Convergent reductive evolution in bee-associated lactic acid bacteria DOI

Ana Pontes,

Marie‐Claire Harrison, Antonis Rokas

и другие.

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

Опубликована: Июль 3, 2024

Abstract Distantly related organisms may evolve similar traits when exposed to environments or engaging in certain lifestyles. Several members of the Lactobacillaceae (LAB) family are frequently isolated from floral niche, mostly bees and flowers. In some LAB species (henceforth referred as bee- associated), distinctive genomic (e.g., genome reduction) phenotypic preference for fructose over glucose fructophily) features were recently documented. These found across distantly species, raising hypothesis that specific evolved convergently during adaptation environment. To test this hypothesis, we examined representative genomes 369 bee-associated non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts towards niche these LAB, observed pervasive, significant reductions size, gene repertoire, GC content. Using machine leaning, could distinguish with 94% accuracy, based on absence genes involved metabolism, osmotic stress, DNA repair. Moreover, most important learning classifier seemingly lost, independently, multiple lineages. One genes, adhE , encodes a bifunctional aldehyde-alcohol dehydrogenase associated evolution fructophily, rare trait was identified many species. results suggest phenotypes has been largely driven by loss same set genes. Importance lactic acid bacteria intimately exhibit unique biochemical properties potential food applications honeybee health. machine-learning approach, our study shows bee environment accompanied trajectory deeply shaped loss. losses occurred independently linked their biotechnologically relevant traits, such (fructophily). This underscores identifying fingerprints detecting instances convergent evolution. Furthermore, it sheds light onto particularities bacteria, thereby deepening understanding positive impact

Язык: Английский

Predicting fungal secondary metabolite activity from biosynthetic gene cluster data using machine learning DOI Creative Commons
Olivia Riedling, Allison S. Walker, Antonis Rokas

и другие.

Microbiology Spectrum, Год журнала: 2024, Номер 12(2)

Опубликована: Янв. 9, 2024

Fungal secondary metabolites (SMs) contribute to the diversity of fungal ecological communities, niches, and lifestyles. Many SMs have one or more medically industrially important activities (e.g., antifungal, antibacterial, antitumor). The genes necessary for SM biosynthesis are typically located right next each other in genome known as biosynthetic gene clusters (BGCs). However, whether bioactivity can be predicted from specific attributes BGCs remains an open question. We adapted machine learning models that bacterial BGC data with accuracies high 80% data. trained our predict cytotoxic/antitumor on two sets: (i) (data set comprised 314 BGCs) (ii) (314 (1,003 BGCs). found had balanced between 51% 68%, whereas training 56% 68%. low prediction accuracy bioactivities likely stems small size set; this lack data, coupled finding including did not substantially change currently limits application approaches studies. With >15,000 characterized SMs, millions putative genomes, increased demand novel drugs, efforts systematically link urgently needed.IMPORTANCEFungi key sources natural products iconic penicillin statins. DNA sequencing has revealed there pathways but chemical structures >99% produced by these remain unknown. used artificial intelligence diverse pathways. predictions were generally low, because only very few known. products, present study suggests is urgent need identify pathways, their bioactivities.

Язык: Английский

Процитировано

11
How genomics can help unravel the evolution of endophytic fungi DOI
Jefferson Brendon Almeida dos Reis, Andrei Stecca Steindorff, Adriana Sturion Lorenzi

и другие.

World Journal of Microbiology and Biotechnology, Год журнала: 2025, Номер 41(5)

Опубликована: Апрель 28, 2025

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 reductive evolution in bee-associated lactic acid bacteria DOI Creative Commons
Ana Pontes, Marie‐Claire Harrison, Antonis Rokas

и другие.

Applied and Environmental Microbiology, Год журнала: 2024, Номер 90(11)

Опубликована: Окт. 23, 2024

ABSTRACT Distantly related organisms may evolve similar traits when exposed to environments or engaging in certain lifestyles. Several members of the Lactobacillaceae [lactic acid bacteria (LAB)] family are frequently isolated from floral niche, mostly bees and flowers. In some LAB species (henceforth referred as bee-associated LAB), distinctive genomic (e.g., genome reduction) phenotypic preference for fructose over glucose fructophily) features were recently documented. These found across distantly species, raising hypothesis that specific evolved convergently during adaptation environment. To test this hypothesis, we examined representative genomes 369 non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts toward bee environment these observed significant reductions size, gene repertoire, GC content. Using machine leaning, could distinguish with 94% accuracy, based on absence genes involved metabolism, osmotic stress, DNA repair. Moreover, most important learning classifier seemingly lost, independently, multiple lineages. One genes, acetaldehyde–alcohol dehydrogenase ( adhE ), encodes a bifunctional aldehyde–alcohol which has been associated evolution fructophily, rare trait is pervasive species. results suggest phenotypes largely driven by losses same sets genes. IMPORTANCE intimately exhibit unique biochemical properties potential food applications honeybee health. learning-based approach, our study shows was accompanied trajectory deeply shaped loss. occurred independently linked their biotechnologically relevant traits, such (fructophily). This underscores identifying fingerprints detecting instances convergent evolution. Furthermore, it sheds light onto particularities bacteria, thereby deepening understanding positive impact

Язык: Английский

Процитировано

1
Convergent reductive evolution in bee-associated lactic acid bacteria DOI

Ana Pontes,

Marie‐Claire Harrison, Antonis Rokas

и другие.

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

Опубликована: Июль 3, 2024

Abstract Distantly related organisms may evolve similar traits when exposed to environments or engaging in certain lifestyles. Several members of the Lactobacillaceae (LAB) family are frequently isolated from floral niche, mostly bees and flowers. In some LAB species (henceforth referred as bee- associated), distinctive genomic (e.g., genome reduction) phenotypic preference for fructose over glucose fructophily) features were recently documented. These found across distantly species, raising hypothesis that specific evolved convergently during adaptation environment. To test this hypothesis, we examined representative genomes 369 bee-associated non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts towards niche these LAB, observed pervasive, significant reductions size, gene repertoire, GC content. Using machine leaning, could distinguish with 94% accuracy, based on absence genes involved metabolism, osmotic stress, DNA repair. Moreover, most important learning classifier seemingly lost, independently, multiple lineages. One genes, adhE , encodes a bifunctional aldehyde-alcohol dehydrogenase associated evolution fructophily, rare trait was identified many species. results suggest phenotypes has been largely driven by loss same set genes. Importance lactic acid bacteria intimately exhibit unique biochemical properties potential food applications honeybee health. machine-learning approach, our study shows bee environment accompanied trajectory deeply shaped loss. losses occurred independently linked their biotechnologically relevant traits, such (fructophily). This underscores identifying fingerprints detecting instances convergent evolution. Furthermore, it sheds light onto particularities bacteria, thereby deepening understanding positive impact

Язык: Английский

Процитировано

0