BMC Medical Genomics最新文献

Background: Sepsis and shock are common complications of necrotising soft tissue infections (NSTI). Sepsis encompasses different endotypes that are associated with specific immune responses. Hyperbaric oxygen (HBO₂) treatment activates the cells oxygen sensing mechanisms that are interlinked with inflammatory pathways. We aimed to identify gene expression patterns associated with effects of HBO₂ treatment in patients with sepsis caused by NSTI, and to explore sepsis-NSTI profiles that are more receptive to HBO₂ treatment.

Methods: An observational cohort study examining 83 NSTI patients treated with HBO₂ in the acute phase of NSTI, fourteen of whom had received two sessions of HBO₂ (HBOx2 group), and another ten patients (non-HBO group) who had not been exposed to HBO₂. Whole blood RNA sequencing and clinical data were collected at baseline and after the intervention, and at equivalent time points in the non-HBO group. Gene expression profiles were analysed using machine learning techniques to identify sepsis endotypes, treatment response endotypes and clinically relevant transcriptomic signatures of response to treatment.

Results: We identified differences in gene expression profiles at follow-up between HBO₂-treated patients and patients not treated with HBO₂. Moreover, we identified two patient endotypes before and after treatment that represented an immuno-suppressive and an immune-adaptive endotype respectively, and we characterized the genetic profile of the patients that transition from the immuno-suppressive to the immune-adaptive endotype after treatment. We discovered one gene MTCO2P12 that distinguished individuals who altered their endotype in response to treatment from non-responders.

Conclusion: The global gene expression pattern in blood changed in response to HBO₂ treatment in a direction associated with clinical biochemistry improvement, and the study provides potential novel biomarkers and pathways for monitoring HBO₂ treatment effects and predicting an HBO₂ responsive NSTI-sepsis profile.

Trial registration: Biological material was collected during the INFECT study, registered at ClinicalTrials.gov (NCT01790698) 04/02/2013.

Hearing loss is a prevalent condition with a significant impact on individuals' quality of life. However, comprehensive studies investigating the differential gene expression and regulatory mechanisms associated with hearing loss are lacking, particularly in the context of diverse patient samples. In this study, we integrated data from 10 patients across different regions, age groups, and genders, with their data retrieved from a public transcriptome database, to explore the molecular basis of hearing loss. These samples are mainly from fibroblasts and keratinocytes. Through differential gene expression analysis, we identified key genes, including ICAM1, SLC1A1, and CD24, which have already been shown to play important roles in neurogenic hearing loss. Furthermore, we predicted potential transcriptional regulatory factors that may modulate the expression of these genes. Enrichment analysis revealed biological processes and pathways associated with hearing loss, highlighting the involvement of circadian rhythm disruption and other neuro-related disorders. Although our study is limited by the sample size and the absence of larger-scale investigations, the identified genes and regulatory factors provide valuable insights into the molecular mechanisms underlying hearing loss. Further molecular and cellular experiments are necessary to validate these findings and elucidate the precise regulatory mechanisms involved. In conclusion, our study contributes to the understanding of hearing loss pathogenesis and offers potential targets for molecular diagnostics and gene-based therapies. This provides a foundation for further research into personalized approaches to diagnosing and treating hearing loss.

Amyotrophic lateral sclerosis (ALS) lacks a specific biomarker, but is defined by relatively selective toxicity to motor neurons (MN). As others have highlighted, this offers an opportunity to develop a sensitive and specific biomarker based on detection of DNA released from dying MN within accessible biofluids. Here we have performed whole genome bisulfite sequencing (WGBS) of iPSC-derived MN from neurologically normal individuals. By comparing MN methylation with an atlas of tissue methylation we have derived a MN-specific signature of hypomethylated genomic regions, which accords with genes important for MN function. Through simulation we have optimised the selection of regions for biomarker detection in plasma and CSF cell-free DNA (cfDNA). However, we show that MN-derived DNA is not detectable via WGBS in plasma cfDNA. In support of our experimental finding, we show theoretically that the relative sparsity of lower MN sets a limit on the proportion of plasma cfDNA derived from MN which is below the threshold for detection via WGBS. Our findings are important for the ongoing development of ALS biomarkers. The MN-specific hypomethylated genomic regions we have derived could be usefully combined with more sensitive detection methods and perhaps with study of CSF instead of plasma. Indeed we demonstrate that neuronal-derived DNA is detectable in CSF. Our work is relevant for all diseases featuring death of rare cell-types.

Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.

Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.

Conclusions: In this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.

Artificial intelligence (AI) is revolutionizing biomedical data science at an unprecedented pace, transforming various aspects of the field with remarkable speed and depth. However, a critical issue remains unclear: how reproducible are the AI models and systems employed in biomedical data science? In this study, we examine the challenges of AI reproducibility by analyzing the factors influenced by data, model, and learning complexities, as well as through a game-theoretical perspective. While adherence to reproducibility standards is essential for the long-term advancement of AI, the conflict between following these standards and aligning with researchers' personal goals remains a significant hurdle in achieving AI reproducibility.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease.

Methods: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model.

Results: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD.

Conclusion: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Naegleria fowleri, the causative agent of Primary Amoebic Meningoencephalitis (PAM), is commonly found in warm freshwater environments and can enter the brain through nasal passages during activities like swimming or ablution. PAM has a high fatality rate, raising concerns about its global health impact. In Pakistan, particularly in Karachi, a significant number of cases have been reported, often with no history of recreational water exposure, but with regular ablution using tap water. This study analyzed the physicochemical parameters, abundance of total and fecal coliforms, and detected N. fowleri and other Naegleria species in tap water samples from Karachi using PCR with ITS- and Naegl-primers. Almost all samples exhibited high temperatures, low chlorine levels, and a high presence of coliforms. N. fowleri and other Naegleria species were detected in 11 out of 39 samples. Sequence analysis identified N. fowleri in tap water from the Golimar and Lyari areas of Karachi, while the other nine samples revealed different Naegleria species. This study suggests that the combination of high temperatures, insufficient chlorination, and the presence of coliforms may create favorable conditions for N. fowleri growth. However, these factors are not exclusive to the Golimar and Lyari areas, indicating that other environmental or infrastructural factors, not detailed in this study, may have contributed to the presence of N. fowleri in that specific location.

Purpose: To explore possible pathogenic genes for concomitant exotropia using whole-exome sequencing.

Methods: In this study, 47 individuals from 10 concomitant exotropia (including intermittent exotropia and constant exotropia) pedigrees were enrolled. Whole-exome sequencing was used to screen mutational profiles in 25 affected individuals and 10 unaffected individuals. Sanger sequencing and in silico analysis were performed for all participants. Two target genes were used to capture the sequences of 220 sporadic samples.

Results: All 10 concomitant exotropia pedigrees presented autosomal dominant inheritance with childhood onset (3.35 ± 1.51 years old). Eleven different missense variants were identified among seven potential pathogenic genes (COL4A2, SYNE1, LOXHD1, AUTS2, GTDC2, HERC2 and CDH3) that cosegregated with pedigree members. All variants were predicted to be deleterious and had low frequencies in the general population. Distinct variants of COL4A2 were present in three pedigrees, and distinct variants of SYNE1 were present in two pedigrees. Fifteen variants in AUTS2 and four variants in GTDC2 were identified in 220 patients with sporadic concomitant exotropia using a target-capture sequencing approach.

Conclusion: This is the first study to explore the genetic mechanism of concomitant exotropia and identify seven associated genes (COL4A2, SYNE1, LOXHD1, AUTS2, GTDC2, HERC2 and CDH3) that may be candidate genes causing concomitant exotropia. More samples and in-depth studies are needed to verify these findings.

Enhancer RNA (eRNA) has emerged as a key player in cancer biology, influencing various aspects of tumor development and progression. In this study, we investigated the role of eRNAs in kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma. Leveraging high-throughput sequencing data and bioinformatics analysis, we identified differentially expressed eRNAs in KIRC and constructed eRNA-centric regulatory networks. Our findings revealed that up-regulated eRNAs in KIRC potentially regulate immune response and hypoxia pathways, while down-regulated eRNAs may impact ion transport, cell cycle, and metabolism. Furthermore, we developed a diagnostic prediction model based on eRNA expression profiles, demonstrating its effectiveness in KIRC diagnosis. Finally, we elucidated the regulatory mechanism of an eRNA (ENSR00000305834) on the expression of SLC15A2, a potential prognostic biomarker in KIRC, through bioinformatics analysis and in vitro validation experiments. In summary, Our study highlights the clinical significance of eRNAs in KIRC and underscores their potential as therapeutic targets.