Moraxella catarrhalis is a symbiotic as well as mucosal infection-causing bacterium unique to humans. Currently, it is considered as one of the leading factors of acute middle ear infection in children. As M. catarrhalis is resistant to multiple drugs, the treatment is unsuccessful; therefore, innovative and forward-thinking approaches are required to combat the problem of antimicrobial resistance (AMR). To better comprehend the numerous processes that lead to antibiotic resistance in M. catarrhalis, we have adopted a computational method in this study. From the NCBI-Genome database, we investigated 12 strains of M. catarrhalis. We explored the interaction network comprising 74 antimicrobial-resistant genes found by analyzing M. catarrhalis bacterial strains. Moreover, to elucidate the molecular mechanism of the AMR system, clustering and the functional enrichment analysis were assessed employing AMR gene interactions networks. According to the findings of our assessment, the majority of the genes in the network were involved in antibiotic inactivation; antibiotic target replacement, alteration and antibiotic efflux pump processes. Additionally, rpoB, atpA, fusA, groEL and rpoL have the highest frequency of relevant interactors in the interaction network and are therefore regarded as the hub nodes. These hub genes only reflects their centrality in cellular function, rather than direct or selective targets for antimicrobial development without reservation. Finally, we believe that our findings could be useful to advance knowledge of the AMR system present in M. catarrhalis via a series of phenotypic assays including MIC testing, and gene expression analysis (RT-qPCR) to confirm the functional expression of AMR genes.

Renal cell carcinoma (RCC) is one of the most prevalent solid tumors, and chromophobe renal cell carcinoma (chRCC) is its third most common subtype. The cuproptosis has become a hot topic in the field of cancer treatment. This study aimed to investigate the potential targets of cuproptosis in chRCC cells. We first downloaded the chRCC mRNA transcriptome data from The Cancer Genome Atlas. Based on the previous reports, we speculated that the expression of LIPT1 was considerably down-regulated in chRCC tissues. The upstream transcription factor (TF) EGR1 was predicted by the hTFtarget web tool, and the interaction between EGR1 and LIPT1 was further verified by dual-luciferase and chromatin immunoprecipitation experiments. The mRNA expression levels of EGR1 and LIPT1 were detected by quantitative polymerase chain reaction. The expression levels of target protein LIPT1 and cuproptosis-associated protein were detected by western blot and immunofluorescence. Cell Counting Kit-8 assay was employed to detect the viability of RCC98 cells. The Transwell assay was utilized to assess the migration and invasion abilities of RCC98 cells. LIPT1 and its upstream TF, EGR1, were significantly down-regulated in chRCC tissues and cells. EGR1 could transcriptionally activate LIPT1. Additionally, overexpression of LIPT1 significantly reduced the cancer-associated malignant phenotype of chRCC and elevated the sensitivity of RCC98 cells to cuproptosis. However, on this basis, knocking down EGR1 restored the anti-cancer effect conferred by overexpression of LIPT1. This work aimed to investigate the transcriptional activation of LIPT1 by EGR1 in RCC98 cells to repress the malignant progression of cancer cells while enhancing the sensitivity of RCC98 cells to cuproptosis.

Genomic imprinting is an epigenetic occurrence that results in the expression of alleles specific to the parent of origin, plays pivotal roles in plant development, stress adaptation, and agronomic trait regulation. While imprinting has been intensively investigated in model plants (e.g. Arabidopsis, maize, and rice), its dynamic regulatory mechanisms and evolutionary implications remain enigmatic. Recent advances in bioinformatics—including single-cell omics, machine learning, and deep learning—have revolutionized the identification, functional annotation, and network modeling of imprinted genes. This review not only provides a detailed summary of the identification, functions and regulatory mechanisms of plant imprinted genes, but also systematically summarizes methodologies for studying plant genomic imprinting, highlights challenges in multi-omics data integration, and envisions artificial intelligence–driven strategies for epigenetic breeding.

Liver cancer (LC) is the second leading cause of cancer-related deaths globally, yet the molecular mechanisms linking its progression with associated risk factors (RFs) remain poorly understood. To address this, we developed an integrative multi-stage framework combining bioinformatics, machine learning-based feature selection, survival modeling, and network analysis to identify robust biomarkers and pathways involved in LC progression. Unlike conventional biomarker discovery approaches, our strategy integrates multi-cohort transcriptomic and clinical datasets, enhancing robustness and reliability of findings. Initially, differentially expressed genes were identified from three Gene Expression Omnibus datasets for LC and its RFs. Next, using shared biomarkers, we constructed a gene-disease association (diseasome) network, revealing 230 unique genes, including 126 shared between LC and liver cirrhosis. Subsequently, RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were analyzed through combined and multivariate Cox survival models, identifying 70 prognostic genes. Among these, we identified RGS5, SULT1C2, CSM3, and CXCL14 as consistent survival-associated markers. Functional investigation of the 70 genes using enrichment and protein–protein interaction networks uncovered ten hub genes involved in key oncogenic pathways, including Oocyte meiosis, Lysine degradation and cell cycle regulation. These findings were further validated through literature and expression-level analysis. Additionally, an independent survival analysis using the full TCGA transcriptomic dataset identified 76 significant genes, with 18 overlapping the risk-associated gene set, reinforcing their prognostic value. Overall, this study demonstrates the potential of an integrative computational approach to uncover meaningful biomarkers and pathways in LC, offering valuable insights for future clinical and therapeutic strategies.

We developed an ordinary differential equations (ODEs) model of hypoxia signaling that, in addition to HIF-1α, takes into account also HIF-2α. Our model can be separated into two parts, the first, describing the production and degradation of the α subunits of HIF-1 and HIF-2, and their accumulation in response to hypoxia; and the second, describing how the α subunits cooperate with the β subunit in binding to cis-regulatory regions and activation of HIF-target genes in response to hypoxia. In our previous work [1], using the first part of our model trained on time-series data from 0.9 % hypoxia, we successfully predicted the response of the system to a further drop of the oxygen to 0.3 % hypoxia. This modeling result contributed to explaining the mechanism of the switch of the control from HIF-1 to HIF-2 during the response of human primary endothelial cells to hypoxia. In another work [2], we experimentally demonstrated a linear proportionality between the counts of motifs assigned to HIF-1 in promoter open chromatin regions of genes and the effects of HIF-1 on the induction of these genes under hypoxia. We furthermore showed that such a proportionality is predicted by the subset of the ODE model of Nguyen et al. (2013) [3] common with the second part of our ODE model. In the current work, we provide the details of our full ODE model and show that it leads to a prediction that HIF-1β can be a limiting factor of the response to hypoxia.

Thyroid cancer is one of the most common endocrine diseases worldwide with phenotypic heterogeneity. Deubiquitinating enzymes (DUBs) participated in ubiquitin (Ub) conjugases-induced signal by removing Ub from the substrates. Dysregulation of DUBs are associated with cancer progression, including thyroid carcinoma. In this review, we outline the main classification and structure of DUBs, the expression of DUBs in thyroid cancer, the association of DUBs with survival, and the possible mechanism of DUBs in thyroid cancer progression. Finally, we summarized the development of USP specific inhibitors, the strategies for designing and identifying selective inhibitors.

Spatial transcriptomics has revolutionized our ability to measure gene expression while preserving spatial information, thus facilitating detailed analysis of tissue structure and function. Identifying spatial domains accurately is key for understanding tissue microenvironments and biological progression. To overcome the challenge of integrating gene expression data with spatial information, we introduce the VARGG deep learning framework. VARGG combines a pretrained Vision Transformer (ViT) with a graph neural network autoencoder, utilizing ViT’s self-attention mechanism to capture global contextual information and enhance understanding of spatial relationships. This framework is further enhanced by multi-layer gated residual graph neural networks and Gaussian noise, which improve feature representation and model generalizability across different data sources. The robustness and scalability of VARGG have been verified on different platforms (10x Visium, Slide-seqV2, Stereo-seq, and MERFISH) and datasets of different sizes (human glioblastoma, mouse embryo, breast cancer). Our results demonstrate that VARGG’s ability to accurately delineate spatial domains can provide a deeper understanding of tissue structure and help identify key molecular markers and potential therapeutic targets, thereby improving our understanding of disease mechanisms and providing opportunities for personalization to inform the development of treatment strategies.