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Studies on Oryza sativa (rice) are crucial for improving agricultural productivity and ensuring global sustenance security, especially considering the increasing drought and heat stress caused by extreme climate change. Currently, the genes and mechanisms underlying drought and heat resistance in rice are not fully understood, and the scope for enhancing the development of new strains remains considerable. To accurately identify the key genes related to drought and heat stress responses in rice, multiple datasets from the Gene Expression Omnibus (GEO) database were integrated in this study. A co-expression network was constructed using a Weighted Correlation Network Analysis (WGCNA) algorithm. We further distinguished the core network and intersected it with differentially expressed genes and multiple expression datasets for screening. Differences in gene expression levels were verified using quantitative real-time polymerase chain reaction (PCR). OsDjC53, MBF1C, BAG6, HSP23.2 , and HSP21.9 were found to be associated with the heat stress response, and it is also possible that UGT83A1 and OsCPn60a1 , although not directly related, are affected by drought stress. This study offers significant insights into the molecular mechanisms underlying stress responses in rice, which could promote the development of stress-tolerant rice breeds., Competing Interests: The authors declare there are no competing interests., (©2024 Cao et al.)

Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleterious passengers. We found that epistasis plays a crucial role in tumor development by promoting the transformation of precancerous clones into rapidly growing tumors through a process that is analogous to evolutionary rescue. The triggering of epistasis-driven rescue is strongly dependent on the intensity of epistasis and could be a key rate-limiting step in many tumors, contributing to their unpredictability. As a result, central genes in cancer epistasis networks appear as key intervention targets for cancer therapy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Alejandre et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The circadian clock is an evolutionarily-conserved molecular oscillator that enables species to anticipate rhythmic changes in their environment. At a molecular level, the core clock genes induce circadian oscillations in thousands of genes in a tissue-specific manner, orchestrating myriad biological processes. While previous studies have investigated how the core clock circuit responds to environmental perturbations such as temperature, the downstream effects of such perturbations on circadian regulation remain poorly understood. By analyzing bulk-RNA sequencing of Drosophila fat bodies harvested from flies subjected to different environmental conditions, we demonstrate a highly condition-specific circadian transcriptome: genes are cycling in a temperature-specific manner, and the distributions of their phases also differ between the two conditions. Further employing a reference-based gene regulatory network (Reactome), we find evidence of increased gene-gene coordination at low temperatures and synchronization of rhythmic genes that are network neighbors. We report that the phase differences between cycling genes increase as a function of geodesic distance in the low temperature condition, suggesting increased coordination of cycling on the gene regulatory network. Our results suggest a potential mechanism whereby the circadian clock mediates the fly's response to seasonal changes in temperature., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference models exhibit the predictive capabilities of capturing latent patterns in genomic data. Such models are emerging as an alternative to the statistical models identifying causative factors driving complex diseases. We present CoVar, an ML-based framework that builds upon the properties of existing inference models, to find the central genes driving perturbed gene expression across biological states. Unlike differentially expressed genes (DEGs) that capture changes in individual gene expression across conditions, CoVar focuses on identifying variational genes that undergo changes in their expression network interaction profiles, providing insights into changes in the regulatory dynamics, such as in disease pathogenesis. Subsequently, it finds core genes from among the nearest neighbors of these variational genes, which are central to the variational activity and influence the coordinated regulatory processes underlying the observed changes in gene expression. Through the analysis of simulated as well as yeast expression data perturbed by the deletion of the mitochondrial genome, we show that CoVar captures the intrinsic variationality and modularity in the expression data, identifying key driver genes not found through existing differential analysis methodologies., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Roy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Nearly all trait-associated variants identified in genome-wide association studies (GWASs) are noncoding. The cis regulatory effects of these variants have been extensively characterized, but how they affect gene regulation in trans has been the subject of fewer studies because of the difficulty in detecting trans-expression quantitative loci (eQTLs). We developed trans-PCO for detecting trans effects of genetic variants on gene networks. Our simulations demonstrate that trans-PCO substantially outperforms existing trans-eQTL mapping methods. We applied trans-PCO to two gene expression datasets from whole blood, DGN (N = 913) and eQTLGen (N = 31,684), and identified 14,985 high-quality trans-eSNP-module pairs associated with 197 co-expression gene modules and biological processes. We performed colocalization analyses between GWAS loci of 46 complex traits and the trans-eQTLs. We demonstrated that the identified trans effects can help us understand how trait-associated variants affect gene regulatory networks and biological pathways., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression., (© 2024. The Author(s).)

Gene regulatory mechanisms (GRMs) control the formation of spatial and temporal expression patterns that can serve as regulatory signals for the development of complex shapes. Synthetic developmental biology aims to engineer such genetic circuits for understanding and producing desired multicellular spatial patterns. However, designing synthetic GRMs for complex, multi-dimensional spatial patterns is a current challenge due to the nonlinear interactions and feedback loops in genetic circuits. Here we present a methodology to automatically design GRMs that can produce any given two-dimensional spatial pattern. The proposed approach uses two orthogonal morphogen gradients acting as positional information signals in a multicellular tissue area or culture, which constitutes a continuous field of engineered cells implementing the same designed GRM. To efficiently design both the circuit network and the interaction mechanisms-including the number of genes necessary for the formation of the target spatial pattern-we developed an automated algorithm based on high-performance evolutionary computation. The tolerance of the algorithm can be configured to design GRMs that are either simple to produce approximate patterns or complex to produce precise patterns. We demonstrate the approach by automatically designing GRMs that can produce a diverse set of synthetic spatial expression patterns by interpreting just two orthogonal morphogen gradients. The proposed framework offers a versatile approach to systematically design and discover complex genetic circuits producing spatial patterns., (© 2024. The Author(s).)

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The etiology of epilepsy is ascribed to the synchronized aberrant neuronal activity within the brain. Circular RNAs (circRNAs), a class of non-coding RNAs characterized by their circular structures and covalent linkage, exert a substantial influence on this phenomenon. CircRNAs possess stereotyped replication, transience, repetitiveness, and paroxysm. Additionally, MicroRNA (miRNA) plays a crucial role in the regulation of diverse pathological processes, including epilepsy. CircRNA is of particular significance due to its ability to function as a competing endogenous RNA, thereby sequestering or inhibiting miRNA activity through binding to target mRNA. Our review primarily concentrates on elucidating the pathological and functional roles, as well as the underlying mechanisms, of circRNA-miRNA-mRNA networks in epilepsy. Additionally, it explores the potential utility of these networks for early detection and therapeutic intervention., (© 2024 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

One of the prime reasons of increasing breast cancer mortality is metastasizing cancer cells. Owing to the side effects of clinically available drugs to treat breast cancer metastasis, it is of utmost importance to understand the underlying biogenesis of breast cancer tumorigenesis. In-silico identification of potential RNAs might help in utilizing the miR-27 family as a therapeutic target in breast cancer. The experimentally verified common interacting mRNAs for miR27 family are retrieved from three publicly available databases- TargetScan, miRDB and miRTarBase. Finally on comparing the common genes with HCMDB and GEPIA data, four breast cancer-associated differentially expressed metastatic mRNAs (GATA3, ENAH, ITGA2 and SEMA4D) are obtained. Corresponding to the miR27 family and associated mRNAs, interacting drugs are retrieved from Sm2mir and CTDbase, respectively. The interaction network-based approach was utilized to obtain the hub RNAs and triad modules by employing the 'Cytohubba' and 'MClique' plugins, respectively in Cytoscape. Further, sample-, subclass- and promoter methylation-based expression analyses reveals GATA3 and ENAH to be the most significant mRNAs in breast cancer metastasis having >10% genetic alteration in both METABRIC Vs TCGA datasets as per their oncoprint analysis via cBioPortal. Additionally, survival analysis in Oncolnc reveals SEMA4D as survival biomarker. Interactions among the miR27 family, their target mRNAs and drugs interacting with miRNAs and mRNAs can be extensively explored in both in-vivo and in-vitro setups to assess their therapeutic potential in the diminution of breast cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Recent developments in single-cell sequencing technology enable the acquisition of entire transcriptome data. Understanding the underlying mechanism and identifying the driving force of transcriptional regulation governing cell function directly from these data remains challenging. This study reconstructs a continuous vector field of the cell cycle based on discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium dynamic landscape-flux. It reveals that large fluctuations disrupt the global landscape and genetic perturbations alter landscape-flux, thus identifying key genes in maintaining cell cycle dynamics and predicting associated functional effects. Additionally, it quantifies the fundamental energy cost of the cell cycle initiation and unveils that sustaining the cell cycle requires curl flux and dissipation to maintain the oscillatory phase coherence. This study enables the inference of the cell cycle gene regulatory networks directly from the single-cell transcriptomic data, including the feedback mechanisms and interaction intensity. This provides a golden opportunity to experimentally verify the landscape-flux theory and also obtain its associated quantifications. It also offers a unique framework for combining the landscape-flux theory and single-cell high-through sequencing experiments for understanding the underlying mechanisms of the cell cycle and can be extended to other nonequilibrium biological processes, such as differentiation development and disease pathogenesis., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Background: Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. Methods: The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an in vitro model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence in situ hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. Result: By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA ZFAS1 was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only SLC26A6 was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the ZFAS1 /hsa-miR-449c-5p/ SLC26A6 ceRNA regulatory axis may play a role in the progression of sepsis. Conclusion: The sepsis ceRNA network, especially the ZFAS1 /hsa-miR-449c-5p/ SLC26A6 regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.

Atypical parkinsonism (AP) is a group of complex neurodegenerative disorders with marked clinical and pathophysiological heterogeneity. The use of systems biology tools may contribute to the characterization of hub-bottleneck genes, and the identification of its biological pathways to broaden the understanding of the bases of these disorders. A systematic search was performed on the DisGeNET database, which integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. The tools STRING 11.0 and Cytoscape 3.8.2 were used for analysis of protein-protein interaction (PPI) network. The PPI network topography analyses were performed using the CytoHubba 0.1 plugin for Cytoscape. The hub and bottleneck genes were inserted into 4 different sets on the InteractiveVenn. Additional functional enrichment analyses were performed to identify Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene ontology for a described set of genes. The systematic search in the DisGeNET database identified 485 genes involved with Atypical Parkinsonism. Superimposing these genes, we detected a total of 31 hub-bottleneck genes. Moreover, our functional enrichment analyses demonstrated the involvement of these hub-bottleneck genes in 3 major KEGG pathways. We identified 31 highly interconnected hub-bottleneck genes through a systems biology approach, which may play a key role in the pathogenesis of atypical parkinsonism. The functional enrichment analyses showed that these genes are involved in several biological processes and pathways, such as the glial cell development, glial cell activation and cognition, pathways were related to Alzheimer disease and Parkinson disease. As a hypothesis, we highlight as possible key genes for AP the MAPT (microtubule associated protein tau), APOE (apolipoprotein E), SNCA (synuclein alpha) and APP (amyloid beta precursor protein) genes., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution., Competing Interests: The authors declare there are no competing interests., (©2024 Ng and Kinjo.)

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., Competing Interests: The authors declare no conflict of interest.

The spike architecture of wheat plays a crucial role in determining grain number, making it a key trait for optimization in wheat breeding programs. In this study, we used a multi-omic approach to analyze the transcriptome and epigenome profiles of the young spike at eight developmental stages, revealing coordinated changes in chromatin accessibility and H3K27me3 abundance during the flowering transition. We constructed a core transcriptional regulatory network (TRN) that drives wheat spike formation and experimentally validated a multi-layer regulatory module involving TaSPL15, TaAGLG1, and TaFUL2. By integrating the TRN with genome-wide association studies, we identified 227 transcription factors, including 42 with known functions and 185 with unknown functions. Further investigation of 61 novel transcription factors using multiple homozygous mutant lines revealed 36 transcription factors that regulate spike architecture or flowering time, such as TaMYC2-A1, TaMYB30-A1, and TaWRKY37-A1. Of particular interest, TaMYB30-A1, downstream of and repressed by WFZP, was found to regulate fertile spikelet number. Notably, the excellent haplotype of TaMYB30-A1, which contains a C allele at the WFZP binding site, was enriched during wheat breeding improvement in China, leading to improved agronomic traits. Finally, we constructed a free and open access Wheat Spike Multi-Omic Database (http://39.98.48.156:8800/#/). Our study identifies novel and high-confidence regulators and offers an effective strategy for dissecting the genetic basis of wheat spike development, with practical value for wheat breeding., (Copyright © 2024 The Author. Published by Elsevier Inc. All rights reserved.)

Purpose: To identify potential biomarkers and the molecular mechanisms associated with repeated implantation failure (RIF), three microarray datasets, GSE71331 (lncRNA + mRNA), GSE111974 (lncRNA + mRNA), and GSE71332 (miRNA), were retrieved from the Gene Expression Omnibus (GEO) database., Methods: The differentially expressed mRNAs (DEMs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between normal control samples (C group) and RIF samples (RIF group) were identified, and then a module partition analysis was performed based on weighted correlation network analysis (WGCNA). Following enrichment analysis of the genes, the lncRNA-miRNA-mRNA interactions (ceRNA) were examined. The mRNAs in the ceRNA network were evaluated using the GSE58144 dataset. Finally, the key RNAs were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR)., Results: Fifty-three DEmiRNAs, 327 DEMs, and 13 DElncRNAs were identified between the C and RIF groups. According to WGCNA, the magenta module was positively correlated with RIF disease status. The lncRNA-mRNA interaction analysis based on genes in the magenta module revealed the intersecting lncRNAs, including peptidylprolyl isomerase E-like pseudogene (PPIEL) and the testis-specific transcript, y-Linked 14 (TTTY14); these lncRNAs are mainly involved in functions, such as plasma membrane organization. The ceRNA network analysis revealed several interactions, such as TTTY14-miR-6088-semaphorin 5 A (SEMA5A). Finally, SEMA5A and the zinc finger protein 555 (ZNF555) were identified to be significantly upregulated in the RIF group compared with those in the C group in the GSE58144 dataset. The RT-qPCR results aligned with the above results., Conclusions: Overall, TTTY14, ZNF555, SEMA5A, PPIEL, and miR-6088 could serve as novel biomarkers of RIF., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Major depressive disorder (MDD) is a heterogeneous mental disorder, and accompanying anxiety symptoms, known as anxious depression (AD), are the most common subtype. However, the pathophysiology of AD may be distinct in depressed patients without anxiety (NAD) and remains unknown. This study aimed to investigate the relationship between functional connectivity and peripheral transcriptional profiles in patients with AD and NAD., Methods: Functional imaging data were collected to identify differences in functional networks among patients with AD (n = 66), patients with NAD (n = 115), and healthy controls (HC, n = 200). The peripheral transcriptional data were clustered as co-expression modules, and their associations with AD, AND, and HC were analyzed. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses of the genes in the significant module were performed. Correlation analysis was performed to identify functional network-associated gene co-expression modules., Results: A network was identified which consisted of 23 nodes and 28 edges that were significantly different among three sample groups. The regions of the network were located in temporal and occipital lobe. Two gene co-expression modules were shown to be associated with NAD, and one of which was correlated with the disrupted network in the AD group. The biological function of this module was enriched in immune regulation pathways., Conclusion: The results suggested that immune-related mechanisms were associated with functional networks in AD., Competing Interests: Declaration of competing interest All authors report no conflicts of interest, financial or otherwise., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Circular RNAs (circRNAs) function as miRNA sponges by adsorbing microRNAs (miRNAs), thereby regulating messenger RNA (mRNA) expression. The circRNA-miRNA-mRNA regulatory network associated with type 2 diabetes mellitus (T2DM) has rarely been explored. A circRNA-miRNA-mRNA regulatory network associated with T2DM was established to help deepen our understanding of the molecular mechanism of and therapeutic targets for T2DM., Methods: Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were derived from the Gene Expression Omnibus (GEO) microarray datasets GSE114248, GSE51674 and GSE95849, respectively. A circRNA-miRNA-mRNA regulatory network associated with T2DM and its subnetwork were constructed. The hub genes were screened using a protein-protein interaction (PPI) network. Finally, a hub gene-related network was constructed. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed., Results: The circRNA-miRNA-mRNA network included 9 circRNAs, 24 miRNAs and 320 mRNAs. When four key circRNAs (circMYO9B, circGRAMD1B, circTHAP4 and circTMC7) were chosen, the subnetwork contained 4 circRNAs, 18 miRNAs and 307 mRNAs. Afterwards, 8 hub genes (SIRT1, GNG7, KDR, FOS, SIN3B, STAT1, SP1, and MAPK3) were extracted from the PPI network. GO and KEGG pathway analyses revealed that the network might be involved in oxidative stress responses, regulation of inflammation, neovascularization, endocrine and cancer-related processes, etc., Conclusions: A circRNA-miRNA-hub gene regulatory network was constructed, and the potential functions of the hub genes were analyzed. Four important circRNAs (circMYO9B, circGRAMD1B, circTHAP4 and circTMC7) might be involved in the occurrence and development of T2DM, and this finding provides new insight into the molecular mechanism of and therapeutic targets for T2DM and its complications. Future studies are needed to validate the sponge effects and mechanisms of these 4 circRNAs.

The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Determining the general laws between evolution and development is a fundamental biological challenge. Developmental hourglasses have attracted increased attention as candidates for such laws, but the necessity of their emergence remains elusive. We conducted evolutionary simulations of developmental processes to confirm the emergence of the developmental hourglass and unveiled its establishment. We considered organisms consisting of cells containing identical gene networks that control morphogenesis and evolved them under selection pressure to induce more cell types. By computing the similarity between the spatial patterns of gene expression of two species that evolved from a common ancestor, a developmental hourglass was observed, that is, there was a correlation peak in the intermediate stage of development. The fraction of pleiotropic genes increased, whereas the variance in individuals decreased, consistent with previous experimental reports. Reduction of the unavoidable variance by initial or developmental noise, essential for survival, was achieved up to the hourglass bottleneck stage, followed by diversification in developmental processes, whose timing is controlled by the slow expression dynamics conserved among organisms sharing the hourglass. This study suggests why developmental hourglasses are observed within a certain phylogenetic range of species., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kohsokabe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method., (© 2024. The Author(s).)

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic β-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring β-cell mass and β-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with β-cell dysfunction., Methods: A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein-protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network., Results: Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets., Conclusions: This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into β-cells., (© 2024. The Author(s).)

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., Competing Interests: Declaration of Competing Interest All of the authors of this article declare that there is no relationship that creates a conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The inference of gene regulatory networks (GRNs) is a widely addressed problem in Systems Biology. GRNs can be modeled as Boolean networks, which is the simplest approach for this task. However, Boolean models need binarized data. Several approaches have been developed for the discretization of gene expression data (GED). Also, the advance of data extraction technologies, such as single-cell RNA-Sequencing (scRNA-Seq), provides a new vision of gene expression and brings new challenges for dealing with its specificities, such as a large occurrence of zero data. This work proposes a new discretization approach for dealing with scRNA-Seq time-series data, named Distribution and Successive Spline Points Discretization (DSSPD), which considers the data distribution and a proper preprocessing step. Here, Cartesian Genetic Programming (CGP) is used to infer GRNs using the results of DSSPD. The proposal is compared with CGP with the standard data handling and five state-of-the-art algorithms on curated models and experimental data. The results show that the proposal improves the results of CGP in all tested cases and outperforms the state-of-the-art algorithms in most cases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Arming human cells with synthetic gene circuits enables to expand their capacity to execute superior sensing and response actions, offering tremendous potential for innovative cellular therapeutics. This can be achieved by assembling components from an ever-expanding molecular toolkit, incorporating switches based on transcriptional, translational, or post-translational control mechanisms. This review provides examples from the three classes of switches, and discusses their advantages and limitations to regulate the activity of therapeutic cells in vivo. Genetic switches designed to recognize internal disease-associated signals often encode intricate actuation programs that orchestrate a reduction in the sensed signal, establishing a closed-loop architecture. Conversely, switches engineered to detect external molecular or physical cues operate in an open-loop fashion, switching on or off upon signal exposure. The integration of such synthetic gene circuits into the next generation of chimeric antigen receptor T-cells is already enabling precise calibration of immune responses in terms of magnitude and timing, thereby improving the potency and safety of therapeutic cells. Furthermore, pre-clinical engineered cells targeting other chronic diseases are gathering increasing attention, and this review discusses the path forward for achieving clinical success. With synthetic biology at the forefront, cellular therapeutics holds great promise for groundbreaking treatments., (© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Motivation: Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory circuits inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory circuits can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based likelihood constraints to determine the global consistency between a candidate TF-gene relation and patterns of genes expressions and region activations, as well as qualify regulations as activations or inhibitions., Results: We introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Louarn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Experimental studies have shown that chromatin modifiers have a critical effect on cellular reprogramming, i.e., the conversion of differentiated cells to pluripotent stem cells. Here, we develop a model of the OCT4 gene regulatory network that includes genes expressing chromatin modifiers TET1 and JMJD2, and the chromatin modification circuit on which these modifiers act. We employ this model to compare three reprogramming approaches that have been considered in the literature with respect to reprogramming efficiency and latency variability. These approaches are overexpression of OCT4 alone, overexpression of OCT4 with TET1, and overexpression of OCT4 with JMJD2. Our results show more efficient and less variable reprogramming when also JMJD2 and TET1 are overexpressed, consistent with previous experimental data. Nevertheless, TET1 overexpression can lead to more efficient reprogramming compared to JMJD2 overexpression. This is the case when the recruitment of DNA methylation by H3K9me3 is weak and the methyl-CpG-binding domain (MBD) proteins are sufficiently scarce such that they do not hamper TET1 binding to methylated DNA. The model that we developed provides a mechanistic understanding of existing experimental results and is also a tool for designing optimized reprogramming approaches that combine overexpression of cell-fate specific transcription factors (TFs) with targeted recruitment of epigenetic modifiers., (© 2024. The Author(s).)

Growing evidence has shown that besides the protein coding genes, the non-coding elements of the genome are indispensable for maintaining the property of self-renewal in human embryonic stem cells and in cell fate determination. However, the regulatory mechanisms and the landscape of interactions between the coding and non-coding elements is poorly understood. In this work, we used weighted gene co-expression network analysis (WGCNA) on transcriptomic data retrieved from RNA-seq and small RNA-seq experiments and reconstructed the core human pluripotency network (called PluriMLMiNet) consisting of 375 mRNA, 57 lncRNA and 207 miRNAs. Furthermore, we derived networks specific to the naïve and primed states of human pluripotency (called NaiveMLMiNet and PrimedMLMiNet respectively) that revealed a set of molecular markers (RPS6KA1, ZYG11A, ZNF695, ZNF273, and NLRP2 for naive state, and RAB34, TMEM178B, PTPRZ1, USP44, KIF1A and LRRN1 for primed state) which can be used to distinguish the pluripotent state from the non-pluripotent state and also to identify the intra-pluripotency states (i.e., naïve and primed state). The lncRNA DANT1 was found to be a crucial as it formed a bridge between the naive and primed state-specific networks. Analysis of the genes neighbouring DANT1 suggested its possible role as a competing endogenous RNA (ceRNA) for the induction and maintenance of human pluripotency. This was computationally validated by predicting the missing DANT1-miRNA interactions to complete the ceRNA circuit. Here we first report that DANT1 might harbour binding sites for miRNAs hsa-miR-30c-2-3p, hsa-miR-210-3p and hsa-let-7b-5p which may influence pluripotency., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest. The work was carried out by AG while working as a JRF at the University of Allahabad., (Copyright © 2023 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.)

Objectives: To utilize an integrative strategy to construct functional miRNA-mRNA regulatory networks by combining the reverse expression relationships between miRNAs and targets and computational predictions for gestational diabetes mellitus (GDM)., Material and Methods: A total of three microarray or RNA-seq datasets (GSE98043, GSE19649 and GSE92772) of plasma samples comparing GDM pregnant women and healthy control pregnant women were downloaded from the GEO database. The differentially expressed genes (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) were analyzed. The target genes of DEmiRNAs were identified using two independent and complementary types of information: computational target predictions and expression relationships. An interaction network was constructed to identify hub genes of GDM. Another dataset (GSE92772) was used to externally verify the predictive ability of the hub genes., Results: A total of 264 DEmiRNAs and 1217 DEmRNAs were identified with Hsa-miR-146a-3p ranked first of DEmiRNAs. Functions of GDM-related miRNAs were mainly enriched in the glypican pathway, proteoglycan syndecan-mediated signaling events, and syndecan-1-mediated signaling events. A total of 47 target genes, including TRAF6, were shared between the computational target predictions and DEmRNAs and were identified as target genes of hsa-miR-146a-3p. The interaction network analysis identified TRAF6, CASP8, PTPN6, and CHD3 as hub genes involved in the pathophysiological process of GDM. Next, independent external validation was performed using the GSE19649 dataset. The expression of TRAF6, CASP8 and CHD3 in eight pairs of GDM blood samples was confirmed to be higher than that in healthy pregnant women blood samples with a AUC of 0.813, 0.813, and 0.703, respectively., Conclusions: Our preliminary analysis revealed that miR-146a-3p/TRAF6 might play a central role in the pathogenesis of GDM. Three hub genes, TRAF6, CASP8, and CHD3, were identified and independently externally validated as potential GDM noninvasive serum markers for future biomarkers research.

Gene regulatory networks (GRNs) represent the interactions between transcription factors (TF) and their target genes. Plant GRNs control transcriptional programs involved in growth, development, and stress responses, ultimately affecting diverse agricultural traits. While recent developments in accessible chromatin (AC) profiling technologies make it possible to identify context-specific regulatory DNA, learning the underlying GRNs remains a major challenge. We developed MINI-AC (Motif-Informed Network Inference based on Accessible Chromatin), a method that combines AC data from bulk or single-cell experiments with TF binding site (TFBS) information to learn GRNs in plants. We benchmarked MINI-AC using bulk AC datasets from different Arabidopsis thaliana tissues and showed that it outperforms other methods to identify correct TFBS. In maize, a crop with a complex genome and abundant distal AC regions, MINI-AC successfully inferred leaf GRNs with experimentally confirmed, both proximal and distal, TF-target gene interactions. Furthermore, we showed that both AC regions and footprints are valid alternatives to infer AC-based GRNs with MINI-AC. Finally, we combined MINI-AC predictions from bulk and single-cell AC datasets to identify general and cell-type specific maize leaf regulators. Focusing on C4 metabolism, we identified diverse regulatory interactions in specialized cell types for this photosynthetic pathway. MINI-AC represents a powerful tool for inferring accurate AC-derived GRNs in plants and identifying known and novel candidate regulators, improving our understanding of gene regulation in plants., (© 2023 Society for Experimental Biology and John Wiley & Sons Ltd.)

Introduction: This study aimed to screen immune-related marker genes of ischemic stroke (IS)., Material and Methods: Two IS-related gene expression datasets were downloaded. The significantly differentially expressed genes (DEGs) and miRNAs (DEMs) between IS and control groups were selected. The differential immune cells were analysed. Weighted gene co-expression network analysis (WGCNA) was applied to analyse immune-related genes, followed by function analysis and interaction network construction. Then, key genes were further screened using optimization algorithm to construct a diagnostic model. Finally, miRNA regulatory network of several key genes was established., Results: In total 321 DEGs and 140 DEMs were obtained. 11 immune cell types were significantly different between IS and control groups. WGCNA identified two key modules, involving 202 differential immune genes. The greenyellow module was enriched in biological processes and pathways associated with T cells, while the midnightblue module was mainly associated with apoptosis, and inflammatory response-related functions and pathways. Protein interaction network identified 10 hub nodes, such as CD8A, ITGAM and TLR4. LASSO regression selected 8 key feature genes, and a risk score model was established. Key model genes were enriched in 63 GO biological processes, such as microglial cell activation, and B cell apoptotic process, and 3 KEGG pathways, such as negative regulation of nuclear cell cycle DNA replication, and hematopoietic cell lineage. Finally, a total of 25 miRNA-target relationship pairs were obtained., Conclusions: This study identified some immune-related marker genes and constructed a diagnostic model based on 8 immune-related genes in IS.

Background: Alzheimer's disease (AD) is the most prevalent neurological disorder worldwide, affecting approximately 24 million individuals. Despite more than a century of research on AD, its pathophysiology is still not fully understood., Objective: Recently, genetic studies of AD have focused on analyzing the general expression profile by employing high-throughput genomic techniques such as microarrays. Current research has leveraged bioinformatics advancements in genetic science to build upon previous efforts., Methods: Data from the GSE118553 dataset used in this investigation, and the analyses carried out using programs such as Limma and BioBase. Differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRs) associated with AD identified in the studied areas of the brain. Target genes of the DEmiRs identified using the MultiMiR package. Gene ontology (GO) completed using the Enrichr website, and the protein-protein interaction (PPI) network for these genes drawn using STRING and Cytoscape software., Results: The findings introduced DEGs including CTNNB1, PAK2, MAP2K1, PNPLA6, IGF1R, FOXL2, DKK3, LAMA4, PABPN1, and GDPD5, and DEmiRs linked to AD (miR-106A, miR-1826, miR-1253, miR-10B, miR-18B, miR-101-2, miR-761, miR-199A1, miR-379 and miR-668), (miR-720, miR-218-2, miR-25, miR-602, miR-1226, miR-548K, miR-H1, miR-410, miR-548F2, miR-181A2), (miR-1470, miR-651, miR-544, miR-1826, miR-195, miR-610, miR-599, miR-323, miR-587 and miR-340), and (miR-1282, miR-1914, miR-642, miR-1323, miR-373, miR-323, miR-1322, miR-612, miR-606 and miR-758) in cerebellum, frontal cortex, temporal cortex, and entorhinal cortex, respectively., Conclusions: The majority of the genes and miRNAs identified by our findings may be employed as biomarkers for prediction, diagnosis, or therapy response monitoring.

Background: Early stress increases the risk for psychiatric disorders. Glucocorticoids are stress mediators that regulate transcriptional activity and morphology in the hippocampus, which is implicated in the pathophysiology of multiple psychiatric conditions. We aimed to establish the relevance of hippocampal glucocorticoid-induced transcriptional activity as a mediator of the effects of early life on later psychopathology in humans., Methods: RNA sequencing was performed with anterior and posterior hippocampal dentate gyrus from adult female macaques (n = 12/group) that were chronically treated with betamethasone (glucocorticoid receptor agonist) or vehicle. Coexpression network analysis identified a preserved gene network in the posterior hippocampal dentate gyrus that was strongly associated with glucocorticoid exposure. The single nucleotide polymorphisms in the genes in this network were used to create an expression-based polygenic score in humans., Results: The expression-based polygenic score significantly moderated the association between early adversity and psychotic disorders in adulthood (UK Biobank, women, n = 44,519) and on child peer relations (ALSPAC [Avon Longitudinal Study of Parents and Children], girls, n = 1666 for 9-year-olds and n = 1594 for 11-year-olds), an endophenotype for later psychosis. Analyses revealed that this network was enriched for glucocorticoid-induced epigenetic remodeling in human hippocampal cells. We also found a significant association between single nucleotide polymorphisms from the expression-based polygenic score and adult brain gray matter density., Conclusions: We provide an approach for the use of transcriptomic data from animal models together with human data to study the impact of environmental influences on mental health. The results are consistent with the hypothesis that hippocampal glucocorticoid-related transcriptional activity mediates the effects of early adversity on neural mechanisms implicated in psychiatric disorders., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in Alzheimer's disease. However, despite recent investigations revealing various aspects of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) networks in Alzheimer's Disease (AD) pathogenesis, few genome-wide studies have explored circRNA-associated profiles in AD patients exhibiting varying degrees of cognitive loss., Objective: To investigate the potential pathogenesis-related molecular biological changes in the various stages of AD progression., Methods: Whole transcriptome sequencing was performed on the peripheral blood of 7 normal cognition (NC) subjects, 8 patients with mild cognitive impairment, 8 AD patients with mild dementia (miD), and 7 AD patients with moderate dementia (moD). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to predict the potential functions of the maternal genes of microRNAs (miRNAs), circRNAs and long non-coding RNAs (lncRNAs). The construction of ceRNA network was performed between the NC group and each diseased group based on the differently expressed RNAs., Results: In total, 3568 messenger RNAs (mRNAs), 142 miRNAs, 990 lncRNAs, and 183 circRNAs were identified as significantly differentially expressed across the four groups. GO and KEGG enrichment analysis revealed the significant roles of GTPase activity and the MAPK signaling pathway in AD pathogenesis. A circRNA-miRNA-lncRNA ceRNA pathway, characterized by the downregulated hsa-miR-7-5p and upregulated hsa_circ_0001170, was identified based on the differentially expressed RNAs between the NC group and the moD group., Conclusion: The study suggests that circRNAs may be independent of mRNAs in AD pathogenesis and holds promise as potential biomarkers for AD clinical manifestations and pathological changes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related., Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment., Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-β 25-35 (Aβ25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes., Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified., Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (P hypergeometric  = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD., (© 2023. The Author(s).)

Autism spectrum disorder (ASD) comprises a large group of neurodevelopmental conditions featuring, over a wide range of severity and combinations, a core set of manifestations (restricted sociality, stereotyped behavior and language impairment) alongside various comorbidities. Common and rare variants in several hundreds of genes and regulatory regions have been implicated in the molecular pathogenesis of ASD along a range of causation evidence strength. Despite significant progress in elucidating the impact of few paradigmatic individual loci, such sheer complexity in the genetic architecture underlying ASD as a whole has hampered the identification of convergent actionable hubs hypothesized to relay between the vastness of risk alleles and the core phenotypes. In turn this has limited the development of strategies that can revert or ameliorate this condition, calling for a systems-level approach to probe the cross-talk of cooperating genes in terms of causal interaction networks in order to make convergences experimentally tractable and reveal their clinical actionability. As a first step in this direction, we have captured from the scientific literature information on the causal links between the genes whose variants have been associated with ASD and the whole human proteome. This information has been annotated in a computer readable format in the SIGNOR database and is made freely available in the resource website. To link this information to cell functions and phenotypes, we have developed graph algorithms that estimate the functional distance of any protein in the SIGNOR causal interactome to phenotypes and pathways. The main novelty of our approach resides in the possibility to explore the mechanistic links connecting the suggested gene-phenotype relations., (© 2023. The Author(s).)

Background: There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD., Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions., Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins., Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation., Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

Single-cell RNA sequencing (scRNA-Seq) technology provides the scope to gain insight into the interplay between intrinsic cellular processes as well as transcriptional and behavioral changes in gene-gene interactions across varying conditions. The high level of scarcity of scRNA-seq data, however, poses a significant challenge for analysis. We propose a complete differential co-expression (DCE) analysis framework for scRNA-Seq data to extract network modules and identify hub-genes. The performance of our method has been shown to be satisfactory after validation using an scRNA-Seq esophageal squamous cell carcinoma (ESCC) dataset. From comparison with four other existing hub-gene finding methods, it has been observed that our method performs better in the majority of cases and has the ability to identify unique potential biomarkers that were not detected by the other methods. The potential biomarker genes identified by our framework, differential co-expression analysis method for single-cell RNA sequencing data (scDiffCoAM), have been validated both statistically and biologically.

Primary ovarian insufficiency (POI) is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in amenorrhea, hypogonadism, and infertility. Biochemical premature ovarian insufficiency (bPOI) is an intermediate stage in the pathogenesis of POI in which the fertility of patients has been reduced. Previous studies suggest that granulosa cells (GCs) play an essential role in the pathogenesis of POI, but their pathogenetic mechanisms remain unclear. To further explore the potential pathophysiological mechanisms of GCs in POI, we constructed a molecular long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network using GC expression data collected from biochemical premature ovarian failure (bPOI) patients in the GEO database. We discovered that the GCs of bPOI patients had differential expression of 131 mRNAs, 191 lncRNAs, and 28 miRNAs. By systematic network analysis, we identified six key genes, including SRSF1, PDIA5, NEURL1B, UNK, CELF2, and CFL2, and five hub miRNAs, namely hsa-miR-27a-3p, hsa-miR-24-3p, hsa-miR-22-3p, hsa-miR-129-5p, and hsa-miR-17-5p, and the results suggest that the expression of these key genes may be regulated by two hub miRNAs, hsa-miR-27a-3p and hsa-miR-17-5p. Additionally, a POI model in vitro was created to confirm the expression of a few important genes. In this study, we discovered a unique lncRNA-miRNA-mRNA network based on the ceRNA mechanism in bPOI for the first time, and we screened important associated molecules, providing a partial theoretical foundation to better understand the pathogenesis of POI., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. Score Lasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Purpose: Recurrent implantation failure (RIF) affects up to 10% of in vitro fertilization (IVF) patients worldwide. However, the pathogenesis of RIF remains unclear. This study was aimed at identifying hub transcription factors (TFs) of RIF in bioinformatics approaches., Methods: The GSE111974 (mRNA), GSE71332 (miRNA), and GSE103465 (mRNA) datasets were downloaded from the Gene Expression Omnibus database from human endometrial tissue using R version 4.2.1 and used to identify differentially expressed TFs (DETFs), differentially expressed miRNAs, and differentially expressed genes for RIF, respectively. DETFs were subjected to functional enrichment analysis and the protein-protein interaction network analysis using the Search Tool for the Retrieval of Interacting Genes (version 11.5) database. Hub TFs were identified using the cytoHubb plug-in, after which a hub TF-miRNA-mRNA network was constructed using Cytoscape v3.8.2., Results: Fifty-seven DETFs were identified, in which Gene Ontology analysis revealed to be mainly involved in the regulation of transcription. Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that DETFs were enriched in transcriptional misregulation in cancer, aldosterone synthesis and secretion, AMPK signaling pathway, and cGMP-PKG signaling pathway. EOMES, NKX2-1, and POU5F1 were identified as hub TFs, and a hub TF-miRNA-mRNA regulatory network was constructed using these three hub TFs, four miRNAs, and four genes., Conclusion: Collectively, we identified three promising molecular biomarkers for the diagnosis of RIF, which may further be potential therapeutic targets. This study provides novel insights into the molecular mechanisms underlying RIF. However, further experiments are required to verify these results., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: To retrieve, and classify PCa miRNAs and identify the functional relationship between miRNAs and their targets through literature collection with computational analysis., Background: MicroRNAs play a role in gene regulation, which can either repress or activate the gene. Hence, the functions of miRNAs are dependent on the target gene. This study will be the first of its kind to combine computational analysis with corpus PCa data. Effectively, our study reported the huge number of miRNAs associated with PCa along with functional information., Objective: The identification and classification of previously known full PCa miRNAs and their targets were made possible by mining the literature data. Systems Biology and curated data mining assisted in identifying optimum miRNAs and their target genes for PCa therapy., Methods: PubMed database was used to collect the PCa literature up to December 2021. Pubmed. mineR package was used to extract the microRNAs associated articles and manual curation was performed to classify the microRNAs based on the function in PCa. PPI was constructed using the STRING database. Pathway analysis was performed using PANTHER and ToppGene Suite Software. Functional analysis was performed using ShinyGO software. Cluster analysis was performed using MCODE 2.0, and Hub gene analysis was performed using cytoHubba. The genemiRNA network was reconstructed using Cytoscape., Results: Unique PCa miRNAs were retrieved and classified from mined PCa literature. Six hundred and five unique miRNAs from 250 articles were considered as oncomiRs to trigger PCa. One hundred and twenty unique miRNAs from 118 articles were considered Tumor Suppressor miRNAs to suppress the PCa. Twenty-four unique miRNAs from 22 articles were utilized as treatment miRNAs to treat PCa. miRNAs target genes and their significant pathways, functions and hub genes were identified., Conclusion: miR-27a, miR-34b, miR-495, miR-23b, miR-100, miR-218, Let-7a family, miR-27a- 5p, miR-34c, miR-34a, miR-143/-145, miR-125b, miR-124 and miR-205 with their target genes AKT1, SRC, CTNNB1, HRAS, MYC and TP53 are significant PCa targets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Importance: We present here a new systems-level approach to decipher genetic factors and biological pathways associated with virulence and/or antibiotic treatment of bacterial pathogens. The power of this approach was demonstrated by application to a well-studied pathogen Pseudomonas aeruginosa PAO1. Our gene co-expression network-based approach unraveled known and unknown genes and their networks associated with pathogenicity in P. aeruginosa PAO1. The systems-level investigation of P. aeruginosa PAO1 helped identify putative pathogenicity and resistance-associated genetic factors that could not otherwise be detected by conventional approaches of differential gene expression analysis. The network-based analysis uncovered modules that harbor genes not previously reported by several original studies on P. aeruginosa virulence and resistance. These could potentially act as molecular determinants of P. aeruginosa PAO1 pathogenicity and responses to antibiotics., Competing Interests: The authors declare no conflict of interest.

NAKED ENDOSPERM1 (NKD1), NKD2, and OPAQUE2 (O2) are transcription factors important for cell patterning and nutrient storage in maize (Zea mays) endosperm. To study the complex regulatory interrelationships among these 3 factors in coregulating gene networks, we developed a set of nkd1, nkd2, and o2 homozygous lines, including all combinations of mutant and wild-type genes. Among the 8 genotypes tested, we observed diverse phenotypes and gene interactions affecting cell patterning, starch content, and storage proteins. From ∼8 to ∼16 d after pollination, maize endosperm undergoes a transition from cellular development to nutrient accumulation for grain filling. Gene network analysis showed that NKD1, NKD2, and O2 dynamically regulate a hierarchical gene network during this period, directing cellular development early and then transitioning to constrain cellular development while promoting the biosynthesis and storage of starch, proteins, and lipids. Genetic interactions regulating this network are also dynamic. The assay for transposase-accessible chromatin using sequencing (ATAC-seq) showed that O2 influences the global regulatory landscape, decreasing NKD1 and NKD2 target site accessibility, while NKD1 and NKD2 increase O2 target site accessibility. In summary, interactions of NKD1, NKD2, and O2 dynamically affect the hierarchical gene network and regulatory landscape during the transition from cellular development to grain filling in maize endosperm., Competing Interests: Conflict of interest statement. None declared., (© American Society of Plant Biologists 2023.)

One challenge in synthetic biology is the tuning of regulatory components within gene circuits to elicit a specific behavior. This challenge becomes more difficult in synthetic microbial consortia since each strain's circuit must function at the intracellular level and their combination must operate at the population level. Here we demonstrate that circuit dynamics can be tuned in synthetic consortia through the manipulation of strain fractions within the community. To do this, we construct a microbial consortium comprised of three strains of engineered Escherichia coli that, when cocultured, use homoserine lactone-mediated intercellular signaling to create a multistrain incoherent type-1 feedforward loop (I1-FFL). Like naturally occurring I1-FFL motifs in gene networks, this engineered microbial consortium acts as a pulse generator of gene expression. We demonstrate that the amplitude of the pulse can be easily tuned by adjusting the relative population fractions of the strains. We also develop a mathematical model for the temporal dynamics of the microbial consortium. This model allows us to identify population fractions that produced desired pulse characteristics, predictions that were confirmed for all but extreme fractions. Our work demonstrates that intercellular gene circuits can be effectively tuned simply by adjusting the starting fractions of each strain in the consortium.

Recent advances in single-cell technologies have led to the discovery of thousands of brain cell types; however, our understanding of the gene regulatory programs in these cell types is far from complete 1-4 . Here we report a comprehensive atlas of candidate cis-regulatory DNA elements (cCREs) in the adult mouse brain, generated by analysing chromatin accessibility in 2.3 million individual brain cells from 117 anatomical dissections. The atlas includes approximately 1 million cCREs and their chromatin accessibility across 1,482 distinct brain cell populations, adding over 446,000 cCREs to the most recent such annotation in the mouse genome. The mouse brain cCREs are moderately conserved in the human brain. The mouse-specific cCREs-specifically, those identified from a subset of cortical excitatory neurons-are strongly enriched for transposable elements, suggesting a potential role for transposable elements in the emergence of new regulatory programs and neuronal diversity. Finally, we infer the gene regulatory networks in over 260 subclasses of mouse brain cells and develop deep-learning models to predict the activities of gene regulatory elements in different brain cell types from the DNA sequence alone. Our results provide a resource for the analysis of cell-type-specific gene regulation programs in both mouse and human brains., (© 2023. The Author(s).)