Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies.

Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

Selection, characterization, and biosensing applications of DNA aptamers targeting cyanotoxin BMAA.

Scientists have established a connection between environmental exposure to toxins like ß-N-methylamino-l-alanine (BMAA) and a heightened risk of neurodegenerative disorders. BMAA is a byproduct from certain strains of cyanobacteria that are present in ecosystems worldwide and is renowned for its bioaccumulation and biomagnification in seafood. The sensitivity, selectivity, and reproducibility of the current analytical techniques are insufficient to support efforts regarding food safety and environment monitoring adequately. This work outlines the in vitro selection of BMAA-specific DNA aptamers via the systematic evolution of ligands through exponential enrichment (SELEX). Screening and characterization of the full-length aptamers was achieved using the SYBR Green (SG) fluorescence displacement assay. Aptamers BMAA_159 and BMAA_165 showed the highest binding affinities, with dissociation constants (Kd) of 2.2 ± 0.1 µM and 0.32 ± 0.02 µM, respectively. After truncation, the binding affinity was confirmed using a BMAA-conjugated fluorescence assay. The Kd values for BMAA_159_min and BMAA_165_min were 6 ± 1 µM and 0.63 ± 0.02 µM, respectively. Alterations in the amino proton region studied using solution nuclear magnetic resonance (NMR) provided further evidence of aptamer-target binding. Additionally, circular dichroism (CD) spectroscopy revealed that BMAA_165_min forms hybrid G-quadruplex (G4) structures. Finally, BMAA_165_min was used in the development of an electrochemical aptamer-based (EAB) sensor that accomplished sensitive and selective detection of BMAA with a limit of detection (LOD) of 1.13 ± 0.02 pM.

Decoding Non-coding Variants: Recent Approaches to Studying Their Role in Gene Regulation and Human Diseases.

Genome-wide association studies (GWAS) have mapped over 90% of disease- and quantitative-trait-associated variants within the non-coding genome. Non-coding regulatory DNA (e.g., promoters and enhancers) and RNA (e.g., 5' and 3' UTRs and splice sites) are essential in regulating temporal and tissue-specific gene expressions. Non-coding variants can potentially impact the phenotype of an organism by altering the molecular recognition of the cis-regulatory elements, leading to gene dysregulation. However, determining causality between non-coding variants, gene regulation, and human disease has remained challenging. Experimental and computational methods have been developed to understand the molecular mechanism involved in non-coding variant interference at the transcriptional and post-transcriptional levels. This review discusses recent approaches to evaluating disease-associated single-nucleotide variants (SNVs) and determines their impact on transcription factor (TF) binding, gene expression, chromatin conformation, post-transcriptional regulation, and translation.

K-Means Clustering Identifies Diverse Clinical Phenotypes in COVID-19 Patients: Implications for Mortality Risks and Remdesivir Impact.

INTRODUCTION: The impact of remdesivir on mortality in patients with COVID-19 is still controversial. We aimed to identify clinical phenotype clusters of COVID-19 hospitalized patients with highest benefit from remdesivir use and validate these findings in an external cohort. METHODS: We included consecutive patients hospitalized between February 2020 and February 2021 for COVID-19. The derivation cohort comprised subjects admitted to Hospital Clinic of Barcelona. The validation cohort included patients from Hospital Universitari Mutua de Terrassa (Terrassa) and Hospital Universitari La Fe (Valencia), all tertiary centers in Spain. We employed K-means clustering to group patients according to reverse transcription polymerase chain reaction (rRT-PCR) cycle threshold (Ct) values and lymphocyte counts at diagnosis, and pre-test symptom duration. The impact of remdesivir on 60-day mortality in each cluster was assessed. RESULTS: A total of 1160 patients (median age 66, interquartile range (IQR) 55-78) were included. We identified five clusters, with mortality rates ranging from 0 to 36.7%. Highest mortality rate was observed in the cluster including patients with shorter pre-test symptom duration, lower lymphocyte counts, and lower Ct values at diagnosis. The absence of remdesivir administration was associated with worse outcome in the high-mortality cluster (10.5% vs. 36.7%; p < 0.001), comprising subjects with higher viral loads. These results were validated in an external multicenter cohort of 981 patients. CONCLUSIONS: Patients with COVID-19 exhibit varying mortality rates across different clinical phenotypes. K-means clustering aids in identifying patients who derive the greatest mortality benefit from remdesivir use.

Serpine1 mRNA confers mesenchymal characteristics to the cell and promotes CD8+ T cells exclusion from colon adenocarcinomas.

Serine protease inhibitor clade E member 1 (SERPINE1) inhibits extracellular matrix proteolysis and cell detachment. However, SERPINE1 expression also promotes tumor progression and plays a crucial role in metastasis. Here, we solve this apparent paradox and report that Serpine1 mRNA per se, independent of its protein-coding function, confers mesenchymal properties to the cell, promoting migration, invasiveness, and resistance to anoikis and increasing glycolytic activity by sequestering miRNAs. Expression of Serpine1 mRNA upregulates the expression of the TRA2B splicing factor without affecting its mRNA levels. Through transcriptional profiling, we found that Serpine1 mRNA expression downregulates through TRA2B the expression of genes involved in the immune response. Analysis of human colon tumor samples showed an inverse correlation between SERPINE1 mRNA expression and CD8+ T cell infiltration, unveiling the potential value of SERPINE1 mRNA as a promising therapeutic target for colon tumors.

Resilience, Self-Efficacy, and Symptoms of Anxiety and Depression in Older Adults during COVID-19 Confinement.

OBJECTIVE: This study examined the relationship between resilience, self-efficacy, anxiety, and depression to test whether self-efficacy affected anxiety and depression and compared how the participants in different age groups experienced anxiety, as well as the differences in anxiety between employed and unemployed participants. METHOD: A cross sectional web-based survey study that included adults aged 60 years or older living in Puerto Rico was performed during April and May 2020. RESULTS: A total of 299 older adults completed the online questionnaire (14% men, 83.6% women). Of the total sample, 25.4% reported having moderate to severe symptoms of anxiety, while 20.8% reported having moderate to severe symptoms of depression. Our path analysis model suggested that while self-efficacy did not directly affect anxiety, it had an impact on resilience, thereby reducing anxiety symptoms. The participants who were 71 years old or older had lower anxiety levels than their younger counterparts did. We also confirmed that work might serve as a protective factor against anxiety. CONCLUSION: Our findings underscore the importance of resilience, self-efficacy, and working later in life to promote well-being and successful aging.

SWI/SNF-dependent genes are defined by their chromatin landscape.

SWI/SNF complexes are evolutionarily conserved, ATP-dependent chromatin remodeling machines. Here, we characterize the features of SWI/SNF-dependent genes using BRM014, an inhibitor of the ATPase activity of the complexes. We find that SWI/SNF activity is required to maintain chromatin accessibility and nucleosome occupancy for most enhancers but not for most promoters. SWI/SNF activity is needed for expression of genes with low to medium levels of expression that have promoters with (1) low chromatin accessibility, (2) low levels of active histone marks, (3) high H3K4me1/H3K4me3 ratio, (4) low nucleosomal phasing, and (5) enrichment in TATA-box motifs. These promoters are mostly occupied by the canonical Brahma-related gene 1/Brahma-associated factor (BAF) complex. These genes are surrounded by SWI/SNF-dependent enhancers and mainly encode signal transduction, developmental, and cell identity genes (with almost no housekeeping genes). Machine-learning models trained with different chromatin characteristics of promoters and their surrounding regulatory regions indicate that the chromatin landscape is a determinant for establishing SWI/SNF dependency.

Prioritizing cardiovascular disease-associated variants altering NKX2-5 and TBX5 binding through an integrative computational approach.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies have mapped >90% of CVD-associated variants within the noncoding genome, which can alter the function of regulatory proteins, such as transcription factors (TFs). However, due to the overwhelming number of single-nucleotide polymorphisms (SNPs) (>500,000) in genome-wide association studies, prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1535 CVD-associated SNPs within TF footprints and putative cardiac enhancers plus 14,218 variants in linkage disequilibrium with genotype-dependent gene expression in cardiac tissues. Using ChIP-seq data from two cardiac TFs (NKX2-5 and TBX5) in human-induced pluripotent stem cell-derived cardiomyocytes, we trained a large-scale gapped k-mer SVM model to identify CVD-associated SNPs that altered NKX2-5 and TBX5 binding. The model was tested by scoring human heart TF genomic footprints within putative enhancers and measuring in vitro binding through electrophoretic mobility shift assay. Five variants predicted to alter NKX2-5 (rs59310144, rs6715570, and rs61872084) and TBX5 (rs7612445 and rs7790964) binding were prioritized for in vitro validation based on the magnitude of the predicted change in binding and are in cardiac tissue eQTLs. All five variants altered NKX2-5 and TBX5 DNA binding. We present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro analysis.

Prioritizing Cardiovascular Disease-Associated Variants Altering NKX2-5 Binding through an Integrative Computational Approach.

Cardiovascular diseases (CVDs) are the leading cause of death worldwide and are heavily influenced by genetic factors. Genome-wide association studies (GWAS) have mapped > 90% of CVD-associated variants within the non-coding genome, which can alter the function of regulatory proteins, like transcription factors (TFs). However, due to the overwhelming number of GWAS single nucleotide polymorphisms (SNPs) (>500,000), prioritizing variants for in vitro analysis remains challenging. In this work, we implemented a computational approach that considers support vector machine (SVM)-based TF binding site classification and cardiac expression quantitative trait loci (eQTL) analysis to identify and prioritize potential CVD-causing SNPs. We identified 1,535 CVD-associated SNPs that occur within human heart footprints/enhancers and 9,309 variants in linkage disequilibrium (LD) with differential gene expression profiles in cardiac tissue. Using hiPSC-CM ChIP-seq data from NKX2-5 and TBX5, two cardiac TFs essential for proper heart development, we trained a large-scale gapped k-mer SVM (LS-GKM-SVM) predictive model that can identify binding sites altered by CVD-associated SNPs. The computational predictive model was tested by scoring human heart footprints and enhancers in vitro through electrophoretic mobility shift assay (EMSA). Three variants (rs59310144, rs6715570, and rs61872084) were prioritized for in vitro validation based on their eQTL in cardiac tissue and LS-GKM-SVM prediction to alter NKX2-5 DNA binding. All three variants altered NKX2-5 DNA binding. In summary, we present a bioinformatic approach that considers tissue-specific eQTL analysis and SVM-based TF binding site classification to prioritize CVD-associated variants for in vitro experimental analysis.

Epidemiology and risk factors for recurrence in biliary source bloodstream infection episodes in oncological patients.

We aimed to describe the characteristics and outcomes of biliary source bloodstream infections (BSIs) in oncological patients. Secondarily, we analyzed risk factors for recurrent BSI episodes. All episodes of biliary source BSIs in oncological patients were prospectively collected (2008-2019) and retrospectively analyzed. Logistic regression analyses were performed. A rule to stratify patients into risk groups for recurrent biliary source BSI was conducted. Four hundred biliary source BSIs were documented in 291 oncological patients. The most frequent causative agents were Escherichia coli (42%) and Klebsiella spp. (27%), and 86 (21.5%) episodes were caused by multidrug-resistant Gram-negative bacilli (MDR-GNB). The rates of MDR-GNB increased over time. Overall, 73 patients developed 118 recurrent BSI episodes. Independent risk factors for recurrent BSI episodes were prior antibiotic therapy (OR 3.781, 95% CI 1.906-7.503), biliary prosthesis (OR 2.232, 95% CI 1.157-4.305), prior admission due to suspected biliary source infection (OR 4.409, 95% CI 2.338-8.311), and BSI episode caused by an MDR-GNB (OR 2.857, 95% CI 1.389-5.874). With these variables, a score was generated that predicted recurrent biliary source BSI with an area under the receiver operating characteristic (ROC) curve of 0.819. Inappropriate empirical antibiotic treatment (IEAT) was administered in 23.8% of patients, and 30-d mortality was 19.5%. As a conclusion, biliary source BSI in oncological patients is mainly caused by GNB, with high and increasing MDR rates, frequent IEAT, and high mortality. Recurrent BSI episodes are frequent. A simple score to identify recurrent episodes was developed to potentially establish prophylactic strategies. IMPORTANCE This study shows that biliary source bloodstream infections (BSIs) in oncological patients are mainly caused by Gram-negative bacilli (GNB), with high and increasing rates of multidrug resistance. Importantly, recurrent biliary source BSI episodes were very frequent and associated with delays in chemotherapy, high rates of inappropriate empirical antibiotic therapy, and high 30-d mortality (19.5%). Using the variable independently associated with recurrent BSI episodes, a score was generated that predicted recurrent biliary source BSI with high accuracy. This score could be used to establish prophylactic strategies and lower the risk of relapsing episodes and the associated morbidity and mortality.

Hidden modes of DNA binding by human nuclear receptors.

Human nuclear receptors (NRs) are a superfamily of ligand-responsive transcription factors that have central roles in cellular function. Their malfunction is linked to numerous diseases, and the ability to modulate their activity with synthetic ligands has yielded 16% of all FDA-approved drugs. NRs regulate distinct gene networks, however they often function from genomic sites that lack known binding motifs. Here, to annotate genomic binding sites of known and unexamined NRs more accurately, we use high-throughput SELEX to comprehensively map DNA binding site preferences of all full-length human NRs, in complex with their ligands. Furthermore, to identify non-obvious binding sites buried in DNA-protein interactomes, we develop MinSeq Find, a search algorithm based on the MinTerm concept from electrical engineering and digital systems design. The resulting MinTerm sequence set (MinSeqs) reveal a constellation of binding sites that more effectively annotate NR-binding profiles in cells. MinSeqs also unmask binding sites created or disrupted by 52,106 single-nucleotide polymorphisms associated with human diseases. By implicating druggable NRs as hidden drivers of multiple human diseases, our results not only reveal new biological roles of NRs, but they also provide a resource for drug-repurposing and precision medicine.

CREPE: a Shiny app for transcription factor cataloguing.

Summary: Transcription factors (TFs) are proteins that directly interpret the genome to regulate gene expression and determine cellular phenotypes. TF identification is a common first step in unraveling gene regulatory networks. We present CREPE, an R Shiny app to catalogue and annotate TFs. CREPE was benchmarked against curated human TF datasets. Next, we use CREPE to explore the TF repertoires of Heliconius erato and Heliconius melpomene butterflies. Availability and implementation: CREPE is available as a Shiny app package available at GitHub (github.com/dirostri/CREPE). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Disease-associated non-coding variants alter NKX2-5 DNA-binding affinity.

Genome-wide association studies (GWAS) have mapped over 90 % of disease- or trait-associated variants within the non-coding genome, like cis-regulatory elements (CREs). Non-coding single nucleotide polymorphisms (SNPs) are genomic variants that can change how DNA-binding regulatory proteins, like transcription factors (TFs), interact with the genome and regulate gene expression. NKX2-5 is a TF essential for proper heart development, and mutations affecting its function have been associated with congenital heart diseases (CHDs). However, establishing a causal mechanism between non-coding genomic variants and human disease remains challenging. To address this challenge, we identified 8475 SNPs predicted to alter NKX2-5 DNA-binding using a position weight matrix (PWM)-based predictive model. Five variants were prioritized for in vitro validation; four of them are associated with traits and diseases that impact cardiovascular health. The impact of these variants on NKX2-5 binding was evaluated with electrophoretic mobility shift assay (EMSA) using purified recombinant NKX2-5 homeodomain. Binding curves were constructed to determine changes in binding between variant and reference alleles. Variants rs7350789, rs7719885, rs747334, and rs3892630 increased binding affinity, whereas rs61216514 decreased binding by NKX2-5 when compared to the reference genome. Our findings suggest that differential TF-DNA binding affinity can be key in establishing a causal mechanism of pathogenic variants.

Data set on mortality of national basketball association (NBA) players.

In this work, we present a data set on the survival times and mortality rates of all 4374 professional basketball players who participated in the National Basketball Association (NBA) from its inception in 1946 until July 2019 [1]. It contains the data of 412 active and 3962 former players. The data were recorded from different internet sources and include information on each player's position, ethnicity, handedness, ages at NBA debut and career end, height, weight, or number of NBA games. The results of the analysis of a previous data set with the same variables of all NBA players from 1946 to 2015 were recently published by Martinez et al. in 2019 [2]. The information provided in the data set can be useful to better understand the mortality risk among NBA players.