ExonSurfer: a web-tool to design primers at exon-exon junctions.

BACKGROUND: Reverse transcription quantitative PCR (RT-qPCR) with intercalating dyes is one of the main techniques to assess gene expression levels used in basic and applied research as well as in diagnostics. However, primer design for RT-qPCR can be complex due to the high demands on primer quality. Primers are best placed on exon junctions, should avoid polymorphic regions, be specific to the target transcripts and also prevent genomic amplification accurately, among others. Current software tools manage to meet all the necessary criteria only insufficiently. Here, we present ExonSurfer, a novel, user-friendly web-tool for qPCR primer design. RESULTS: ExonSurfer combines the different steps of the primer design process, encompassing target selection, specificity and self-complementarity assessment, and the avoidance of issues arising from polymorphisms. Amplification of potentially contaminating genomic DNA is avoided by designing primers on exon-exon junctions, moreover, a genomic alignment is performed to filter the primers accordingly and inform the user of any predicted interaction. In order to test the whole performance of the application, we designed primer pairs for 26 targets and checked both primer efficiency, amplicon melting temperature and length and confirmed the targeted amplicon by Sanger sequencing. Most of the tested primers accurately and selectively amplified the corresponding targets. CONCLUSION: ExonSurfer offers a comprehensive end-to-end primer design, guaranteeing transcript-specific amplification. The user interface is intuitive, providing essential specificity and amplicon details. The tool can also be used by command line and the source code is available. Overall, we expect ExonSurfer to facilitate RT-qPCR set-up for researchers in many fields.

Towards understanding post-COVID-19 condition: A systematic meta-analysis of transcriptomic alterations with sex-specific insights.

BACKGROUND: Post COVID-19 Condition (PCC), characterized by lingering symptoms post-acute COVID-19, poses clinical challenges, highlighting the need to understand its underlying molecular mechanisms. This meta-analysis aims to shed light on the transcriptomic landscapes and sex-specific molecular dynamics intrinsic to PCC. METHODS: A systematic review identified three studies suitable for comprehensive meta-analysis, encompassing 135 samples (57 PCC subjects and 78 recovered subjects). We performed meta-analysis on differential gene expression, a gene set enrichment analysis of Reactome pathways, and weighted gene co-expression network analysis (WGCNA). We performed a drug and disease enrichment analysis and also assessed sex-specific differences in expression patterns. KEY FINDINGS: A clear difference was observed in the transcriptomic profiles of PCC subjects, with 530 differentially expressed genes (DEGs) identified. Enrichment analysis revealed that the altered pathways were predominantly implicated in cell cycle processes, immune dysregulation and histone modifications. Antioxidant compounds such as hesperitin were predominantly linked to the hub genes of the DEGs. Sex-specific analyses highlighted disparities in DEGs and altered pathways in male and female PCC patients, revealing a difference in the expression of ribosomal proteins. PCC in men was mostly linked to neuro-cardiovascular disorders, while women exhibited more diverse disorders, with a high index of respiratory conditions. CONCLUSION: Our study reveals the intricate molecular processes underlying PCC, highlighting that the differences in molecular dynamics between males and females could be key to understanding and effectively managing the varied symptomatology of this condition.

Comprehensive mapping of the AOP-Wiki database: identifying biological and disease gaps.

Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.

Urine Metabolite Analysis to Identify Pathomechanisms of Long COVID: A Pilot Study.

Background: Around 10% of people who had COVID-9 infection suffer from persistent symptoms such as fatigue, dyspnoea, chest pain, arthralgia/myalgia, sleep disturbances, cognitive dysfunction and impairment of mental health. Different underlying pathomechanisms appear to be involved, in particular inflammation, alterations in amino acid metabolism, autonomic dysfunction and gut dysbiosis. Aim: As routine tests are often inconspicuous in patients with Long COVID (LC), similarly to patients suffering from myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), accessible biomarkers indicating dysregulation of specific pathways are urgently needed to identify underlying pathomechanisms and enable personalized medicine treatment. Within this pilot study we aimed to proof traceability of altered metabolism by urine analysis. Patients and Methods: Urine metabolome analyses were performed to investigate the metabolic signature of patients with LC (n = 25; 20 women, 5 men) in comparison to healthy controls (Ctrl, n = 8; 7 women, 1 man) and individuals with ME/CFS (n = 8; 2 women, 6 men). Concentrations of neurotransmitter precursors tryptophan, phenylalanine and their downstream metabolites, as well as their association with symptoms (fatigue, anxiety and depression) in the patients were examined. Results and Conclusion: Phenylalanine levels were significantly lower in both the LC and ME/CFS patient groups when compared to the Ctrl group. In many LC patients, the concentrations of downstream metabolites of tryptophan and tyrosine, such as serotonin, dopamine and catecholamines, deviated from the reference ranges. Several symptoms (sleep disturbance, pain or autonomic dysfunction) were associated with certain metabolites. Patients experiencing fatigue had lower levels of kynurenine, phenylalanine and a reduced kynurenine to tryptophan ratio (Kyn/Trp). Lower concentrations of gamma-aminobutyric acid (GABA) and higher activity of kynurenine 3-monooxygenase (KMO) were observed in patients with anxiety. Conclusively, our results suggest that amino acid metabolism and neurotransmitter synthesis is disturbed in patients with LC and ME/CFS. The identified metabolites and their associated dysregulations could serve as potential biomarkers for elucidating underlying pathomechanisms thus enabling personalized treatment strategies for these patient populations.

Positive Effects of Probiotic Therapy in Patients with Post-Infectious Fatigue.

Post-infectious fatigue is a common complication that can lead to decreased physical efficiency, depression, and impaired quality of life. Dysbiosis of the gut microbiota has been proposed as a contributing factor, as the gut-brain axis plays an important role in regulating physical and mental health. This pilot study aimed to investigate the severity of fatigue and depression, as well as the quality of life of 70 patients with post-infectious fatigue who received a multi-strain probiotic preparation or placebo in a double-blind, placebo-controlled trial. Patients completed questionnaires to assess their fatigue (fatigue severity scale (FSS)), mood (Beck Depression Inventory II (BDI-II)), and quality of life (short form-36 (SF-36)) at baseline and after 3 and 6 months of treatment. Routine laboratory parameters were also assessed, including immune-mediated changes in tryptophan and phenylalanine metabolism. The intervention was effective in improving fatigue, mood, and quality of life in both the probiotic and placebo groups, with greater improvements seen in the probiotic group. FSS and BDI-II scores declined significantly under treatment with both probiotics and placebo, but patients who received probiotics had significantly lower FSS (p < 0.001) and BDI-II (p < 0.001) scores after 6 months. Quality of life scores improved significantly in patients who received probiotics (p < 0.001), while patients taking a placebo only saw improvements in the "Physical limitation" and "Energy/Fatigue" subcategories. After 6 months neopterin was higher in patients receiving placebo, while no longitudinal changes in interferon-gamma mediated biochemical pathways were observed. These findings suggest that probiotics may be a promising intervention for improving the health of patients with post-infectious fatigue, potentially through modulating the gut-brain axis.

The microbiome of the ice-capped Cayambe Volcanic Complex in Ecuador.

A major challenge in microbial ecology is to understand the principles and processes by which microbes associate and interact in community assemblages. Microbial communities in mountain glaciers are unique as first colonizers and nutrient enrichment drivers for downstream ecosystems. However, mountain glaciers have been distinctively sensitive to climate perturbations and have suffered a severe retreat over the past 40 years, compelling us to understand glacier ecosystems before their disappearance. This is the first study in an Andean glacier in Ecuador offering insights into the relationship of physicochemical variables and altitude on the diversity and structure of bacterial communities. Our study covered extreme Andean altitudes at the Cayambe Volcanic Complex, from 4,783 to 5,583 masl. Glacier soil and ice samples were used as the source for 16S rRNA gene amplicon libraries. We found (1) effects of altitude on diversity and community structure, (2) the presence of few significantly correlated nutrients to community structure, (3) sharp differences between glacier soil and glacier ice in diversity and community structure, where, as quantified by the Shannon γ-diversity distribution, the meta-community in glacier soil showed more diversity than in glacier ice; this pattern was related to the higher variability of the physicochemical distribution of variables in the former substrate, and (4) significantly abundant genera associated with either high or low altitudes that could serve as biomarkers for studies on climate change. Our results provide the first assessment of these unexplored communities, before their potential disappearance due to glacier retreat and climate change.

MIO: microRNA target analysis system for immuno-oncology.

SUMMARY: MicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers. AVAILABILITY AND IMPLEMENTATION: https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.