Biology subjects

Wise, M. J.

Publications and source records attributed to Wise, M. J..

4 recordsLinked to original sources

bioRxiv2023

Exploring the genetic architecture of ecophysiological traits in microbial ecology using statistical analysis of the properties of protein sequences: application to the glycogen accumulating organism (GAO) phenotype

Nearly 50 years ago, King and Wilson introduced a key distinction between two types of genetic variation-named structural and regulatory variation-for exploring the molecular basis for phenotypic traits. Structural variation is genetic variation that will influence gene product structure via changes in coding sequence, whereas regulatory variation will influence the control of gene product expression. Here we repurpose these concepts to study the molecular basis of eco-physiological traits observed in microbial ecology, focusing on a specialized glycogen accumulating phenotype, known to be exhibited by a phylogenetically diverse group of microbes. We analyse the statistical properties of the protein sequence of the 1,4--glucan branching enzyme (glgB), a key enzyme responsible for formation of glycogen from linear glucans. We show that the glgB proteins in a subgroup of these organisms show unusual statistical properties of protein sequence length, sequence similarity and patterning of functional domains, compared to organisms that do not exhibit this phenotype. These findings suggest a role for structural genetic variation in determining this phenotype in some species. Our analysis holds implications for dissecting the genetic/genomic architecture of complex traits exhibited by microbial communities and also provides a complementary framework to presence-absence level analyses that are ubiquitous in microbial ecology.

genomics↗

bioRxiv2022

Deep learning models for RNA secondary structure prediction (probably) do not generalise across families

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSThe secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions, but seldom address the much more difficult (and practical) inter-family problem. ResultsWe demonstrate it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modeled after structure mapping data, that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalisation despite the widespread assumption in the literature, and provide strong evidence that many existing learning-based models have not generalised inter-family. AvailabilitySource code and data is available at https://github.com/marcellszi/dl-rna.

bioinformatics↗

bioRxiv2020

Temporal Microbial Community Dynamics within a Unique Acid Saline Lake

Lake Magic is one of the acidic hypersaline lakes (ca. 1 km in diameter) present within the Yilgarn Craton in WA. This unique lake exhibits extremely low pH (<1.6) coupled to very high salinity (32% TDS) with the highest concentration of aluminium (1774 mg/L) and silica (510 mg/L) in the world. Previous studies on Lake Magic diversity has revealed that the lake hosts acidophilic, acidotolerant, halophilic and halotolerant bacterial species. These studies provide indicators of the population residing within the lake. However, they do not emphasize the survival mechanisms adopted by the resident microorganisms and how the diversity of microbial populations residing within the lake changes during the dynamic stages of flooding, evapo-concentration and desiccation. We have studied the bacterial and fungal diversity in Lake Magic via amplicon sequencing and functional analysis through different stages of the lake in a span of one year, in the salt and sediment layer. Our results highlight that the diversity in Lake Magic is strongly driven by the pH and salt concentrations at different stages of the lake. The microbial community becomes more specialised in specific functions during more extreme stages. This also suggests that microbial interactions are involved in stabilising the ecosystem and is responsible for the resistance and resilience of these communities as the interactions of these microbes create a safe haven for other microbes to survive during more extreme stages.

microbiology↗

bioRxiv2019

Systematic Analysis of Metabolic Pathway Distributions of Bacterial Energy Reserves

Metabolism of energy reserves are essential for bacterial functions such as pathogenicity, metabolic adaptation, and environmental persistence, etc. Previous bioinformatics studies have linked gain or loss of energy reserves such as glycogen and polyphosphate (polyP) with host-pathogen interactions and bacterial virulence based on a comparatively small number of bacterial genomes or proteomes. Thus, understanding the distribution patterns of energy reserves metabolism across bacterial species provides a shortcut route to look into bacterial lifestyle and physiology theoretically. So far, five major energy reserves have been identified in bacteria due to their effective capacity to support bacterial persistence under nutrient deprivation conditions, which include wax ester (WE), triacylglycerol (TAG), polyhydroxyalkanoates (PHA), polyphosphate, and glycogen. Although unknown pathways directly involved in energy reserves keep being discovered with the continuous endeavour of molecular microbiologists and it is currently rather clear about the enzymes related with the metabolism of energy reserves, there is a lack of systematic study of the pathway or key enzyme distributions of the five energy reserves in bacteria from an evolutionary point of view. With the fast development of sequencing technology, abundant bacterial proteomes are available in public database now. In this study, we sourced 8214 manually reviewed bacterial reference proteomes from UniProt database and used statistical models to search homologous sequences of key enzymes related with energy reserves. The distribution patterns of the pathways for energy reserves metabolism are visualized in taxonomy-based phylogenetic trees. According to the study, it was revealed that specific pathways and enzymes are associated with certain types of bacterial groups, which provides evolutionary insights into the understanding of their origins and functions. In addition, the study also confirmed that loss of energy reserves is correlated with bacterial genome reduction. Through this analysis, a much clearer picture about energy reserves metabolism in bacteria is present, which could serve a guide for further theoretical and experimental analyses of energy reserves metabolism in bacteria.

microbiology↗