Briefings in Functional Genomics最新文献

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.

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.

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.

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.

Non-coding RNAs (ncRNAs) are RNA molecules that are transcribed from DNA but are not translated into proteins. Studies over the past decades have revealed that ncRNAs can be classified into small RNAs, long non-coding RNAs and circular RNAs by genomic size and structure. Accumulated evidences have eludicated the critical roles of these non-coding transcripts in regulating gene expression through transcription and translation, thereby shaping cellular function and disease pathogenesis. Notably, recent studies have investigated the function of ncRNAs as competitive endogenous RNAs (ceRNAs) that sequester miRNAs and modulate mRNAs expression. The ceRNAs network emerges as a pivotal regulatory function, with significant implications in various diseases such as cancer and neurodegenerative disease. Therefore, we highlighted multiple bioinformatics tools and databases that aim to predict ceRNAs interaction. Furthermore, we discussed limitations of using current technologies and potential improvement for ceRNAs network detection. Understanding of the dynamic interplay within ceRNAs may advance the biological comprehension, as well as providing potential targets for therapeutic intervention.

Molecular epidemiology of Foot-and-mouth disease (FMD) is crucial to implement its control strategies including vaccination and containment, which primarily deals with knowing serotype, topotype, and lineage of the virus. The existing approaches including serotyping are biological in nature, which are time-consuming and risky due to live virus handling. Thus, novel computational tools are highly required for large-scale molecular epidemiology of the FMD virus. This study reported a comprehensive computational tool for FMD molecular epidemiology. Ten learning algorithms were initially evaluated on cross-validated and ten independent secondary datasets for serotype prediction using sequence-based features through accuracy, sensitivity and 14 other metrics. Next, best performing algorithms, with higher serotype predictive accuracies, were evaluated for topotype and lineage prediction using cross-validation. These algorithms are implemented in the computational tool. Then, performance of the developed approach was assessed on five independent secondary datasets, never seen before, and primary experimental data. Our cross-validated and independent evaluation of learning algorithms for serotype prediction revealed that support vector machine, random forest, XGBoost, and AdaBoost algorithms outperformed others. Then, these four algorithms were evaluated for topotype and lineage prediction, which achieved accuracy ≥96% and precision ≥95% on cross-validated data. These algorithms are implemented in the web-server (https://nifmd-bbf.icar.gov.in/MolEpidPred), which allows rapid molecular epidemiology of FMD virus. The independent validation of the MolEpidPred observed accuracies ≥98%, ≥90%, and ≥ 80% for serotype, topotype, and lineage prediction, respectively. On wet-lab data, the MolEpidPred tool provided results in fewer seconds and achieved accuracies of 100%, 100%, and 96% for serotype, topotype, and lineage prediction, respectively, when benchmarked with phylogenetic analysis. MolEpidPred tool provides an innovative platform for large-scale molecular epidemiology of FMD virus, which is crucial for tracking FMD virus infection and implementing control program.

Melanoma is characterized by its rapid progression and high mortality rates, making early and accurate detection essential for improving patient outcomes. This paper presents a comprehensive review of significant advancements in early melanoma detection, with a focus on integrating computer vision and deep learning techniques. This study investigates cutting-edge neural networks such as YOLO, GAN, Mask R-CNN, ResNet, and DenseNet to explore their application in enhancing early melanoma detection and diagnosis. These models were critically evaluated for their capacity to enhance dermatological imaging and diagnostic accuracy, crucial for effective melanoma treatment. Our research demonstrates that these AI technologies refine image analysis and feature extraction, and enhance processing capabilities in various clinical settings. Additionally, we emphasize the importance of comprehensive dermatological datasets such as PH2, ISIC, DERMQUEST, and MED-NODE, which are crucial for training and validating these sophisticated models. Integrating these datasets ensures that the AI systems are robust, versatile, and perform well under diverse conditions. The results of this study suggest that the integration of AI into melanoma detection marks a significant advancement in the field of medical diagnostics and is expected to have the potential to improve patient outcomes through more accurate and earlier detection methods. Future research should focus on enhancing these technologies further, integrating multimodal data, and improving AI decision interpretability to facilitate clinical adoption, thus transforming melanoma diagnostics into a more precise, personalized, and preventive healthcare service.

Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.