Briefings in Functional Genomics最新文献

High-throughput gene expression data have been extensively generated and utilized in biological mechanism investigations, biomarker detection, disease diagnosis and prognosis. These applications encompass not only bulk transcriptome, but also single cell RNA-seq data. However, extracting reliable biological information from transcriptome data remains challenging due to the constrains of Compositional Data Analysis. Current data preprocessing methods, including dataset normalization and batch effect correction, are insufficient to address these issues and improve data quality for downstream analysis. Alternatively, qualification methods focusing on the relative order of gene expression (ROGER) are more informative than the quantification methods that rely on gene expression abundance. The Pairwise Analysis of Gene expression method is an enhancement of ROGER, designed for data integration in either sample space or feature space. In this review, we summarize the methods applied to transcriptome data analysis and discuss their potentials in predicting clinical outcomes.

Objective: The primary objective of this study is to investigate various applications of artificial intelligence (AI) and statistical methodologies for analyzing and managing peritoneal metastases (PM) caused by gastrointestinal cancers.

Methods: Relevant keywords and search criteria were comprehensively researched on PubMed and Google Scholar to identify articles and reviews related to the topic. The AI approaches considered were conventional machine learning (ML) and deep learning (DL) models, and the relevant statistical approaches included biostatistics and logistic models.

Results: The systematic literature review yielded nearly 30 articles meeting the predefined criteria. Analyses of these studies showed that AI methodologies consistently outperformed traditional statistical approaches. In the AI approaches, DL consistently produced the most precise results, while classical ML demonstrated varied performance but maintained high predictive accuracy. The sample size was the recurring factor that increased the accuracy of the predictions for models of the same type.

Conclusions: AI and statistical approaches can detect PM developing among patients with gastrointestinal cancers. Therefore, if clinicians integrated these approaches into diagnostics and prognostics, they could better analyze and manage PM, enhancing clinical decision-making and patients' outcomes. Collaboration across multiple institutions would also help in standardizing methods for data collection and allowing consistent results.

The misuse and overprescription of antibiotics have accelerated the rise of antimicrobial resistance (AMR), rendering many antibiotics ineffective and leading to significant clinical challenges. The conventional treatment methods have become progressively challenging, posing a threat of evolving into an impending silent pandemic. The long track record of bacteriophages combating bacterial infections has renewed hope into the potential therapeutic benefits of bacteriophages. Bacteriophage therapy offers a promising alternative to antibiotics, particularly against multidrug-resistant (MDR) pathogens. This article explores the promise of phages as a potential means to combat superbugs from the perspective of the genomic and transcriptomic landscape of the phages and their bacterial host. Advances in bacteriophage genomics have expedited the detection of new phages and AMR genes, enhancing our understanding of phage-host interactions and enabling the identification of potential treatments for antibiotic-resistant bacteria. At the same time, holo-transcriptomic studies hold potential for discovering disease and context-specific transcriptionally active phages vis-à-vis disease severity. Holo-transcriptomic profiling can be applied to investigate the presence of AMR-bacteria, highlighting COVID-19 and Dengue diseases, in addition to the globally recognized ESKAPE pathogens. By simultaneously capturing phage, bacterial and host transcripts, this approach enables a better comprehension of the bacteriophage dynamics. Moreover, insight into these defence and counter-defence interactions is essential for augmenting the adoption of phage therapy at scale and advancing bacterial control in clinical settings.

MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors. Despite the fact that numerous MDM2 inhibitors and degraders have been assessed in clinical studies for various human cancers, no FDA-approved drug targeting MDM2 is presently available in the market. Researchers have investigated the effects of various drugs, which are involved in cancer therapies with known mechanisms, on well-characterized cancer cell lines. The prediction of drug inhibition responses becomes crucial to enhance the effectiveness and personalization of cancer treatments. Such findings can provide new perceptions aimed at designing new drugs for targeted cancer therapies. In our current insilico work, a robust response was observed for Idasanutlin in cancer cell lines, indicating the drug's significant impact on gene expression. We also identified transcriptional response signatures, which were informative about the drug's mechanism of action and potential clinical application. Further, we applied a similarity search approach for the identification of potential lead compounds from the ChEMBL database and validated them by molecular docking and dynamics studies. The study highlights the potential of incorporating machine learning with omics and single-cell RNA-seq data for predicting drug responses in cancer cells. Our findings could provide valuable insights for improving cancer treatment in the future, particularly in developing effective therapies.

Recently, the mRNA presence of pregnancy-specific glycoproteins (PSGs) in cancer biopsies has been shown to be associated with poor survival. Given the pregnancy-related function of PSGs, we hypothesized that PSGs might act in a sex-dependent behavior in cancer patients. A differential sex effect of PSG genes with respect to tumor immune landscape and cancer outcomes was investigated using statistical, bioinformatic, and machine learning analyses in The Cancer Genome Atlas (TCGA) data. The resulting findings were then validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data. In a pan-cancer TCGA data analysis, the strongest PSG-related sex difference for the prognostic association was found in lung adenocarcinoma (LUAD). Kaplan-Meier analysis revealed that expression of PSG genes is strongly associated with overall survival rate in the female group on the TCGA, but not in the male group. This sex-specific association was validated in an independent dataset from the CPTAC study. A combination of PSG3, PSG7, and PSG8 expression was most significantly linked to poor prognosis in females (P = 8.67E-06 in TCGA and P = .0382 in CPTAC). Pathway analysis revealed enrichment of the 'KRAS Signaling Down' pathway in the high-risk female group. A predictive model showed good predictive performance for the female group (validated C-index = 0.78 in CPTAC), but poor predictive performance for the male group. These findings suggest that PSGs may have a sex-specific negative impact on survival in female LUAD patients, and the mechanism may be related to KRAS signaling pathway modulation.

Enhancer RNA (eRNA), a type of non-coding RNA transcribed from enhancer regions, serves as a class of critical regulatory elements in gene expression. In cancer biology, eRNAs exhibit profound roles in tumorigenesis, metastasis, and therapeutic response modulation. In this review, we outline eRNA identification methods utilizing enhancer region prediction, histone H3 lysine 4 monomethyl chromatin signatures, and nucleosome positioning analysis. We quantitate eRNA expression through RNA-seq, single-cell transcriptomics, and epigenomic integration approaches. Functionally, eRNAs regulate gene expression, protein function modulation, and chromatin modification. Key databases detailing eRNA annotations and interactions are highlighted. Furthermore, we analyze the connection of eRNA with immune cells and its potential in immunotherapy. Emerging evidence demonstrates eRNA's critical involvement in immune cell crosstalk and tumor microenvironment reprogramming. Notably, eRNA signatures show promise as predictive biomarkers for immunotherapy response and chemoresistance monitoring in multiple malignancies. This review underscores eRNA's transformative potential in precision oncology, advocating for integrated multiomics approaches to fully realize their clinical applicability.

Single-cell technology opened up a new avenue to delineate cellular status at a single-cell resolution and has become an essential tool for studying human diseases. Multiplexing allows cost-effective experiments by combining multiple samples and effectively mitigates batch effects. It starts by giving each sample a unique tag and then pooling them together for library preparation and sequencing. After sequencing, sample demultiplexing is performed based on tag detection, where cells belonging to one sample are expected to have a higher amount of the corresponding tag than cells from other samples. However, in reality, demultiplexing is not straightforward due to the noise and contamination from various sources. Successful demultiplexing depends on the efficient removal of such contamination. Here, we perform a systematic benchmark combining different normalization methods and demultiplexing approaches using real-world data and simulated datasets. We show that accounting for sequencing depth variability increases the separability between tagged and untagged cells, and the clustering-based approach outperforms existing tools. The clustering-based workflow is available as an R package from https://github.com/hwlim/hashDemux.

Phenotyping of animals is a routine task in agriculture which can provide large datasets for the functional annotation of genomes. Using the livestock farming sector to study complex traits enables genetics researchers to fully benefit from the digital transformation of society as economies of scale substantially reduces the cost of phenotyping animals on farms. In the agricultural sector genomics has transitioned towards a model of 'Genomics without the genes' as a large proportion of the genetic variation in animals can be modelled using the infinitesimal model for genomic breeding valuations. Combined with third generation sequencing creating pan-genomes for livestock the digital infrastructure for trait collection and precision farming provides a unique opportunity for high-throughput phenotyping and the study of complex traits in a controlled environment. The emphasis on cost efficient data collection mean that mobile phones and computers have become ubiquitous for cost-efficient large-scale data collection but that the majority of the recorded traits can still be recorded manually with limited training or tools. This is especially valuable in low- and middle income countries and in settings where indigenous breeds are kept at farms preserving more traditional farming methods. Digitalization is therefore an important enabler for high-throughput phenotyping for smaller livestock herds with limited technology investments as well as large-scale commercial operations. It is demanding and challenging for individual researchers to keep up with the opportunities created by the rapid advances in digitalization for livestock farming and how it can be used by researchers with or without a specialization in livestock. This review provides an overview of the current status of key enabling technologies for precision livestock farming applicable for the functional annotation of genomes.

Metastatic melanoma originates from melanocytes of the skin. Melanoma metastasis results in poor treatment prognosis for patients and is associated with epigenetic and transcriptional changes that reflect the developmental program of melanocyte differentiation from neural crest stem cells. Several studies have explored melanoma transcriptional heterogeneity using microarray, bulk and single-cell RNA-sequencing technologies to derive data-driven models of the transcriptional-state change which occurs during melanoma progression. No study has systematically examined how different models of melanoma progression derived from different data types, technologies and biological conditions compare. Here, we perform a cross-sectional study to identify averaging effects of bulk-based studies that mask and distort apparent melanoma transcriptional heterogeneity; we describe new transcriptionally distinct melanoma cell states, identify differential co-expression of genes between studies and examine the effects of predicted drug susceptibilities of different cell states between studies. Importantly, we observe considerable variability in drug-target gene expression between studies, indicating potential transcriptional plasticity of melanoma to down-regulate these drug targets and thereby circumvent treatment. Overall, observed differences in gene co-expression and predicted drug susceptibility between studies suggest bulk-based transcriptional measurements do not reliably gauge heterogeneity and that melanoma transcriptional plasticity is greater than described when studies are considered in isolation.