MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence-derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the-state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post-transcriptional regulation.

Intermittent cytomegalovirus infection alters neurobiological metabolism and induces cognitive deficits in mice.

Risk factors contributing to dementia are multifactorial. Accumulating evidence suggests a role for pathogens as risk factors, but data is largely correlative with few causal relationships. Here, we demonstrate that intermittent murine cytomegalovirus (MCMV) infection of mice, alters blood brain barrier (BBB) permeability and metabolic pathways. Increased basal mitochondrial function is observed in brain microvessels cells (BMV) exposed to intermittent MCMV infection and is accompanied by elevated levels of superoxide. Further, mice score lower in cognitive assays compared to age-matched controls who were never administered MCMV. Our data show that repeated systemic infection with MCMV, increases markers of neuroinflammation, alters mitochondrial function, increases markers of oxidative stress and impacts cognition. Together, this suggests that viral burden may be a risk factor for dementia. These observations provide possible mechanistic insights through which pathogens may contribute to the progression or exacerbation of dementia.

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models.

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

Accurately predicting nitrosylated tyrosine sites using probabilistic sequence information.

Post-translational modification (PTM) is defined as the enzymatic changes of proteins after the translation process in protein biosynthesis. Nitrotyrosine, which is one of the most important modifications of proteins, is interceded by the active nitrogen molecule. It is known to be associated with different diseases including autoimmune diseases characterized by chronic inflammation and cell damage. Currently, nitrotyrosine sites are identified using experimental approaches which are laborious and costly. In this study, we propose a new machine learning method called PredNitro to accurately predict nitrotyrosine sites. To build PredNitro, we use sequence coupling information from the neighboring amino acids of tyrosine residues along with a support vector machine as our classification technique.Our results demonstrates that PredNitro achieves 98.0% accuracy with more than 0.96 MCC and 0.99 AUC in both 5-fold cross-validation and jackknife cross-validation tests which are significantly better than those reported in previous studies. PredNitro is publicly available as an online predictor at: http://103.99.176.239/PredNitro.

Survival and prognostic association in stage IV colorectal cancer patients treated with chemotherapy in Bangladesh.

OBJECTIVE: Prognostic factors in colorectal cancer have lesser been evaluated in developing countries. This study aims to determine overall survival and prognostic factors for metastatic colorectal cancer patients who were non-operable and received chemotherapy. METHODS: The study retrospectively investigated 67 inoperable metastatic colorectal cancer patients at Square Hospital, Bangladesh. The primary endpoint was overall survival, and the secondary endpoints were prognostic association with factors. Survival probabilities were calculated by non-parametric Kaplan-Meier method and compared by log-rank test. Univariate and multivariable Cox proportional hazard models were implemented to assess the prognostic association. RESULTS: Median survival of the entire cohort was 14 months (95% confidence interval: 11-25). In multivariable analysis, two prognostic factors were independently associated with survival: Karnofsky performance status and carcinoembryonic antigen. Patients with Karnofsky performance status <70 had significant higher risk of death than those with Karnofsky performance status ≥70 (adjusted hazard ratio 4.25, 95% confidence interval: 2.15-8.39). Higher risk of death was found to be associated with higher carcinoembryonic antigen: adjusted hazard ratio was 1.72 (95% confidence interval: 0.81-3.68) and 2.96 (95% confidence interval: 1.25-7.01) for patients with carcinoembryonic antigen 10-100 and >100 ng/ml, respectively, while comparing with carcinoembryonic antigen <10 ng/ml. The presence of peritoneal metastasis and grade-III tumour significantly worsened the survival in univariate analysis (hazard ratio 2.46, 95% confidence interval: 1.32-4.57 and hazard ratio 1.74, 95% confidence interval: 1.01-3.03, respectively) but not in multivariable analysis (adjusted hazard ratio 1.92, 95% confidence interval: 0.88-4.18 and adjusted hazard ratio 1.25, 95% confidence interval: 0.66-2.36, respectively). CONCLUSION: The study reported survival of stage IV colorectal cancer patients undergo chemotherapy and identified that Karnofsky performance status and carcinoembryonic antigen are the poor prognostic factors to this cohort adjusting for other factors.

Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach.

BACKGROUND: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used. RESULTS: In this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix. CONCLUSION: The high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.