Abstract Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses ...on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.
Abstract Pseudomonas aeruginosa is a complex nosocomial infectious agent responsible for numerous illnesses, with its growing resistance variations complicating treatment development. Studies have ...emphasized the importance of virulence factors OprE and OprF in pathogenesis, highlighting their potential as vaccine candidates. In this study, B-cell, MHC-I, and MHC-II epitopes were identified, and molecular linkers were active to join these epitopes with an appropriate adjuvant to construct a vaccine. Computational tools were employed to forecast the tertiary framework, characteristics, and also to confirm the vaccine’s composition. The potency was weighed through population coverage analysis and immune simulation. This project aims to create a multi-epitope vaccine to reduce P. aeruginosa–related illness and mortality using immunoinformatics resources. The ultimate complex has been determined to be stable, soluble, antigenic, and non-allergenic upon inspection of its physicochemical and immunological properties. Additionally, the protein exhibited acidic and hydrophilic characteristics. The Ramachandran plot, ProSA-web, ERRAT, and Verify3D were employed to ensure the final model’s authenticity once the protein’s three-dimensional structure had been established and refined. The vaccine model showed a significant binding score and stability when interacting with MHC receptors. Population coverage analysis indicated a global coverage rate of 83.40%, with the USA having the highest coverage rate, exceeding 90%. Moreover, the vaccine sequence underwent codon optimization before being cloned into the Escherichia coli plasmid vector pET-28a (+) at the EcoRI and EcoRV restriction sites. Our research has developed a vaccine against P. aeruginosa that has strong binding affinity and worldwide coverage, offering an acceptable way to mitigate nosocomial infections.
Abstract Chimeric antigen receptor (CAR) therapy has emerged as a ground-breaking advancement in cancer treatment, harnessing the power of engineered human immune cells to target and eliminate cancer ...cells. The escalating interest and investment in CAR therapy in recent years emphasize its profound significance in clinical research, positioning it as a rapidly expanding frontier in the field of personalized cancer therapies. A crucial step in CAR therapy design is choosing the right target as it determines the therapy’s effectiveness, safety and specificity against cancer cells, while sparing healthy tissues. Herein, we propose a suite of tools for the identification and analysis of potential CAR targets leveraging expression data from The Cancer Genome Atlas and Genotype-Tissue Expression Project, which are implemented in CARTAR website. These tools focus on pinpointing tumor-associated antigens, ensuring target selectivity and assessing specificity to avoid off-tumor toxicities and can be used to rationally designing dual CARs. In addition, candidate target expression can be explored in cancer cell lines using the expression data for the Cancer Cell Line Encyclopedia. To our best knowledge, CARTAR is the first website dedicated to the systematic search of suitable candidate targets for CAR therapy. CARTAR is publicly accessible at https://gmxenomica.github.io/CARTAR/.
Abstract With the increasing prevalence of age-related chronic diseases burdening healthcare systems, there is a pressing need for innovative management strategies. Our study focuses on the gut ...microbiota, essential for metabolic, nutritional, and immune functions, which undergoes significant changes with aging. These changes can impair intestinal function, leading to altered microbial diversity and composition that potentially influence health outcomes and disease progression. Using advanced metagenomic sequencing, we explore the potential of personalized probiotic supplements in 297 older adults by analyzing their gut microbiota. We identified distinctive Lactobacillus and Bifidobacterium signatures in the gut microbiota of older adults, revealing probiotic patterns associated with various population characteristics, microbial compositions, cognitive functions, and neuroimaging results. These insights suggest that tailored probiotic supplements, designed to match individual probiotic profile, could offer an innovative method for addressing age-related diseases and functional declines. Our findings enhance the existing evidence base for probiotic use among older adults, highlighting the opportunity to create more targeted and effective probiotic strategies. However, additional research is required to validate our results and further assess the impact of precision probiotics on aging populations. Future studies should employ longitudinal designs and larger cohorts to conclusively demonstrate the benefits of tailored probiotic treatments.
Abstract Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the ...spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.
Abstract Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the ...thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.
Abstract Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic ...basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.
Hand-foot-and-mouth disease (HFMD), which is typically seen in the younger pediatric population, is uncommon in older adolescent and young adult populations. We report on an atypical outbreak of HFMD ...among college students at a mid-size university in the mid-Atlantic U.S. The outbreak included 138 qualifying cases of HFMD among students at the institution between August and November 2018. All tested samples were positive for CVA6, a less common cause of HFMD. Signs and symptoms and campus mitigation strategies are described. This case report aims to highlight an atypical outbreak of HFMD for clinicians who care for older adolescent and college-aged patients in primary care and community settings, and may see cases and/or outbreaks of HFMD in these populations.
Low back pain is the most common musculoskeletal complaint accounting for over 30 million visits to primary care physicians annually. Serious pathology is found in less than 1% of these visits. ...Therefore it is often a challenge to distinguish worrisome findings requiring further workup and treatment from common complaints of pain. Gout is an inflammatory arthritis that most commonly affects the appendicular skeleton. It is characterized by the saturation of uric acid and deposition of monosodium urate crystals in joints and tissues. Spinal involvement is rare and is not typically considered on the differential diagnosis for a patient presenting with acute low back pain. We present such a case of a 35-year-old male who presented with intractable back pain, highlighting the need to recognize signs and symptoms that raise suspicion for spinal gout.
