Biology subjects

Backes, C.

Publications and source records attributed to Backes, C..

10 recordsLinked to original sources

bioRxiv2026

A Neuronally Active Bacterial Siderophore Manipulates Host Behavior and Longevity

Aging populations face a growing burden of neurodegenerative disease and behavioral decline. The microbiota is increasingly implicated in host physiology, highlighting an urgent need to understand how gut microbes modulate brain function and late life health. Here, we develop a high-throughput microbe-host screening platform to systematically dissect how bacterial genes shape organismal behavior. Using the Escherichia coli Keio library of 3,985 distinct non-essential single-gene deletion mutants in combination with Caenorhabditis elegans, we quantitatively profiled 768 behavioral features per bacterial clone and uncovered a coherent module of bacterial genes involved in iron homeostasis that robustly reprogrammed host behavior. Bacterial omics analyses revealed that alterations in iron homeostasis amplify siderophore production in a TCA cycle-dependent manner, and loss of enterobactin biosynthesis abolished the bacteria-induced changes in worm movement. Transcriptional, molecular and functional studies demonstrated that bacterial enterobactin activates host neuromuscular responses to shape both lifespan and healthspan in a manner dependent on the mitochondrial unfolded protein response. Thermal proteome profiling identified mitochondrial glutaryl-CoA dehydrogenase and the electron-transfer flavoproteins A and B as enterobactin-binding targets that control mitochondrial function. Finally, genetic manipulation of neuronal mitochondrial and cytosolic reactive oxygen species via superoxide dismutase and catalase revealed that redox state modulates enterobactin-driven changes in movement and lifespan through the secretin-family GPCR neuropeptide receptor PDFR-1. Together, these findings establish microbial enterobactin as a non-canonical neurotransmitter regulating neuronal mitochondrial function, behavior and aging, defining a gut bacteria-brain axis with broad implications for maintaining late-life health.

animal behavior and cognition↗

bioRxiv2026

A protein language model unveils the E. coli pangenome functional landscape regulating host proteostasis

Understanding how bacterial diversity at strain level resolution shapes host physiology is a central challenge in microbiome research. The vast, functionally unknown genetic diversity within a species pangenome makes it difficult to connect genes to function and their impact on host physiology. Here, we explore how the functional landscape of the Escherichia coli pangenome impacts transcriptional responses in Caenorhabditis elegans and show that traditional gene-centric methods fail to provide significant functional associations with the host. Thus, we developed a pangenome framework that leverages the protein language model ProtT5 and generates unique strain embeddings representing the functional potential of each 9,558 E. coli isolate. Stratification of the pangenome into distinct functional guilds aligned with key host processes such as cell division, metabolism and proteostasis. Further, we identify a critical interplay between the extensive network of bacterial chaperones and proteases in regulating host proteostasis. We find that the bacterial chaperone DNAK/HSP70 and protease ClpX fine-tune the host ubiquitin-proteasome system by controlling propionate and vitamin B12 availability. These findings reveal a conserved co-proteostasis mechanism as a key phenomenon modulating host-microbe interactions through metabolic communication. Our pangenome-to-phenotype approach offers a powerful strategy to decode bacterial pangenome functional diversity, directly linking microbial genomic variation to host physiological outcomes.

systems biology↗

bioRxiv2020

Deep sncRNA-seq of the PPMI cohort to study Parkinson's disease progression

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinsons diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinsons Progression Marker Initiative (PPMI) and Luxembourg Parkinsons Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.

neuroscience↗

bioRxiv2020

Metabolic exchange and energetic coupling between nutritionally stressed bacterial species, the possible role of QS molecules

To clarify the principles controlling inter-species interactions, we previously developed a co-culture model with two anaerobic bacteria, Clostridium acetobutylicum and Desulfovibrio vulgaris Hildenborough, in which nutritional stress for D. vulgaris induced tight cell-cell inter-species interaction. Here we show that exchange of metabolites produced by C. acetobutylicum allows D. vulgaris to duplicate its DNA, and to be energetically viable even without its substrates. Physical interaction between C. acetobutylicum and D. vulgaris (or Escherichia coli and D. vulgaris) is linked to the quorum-sensing molecule AI-2, produced by C. acetobutylicum and E. coli. With nutrients D. vulgaris produces a small molecule that inhibits in vitro the AI-2 activity, and could act as an antagonist in vivo. Sensing of AI-2 by D. vulgaris could induce formation of an intercellular structure that allows directly or indirectly metabolic exchange and energetic coupling between the two bacteria.

microbiology↗

bioRxiv2020

Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis

Small non-coding RNAs such as microRNAs are master regulators of gene expression. One of the most promising applications of miRNAs is the use as liquid biopsy. Especially early diagnosis is an effective means to increase patients overall survival. E.g. in oncology a tumor is detected at best prior to its clinical manifestation. We generated genome-wide miRNA profiles from serum of patients and controls from the population-based Janus Serum Bank (JSB) and analyzed them by bioinformatics and artificial intelligence approaches. JSB contains sera from 318,628 originally healthy persons, more than 96,000 of whom later developed cancer. We selected 210 serum samples of patients with lung, colon or breast cancer at three time points prior to diagnosis, after cancer diagnosis and controls. The controls were matched with regard to age of the blood donor and to the time points of blood drawing, which were 27, 32, or 38 years prior to diagnosis. Using ANOVA we report 70 significantly deregulated markers (adjusted p-value<0.05). The driver for the significance was the diagnostic time point (miR-575, miR-6821-5p, miR-630 had adjusted p-values<10-10). Further, 91miRNAs were differently expressed in pre-diagnostic samples as compared to controls (nominal p<0.05). Unsupervised competitive learning by self-organized maps indicated larges effects in lung cancer samples while breast cancer samples showed the least pronounced changes. Self-organized maps also highlighted cancer and time point specific miRNA dys-regulation. Intriguingly, a detailed breakdown of the results highlighted that 51% of all miRNAs were highly specific, either for a time-point or a cancer entity. Our results indicate that tumors may be indicated by serum miRNAs decades prior the clinical manifestation.

cancer biology↗

bioRxiv2020

miRSwitch: Detecting microRNA arm shift and switch events

Arm selection, the preferential expression of a 3' or 5' mature microRNA (miRNA), is a highly dynamic and tissue-specific process. Time-dependent expression shifts or switches between the arms are also relevant for human diseases. We present miRSwitch, a web server to facilitate the analysis and interpretation of arm selection events. Our species-independent tool evaluates pre-processed small non-coding RNA sequencing (sncRNA-seq) data, i.e. expression matrices or output files from miRNA quantification tools (miRDeep2, miRMaster, sRNAbench). miRSwitch highlights potential changes in the distribution of mature miRNAs from the same precursor. Group comparisons from one or several user-provided annotations (e.g. disease states) are possible. Results can be dynamically adjusted by choosing from a continuous range of highly specific to very sensitive parameters. Users can compare potential arm shifts in the provided data to a human reference map of pre-computed arm shift frequencies. We created this map from 46 tissues and 30,521 samples. As case studies we present novel arm shift information in a Alzheimers disease biomarker data set and from a comparison of tissues in Homo sapiens and Mus musculus. In summary, miRSwitch offers a broad range of customised arm switch analyses along with comprehensive visualisations, and is freely available at: https://www.ccb.uni-saarland.de/mirswitch/.

bioinformatics↗

bioRxiv2020

miEAA 2.0: Integrating multi-species microRNA enrichment analysis and workflow management systems

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from Homo sapiens, Mus musculus, and Rattus norvegicus. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miR-Base miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualisations such as heatmaps and running sum curves with background distributions. Lastly, additional methods to correct for multiple hypothesis testing were implemented. We demonstrate the new features using case studies for human kidney cancer and mouse samples. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

bioinformatics↗

bioRxiv2019

Systematic assessment of blood-borne microRNAs highlights molecular profiles of endurance sport and carbohydrate uptake

Multiple studies endorsed the positive effect of regular exercising on mental and physical health. However, the molecular mechanisms underlying training-induced fitness in combination with personal life-style remain largely unexplored. Circulating biomarkers such as microRNAs (miRNAs) offer themselves for studying systemic and cellular changes since they can be collected from the bloodstream in a low-invasive manner. In Homo sapiens miRNAs are known to regulate a substantial number of protein-coding genes in a post-transcriptional manner and hence are of great interest to understand differential gene expression profiles, offering a cost-effective mechanism to study molecular training adaption, and connecting the dots from genomics to observed phenotypes.\n\nHere, we investigated molecular expression patterns of 2, 549 miRNAs in whole-blood samples from 23 healthy and untrained adult participants of a cross-over study, consisting of 8 weeks of endurance training, with several sessions per week, followed by 8 weeks of washout and another 8 weeks of running, using microarrays. Participants were randomly assigned to one of the two study groups, one of which administered carbohydrates before each session in the first training period, switching the treatment group for the second training period. During running sessions clinical parameters as heartbeat frequency were recorded. This information was extended with four measurements of maximum oxygen uptake (VO2 max) for each participant.\n\nWe observed that multiple circulating miRNAs show expression changes after endurance training, leveraging the capability to separate the blood samples by training status. To this end, we demon-strate that most of the variance in miRNA expression can be explained by both common and known biological and technical factors. Our findings highlight six distinct clusters of miRNAs, each exhibiting an oscillating expression profile across the four study timepoints, that can effectively be utilized to predict phenotypic VO2 max levels. In addition, we identified miR-532-5p as a candidate marker to determine personal alterations in physical training performance on a case-by-case analysis taking the influence of a carbohydrate-rich nutrition into account. In literature, miR-532-5p is known as a common down-regulated miRNA in diabetes and obesity, possibly providing a molecular link between cellular homeostasis, personal fitness levels, and health in aging.\n\nWe conclude that circulating miRNAs expression can be altered due to regular endurance training, independent of the carbohydrate availability in the timeframe around training. Further validation studies are required to confirm the role of exercise-affected miRNAs and the extraordinary function of miR-532-5p in modulating the metabolic response to a high availability of glucose.

systems biology↗

bioRxiv2019

Machine learning to detect Alzheimer’s disease from circulating non-coding RNAs

BackgroundTo develop therapeutics for Alzheimers disease, early detection of patients awakes new hope. Circulating small non-coding RNAs are among the prominent candidates for a blood-based diagnosis, requiring however growing cohort sizes.\n\nMethodsWe determined abundance levels of 21 known circulating microRNAs in 465 individuals encompassing Alzheimers patients and controls recruited in US and Germany. We computed models to assess the relation between microRNA-expression and phenotypes, gender, age and disease severity (Mini-Mental State Examination MMSE).\n\nResults20 of 21 miRNAs were consistently dys-regulated in the US and Germany. 18 were significantly correlated to neurodegeneration (adjusted p<0.05) with highest significance for miR-532-5p (adjusted p=4.8x10-30). Ten miRNAs were significantly correlated with MMSE, in particular miR-26a/26b-5p (adjusted p=0.0002). Machine learning models reached an AUC value of 87.6% in differentiating AD patients from controls.\n\nConclusionsOur data provide strong evidence for the relevance of circulating non-coding RNAs to detect Alzheimers from a blood sample.

bioinformatics↗

bioRxiv2018

Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates

Emerging antibiotic resistance is a major global health threat. The analysis of nucleic acid sequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistance determinants to inform molecular diagnostics and drug development. We collected genetic data (11,087 newly sequenced whole genomes) and culture-based resistance profiles (10,991 of 11,087 isolates were comprehensively tested against 22 antibiotics in total) of clinical isolates including 18 main species spanning a time period of 30 years. Species and drug specific resistance patterns could be observed including increasing resistance rates for Acinetobacter baumannii to carbapenems and for Escherichia coli to fluoroquinolones. Species-level pan-genomes were constructed to reflect the genetic repertoire of the respective species such as conserved essential genes and known resistance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed to infer gene-drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into a resource, GEAR-base, available for academic research use free of charge at https://gear-base.com.

genomics↗