Gene Ontology terms, and Kyoto Encyclopedia of Genes and Genomes pathways in human trabecular bone (HTB)-derived cells of dystrophic scoliosis secondary to neurofibromatosis type 1 (DS-NF1) and compare these to normal controls. The pathogenesis of DS-NF1 and the accompanying generalized osteopenia remain unclear. We hypothesized that HTBs may play a significant role in the etiology and pathogenesis of DS-NF1. Microarray analysis was used to identify differentially expressed genes of HTBs from patients with DS-NF1 compared with those from healthy individuals. Functional and pathway enrichment analysis were implemented through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway database. Then, the "search tool for the retrieval of interacting genes/proteins" database, Cytoscape, and "Molecular Complex Detection" were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Pathway enrichment analysis was further performed for hub genes and gene clusters identified through module analysis. Six potential crucial genes were selected for validation by reverse transcription polymerase chain reaction. Bioinformatic analysis revealed that there are 401 previously unrecognized differentially expressed genes (238 up and 163 downregulated genes) in HTBs from patients with DS-NF1, and they were mainly enriched in terms of immune response, type-I interferon (IFN) signaling, TNF signaling pathway and etinoic acid inducible gene I-like receptor signaling pathway. Five hub genes, including signal transducer and activator of transcription 1, 2'-5'-oligoadenylate synthetase-like, IFN induced with helicase C domain 1, IFN regulatory factor 7, and MX dynamin-like GTPase 1 were identified through PPI network, which were mainly enriched in terms of Jak-STAT and etinoic acid inducible gene I-like receptor signaling pathway. An independently dysregulated protein cluster containing CCL2, CXCL1, CXCL3, CX3CL1, TLR1 , and CXCL12 was also identified through the PPI network. This indicated that the upper abnormally expressed genes may play essential roles in DS-NF1 pathogenesis and accompanied osteopenia. Six key genes were identified in the progression of DS-NF1-related osteopenia. Immune response might play a key role in the progression of osteopenia, whereas a CXCL12 -mediated osteogenic effect might play a protective role.

Integrative Transcriptomic Analyses of Hippocampal-Entorhinal System Subfields Identify Key Regulators in Alzheimer's Disease.

The hippocampal-entorhinal system supports cognitive function and is selectively vulnerable to Alzheimer's disease (AD). Little is known about global transcriptomic changes in the hippocampal-entorhinal subfields during AD. Herein, large-scale transcriptomic analysis is performed in five hippocampal-entorhinal subfields of postmortem brain tissues (262 unique samples). Differentially expressed genes are assessed across subfields and disease states, and integrated genotype data from an AD genome-wide association study. An integrative gene network analysis of bulk and single-nucleus RNA sequencing (snRNA-Seq) data identifies genes with causative roles in AD progression. Using a system-biology approach, pathology-specific expression patterns for cell types are demonstrated, notably upregulation of the A1-reactive astrocyte signature in the entorhinal cortex (EC) during AD. SnRNA-Seq data show that PSAP signaling is involved in alterations of cell- communications in the EC during AD. Further experiments validate the key role of PSAP in inducing astrogliosis and an A1-like reactive astrocyte phenotype. In summary, this study reveals subfield-, cell type-, and AD pathology-specific changes and demonstrates PSAP as a potential therapeutic target in AD.

An integrated nitrogen utilization gene network and transcriptome analysis reveal candidate genes in response to nitrogen deficiency in Brassica napus.

Nitrogen (N) is an essential factor for crop yield. Here, we characterized 605 genes from 25 gene families that form the complex gene networks of N utilization pathway in Brassica napus. We found unequal gene distribution between the An- and Cn-sub-genomes, and that genes derived from Brassica rapa were more retained. Transcriptome analysis indicated that N utilization pathway gene activity shifted in a spatio-temporal manner in B. napus. A low N (LN) stress RNA-seq of B. napus seedling leaves and roots was generated, which proved that most N utilization related genes were sensitive to LN stress, thereby forming co-expression network modules. Nine candidate genes in N utilization pathway were confirmed to be significantly induced under N deficiency conditions in B. napus roots, indicating their potential roles in LN stress response process. Analyses of 22 representative species confirmed that the N utilization gene networks were widely present in plants ranging from Chlorophyta to angiosperms with a rapid expansion trend. Consistent with B. napus, the genes in this pathway commonly showed a wide and conserved expression profile in response to N stress in other plants. The network, genes, and gene-regulatory modules identified here represent resources that may enhance the N utilization efficiency or the LN tolerance of B. napus.

Integrated gene network analysis sheds light on understanding the progression of Osteosarcoma.

Osteosarcoma is a rare disorder among cancer, but the most frequently occurring among sarcomas in children and adolescents. It has been reported to possess the relapsing capability as well as accompanying collateral adverse effects which hinder the development process of an effective treatment plan. Using networks of omics data to identify cancer biomarkers could revolutionize the field in understanding the cancer. Cancer biomarkers and the molecular mechanisms behind it can both be understood by studying the biological networks underpinning the etiology of the disease. In our study, we aimed to highlight the hub genes involved in gene-gene interaction network to understand their interaction and how they affect the various biological processes and signaling pathways involved in Osteosarcoma. Gene interaction network provides a comprehensive overview of functional gene analysis by providing insight into how genes cooperatively interact to elicit a response. Because gene interaction networks serve as a nexus to many biological problems, their employment of it to identify the hub genes that can serve as potential biomarkers remain widely unexplored. A dynamic framework provides a clear understanding of biological complexity and a pathway from the gene level to interaction networks. Our study revealed various hub genes viz. TP53, CCND1, CDK4, STAT3, and VEGFA by analyzing various topological parameters of the network, such as highest number of interactions, average shortest path length, high cluster density, etc. Their involvement in key signaling pathways, such as the FOXM1 transcription factor network, FAK-mediated signaling events, and the ATM pathway, makes them significant candidates for studying the disease. The study also highlighted significant enrichment in GO terms (Biological Processes, Molecular Function, and Cellular Processes), such as cell cycle signal transduction, cell communication, kinase binding, transcription factor activity, nucleoplasm, PML body, nuclear body, etc. To develop better therapeutics, a specific approach toward the disease targeting the hub genes involved in various signaling pathways must have opted to unravel the complexity of the disease. Our study has highlighted the candidate hub genes viz. TP53, CCND1 CDK4, STAT3, VEGFA. Their involvement in the major signaling pathways of Osteosarcoma makes them potential candidates to be targeted for drug development. The highly enriched signaling pathways include FOXM1 transcription pathway, ATM signal-ling pathway, FAK mediated signaling events, Arf6 signaling events, mTOR signaling pathway, and Integrin family cell surface interactions. Targeting the hub genes and their associated functional partners which we have reported in our studies may be efficacious in developing novel therapeutic targets.

An integrated gene network analysis to decode the multi-drug resistance mechanism in Klebsiella pneumoniae

Antimicrobial resistance (AMR) among microorganisms has become one of the worldwide concerns of this century and continues to challenge us. To properly understand this problem, it is essential to know the genes that cause AMR and their resistance mechanisms. Our present study focused on Klebsiella pneumoniae, which possesses AMR genes conferring resistance against multiple antibiotics. A gene interaction network of 42 functional partners was constructed and analyzed to broaden our understanding. Three closely related clusters (C1–C3) having an association with multi-drug resistance mechanisms were identified by clustering analysis. The enrichment analysis illustrated 30 genes in biological processes, 24 genes in molecular function, and 25 genes in cellular components having a significant role. The analysis of the gene interaction network revealed genes birA2, folP, pabC, folA, gyrB, glmM, gyrA, thyA_2 had maximum no. of interactions with their functional partners viz. 26, 25, 25, 24, 23, 23, 23, 23 respectively and can be considered as hub genes. Analyzing the enriched pathways and Gene Ontologies provides insight into AMR's molecular basis. In addition, the proposed study could aid the researchers in developing new treatment options to combat multi-drug resistant K. pneumoniae.

The landscape of human tissue and cell type specific expression and co-regulation of senescence genes

BackgroundCellular senescence is a complex stress response that impacts cellular function and organismal health. Multiple developmental and environmental factors, such as intrinsic cellular cues, radiation, oxidative stress, oncogenes, and protein accumulation, activate genes and pathways that can lead to senescence. Enormous efforts have been made to identify and characterize senescence genes (SnGs) in stress and disease systems. However, the prevalence of senescent cells in healthy human tissues and the global SnG expression signature in different cell types are poorly understood.MethodsThis study performed an integrative gene network analysis of bulk and single-cell RNA-seq data in non-diseased human tissues to investigate SnG co-expression signatures and their cell-type specificity.ResultsThrough a comprehensive transcriptomic network analysis of 50 human tissues in the Genotype-Tissue Expression Project (GTEx) cohort, we identified SnG-enriched gene modules, characterized SnG co-expression patterns, and constructed aggregated SnG networks across primary tissues of the human body. Our network approaches identified 51 SnGs highly conserved across the human tissues, including CDKN1A (p21)-centered regulators that control cell cycle progression and the senescence-associated secretory phenotype (SASP). The SnG-enriched modules showed remarkable cell-type specificity, especially in fibroblasts, endothelial cells, and immune cells. Further analyses of single-cell RNA-seq and spatial transcriptomic data independently validated the cell-type specific SnG signatures predicted by the network analysis.ConclusionsThis study systematically revealed the co-regulated organizations and cell type specificity of SnGs in major human tissues, which can serve as a blueprint for future studies to map senescent cells and their cellular interactions in human tissues.

Prioritizing Candidate Genes for Type 2 Diabetes Mellitus using Integrated Network and Pathway Analysis.

Background:Type 2 Diabetes Mellitus (T2DM) has emerged as a major threat to global health that fosters life-threatening clinical complications, taking a huge toll on our society. More than 65 million Indians suffer from T2DM, making it one of the leading causes of death. T2DM and associated complications have to be constantly monitored and managed which reduces the overall quality of life and increases socioeconomic burden. Therefore, it is crucial to develop specific treatment and management strategies. In order to achieve this, it is essential to understand the underlying genetic causes and molecular mechanisms.Methods:Integrated gene network and ontology analyses facilitate prioritization of plausible candidate genes for T2DM and also aid in understanding their mechanistic pathways. In this study, T2DM-associated genes were subjected to sequential interaction network and gene set enrichment analysis. High ranking network clusters were derived and their interrelation with pathways was assessed.Results:About 23 significant candidate genes were prioritized from 615 T2DM-associated genes which were overrepresented in pathways related to insulin resistance, type 2 diabetes, signaling cascades such as insulin receptor signaling pathway, PI3K signaling, IGFR signaling pathway, ERBB signaling pathway, MAPK signaling pathway and their regulatory mechanisms.Conclusion:Of these, two tyrosine kinase receptor genes-EGFR and IGF1R were identified as common nodes and can be considered to be significant candidate genes in T2DM.

Comprehensive Transcriptomic Analysis Identifies Novel Antiviral Factors Against Influenza A Virus Infection.

Influenza A virus (IAV) has a higher genetic variation, leading to the poor efficiency of traditional vaccine and antiviral strategies targeting viral proteins. Therefore, developing broad-spectrum antiviral treatments is particularly important. Host responses to IAV infection provide a promising approach to identify antiviral factors involved in virus infection as potential molecular drug targets. In this study, in order to better illustrate the molecular mechanism of host responses to IAV and develop broad-spectrum antiviral drugs, we systematically analyzed mRNA expression profiles of host genes in a variety of human cells, including transformed and primary epithelial cells infected with different subtypes of IAV by mining 35 microarray datasets from the GEO database. The transcriptomic results showed that IAV infection resulted in the difference in expression of amounts of host genes in all cell types, especially those genes participating in immune defense and antiviral response. In addition, following the criteria of P<0.05 and |logFC|≥1.5, we found that some difference expression genes were overlapped in different cell types under IAV infection via integrative gene network analysis. IFI6, IFIT2, ISG15, HERC5, RSAD2, GBP1, IFIT3, IFITM1, LAMP3, USP18, and CXCL10 might act as key antiviral factors in alveolar basal epithelial cells against IAV infection, while BATF2, CXCL10, IFI44L, IL6, and OAS2 played important roles in airway epithelial cells in response to different subtypes of IAV infection. Additionally, we also revealed that some overlaps (BATF2, IFI44L, IFI44, HERC5, CXCL10, OAS2, IFIT3, USP18, OAS1, IFIT2) were commonly upregulated in human primary epithelial cells infected with high or low pathogenicity IAV. Moreover, there were similar defense responses activated by IAV infection, including the interferon-regulated signaling pathway in different phagocyte types, although the differentially expressed genes in different phagocyte types showed a great difference. Taken together, our findings will help better understand the fundamental patterns of molecular responses induced by highly or lowly pathogenic IAV, and the overlapped genes upregulated by IAV in different cell types may act as early detection markers or broad-spectrum antiviral targets.

A new take on organelle-mediated stress sensing in plants.

Plants are able to adjust phenotype in response to changes in the environment. This system depends on an internal capacity to sense environmental conditions and to process this information to plant response. Recent studies have pointed to mitochondria and plastids as important environmental sensors, capable of perceiving stressful conditions and triggering gene expression, epigenomic, metabolic and phytohormone changes in the plant. These processes involve integrated gene networks that ultimately modulate the energy balance between growth and plant defense. This review attempts to link several unusual recent findings into a comprehensive hypothesis for the regulation of plant phenotypic plasticity.

DNA methylation analysis improves the prognostication of acute myeloid leukemia.

Integration of orthogonal data could provide new opportunities to pinpoint the underlying molecular mechanisms of hematologic disorders. Using a novel gene network approach, we integrated DNA methylation data from The Cancer Genome Atlas (n = 194 cases) with the corresponding gene expression profile. Our integrated gene network analysis classified AML patients into low‐, intermediate‐, and high‐risk groups. The identified high‐risk group had significantly shorter overall survival compared to the low‐risk group (p‐value ≤10−11). Specifically, our approach identified a particular subgroup of nine high‐risk AML cases that died within 2 years after diagnosis. These high‐risk cases otherwise would be incorrectly classified as intermediate‐risk solely based on cytogenetics, mutation profiles, and common molecular characteristics of AML. We confirmed the prognostic value of our integrative gene network approach using two independent datasets, as well as through comparison with European LeukemiaNet and LSC17 criteria. Our approach could be useful in the prognostication of a subset of borderline AML cases. These cases would not be classified into appropriate risk groups by other approaches that use gene expression, but not DNA methylation data. Our findings highlight the significance of epigenomic data, and they indicate integrating DNA methylation data with gene coexpression networks can have a synergistic effect.

Association of neurogranin gene expression with Alzheimer's disease pathology in the perirhinal cortex.

IntroductionSynaptic damage is a key pathology of Alzheimer's disease (AD). The mechanism underlying synaptic vulnerability in AD remains elusive.MethodsUsing a large‐scale transcriptomic dataset, we analyzed the neurogranin‐centered integrative gene network and assessed the correlation of neurogranin (NRGN) gene expression with AD pathology in post mortem brains. We studied the association of NRGN expression with Clinical Dementia Rating (CDR) and neuropathological diagnosis of AD.ResultsWe find that the genes positively correlated with NRGN expression in AD are involved in synaptic transmission and cation channel pathways. NRGN expression is correlated with amyloid and tau pathology in the perirhinal cortex of post mortem brains. NRGN expression is associated with the diagnosis of AD and correlated with CDR.DiscussionTranscriptional regulation of the gene encoding for synaptic protein is involved in selective synaptic damage in AD. Identifying the genes associated with synaptic damage pathways in AD may provide targets for intervention.

Comprehensive Transcriptomic Analysis Identifies Cellular Immune Response Against Influenza A Virus Infection

Influenza A virus (IAV) has the higher genetic variation and reassortment, the traditional vaccine and virus-directed antiviral strategies have hardly shown successful efficacy and even lead to serious resistance, therefor developing the broad-spectrum antiviral treatments is particularly important. Host response to IAV infection provides a promising alternative approach to find host factors that are involved in the replication of viruses as molecular drug target. In this study, in order to better illustrate how IAV infection triggers cellular response and understand virus-host interaction, we systematically studied the expression profiles of host gene in a variety of human cells (including epithelial cells, primary epithelial cells) infected with different subtypes of IAV in this research by collecting and excavating 40 microarray data from Gene Expression Omnibus (GEO). The transcriptome analysis results showed that each of influenza strains led to the certain amounts of genes differentially regulated in all cell types, especially those genes belonging to immune defense and antiviral response, but we found that the overlapping regulated genes accounted for only a few in all cell types with different subtypes of IAV infection following the the criteria of P<0.05 and |logFC|≥1.5, because of the difference of IAV strains and infected cell types. Integrative gene network analysis found that IFI6, IFIT2, ISG15, HERC5, RSAD2, GBP1, IFIT3, IFITM1, LAMP3, USP18 and CXCL10 may act as key signatures in alveolar basal epithelial cells for IAV infection, while BATF2, CXCL10, IFI44L, IL6 and OAS2 played a key role in airway epithelial cell response to different subtypes of IAV infection. In addition, we also revealed that some overlaps (BATF2, IFI44L, IFI44, HERC5, OASL, CXCL10, MX2, OAS2, XAF1, IFIT3, USP18, IFIH1, OAS1, IFIT2 and IFIT1) were commonly upregulated in human primary epithelial cells infected by different influenza strains of high or low pathogenicity. Additionally, in the phagocytes, the difference gene expression pattern in different subtypes of IAV infection was found, but phagocytes could elicit the similar defence response against virus infection. Our findings will help us understand the fundamental patterns of molecular responses induced by highly or lowly pathogenic IAV originating from different species and the overlapped genes upregulated by IAV in different cell types may be as key biomarkers to improve effective early detection and treatment. Funding Statement: This work was supported by Key Lab of Process Analysis and Control of Sichuan Universities (No.2018001), The Project-sponsored by Sichuan Province for ROCS (0903/00021728) and NSFC (81902073). Declaration of Interests: No potential conflict of interest was reported by the authors.

Selective association of neurogranin gene expression with amyloid and tau pathology in the parahippocampal gyrus in Alzheimer’s disease

AbstractBackgroundBrain region‐specific neuronal and synaptic damage is associated with clinical symptomatology and pathogenesis of Alzheimer’s disease (AD). The mechanism underlying such regional vulnerability in AD remains elusive.MethodPublic access datasets from the Mount Sinai NIH NeuroBioBank (MSBB), The Religious order Study and the Memory and Aging Project(ROSMAP) and the Harvard Brain Tissue Resource Center (HBTRC) were used in this study. Using a large‐scale transcriptomic dataset from the prefrontal cortex (BA 10, 261 samples), the superior temporal gyrus (BA 22, 240 samples), the parahippocampal gyrus (BA 36, 215 samples) and the pars opercularis (BA 44, 222 samples) of MBSS post‐mortem brains of AD and normal control subjects, we studied the association of the post‐synaptic protein neurogranin (NRGN) gene expression with amyloid and tau pathology (plaque and tangle counts), clinical dementia scale (CDR), and pathological diagnosis of AD. Integrative gene network analysis was conducted by using a combination of the datasets from three brain banks.ResultWe show that NRGN gene expression in post‐mortem brain is associated with the certainty of pathological diagnosis of AD, and negatively associated with amyloid and tau pathology only in parahippocampal gyrus among the four brain regions examined. NRGN gene expression in parahippocampal gyrus is also negatively associated with CDR scores. Integrative gene network analysis reveals that the positively correlated genes with NRGN in AD are associated with synaptic transmission and cation channel pathways.ConclusionOur results suggest that synaptic damage in selective brain regions contributes to the clinical phenotype in AD. Understanding of synaptic functional pathways associated with synaptic damage may provide another avenue to prioritize targets for AD.

Identification of key regulators in parathyroid adenoma using an integrative gene network analysis.

Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyroid adenomas and healthy parathyroid gland tissues from Gene Expression Omnibus (GEO) database. We identified a total of 280 differentially expressed genes (196 up-regulated, 84 down-regulated), which are involved in a wide array of biological processes. We further constructed a gene interaction network and analyzed its topological properties to know the network structure and its hidden mechanism. This will help to understand the molecular mechanisms underlying parathyroid adenoma development. We thus identified 13 key regulators (PRPF19, SMC3, POSTN, SNIP1, EBF1, MEIS2, PAX9, SCUBE2, WNT4, ARHGAP10, DOCK5, CAV1 and VSIR), which are deep-rooted from top to bottom in the gene interaction network forming a backbone for the network. The structural features of the network are probably maintained by crosstalk between important genes within the network along with associated functional modules.Thus, gene-expression profiling and network approach could be used to provide an independent platform to glen insights from available clinical data.

Global gene network exploration based on explainable artificial intelligence approach.

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial–mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

Gender-Dependent Effects of Endocrine Disrupting Chemicals on Retinoic Acid-Related Orphan Receptor Alpha [RORA] and Aromatase Genes on Human Progenitor Neurons

Normally we encounter a myriad of chemicals in our daily lives. Endocrine disrupting chemicals [EDCs] are ubiquitous in our environment and upon bodily entry many be stored in adipose tissue and, in pregnant woman, can reach the developing fetal brain, disrupting normal fetal brain development. EDC-induced aberrant levels of sex hormones can alter sexual dimorphism [i.e. degree of feminization or masculinization; sex differences in brain and behavior] and may contribute to the differential susceptibility of males and females to autism. Fetal development is guarded by the placenta which expresses high levels of aromatase, an enzyme that converts testosterone [T] to estrogen [E], and it appears that this process prevents the transfer of maternal T to the vulnerable developing fetus. Many investigators have shown a clear association between exposure to high levels of T during the prenatal and early postnatal periods, and development of autism spectrum disorder [ASD]. Retinoic acid-related orphan receptor-alpha [RORA] is a transcription factor that regulates aromatase. We analyzed the effects of six commonly encountered EDCs on human progenitor neurons from both genders and evaluated the differential expression of both aromatase and RORA by real-time PCR using an in vitro approach. We also evaluated the effects of the EDCs on neurite formation. Male and female human neuroblastoma cell lines were used to evaluate the effects of EDCs on neurite formation and the expression of RORA and CYP19A1. The morphologic analyses showed significant neuromodifications in neurite formation and a significant differential downregulation of RORA in male, but not in female neurons. Dysregulation in CYP19A1 was not gender-associated but was dependent on the dose of EDCs. Our findings confirm previous reports of differential downregulation of RORA in male neurons while expanding the gene-environment connection where estrogenic or androgenic EDCs can profoundly dysregulate RORA and aromatase gene expression, leading to a cascade of a highly integrated gene-network, associated with ASD.

Transcriptomic analysis at organ and time scale reveals gene regulatory networks controlling the sulfate starvation response of Solanum lycopersicum

BackgroundSulfur is a major component of biological molecules and thus an essential element for plants. Deficiency of sulfate, the main source of sulfur in soils, negatively influences plant growth and crop yield. The effect of sulfate deficiency on plants has been well characterized at the physiological, transcriptomic and metabolomic levels in Arabidopsis thaliana and a limited number of crop plants. However, we still lack a thorough understanding of the molecular mechanisms and regulatory networks underlying sulfate deficiency in most plants. In this work we analyzed the impact of sulfate starvation on the transcriptome of tomato plants to identify regulatory networks and key transcriptional regulators at a temporal and organ scale.ResultsSulfate starvation reduces the growth of roots and leaves which is accompanied by major changes in the organ transcriptome, with the response being temporally earlier in roots than leaves. Comparative analysis showed that a major part of the Arabidopsis and tomato transcriptomic response to sulfate starvation is conserved between these plants and allowed for the identification of processes specifically regulated in tomato at the transcript level, including the control of internal phosphate levels. Integrative gene network analysis uncovered key transcription factors controlling the temporal expression of genes involved in sulfate assimilation, as well as cell cycle, cell division and photosynthesis during sulfate starvation in tomato roots and leaves. Interestingly, one of these transcription factors presents a high identity with SULFUR LIMITATION1, a central component of the sulfate starvation response in Arabidopsis.ConclusionsTogether, our results provide the first comprehensive catalog of sulfate-responsive genes in tomato, as well as novel regulatory targets for future functional analyses in tomato and other crops.

Depicting the genetic architecture of pediatric cancers through an integrative gene network approach

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.