Biology subjects

Weber, T.

Publications and source records attributed to Weber, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Secondary metabolites shape Streptomyces-Streptomyces interaction: Mass Spectrometry Imaging reveals lydicamycins broadly induce sporulation

Streptomyces are major players in soil microbiomes, however, interactions involving Streptomyces and other actinobacteria are rarely described. The complex developmental cycle of actinobacteria necessitates a multi-omics approach to unravel the web of information. This study resulted from the observation of induced morphogenesis between two environmental isolates from the same site, Kitasatospora sp. P9-2B1 and Streptomyces sp. P9-2B2. When co-cultivated on potato dextrose agar (PDA), P9-2B2 induced a wave-like sporulation in strain P9-2B1. Using mass spectrometry imaging, we revealed that a suite of lydicamycins were present in this induced sporulation zone. Lydicamycin deficient mutants were generated using CRISPR Base-Editing and the inducible sporulation ceased, confirming their role in triggering morphological differentiation. In agar diffusion assays, pure lydicamycin was inhibitory when added concurrently with bacterial inoculation and induced sporulation with delayed addition. Subsequent testing of additional environmental isolates resulted in the same inducible sporulation wave phenomenon, including Streptomyces coelicolor M145 and M1146. Using transcriptomics, we observed the differential expression of genes related to early aerial mycelium development four days into cocultivation and the transitional genes responsible for development of spores on day 9. Along with these differentially expressed genes, we also observed numerous overall stress responses, specifically cell envelope stress responses. These findings uncovered actinobacteria interactions mediated by lydicamycins, pointing to a potential role of certain groups of bioactive metabolites in nature. ImportanceShifting away from an antibiotic discovery mindset, uncovering the chemical ecology of secondary metabolites is key to maximizing their biotechnological application. The reduced complexity of dual cultures allows for in depth analysis and investigation of these interactions via multi-omics provides orthogonal data leading to more robust conclusions. This study provides insight into the role of lydicamycins in dual cultures with other actinobacteria and provides an integral roadmap for future chemical ecology work between microorganisms, especially through the use of mass spectrometry imaging.

ecology↗

bioRxiv2024

Critical assessment of missense variant effect predictors on disease-relevant variant data

Regular, systematic, and independent assessment of computational tools used to predict the pathogenicity of missense variants is necessary to evaluate their clinical and research utility and suggest directions for future improvement. Here, as part of the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, we assess missense variant effect predictors (or variant impact predictors) on an evaluation dataset of rare missense variants from disease-relevant databases. Our assessment evaluates predictors submitted to the CAGI6 Annotate-All-Missense challenge, predictors commonly used by the clinical genetics community, and recently developed deep learning methods for variant effect prediction. To explore a variety of settings that are relevant for different clinical and research applications, we assess performance within different subsets of the evaluation data and within high-specificity and high-sensitivity regimes. We find strong performance of many predictors across multiple settings. Meta-predictors tend to outperform their constituent individual predictors; however, several individual predictors have performance similar to that of commonly used meta-predictors. The relative performance of predictors differs in high-specificity and high-sensitivity regimes, suggesting that different methods may be best suited to different use cases. We also characterize two potential sources of bias. Predictors that incorporate allele frequency as a predictive feature tend to have reduced performance when distinguishing pathogenic variants from very rare benign variants, and predictors supervised on pathogenicity labels from curated variant databases often learn label imbalances within genes. Overall, we find notable advances over the oldest and most cited missense variant effect predictors and continued improvements among the most recently developed tools, and the CAGI Annotate-All-Missense challenge (also termed the Missense Marathon) will continue to assess state-of-the-art methods as the field progresses. Together, our results help illuminate the current clinical and research utility of missense variant effect predictors and identify potential areas for future development.

genetics↗

bioRxiv2024

An in vitro assay of MCTS1-DENR-dependent re-initiation and ribosome profiling uncover the activity of MCTS2 and distinct function of eIF2D

Ribosomes scanning from the mRNA 5' cap to the start codon may initiate at upstream open reading frames (uORFs), decreasing protein biosynthesis. Termination at a uORF can lead to re-initiation, where the 40S subunit resumes scanning and initiates another translation event downstream. In mammals, the noncanonical translation factors MCTS1-DENR participate in re-initiation at specific uORFs, but knowledge of other trans-acting factors and uORF features influencing re-initiation is limited. Here, we describe a cell-free re-initiation assay using HeLa cell lysates. Comparing in vivo and in vitro re-initiation activities on uORF-containing model reporters, we validate that MCTS1-DENR-dependent re-initiation is accurately recapitulated in vitro. Using this system and ribosome profiling in cultured cells, we found that knockdown of the homolog eIF2D causes widespread gene expression deregulation unrelated to uORF translation, suggesting distinct functions from MCTS1-DENR. Additionally, we identified MCTS2, encoded by a retrogene copy of Mcts1, as an alternative DENR partner that promotes re-initiation in vitro, providing a plausible explanation for the striking clinical differences associated with Denr vs. Mcts1 mutations in humans. Our findings on re-initiation and the new assay provide valuable insights and a powerful tool for future research on uORF features and trans-acting factors.

molecular biology↗

bioRxiv2024

Expanding the genome information on Bacillales for biosynthetic gene cluster discovery

This study showcases 121 new genomes of spore-forming Bacillales from strains collected globally from a variety of habitats, assembled using Oxford Nanopore long-read and MGI short-read sequences. Bacilli are renowned for their capacity to produce diverse secondary metabolites with use in agriculture, biotechnology, and medicine. These secondary metabolites are encoded within biosynthetic gene clusters (smBGCs). smBGCs have significant research interest due to their potential for the discovery of new bioactivate compounds. Our dataset includes 62 complete genomes, 2 at chromosome level, and 57 at contig level, covering a genomic size range from 3.50 Mb to 7.15 Mb. Phylotaxonomic analysis revealed that these genomes span 16 genera, with 69 of them belonging to Bacillus. A total of 1,176 predicted BGCs were identified by in silico genome mining. We anticipate that the open-access data presented here will expand the reported genomic information of spore-forming Bacillales and facilitate a deeper understanding of the genetic basis of Bacillales potential for secondary metabolite production.

microbiology↗

bioRxiv2024

Pangenome mining of the Streptomyces genus redefines their biosynthetic potential

BackgroundStreptomyces is a highly diverse genus known for the production of secondary or specialized metabolites with a wide range of applications in the medical and agricultural industries. Several thousand complete or nearly-complete Streptomyces genome sequences are now available, affording the opportunity to deeply investigate the biosynthetic potential within these organisms and to advance natural product discovery initiatives. ResultWe performed pangenome analysis on 2,371 Streptomyces genomes, including approximately 1,200 complete assemblies. Employing a data-driven approach based on genome similarities, the Streptomyces genus was classified into 7 primary and 42 secondary MASH-clusters, forming the basis for a comprehensive pangenome mining. A refined workflow for grouping biosynthetic gene clusters (BGCs) redefined their diversity across different MASH-clusters. This workflow also reassigned 2,729 known BGC families to only 440 families, a reduction caused by inaccuracies in BGC boundary detections. When the genomic location of BGCs is included in the analysis, a conserved genomic structure (synteny) among BGCs becomes apparent within species and MASH-clusters. This synteny suggests that vertical inheritance is a major factor in the acquisition of new BGCs. ConclusionOur analysis of a genomic dataset at a scale of thousands of genomes refined predictions of BGC diversity using MASH-clusters as a basis for pangenome analysis. The observed conservation in the order of BGCs genomic locations showed that the BGCs are vertically inherited. The presented workflow and the in-depth analysis pave the way for large-scale pangenome investigations and enhance our understanding of the biosynthetic potential of the Streptomyces genus.

systems biology↗

bioRxiv2024

Maramycin, a cytotoxic isoquinolinequinone terpenoid produced through heterologous expression of a bifunctional indole prenyltransferase /tryptophan indole-lyase in S. albidoflavus

Isoquinolinequinones represent an important family of natural alkaloids with profound biological activities. Heterologous expression of a rare bifunctional indole prenyltransferase /tryptophan indole-lyase enzyme from Streptomyces mirabilis P8-A2 in S. albidoflavus J1074 led to the activation of a putative isoquinolinequinone biosynthetic gene cluster and production of a novel isoquinolinequinone alkaloid, named maramycin (1). The structure of maramycin was determined by analysis of spectroscopic (1D/2D NMR) and MS spectrometric data. The prevalence of this bifunctional biosynthetic enzyme was explored and found to be a recent evolutionary event with only a few representatives in Nature. Maramycin exhibited moderate cytotoxicity against human prostate cancer cell lines, LNCaP and C4-2B. The discovery of maramycin (1) enriched the chemical diversity of natural isoquinolinequinones and also provided new insights into crosstalk between the host biosynthetic genes and the heterologous biosynthetic genes in generating new chemical scaffolds.

bioengineering↗

bioRxiv2024

A treasure trove of 1,034 actinomycete genomes

Filamentous Actinobacteria, recently renamed Actinomycetia, are the most prolific source of microbial bioactive natural products. Studies on biosynthetic gene clusters benefit from or require chromosome-level assemblies. Here, we provide DNA sequences from more than 1,000 isolates: 881 complete genomes and 153 near-complete genomes, representing 28 genera and 389 species, including 244 likely novel species. All genomes are from filamentous isolates of the class Actinomycetia from the NBC culture collection. The largest genus is Streptomyces with 886 genomes including 742 complete assemblies. We use this data to show that analysis of complete genomes can bring biological understanding not previously derived from more fragmented sequences or less systematic datasets. We document the central and structured location of core genes and distal location of specialized metabolite biosynthetic gene clusters and duplicate core genes on the linear Streptomyces chromosome, and analyze the content and length of the terminal inverted repeats which are characteristic for Streptomyces. We then analyze the diversity of trans-AT polyketide synthase biosynthetic gene clusters, which encodes the machinery of a biotechnologically highly interesting compound class. These insights have both ecological and biotechnological implications in understanding the importance of high quality genomic resources and the complex role synteny plays in Actinomycetia biology.

microbiology↗

bioRxiv2023

Biosynthesis of the azoxy compound azodyrecin from Streptomyces mirabilis P8-A2

Azoxy compounds are a distinctive group of bioactive secondary metabolites, characterized by a unique RN=N+(O-)R moiety. The azoxy moiety is present in various classes of metabolites that exhibit various biological activities. The enzymatic mechanisms underlying azoxy bond formation remain enigmatic. Azodyrecins are cytotoxic azoxy metabolites produced by Streptomyces mirabilis P8-A2. Here we cloned and confirmed the putative azd biosynthetic gene cluster through CATCH cloning followed by expression and production of azodyrecins in two heterologous hosts, S. albidoflavus J1074 and S. coelicolor M1146, respectively. We explored the function of 14 enzymes in azodyrecin biosynthesis through gene knock-out using CRISPR-Cas9 base editing in the native producer, S. mirabilis P8-A2. The key intermediates were analyzed in the mutants through MS/MS fragmentation studies, revealing azoxy bond formation via the conversion of hydrazine to azo compound; followed by further oxygenation. Additionally, N-oxygenase and dehydrogenase activities were confirmed among 8 core biosynthetic genes and five helper genes. Moreover, the distribution of the azoxy biosynthetic gene clusters across Streptomyces spp. genomes is explored, highlighting the presence of these clusters in over 20% of the Streptomyces spp. genomes and revealing that azoxymycin and valanimycin are scarce, while azodyrecin and KA57A like clusters are widely distributed across the phylogenetic tree.

microbiology↗

bioRxiv2023

A discovery platform to identify inducible synthetic circuitry from varied microbial sources

Gut microbes encode a variety of systems for molecular sensing and controlling conditional gene expression within the mammalian gut. Synthetic biology approaches such as whole-cell biosensing and sense-and-respond therapeutics aim to tap into this vast sensing repertoire to drive clinical and pre-clinical applications. An ongoing constraint is the limited number of well-characterized inducible circuit components to specifically sense in vivo conditions of interest, such as disease. Here, we extend the flexibility and power of a biosensor screening platform using bacterial memory circuits encoded in a gut commensal E. coli. We construct libraries driven by potential sensory components derived from a combination of E. coli promoters or bacterial two-component systems (TCSs) sourced from diverse gut bacteria. Each is tagged with unique DNA barcodes using a pooled construction method. Using our pipeline, we evaluate sensor activity and performance heterogeneity across in vitro and in vivo conditions including using a mouse inflammation model. We demonstrate the methods ability to identify biosensors of interest, including the identification of unannotated TCSs. Following the optimisation of library construction, analysis, and delivery to account for the challenges of working with engineered bacteria within a conventional mammalian gut microbiome, we identify and validate several further biosensors of interest responding to the murine gut environment, and specifically during inflammatory conditions. This approach can be applied to transcriptionally activated sensing elements of any type and will allow for rapid development of new biosensors that can advance synthetic biology approaches for complex environments.

synthetic biology↗

bioRxiv2023

getphylo: rapid and automatic generation of multi-locus phylogentic trees from genbank files

MotivationPhylogenetic trees are the primary tool for visualising evolutionary relationships. Traditionally, phylogenies are inferred from manually curated sets of marker genes. As available genomic data increases, there is increasing demand for tools to automatically build phylogenies from assembled genomes. Existing tools rely on reference databases of preselected marker genes, limiting their taxonomic scope. We sought to develop a tool that could quickly build phylogeny from input genomes alone. ResultsWe developed getphylo, a tool to automatically generate multi-locus phylogenetic trees from GenBank files. It has a low barrier to entry with minimal dependencies. getphylo uses a parallelised, heuristic workflow to keep runtime and system requirements as low as possible. getphylo consistently produces trees with topologies comparable to other tools in less time. Furthermore, as getphylo does not rely on reference databases, it has a virtually unlimited scope in terms of taxonomy (e.g., not limited to bacteria) and genetic scale (e.g., can analyse plasmids, prophage, and gene clusters). This combination of speed and flexibility makes getphylo a valuable addition to the phylogenetics toolkit. Availabilitygetphylo is freely available and is downloadable through the Python Package Index (pip install getphylo; https://pypi.org/project/getphylo/) and GitHub (https://github.com/drboothtj/getphylo).

bioinformatics↗

bioRxiv2023

Literate programming for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of complex biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of a metabolic pathway designs for production of a key precursor to medicinal alkaloids. The teemi platform is publicly available at PyPi and GitHub.

bioinformatics↗

bioRxiv2023

BGCFlow: Systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as; i) data selection, ii) functional annotation, iii) phylogenetic analysis, iv) genome mining, and v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant, and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge in the ever-growing genomic datasets of biotechnologically relevant bacterial species. BGCFlow is available for downloading at https://github.com/NBChub/bgcflow.

bioinformatics↗

bioRxiv2023

Dissecting the impact of somatic hypermutation on SARS-CoV-2 neutralization and viral escape

Somatic hypermutation (SHM) drives affinity maturation and continues over months in SARS-CoV-2 neutralizing antibodies. Yet, several potent SARS-CoV-2 antibodies carry no or only few mutations, leaving the question of how ongoing SHM affects neutralization. Here, we reverted variable region mutations of 92 antibodies and tested their impact on SARS-CoV-2 binding and neutralization. Reverting higher numbers of mutations correlated with decreasing antibody functionality. However, some antibodies, including the public clonotype VH1-58, remained unaffected for Wu01 activity. Moreover, while mutations were dispensable for Wu01-induced VH1-58 antibodies to neutralize Alpha, Beta, and Delta variants, they were critical to neutralize Omicron BA.1/BA.2. Notably, we exploited this knowledge to convert the clinical antibody tixagevimab into a BA.1/BA.2-neutralizer. These findings substantially broaden our understanding of SHM as a mechanism that not only improves antibody responses during affinity maturation, but also counteracts antigenic imprinting through antibody diversification and thus increases the chances of neutralizing viral escape variants.

immunology↗

bioRxiv2023

CASCADE-Cas3 Enables Highly Efficient Genome Engineering in Streptomyces Species

Type I CRISPR systems are widespread in bacteria and archaea. The main differences compared to more widely applied type II systems are multi-effector CASCADE needed for crRNA processing and target recognition, as well as the processive nature of the hallmark nuclease Cas3. Given the widespread nature of type I systems, the processive nature of Cas3, as well as the recombinogenic overhangs created by Cas3, we hypothesized that Cas3 would be uniquely positioned to enable efficient genome engineering in streptomycetes. Here, we report a new type I based CRISPR genome engineering tool for streptomycetes. The plasmid system, called pCRISPR-Cas3, utilizes a compact type I-C CRISPR system and enables highly efficient genome engineering. pCRISPR-Cas3, outperforms pCRISPR-Cas9 and facilitates targeted and random sized deletions, as well as substitutions of large genomic regions such as biosynthetic gene clusters. Without additional modifications, pCRISPR-Cas3 enabled genome engineering in several Streptomyces species at high efficiencies.

synthetic biology↗

bioRxiv2023

DiscoGen: Learning to Discover Gene Regulatory Networks

Accurately inferring Gene Regulatory Networks (GRNs) is a critical and challenging task in biology. GRNs model the activatory and inhibitory interactions between genes and are inherently causal in nature. To accurately identify GRNs, perturbational data is required. However, most GRN discovery methods only operate on observational data. Recent advances in neural network-based causal discovery methods have significantly improved causal discovery, including handling interventional data, improvements in performance and scalability. However, applying state-of-the-art (SOTA) causal discovery methods in biology poses challenges, such as noisy data and a large number of samples. Thus, adapting the causal discovery methods is necessary to handle these challenges. In this paper, we introduce DiscoGen, a neural network-based GRN discovery method that can denoise gene expression measurements and handle interventional data. We demonstrate that our model outperforms SOTA neural network-based causal discovery methods.

genomics↗

bioRxiv2022

Streptomyces alleviate abiotic stress in plant by producing pteridic acids

Soil microbiota can confer fitness advantages to plants and increase crop resilience to drought and other abiotic stressors. However, there is little evidence on the mechanisms correlating a microbial trait with plant abiotic stress tolerance. Here, we report that Streptomyces effectively alleviates the drought and salinity stress by producing spiroketal polyketide pteridic acid H (1) and its isomer F (2), both of which promote root growth in Arabidopsis at a concentration of 1.3 nM under abiotic stress. Pteridic acids induce stress response genes expression in salinity-stressed Arabidopsis seedlings. The bifunctional biosynthetic gene cluster of pteridic acids and antimicrobial elaiophylin is confirmed in vivo and mainly disseminated by vertical transmission which is geographically distributed in various environments. This discovery reveals a perspective for understanding plant-Streptomyces interactions and provides a promising approach for utilising beneficial Streptomyces and their secondary metabolites in agriculture to mitigate the detrimental effects of climate change.

microbiology↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2022

Bayesian multi-level calibration of a process-based maize phenology model

Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They are usually estimated by calibrating the model to observations. However, at regional scales in which different cultivars of a crop species may be grown, not accounting for inherent differences in phenological development between cultivars in the model and the presence of model deficits lead to inaccurate parameter estimates. To account for inherent differences between cultivars and to identify model deficits, we used a Bayesian multi-level approach to calibrate a phenology model (SPASS) to observations of silage maize grown across Germany between 2009 and 2017. We evaluated four multi-level models of increasing complexity, where we accounted for different combinations of ecological, weather, and year effects, as well as the hierarchical classification of cultivars nested within ripening groups of the maize species. We compared the calibration quality from this approach to the commonly used pooled approach in which none of these factors are considered. Our approach proved successful in improving calibration quality by incorporating the hierarchical classification of cultivars within ripening groups of maize. Our findings have implications for regional model calibration and data-gathering studies, since it emphasizes that ripening group and cultivar information is essential. Furthermore, we found that if this information is not available, at least weather, ecological regions and year effects should be taken into account. Our results can facilitate model improvement studies since we identified possible model limitations related to temperature effects in the reproductive (post-flowering) phase and to soil-moisture. We demonstrate that Bayesian multi-level calibration of a phenology model facilitates the incorporation of hierarchical dependencies and the identification of model limitations. Our approach can be extended to full crop models at different spatial scales.

plant biology↗