Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in triple negative breast cancer

Breast cancers (BRCA) exhibit substantial transcriptional heterogeneity, posing a significant clinical challenge. The global transcriptional changes in a disease context, however, are likely mediated by few key genes which reflect disease etiology better than the differentially expressed genes (DEGs). We apply our network-based tool PathExt to 1,059 BRCA tumors across 4 subtypes to identify key mediator genes in each subtype. Compared to conventional differential expression analysis, PathExt-identified genes exhibit greater concordance across tumors, revealing shared and subtype-specific biological processes; better recapitulate BRCA-associated genes in multiple benchmarks, and are more essential in BRCA subtype-specific cell lines. Single-cell transcriptomic analysis reveals a subtype-specific distribution of PathExt-identified genes in multiple cell types from the tumor microenvironment. Application of PathExt to a TNBC chemotherapy response dataset identified subtype-specific key genes and biological processes associated with resistance. We described putative drugs that target key genes potentially mediating drug resistance.

A Principal Components Analysis and Functional Annotation of Differentially Expressed Genes in Brain Regions of Gray Rats Selected for Tame or Aggressive Behavior

The process of domestication, despite its short duration as it compared with the time scale of the natural evolutionary process, has caused rapid and substantial changes in the phenotype of domestic animal species. Nonetheless, the genetic mechanisms underlying these changes remain poorly understood. The present study deals with an analysis of the transcriptomes from four brain regions of gray rats (Rattus norvegicus), serving as an experimental model object of domestication. We compared gene expression profiles in the hypothalamus, hippocampus, periaqueductal gray matter, and the midbrain tegmental region between tame domesticated and aggressive gray rats and revealed subdivisions of differentially expressed genes by principal components analysis that explain the main part of differentially gene expression variance. Functional analysis (in the DAVID (Database for Annotation, Visualization and Integrated Discovery) Bioinformatics Resources database) of the differentially expressed genes allowed us to identify and describe the key biological processes that can participate in the formation of the different behavioral patterns seen in the two groups of gray rats. Using the STRING- DB (search tool for recurring instances of neighboring genes) web service, we built a gene association network. The genes engaged in broad network interactions have been identified. Our study offers data on the genes whose expression levels change in response to artificial selection for behavior during animal domestication.

Use of a combined antibacterial synergy approach and the ANNOgesic tool to identify novel targets within the gene networks of multidrug-resistant Klebsiella pneumoniae

Since the 1980s, the development of new drug classes for the treatment of multidrug-resistant Klebsiella pneumoniae has become limited, highlighting the urgent need for novel antibiotics. To address this challenge, this study aimed to explore the synergistic interactions between chemical compounds and representative antibiotics, such as carbapenem and colistin. The primary objective of this study was not only to mitigate the adverse impact of multidrug-resistant K. pneumoniae on public health but also to establish a sustainable balance among humans, animals, and the environment. Phenotypical measurements were conducted using the broth microdilution technique to determine the drug sensitivity of bacterial strains. Additionally, a genotypical approach was employed, involving traditional RNA sequencing analysis to identify differentially expressed genes and the computational ANNOgesic tool to detect noncoding RNAs. This study revealed the existence of various pathways and regulatory RNA elements that form a functional network. These pathways, characterized by the expression of specific genes, contribute to the combined treatment effect and bacterial survival strategies. The connections between pathways are facilitated by regulatory RNA elements that respond to environmental changes. These findings suggest an adaptive response of bacteria to harsh environmental conditions.IMPORTANCENoncoding RNAs were identified as key players in post-transcriptional regulation. Moreover, this study predicted the presence of novel small regulatory RNAs that interact with target genes, as well as the involvement of riboswitches and RNA thermometers in conjunction with associated genes. These findings will contribute to the discovery of potential antimicrobial therapeutic candidates. Overall, this study offers valuable insights into the synergistic effects of chemical compounds and antibiotics, highlighting the role of regulatory RNA elements in bacterial response, and survival strategies. The identification of novel noncoding RNAs and their interactions with target genes, riboswitches, and RNA thermometers holds promise for the development of antimicrobial therapies.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

A method to improve the prediction performance of cancer-gene association by screening negative training samples through gene network data

This work focuses on data sampling in cancer-gene association prediction. Currently, researchers are using machine learning methods to predict genes that are more likely to produce cancer-causing mutations. To improve the performance of machine learning models, methods have been proposed, one of which is to improve the quality of the training data. Existing methods focus mainly on positive data, i.e. cancer driver genes, for screening selection. This paper proposes a low-cancer-related gene screening method based on gene network and graph theory algorithms to improve the negative samples selection. Genetic data with low cancer correlation is used as negative training samples. After experimental verification, using the negative samples screened by this method to train the cancer gene classification model can improve prediction performance. The biggest advantage of this method is that it can be easily combined with other methods that focus on enhancing the quality of positive training samples. It has been demonstrated that significant improvement is achieved by combining this method with three state-of-the-arts cancer gene prediction methods.

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.

Tissue-specific RNA-seq defines genes governing male tail tip morphogenesis in C. elegans

Caenorhabditis elegans males undergo sex-specific tail tip morphogenesis (TTM) under the control of the transcription factor DMD-3. To find genes regulated by DMD-3, We performed RNA-seq of laser-dissected tail tips. We identified 564 genes differentially expressed (DE) in wild-type males vs. dmd-3(-) males and hermaphrodites. The transcription profile of dmd-3(-) tail tips is similar to that in hermaphrodites. For validation, we analyzed transcriptional reporters for 49 genes and found male-specific or male-biased expression for 26 genes. Only 11 DE genes overlapped with genes found in a previous RNAi screen for defective TTM. GO enrichment analysis of DE genes finds upregulation of genes within the UPR (unfolded protein response) pathway and downregulation of genes involved in cuticle maintenance. Of the DE genes, 40 are transcription factors, indicating that the gene network downstream of DMD-3 is complex and potentially modular. We propose modules of genes that act together in TTM and are coregulated by DMD-3, among them the chondroitin synthesis pathway and the hypertonic stress response.

A Study and Analysis of Disease Identification using Genomic Sequence Processing Models: An Empirical Review

Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incremental-performance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

The central regulatory circuit in the gene network controlling the morphogenesis of Drosophila mechanoreceptors: an in silico analysis

Identification of the mechanisms underlying the genetic control of spatial structure formation is among the relevant tasks of developmental biology. Both experimental and theoretical approaches and methods are used for this purpose, including gene network methodology, as well as mathematical and computer modeling. Reconstruction and analysis of the gene networks that provide the formation of traits allow us to integrate the existing experimental data and to identify the key links and intra-network connections that ensure the function of networks. Mathematical and computer modeling is used to obtain the dynamic characteristics of the studied systems and to predict their state and behavior. An example of the spatial morphological structure is the Drosophila bristle pattern with a strictly defined arrangement of its components - mechanoreceptors (external sensory organs) - on the head and body. The mechanoreceptor develops from a single sensory organ parental cell (SOPC), which is isolated from the ectoderm cells of the imaginal disk. It is distinguished from its surroundings by the highest content of proneural proteins (ASC), the products of the achaete-scute proneural gene complex (AS-C). The SOPC status is determined by the gene network we previously reconstructed and the AS-C is the key component of this network. AS-C activity is controlled by its subnetwork - the central regulatory circuit (CRC) comprising seven genes: AS-C, hairy, senseless (sens), charlatan (chn), scratch (scrt), phyllopod (phyl), and extramacrochaete (emc), as well as their respective proteins. In addition, the CRC includes the accessory proteins Daughterless (DA), Groucho (GRO), Ubiquitin (UB), and Seven-in-absentia (SINA). The paper describes the results of computer modeling of different CRC operation modes. As is shown, a cell is determined as an SOPC when the ASC content increases approximately 2.5-fold relative to the level in the surrounding cells. The hierarchy of the effects of mutations in the CRC genes on the dynamics of ASC protein accumulation is clarified. AS-C as the main CRC component is the most significant. The mutations that decrease the ASC content by more than 40 % lead to the prohibition of SOPC segregation.

A stochastic block Ising model for multi-layer networks with inter-layer dependence

Community detection has attracted tremendous interests in network analysis, which aims at finding group of nodes with similar characteristics. Various detection methods have been developed to detect homogeneous communities in multi-layer networks, where inter-layer dependence is a widely acknowledged but severely under-investigated issue. In this paper, we propose a novel stochastic block Ising model (SBIM) to incorporate the inter-layer dependence to help with community detection in multi-layer networks. The community structure is modeled by the stochastic block model (SBM) and the inter-layer dependence is incorporated via the popular Ising model. Furthermore, we develop an efficient variational EM algorithm to tackle the resultant optimization task and establish the asymptotic consistency of the proposed method. Extensive simulated examples and a real example on gene co-expression multi-layer network data are also provided to demonstrate the advantage of the proposed method.

CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model

Millions of RNA sequencing samples have been deposited into public databases, providing a rich resource for biological research. These datasets encompass tens of thousands of experiments and offer comprehensive insights into human cellular regulation. However, a major challenge is how to integrate these experiments that acquired at different conditions. We propose a new statistical tool based on beta-binomial distributions that can construct robust gene co-regulation network (CoRegNet) across tens of thousands of experiments. Our analysis of over 12 000 experiments involving human tissues and cells shows that CoRegNet significantly outperforms existing gene co-expression-based methods. Although the majority of the genes are linearly co-regulated, we did discover an interesting set of genes that are non-linearly co-regulated; half of the time they change in the same direction and the other half they change in the opposite direction. Additionally, we identified a set of gene pairs that follows the Simpson's paradox. By utilizing public domain data, CoRegNet offers a powerful approach for identifying functionally related gene pairs, thereby revealing new biological insights.

Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks

Stochastic gene expression dynamics can be modelled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiner's Poisson representation. Here, we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. This generalizes the results obtained in Dattani & Barahona (Dattani & Barahona 2017 J. R. Soc. Interface 14, 20160833 (doi:10.1098/rsif.2016.0833)). In addition, using a simple counter-example, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.

Genome-Wide Analysis of WUSCHEL-Related Homeobox Gene Family in Sacred Lotus (Nelumbo nucifera)

WUSCHEL-related homeobox (WOX) is a plant-specific transcription factor (TF), which plays an essential role in the regulation of plant growth, development, and abiotic stress responses. However, little information is available on the specific roles of WOX TFs in sacred lotus (Nelumbo nucifera), which is a perennial aquatic plant with important edible, ornamental, and medicinal values. We identified 15 WOX TFs distributing on six chromosomes in the genome of N. nucifera. A total of 72 WOX genes from five species were divided into three clades and nine subclades based on the phylogenetic tree. NnWOXs in the same subclades had similar gene structures and conserved motifs. Cis-acting element analysis of the promoter regions of NnWOXs found many elements enriched in hormone induction, stress responses, and light responses, indicating their roles in growth and development. The Ka/Ks analysis showed that the WOX gene family had been intensely purified and selected in N. nucifera. The expression pattern analysis suggested that NnWOXs were involved in organ development and differentiation of N. nucifera. Furthermore, the protein-protein interaction analysis showed that NnWOXs might participate in the growth, development, and metabolic regulation of N. nucifera. Taken together, these findings laid a foundation for further analysis of NnWOX functions.

The master regulator for entry into sporulation in Bacillus subtilis becomes a mother cell-specific transcription factor for forespore engulfment

The Spo0A transcription factor is activated by phosphorylation in starving Bacillus subtilis cells. The activated Spo0A (Spo0A~P) regulates genes controlling entry into sporulation and appears to control mother-cell-specific gene expression after asymmetric division, but the latter remains elusive. Here, we found that Spo0A~P directly binds to three conserved DNA sequences (0A1-3) in the promoter region of the mother cell-specific lytic transglycosylase gene spoIID, which is transcribed by σE -RNA polymerase (RNAP) and negatively controlled by the SpoIIID transcription factor and required for forespore engulfment. Systematic mutagenesis of the 0A boxes revealed that the 0A1 and 0A2 boxes located upstream of the promoter positively control the transcription of spoIID. In contrast, the 0A3 box located downstream of the promoter negatively controls the transcription of spoIID. The mutated SpoIIID binding site located between the -35 and -10 promoter elements causes increased expression of spoIID and reduced sporulation. When the mutations of 0A1, 0A2, and IIID sites are combined, sporulation is restored. Collectively, our data suggest that the mother cell-specific spoIID expression is precisely controlled by the coordination of three factors, Spo0A~P, SpoIIID, and σE -RNAP, for proper sporulation. The conservation of this mechanism across spore-forming species was discussed.

Pou3f1 orchestrates a gene regulatory network controlling contralateral retinogeniculate projections

The balance of contralateral and ipsilateral retinogeniculate projections is critical for binocular vision, but the transcriptional programs regulating this process remain ill defined. Here we show that the Pou class homeobox protein POU3F1 is expressed in nascent mouse contralateral retinal ganglion cells (cRGCs) but not ipsilateral RGCs (iRGCs). Upon Pou3f1 inactivation, the proportion of cRGCs is reduced in favor of iRGCs, leading to abnormal projection ratios at the optic chiasm. Conversely, misexpression of Pou3f1 in progenitors increases the production of cRGCs. Using CUT&RUN and RNA sequencing in gain- and loss-of-function assays, we demonstrate that POU3F1 regulates expression of several key members of the cRGC gene regulatory network. Finally, we report that POU3F1 is sufficient to induce RGC-like cell production, even in late-stage retinal progenitors of Atoh7 knockout mice. This work uncovers POU3F1 as a regulator of the cRGC transcriptional program, opening possibilities for optic nerve regenerative therapies.

Graph Random Forest: A Graph Embedded Algorithm for Identifying Highly Connected Important Features

Random Forest (RF) is a widely used machine learning method with good performance on classification and regression tasks. It works well under low sample size situations, which benefits applications in the field of biology. For example, gene expression data often involve much larger numbers of features (p) compared to the size of samples (n). Though the predictive accuracy using RF is often high, there are some problems when selecting important genes using RF. The important genes selected by RF are usually scattered on the gene network, which conflicts with the biological assumption of functional consistency between effective features. To improve feature selection by incorporating external topological information between genes, we propose the Graph Random Forest (GRF) for identifying highly connected important features by involving the known biological network when constructing the forest. The algorithm can identify effective features that form highly connected sub-graphs and achieve equivalent classification accuracy to RF. To evaluate the capability of our proposed method, we conducted simulation experiments and applied the method to two real datasets-non-small cell lung cancer RNA-seq data from The Cancer Genome Atlas, and human embryonic stem cell RNA-seq dataset (GSE93593). The resulting high classification accuracy, connectivity of selected sub-graphs, and interpretable feature selection results suggest the method is a helpful addition to graph-based classification models and feature selection procedures.

Identification of Key Modules and Hub Genes Involved in Regulating the Color of Chicken Breast Meat Using WGCNA

Meat color is one of the most important economic traits in chickens. However, the gene network and regulatory mechanisms contributing to meat color traits in chickens remain largely unknown. In the present study, we performed weighted gene co-expression network analysis (WGCNA) based on RNA-Seq datasets of 16 pectoralis major muscle samples from two yellow-feather chicken breeds to identify the modules and hub genes related to meat color in chickens. A total of 18,821 genes were used to construct the weighted gene co-expression network, and 29 co-expression gene modules were identified. Among these modules, five modules including blue, brown, steel blue, paleturquoise and orange modules were found to be significantly correlated with meat color traits. Furthermore, several genes within the association module involved in the regulation of mitochondrial activity (e.g., ATP5L, UQCR10 and COX7C) and lipid oxidation (e.g., CAV3, RBP4A and APOH) were identified as hub genes that may play a crucial role in the regulation of meat color. These results provide valuable information to improve our understanding of gene expression and regulation in relation to meat color traits and contribute to future molecular breeding for improving meat color in chickens.

Evaluation of the Antitumor Potential of Soloxolone Tryptamide against Glioblastoma Multiforme Using in silico, in vitro, and in vivo Approaches

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor characterized by uncontrollable diffusive growth, resistance to chemo- and radiotherapy, and a high recurrence rate leading to a low survival rate of patients with GBM. Due to a large number of signaling pathways regulating GBM pathogenesis, one of the promising directions is development of novel anti-glioblastoma compounds based on natural metabolites capable of affecting multiple targets. Here, we investigated the antitumor potential of the semisynthetic triterpenoid soloxolone tryptamide (STA) against human glioblastoma U87 cells. STA efficiently blocked the growth of U87 cells in 2D and 3D cultures, enhanced adhesiveness of tumor cells, and displayed synergistic cytotoxicity with temozolomide. In silico analysis suggested that the anti-glioblastoma activity of STA can be explained by its direct interaction with EGFR, ERBB2, and AKT1 which play an important role in the regulation of GBM malignancy. Along with direct effect on U87 cells, STA normalized tumor microenvironment in murine heterotopic U87 xenograft model by suppressing the development of immature blood vessels and elastin production in the tumor tissue. Taken together, our results clearly demonstrate that STA can be a novel promising antitumor candidate for GMB treatment.

Synthetic Adrenocorticotropic Peptides Modulate the Expression Pattern of Immune Genes in Rat Brain following the Early Post-Stroke Period

Ischemic stroke is an acute local decrease in cerebral blood flow due to a thrombus or embolus. Of particular importance is the study of the genetic systems that determine the mechanisms underlying the formation and maintenance of a therapeutic window (a time interval of up to 6 h after a stroke) when effective treatment can be provided. Here, we used a transient middle cerebral artery occlusion (tMCAO) model in rats to study two synthetic derivatives of adrenocorticotropic hormone (ACTH). The first was ACTH(4-7)PGP, which is known as Semax. It is actively used as a neuroprotective drug. The second was the ACTH(6-9)PGP peptide, which is elucidated as a prospective agent only. Using RNA-Seq analysis, we revealed hundreds of ischemia-related differentially expressed genes (DEGs), as well as 131 and 322 DEGs related to the first and second peptide at 4.5 h after tMCAO, respectively, in dorsolateral areas of the frontal cortex of rats. Furthermore, we showed that both Semax and ACTH(6-9)PGP can partially prevent changes in the immune- and neurosignaling-related gene expression profiles disturbed by the action of ischemia at 4.5 h after tMCAO. However, their different actions with regard to predominantly immune-related genes were also revealed. This study gives insight into how the transcriptome depends on the variation in the structure of the related peptides, and it is valuable from the standpoint of the development of measures for early post-stroke therapy.

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

Apoptosis Genes as a Key to Identification of Inverse Comorbidity of Huntington's Disease and Cancer

Cancer and neurodegenerative disorders present overwhelming challenges for healthcare worldwide. Epidemiological studies showed a decrease in cancer rates in patients with neurodegenerative disorders, including the Huntington disease (HD). Apoptosis is one of the most important processes for both cancer and neurodegeneration. We suggest that genes closely connected with apoptosis and associated with HD may affect carcinogenesis. We applied reconstruction and analysis of gene networks associated with HD and apoptosis and identified potentially important genes for inverse comorbidity of cancer and HD. The top 10 high-priority candidate genes included APOE, PSEN1, INS, IL6, SQSTM1, SP1, HTT, LEP, HSPA4, and BDNF. Functional analysis of these genes was carried out using gene ontology and KEGG pathways. By exploring genome-wide association study results, we identified genes associated with neurodegenerative and oncological disorders, as well as their endophenotypes and risk factors. We used publicly available datasets of HD and breast and prostate cancers to analyze the expression of the identified genes. Functional modules of these genes were characterized according to disease-specific tissues. This integrative approach revealed that these genes predominantly exert similar functions in different tissues. Apoptosis along with lipid metabolism dysregulation and cell homeostasis maintenance in the response to environmental stimulus and drugs are likely key processes in inverse comorbidity of cancer in patients with HD. Overall, the identified genes represent the promising targets for studying molecular relations of cancer and HD.

Transcriptome and Metabolomics Integrated Analysis Reveals MdMYB94 Associated with Esters Biosynthesis in Apple (Malus × domestica)

Volatile esters are major aromas contributing to the organoleptic quality of apple fruit. However, the molecular mechanisms underlying the regulation of volatile ester biosynthesis in apple remain elusive. This study investigated the volatile profiles and transcriptomes of 'Qinguan' (QG) apple fruit during development and/or postharvest storage. Although the constitution of volatiles varied widely between the peel and flesh, the volatile profiles of the peel and flesh of ripening QG fruit were dominated by volatile esters. WGCNA results suggested that 19 genes belonging to ester biosynthesis pathways and 11 hub transcription factor genes potentially participated in the biosynthesis and regulation of esters. To figure out key regulators of ester biosynthesis, correlation network analysis, dual-luciferase assays, and yeast one-hybrid assay were conducted and suggested that MdMYB94 trans-activated the MdAAT2 promoter and participated in the regulation of ester biosynthesis. This study provides a framework for understanding ester biosynthesis and regulation in apple.

Novel genomic regions on chromosome 5B controlling wheat powdery mildew seedling resistance under Egyptian conditions

Wheat powdery mildew (PM) causes significant yield losses worldwide. None of the Egyptian wheat cultivars was detected to be highly resistant to such a severe disease. Therefore, a diverse spring wheat panel was evaluated for PM seedling resistance using different Bgt conidiospores collected from Egyptian fields in two growing seasons. The evaluation was done in two separate experiments. Highly significant differences were found between the two experiments suggesting the presence of different isolates populations. Highly significant differences were found among the tested genotypes confirming the ability to improve PM resistance using the recent panel. Genome-wide association study (GWAS) was done for each experiment separately and a total of 71 significant markers located within 36 gene models were identified. The majority of these markers are located on chromosome 5B. Haplotype block analysis identified seven blocks containing the significant markers on chromosome 5B. Five gene models were identified on the short arm of the chromosome. Gene enrichment analysis identified five and seven pathways based on the biological process and molecular functions respectively for the detected gene models. All these pathways are associated with disease resistance in wheat. The genomic regions on 5B seem to be novel regions that are associated with PM resistance under Egyptian conditions. Selection of superior genotypes was done and Grecian genotypes seem to be a good source for improving PM resistance under Egyptian conditions.

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph-a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.

ZBTB7A as a novel vulnerability in neuroendocrine prostate cancer

Neuroendocrine prostate cancer (NEPC) is a highly aggressive subtype of prostate cancer. NEPC is characterized by the loss of androgen receptor (AR) signaling and transdifferentiation toward small-cell neuroendocrine (SCN) phenotypes, which results in resistance to AR-targeted therapy. NEPC resembles other SCN carcinomas clinically, histologically and in gene expression. Here, we leveraged SCN phenotype scores of various cancer cell lines and gene depletion screens from the Cancer Dependency Map (DepMap) to identify vulnerabilities in NEPC. We discovered ZBTB7A, a transcription factor, as a candidate promoting the progression of NEPC. Cancer cells with high SCN phenotype scores showed a strong dependency on RET kinase activity with a high correlation between RET and ZBTB7A dependencies in these cells. Utilizing informatic modeling of whole transcriptome sequencing data from patient samples, we identified distinct gene networking patterns of ZBTB7A in NEPC versus prostate adenocarcinoma. Specifically, we observed a robust association of ZBTB7A with genes promoting cell cycle progression, including apoptosis regulating genes. Silencing ZBTB7A in a NEPC cell line confirmed the dependency on ZBTB7A for cell growth via suppression of the G1/S transition in the cell cycle and induction of apoptosis. Collectively, our results highlight the oncogenic function of ZBTB7A in NEPC and emphasize the value of ZBTB7A as a promising therapeutic strategy for targeting NEPC tumors.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

A molecular study of pediatric pilomyxoid and pilocytic astrocytomas: Genome-wide copy number screening, retrospective analysis of clinicopathological features and long-term clinical outcome

Background

Pilocytic Astrocytoma (PA) is the most common pediatric brain tumors. PAs are slow-growing tumors with high survival rates. However, a distinct subgroup of tumors defined as pilomyxoid astrocytoma (PMA) presents unique histological characteristics and have more aggressive clinical course. The studies on genetics of PMA are scarce.

Methods

In this study, we report one of the largest cohort of pediatric patients with pilomyxoid (PMA) and pilocytic astrocytomas (PA) in Saudi population providing a comprehensive clinical picture, retrospective analysis with long-term follow-up, genome-wide copy number changes, and clinical outcome of these pediatric tumors. We examined and compared genome-wide copy number aberrations (CNAs) and the clinical outcome of the patients with PA and PMA.

Results

The median progression free survival for the whole cohort was 156 months and it was 111 months for the PMA, however, not statistically significantly different between the groups (log-rank test, P = 0.726). We have identified 41 CNAs (34 gains and 7 losses) in all tested patients. Our study yielded the previously reported KIAA1549-BRAF Fusion gene in over 88% of the tested patients (89% and 80% in PMA and PA, respectively). Besides the fusion gene, twelve patients had additional genomic CNAs. Furthermore, pathway and gene network analyses of genes in the fusion region revealed alterations in retinoic acid mediated apoptosis and MAPK signaling pathways and key hub genes that may potentially be involved in tumor growth and progression, including BRAF, LUC7L2, MKRN1, RICTOR, TP53, HIPK2, HNF4A, POU5F, and SOX4.

Conclusion

Our study is the first report of a large cohort of patients with PMA and PA in the Saudi population that provides detailed clinical features, genomic copy number changes, and outcome of these pediatric tumors and may help better diagnosis and characterization of PMA.

In-Depth Understanding of the Genetic Control of Stripe Rust Resistance (Puccinia striiformis f. sp. tritici) Induced in Wheat (Triticum aestivum) by Trichoderma asperellum T34

Wheat stripe rust (caused by Puccinia striiformis f. tritici Erikss.) causes severe yield losses worldwide. Due to the continuous appearance of new stripe rust races, resistance has been broken in most of the highly resistant genotypes in Egypt and worldwide. Therefore, looking for new ways to resist such a severe disease is urgently needed. Trichoderma asperellum strain T34 has been known as an effective bioagent against many crop diseases. It exists naturally in Egyptian fields. Therefore, in our study, the effectiveness of strain T34 was tested as a bioagent against wheat stripe rust. For this purpose, 198 spring wheat genotypes were tested for their resistance against two different P. striiformis f. tritici populations collected from the Egyptian fields. The most highly aggressive P. striiformis f. tritici population was used to test the effectiveness of strain T34. Highly significant differences were found between strain T34 and stripe rust, suggesting the effectiveness of strain T34 in stripe rust resistance. A genome-wide association study identified 48 gene models controlling resistance under normal conditions and 46 gene models controlling strain T34-induced resistance. Of these gene models, only one common gene model was found, suggesting the presence of two different genetic systems controlling resistance under each condition. The pathways of the biological processes were investigated under both conditions. This study provided in-depth understanding of genetic control and, hence, will accelerate the future of wheat breeding programs for stripe rust resistance.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data

The sparse nature of single-cell omics data makes it challenging to dissect the wiring and rewiring of the transcriptional and signaling drivers that regulate cellular states. Many of the drivers, referred to as "hidden drivers", are difficult to identify via conventional expression analysis due to low expression and inconsistency between RNA and protein activity caused by post-translational and other modifications. To address this issue, we developed scMINER, a mutual information (MI)-based computational framework for unsupervised clustering analysis and cell-type specific inference of intracellular networks, hidden drivers and network rewiring from single-cell RNA-seq data. We designed scMINER to capture nonlinear cell-cell and gene-gene relationships and infer driver activities. Systematic benchmarking showed that scMINER outperforms popular single-cell clustering algorithms, especially in distinguishing similar cell types. With respect to network inference, scMINER does not rely on the binding motifs which are available for a limited set of transcription factors, therefore scMINER can provide quantitative activity assessment for more than 6,000 transcription and signaling drivers from a scRNA-seq experiment. As demonstrations, we used scMINER to expose hidden transcription and signaling drivers and dissect their regulon rewiring in immune cell heterogeneity, lineage differentiation, and tissue specification. Overall, activity-based scMINER is a widely applicable, highly accurate, reproducible and scalable method for inferring cellular transcriptional and signaling networks in each cell state from scRNA-seq data. The scMINER software is publicly accessible via: https://github.com/jyyulab/scMINER.

Identification of New Candidate Genes Related to Semen Traits in Duroc Pigs through Weighted Single-Step GWAS

Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility-sperm progressive motility, sperm abnormality rate, and total sperm count-explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars.

Targeted next-generation sequencing for genetic variants of left ventricular mass status among community-based adults in Taiwan

Background: Left ventricular mass is a highly heritable disease. Previous studies have suggested common genetic variants to be associated with left ventricular mass; however, the roles of rare variants are still unknown. We performed targeted next-generation sequencing using the TruSight Cardio panel, which provides comprehensive coverage of 175 genes with known associations to 17 inherited cardiac conditions. Methods: We conducted next-generation sequencing using the Illumina TruSight Cardiomyopathy Target Genes platform using the 5% and 95% extreme values of left ventricular mass from community-based participants. After removing poor-quality next-generation sequencing subjects, including call rate <98% and Mendelian errors, 144 participants were used for the analysis. We performed downstream analysis, including quality control, alignment, coverage length, and annotation; after setting filtering criteria for depths more than 60, we found a total of 144 samples and 165 target genes for further analysis. Results: Of the 12,287 autosomal variants, most had minor allele frequencies of <1% (rare frequency), and variants had minor allele frequencies ranging from 1% to 5%. In the multi-allele variant analyses, 16 loci in 15 genes were significant using the false discovery rate of less than .1. In addition, gene-based analyses using continuous and binary outcomes showed that three genes (CASQ2, COL5A1, and FXN) remained to be associated with left ventricular mass status. One single-nucleotide polymorphism (rs7538337) was enriched for the CASQ2 gene expressed in aorta artery (p = 4.6 × 10-18), as was another single-nucleotide polymorphism (rs11103536) for the COL5A1 gene expressed in aorta artery (p = 2.0 × 10-9). Among the novel genes discovered, CASQ2, COL5A1, and FXN are within a protein-protein interaction network with known cardiovascular genes. Conclusion: We clearly demonstrated candidate genes to be associated with left ventricular mass. Further studies to characterize the target genes and variants for their functional mechanisms are warranted.

Mechanism of CAV and CAVIN Family Genes in Acute Lung Injury based on DeepGENE

BACKGROUND

The fatality rate of acute lung injury (ALI) is as high as 40% to 60%. Although various factors, such as sepsis, trauma, pneumonia, burns, blood transfusion, cardiopulmonary bypass, and pancreatitis, can induce ALI, patients with these risk factors will eventually develop ALI. The rate of developing ALI is not high, and the outcomes of ALI patients vary, indicating that it is related to genetic differences between individuals. In a previous study, we found multiple functions of cavin-2 in lung function. In addition, many other studies have revealed that CAV1 is a critical regulator of lung injury. Due to the strong relationship between cavin-2 and CAV1, we suspect that cavin-2 is also associated with ALI. Furthermore, we are curious about the role of the CAV family and cavin family genes in ALI.

METHODS

To reveal the mechanism of CAV and CAVIN family genes in ALI, we propose DeepGENE to predict whether CAV and CAVIN family genes are associated with ALI. This method constructs a gene interaction network and extracts gene expression in 84 tissues. We divided these features into two groups and used two network encoders to encode and learn the features.

RESULTS

Compared with DNN, GBDT, RF and KNN, the AUC of DeepGENE increased by 7.89%, 16.84%, 20.19% and 32.01%, respectively. The AUPR scores increased by 8.05%, 15.58%, 22.56% and 23.34%. DeepGENE shows that CAVIN-1, CAVIN-2, CAVIN-3 and CAV2 are related to ALI.

CONCLUSION

DeepGENE is a reliable method for identifying acute lung injury-related genes. Multiple CAV and CAVIN family genes are associated with acute lung injury-related genes through multiple pathways and gene functions.

Comprehensive genomic identification of cotton starch synthase genes reveals that GhSS9 regulates drought tolerance

Introduction

Starch metabolism is involved in the stress response. Starch synthase (SS) is the key enzyme in plant starch synthesis, which plays an indispensable role in the conversion of pyrophosphoric acid to starch. However, the SS gene family in cotton has not been comprehensively identified and systematically analyzed.

Result

In our study, a total of 76 SS genes were identified from four cotton genomes and divided into five subfamilies through phylogenetic analysis. Genetic structure analysis proved that SS genes from the same subfamily had similar genetic structure and conserved sequences. A cis-element analysis of the SS gene promoter showed that it mainly contains light response elements, plant hormone response elements, and abiotic stress elements, which indicated that the SS gene played key roles not only in starch synthesis but also in abiotic stress response. Furthermore, we also conducted a gene interaction network for SS proteins. Silencing GhSS9 expression decreased the resistance of cotton to drought stress. These findings suggested that SS genes could be related to drought stress in cotton, which provided theoretical support for further research on the regulation mechanism of SS genes on abiotic starch synthesis and sugar levels.

Gene Expression Network and Circ_0008012 Promote Progression in MLL/AF4 Positive Acute Lymphoblastic Leukemia

BACKGROUND

Acute lymphoblastic leukemia with MLL/AF4 rearrangement remains a major hurdle to improving outcomes. Gene network and circRNAs have been found to participate in tumorigenesis, while their roles in leukemia still need to be explored. Recent patents have shown that circRNAs exhibit the markers for the children ALL, although the target and related mechanism remain to be elucidated.

OBJECTIVE

This study aims to explore the possible targets and mechanisms of ALL with MLLAF4 rearrangement.

METHODS

We first generated a gene network focusing on MLL-AF4 rearrangement. Cell viability was determined with Cell Counting Kit-8 assay. The cell apoptosis was tested by the Annexin V/PI assay. The RNA-protein complexes were analyzed by qRT-PCR, and the pathway proteins were analyzed by western blot.

RESULTS

This gene network was associated with biological processes, such as nucleic acid metabolism and immunity, indicating its key role in inflammation. We found that circ_0008012 was upregulated in MLL/AF4 ALL cells and regulated cell proliferation and apoptosis. Further computed simulation and RIP showed that IKKβ was the strongest protein in the NF-κB pathway binding with circ_0008012. As a result, possible regulation of circ_0008012 is suggested by binding IKKβ in the IKKα:IKKβ:IKKγ compound, which then phosphorylates IκB and activates NF- κB:p65:p300 compound in cell nucleus, thereby leading to leukemia.

CONCLUSION

We identified a gene network for MLL/AF4 ALL. Moreover, circ_0008012 may be a therapeutic target for this subtype of ALL.

Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions

The genomic, environmental, and evolutionary interruptions caused the changes in bacterial growth, which were stringently associated with changes in gene expression. The growth and gene expression changes remained unclear in response to these interruptions that occurred combinative. As a pilot study, whether and how bacterial growth was affected by the individual and dual interruptions of genome reduction, environmental stress, and adaptive evolution were investigated. Growth assay showed that the presence of the environmental stressors, i.e., threonine and chloramphenicol, significantly decreased the growth rate of the wild-type Escherichia coli, whereas not that of the reduced genome. It indicated a canceling effect in bacterial growth due to the dual interruption of the genomic and environmental changes. Experimental evolution of the reduced genome released the canceling effect by improving growth fitness. Intriguingly, the transcriptome architecture maintained a homeostatic chromosomal periodicity regardless of the genomic, environmental, and evolutionary interruptions. Negative epistasis in transcriptome reorganization was commonly observed in response to the dual interruptions, which might contribute to the canceling effect. It was supported by the changes in the numbers of differentially expressed genes (DEGs) and the enriched regulons and functions. Gene network analysis newly constructed 11 gene modules, one out of which was correlated to the growth rate. Enrichment of DEGs in these modules successfully categorized them into three types, i.e., conserved, responsive, and epistatic. Taken together, homeostasis in transcriptome architecture was essential to being alive, and it might be attributed to the negative epistasis in transcriptome reorganization and the functional differentiation in gene modules. The present study directly connected bacterial growth fitness with transcriptome reorganization and provided a global view of how microorganisms responded to genomic, environmental, and evolutionary interruptions for survival from wild nature.

Bioinformatic Analysis Revealed the Essential Regulatory Genes and Pathways of Early and Advanced Atherosclerotic Plaque in Humans

Atherosclerosis (AS) is a lipid-induced, chronic inflammatory, autoimmune disease affecting multiple arteries. Although much effort has been put into AS research in the past decades, it is still the leading cause of death worldwide. The complex genetic network regulation underlying the pathogenesis of AS still needs further investigation to provide effective targeted therapy for AS. We performed a bioinformatic microarray data analysis at different atherosclerotic plaque stages from the Gene Expression Omnibus database with accession numbers GSE43292 and GSE28829. Using gene set enrichment analysis, we further confirmed the immune-related pathways that play an important role in the development of AS. We are reporting, for the first time, that the metabolism of the three branched-chain amino acids (BCAAs; leucine, isoleucine, and valine) and short-chain fatty acids (SCFA; propanoate, and butanoate) are involved in the progression of AS using microarray data of atherosclerotic plaque tissue. Immune and muscle system-related pathways were further confirmed as highly regulated pathways during the development of AS using gene expression pattern analysis. Furthermore, we also identified four modules mainly involved in histone modification, immune-related processes, macroautophagy, and B cell activation with modular differential connectivity in the dataset of GSE43292, and three modules related to immune-related processes, B cell activation, and nuclear division in the dataset of GSE28829 also display modular differential connectivity based on the weighted gene co-expression network analysis. Finally, we identified eight key genes related to the pathways of immune and muscle system function as potential therapeutic biomarkers to distinguish patients with early or advanced stages in AS, and two of the eight genes were validated using the gene expression dataset from gene-deficient mice. The results of the current study will improve our understanding of the molecular mechanisms in the progression of AS. The key genes and pathways identified could be potential biomarkers or new drug targets for AS management.

Multi-omics integration reveals a core network involved in host defence and hyperkeratinization in psoriasis

OBJECTIVES

The precise pathogenesis of psoriasis remains incompletely explored. We aimed to better understand the underlying mechanisms of psoriasis, using a systems biology approach based on transcriptomics and microbiome profiling.

METHODS

We collected the skin tissue biopsies and swabs in both lesional and non-lesional skin of 13 patients with psoriasis, 15 patients with psoriatic arthritis and healthy skin from 12 patients with ankylosing spondylitis. To study the similarities and differences in the molecular profiles between these three conditions, and the associations between the host defence and microbiota composition, we performed high-throughput RNA-sequencing to quantify the gene expression profile in tissues. The metagenomic composition of 16S on local skin sites was quantified by clustering amplicon sequences and counted into operational taxonomic units. We further analysed associations between the transcriptome and microbiome profiling.

RESULTS

We found that lesional and non-lesional samples were remarkably different in terms of their transcriptome profiles. The functional annotation of differentially expressed genes showed a major enrichment in neutrophil activation. By using co-expression gene networks, we identified a gene module that was associated with local psoriasis severity at the site of biopsy. From this module, we found a 'core' set of genes that was functionally involved in neutrophil activation, epidermal cell differentiation and response to bacteria. Skin microbiome analysis revealed that the abundances of Enhydrobacter, Micrococcus and Leptotrichia were significantly correlated with the genes in core network.

CONCLUSIONS

We identified a core gene network that associated with local disease severity and microbiome composition, involved in the inflammation and hyperkeratinization in psoriatic skin.

A Genome-Wide Association Study Reveals a BDNF-Centered Molecular Network Associated with Alcohol Dependence and Related Clinical Measures

At least 50% of factors predisposing to alcohol dependence (AD) are genetic and women affected with this disorder present with more psychiatric comorbidities, probably indicating different genetic factors involved. We aimed to run a genome-wide association study (GWAS) followed by a bioinformatic functional annotation of associated genomic regions in patients with AD and eight related clinical measures. A genome-wide significant association of rs220677 with AD (p-value = 1.33 × 10^-8 calculated with the Yates-corrected χ² test under the assumption of dominant inheritance) was discovered in female patients. Associations of AD and related clinical measures with seven other single nucleotide polymorphisms listed in previous GWASs of psychiatric and addiction traits were differently replicated in male and female patients. The bioinformatic analysis showed that regulatory elements in the eight associated linkage disequilibrium blocks define the expression of 80 protein-coding genes. Nearly 68% of these and of 120 previously published coding genes associated with alcohol phenotypes directly interact in a single network, where BDNF is the most significant hub gene. This study indicates that several genes behind the pathogenesis of AD are different in male and female patients, but implicated molecular mechanisms are functionally connected. The study also reveals a central role of BDNF in the pathogenesis of AD.

In silico strategies to identify protein-protein interaction modulator in cell-to-cell transmission of SARS CoV2

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high-throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only 1 with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e - 05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell-cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, a hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins, such as integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2.

Integrative metabolomic and transcriptomic analyses reveal the mechanisms of Tibetan hulless barley grain coloration

Cereal grains accumulate anthocyanin during developmental process. The anthocyanin content increases at grain filling stages to develop grain coloration in cereals. However, anthocyanin biosynthesis responsible for grain coloring and its regulatory mechanisms controlled by structural and functional genes remain unclear. Therefore, this study aimed to explore the global map of metabolic changes linked to grain coloration of Tibetan hulless barley (qingke) using an integrative metabolome and transcriptome approach. Grains from three colored qingke cultivars at different developmental stages were considered for molecular and metabolic investigations. A total of 120 differentially accumulated metabolites (DAMs) and 8,327 differentially expressed genes (DEGs) were filtered. DEGs were mainly enriched in the phenylpropanoid and flavonoid pathways. The transcript levels of anthocyanin biosynthesis genes (PAL, C4H, 4CL, CHS, FLS, F3H, F3'H, DFR, ANS, GT, OMT, and MAT) significantly upregulate in colored qingke compared to the non-colored variety. During grain development and maturation, the strong correlation of HvMYC2 expression with anthocyanin contents and anthocyanin biosynthesis genes suggested it as a critical gene in anthocyanin accumulation. Further results confirmed that HvMYC2 could be activated by HvMYB and be a positive regulator of UV-B and cold tolerance in qingke. In addition, verification based on enzymatic assays indicated that six key modifier enzymes could catalyze glycosylation, malonylation, and methylation of anthocyanins, thereby dissecting the major anthocyanin modification pathway in colored qingke. Overall, our study provides global insight into anthocyanin accumulation and the mechanism underlying grain coloration in qingke.

Transcriptome Analysis of Developing Grains from Wheat Cultivars TAM 111 and TAM 112 Reveal Cultivar-Specific Regulatory Networks

Wheat flour's end-use quality is tightly linked to the quantity and composition of storage proteins in the endosperm. TAM 111 and TAM 112 are two popular cultivars grown in the Southern US Great Plains with significantly different protein content. To investigate regulatory differences, transcriptome data were analyzed from developing grains at early- and mid-filling stages. At the mid-filling stage, TAM 111 preferentially upregulated starch metabolism-related pathways compared to TAM 112, whereas amino acid metabolism and transporter-related pathways were over-represented in TAM 112. Elemental analyses also indicated a higher N percentage in TAM 112 at the mid-filling stage. To explore the regulatory variation, weighted correlation gene network was constructed from publicly available RNAseq datasets to identify the modules differentially regulated in TAM 111 and TAM 112. Further, the potential transcription factors (TFs) regulating those modules were identified using graphical least absolute shrinkage and selection operator (GLASSO). Homologs of the OsNF-Y family members with known starch metabolism-related functions showed higher connectivities in TAM 111. Multiple TFs with high connectivity in TAM 112 had predicted functions associated with ABA response in grain. These results will provide novel targets for breeders to explore and further our understanding in mechanisms regulating grain development.

Transcriptome alterations in chicken HD11 cells with steady knockdown and overexpression of RIPK2 gene

Receptor interacting protein kinase 2 (RIPK2) is involved in a variety of signaling pathway to produce a series of inflammatory cytokines in response to a diverse of bacterial, viral and protozoal pathogens. However, the underlying regulating of RIPK2 remain unknown. Transcriptome alterations in chicken HD11 cells following RIPK2 overexpression or silencing by shRNA were analyzed by next-generation sequencing. Both overexpression and knockdown of the RIPK2 gene caused wide-spread changes in gene expression in chicken HD11 cells. Differentially expressed genes (DEGs) caused by altered RIPK2 gene expression were associated with multiple biological processes linked with biological regulation, response to stimulus, cell communication, and signal transduction etc. KEGG analysis revealed that many of the DEGs were enriched in VEGF signaling pathway, ECM-receptor interaction, Focal adhesion, TGF-beta signaling pathway etc. Moreover, we show that initiation genes, TGFB1 and TGFB3, in the TGF-beta signaling pathway are biological targets regulated by RIPK2 in chicken HD11 cells. This is the first transcriptome-wide study in which RIPK2-regulated genes in chicken cells have been screened. Our findings elucidate the molecular events associated with RIPK2 in chicken HD11 cells.

Epigenetic factor competition reshapes the EMT landscape

The emergence of and transitions between distinct phenotypes in isogenic cells can be attributed to the intricate interplay of epigenetic marks, external signals, and gene-regulatory elements. These elements include chromatin remodelers, histone modifiers, transcription factors, and regulatory RNAs. Mathematical models known as gene-regulatory networks (GRNs) are an increasingly important tool to unravel the workings of such complex networks. In such models, epigenetic factors are usually proposed to act on the chromatin regions directly involved in the expression of relevant genes. However, it has been well-established that these factors operate globally and compete with each other for targets genome-wide. Therefore, a perturbation of the activity of a regulator can redistribute epigenetic marks across the genome and modulate the levels of competing regulators. In this paper, we propose a conceptual and mathematical modeling framework that incorporates both local and global competition effects between antagonistic epigenetic regulators, in addition to local transcription factors, and show the counterintuitive consequences of such interactions. We apply our approach to recent experimental findings on the epithelial-mesenchymal transition (EMT). We show that it can explain the puzzling experimental data, as well as provide verifiable predictions.

Comparative transcriptomic profiling in the pulp and peel of pitaya fruit uncovers the gene networks regulating pulp color formation

Pitaya (genus Hylocereus) is a popular fruit. To develop pitaya fruit with greater marketability and high nutritional value, it is important to elucidate the roles of candidate genes and key metabolites that contribute to the coloration of the pitaya pulp and peel. By combining transcriptome and biochemical analyses, we compared and analyzed the dynamic changes in the peel and pulp of H. undatus (white pulp) and H. polyrhizus (red pulp) fruits at four key time points during ripening. Differential expression analysis and temporal analysis revealed the difference regulation in pathways of plant hormone signal transduction, phenylpropanoid biosynthesis, and betalain biosynthesis. Our results suggest that color formation of purple-red peel and pulp of pitaya is influenced by betalains. Increased tyrosine content and fluctuation in acylated betalain content may be responsible for pulp color formation, while some of the key genes in this network showed differential expression patterns during ripening between white pulp and red pulp fruits. The data and analysis results of this study provide theoretical basis for the red color formation mechanism of pitaya, which will facilitate future work to improve pitaya fruit physical appearance and marketability.

The Community Coevolution Model with Application to the Study of Evolutionary Relationships between Genes based on Phylogenetic Profiles

Organismal traits can evolve in a coordinated way, with correlated patterns of gains and losses reflecting important evolutionary associations. Discovering these associations can reveal important information about the functional and ecological linkages among traits. Phylogenetic profiles treat individual genes as traits distributed across sets of genomes and can provide a fine-grained view of the genetic underpinnings of evolutionary processes in a set of genomes. Phylogenetic profiling has been used to identify genes that are functionally linked, and to identify common patterns of lateral gene transfer in microorganisms. However, comparative analysis of phylogenetic profiles and other trait distributions should take into account the phylogenetic relationships among the organisms under consideration. Here we propose the Community Coevolution Model (CCM), a new coevolutionary model to analyze the evolutionary associations among traits, with a focus on phylogenetic profiles. In the CCM, traits are considered to evolve as a community with interactions, and the transition rate for each trait depends on the current states of other traits. Surpassing other comparative methods for pairwise trait analysis, CCM has the additional advantage of being able to examine multiple traits as a community to reveal more dependency relationships. We also develop a simulation procedure to generate phylogenetic profiles with correlated evolutionary patterns that can be used as benchmark data for evaluation purposes. A simulation study demonstrates that CCM is more accurate than other methods including the Jaccard Index and three tree-aware methods. The parameterization of CCM makes the interpretation of the relations between genes more direct, which leads to Darwin's scenario being identified easily based on the estimated parameters. We show that CCM is more efficient and fits real data better than other methods resulting in higher likelihood scores with fewer parameters. An examination of 3786 phylogenetic profiles across a set of 659 bacterial genomes highlights linkages between genes with common functions, including many patterns that would not have been identified under a non-phylogenetic model of common distribution. We also applied the CCM to 44 proteins in the well-studied Mitochondrial Respiratory Complex I and recovered associations that mapped well onto the structural associations that exist in the complex.

Identification of the Key Pathways and Genes Involved in the Wax Biosynthesis of the Chinese White Wax Scale Insect (Ericerus pela Chavannes) by Integrated Weighted Gene Coexpression Network Analysis

The white wax secreted by the male insects of the Chinese white wax scale (CWWS) is a natural high-molecular-weight compound with important economic value. However, its regulatory mechanism of wax biosynthesis is still unclear. In this study, a weighted gene coexpression network analysis (WGCNA) was used to analyze transcriptome data of first- and second-instar females, early and late female adults, and first- and second-instar males. A total of 19 partitioned modules with different topological overlaps were obtained, and three modules were identified as highly significant for wax secretion (p < 0.05). A total of 30 hub genes were obtained through screening, among which elongation of very-long-chain fatty acids protein (ELOVL) and fatty acyl-CoA reductase (FAR) are important catalytic enzymes of fatty acid metabolism. Furthermore, their metabolic catalytic products are involved in the synthesis of wax biosynthesis. The results demonstrate that WGCNA can be used for insect transcriptome analysis and effectively screen out the key genes related to wax biosynthesis.

Temporally Regulated Plant-Nematode Gene Networks Implicate Metabolic Pathways

Root-knot nematodes (RKN) (Meloidogyne spp.) constantly communicate with their host to establish and maintain specialized feeding cells. They likely regulate this interaction by monitoring host biology. As plant host biology is influenced by light and gene expression varies correspondingly, RKN gene transcription and biology likely follow similar patterns. We profiled RKN transcripts over a period of 24 h and identified approximately 1,000 differentially expressed genes (DEG) in nematode and model host Medicago truncatula, with the majority of DEG occurring in the middle of the dark period. Many of the plant DEG are involved in defense-response pathways, while the nematode DEG are involved in establishing infection, suggesting a strong host-nematode interaction occurring during the dark. To identify interacting genes, we developed a plant-nematode gene network based on DEG signals. The phenylpropanoid pathway was identified as a significant plant-nematode interacting pathway, representing four of 33 genes in the network. We further examined if this pathway interacts similarly in another host, tomato, by quantifying phenolic and flavonoid compounds produced by this pathway. Phenolic compounds showed a significant increase in production during the day in uninoculated plants as compared with during the night. However, during the dark period, there was an increase in flavonoid content in infected plants when compared with uninfected controls, indicating potential host defense mechanisms active during the height of nematode activity at night. This study elucidated cross-species interacting pathways that could be targeted to develop novel management strategies to these important pests.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.