Genomics & informatics最新文献

Spatial transcriptomics technologies have significantly enhanced the analysis of gene expression profiles by retaining the spatial information of intact tissue sections and enabling the possibility of a more profound comprehension of tissue structures and cellular relationships. Despite this, most platforms have limited resolution, and at numerous capture spots, multiple signals from various cells are present, requiring deconvolution, a set of computational steps to deduce the underlying cellular composition. Over the last few years, a range of algorithms has been proposed to address this problem, each employing distinct computational principles and processing paradigms. The present review seeks to present a comprehensive analysis of twenty such algorithms, focusing on their methodological foundations. We contrast the underlying computational algorithms, modeling methods, and data processing pipelines that underlie them, and how they deal with external references, noise and sparsity in the data. By drawing out the conceptual as well as technical foundations of each algorithm, we aim to provide researchers a complete and hands-on grasp of the computational landscape of spatial transcriptomics deconvolution. This review is a methodological handbook to enable deep understanding of current deconvolution methods to develop novel strategies and help in selecting or applying these existing tools for different biological contexts.

Schistosomiasis remains a significant public health burden, necessitating the development of effective vaccines against it. In this study, a multi-epitope subunit vaccine was designed against adenylate kinase 2 protein and evaluated for its potential to elicit protective immunity against three Schistosoma species. CTL, HTL, and B-cell epitopes were identified using immunoinformatics tools and linked using AAY and KK linkers, respectively. The 50S ribosomal protein L7/L12, a known TLR4 agonist, was incorporated as an adjuvant to enhance immune activation in the vaccine. Molecular docking and molecular dynamics (MD) simulations demonstrated a strong binding affinity between the vaccine and human TLR4, supported by low RMSD and Rg values, indicating structural stability. The negative binding energy further validated the vaccine's potential for engaging TLR4. The immunogenic profile predicted robust activation of CD4+ and CD8+ T cells, as well as neutralizing antibody responses. These findings highlight the potential of the vaccine to stimulate both cell-mediated and humoral immunity, making it a promising candidate for further experimental validation against schistosomiasis.

DNA is often described as the "language of life" because it encodes biological information using nucleotide sequences. Unlike the traditional view focused on codon-to-amino acid mapping in coding regions, the vast non-coding genome reveals complex organizational patterns resembling natural language. This paper outlines essential approaches in DNA linguistics, including formal language theory, RNA secondary structure modeling, statistical methods, and phylogenetic analysis. Additionally, recent research on Indo-European populations shows correlations between lexical and phonemic traits and asymmetrical patterns of genetic inheritance. Together, these perspectives deepen our understanding of genome regulation, evolution, and the striking parallels between genetic and linguistic systems.

Background: The COVID-19 pandemic has highlighted the need for survival models to assess risk factors and time-dependent effects in infectious diseases. However, the Cox proportional hazards (PH) model, which assumes constant covariate effects, struggles to capture disease dynamics. This underscores the need for advanced models that incorporate time-dependent coefficients and covariates for improved accuracy.

Methods: To address the need for modeling time-dependent effects and covariates, we applied a stratified Cox PH model with multiple time intervals to better satisfy the PH assumption. We conducted simulations to evaluate the performance of machine learning and deep learning survival models, including random survival forest (RSF), DeepSurv, and DeepHit. To improve time-dependent effect estimation, we introduced a refined time-interval division and a weighted sum approach for integrated hazard ratios of COVID-19 variants. The event of interest was death, and the specific risk compared was the risk of death from the start of the study to either death or the last follow-up among infected versus uninfected individuals.

Results: Our results showed that increasing the number of time intervals improved predictive accuracy. When the PH assumption held, the Cox PH model outperformed machine learning and deep learning models. Applying our approach to UK Biobank data, expanding time intervals from five to fifteen enhanced performance. The previously reported hazard ratio of 7.333 for the pre-Delta period was refined to 29.359 for the Early variant, 20.734 for EU1, and 4.079 for Alpha, revealing a decline in risk across variants.

Conclusions: These findings suggest that refining time intervals improves the understanding of time-dependent effects in infectious diseases. Incorporating stratified intervals and advanced models enhances risk assessment and predictive accuracy for COVID-19 and other evolving diseases.

Background: Polycystic ovary syndrome (PCOS) is a common metabolic problem in women of reproductive age that can lead to infertility and other metabolic disorders. Recent evidence indicates that inflammation might be one of the contributing factors in PCOS progression. However, there is a lack of information on the regulation of inflammatory genes in PCOS. Therefore, the aim of the study is to investigate the role of inflammation-associated genes and pathways in relation to PCOS.

Method: The bulk RNA-seq data of granulosa cells of human ovaries of PCOS-affected and healthy women were analyzed to evaluate the inflammatory regulation in PCOS. After quality trimming, the raw RNA-seq data were aligned to the human genome, and gene expression was quantified using featureCounts with Ensembl annotation. Further, downstream analyses of the resulting count matrix were performed in R Studio, where differentially expressed genes (DEG) were identified and CO-DEG analysis was performed.

Results: The study identifies the various differentially expressed inflammatory genes in the case of PCOS such as SPI1, HSPB1, MNDA, and ITGA. These DEG are closely associated with the activation of inflammatory responses, i.e., activation of lymphocytes and leukocytes, leukocyte migration and mononuclear cell proliferation, stimulating binding of various cytokines, immunoglobulins, and chemokines. PCOS group also exhibited an increased expression of androgen-mediated genes (SPI1 and ETS transcription factors) and genes associated with hyperlipidemia and insulin resistance (TNFRSF1B). Further, KEGG pathway enrichment analysis revealed significant upregulation of various pathways (autophagy, endocytosis) in the PCOS group. In addition, network analysis (cnetplot) of the top 10 KEGG GSEA pathways also highlights the key pathways in the PCOS group such as SNARE complex assembly pathway, SNAP-25, nucleophagy, and regulation of mast cell activation.

Conclusion: Therefore, the study highlights that inflammation is a major effector in PCOS, which also fuels obesity, an independent effector that further worsens the PCOS condition. In addition, the genes related to hyperandrogenism, hyperlipidemia, and insulin resistance were also overexpressed in PCOS, exacerbating the condition.

Background: The biological functions of transposable element (TE)-derived transcripts during physiological development, disease development, and progression have been previously reported. However, research on locus-specific TE-derived transcript expression in various human cell types remains limited.

Methods: We processed 2596 publicly available human long-read RNA-sequencing (LR RNA-seq) datasets covering 21 organs and 71 cell lines in both healthy individuals and diseased patients with various conditions to compile this TE-derived transcript annotation. We established a pipeline for assembling transcripts containing TE sequences to measure transcriptionally active TE-derived transcripts in diverse tissues and cell types. Next, we applied our TE annotation to the Genotype-Tissue Expression (GTEx) single-cell RNA-sequencing (scRNA-seq) data from eight tissues.

Results: We constructed the first transcriptom6e-based TE annotation using massive amounts of human LR RNA-seq data for use as a comprehensive reference to detect locus-specific TE-derived transcripts. Our annotation showed better detection accuracy for TE-derived transcripts than the RepeatMasker and GENCODE nonTE gene annotations. This annotation enabled the identification of novel TE-derived transcripts and their isoforms. We also identified alternative transcription end sites for long noncoding genes and confirmed previously annotated TE-nonTE gene fusion transcripts. Next, we applied our TE-derived transcript annotation to public scRNA-seq data from various human tissues and identified several cell-type-specific TE-derived transcripts in a locus-specific manner.

Conclusions: We generated a comprehensive, TE-derived transcript annotation using large-scale, LR RNA-seq data. Researchers can use our TE reference annotation to analyze active TE transcripts and their splicing isoforms in specific transcriptome datasets and to detect de novo TE transcripts. The discovery of cell-type-specific TE-derived transcripts may help explain mechanisms underlying the maintenance of cellular identity and provide new insights into the pathological mechanisms of various diseases.

Introduction/objectives: Despite widespread vaccination, the increasing incidence of pertussis underscores the urgent need for innovative vaccine strategies. This study aims to design and analyze, using in silico methods, a multiepitope protein that incorporates epitopes from the S1 subunit of pertussis toxin and the type 1 immunodominant domain of filamentous hemagglutinin (F1). The goal is to enhance both systemic and mucosal immunity through the incorporation of the C-terminal fragment of Clostridium perfringens enterotoxin (C-CPE).

Methods: Using reverse vaccinology, we predicted immunogenic epitopes for lymphocytes derived from the S1 and F1 proteins. The epitopes were assembled into a multiepitope construct named mF1S1-C-CPE, which was then evaluated for its physicochemical, immunological, and biological properties. Immunoinformatics tools were employed to analyze antigenicity, allergenicity, and population coverage. Additionally, molecular docking simulations of peptide‒MHC and mF1S1-C-CPE_TLR2/TLR4 binding were conducted.

Results: Structural analysis indicated that the final multiepitope construct maintained stability and solubility in aqueous environments. Immunoinformatic analysis revealed strong immunogenic properties, effectively eliciting both systemic and mucosal immune responses. Molecular docking demonstrated high-affinity binding patterns between the peptides (both individual or within the mF1S1-C-CPE) and corresponding HLA molecules. Additionally, molecular docking simulations of mF1S1-C-CPE and TLR2/TLR4 indicated strong binding affinity to receptors of innate immunity. The construct was predicted to be stable, soluble, and suitable for expression in Escherichia coli (CAI 0.93; GC content 54.9%).

Conclusion: This innovative approach holds promise for enhancing pertussis vaccination strategies by improving mucosal immune responses. Further in vivo studies are essential to validate the efficacy of this multiepitope vaccine candidate.

Background: Colorectal cancer (CRC) is one of the most common malignancies and the second most common cause of cancer-related mortality worldwide. Despite extensive research, the mechanism underlying CRC development remains unclear. This study aimed to understand the development and progression of CRC.

Methods: Gene network analysis of tumors with their paired normal tissues was performed using the differentially expressed genes dataset for CRC from the Cancer Genome Atlas. Further investigation of the regulatory relationship between hub genes and tumor development was conducted by protein-protein interaction network, Gene Ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the selected hub genes.

Results: The network was more centered, and a common hub as well as a hub of hub genes were more connected to each other in the tumor than in the normal tissue, indicating changes in the network from normal to tumor. Eight downregulated and two upregulated hub genes (CDK1 and KIF11) in the tumor were identified. Further, the regulatory pathway was altered, especially in cell cycle and cell division. All R implementation codes are available on the journal website as supplementary materials.

Conclusions: Our findings may help understand the biological processes underlying tumor development and progression and suggest CDK1 and KIF11 as possible key molecules in the development of CRC.

In single-cell RNA sequencing (scRNA-seq) data, issues related to the high expression of non-variable RNAs often arise due to organ traits or sample quality. Computational methods, such as SoupX (Young (Gigascience 9:giaa151, 2020)), have been used to solve this problem but it may remove biologically relevant data. This study presents a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9-based method that selectively removes non-variable RNAs. We applied this approach to scRNA-seq data from human intestinal tissues of 17 patients. By targeting non-variable genes, including ribosomal and mitochondrial RNAs, CRISPR-Cas9 treatment effectively reduced their expression, outperforming computational methods in both the number and extent of gene removal. The CRISPR-Cas9 treated samples, sequenced at half the depth compared to untreated samples, maintained comparable sequencing quality, and saturation, demonstrating that this approach can reduce sequencing costs while preserving data quality. Cell type composition and gene expression patterns remained consistent between treated and original datasets, with no unintended gene deletions. Overall, our findings suggest that the CRISPR-Cas9-based method offers a cost-effective solution for improving scRNA-seq data quality, particularly for tissues with high levels of non-variable RNAs, without compromising biological integrity.

Single-cell RNA-sequencing (scRNA-seq) technology brought about a revolutionary change in the transcriptomic world, paving the way for comprehensive analysis of cellular heterogeneity in complex biological systems. It enabled researchers to see how different cells behaved at single-cell levels, providing new insights into the process. However, despite all these advancements, scRNA-seq also experiences challenges related to the complexity of data analysis, interpretation, and multi-omics data integration. In this review, these complications were discussed in detail, directly pointing at the optimization of scRNA-seq approaches and understanding the world of single-cell and its dynamics. Different protocols and currently functional single-cell databases were also covered. This review highlights different tools for the analysis of scRNA-seq and their methodologies, emphasizing innovative techniques that enhance resolution and accuracy at a single-cell level. Various applications were explored across domains including drug discovery, tumor microenvironment (TME), biomarker discovery, and microbial profiling, and case studies were discussed to explain the importance of scRNA-seq by uncovering novel and rare cell types and their identification. This review underlines a crucial aspect of scRNA-seq in the advancement of personalized medicine and highlights its potential to understand the complexity of biological systems.