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New Method for Identifying Fungal Kingdom Enzyme Hotspots from Genome Sequences.

ABSTRACT: Fungal genome sequencing data represent an enormous pool of information for enzyme discovery. Here, we report a new approach to identify and quantitatively compare biomass-degrading capacity and diversity of fungal genomes via integrated function-family annotation of carbohydrate-active enzymes (CAZymes) encoded by the genomes. Based on analyses of 1932 fungal genomes the most potent hotspots of fungal biomass processing CAZymes are identified and ranked according to substrate degradation capacity. The analysis is achieved by a new bioinformatics approach, Conserved Unique Peptide Patterns (CUPP), providing for CAZyme-family annotation and robust prediction of molecular function followed by conversion of the CUPP output to lists of integrated "Function;Family" (e.g., EC 3.2.1.4;GH5) enzyme observations. An EC-function found in several protein families counts as different observations. Summing up such observations allows for ranking of all analyzed genome sequenced fungal species according to richness in CAZyme function diversity and degrading capacity. Identifying fungal CAZyme hotspots provides for identification of fungal species richest in cellulolytic, xylanolytic, pectinolytic, and lignin modifying enzymes. The fungal enzyme hotspots are found in fungi having very different lifestyle, ecology, physiology and substrate/host affinity. Surprisingly, most CAZyme hotspots are found in enzymatically understudied and unexploited species. In contrast, the most well-known fungal enzyme producers, from where many industrially exploited enzymes are derived, are ranking unexpectedly low. The results contribute to elucidating the evolution of fungal substrate-digestive CAZyme profiles, ecophysiology, and habitat adaptations, and expand the knowledge base for novel and improved biomass resource utilization.

SUBMITTER: Lange L

PROVIDER: S-EPMC8000046 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A key mechanism controlling cardiac function is the electrical activation sequence of the heart's main pumping chambers termed the ventricles. As such, personalization of the ventricular activation sequences is of pivotal importance for the clinical utility of computational models of cardiac electrophysiology. However, a direct observation of the activation sequence throughout the ventricular volume is virtually impossible. In this study, we report on a novel method for identification of activation sequences from activation maps measured at the outer surface of the heart termed the epicardium. Conceptually, the method attempts to identify the key factors governing the ventricular activation sequence - the timing of earliest activation sites (EAS) and the velocity tensor field within the ventricular walls - from sparse and noisy activation maps sampled from the epicardial surface and fits an Eikonal model to the observations. Regularization methods are first investigated to overcome the severe ill-posedness of the inverse problem in a simplified 2D example. These methods are then employed in an anatomically accurate biventricular model with two realistic activation models of varying complexity - a simplified trifascicular model (3F) and a topologically realistic model of the His-Purkinje system (HPS). Using epicardial activation maps at full resolution, we first demonstrate that reconstructing the volumetric activation sequence is, in principle, feasible under the assumption of known location of EAS and later evaluate robustness of the method against noise and reduced spatial resolution of observations. Our results suggest that the FIMIN algorithm is able to robustly recover the full 3D activation sequence using epicardial activation maps at a spatial resolution achievable with current mapping systems and in the presence of noise. Comparing the accuracy achieved in the reconstructed activation maps with clinical data uncertainties suggests that the FIMIN method may be suitable for the patient- specific parameterization of activation models.

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New Method for Identifying Fungal Kingdom Enzyme Hotspots from Genome Sequences.

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