Liver abscesses in feedlot cattle are polymicrobial infections. Culture-based studies have identified Fusobacterium necrophorum as the primary causative agent, but a number of other bacterial species are frequently isolated. The incidence of liver abscesses is highly variable and is affected by a number of factors, including cattle type. Holstein steers raised for beef production have a higher incidence than crossbred feedlot cattle. Tylosin is the commonly used antimicrobial feed additive to reduce the incidence of liver abscesses. The objective of this study was to utilize 16S ribosomal RNA amplicon sequence analyses to analyze the bacterial community composition of purulent material of liver abscesses of crossbred cattle (n = 24) and Holstein steers (n = 24), each fed finishing diet with or without tylosin. DNA was extracted and the V3 and V4 regions of the 16S rRNA gene were amplified, sequenced, and analyzed. The minimum, mean, and maximum sequence reads per sample were 996, 177,070, and 877,770, respectively, across all the liver abscess samples. Sequence analyses identified 5 phyla, 14 families, 98 genera, and 102 amplicon sequence variants (ASV) in the 4 treatment groups. The dominant phyla identified were Fusobacteria (52% of total reads) and Proteobacteria (33%). Of the top 25 genera identified, 17 genera were Gram negative and 8 were Gram positive. The top 3 genera, which accounted for 75% of the total reads, in the order of abundance, were Fusobacterium, Pseudomonas, and Bacteroides. The relative abundance, expressed as percent of total reads, of phyla, family, and genera did not differ (P > 0.05) between the 4 treatment groups. Generic richness and evenness, determined by Shannon–Weiner and Simpson’s diversity indices, respectively, did not differ between the groups. The UniFrac distance matrices data revealed no clustering of the ASV indicating variance between the samples within each treatment group. Co-occurrence network analysis at the genus level indicated a strong association of Fusobacterium with 15 other genera, and not all of them have been previously isolated from liver abscesses. In conclusion, the culture-independent method identified the bacterial composition of liver abscesses as predominantly Gram negative and Fusobacterium as the dominant genus, followed by Pseudomonas. The bacterial community composition did not differ between crossbred and Holstein steers fed finishing diets with or without tylosin.

Usher syndrome, a rare genetic disorder causing both hearing and vision loss, presents significant diagnostic and therapeutic challenges due to its complex genetic basis. The identification of reliable biomarkers for early detection and intervention is crucial for improving patient outcomes. In this study, we present a machine learning-based hybrid sequential feature selection approach to identify key mRNA biomarkers associated with Usher syndrome. Beginning with a dataset of 42,334 mRNA features, our approach successfully reduced dimensionality and identified 58 top mRNA biomarkers that distinguish Usher syndrome from control samples. We employed a combination of feature selection techniques, including variance thresholding, recursive feature elimination, and Lasso regression, integrated within a nested cross-validation framework. The selected biomarkers were further validated using multiple machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, demonstrating robust classification performance. To assess the biological relevance of the computationally identified mRNA biomarkers, we experimentally validated candidates from the top 10 selected mRNAs using droplet digital PCR (ddPCR). The ddPCR results were consistent with expression patterns observed in the integrated transcriptomic metadata, reinforcing the credibility of our machine learning-driven biomarker discovery framework. Our findings highlight the potential of machine learning-driven biomarker discovery to enhance the detection of Usher syndrome.

Background: The human skin contains a diverse microbiome that provides protective functions against environmental pathogens. Studies have demonstrated that bacteriophages modulate bacterial community composition and facilitate the transfer of host-specific genes, potentially influencing host cellular functions. However, little is known about the human skin virome and its role in human health. Especially, how viral-host relationships influence skin microbiome structure and function is poorly understood. Results: Population dynamics and genetic diversity of bacteriophage communities in viral metagenomic data collected from three anatomical skin locations from 60 subjects at five different time points revealed that cutaneous bacteriophage populations are mainly composed of tailed Caudovirales phages that carry auxiliary genes to help improve metabolic remodeling to increase bacterial host fitness through antimicrobial resistance. Sequence variation in the MRSA associated antimicrobial resistance gene, erm(C) was evaluated using targeted sequencing to further confirm the presence of antimicrobial resistance genes in the human virome and to demonstrate how functionality of such genes may influence persistence and in turn stabilization of bacterial host and their functions. Conclusions: This large temporal study of human skin associated viruses indicates that the human skin virome is associated with auxiliary metabolic genes and antimicrobial resistance genes to help increase bacterial host fitness.

Usher syndrome (USH) is an inherited disorder characterized by sensorineural hearing loss (SNHL), retinitis pigmentosa (RP)-related vision loss, and vestibular dysfunction. USH presents itself as three distinct clinical types, 1, 2, and 3, with no biomarker for early detection. This study aimed to explore whether microRNA (miRNA) expression in USH cell lines is dysregulated compared to the miRNA expression pattern in a cell line derived from a healthy human subject. Lymphocytes from USH patients and healthy individuals were isolated and transformed into stable cell lines using Epstein–Barr virus (EBV). DNA from these cell lines was sequenced using a targeted panel to identify gene variants associated with USH types 1, 2, and 3. Microarray analysis was performed on RNA from both USH and control cell lines using NanoString miRNA microarray technology. Dysregulated miRNAs identified by the microarray were validated using droplet digital PCR technology. DNA sequencing revealed that two USH patients had USH type 1 with gene variants in USH1B (MYO7A) and USH1D (CDH23), while the other two patients were classified as USH type 2 (USH2A) and USH type 3 (CLRN-1), respectively. The NanoString miRNA microarray detected 92 differentially expressed miRNAs in USH cell lines compared to controls. Significantly altered miRNAs exhibited at least a twofold increase or decrease with a p value below 0.05. Among these miRNAs, 20 were specific to USH1, 14 to USH2, and 5 to USH3. Three miRNAs that are known as miRNA-183 family which are crucial for inner ear and retina development, have been significantly downregulated as compared to control cells. Subsequently, droplet digital PCR assays confirmed the dysregulation of the 12 most prominent miRNAs in USH cell lines. This study identifies several miRNA signatures in USH cell lines which may have potential utility in Usher syndrome identification.

Usher syndrome (USH) is a rare genetic disorder affecting vision, hearing, and balance. Identifying reliable biomarkers is crucial for early diagnosis and understanding disease mechanisms. MicroRNAs (miRNAs), key regulators of gene expression, hold promise as biomarkers for USH. This study aimed to identify a minimal subset of miRNAs that could serve as biomarkers to effectively differentiate USH from controls. We employed ensemble feature selection techniques to select the top miRNAs appearing in at least three algorithms. Machine learning models were trained and tested using this subset, followed by validation on an independent 10% sample. Our approach identified 10 key miRNAs as potential biomarkers for USH. To further validate their biological relevance, we conducted pathway analysis, which revealed significant pathways associated with USH. Furthermore, our approach achieved high classification performance, with an accuracy of 97.7%, sensitivity of 98%, specificity of 92.5%, F1 score of 95.8%, and an AUC of 97.5%. These findings demonstrate that combining ensemble feature selection with machine learning provides a robust strategy for miRNA biomarker discovery, advancing USH diagnosis and molecular understanding.

Reliable detection of robust biomarkers from high-dimensional transcriptomic data remains a major challenge in computational oncology. Traditional approaches often suffer from overfitting and poor generalization due to the high dimensionality of genomic data and limited sample sizes. This study aims to identify an optimal, biologically meaningful subset of mRNA biomarkers capable of distinguishing ovarian cancer samples from healthy controls using an integrated machine learning-based feature selection framework. We analyzed mRNA expression data encompassing approximately 63,000 transcripts from ovarian cancer and control samples derived from cell lines. A hybrid feature selection pipeline combining statistical filtering, recursive elimination, and regularization was implemented under stratified cross-validation to derive stable biomarkers. Model validation was performed using Logistic Regression, Random Forest, XGBoost, and Support Vector Machine classifiers, while experimental validation was conducted through droplet digital PCR (ddPCR). Statistical analyses included ANOVA, t-tests, and pathway enrichment. The pipeline identified 80 discriminative mRNA biomarkers with exceptionally high classification performance (accuracy = 1.00, sensitivity = 1.00, specificity = 1.00 for top models). ddPCR confirmed consistent expression patterns, with significant downregulation of ADAMTS12, FN1, and ABI3BP and overexpression of EPCAM, COX6C, and TMT1B in ovarian cancer. Pathway enrichment revealed involvement in DNA repair, RNA processing, protein translation, immune regulation, and metabolic reprogramming. This hybrid feature selection framework applied to patient derived cell lines, effectively reduces dimensionality, enhances biomarker reliability, and uncovers biologically interpretable mRNA signatures associated with ovarian cancer, demonstrating potential for diagnostic and therapeutic applications.