Pseudomonas kulmbachensis sp. nov. and Pseudomonas paraveronii sp. nov., originating from chilled beef and chicken breast.

By investigating wet and dry age-related ripening of beef, Pseudomonas strains V3/3/4/13T and V3/K/3/5T were isolated. Strain V3/3/4/13T exhibited more than 99 % 16S rRNA gene-based similarity to Pseudomonas fragi and other members of this group, while isolate V3/K/3/5T was very close to Pseudomonas veronii and a number of relatives within the Pseudomonas fluorescens group. Additional comparisons of complete rpoB sequences and draft genomes allowed us to place isolate V3/3/4/13T close to Pseudomonas deceptionensis DSM 26521T. In the case of V3/K/3/5T the closest relative was P. veronii DSM 11331T. Average nucleotide identity (ANIb) and digital DNA-DNA hybridization (dDDH) values calculated from the draft genomes of V3/3/4/13T and P. deceptionensis DSM 26521T were 88.5 and 39.8 %, respectively. For V3/K/3/5T and its closest relative P. veronii DSM 11331T, the ANIb value was 95.1 % and the dDDH value was 60.7 %. The DNA G+C contents of V3/3/4/13T and V3/K/3/5T were 57.4 and 60.8 mol%, respectively. Predominant fatty acids were C16 : 0, C18 : 1 ω7c, C17 : 0 cyclo and summed feature C16 : 1 ω7ct/C15 : 0 iso 2OH. The main respiratory quinones were Q9, with minor proportions of Q8 and, in the case of V3/K/3/5T, additional Q10. The main polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine and, in the case of V3/K/3/5T, additional phosphatidylcholine. Based on the combined data, isolates V3/3/4/13T and V3/K/3/5T should be considered as representatives of two novel Pseudomonas species. The type strain of the newly proposed Pseudomonas kulmbachensis sp. nov. is V3/3/4/13T (=DSM 113654T=LMG 32520T), a second strain belonging to the same species is FLM 004-28 (=DSM 113604=LMG 32521); the type strain for the newly proposed Pseudomonas paraveronii sp. nov. is V3/K/3/5T (=DSM 113573T=LMG 32518T) with a second isolate FLM 11 (=DSM 113572=LMG 32519).

Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data.

Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a cumbersome process often executed manually, hence error-prone and lacking reproducibility. For corresponding data processing, we introduce Curare, an easy-to-use yet versatile workflow builder for analyzing high-throughput RNA-Seq data focusing on differential gene expression experiments. Data analysis with Curare is customizable and subdivided into preprocessing, quality control, mapping, and downstream analysis stages, providing multiple options for each step while ensuring the reproducibility of the workflow. For a fast and straightforward exploration and visualization of differential gene expression results, we provide the gene expression visualizer software GenExVis. GenExVis can create various charts and tables from simple gene expression tables and DESeq2 results without the requirement to upload data or install software packages. In combination, Curare and GenExVis provide a comprehensive software environment that supports the entire data analysis process, from the initial handling of raw RNA-Seq data to the final DGE analyses and result visualizations, thereby significantly easing data processing and subsequent interpretation.

Draft genome sequences of 21 Pedobacter strains isolated from amphibian specimens.

The genomes of 21 Pedobacter strains isolated from the European salamander Salamandra salamandra and different Madagascan frog species were sequenced using Illumina sequencing. Here, we report their draft genome sequences (~4.7-7.2 Mbp in size) to allow comparative genomics and taxonomic assignment of these strains.

A MoClo-Compatible Toolbox of ECF Sigma Factor-Based Regulatory Switches for Proteobacterial Chassis.

The construction of complex synthetic gene circuits with predetermined and reliable output depends on orthogonal regulatory parts that do not inadvertently interfere with the host machinery or with other circuit components. Previously, extracytoplasmic function sigma factors (ECFs), a diverse group of alternative sigma factors with distinct promoter specificities, were shown to have great potential as context-independent regulators, but so far, they have only been used in a few model species. Here, we show that the alphaproteobacterium Sinorhizobium meliloti, which has been proposed as a plant-associated bacterial chassis for synthetic biology, has a similar phylogenetic ECF acceptance range as the gammaproteobacterium Escherichia coli. A common set of orthogonal ECF-based regulators that can be used in both bacterial hosts was identified and used to create 2-step delay circuits. The genetic circuits were implemented in single copy in E. coli by chromosomal integration using an established method that utilizes bacteriophage integrases. In S. meliloti, we demonstrated the usability of single-copy pABC plasmids as equivalent carriers of the synthetic circuits. The circuits were either implemented on a single pABC or modularly distributed on 3 such plasmids. In addition, we provide a toolbox containing pABC plasmids compatible with the Golden Gate (MoClo) cloning standard and a library of basic parts that enable the construction of ECF-based circuits in S. meliloti and in E. coli. This work contributes to building a context-independent and species-overarching ECF-based toolbox for synthetic biology applications.

Correction: GABenchToB: A Genome Assembly Benchmark Tuned on Bacteria and Benchtop Sequencers.

[This corrects the article DOI: 10.1371/journal.pone.0107014.].

The Viscum album Gene Space database.

The hemiparasitic flowering plant Viscum album (European mistletoe) is known for its very special life cycle, extraordinary biochemical properties, and extremely large genome. The size of its genome is estimated to be 30 times larger than the human genome and 600 times larger than the genome of the model plant Arabidopsis thaliana. To achieve insights into the Gene Space of the genome, which is defined as the space including and surrounding protein-coding regions, a transcriptome project based on PacBio sequencing has recently been conducted. A database resulting from this project contains sequences of 39,092 different open reading frames encoding 32,064 distinct proteins. Based on 'Benchmarking Universal Single-Copy Orthologs' (BUSCO) analysis, the completeness of the database was estimated to be in the range of 78%. To further develop this database, we performed a transcriptome project of V. album organs harvested in summer and winter based on Illumina sequencing. Data from both sequencing strategies were combined. The new V. album Gene Space database II (VaGs II) contains 90,039 sequences and has a completeness of 93% as revealed by BUSCO analysis. Sequences from other organisms, particularly fungi, which are known to colonize mistletoe leaves, have been removed. To evaluate the quality of the new database, proteome data of a mitochondrial fraction of V. album were re-analyzed. Compared to the original evaluation published five years ago, nearly 1000 additional proteins could be identified in the mitochondrial fraction, providing new insights into the Oxidative Phosphorylation System of V. album. The VaGs II database is available at https://viscumalbum.pflanzenproteomik.de/. Furthermore, all V. album sequences have been uploaded at the European Nucleotide Archive (ENA).

Functional Degeneracy in Paracoccus denitrificans Pd1222 Is Coordinated via RamB, Which Links Expression of the Glyoxylate Cycle to Activity of the Ethylmalonyl-CoA Pathway.

Metabolic degeneracy describes the phenomenon that cells can use one substrate through different metabolic routes, while metabolic plasticity, refers to the ability of an organism to dynamically rewire its metabolism in response to changing physiological needs. A prime example for both phenomena is the dynamic switch between two alternative and seemingly degenerate acetyl-CoA assimilation routes in the alphaproteobacterium Paracoccus denitrificans Pd1222: the ethylmalonyl-CoA pathway (EMCP) and the glyoxylate cycle (GC). The EMCP and the GC each tightly control the balance between catabolism and anabolism by shifting flux away from the oxidation of acetyl-CoA in the tricarboxylic acid (TCA) cycle toward biomass formation. However, the simultaneous presence of both the EMCP and GC in P. denitrificans Pd1222 raises the question of how this apparent functional degeneracy is globally coordinated during growth. Here, we show that RamB, a transcription factor of the ScfR family, controls expression of the GC in P. denitrificans Pd1222. Combining genetic, molecular biological and biochemical approaches, we identify the binding motif of RamB and demonstrate that CoA-thioester intermediates of the EMCP directly bind to the protein. Overall, our study shows that the EMCP and the GC are metabolically and genetically linked with each other, demonstrating a thus far undescribed bacterial strategy to achieve metabolic plasticity, in which one seemingly degenerate metabolic pathway directly drives expression of the other. IMPORTANCE Carbon metabolism provides organisms with energy and building blocks for cellular functions and growth. The tight regulation between degradation and assimilation of carbon substrates is central for optimal growth. Understanding the underlying mechanisms of metabolic control in bacteria is of importance for applications in health (e.g., targeting of metabolic pathways with new antibiotics, development of resistances) and biotechnology (e.g., metabolic engineering, introduction of new-to-nature pathways). In this study, we use the alphaproteobacterium P. denitrificans as model organism to study functional degeneracy, a well-known phenomenon of bacteria to use the same carbon source through two different (competing) metabolic routes. We demonstrate that two seemingly degenerate central carbon metabolic pathways are metabolically and genetically linked with each other, which allows the organism to control the switch between them in a coordinated manner during growth. Our study elucidates the molecular basis of metabolic plasticity in central carbon metabolism, which improves our understanding of how bacterial metabolism is able to partition fluxes between anabolism and catabolism.

Draft Genome Sequences of Algoriphagus sp. Strain PAP.12 and Roseivirga sp. Strain PAP.19, Isolated from Marine Samples from Papua, Indonesia.

Algoriphagus sp. strain PAP.12 (EXT111900) and Roseivirga sp. strain PAP.19 (EXT111901) were isolated from marine samples. Here, we report their draft genome sequences, 5.032 Mbp and 4.583 Mbp in size, respectively, and rate their specialized metabolite production capacity. Taxonomic ranks established by genome-based analysis indicate that Algoriphagus sp. strain PAP.12 represents a candidate new species.

Draft Genome Sequence of Sinomicrobium sp. Strain PAP.21, Isolated from a Coast Sample of Papua, Indonesia.

Sinomicrobium sp. strain PAP.21 (EXT111902) was isolated from the coast of Cenderawasih Bay National Park in West Papua, Indonesia. Its genome was assembled into 151 contigs with a total size of 5.439 Mbp, enabling the prediction of its specialized metabolite production capacity.

Deep Transfer Learning Enables Robust Prediction of Antimicrobial Resistance for Novel Antibiotics.

Antimicrobial resistance (AMR) has become one of the serious global health problems, threatening the effective treatment of a growing number of infections. Machine learning and deep learning show great potential in rapid and accurate AMR predictions. However, a large number of samples for the training of these models is essential. In particular, for novel antibiotics, limited training samples and data imbalance hinder the models' generalization performance and overall accuracy. We propose a deep transfer learning model that can improve model performance for AMR prediction on small, imbalanced datasets. As our approach relies on transfer learning and secondary mutations, it is also applicable to novel antibiotics and emerging resistances in the future and enables quick diagnostics and personalized treatments.

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

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 independent of their organization in functional gene clusters. Here, we explored more than 3,000 Acinetobacter genomes in a phylogenomic framework integrating orthology-based phylogenetic profiling and microsynteny conservation analyses. We 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 the pathogenic ACB clade or are preferentially found therein. They provide a high-resolution picture of genetic and functional changes that coincide with the manifestation of the pathogenic phenotype in 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. We could show experimentally 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. It is a comprehensive resource for future research into novel therapeutic strategies.

What goes around comes around: Artificial circular RNAs bypass cellular antiviral responses.

Natural circular RNAs have been found to sequester microRNAs and suppress their function. We have used this principle as a molecular tool and produced artificial circular RNA sponges in a cell-free system by in vitro transcription and ligation. Formerly, we were able to inhibit hepatitis C virus propagation by applying a circular RNA decoy strategy against microRNA-122, which is essential for the viral life cycle. In another proof-of-principle study, we used circular RNAs to sequester microRNA-21, an oncogenic and pro-proliferative microRNA. This strategy slowed tumor growth in a 3D cell culture model system, as well as in xenograft mice upon systemic delivery. In the wake of the global use of an in vitro transcribed RNA in coronavirus disease 2019 (COVID-19) vaccines, the question arose whether therapeutic circular RNAs trigger cellular antiviral defense mechanisms when delivered systemically. In this study, we present data on the cellular innate immune response as a consequence of liposome-based transfection of the circular RNA sponges we previously used to inhibit microRNA function. We find that circular RNAs produced by the presented methodology do not trigger the antiviral response and do not activate innate immune-signaling pathways.

Multi-label classification for multi-drug resistance prediction of Escherichia coli.

Antimicrobial resistance (AMR) is a global health and development threat. In particular, multi-drug resistance (MDR) is increasingly common in pathogenic bacteria. It has become a serious problem to public health, as MDR can lead to the failure of treatment of patients. MDR is typically the result of mutations and the accumulation of multiple resistance genes within a single cell. Machine learning methods have a wide range of applications for AMR prediction. However, these approaches typically focus on single drug resistance prediction and do not incorporate information on accumulating antimicrobial resistance traits over time. Thus, identifying multi-drug resistance simultaneously and rapidly remains an open challenge. In our study, we could demonstrate that multi-label classification (MLC) methods can be used to model multi-drug resistance in pathogens. Importantly, we found the ensemble of classifier chains (ECC) model achieves accurate MDR prediction and outperforms other MLC methods. Thus, our study extends the available tools for MDR prediction and paves the way for improving diagnostics of infections in patients. Furthermore, the MLC methods we introduced here would contribute to reducing the threat of antimicrobial resistance and related deaths in the future by improving the speed and accuracy of the identification of pathogens and resistance.

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning.

MOTIVATION: Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done. RESULTS: In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic. AVAILABILITY AND IMPLEMENTATION: Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bakta: rapid and 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 International Nucleotide Sequence Database Collaboration (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.

Genome Analyses of the Less Aggressive Rhizoctonia solani AG1-IB Isolates 1/2/21 and O8/2 Compared to the Reference AG1-IB Isolate 7/3/14.

Rhizoctonia solani AG1-IB of the phylum Basidiomycota is known as phytopathogenic fungus affecting various economically important crops, such as bean, rice, soybean, figs, cabbage and lettuce. The isolates 1/2/21 and O8/2 of the anastomosis group AG1-IB originating from lettuce plants with bottom rot symptoms represent two less aggressive R. solani isolates, as confirmed in a pathogenicity test on lettuce. They were deeply sequenced on the Illumina MiSeq system applying the mate-pair and paired-end mode to establish their genome sequences. Assemblies of obtained sequences resulted in 2092 and 1492 scaffolds, respectively, for isolate 1/2/21 and O8/2, amounting to a size of approximately 43 Mb for each isolate. Gene prediction by applying AUGUSTUS (v. 3.2.1.) yielded 12,827 and 12,973 identified genes, respectively. Based on automatic functional annotation, genes potentially encoding cellulases and enzymes involved in secondary metabolite synthesis were identified in the AG1-IB genomes. The annotated genome sequences of the less aggressive AG1-IB isolates were compared with the isolate 7/3/14, which is highly aggressive on lettuce and other vegetable crops such as bean, cabbage and carrot. This analysis revealed the first insights into core genes of AG1-IB isolates and unique determinants of each genome that may explain the different aggressiveness levels of the strains.

Satellite-Like W-Elements: Repetitive, Transcribed, and Putative Mobile Genetic Factors with Potential Roles for Biology and Evolution of Schistosoma mansoni.

A large portion of animal and plant genomes consists of noncoding DNA. This part includes tandemly repeated sequences and gained attention because it offers exciting insights into genome biology. We investigated satellite-DNA elements of the platyhelminth Schistosoma mansoni, a parasite with remarkable biological features. Schistosoma mansoni lives in the vasculature of humans causing schistosomiasis, a disease of worldwide importance. Schistosomes are the only trematodes that have evolved separate sexes, and the sexual maturation of the female depends on constant pairing with the male. The schistosome karyotype comprises eight chromosome pairs, males are homogametic (ZZ) and females are heterogametic (ZW). Part of the repetitive DNA of S. mansoni are W-elements (WEs), originally discovered as female-specific satellite DNAs in the heterochromatic block of the W-chromosome. Based on new genome and transcriptome data, we performed a reanalysis of the W-element families (WEFs). Besides a new classification of 19 WEFs, we provide first evidence for stage-, sex-, pairing-, gonad-, and strain-specific/preferential transcription of WEs as well as their mobile nature, deduced from autosomal copies of full-length and partial WEs. Structural analyses suggested roles as sources of noncoding RNA-like hammerhead ribozymes, for which we obtained functional evidence. Finally, the variable WEF occurrence in different schistosome species revealed remarkable divergence. From these results, we propose that WEs potentially exert enduring influence on the biology of S. mansoni. Their variable occurrence in different strains, isolates, and species suggests that schistosome WEs may represent genetic factors taking effect on variability and evolution of the family Schistosomatidae.

Next-Generation Sequencing Analysis of the Tineola bisselliella Larval Gut Transcriptome Reveals Candidate Enzymes for Keratin Digestion.

The clothes moth Tineola bisselliella is one of a few insects that can digest keratin, leading to the destruction of clothing, textiles and artwork. The mechanism of keratin digestion is not yet fully understood, partly reflecting the lack of publicly available genomic and transcriptomic data. Here we present a high-quality gut transcriptome of T. bisselliella generated from larvae reared on keratin-rich and keratin-free diets. The overall transcriptome consists of 428,221 contigs that were functionally annotated and screened for candidate enzymes involved in keratin utilization. As a mechanism for keratin digestion, we identified cysteine synthases, cystathionine ß-synthases and cystathionine γ-lyases. These enzymes release hydrogen sulfite, which may reduce the disulfide bonds in keratin. The dataset also included 27 differentially expressed contigs with trypsin domains, among which 20 were associated with keratin feeding. Finally, we identified seven collagenases that were upregulated on the keratin-rich diet. In addition to this enzymatic repertoire potentially involved in breaking down keratin, our analysis of poly(A)-enriched and poly(A)-depleted transcripts suggested that T. bisselliella larvae possess an unstable intestinal microbiome that may nevertheless contribute to keratin digestion.

mRNA Inventory of Extracellular Vesicles from Ustilago maydis.

Extracellular vesicles (EVs) can transfer diverse RNA cargo for intercellular communication. EV-associated RNAs have been found in diverse fungi and were proposed to be relevant for pathogenesis in animal hosts. In plant-pathogen interactions, small RNAs are exchanged in a cross-kingdom RNAi warfare and EVs were considered to be a delivery mechanism. To extend the search for EV-associated molecules involved in plant-pathogen communication, we have characterised the repertoire of EV-associated mRNAs secreted by the maize smut pathogen, Ustilago maydis. For this initial survey, we examined EV-enriched fractions from axenic filamentous cultures that mimic infectious hyphae. EV-associated RNAs were resistant to degradation by RNases and the presence of intact mRNAs was evident. The set of mRNAs enriched inside EVs relative to the fungal cells are functionally distinct from those that are depleted from EVs. mRNAs encoding metabolic enzymes are particularly enriched. Intriguingly, mRNAs of some known effectors and other proteins linked to virulence were also found in EVs. Furthermore, several mRNAs enriched in EVs are also upregulated during infection, suggesting that EV-associated mRNAs may participate in plant-pathogen interactions.

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.