Genes & genomics最新文献

Background: Traumatic brain injury (TBI) induces broad molecular changes in the human brain, altering gene expression in diverse neural and glial cells. While the transcriptional effects of TBI on protein-coding genes are well characterized, the roles of long noncoding RNAs (lncRNAs), key regulators of gene expression and chromatin, remain largely unknown.

Objective: Our objective was to identify lncRNAs altered in TBI and explore their potential regulatory functions.

Methods: We applied an integrative multi-omics approach combining single-nucleus RNA sequencing (snRNA-seq), isoform-level transcriptomics, transposable element (TE) annotation, and RNA-binding protein (RBP) interaction analyses. Public snRNA-seq datasets from cortical tissues of 12 TBI patients and 5 controls were analyzed to resolve injury-driven transcriptional signatures. We have performed differential expression analysis on 12,801 human lncRNAs, examined isoform-specific expression with TE content, and explored RBP-lncRNA interactions using CLIP-seq data.

Results: Cell-type diversity decreased in TBI, and reactive and progenitor-like states were expanded. We identified 190 upregulated lncRNAs, mainly in glial cells. Among these, LINC00486 emerged as a brain-enriched lncRNA consistently increased after TBI. Isoform analysis showed its dominant brain isoform contains LINEs and LTRs, linking it to regulatory networks associated with endogenous retroelement activation. Functional enrichment connected LINC00486 to neurodevelopment, serotonin metabolism, and neuroinflammatory pathways. CLIP-seq data confirmed its interactions with stress-responsive RBPs such as AGO2 and TARDBP.

Conclusions: Our multi-omics analysis identifies LINC00486 as a potential regulator of transcriptional plasticity in TBI. Its TE content and RBP interactions suggest a role in lncRNA-mediated regulatory networks during injury, highlighting possible therapeutic targets in neurotrauma.

Background: The high prevalence of non-biological zero counts, arising from low sequencing depth and sampling variation, presents a significant challenge in microbiome data analysis. These zeros can distort taxon abundance distributions and hinder the identification of true biological signals, complicating downstream analyses.

Objective: To address the challenges of non-biological zeros in microbiome datasets, we propose DeepIMB, a deep learning-based imputation method for microbiome data, specifically designed to accurately identify and impute non-biological zero counts while preserving biological integrity.

Methods: DeepIMB operates in two main phases. First, it identifies non-biological zeros using a gamma-normal mixture model applied to the normalized, log-transformed taxon count matrix. Second, it imputes these zeros with a deep neural network model that integrates diverse sources of information, including taxon abundances, sample covariates, and phylogenetic distances, thereby learning complex, nonlinear relationships within microbiome data.

Results: By leveraging integrated information from multiple data types, DeepIMB accurately imputes non-biological zeros while preserving true biological signals. In our two simulation studies, DeepIMB outperformed existing imputation methods in terms of mean squared error, Pearson correlation coefficient, and Wasserstein distance.

Conclusion: DeepIMB effectively addresses the challenges posed by non-biological zeros in microbiome data. By improving the quality of the data and the reliability of downstream analyses, DeepIMB represents a significant advancement in microbiome research methodologies.

BRCA1 is a tumor suppressor gene encoding a protein which plays an essential role in the repair of DNA double strand break. Approximately 40% of its introns are composed of Alu elements. It is known that retrotransposons create an environment prone to non-allelic recombination (NAHR) between homologous sequences, which frequently cause deletion or duplication of exon(s) resulting in structural variations in the gene. Some mutations can directly impact protein function by causing frameshifts, the deletion of key domains, and abnormal RNA processing, possibly increasing tumorigenicity. Indeed, large-scale BRCA1 rearrangements caused by Alu elements have been observed in approximately 10-15% of hereditary breast cancer patients, most notably deletions of exons 5 through 7. Occasionally, mutated exons are excluded from splicing, resulting in protein isoforms with a limited function, which may be associated with drug resistance during treatment. Tumors with BRCA1 mutations exhibit homologous recombination deficiency (HRD), resulting in high sensitivity to PARP inhibitors and platinum-based chemotherapy. However, in some tumors, gene function may be partially restored through subsequent secondary rearrangements, which can lead to long-term drug resistance, demanding continuous molecular surveillance. It has been difficult to detect mutations in the BRCA gene caused by Alu elements using conventional PCR-based analysis or short-read next-generation sequencing (NGS) technologies. However, the recent introduction of MLPA and long-read NGS technologies has significantly improved a detection rate of Alu-involved mutations in the BRCA gene. Advances in long-read sequencing technologies, such as Oxford Nanopore and PacBio, and in optical genome mapping tools provide the capability to analyze complex structural mutations. Furthermore, machine learning-based prediction tools, such as HRDetect and SVScore, have been developed to analyze genome-wide structural mutation patterns and HRD indicators more comprehensively, thereby contributing to the prediction of BRCA1/2 defects and treatment responses. The integration of these technologies is expected to enhance our comprehension of BRCA1-related structural mutations and serve as an important foundation for developing personalized treatment strategies for patients.

Background: Whole genome data are invaluable resources for both conservation and adaptation studies, especially for endemic species, providing insights into the evolution of genes involved in genomic adaptation across different environments.

Objective: We compare the newly generated genomic and transcriptomic data of the Cretan endemic lizard species Podarcis cretensis to other Podarcis species to obtain an overview of gene family evolution and genome structure within the genus.

Methods: Comparative genomic and transcriptomic analyses were performed using the newly published genome of P. cretensis. A gene set was predicted using RNA-seq data from 36 samples, comprising three tissues (liver, brain, and muscle) from both male and female individuals across three distinct habitats.

Results: The main findings revealed that P. cretensis and P. raffonei present the best genome assemblies and the most syntenic among the Podarcis species examined. Moreover, P. cretensis displayed the highest percentage of single-copy genes and the lowest percentage of duplicated genes. These duplicated genes are primarily associated with immune and sensory-related gene families, including chemokines, interleukins, immunoglobulin-like domain proteins, secreted proteins, and vomeronasal type-2 receptors.

Conclusions: This study deepens our understanding of chromosome structure, gene expression, and genome evolution in the Podarcis genus, representing the most extensive comparative analysis to date. The newly predicted gene set of the insular endemic species P. cretensis offers initial insights into gene expression related to adaptation across environments and tissues. Comparative genomic analyses further revealed gene families potentially involved in environmental adaptation.

Background: Wilson disease (WD) is a hereditary disorder characterized by abnormal copper metabolism. WD in the liver can result in dyslipidemia, typically manifesting as decreased lipid metabolism. Familial hypercholesterolemia (FH) is an inherited disorder with markedly elevated low-density lipoprotein cholesterol (LDL-C) levels and mainly attributed to disease-causing variants in the low-density lipoprotein receptor (LDLR) gene. LDLR c.599T > G (p.Phe200Cys) variant has neither been reported in WD with FH, nor has the pathogenicity study and function prediction of LDLR c.599T > G (p.Phe200Cys) variant been reported.

Objective: In this study, a pediatric patient with a body mass index (BMI) of 13.7, without fatty liver, presented with elevated transaminase and blood lipid levels (LDL-C 8.64 mmol/L). He was diagnosed with WD and probable FH. Despite treatment for WD, which reduced the patient's transaminase levels, blood lipid levels did not improve. We performed genetic testing, clinical surveys, pedigree analysis, and pathogenic identification to clarify the cause of the patient's dyslipidemia.

Methods: Clinical and biochemical data from the patient and seven family members were evaluated using the Dutch Lipid Clinic Network (DLCN) diagnostic criteria. Whole-exome and Sanger sequencing were used to explore dyslipidemia-related variants and validate candidate variants, respectively. Bioinformatics analysis was used to evaluate the pathogenicity of the candidate variant and its structure‒function relationship.

Results: The patient was clinically diagnosed with definite FH, and his mother and eldest maternal uncle were clinically diagnosed with probable FH and possible FH, respectively. The three patients carried a rare LDLR c.599T > G (p.Phe200Cys) variant, which was classified according to the American College of Medical Genetics and Genomics guidelines as likely pathogenic. Bioinformatics analyses categorized this variant, located in the fifth LDL receptor type A (LA) modules of ligand-binding domain (LBD) and affecting the random coil structure of LDLR.

Conclusions: The LDLR c.599T > G (p.Phe200Cys) variant was associated with FH combined with WD, and the heterozygous variant site was considered likely pathogenic. The variant may affect the ligand-binding function of LDLR by altering the random coil structure.

Background: Clonorchis sinensis infection triggers various hepatobiliary complications, including epithelial hyperplasia, chronic inflammation, periductal fibrosis, and cholangiocarcinogenesis through direct contact with worms and their excretory-secretory products (ESPs). We previously profiled differential transcriptome expression from three-dimensional cholangiocyte (H69 cells) spheroids constitutively and repetitively exposed to ESPs using microarrays and RNA-seq analysis. TRIM22 was upregulated in response to ESP exposure.

Objective: This study aimed to elucidate the pathophysiological mechanism of TRIM22-mediated hepatobiliary abnormalities during C. sinensis infection.

Methods: Quantitative reverse transcription polymerase chain reaction and immunoblot analyses were used to examine TRIM22 transcript and protein expression in ESP-treated H69 spheroids and 2D cultured cells. Transfection with TRIM22-specific small interfering RNA was performed to examine changes in intracellular reactive oxygen species levels and the expression of the redox-active transcription factor signaling pathway (mTOR/Nrf2) and its target genes. Liver tissues from C. sinensis-infected mice were stained with TRIM22 and Nrf2 polyclonal antibodies.

Results: Treatment of H69 cells with ESPs increased TRIM22 expression and AKT/mTOR signaling pathway activation. TRIM22 knockdown abolished ESP-induced mTOR activation but elevated Nrf2 nuclear translocation with subsequent increased expression of antioxidant and phase II detoxifying enzymes. The immunoreactivity of TRIM22 and Nrf2 showed distinctly different patterns in the liver of mouse infected with C. sinensis for three months.

Conclusion: TRIM22 upregulation during C. sinensis infection contributes to hepatobiliary abnormalities by disrupting redox homeostasis.