Regulatory Interaction Networks Research Articles

Quantitative proteomics reveals the complex regulatory networks of LTTR-type regulators in pleiotropic functions of Aeromonas hydrophila

LysR-type transcriptional regulators (LTTRs) are ubiquitously distributed and abundant transcriptional regulators in prokaryotes, playing pivotal roles in diverse physiological processes. Nonetheless, despite their prevalence, the intricate functionalities and physiological implications of this protein family remain incompletely elucidated. In this study, we employed a comprehensive approach to deepen our understanding of LTTRs by generating a collection of 20 LTTR gene-deletion strains in Aeromonas hydrophila, accounting for 42.6 % of the predicted total LTTR repertoire, and subjected them to meticulous assessment of their physiological phenotypes. Leveraging quantitative proteomics, we conducted a comparative analysis of protein expression variations between six representative mutants and the wild-type strain. Subsequent bioinformatics analysis unveiled the involvement of these LTTRs in modulating a wide array of biological processes, notably including two-component regulatory systems (TCSs) and intracellular central metabolism. Moreover, employing subsequent microbiological methodologies, we experimentally verified the direct involvement of at least six LTTRs in the regulation of galactose metabolism. Importantly, through ELISA and competitive ELISA assays, we demonstrated the competitive binding capabilities of these LTTRs with the promoter of the α-galactosidase gene AHA_1897 and identified that four LTTRs (XapR, YidZ, YeeY, and AHA_1805) do not engage in competitive binding with other LTTRs. Overall, our comprehensive findings not only provide fundamental insights into the regulatory mechanisms governing crucial physiological functions of bacteria through LTTR family proteins but also uncover an intricate and interactive regulatory network mediated by LTTRs.

Gut bacterium Burkholderia cepacia (BsNLG8) and immune gene Defensin A contribute to the resistance against Nicotine-induced stress in Nilaparvata lugens (Stål)

Nicotine, a naturally occurring alkaloid found in tobacco, is a potent neurotoxin extensively used to control Nilaparvata lugens (Stål), a destructive insect pest of rice crops. The insect gut harbors a wide array of resident microorganisms that profoundly influence several biological processes, including host immunity. Maintaining an optimal gut microbiota and immune homeostasis requires a complex network of reciprocal regulatory interactions. However, the underlying molecular mechanisms driving these symbiotic exchanges, particularly between specific gut microbe and immunity, remain largely unknown in insects. Our previous investigations identified and isolated a nicotine-degrading Burkholderia cepacia strain (BsNLG8) with antifungal properties. Building on those findings, we found that nicotine intake significantly increased the abundance of a symbiotic bacteria BsNLG8, induced a stronger bacteriostatic effect in hemolymph, and enhanced the nicotine tolerance of N. lugens. Additionally, nicotine-induced antimicrobial peptides (AMPs) exhibited significant antibacterial effects against Staphylococcus aureus. We adopted RNA-seq to explore the underlying immunological mechanisms in nicotine-stressed N. lugens. Bioinformatic analyses identified numerous differentially expressed immune genes, including recognition/immune activation (GRPs and Toll) and AMPs (i.e., Defensin, Lugensin, lysozyme). Temporal expression profiling (12, 24, and 48 hours) of immune genes revealed pattern recognition proteins and immune effectors as primary responders to nicotine-induced stress. Defensin A, a broad-spectrum immunomodulatory cationic peptide, exhibited significantly high expression. RNA interference-mediated silencing of Defensin A reduced the survival, enhanced nicotine sensitivity of N. lugens to nicotine, and decreased the abundance of BsNLG8. The reintroduction of BsNLG8 improved the expression of immune genes, aiding nicotine resistance of N. lugens. Our findings indicate a potential reciprocal immunomodulatory interaction between Defensin A and BsNLG8 under nicotine stress. Moreover, this study offers novel and valuable insights for future research into enhancing nicotine-based pest management programs and developing alternative biocontrol methods involving the implication of insect symbionts.

Regulation of T helper cell differentiation by the interplay between histone modification and chromatin interaction

The development and function of the immune system are controlled by temporospatial gene expression programs, which are regulated by cis-regulatory elements, chromatin structure, and trans-acting factors. In this study, we cataloged the dynamic histone modifications and chromatin interactions at regulatory regions during T helper (Th) cell differentiation. Our data revealed that the H3K4me1 landscape established by MLL4 in naive CD4+ Tcells is critical for restructuring the regulatory interaction network and orchestrating gene expression during the early phase of Th differentiation. GATA3 plays a crucial role in further configuring H3K4me1 modification and the chromatin interaction network during Th2 differentiation. Furthermore, we demonstrated that HSS3-anchored chromatin loops function to restrict the activity of the Th2 locus control region (LCR), thus coordinating the expression of Th2 cytokines. Our results provide insights into the mechanisms of how the interplay between histone modifications, chromatin looping, and trans-acting factors contributes to the differentiation of Th cells.

LogoMotif: A Comprehensive Database of Transcription Factor Binding Site Profiles in Actinobacteria

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations in Streptomyces model organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts.

Key anti-freeze genes and pathways of Lanzhou lily (Lilium davidii, var. unicolor) during the seedling stage.

Temperature is one of the most important environmental factors for plant growth, as low-temperature freezing damage seriously affects the yield and distribution of plants. The Lanzhou lily (Lilium davidii, var. unicolor) is a famous ornamental plant with high ornamental value. Using an Illumina HiSeq transcriptome sequencing platform, sequencing was conducted on Lanzhou lilies exposed to two different temperature conditions: a normal temperature treatment at 20°C (A) and a cold treatment at -4°C (C). After being treated for 24 hours, a total of 5848 differentially expressed genes (DEGs) were identified, including 3478 significantly up regulated genes and 2370 significantly down regulated genes, accounting for 10.27% of the total number of DEGs. Quantitative real-time PCR (QRT-PCR) analysis showed that the expression trends of 10 randomly selected DEGs coincided with the results of high-throughput sequencing. In addition, genes responding to low-temperature stress were analyzed using the interaction regulatory network method. The anti-freeze pathway of Lanzhou lily was found to involve the photosynthetic and metabolic pathways, and the key freezing resistance genes were the OLEO3 gene, 9 CBF family genes, and C2H2 transcription factor c117817_g1 (ZFP). This lays the foundation for revealing the underlying mechanism of the molecular anti-freeze mechanism in Lanzhou lily.

Integrated Transcriptome and Proteome Analyses of β-Conglycinin-Induced Intestinal Damage in Piglets.

β-conglycinin (β-CG) induces intestinal damage in piglets; however, its regulatory mechanisms are not fully understood. This study aimed to investigate the molecular mechanisms by which β-CG regulates intestinal injury in piglets through downstream genes and proteins. Our findings revealed that β-CG significantly reduced villus height while increasing the crypt depth. In addition, we analyzed the transcriptome and proteome of jejunum tissues after the β-CG treatment. In total, 382 differentially expressed genes (DEGs) and 292 differentially expressed proteins (DEPs) were identified between the treatment and the control groups. The expression levels of DEGs and DEPs were validated by using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting, respectively. The findings revealed a consistent correlation between their expression levels and transcriptomic and proteomic data. In addition, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs and DEPs revealed their enrichment in oxidation-related GOs, as well as in lysosome-related pathways. A protein-protein interaction (PPI) regulatory network was constructed based on the DEPs. The integration of transcriptomic and proteomic analyses identified six genes that were significantly different at both the transcript and the protein levels. This study provides valuable insights into the molecular mechanisms underlying β-CG-induced intestinal injury in piglets.

Integrated analysis of lncRNA, miRNA and mRNA expression profiles reveals regulatory pathways associated with pig testis function

Long noncoding RNA (lncRNA) and microRNA (miRNA) are known to play pivotal roles in mammalian testicular function and spermatogenesis. However, their impact on porcine male reproduction has yet to be well unraveled. Here, we sequenced and identified lncRNA and miRNA expressed in the testes of Chinese indigenous Banna mini-pig inbred line (BMI) and introduced Western Duroc (DU) and Large White (LW) pigs. By pairwise comparison (BMI vs DU, BMI vs LW, and DU vs LW), we found the gene expression differences in the testes between Chinese local pigs and introduced Western commercial breeds were more striking than those between introduced commercial breeds. Furthermore, we found 1622 co-differentially expressed genes (co-DEGs), 122 co-differentially expressed lncRNAs (co-DELs), 39 co-differentially expressed miRNAs (co-DEMs) in BMI vs introduced commercial breeds (DU and LW). Functional analysis revealed that these co-DEGs and co-DELs/co-DEMs target genes were enriched in male sexual function pathways, including MAPK, AMPK, TGF-β/Smad, Hippo, NF-kappa B, and PI3K/Akt signaling pathways. Additionally, we established 10,536 lncRNA-mRNA, 11,248 miRNA-mRNA pairs, and 62 ceRNA (lncRNA-miRNA-mRNA) networks. The ssc-miR-1343 had the most interactive factors in the ceRNA network, including 20 mRNAs and 3 lncRNAs, consisting of 56 ceRNA pairs. These factors played extremely important roles in the regulation of testis function as key nodes in the interactive regulatory network. Our results provide insight into the functional roles of lncRNAs and miRNAs in porcine testis and offer a valuable resource for understanding the differences between Chinese indigenous and introduced Western pigs.

Predicting Diagnostic Biomarkers Associated with Pyroptosis in Neuropathic Pain Based on Machine Learning and Experimental Validation.

Previous studies have shown that pyroptosis plays a vital role in the progress of neuropathic pain (NP), but the molecular mechanisms have not been fully elucidated. The aim of this study was to identify crucial pyroptosis-related genes (PRGs) in NP. We identified pyroptosis-related differentially expressed genes (PRDEGs) in NP by machine learning analysis of the GSE24982 and GSE60670 datasets. Furthermore, these PRDEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Friends analysis, respectively. Meanwhile, receiver operator characteristic (ROC) analysis was performed to assess the diagnostic value of PRDEGs in NP. Finally, we performed immune infiltration analysis of key PRDEGs using CIBERSORTR R package. We found that 5 PRDEGs by least absolute shrinkage and selection operator (LASSO) regression and random forest and verified by RT-qPCR. GO, KEGG and GSEA revealed that these PRDEGs were mainly enriched in regulation of neuron death, IL-4 signaling, IL-23 pathway, and NF-κB pathway. ROC analysis revealed that most of the PRDEGs performed well in diagnosing NP. We also revealed transcription factors, miRNA regulatory networks and drug interaction networks of PRDEGs. For immune infiltration analysis, PRDEGs were mainly correlated with dendritic cells, monocytes and follicular T helper cells, suggested that it might be involved in the regulation of neuroimmune-related signaling. A total of five PRDEGs were can be employed as NP biomarkers, particularly Tlr4, Il1b and Casp8, and provide additional evidence for a vital role of pyroptosis in NP.

Transcription Analysis of the THBS2 Gene through Regulation by Potential Noncoding Diagnostic Biomarkers and Oncogenes of Gastric Cancer in the ECM-Receptor Interaction Signaling Pathway: Integrated System Biology and Experimental Investigation.

Gastric cancer (GC) is the second most frequent cause of cancer-related death worldwide and the fourth most common malignancy. Despite significant improvements in patient survival over the past few decades, the prognosis for patients with GC remains dismal because of the high recurrence rate. In this comprehensive system biology and experimental investigation, we aimed to find new novel diagnostic biomarkers of GC through a regulatory RNA interaction network. Gene expression, coexpression, and survival analyses were performed using microarray and RNAseq datasets (analyzed by RStudio, GEPIA2, and ENCORI). RNA interaction analysis was performed using miRWalk and ENCORI online databases. Gene set enrichment analysis (GSEA) was performed to find related signaling pathways of up- and downregulated genes in the microarray dataset. Gene ontology and pathway enrichment analysis were performed by the enrichr database. Protein interaction analysis was performed by STRING online database. Validation of expression and coexpression analyses was performed using a qRT-PCR experiment. Based on bioinformatics analyses, THBS2 (FC: 7.14, FDR < 0.0001) has a significantly high expression in GC samples. lncRNAs BAIAP2-AS1, TSIX, and LINC01215 have RNA interaction with THBS2. BAIAP2-AS1 (FC: 1.44, FDR: 0.018), TSIX (FC: 1.34, FDR: 0.038), and LINC01215 (FC: 1.19, FDR: 0.046) have significant upregulation in GC samples. THBS2 has a significant role in the regulation of the ECM-receptor signaling pathway. miR-4677-5p has a significant RNA interaction with THBS2. The expression level of THBS2, BAIAP2-AS1, TSIX, and LINC01215 has a nonsignificant negative correlation with the survival rate of GC patients (HR: 0.28, logrank p: 0.28). qRT-PCR experiment validates mentioned bioinformatics expression analyses. BAIAP2-AS1 (AUC: 0.7136, p value: 0.0096), TSIX (AUC: 0.7456, p value: 0.0029), and LINC01215 (AUC: 0.7872, p value: 0.0005) could be acceptable diagnostic biomarkers of GC. BAIAP2-AS1, lncRNA LINC01215, lncRNA TSIX, and miR-4677-5p might modulate the ECM-receptor signaling pathway via regulation of THBS2 expression level, as the high-expressed noncoding RNAs in GC. Furthermore, mentioned lncRNAs could be considered potential diagnostic biomarkers of GC.

Genome-Wide Identification of Cotton MicroRNAs Predicted for Targeting Cotton Leaf Curl Kokhran Virus-Lucknow

Cotton leaf curl Kokhran virus (CLCuKoV) (genus, Begomovirus; family, Geminiviridae) is one of several plant virus pathogens of cotton (Gossypium hirsutum L.) that cause cotton leaf curl disease in Pakistan. Begomoviruses are transmitted by the whitefly Bemisia tabaci cryptic species group and cause economic losses in cotton and other crops worldwide. The CLCuKoV strain, referred to as CLCuKoV-Bur, emerged in the vicinity of Burewala, Pakistan, and was the primary causal virus associated with the second CLCuD epidemic in Pakistan. The monopartite ssDNA genome of (2.7 Kb) contains six open reading frames that encode four predicted proteins. RNA interference (RNAi)-mediated antiviral immunity is a sequence-specific biological process in plants and animals that has evolved to combat virus infection. The objective of this study was to design cotton locus-derived microRNA (ghr-miRNA) molecules to target strains of CLCuKoV, with CLCuKoV-Lu, as a typical CLCuD-begomovirus genome, predicted by four algorithms, miRanda, RNA22, psRNATarget, and RNA hybrid. Mature ghr-miRNA sequences (n = 80) from upland cotton (2n = 4x = 52) were selected from miRBase and aligned with available CLCuKoV-Lu genome sequences. Among the 80 cotton locus-derived ghr-miRNAs analyzed, ghr-miR2950 was identified as the most optimal, effective ghr-miRNA for targeting the CLCuKoV-Lu genome (nucleotide 82 onward), respectively, based on stringent criteria. The miRNA targeting relies on the base pairing of miRNA–mRNA targets. Conservation and potential base pairing of binding sites with the ghr-miR2950 were validated by multiple sequence alignment with all available CLCuKoV sequences. A regulatory interaction network was constructed to evaluate potential miRNA–mRNA interactions with the predicted targets. The efficacy of miRNA targeting of CLCuKoV was evaluated in silico by RNAi-mediated mRNA cleavage. This predicted targets for the development of CLCuD-resistant cotton plants.

SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks.

Silencers are repressive cis-regulatory elements that play crucial roles in transcriptional regulation. Experimental methods for identifying silencers are always costly and time-consuming. Computational methods, which relies on genomic sequence features, have been introduced as alternative approaches. However, silencers do not have significant epigenomic signature. Therefore, we explore a new way to computationally identify silencers, by incorporating chromatin structural information. We propose the SilenceREIN method, which focuses on finding silencers on anchors of chromatin loops. By using graph neural networks, we extracted chromatin structural information from a regulatory element interaction network. SilenceREIN integrated the chromatin structural information with linear genomic signatures to find silencers. The predictive performance of SilenceREIN is comparable or better than other states-of-the-art methods. We performed a genome-wide scanning to systematically find silencers in human genome. Results suggest that silencers are widespread on anchors of chromatin loops. In addition, enrichment analysis of transcription factor binding motif support our prediction results. As far as we can tell, this is the first attempt to incorporate chromatin structural information in finding silencers. All datasets and source codes of SilenceREIN have been deposited in a GitHub repository (https://github.com/JianHPan/SilenceREIN).

Chromatin priming elements direct tissue-specific gene activity before hematopoietic specification.

Tissue-specific gene regulation during development involves the interplay between transcription factors and epigenetic regulators binding to enhancer and promoter elements. The pattern of active enhancers defines the cellular differentiation state. However, developmental gene activation involves a previous step called chromatin priming which is not fully understood. We recently developed a genome-wide functional assay that allowed us to functionally identify enhancer elements integrated in chromatin regulating five stages spanning the in vitro differentiation of embryonic stem cells to blood. We also measured global chromatin accessibility, histone modifications, and transcription factor binding. The integration of these data identified and characterised cis-regulatory elements which become activated before the onset of gene expression, some of which are primed in a signalling-dependent fashion. Deletion of such a priming element leads to a delay in the up-regulation of its associated gene in development. Our work uncovers the details of a complex network of regulatory interactions with the dynamics of early chromatin opening being at the heart of dynamic tissue-specific gene expression control.

Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions.

The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.

Ultrafine mapping of chromosome conformation at hundred basepair resolution reveals regulatory genome architecture.

The resolution limit of chromatin conformation capture methodologies (3Cs) has restrained their application in detection of fine-level chromatin structure mediated by cis-regulatory elements (CREs). Here, we report two 3C-derived methods, Tri-4C and Tri-HiC, which utilize multirestriction enzyme digestions for ultrafine mapping of targeted and genome-wide chromatin interaction, respectively, at up to one hundred basepair resolution. Tri-4C identified CRE loop interaction networks and quantitatively revealed their alterations underlying dynamic gene control. Tri-HiC uncovered global fine-gauge regulatory interaction networks, identifying >20-fold more enhancer:promoter (E:P) loops than in situ Hi-C. In addition to vastly improved identification of subkilobase-sized E:P loops, Tri-HiC also uncovered interaction stripes and contact domain insulation from promoters and enhancers, revealing their loop extrusion behaviors resembling the topologically associating domain boundaries. Tri-4C and Tri-HiC provide robust approaches to achieve the high-resolution interactome maps required for characterizing fine-gauge regulatory chromatin interactions in analysis of development, homeostasis, and disease.

Bioinformatic analysis of the ABC transporter protein family and their function in Penicillium digitatum

ATP-binding cassette transporter (ABC transporter) has prominent effects on the normal growth, development, substance transport and drug detoxification of organisms. However, the function of most of the ABC family members in filamentous fungi remains unknown. In the present research, 171 ABCs were identified from fruit postharvest pathogenic Penicillium spp. fungi including P. digitatum, P. expansum and P. italicum, of which 45 were from P. digitatum. Bioinformatics analysis showed that all 45 PdABCs had ABC_tran domain, belonging to seven subfamilies and containing 1–12 introns, distributed on six chromosomes of P. digitatum. The transcriptome data of spore germination, infection and sodium dehydroacetate (SD) inhibition showed that 43 PdABCs were detected as expressed in at least one stage, 6 PdABCs were all significantly differenially expressed during spore germination, infection or SD repression, five, eight and six PdABCs were significantly differentially expressed at the spore germination, infection and SD repression stages, respectively. RT-qPCR experiments further validated these six co-differentially expressed PdABCs. Cis-acting element (CAE) analysis showed that C2H2, C6 and Heat shock factors family-specific binding elements were highly present in their promoters, and the interaction regulatory network analysis indicated that there were interactions between PdABCs and PdABCs, major facilitator superfamily (MFS) transporter proteins, cytochrome P450 proteins, TFs (PdRING1、PdHeat1、PdbZIP1、PdzincC6). This study contributes to the screening of key ABCs and provides foundations for further investigation into the function of PdABCs and the creation of novel antifungal drugs to combat citrus green mold.

Identification of target hub genes and construction of a novel miRNA regulatory network in autism spectrum disorder by integrated analysis.

The incidence of autism spectrum disorder (ASD) is increasing year by year in children. The aim of the study was to find possible biomarkers for ASD diagnosis as well as examine MicroRNA (miRNA) signatures and crucial pathways. We conducted a two-stage study to explore potential target genes and functional miRNAs. Peripheral blood samples of children with ASD were enrolled and performed RNA sequencing analysis. The overlapped candidate genes were further screened in combination with differentially expressed genes (DEGs) of GSE77103 datasets. STRING established a protein-protein interaction network comprising DEGs. The hub genes were filtered out using the CytoHubba. Then, we set up a miRNA-mRNA regulatory network. Correlational analyses between hub genes and immune cells associated with ASD were carried out using the CIBERSORT software to assess the diversity of immune cell types in ASD. RNA-sequencing analysis was used to confirm the differential expression of 3 hub genes. Briefly, after blood samples were sequenced interrogating 867 differential genes in our internal screening dataset. After screening GEO databases, 551 DEGs obtained from GSE77103. Fourteen common genes were overlapped through DEGs of GEO datasets and internal screening dataset. Among protein-protein interaction network, 10 hub genes with high degree algorithm were screened out and 3 hub genes of them - ADIPOR1, LGALS3, and GZMB - that were thought to be most associated with the emergence of ASD. Then, we developed a network of miRNA-mRNA regulatory interactions by screening miRNAs (such as hsa-miR-20b-5p, hsa-miR-17-5p, and hsa-miR-216b-5p) that were closely associated to 3 hub genes. Additionally, we discovered 18 different immune cell types associated with ASD using the CIBERSORT algorithm, and we discovered that mononuclear macrophages differed considerably between the 2 groups. Overall, 3 hub genes (ADIPOR1, LGALS3, and GZMB) and 15 candidates miRNAs-target 3 genes regulatory pathways representing potentially novel biomarkers of ASD diseases were revealed. These findings could enhance our knowledge of ASD and offer possible therapeutic targets of ASD patients in the future.

ScKINETICS: inference of regulatory velocity with single-cell transcriptomics data.

Transcriptional dynamics are governed by the action of regulatory proteins and are fundamental to systems ranging from normal development to disease. RNA velocity methods for tracking phenotypic dynamics ignore information on the regulatory drivers of gene expression variability through time. We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. Fitting is accomplished through an expectation-maximization approach designed to learn the impact of each regulator on its target genes, leveraging biologically motivated priors from epigenetic data, gene-gene coexpression, and constraints on cells' future states imposed by the phenotypic manifold. Applying this approach to an acute pancreatitis dataset recapitulates a well-studied axis of acinar-to-ductal transdifferentiation whilst proposing novel regulators of this process, including factors with previously appreciated roles in driving pancreatic tumorigenesis. In benchmarking experiments, we show that scKINETICS successfully extends and improves existing velocity approaches to generate interpretable, mechanistic models of gene regulatory dynamics. All python code and an accompanying Jupyter notebook with demonstrations are available at http://github.com/dpeerlab/scKINETICS.

Dynamics-Based Regulatory Switches of Type II Antitoxins: Insights into New Antimicrobial Discovery.

Type II toxin-antitoxin (TA) modules are prevalent in prokaryotes and are involved in cell maintenance and survival under harsh environmental conditions, including nutrient deficiency, antibiotic treatment, and human immune responses. Typically, the type II TA system consists of two protein components: a toxin that inhibits an essential cellular process and an antitoxin that neutralizes its toxicity. Antitoxins of type II TA modules typically contain the structured DNA-binding domain responsible for TA transcription repression and an intrinsically disordered region (IDR) at the C-terminus that directly binds to and neutralizes the toxin. Recently accumulated data have suggested that the antitoxin's IDRs exhibit variable degrees of preexisting helical conformations that stabilize upon binding to the corresponding toxin or operator DNA and function as a central hub in regulatory protein interaction networks of the type II TA system. However, the biological and pathogenic functions of the antitoxin's IDRs have not been well discussed compared with those of IDRs from the eukaryotic proteome. Here, we focus on the current state of knowledge about the versatile roles of IDRs of type II antitoxins in TA regulation and provide insights into the discovery of new antibiotic candidates that induce toxin activation/reactivation and cell death by modulating the regulatory dynamics or allostery of the antitoxin.

Identification of anoikis-related genes classification patterns and immune infiltration characterization in ischemic stroke based on machine learning.

Ischemic stroke (IS) is a type of stroke that leads to high mortality and disability. Anoikis is a form of programmed cell death. When cells detach from the correct extracellular matrix, anoikis disrupts integrin junctions, thus preventing abnormal proliferating cells from growing or attaching to an inappropriate matrix. Although there is growing evidence that anoikis regulates the immune response, which makes a great contribution to the development of IS, the role of anoikis in the pathogenesis of IS is rarely explored. First, we downloaded GSE58294 set and GSE16561 set from the NCBI GEO database. And 35 anoikis-related genes (ARGs) were obtained from GSEA website. The CIBERSORT algorithm was used to estimate the relative proportions of 22 infiltrating immune cell types. Next, consensus clustering method was used to classify ischemic stroke samples. In addition, we used least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms to screen the key ARGs in ischemic stroke. Next, we performed receiver operating characteristics (ROC) analysis to assess the accuracy of each diagnostic gene. At the same time, the nomogram was constructed to diagnose IS by integrating trait genes. Then, we analyzed the correlation between gene expression and immune cell infiltration of the diagnostic genes in the combined database. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on these genes to explore differential signaling pathways and potential functions, as well as the construction and visualization of regulatory networks using NetworkAnalyst and Cytoscape. Finally, we investigated the expression pattern of ARGs in IS patients across age or gender. Our study comprehensively analyzed the role of ARGs in IS for the first time. We revealed the expression profile of ARGs in IS and the correlation with infiltrating immune cells. And The results of consensus clustering analysis suggested that we can classify IS patients into two clusters. The machine learning analysis screened five signature genes, including AKT1, BRMS1, PTRH2, TFDP1 and TLE1. We also constructed nomogram models based on the five risk genes and evaluated the immune infiltration correlation, gene-miRNA, gene-TF and drug-gene interaction regulatory networks of these signature genes. The expression of ARGs did not differ by sex or age. This study may provide a beneficial reference for further elucidating the pathogenesis of IS, and render new ideas for drug screening, individualized therapy and immunotherapy of IS.

Potential biomarkers for the prognosis and treatment of HCC immunotherapy.

The liver is a unique organ containing large populations of immune cells. Immunotherapy for liver cancer is a promising yet particularly challenging method. Therefore, it harbors great significance for the identification of immune-related subtypes and the potential therapeutic targets for hepatocellular carcinoma (HCC). Firstly, we classified the HCC samples downloaded from the dataset of Cancer Genome Atlas (TCGA) into two clusters based on the immune cell infiltration. Thereafter, we identified the significant module and regulatory factors using the weighted gene co-expression network analysis (WGCNA). The immune competence of the regulatory factors was delineated through the ESTIMATE algorithm, the analysis of the tumor microenvironment, and pan-cancer analysis. In the single-cell RNA sequencing analysis, we further explored the immune competence of regulatory factors. We also collected the potential drugs targeting the regulatory factors. In addition, we constructed lncRNA-miRNA-mRNA interaction regulatory networks. Finally, western blot and quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to verify the protein expression of regulatory genes in HCC cell lines and tissues. According to the immune cell infiltration, two immune-related subtypes-cluster 1 and cluster 2-were found. Patients in cluster 2 had a more significant immune infiltration than in cluster 1. Afterward, six significant regulatory genes were identified through WGCNA, and the expression in cluster 2 was high in cluster 1. We performed a comprehensive analysis to clarify the immune signature. The results showed that the six genes had significant immunological competence. Moreover, the expression of the six genes was similar to the subtypes' classification. In the analysis of the prognosis value, patients in cluster 2 had a better prognosis. In addition, the lncRNA in the lncRNA-miRNA-mRNA interaction regulatory networks was located in the nucleus and cytoplasm. In the single-cell RNA sequencing analysis, the six genes were related to the immune cell. We also identified potential drugs for CD6 and CLEC12A, which may provide potential therapeutic drugs. Finally, the regulatory genes were verified in the western blot and quantitative real-time polymerase chain reaction. The classification into two clusters based on the immune cell infiltration may provide a promising prospect for HCC through immunotherapy. The six regulatory genes may be potential therapeutic targets in the treatment of HCC.