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Soltysiak, M. P. M.

Publications and source records attributed to Soltysiak, M. P. M..

6 recordsLinked to original sources

Identification and deletion of the extracellular polysaccharide slime pathway across the genus Xanthobacter

Foods and commodities made by microbial bioproduction offer a sustainable alternative to those produced from our traditional petrochemical-based supply chains. Increasingly, Xanthobacter strains are used for bioproduction because of their unique metabolic properties (e.g., CO2 and N2 fixation) and wide feedstock range, including growth on agricultural products (e.g., fructose, sucrose) and electrically generated substrates (e.g., hydrogen gas, methanol, acetate, and formate). Many Xanthobacter strains, however, produce an extracellular polysaccharide (EPS) slime that makes plating, pelleting, and handling these cultures challenging and shuttles away resources from biomass and other biosynthetic pathways. While slime-free mutants of slime-producing Xanthobacter strains have been isolated, the causative mutations were unknown. Thus, slime-free mutants need to be painstakingly reisolated based on phenotypic properties in each academically and industrially relevant strain, unless we know the gene to target. Here, we identified the gene and underlying mutation that causes a slime-free phenotype in the X. autotrophicus mutant 7CSF. When we deleted this gene, xsbA, slime production was abolished in the wild-type strain. We showed that deleting xsbA homologs similarly abolished slime production in slime-producers across the genus Xanthobacter (X. versatilis, X. autotrophicus, X. flavus, and X. tagetidis). We also identified a Wzx/Wzy-dependent EPS pathway downstream of xsbA that is conserved across slime-producing Xanthobacter strains. Lastly, abolishing slime production improved colony formation and pelleting, with minimal impact on growth. We demonstrated that challenging phenotypic properties in Xanthobacter strains can be removed, improving their usability as a chassis for sustainable bioproduction.

microbiology↗

Expansion and revision of the genus Xanthobacter and proposal of Roseixanthobacter gen. nov.

The nitrogen-fixing, chemolithoautotrophic genus Xanthobacter is found worldwide across numerous diverse environments and is an important member of many ecosystems. These species serve as model systems for their metabolic properties in academic settings and have industrial applications in bioremediation and sustainable protein, food, and fertilizer production. Despite their abundance, interest, and importance, the majority of Xanthobacter strains are without a genome sequence, and only 8 validly published species are known to date. To expand our understanding of the diversity and evolutionary history of the genus, we sequenced the genomes of 37 repository strains and 26 novel environmental strains we isolated. After performing comparative phylogenomic analyses, we expand and revise the genus Xanthobacter and propose the novel genus Roseixanthobacter gen. nov. For the Xanthobacter, we describe 10 novel species, bringing the total to 18: Xanthobacter agilis, Xanthobacter albus sp. nov., Xanthobacter aminoxidans, Xanthobacter autotrophicus, Xanthobacter cornucopiae sp. nov., Xanthobacter dioxanivorans, Xanthobacter flavus, Xanthobacter lutulentifluminis sp. nov., Xanthobacter nonsaccharivorans sp. nov., Xanthobacter oligotrophicus, Xanthobacter pseudotagetidis sp. nov., Xanthobacter sediminis sp. nov., Xanthobacter tagetidis, Xanthobacter toluenivorans sp. nov., Xanthobacter variabilis sp. nov., Xanthobacter versatilis sp. nov., Xanthobacter viscosus, and Xanthobacter wiegelii sp. nov. For the Roseixanthobacter gen. nov., we describe 5 novel species formerly classified as Xanthobacter: Roseixanthobacter finlandensis sp. nov., Roseixanthobacter glucoisosaccharinivorans sp. nov., Roseixanthobacter liquoris sp. nov., Roseixanthobacter pseudopolyaromaticivorans sp. nov., and Roseixanthobacter psychrophilus sp. nov. We characterized the phenotypic properties of these type strains, including temperature, salinity and pH ranges, carbon substrate utilization, motility, antibiotic susceptibility, slime production, autotrophic growth, and enzymatic activities. We discovered a more diverse range of phenotypes across the genus Xanthobacter than previously known and elucidated the evolutionary history within the genus. These findings and genome sequences will help further the application of Xanthobacter biology in academic, industrial, and environmental settings and provide additional insight into the unique biological properties that make these species attractive for such applications.

microbiology↗

Environment and taxonomy shape the genomic signature of prokaryotic extremophiles

This study provides comprehensive quantitative evidence suggesting that adaptations to extreme temperatures and pH imprint a discernible environmental component in the genomic signature of microbial extremophiles. Both supervised and unsupervised machine learning algorithms were used to analyze genomic signatures, each computed as the k-mer frequency vector of a 500 kbp DNA fragment arbitrarily selected to represent a genome. Computational experiments classified/clustered genomic signatures extracted from a curated dataset of [~]700 extremophile (temperature, pH) bacteria and archaea genomes, at multiple scales of analysis, 1 [≤]k [≤]6. The supervised learning resulted in high accuracies for taxonomic classifications at 2 [≤]k [≤]6, and medium to medium-high accuracies for environment category classifications of the same datasets at 3 [≤]k [≤]6. For k = 3, our findings were largely consistent with amino acid compositional biases and codon usage patterns in coding regions, previously attributed to extreme environment adaptations. The unsupervised learning of unlabelled sequences identified several exemplars of hyperthermophilic organisms with large similarities in their genomic signatures, in spite of belonging to different domains in the Tree of Life.

bioinformatics↗

SomaticSiMu: a mutational signature simulator

SummarySomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates, and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by 1) supervised machine learning classification of simulated sequences with different mutation types and burdens, and 2) mutational signature extraction from simulated mutational catalogs. Availability and ImplementationSomaticSiMu is written in Python 3.8.3. The open-source code, documentation, and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the Creative Commons Attribution 4.0 International License. Contactkhill22@uwo.ca Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses

As of February 20, 2020, the 2019 novel coronavirus (renamed to COVID-19) spread to 30 countries with 2130 deaths and more than 75500 confirmed cases. COVID-19 is being compared to the infamous SARS coronavirus, which resulted, between November 2002 and July 2003, in 8098 confirmed cases worldwide with a 9.6% death rate and 774 deaths. Though COVID-19 has a death rate of 2.8% as of 20 February, the 75752 confirmed cases in a few weeks (December 8, 2019 to February 20, 2020) are alarming, with cases likely being under-reported given the comparatively longer incubation period. Such outbreaks demand elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 genomes. The proposed method combines supervised machine learning with digital signal processing for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearmans rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp. Our results support a hypothesis of a bat origin and classify COVID-19 as Sarbecovirus, within Betacoronavirus. Our method achieves high levels of classification accuracy and discovers the most relevant relationships among over 5,000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.

bioinformatics↗

Rapid method for generating designer algal mitochondrial genomes

With synthetic biology, we can turn algae into bio-factories that produce high-value molecules (e.g. medicines or biofuels) or tackle global challenges (e.g. malnutrition and climate change). This realization has provoked rapid progress towards the creation of genetic tools for multiple algal species, notably Phaeodactylum tricornutum. The power of synthetic biology to generate more useful or productive organisms is contingent on the ability to produce diverse DNA molecules and rapidly screen them for beneficial variants. However, it is still relatively expensive to synthesize DNA, and delivering large DNA (>50 kbp) to eukaryotic cellular compartments remains challenging. In this study, we establish a robust system for building algal mitochondrial genomes as a practical alternative to DNA synthesis. Our approach permits an inexpensive and rapid generation of mitochondrial derivatives designed for testing targeted DNA delivery. First, we cloned the mitochondrial genome of P. tricornutum into the eukaryotic host organism S. cerevisiae using two different techniques: transformation-associated recombination; and PCR-based cloning. Next, we analyzed the cloned genomes by multiplex PCR, and correct genomes were transferred to prokaryotic host E. coli. Then these genomes were again analyzed by multiplex PCR, followed by diagnostic digest and complete-plasmid sequencing to evaluate the fidelity of each cloning method. Finally, we assessed the burden on eukaryotic and prokaryotic hosts to propagate the cloned genomes. We conclude that our system can reliably generate variants for genome-level engineering of algal mitochondria. HIGHLIGHTSO_LITAR-cloned mitochondrial genome of P. tricornutum in yeast C_LIO_LIDeveloped PCR-based cloning method to create designer algal mitochondrial genomes C_LIO_LIStably propagated algal mitochondrial genomes in S. cerevisiae and E. coli hosts C_LI

synthetic biology↗