BMC Medical Genomics最新文献

Background: The growth in obesity and rates of abdominal obesity in developing countries is due to the dietary transition, meaning a shift from traditional, fiber-rich diets to Westernized diets high in processed foods, sugars, and unhealthy fats. Environmental changes, such as improving the quality of dietary fat consumed, may be useful in preventing or mitigating the obesity or unhealthy obesity phenotype in individuals with a genetic predisposition, although this has not yet been confirmed. Therefore, in this study, we investigated how dietary fat quality indices with metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on the Karelis criterion interact with genetic susceptibility in Iranian female adults.

Methods: In the current cross-sectional study, 279 women with overweight or obesity participated. Dietary intake was assessed using a 147-item food frequency questionnaire and dietary fat quality was assessed using the cholesterol-saturated fat index (CSI) and the ratio of omega-6/omega-3 (N6/N3) essential fatty acids. Three single nucleotide polymorphisms-MC4R (rs17782313), CAV-1 (rs3807992), and Cry-1(rs2287161) were genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique and were combined to produce the genetic risk score (GRS). Body composition was evaluated using a multi-frequency bioelectrical impedance analyzer. Participants were divided into MHO or MUO phenotypes after the metabolic risk assessment based on the Karelis criteria.

Results: We found significant interactions between GRS and N6/N3 in the adjusted model controlling for confounding factors (age, body mass index, energy, and physical activity) (β = 2.26, 95% CI: 0.008 to 4.52, P = 0.049). In addition, we discovered marginally significant interactions between GRS and N6/N3 in crude (β = 1.92, 95% CI: -0.06 to 3.91, P = 0.058) and adjusted (age and energy) (β = 2.00, 95% CI: -0.05 to 4.05, P = 0.057) models on the MUH obesity phenotype. However, no significant interactions between GRS and CSI were shown in both crude and adjusted models.

Conclusion: This study highlights the importance of personalized nutrition and recommends further study of widely varying fat intake based on the findings on gene-N6/N3 PUFA interactions.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a formidable public scourge causing worldwide mild to severe life-threatening infections. The ability of this strain to swiftly spread, evolve, and acquire resistance genes and virulence factors such as pvl genes has further rendered this strain difficult to treat. Of concern, is a recently recognized ability to resist antiseptic/disinfectant agents used as an essential part of treatment and infection control practices. This study aimed at detecting the presence of pvl genes and determining the distribution of antiseptic resistance genes in Methicillin-resistant Staphylococcus aureus isolates through whole genome sequencing technology.

Materials and methods: A descriptive cross-sectional study was conducted across six regional referral hospitals-Dodoma, Songea, Kitete-Kigoma, Morogoro, and Tabora on the mainland, and Mnazi Mmoja from Zanzibar islands counterparts using the archived isolates of Staphylococcus aureus bacteria. The isolates were collected from Inpatients and Outpatients who attended these hospitals from January 2020 to Dec 2021. Bacterial analysis was carried out using classical microbiological techniques and whole genome sequencing (WGS) using the Illumina Nextseq 550 sequencer platform. Several bioinformatic tools were used, KmerFinder 3.2 was used for species identification, MLST 2.0 tool was used for Multilocus Sequence Typing and SCCmecFinder 1.2 was used for SCCmec typing. Virulence genes were detected using virulenceFinder 2.0, while resistance genes were detected by ResFinder 4.1, and phylogenetic relatedness was determined by CSI Phylogeny 1.4 tools.

Results: Out of the 80 MRSA isolates analyzed, 11 (14%) were found to harbor LukS-PV and LukF-PV, pvl-encoding genes in their genome; therefore pvl-positive MRSA. The majority (82%) of the MRSA isolates bearing pvl genes were also found to exhibit the antiseptic/disinfectant genes in their genome. Moreover, all (80) sequenced MRSA isolates were found to harbor SCCmec type IV subtype 2B&5. The isolates exhibited 4 different sequence types, ST8, ST88, ST789 and ST121. Notably, the predominant sequence type among the isolates was ST8 72 (90%).

Conclusion: The notably high rate of antiseptic resistance particularly in the Methicillin-resistant S. aureus strains poses a significant challenge to infection control measures. The fact that some of these virulent strains harbor the LukS-PV and LukF-PV, the pvl encoding genes, highlight the importance of developing effective interventions to combat the spreading of these pathogenic bacterial strains. Certainly, strengthening antimicrobial resistance surveillance and stewardship will ultimately reduce the selection pressure, improve the patient's treatment outcome and public health in Tanzania.

Background: Noninvasive prenatal testing (NIPT) is increasingly used to screen for fetal chromosomal aneuploidy by analyzing cell-free DNA (cfDNA) in peripheral maternal blood. The method provides an opportunity for early detection of large genetic abnormalities without an increased risk of miscarriage due to invasive procedures. Commercial applications for use at clinical laboratories often take advantage of DNA sequencing technologies and include the bioinformatic workup of the sequence data. The interpretation of the test results and the clinical report writing, however, remains the responsibility of the diagnostic laboratory. In order to facilitate this step, we developed NIPTviewer, a web-based application to visualize and guide the interpretation of NIPT data results.

Results: NIPTviewer has a database functionality to store the NIPT results and a web interface for user interaction and visualization. The application has been implemented as part of a novel analysis pipeline for NIPT in a diagnostic laboratory at Uppsala University Hospital. The validation data set included 84 previously analyzed plasma samples with known results regarding chromosomes 13, 18, 21, X and Y. They were sequenced in six different experiments, uploaded to NIPTviewer and assigned to a clinical laboratory geneticist for interpretation. The results of all previously analyzed samples were replicated.

Conclusion: NIPTviewer facilitates NIPT results interpretation and has been implemented as part of a NIPT analysis routine that was accredited by the national accreditation body for Sweden (Swedac).

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.