Genomic factors shaping codon usage across the Saccharomycotina subphylum DOI Creative Commons

Bryan Zavala,

Lauren Dineen, Kaitlin J. Fisher

и другие.

G3 Genes Genomes Genetics, Год журнала: 2024, Номер 14(11)

Опубликована: Авг. 30, 2024

Abstract Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. It has been implicated in many cellular functions, such as translation dynamics transcript stability, but can also be shaped by neutral forces. We characterized codon 1,154 strains from 1,051 species fungal subphylum Saccharomycotina to gain insight into biases, molecular mechanisms, evolution, genomic features contributing patterns. found a general preference for A/T-ending codons correlations GC content, tRNA-ome size. bias distinct 12 orders degree that yeasts classified with an accuracy >90% using machine learning algorithm. which impacted translational selection. it was influenced combination features, including number coding sequences, BUSCO count, genome length. Our analysis revealed extreme Saccharomycodales associated lack predicted arginine tRNAs decode CGN leaving only AGN encode arginine. Analysis gene expression, tRNA evolution suggests avoidance decline function. Consistent previous findings, within bias. However, we find cases along yeast lineages, suggesting additional forces may shaping specific codons.

Язык: Английский

Celebrating the fifth edition of the International Symposium on Fungal Stress – ISFUS, a decade after its 2014 debut DOI

Alene Alder-Rangel,

Amanda E.A. Rangel,

Arturo Casadevall

и другие.

Fungal Biology, Год журнала: 2025, Номер unknown, С. 101590 - 101590

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

1
Diverse signatures of convergent evolution in cactus-associated yeasts DOI Creative Commons
Carla Gonçalves, Marie‐Claire Harrison, Jacob L. Steenwyk

и другие.

PLoS Biology, Год журнала: 2024, Номер 22(9), С. e3002832 - e3002832

Опубликована: Сен. 23, 2024

Many distantly related organisms have convergently evolved traits and lifestyles that enable them to live in similar ecological environments. However, the extent of phenotypic convergence evolving through same or distinct genetic trajectories remains an open question. Here, we leverage a comprehensive dataset genomic data from 1,049 yeast species subphylum Saccharomycotina (Kingdom Fungi, Phylum Ascomycota) explore signatures convergent evolution cactophilic yeasts, specialists associated with cacti. We inferred association yeasts cacti arose independently approximately 17 times. Using machine learning-based approach, further found cactophily can be predicted 76% accuracy both functional data. The most informative feature for predicting was thermotolerance, which likely altered evolutionary rates genes impacting cell envelope several lineages. also identified horizontal gene transfer duplication events plant wall-degrading enzymes clades, suggesting putatively adaptive disparate molecular mechanisms. Notably, multiple their close relatives been reported as emerging human opportunistic pathogens, lifestyle-and perhaps more generally favoring thermotolerance-might preadapt cause disease. This work underscores potential multifaceted approach involving high-throughput shed light onto adaptation highlights how wild environments could facilitate transition pathogenicity.

Язык: Английский

Процитировано

5
Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges DOI
Xiaoyan Huang, Xue Zhang, Lei Xing

и другие.

Bioresource Technology, Год журнала: 2025, Номер unknown, С. 132434 - 132434

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Optimizing lipid production in oleaginous yeasts for sustainable bioenergy: A review of process parameters, cultivation strategies, and machine learning integration DOI

Wannapawn Watsuntorn,

Nuttha Chuengcharoenphanich,

Piroonporn Srimongkol

и другие.

Biomass and Bioenergy, Год журнала: 2025, Номер 197, С. 107810 - 107810

Опубликована: Март 29, 2025

Язык: Английский

Процитировано

0
Tire Rubber Antioxidant 6PPD and 6PPD-quinone Disrupt the Energy Supply and Lipid Metabolism of Earthworms DOI
Ruiying Shi, Yanyu Bao, Weitao Liu

и другие.

Environmental Science & Technology, Год журнала: 2025, Номер unknown

Опубликована: Май 5, 2025

With the increase in traffic due to urbanization, tire wear particles (TWPs) derived compounds persistently accumulate soil environment. This study addresses critical knowledge gaps regarding ecotoxicological effects of TWP-derived contaminants, N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6PPD) and its precursor, 6PPD-quinone (6PPD-Q), on soil-dwelling organisms. The findings demonstrated that 6PPD-Q accumulated at a higher concentration (6.77 ± 0.124 ng/g) earthworms (Eisenia fetida) compared 6PPD (5.41 0.002 ng/g), triggering more severe oxidative stress cellular homeostatic imbalance. Specifically, 100 ng/g significantly elevated reactive oxygen species (ROS) levels by 180.77% suppressed acetylcholinesterase (AchE) Ca2+-ATPase activities 17.14% 44.70%, respectively. Notably, uniquely disrupted nitrogen balance disturbed energy supply strongly inhibiting fatty acid degradation peroxisome proliferator-activated receptor (PPAR) signaling pathways. Additionally, profoundly altered abundance key microbes microbial network stability, affecting intestinal functions such as bile secretion, hormone synthesis, lipid digestion, thus exacerbating metabolic imbalance earthworms. deciphers molecular toxicity mechanisms contaminants earthworms, providing crucial insights for developing risk assessment frameworks mitigation strategies ecosystems.

Язык: Английский

Процитировано

0
Machine learning identifies novel signatures of antifungal drug resistance inSaccharomycotinayeasts DOI Creative Commons
Marie‐Claire Harrison, David C. Rinker, Abigail L. LaBella

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Май 10, 2025

Abstract Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute acquired clinical isolates of specific pathogens, but whether they broadly explain natural across entire lineages unknown. We leveraged genomic, ecological, and phenotypic trait data from naturally sampled strains nearly all species subphylum Saccharomycotina examine the evolution eight antifungal drugs. The phylogenetic distribution varied by drug; fluconazole was widespread, while 5-fluorocytosine rare, except Lipomycetales . A random forest algorithm trained on predicted drug-resistant yeasts with 54-75% accuracy. In general, frequency correlated prediction accuracy, being consistently highest accuracy (74.9%). Fluconazole similar between models genome-wide variation presence number InterPro protein annotations (74.9% accuracy) those amino acid sequence alignment Erg11, be involved (74.3-74.9% accuracy). Interestingly, top Erg11 residues for predicting do not overlap with, spatially close to, less conserved than previously linked Candida albicans silico deep mutational scanning C. revealed that variants implicated cases almost universally destabilizing our most informative energetically more neutral, explaining why latter much common former populations. Importantly, previous experimental analyses have shown residues, despite having never been directly cases, can resistance. Our results suggest studies yeast encountered clinic will yield fuller understanding

Язык: Английский

Процитировано

0
Convergent expansions of keystone gene families drive metabolic innovation in Saccharomycotina yeasts DOI Creative Commons
Kyle T. David, Joshua G. Schraiber, Johnathan G. Crandall

и другие.

Proceedings of the National Academy of Sciences, Год журнала: 2025, Номер 122(23)

Опубликована: Июнь 3, 2025

Many remarkable phenotypes have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by same underlying gene families, while other times, many different families involved. Conversely, a family may be recruited for single trait or traits. To understand general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic count in 14,785 from 993 Saccharomycotina yeasts. Using recently developed phylogenetic approach that reduces spurious correlations, found expansion contraction were significantly linked to gain loss 45/56 (80%) While 595/739 (81%) significant associated with only one trait, also identified several "keystone" up 13/56 (23%) all Strikingly, most these known encode enzymes transporters, including members industrially relevant MAL tose fermentation loci baker's yeast Saccharomyces cerevisiae. These results indicate evolution level more widespread deeper timescales than previously believed.

Язык: Английский

Процитировано

0
Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework DOI Creative Commons
Marie‐Claire Harrison, Dana A. Opulente, John F. Wolters

и другие.

Yeast, Год журнала: 2024, Номер 41(10), С. 615 - 628

Опубликована: Сен. 18, 2024

ABSTRACT Yeasts in the subphylum Saccharomycotina are found across globe disparate ecosystems. A major aim of yeast research is to understand diversity and evolution ecological traits, such as carbon metabolic breadth, insect association, cactophily. This includes studying aspects traits like genetic architecture or association with other phenotypic traits. Genomic resources have grown rapidly. Ecological data, however, still limited for many species, especially those only known from species descriptions where usually a number strains studied. Moreover, information recorded natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework analysis ecology. total 1,088 were added Ontology Yeast Environments (OYE) analyzed machine‐learning connect genotype flexible can be extended additional isolates, environmental sequencing data. Widespread adoption OYE would greatly aid study macroecology subphylum.

Язык: Английский

Процитировано

2
Convergent expansions of keystone gene families drive metabolic innovation in a major eukaryotic clade DOI Creative Commons
Kyle T. David, Joshua G. Schraiber, Johnathan G. Crandall

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 23, 2024

Many remarkable innovations have repeatedly occurred across vast evolutionary distances. When convergent traits emerge on the tree of life, they are sometimes driven by same underlying gene families, while other times many different families involved. Conversely, a family may be recruited for single trait or traits. To understand general rules governing convergence at both genomic and phenotypic levels, we systematically tested associations between 56 binary metabolic count in 14,710 from 993 species Saccharomycotina yeasts. Using recently developed phylogenetic approach that reduces spurious correlations, discovered expansion contraction was significantly linked to gain loss 45/56 (80%) While 601/746 (81%) significant were associated with only one trait, also identified several 'keystone' up 13/56 (23%) all These results indicate yeasts governed narrow set major genetic elements mechanisms.

Язык: Английский

Процитировано

1
Machine learning reveals genes impacting oxidative stress resistance across yeasts DOI Creative Commons

Katarina Aranguiz,

Linda C. Horianopoulos,

Logan Elkin

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Авг. 14, 2024

Abstract Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and interactions with other organisms, including host infection. Here, we characterized the variation in resistance to ROS across ancient yeast subphylum Saccharomycotina used machine learning (ML) identify gene families whose sizes were predictive of resistance. The most features enriched related cell wall organization included two reductase families. We estimated quantitative contributions each species’ classification guide experimental validation showed that overexpression old yellow enzyme (OYE) increased Kluyveromyces lactis , while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes hypersensitive ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait diverse inform manipulation clinical biotechnological applications.

Язык: Английский

Процитировано

1