Biology subjects

Goesmann, A.

Publications and source records attributed to Goesmann, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Predicted Bacterial uRBSs Reveal Translational Coupling and Ribosome Mediated RBS Occlusion as Gene-Controlling Mechanisms

Upstream open reading frames (uORFs) in the 5' leader of bacterial mRNAs can modulate gene expression, yet genome-wide identification remains limited. We combined bioinformatic prediction of ribosome-binding sites (RBSs) - a Shine-Dalgarno sequence and a start codon - with experimental validation to uncover new uORFs in Sinorhizobium meliloti 2011. From totally 1106 predicted upstream RBSs (uRBSs), we first examined 15 candidates using eGFP reporters and integrating existing RNA-seq and Ribo-seq data. Translation was detected at 13 sites, with fluorescence intensity broadly correlating with predicted initiation rates. Two uRBSs correspond to gene start sites, thereby refining gene annotations. In nine cases, uRBS mutations affected downstream gene expression in reporter fusions. Among others, the data suggests that a Type I secretion system operon, the RNA chaperone gene hfq, and metabolic genes are regulated by uORFs. Four uORFs acted through translational coupling. We also identified uRBSs that were ribosome-occupied yet (nearly) silent in eGFP assays, and closely spaced to the downstream main RBS (mRBS). These uRBSs probably mediate ribosomal occlusion downregulating lacR and SM2011_RS36230. A re-screen of the prediction set revealed 335 close uRBS/mRBS pairs. Three of them were analyzed, supporting the proposed ribosomal occlusion mechanism for SM2011_RS03630 and SM2011_RS22110, while for glnK translational coupling to an uORF was suggested. These results indicate that uORFs are more widespread in bacteria than previously recognized and suggest that direct ribosomal occlusion of the mRBS is a novel mechanism for down-regulating protein synthesis.

molecular biology↗

bioRxiv2026

Baktfold: Sensitive protein functional annotation across the microbial tree of life using structural information

The functional annotation of protein sequences has undergone tremendous progress over recent years, but still too-many protein sequences remain as so-called hypothetical proteins after applying state-of-the-art genome annotation software pipelines. Here, we introduce Baktfold, a new command line software tool for the ultra-sensitive but taxon-independent fast annotation of protein sequences across the microbial tree of life. Baktfold conducts sequential protein structure-based searches against four complementary structure databases. Protein sequences are transformed into Foldseek 3Di tokens via the ProstT5 protein language model and subsequently searched against structure databases via Foldseek. All results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis 100% interoperable with the popular bacterial annotation tool Bakta. We compared Baktfolds performance in terms of wallclock runtime and functional annotation of hypothetical proteins from various sources including bacterial and archaeal isolates, plasmids, metagenomic-assembled genomes and micro-eukaryotes. When benchmarked on over three hundred thousand species representatives across the prokaryotic tree of life, Baktfolds median overall bacterial genome annotation rate is 87.8% compared to 72.9% with Bakta, while Baktfolds median bacterial annotation rate of remaining hypothetical proteins is 50.1% (n=290258). For archaea, Baktfolds overall median annotation rate is 71.5% compared to Prokkas 35.8%, with a median archaeal annotation rate of hypothetical proteins of 68.0% (n=14058), making Baktfold the most sensitive automated archaeal annotation method by far. Baktfold is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a MIT license at https://github.com/gbouras13/baktfold. Data SummaryO_LIBaktfold was developed in Python as a command line application for Linux and MacOS C_LIO_LIThe complete source code and documentation are available on GitHub under an MIT license: https://github.com/gbouras13/baktfold C_LIO_LIThe Baktfold database is hosted at Zenodo (https://zenodo.org/records/17347516) mirrored on HuggingFace (https://huggingface.co/datasets/gbouras13/baktfold-db) C_LIO_LIBaktfold is available via bioconda (https://anaconda.org/bioconda/baktfold) and PyPI (https://pypi.org/project/baktfold/) C_LIO_LIBaktfold can also be run without local installation using Google Colab at https://colab.research.google.com/github/gbouras13/baktfold/blob/main/run_baktfold. ipynb C_LIO_LIAll supplementary code, data and files required to reproduce the results of this manuscript are available at https://github.com/gbouras13/baktfold-analysis (code and small data) and https://zenodo.org/records/19333697 (large data) C_LI

bioinformatics↗

bioRxiv2026

Towards a holistic epidemiology of Streptococcus agalactiae using the BakRep repository

Streptococcus agalactiae is a versatile multi-host pathogen that can cause major neonatal disease in humans, as well as mastitis in dairy animals. Its ability to infect a wide range of hosts is largely driven by its high genomic plasticity and the acquisition of distinct accessory genes. The global population of S. agalactiae is characterized by multiple of capsular serotypes and clonal complexes that differ in their propensity to cause invasive disease, including hypervirulent CC17 (often serotype III) associated with neonatal meningitis, whereas CC1/CC19/CC23 are more often colonizing lineages. Although widely studied, most research is limited to particular regions or single outbreak events, offering only fragmented snapshots instead of a comprehensive global picture. To move beyond region- or outbreak-limited studies, this work has analyzed 37970 S.agalactiae genomes from BakRep, integrating serotypes, MLST, AMR genes, lineage-specific genes, and descriptive metadata to map current trends and identify potential gaps in public data. The dataset largely matched the known population structure with serotype III, Ia and V most common and stable serotype/clonal complex lineages (e.g. III-2/CC17, Ia/CC23, CC1/V), while also rising serotype diversity. Lineages differed in their accessory-gene profiles, with III-2/CC17 being enriched for virulence and adhesion genes, while other groups showed either greater genomic plasticity (mobile/phage genes) or niche specialization. AMR was widespread with very high tetracycline resistance (>80%), frequent MLSB resistance determinants, and emerging aminoglycoside resistance in some genomes. But overall it became evident that the associated metadata contained substantial gaps. Missing or incomplete information limits biological interpretation, underscoring that rigorously curated, structured metadata is essential for maximizing the value of ongoing sequencing efforts.

genomics↗

bioRxiv2025

Generative models for antimicrobial peptide design: auto-encoders and beyond

BackgroundSince the number of multi-resistant pathogens is growing rapidly, new strategies to accelerate the development of antimicrobial drugs are urgently needed. A promising candidate class for new antibiotics are antimicrobial peptides, showing lower tendency to induce antibiotic resistance. High-throughput in silico strategies for candidate mining, such as generative deep learning algorithms, have become popular over the last few years and offer novel ways for peptide discovery. MethodsThis study presents a comparative analysis of contemporary deep learning models generative performance for generating novel antimicrobial peptides. The models examined include Variational Auto-Encoders, a Wasserstein Auto-Encoder, a Recurrent Neural Network and a Language Model. The primary focus of this study is the systematic comparison and evaluation of various methods and sampling options to identify the most suitable model and sampling strategy combination for different use cases. ResultsThe findings demonstrate the models capacity to generate peptide sequences exhibiting analogous properties to those of naturally occurring active peptides, which are utilized for model training while featuring an appropriate degree of sequence diversity. Auto-encoder-based models, particularly the Wasserstein auto-encoder, have generated novel and remarkably diverse sequences compared to recurrent neural networks and language models. This model category exhibits a propensity to prioritize the frequencies of individual amino acids during the learning process, in contrast to variational auto-encoders. Furthermore, latent space models have been shown to possess the capacity to utilize diverse methodologies for generating novel peptides. However, it is imperative to note that these sampling strategies are not universally advantageous or disadvantageous; their optimal selection is contingent on the specificities of each individual use case. ConclusionThe present study investigates the strengths and weaknesses of various generative models for antimicrobial peptides and suggests which model and sampling strategy combination should be favoured for specific individual applications.

bioinformatics↗

bioRxiv2025

A single-domain response regulator activates exopolysaccharide synthesis by interacting with the initiating phosphoglycosyl transferase

Exported polysaccharides have crucial functions in bacteria. Polysaccharide biosynthesis in the ubiquitous Wzx/Wzy- and ABC-transporter-dependent pathways starts with the transfer of a sugar-1-phosphate from a nucleotide-sugar donor to undecaprenyl phosphate, a reaction catalyzed by a phosphoglycosyl transferase (PGT). Both reaction substrates are limited and shared among multiple glycoconjugate pathways, raising the question of how bacteria regulate these pathways. In Myxococcus xanthus, EpsZ, which belongs to the family of large monotopic PGTs (monoPGTs), starts the Wzx/Wzy-dependent exopolysaccharide (EPS) biosynthesis. The Dif chemosensory system regulates EPS biosynthesis by an unknown mechanism via the phosphorylated single-domain response regulator EpsW (EpsW[~]P). Here, we show that EpsW[~]P stimulates EPS biosynthesis at the post-translational level. Moreover, MiniTurbo-based proximity labeling experiments suggest that EpsW[~]P interacts directly with EpsZ. Additionally, heterologous expression of these two proteins in Salmonella enterica demonstrates that EpsW stimulates EpsZ enzymatic activity. S. enterica WbaP, the prototype large monoPGT, forms a functional homodimer, with dimerization involving a distinct cytoplasmic {beta}-hairpin. However, AlphaFold-based structural modeling shows that EpsZ lacks this {beta}-hairpin, suggesting an alternative mechanism for dimerization. Our structural modeling of a EpsZ-EpsW heterocomplex suggests that EpsW[~]P, by direct interaction, promotes the formation of the stable EpsZ dimer. These findings uncover a new model for the regulation of polysaccharide biosynthesis in which EpsW[~]P allosterically facilitates the formation of the active, dimeric conformation of EpsZ, thereby activating EPS biosynthesis at its initial step. Genomics and structural modeling suggest that the regulation of large monoPGTs by a single-domain response regulator is widespread in myxobacteria and potentially beyond. SignificanceBacteria produce various polysaccharides with important biological functions and biotechnological applications. Polysaccharide synthesis is energy-costly and requires substrates that are in limited supply raising the question of how bacteria regulate these pathways. Here, we explored the regulation of exopolysaccharide biosynthesis in Myxococcus xanthus. We demonstrate that the phosphorylated single-domain response regulator EpsW activates exopolysaccharide biosynthesis at the post-translational level by stimulating the activity of the phosphoglycosyl transferase EpsZ. By directly interacting with EpsZ, phosphorylated EpsW facilitates the formation of the active, dimeric conformation of EpsZ, thereby activating exopolysaccharide biosynthesis at its initial step. We propose that this previously unrecognized regulatory mechanism is broadly conserved, not only in myxobacteria but also beyond.

microbiology↗

bioRxiv2025

Evidence of a novel sublineage of Streptococcus agalactiae in elephants from zoo populations in Germany

Streptococcus agalactiae research primarily centers on investigating human and bovine infections, although this pathogen also can be carried and cause infections in a wider range of animal species. Moreover, infections with S. agalactiae are posing significant health implications and, also, recent studies are highlighting a potential zoonotic risk. Despite the comparatively frequent isolation of S. agalactiae from elephants, only a few reports document infections in wild and zoo populations. We performed a comparative genomic analysis of 24 elephant isolates from three different zoos in Germany, to achieve a comprehensive characterization. Elephant isolates showed pronounced phylogenetic divergence from isolates of other host species, while also forming clusters based on their zoo of origin and their genotypes (MLST profiles). Capsular serotypes could not be identified for the majority of the isolates (n=20/24). Several genes, associated exclusively with the elephant host may underlie the pathogens capacity to improve its survival and virulence across varied ecological niches. This study not only deepens our understanding of S. agalactiae across diverse species and environments but also represents the first whole-genome sequencing characterization of S. agalactiae isolates from elephants, helping to expand our knowledge about infections in exotic animals.

bioinformatics↗

bioRxiv2024

Broad-scale phenotyping in Arabidopsis reveals varied involvement of RNA interference across diverse plant-microbe interactions

RNA interference (RNAi) is a crucial mechanism that can contribute to immunity against infectious microbes through the action of DICER-LIKE (DCL) and ARGONAUTE (AGO) proteins. In the case of the fungal pathogen Botrytis cinerea and the oomycete Hyaloperonospora arabidopsidis, plant DCL and AGO proteins have proven roles as negative regulators of immunity, suggesting functional specialization of these proteins. To address this aspect in a broader taxonomic context, we characterized the colonization pattern of an informative set of DCL and AGO loss-of-function mutants in Arabidopsis thaliana upon infection with a panel of pathogenic microbes with different lifestyles, and a fungal mutualist. Our results revealed that AGO1 and AGO4 function as positive regulators of immunity to a bacterial and a fungal pathogen, respectively. Additionally, AGO2 and AGO10 positively modulated the colonization by a fungal mutualist. Therefore, analysing the role of RNAi across a broader range of plant-microbe interactions has identified previously unknown functions for AGO proteins. For some pathogen interactions, however, all tested mutants exhibited wild type-like infection phenotypes, suggesting that the roles of AGO and DCL proteins in these interactions may be more complex to elucidate.

plant biology↗

bioRxiv2024

sORFdb - A database for sORFs, small proteins, and small protein families in bacteria

Small proteins with fewer than 100, particularly fewer than 50, amino acids are still largely unexplored. Nonetheless, they represent an essential part of bacterias often neglected genetic repertoire. In recent years, the development of ribosome profiling protocols has led to the detection of an increasing number of previously unknown small proteins. Despite this, they are overlooked in many cases by automated genome annotation pipelines, and often, no functional descriptions can be assigned due to a lack of known homologs. To understand and overcome these limitations, the current abundance of small proteins in existing databases was evaluated, and a new dedicated database for small proteins and their potential functions, called sORFdb, was created. To this end, small proteins were extracted from annotated bacterial genomes in the GenBank database. Subsequently, they were quality-filtered, compared, and complemented with proteins from Swiss-Prot, UniProt, and SmProt to ensure reliable identification and characterization of small proteins. Families of similar small proteins were created using bidirectional best BLAST hits followed by Markov clustering. Analysis of small proteins in public databases revealed that their number is still limited due to historical and technical constraints. Additionally, functional descriptions were often missing despite the presence of potential homologs. As expected, a taxonomic bias was evident in over-represented clinically relevant bacteria. This new and comprehensive database is accessible via a feature-rich website providing specialized search features for sORFs and small proteins of high quality. Additionally, small protein families with Hidden Markov Models and information on taxonomic distribution and other physicochemical properties are available. In conclusion, the novel small protein database sORFdb is a specialized, taxonomy-independent database that improves the findability and classification of sORFs, small proteins, and their functions in bacteria, thereby supporting their future detection and consistent annotation. All sORFdb data is freely accessible via https://sorfdb.computational.bio.

bioinformatics↗

bioRxiv2024

BakRep - A searchable large-scale web repository for bacterial genomes, characterizations and metadata

Bacteria are fascinating research objects in many disciplines for countless reasons, and whole-genome sequencing has become the paramount methodology to advance our microbiological understanding. Meanwhile, access to cost-effective sequencing platforms has accelerated bacterial whole-genome sequencing to unprecedented levels introducing new challenges in terms of data accessibility, computational demands, heterogeneity of analysis workflows, and thus, ultimately its scientific usability. To that end, Blackwell et al. released a uniformly processed set of 661,405 bacterial genome assemblies obtained from the European Nucleotide Archive as of November 2018. Building on these accomplishments, we conducted further genome-based analyses like taxonomic classification, MLST subtyping and annotation of all genomes. Here we present BakRep, a searchable large-scale web repository of these genomes enriched with consistent genome characterizations and original metadata. The platform provides a flexible search engine combining taxonomic, genomic and metadata information, as well as interactive elements to visualize genomic features. Furthermore, all results can be downloaded for offline analyses via an accompanying command line tool. The web repository is accessible via https://bakrep.computational.bio.

bioinformatics↗

bioRxiv2023

MGX 2.0: Shotgun- and assembly-based metagenome and metatranscriptome analysis from a single source

Metagenomics studies have enabled scientists to analyze the genetic information of natural habitats or even complete ecosystems, including otherwise unculturable microbes. The processing of such datasets, however, remains a challenging task requiring extensive computational resources. MGX 2.0 is a versatile solution for the analysis and interpretation of microbial community sequence data. MGX 2.0 supports the processing of raw metagenomes and metatranscriptomes, but also enables assembly-based strategies, including downstream taxonomic binning, bin quality assessment, abundance quantification, and subsequent annotation coming from a single source. Due to the modular design of MGX, users are able to choose from a wide range of different methods for microbial community sequence data analysis, allowing them to directly compare between read-based and assembly-based approaches or to evaluate different strategies to analyze their data.

bioinformatics↗

bioRxiv2022

Evolutionarily stable gene clusters shed light on the common grounds of pathogenicity in the Acinetobacter calcoaceticus-baumanniicomplex

Nosocomial pathogens of the Acinetobacter calcoaceticus-baumannii (ACB) complex are a cautionary example for the world-wide spread of multi- and pan-drug resistant bacteria. Aiding the urgent demand for novel therapeutic targets, comparative genomics studies between pathogens and their apathogenic relatives shed light on the genetic basis of human-pathogen interaction. Yet, existing studies are limited in taxonomic scope, sensing of the phylogenetic signal, and resolution by largely analyzing genes isolated from their functional contexts. Here, we explored more than 3,000 Acinetobacter genomes in a phylogenomic framework integrating orthology-based phylogenetic profiling and microsynteny conservation analyses. This allowed to delineate gene clusters in the type strain A. baumannii ATCC 19606 whose evolutionary conservation indicates a functional integration of the subsumed genes. These evolutionarily stable gene clusters (ESGCs) reveal metabolic pathways, transcriptional regulators residing next to their targets but also tie together sub-clusters with distinct functions to form higher-order functional modules. We shortlisted 150 ESGCs that either co-emerged with, or are found preferentially in, the pathogenic ACB clade. They unveil, at an unprecedented resolution, the genetic makeup that coincides with the manifestation of the pathogenic phenotype in the last common ancestor of the ACB clade. Key innovations are the remodeling of the regulatory-effector cascade connecting LuxR/LuxI quorum sensing via an intermediate messenger to biofilm formation, the extension of micronutrient scavenging systems, and the increase of metabolic flexibility by exploiting carbon sources that are provided by the human host. Specifically, we could show that only members of the ACB clade use kynurenine as a sole carbon and energy source, a substance produced by humans to fine-tune the antimicrobial innate immune response. In summary, this study provides a rich and unbiased set of novel testable hypotheses on how pathogenic Acinetobacter interact with and ultimately infect their human host. They disclose promising routes for future therapeutic strategies. Author SummaryThe spread of multi- and pan-drug resistant bacterial pathogens is a world-wide threat to human health. Understanding the genetics of host colonization and infection is essential for devising novel ways of treatment. Acinetobacter baumannii, a nosocomial pathogen ranked top by the World Health Organization in the list of bacteria for which novel therapeutic approaches are needed, is a prime example. Here, we have carved out the genetic make-up that distinguishes A. baumannii and its pathogenic next relatives comprising the A. baumannii-calcoaceticus complex from other and mostly apathogenic Acinetobacter species. We found a rich spectrum of pathways and regulatory modules that reveals how the pathogens have modified biofilm formation, iron scavenging, and their carbohydrate metabolism to adapt to their human host. Among these, the capability to metabolize kynurenine is particularly intriguing. Humans produce this substance to contain bacterial invaders and to fine-tune the innate immune response. But A. baumannii and its allies have turned the table. They feed on kynurenine, and thus most likely also dysregulate human immune response. In summary, our study substantially deepens the understanding of how a highly critical pathogen interacts with its host, which substantially eases the identification of novel targets for innovative therapeutic strategies.

genomics↗

bioRxiv2021

The CRP-like transcriptional regulator MrpC curbs c-di-GMP and 3,3 cGAMP nucleotide levels during development in Myxococcus xanthus

Myxococcus xanthus has a nutrient-regulated biphasic lifecycle forming predatory swarms in the presence of nutrients and spore-filled fruiting bodies in the absence of nutrients. The second messenger c-di-GMP is essential during both stages of the lifecycle; however, different enzymes involved in c-di-GMP synthesis and degradation as well as several c-di-GMP receptors are important during distinct lifecycle stages. To address this stage specificity, we determined transcript levels using RNA-seq and transcription start sites using Cappable-seq during growth and development at a genome-wide scale. All 70 genes encoding c-di-GMP associated proteins were expressed, with 28 up-regulated and 10 down-regulated during development. In particular, the three genes encoding enzymatically active proteins with a stage-specific function were expressed stage-specifically. By combining operon mapping with published ChIP-seq data for MrpC (Robinson et al., 2014), the CRP-like master regulator of development, we identified nine developmentally regulated genes as regulated by MrpC. In particular, MrpC directly represses expression of dmxB, which encodes the diguanylate cyclase DmxB that is essential for development and responsible for the c-di-GMP increase during development. Moreover, MrpC directly activates transcription of pmxA, which encodes a bifunctional phosphodiesterase that degrades c-di-GMP and 3, 3 cGAMP in vitro and is essential for development. Thereby, MrpC regulates and curbs the cellular pools of c-di-GMP and 3, 3 cGAMP during development. We conclude that temporal regulation of the synthesis of proteins involved in c-di-GMP metabolism contributes to c-di-GMP signaling specificity. MrpC is important for this regulation, thereby being a key regulator of developmental cyclic di-nucleotide metabolism in M. xanthus. ImportanceThe second messenger c-di-GMP is important during both stages of the nutrient-regulated biphasic lifecycle of Myxococcus xanthus with the formation of predatory swarms in the presence of nutrients and spore-filled fruiting bodies in the absence of nutrients. However, different enzymes involved in c-di-GMP synthesis and degradation are important during distinct lifecycle stages. Here, we show that the three genes encoding enzymatically active proteins with a stage-specific function are expressed stage-specifically. Moreover, we find that the master transcriptional regulator of development MrpC directly regulates expression of dmxB, which encodes the diguanylate cyclase DmxB that is essential for development, and of pmxA, which encodes a bifunctional phosphodiesterase that degrades c-di-GMP and 3, 3 cGAMP in vitro and is essential for development. We conclude that temporal regulation of the synthesis of proteins involved in c-di-GMP metabolism contributes to c-di-GMP signaling specificity, and that MrpC plays an important role in this regulation.

microbiology↗

bioRxiv2021

Bakta: Rapid & standardized annotation of bacterial genomes via alignment-free sequence identification

Command line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command line software pipelines heavily depend on taxon specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command line software tool for the robust, taxon-independent, thorough and nonetheless fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross references. Annotation results are exported in GFF3 and INSDC-compliant flat files as well as comprehensive JSON files facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references whilst providing comparable wall clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

bioinformatics↗

bioRxiv2021

Countering reproducibility issues in mathematical models with software engineering techniques: A case study using a one-dimensional mathematical model of the atrioventricular node

One should assume that in silico experiments in systems biology are less susceptible to reproducibility issues than their wet-lab counterparts, because they are free from natural biological variations and their environment can be fully controlled. However, recent studies show that only half of the published mathematical models of biological systems can be reproduced without substantial effort. In this article we examine the potential causes for failed or cumbersome reproductions in a case study of a one-dimensional mathematical model of the atrioventricular node, which took us four months to reproduce. The model demonstrates that even otherwise rigorous studies can be hard to reproduce due to missing information, errors in equations and parameters, a lack in available data files, non-executable code, missing or incomplete experiment protocols, and missing rationales behind equations. Many of these issues seem similar to problems that have been solved in software engineering using techniques such as unit testing, regression tests, continuous integration, version control, archival services, and a thorough modular design with extensive documentation. Applying these techniques, we reimplement the examined model using the modeling language Modelica. The resulting workflow is independent of the model and can be translated to SBML, CellML, and other languages. It guarantees methods reproducibility by executing automated tests in a virtual machine on a server that is physically separated from the development environment. Additionally, it facilitates results reproducibility, because the model is more understandable and because the complete model code, experiment protocols, and simulation data are published and can be accessed in the exact version that was used in this article. We found the additional design and documentation effort well justified, even just considering the immediate benefits during development such as easier and faster debugging, increased understandability of equations, and a reduced requirement for looking up details from the literature. Author summaryReproducibility is one of the cornerstones of the scientific method. In order to draw reliable conclusions, an experiment must yield the same results when it is repeated using the same methods. However, biological systems are complex, which makes experiments cumbersome. It is therefore desirable to build a mathematical representation of the biological system, which captures its essential behavior in a set of variables and equations and allows for easier and faster experimentation. Unfortunately, recent studies have shown that half of the published mathematical models are not immediately reproducible due to missing information, mathematical errors, and incomplete documentation. These issues are similar to those faced in software engineering: A single missing file or a buggy line of code can render any kind of software useless. Software engineering has turned to rigorous software testing, automated development pipelines, and version control systems to overcome these challenges, but these techniques are not yet widely applied to mathematical modeling. In this paper we demonstrate their benefit for the reproducibility of a large mathematical model of the atrioventricular node. The software engineering solutions that we employ can be applied to any mathematical model and could therefore facilitate scientific progress by encouraging and simplifying model reuse.

systems biology↗

bioRxiv2020

Platon: identification and characterization of bacterial plasmid contigs in short-read draft assembliesexploiting protein-sequence-based replicon distribution scores

Plasmids are extrachromosomal genetic elements replicating independently of the chromosome which play a vital role in the environmental adaptation of bacteria. Due to potential mobilization or conjugation capabilities, plasmids are important genetic vehicles for antimicrobial resistance genes and virulence factors with huge and increasing clinical implications. They are therefore subject to large genomic studies within the scientific community worldwide. As a result of rapidly improving next generation sequencing methods, the amount of sequenced bacterial genomes is constantly increasing, in turn raising the need for specialized tools to (i) extract plasmid sequences from draft assemblies, (ii) derive their origin and distribution, and (iii) further investigate their genetic repertoire. Recently, several bioinformatic methods and tools have emerged to tackle this issue; however, a combination of both high sensitivity and specificity in plasmid sequence identification is rarely achieved in a taxon-independent manner. In addition, many software tools are not appropriate for large high-throughput analyses or cannot be included into existing software pipelines due to their technical design or software implementation. In this study, we investigated differences in the replicon distributions of protein-coding genes on a large scale as a new approach to distinguish plasmid-borne from chromosome-borne contigs. We defined and computed statistical discrimination thresholds for a new metric: the replicon distribution score (RDS) which achieved an accuracy of 96.6%. The final performance was further improved by the combination of the RDS metric with heuristics exploiting several plasmid specific higher-level contig characterizations. We implemented this workflow in a new high-throughput taxon-independent bioinformatics software tool called Platon for the recruitment and characterization of plasmid-borne contigs from short-read draft assemblies. Compared to PlasFlow, Platon achieved a higher accuracy (97.5%) and more balanced predictions (F1=82.6%) tested on a broad range of bacterial taxa and better or equal performance against the targeted tools PlasmidFinder and PlaScope on sequenced E. coli isolates. Platon is available at: platon.computational.bio Data SummaryO_LIPlaton was developed as a Python 3 command line application for Linux. C_LIO_LIThe complete source code and documentation is available on GitHub under a GPL3 license: https://github.com/oschwengers/platon and platon.computational.bio. C_LIO_LIAll database versions are hosted at Zenodo: DOI 10.5281/zenodo.3349651. C_LIO_LIPlaton is available via bioconda package platon C_LIO_LIPlaton is available via PyPI package cb-platon C_LIO_LIBacterial representative sequences for UniProts UniRef90 protein clusters, complete bacterial genome sequences from the NCBI RefSeq database, complete plasmid sequences from the NCBI genomes plasmid section, created artificial contigs, RDS threshold metrics and raw protein replicon hit counts used to create and evaluate the marker protein sequence database are hosted at Zenodo: DOI 10.5281/zenodo.3759169 C_LIO_LI24 Escherichia coli isolates sequenced with short read (Illumina MiSeq) and long read sequencing technologies (Oxford Nanopore Technology GridION platform) used for real data benchmarks are available under the following NCBI BioProjects: PRJNA505407, PRJNA387731 C_LI Impact StatementPlasmids play a vital role in the spread of antibiotic resistance and pathogenicity genes. The increasing numbers of clinical outbreaks involving resistant pathogens worldwide pushed the scientific community to increase their efforts to comprehensively investigate bacterial genomes. Due to the maturation of next-generation sequencing technologies, nowadays entire bacterial genomes including plasmids are sequenced in huge scale. To analyze draft assemblies, a mandatory first step is to separate plasmid from chromosome contigs. Recently, many bioinformatic tools have emerged to tackle this issue. Unfortunately, several tools are implemented only as interactive or web-based tools disabling them for necessary high-throughput analysis of large data sets. Other tools providing such a high-throughput implementation however often come with certain drawbacks, e.g. providing taxon-specific databases only, not providing actionable, i.e. true binary classification or achieving biased classification performances towards either sensitivity or specificity. Here, we introduce the tool Platon implementing a new replicon distribution-based approach combined with higher-level contig characterizations to address the aforementioned issues. In addition to the plasmid detection within draft assemblies, Platon provides the user with valuable information on certain higher-level contig characterizations. We show that Platon provides a balanced classification performance as well as a scalable implementation for high-throughput analyses. We therefore consider Platon to be a powerful, species-independent and flexible tool to scan large amounts of bacterial whole-genome sequencing data for their plasmid content.

bioinformatics↗

bioRxiv2020

Host-induced gene silencing involves transfer of dsRNA-derived siRNA via extracellular vesicles

Small (s)RNAs and their double-stranded (ds)RNA precursors have been adopted to control diseases in crop plants through expression in transgenic plants and targeted gene silencing (host-induced gene silencing, HIGS). While HIGS strategies proved to be effective, the mechanism of RNA transfer at the plant - pathogen interface is widely unknown. Here we show that extracellular vesicles (EVs) purified from Arabidopsis thaliana plants expressing CYP3RNA, a dsRNA originally designed to target the three CYP51 genes of the fungal pathogen Fusarium graminearum, contain CYP3RNA-derived small interfering (si)RNAs as shown by RNA sequencing (RNA-seq) analysis. These transgene specific siRNAs had a length of 21 and 22 nucleotides with a bias towards 5-uracil (U) and 5-adenine (A). Notably, stringent protease and RNase treated EV fractions contained >70% less CYP3RNA-derived siRNAs, suggesting the presence of co-purified extravesicular nucleoprotein complexes stabilizing siRNAs outside of EVs. In addition, mutants of the ESCRT-III complex showed a loss of HIGS-mediated disease resistance and EVs isolated from these mutants were free of CYP3RNA-derived siRNAs. Together, these findings support the view that endosomal vesicle trafficking is required for HIGS mediating the transfer of transgene-derived siRNAs between donor host cells and recipient fungal cells probably in an EV-independent manner.

plant biology↗

bioRxiv2019

Expansion and re-classification of the extracytoplasmic function (ECF) σ factor family

Extracytoplasmic function {sigma} factors (ECFs) represent one of the major bacterial signal transduction mechanisms in terms of abundance, diversity and importance, particularly in mediating stress responses. Here, we performed a comprehensive phylogenetic analysis of this protein family by scrutinizing all proteins in the NCBI database. As result, we identified [~]10 ECFs per bacterial genome on average and classified them into 157 phylogenetic ECF groups that feature a conserved genetic neighborhood and a similar regulation mechanism. Our analysis expands the number of unique ECF sequences [~]50-fold relative to previous classification efforts, enriches many original ECF groups with previously unclassified proteins and identifies 22 entirely new ECF groups. The ECF groups are hierarchically related to each other and are further composed of subgroups with closely related sequences. This two-tiered classification allows for the accurate prediction of common promoter motifs and the inference of putative regulatory mechanisms across subgroups composing an ECF group. This comprehensive, high-resolution description of the phylogenetic distribution of the ECF family, together with the massive expansion of classified ECF sequences, enables the application of in silico tools for the prediction of important functional residues, and serves as a powerful hypothesis-generator to guide future research in the field.

bioinformatics↗

bioRxiv2019

ReferenceSeeker: rapid determination of appropriate reference genomes

SummaryThe large and growing number of microbial genomes available in public databases makes the optimal selection of reference genomes necessary for many in-silico analyses, e.g. single nucleotide polymorphism detection, scaffolding and comparative genomics, increasingly difficult. Here, we present ReferenceSeeker, a novel command line tool combining a fast kmer profile-based database lookup of candidate reference genomes with subsequent calculation of highly specific average nucleotide identity (ANI) values for the rapid determination of appropriate reference genomes. Pre-built databases for bacteria, archaea, fungi, protozoa and viruses based on the RefSeq database are provided for download. Availability and ImplementationReferenceSeeker is open source software implemented in Python. Source code and binaries are freely available for download at https://github.com/oschwengers/referenceseeker under the GNU GPL3 license. Contactreferenceseeker@computational.bio

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