Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF–target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF–target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF–target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species.

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Intestinal microbiota has been extensively studied in the context of host health benefit, and it has recently become clear that the gut microbiota influences drug pharmacokinetics and correspondingly efficacy. Intestinal microbiota dysbiosis is closely related with liver cirrhosis, especially the depletion of Lactobacillus. Therefore, the bioavailability of orally administered glycyrrhizic acid (GL) was speculated to be influenced under a pathological state. In the present study, L. murinus was isolated and screened for GL bioconversion capacity in vitro. Compared with Lactobacillus rhamnosus and Lactobacillus acidophilus, L. murinus was chosen for further investigation because it has the highest biotransformation rate. Our results showed that L. murinus could significantly improve the translocation of GL on Caco-2 cell models. Meanwhile, L. murinus was observed to have the ability to bind with the surface of Caco-2 cells and prominently downregulate the transporter gene expression level of multidrug resistance gene 1 (MDR1) and multidrug resistance protein 2 (MRP2), which were involved in the efflux of drugs. Furthermore, L. murinus was selected to be orally administred into rats in healthy and liver cirrhosis groups by a daily gavage protocol. Our data highlighted that supplements of L. murinus significantly improved the bioavailability of orally administered GL in rats, especially under a pathological condition, which may provide a novel strategy for improving the clinical therapeutic effect of liver protective drugs.

Inchworms have been one of the most widely used bionic templates for designing soft robotic devices. Bioresearch has shown that muscles of inchworms exhibit nonlinear hysteresis and their body structures are with hydrostatic skeleton. But effects of these properties on their dynamic movements have not been studied yet. In this work, a dynamic model based on the principle of virtual power of an inchworm is established to examine the problem. A spring-damper model with time-varying stiffness and damping coefficients is used to model controllable nonlinear properties of the inchworm muscles. The hydrostatic skeleton is applied to the model as a constant volume constraint for each segment. Based on this, simulations of three typical movements including omega-shaped arching motion, cantilevered probing motion and surprising fast looping motion are presented. The effects of the nonlinear properties including variable stiffness and damping properties of muscles on these dynamic behaviors of inchworms are illustrated. Some inspiration for designing bio-inspired crawling robots and soft slender robotic devices is obtained. And we think this work will hopefully provide better understanding and guidance for design and control of these robotic devices.

This study analyzes the correlation and interaction of miRNAs and mRNAs and their biological function in the malignant transformation of BEAS-2B cells induced by cigarette smoke (CS). Normal human bronchial epithelial cells (BEAS-2B) were continuously exposed to CS for 30 passages (S30) to establish an in vitro cell model of malignant transformation. The transformed cells were validated by scratch wound healing assay, transwell migration assay, colony formation and tumorigenicity assay. The miRNA and mRNA sequencing analysis were performed to identify differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) between normal BEAS-2B and S30 cells. The miRNA-seq data of lung cancer with corresponding clinical data obtained from TCGA was used to further identify lung cancer-related DEMs and their correlations with smoking history. The target genes of these DEMs were predicted using the miRDB database, and their functions were analyzed using the online tool “Metascape.” It was found that the migration ability, colony formation rate and tumorigenicity of S30 cells enhanced. A total of 42 miRNAs and 753 mRNAs were dysregulated in S30 cells. The change of expression of top five DEGs and DEMs were consistent with our sequencing results. Among these DEMs, eight miRNAs were found dysregulated in lung cancer tissues based on TCGA data. In these eight miRNAs, six of them including miR-96-5p, miR-93-5p, miR-106-5p, miR-190a-5p, miR-195-5p, and miR-1-3p, were found to be associated with smoking history. Several DEGs, including THBS1, FN1, PIK3R1, CSF1, CORO2B, and PREX1, were involved in many biological processes by enrichment analysis of miRNA and mRNA interaction. We identified the negatively regulated miRNA-mRNA pairs in the CS-induced lung cancer, which were implicated in several cancer-related (especially EMT-related) biological process and KEGG pathways in the malignant transformation progress of lung cells induced by CS. Our result demonstrated the dysregulation of miRNA-mRNA profiles in cigarette smoke-induced malignant transformed cells, suggesting that these miRNAs might contribute to cigarette smoke-induced lung cancer. These genes may serve as biomarkers for predicting lung cancer pathogenesis and progression. They can also be targets of novel anticancer drug development.