RESUMO
Although SARS-CoV-2 primarily targets the respiratory system, patients with and survivors of COVID-19 can suffer neurological symptoms1-3. However, an unbiased understanding of the cellular and molecular processes that are affected in the brains of patients with COVID-19 is missing. Here we profile 65,309 single-nucleus transcriptomes from 30 frontal cortex and choroid plexus samples across 14 control individuals (including 1 patient with terminal influenza) and 8 patients with COVID-19. Although our systematic analysis yields no molecular traces of SARS-CoV-2 in the brain, we observe broad cellular perturbations indicating that barrier cells of the choroid plexus sense and relay peripheral inflammation into the brain and show that peripheral T cells infiltrate the parenchyma. We discover microglia and astrocyte subpopulations associated with COVID-19 that share features with pathological cell states that have previously been reported in human neurodegenerative disease4-6. Synaptic signalling of upper-layer excitatory neurons-which are evolutionarily expanded in humans7 and linked to cognitive function8-is preferentially affected in COVID-19. Across cell types, perturbations associated with COVID-19 overlap with those found in chronic brain disorders and reside in genetic variants associated with cognition, schizophrenia and depression. Our findings and public dataset provide a molecular framework to understand current observations of COVID-19-related neurological disease, and any such disease that may emerge at a later date.
Assuntos
Astrócitos/patologia , Encéfalo/patologia , COVID-19/diagnóstico , COVID-19/patologia , Plexo Corióideo/patologia , Microglia/patologia , Neurônios/patologia , Idoso , Idoso de 80 Anos ou mais , Encéfalo/metabolismo , Encéfalo/fisiopatologia , Encéfalo/virologia , COVID-19/genética , COVID-19/fisiopatologia , Núcleo Celular/genética , Plexo Corióideo/metabolismo , Plexo Corióideo/fisiopatologia , Plexo Corióideo/virologia , Feminino , Humanos , Inflamação/virologia , Masculino , Pessoa de Meia-Idade , SARS-CoV-2/crescimento & desenvolvimento , SARS-CoV-2/patogenicidade , Análise de Célula Única , Transcriptoma , Replicação ViralRESUMO
The human microbiome has emerged as a rich source of diverse and bioactive natural products, harboring immense potential for therapeutic applications. To facilitate systematic exploration and analysis of its biosynthetic landscape, we present ABC-HuMi: the Atlas of Biosynthetic Gene Clusters (BGCs) in the Human Microbiome. ABC-HuMi integrates data from major human microbiome sequence databases and provides an expansive repository of BGCs compared to the limited coverage offered by existing resources. Employing state-of-the-art BGC prediction and analysis tools, our database ensures accurate annotation and enhanced prediction capabilities. ABC-HuMi empowers researchers with advanced browsing, filtering, and search functionality, enabling efficient exploration of the resource. At present, ABC-HuMi boasts a catalog of 19 218 representative BGCs derived from the human gut, oral, skin, respiratory and urogenital systems. By capturing the intricate biosynthetic potential across diverse human body sites, our database fosters profound insights into the molecular repertoire encoded within the human microbiome and offers a comprehensive resource for the discovery and characterization of novel bioactive compounds. The database is freely accessible at https://www.ccb.uni-saarland.de/abc_humi/.
Assuntos
Vias Biossintéticas , Bases de Dados Genéticas , Microbiota , Família Multigênica , Humanos , Vias Biossintéticas/genética , Biologia Computacional/instrumentação , Internet , Microbiota/genética , Família Multigênica/genética , Metagenoma/genéticaRESUMO
Quantifying microbiome species and composition from metagenomic assays is often challenging due to its time-consuming nature and computational complexity. In Bioinformatics, k-mer-based approaches were long established to expedite the analysis of large sequencing data and are now widely used to annotate metagenomic data. We make use of k-mer counting techniques for efficient and accurate compositional analysis of microbiota from whole metagenome sequencing. Mibianto solves this problem by operating directly on read files, without manual preprocessing or complete data exchange. It handles diverse sequencing platforms, including short single-end, paired-end, and long read technologies. Our sketch-based workflow significantly reduces the data volume transferred from the user to the server (up to 99.59% size reduction) to subsequently perform taxonomic profiling with enhanced efficiency and privacy. Mibianto offers functionality beyond k-mer quantification; it supports advanced community composition estimation, including diversity, ordination, and differential abundance analysis. Our tool aids in the standardization of computational workflows, thus supporting reproducibility of scientific sequencing studies. It is adaptable to small- and large-scale experimental designs and offers a user-friendly interface, thus making it an invaluable tool for both clinical and research-oriented metagenomic studies. Mibianto is freely available without the need for a login at: https://www.ccb.uni-saarland.de/mibianto.
Assuntos
Metagenômica , Microbiota , Software , Metagenômica/métodos , Microbiota/genética , Humanos , Metagenoma , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Internet , Fluxo de Trabalho , Análise de Sequência de DNA/métodos , Biologia Computacional/métodosRESUMO
MicroRNAs (miRNAs) are small non-coding RNAs that play a critical role in regulating diverse biological processes. Extracting functional insights from a list of miRNAs is challenging, as each miRNA can potentially interact with hundreds of genes. To address this challenge, we developed miEAA, a flexible and comprehensive miRNA enrichment analysis tool based on direct and indirect miRNA annotation. The latest release of miEAA includes a data warehouse of 19 miRNA repositories, covering 10 different organisms and 139 399 functional categories. We have added information on the cellular context of miRNAs, isomiRs, and high-confidence miRNAs to improve the accuracy of the results. We have also improved the representation of aggregated results, including interactive Upset plots to aid users in understanding the interaction among enriched terms or categories. Finally, we demonstrate the functionality of miEAA in the context of ageing and highlight the importance of carefully considering the miRNA input list. MiEAA is free to use and publicly available at https://www.ccb.uni-saarland.de/mieaa/.
Assuntos
MicroRNAs , Software , MicroRNAs/genética , Bases de Dados de Ácidos NucleicosRESUMO
A significant fraction of mature miRNA transcripts carries sequence and/or length variations, termed isomiRs. IsomiRs are differentially abundant in cell types, tissues, body fluids or patients' samples. Not surprisingly, multiple studies describe a physiological and pathophysiological role. Despite their importance, systematically collected and annotated isomiR information available in databases remains limited. We thus developed isomiRdb, a comprehensive resource that compiles miRNA expression data at isomiR resolution from various sources. We processed 42 499 human miRNA-seq datasets (5.9 × 1011 sequencing reads) and consistently analyzed them using miRMaster and sRNAbench. Our database provides online access to the 90 483 most abundant isomiRs (>1 RPM in at least 1% of the samples) from 52 tissues and 188 cell types. Additionally, the full set of over 3 million detected isomiRs is available for download. Our resource can be queried at the sample, miRNA or isomiR level so users can quickly answer common questions about the presence/absence of a particular miRNA/isomiR in tissues of interest. Further, the database facilitates to identify whether a potentially interesting new isoform has been detected before and its frequency. In addition to expression tables, isomiRdb can generate multiple interactive visualisations including violin plots and heatmaps. isomiRdb is free to use and publicly available at: https://www.ccb.uni-saarland.de/isomirdb.
Assuntos
Bases de Dados Genéticas , MicroRNAs , Humanos , Sequenciamento de Nucleotídeos em Larga Escala , MicroRNAs/genética , MicroRNAs/metabolismo , Isoformas de Proteínas/genética , Análise de Sequência de RNARESUMO
Non-thermal plasma, a partially ionized gas, holds significant potential for clinical applications, including wound-healing support, oral therapies, and anti-tumour treatments. While its applications showed promising outcomes, the underlying molecular mechanisms remain incompletely understood. We thus apply non-thermal plasma to mouse auricular skin and conducted non-coding RNA sequencing, as well as single-cell blood sequencing. In a time-series analysis (five timepoints spanning 2 hours), we compare the expression of microRNAs in the plasma-treated left ears to the unexposed right ears of the same mice as well as to the ears of unexposed control mice. Our findings indicate specific effects in the treated ears for a set of five miRNAs: mmu-miR-144-5p, mmu-miR-144-3p, mmu-miR-142a-5p, mmu-miR-223-3p, and mmu-miR-451a. Interestingly, mmu-miR-223-3p also exhibits an increase over time in the right non-treated ear of the exposed mice, suggesting systemic effects. Notably, this miRNA, along with mmu-miR-142a-5p and mmu-miR-144-3p, regulates genes and pathways associated with wound healing and tissue regeneration (namely ErbB, FoxO, Hippo, and PI3K-Akt signalling). This co-regulation is particularly remarkable considering the significant seed dissimilarities among the miRNAs. Finally, single-cell sequencing of PBMCs reveals the downregulation of 12 from 15 target genes in B-cells, Cd4+ and Cd8+ T-cells. Collectively, our data provide evidence for a systemic effect of non-thermal plasma.
Assuntos
Regulação da Expressão Gênica , MicroRNAs , Gases em Plasma , Pele , MicroRNAs/genética , Animais , Camundongos , Pele/metabolismo , Gases em Plasma/farmacologia , Regulação da Expressão Gênica/efeitos dos fármacos , Perfilação da Expressão Gênica , Cicatrização/efeitos dos fármacos , Transdução de Sinais , Sistema Imunitário/metabolismoRESUMO
Plasmids are known to contain genes encoding for virulence factors and antibiotic resistance mechanisms. Their relevance in metagenomic data processing is steadily growing. However, with the increasing popularity and scale of metagenomics experiments, the number of reported plasmids is rapidly growing as well, amassing a considerable number of false positives due to undetected misassembles. Here, our previously published database PLSDB provides a reliable resource for researchers to quickly compare their sequences against selected and annotated previous findings. Within two years, the size of this resource has more than doubled from the initial 13,789 to now 34,513 entries over the course of eight regular data updates. For this update, we aggregated community feedback for major changes to the database featuring new analysis functionality as well as performance, quality, and accessibility improvements. New filtering steps, annotations, and preprocessing of existing records improve the quality of the provided data. Additionally, new features implemented in the web-server ease user interaction and allow for a deeper understanding of custom uploaded sequences, by visualizing similarity information. Lastly, an application programming interface was implemented along with a python library, to allow remote database queries in automated workflows. The latest release of PLSDB is freely accessible under https://www.ccb.uni-saarland.de/plsdb.
Assuntos
Bactérias/genética , Bases de Dados Genéticas , Plasmídeos/química , Interface Usuário-Computador , Actinobacteria/genética , Actinobacteria/patogenicidade , Bactérias/classificação , Bactérias/patogenicidade , Bacteroidetes/genética , Bacteroidetes/patogenicidade , Resistência Microbiana a Medicamentos/genética , Firmicutes/genética , Firmicutes/patogenicidade , Internet , Metagenômica/métodos , Anotação de Sequência Molecular , Plasmídeos/classificação , Plasmídeos/metabolismo , Proteobactérias/genética , Proteobactérias/patogenicidade , Spirochaetales/genética , Spirochaetales/patogenicidade , Tenericutes/genética , Tenericutes/patogenicidade , Virulência/genéticaRESUMO
Despite recent methodology and reference database improvements for taxonomic profiling tools, metagenomic assembly and genomic binning remain important pillars of metagenomic analysis workflows. In case reference information is lacking, genomic binning is considered to be a state-of-the-art method in mixed culture metagenomic data analysis. In this light, our previously published tool BusyBee Web implements a composition-based binning method efficient enough to function as a rapid online utility. Handling assembled contigs and long nanopore generated reads alike, the webserver provides a wide range of supplementary annotations and visualizations. Half a decade after the initial publication, we revisited existing functionality, added comprehensive visualizations, and increased the number of data analysis customization options for further experimentation. The webserver now allows for visualization-supported differential analysis of samples, which is computationally expensive and typically only performed in coverage-based binning methods. Further, users may now optionally check their uploaded samples for plasmid sequences using PLSDB as a reference database. Lastly, a new application programming interface with a supporting python package was implemented, to allow power users fully automated access to the resource and integration into existing workflows. The webserver is freely available under: https://www.ccb.uni-saarland.de/busybee.
Assuntos
Algoritmos , Metagenoma , Software , Metagenômica/métodos , Fluxo de Trabalho , Análise de Sequência de DNARESUMO
BACKGROUND: Different production systems of livestock animals influence various factors, including the gut microbiota. METHODS: We investigated whether changing the conditions from barns to free-range chicken farming impacts the microbiome over the course of three weeks. We compared the stool microbiota of chicken from industrial barns after introducing them either in community or separately to a free-range environment. RESULTS: Over the six time points, 12 taxa-mostly lactobacilli-changed significantly. As expected, the former barn chicken cohort carries more resistances to common antibiotics. These, however, remained positive over the observed period. At the end of the study, we collected eggs and compared metabolomic profiles of the egg white and yolk to profiles of eggs from commercial suppliers. Here, we observed significant differences between commercial and fresh collected eggs as well as differences between the former barn chicken and free-range chicken. CONCLUSION: Our data indicate that the gut microbiota can undergo alterations over time in response to changes in production systems. These changes subsequently exert an influence on the metabolites found in the eggs. The preliminary results of our proof-of-concept study motivate larger scale observations with more individual chicken and longer observation periods.
RESUMO
In 2019, researchers from the EAT-Lancet Commission developed the 'Planetary Health (PH) diet'. Specifically, they provided recommendations pertaining to healthy diets derived from sustainable food systems. Thus far, it has not been analysed how such a diet affects the human intestinal microbiome, which is important for health and disease development. Here, we present longitudinal genome-wide metagenomic sequencing and mass spectrometry data on the gut microbiome of healthy volunteers adhering to the PH diet, as opposed to vegetarian or vegan (VV) and omnivorous (OV) diets. We obtained basic epidemiological information from 41 healthy volunteers and collected stool samples at inclusion and after 2, 4, and 12 weeks. Individuals opting to follow the PH diet received detailed instructions and recipes, whereas individuals in the control groups followed their habitual dietary pattern. Whole-genome DNA was extracted from stool specimens and subjected to shotgun metagenomic sequencing (~3 GB per patient). Conventional bacterial stool cultures were performed in parallel and bacterial species were identified with matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. We analysed samples from 16 PH, 16 OV, and 9 VV diet patterns. The α-diversity remained relatively stable for all dietary groups. In the PH group, we observed a constant increase from 3.79% at inclusion to 4.9% after 12 weeks in relative abundance of Bifidobacterium adolescentis. Differential PH abundance analysis highlighted a non-significant increase in possible probiotics such as Paraprevotella xylaniphila and Bacteroides clarus. The highest abundance of these bacteria was observed in the VV group. Dietary modifications are associated with rapid alterations to the human gut microbiome, and the PH diet led to a slight increase in probiotic-associated bacteria at ≥4 weeks. Additional research is required to confirm these findings.
Assuntos
Microbioma Gastrointestinal , Humanos , Dieta Saudável , Fezes/microbiologia , Dieta , Bactérias/genéticaRESUMO
For decades, in diffusion cloud chambers, different types of subatomic particle tracks from radioactive sources or cosmic radiation had to be identified with the naked eye which limited the amount of data that could be processed. In order to allow these classical particle detectors to enter the digital era, we successfully developed a neuro-explicit artificial intelligence model that, given an image from the cloud chamber, automatically annotates most of the particle tracks visible in the image according to the type of particle or process that created it. To achieve this goal, we combined the attention U-Net neural network architecture with methods that model the shape of the detected particle tracks. Our experiments show that the model effectively detects particle tracks and that the neuro-explicit approach decreases the misclassification rate of rare particles by 73% compared with solely using the attention U-Net.
RESUMO
We analyzed consecutive clinical cases of infections due to carbapenemase-producing gram-negative bacteria detected in war-wounded patients from Ukraine who were treated at one university medical center in southwest Germany between June and December 2022. The isolates of multiresistant gram-negative bacteria were subjected to a thorough microbiological characterization and whole genome sequencing (WGS). We identified five war-wounded Ukrainian patients who developed infections with New Delhi metallo-ß-lactamase 1-positive Klebsiella pneumoniae. Two isolates also carried OXA-48 carbapenemases. The bacteria were resistant to novel antibiotics, such as ceftazidime/avibactam and cefiderocol. The used treatment strategies included combinations of ceftazidime/avibactam + aztreonam, colistin, or tigecycline. WGS suggested transmission during primary care in Ukraine. We conclude that there is an urgent need for thorough surveillance of multiresistant pathogens in patients from war zones.
Assuntos
Ceftazidima , Refugiados , Humanos , Ceftazidima/uso terapêutico , Ucrânia/epidemiologia , Antibacterianos/farmacologia , Antibacterianos/uso terapêutico , beta-Lactamases/genética , Proteínas de Bactérias/genética , Compostos Azabicíclicos/uso terapêutico , Combinação de Medicamentos , Bactérias Gram-Negativas/genética , Testes de Sensibilidade Microbiana , Klebsiella pneumoniae/genéticaRESUMO
BACKGROUND: One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. RESULTS: We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. CONCLUSION: We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. REVIEWERS: This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.