Convergent reductive evolution in bee-associated lactic acid bacteria DOI

Ana Pontes,

Marie‐Claire Harrison, Antonis Rokas

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 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

Language: Английский

Machine learning enables identification of an alternative yeast galactose utilization pathway DOI
Marie‐Claire Harrison, Emily J. Ubbelohde, Abigail L. LaBella

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(18)

Published: April 26, 2024

How genomic differences contribute to phenotypic is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources conditions 1,154 strains from 1,049 fungal species (nearly all known) the yeast subphylum Saccharomycotina provide powerful, yet complex, dataset for addressing this question. We used random forest algorithm trained these genomic, metabolic, environmental data predict several carbon with high accuracy. Known structural genes involved assimilation presence/absence other were important features contributing prediction By further examining galactose, we found that it can be predicted accuracy either (92.2%) or (82.6%) but not environment (65.6%). Prediction was even higher (93.3%) when combined data. After

Language: Английский

Citations

12
Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products DOI Creative Commons
Christine Mae F. Ancajas, Abiodun S. Oyedele, Caitlin M. Butt

et al.

Natural Product Reports, Journal Year: 2024, Volume and Issue: 41(10), P. 1543 - 1578

Published: Jan. 1, 2024

This review highlights methods for studying structure activity relationships of natural products and proposes that these are complementary could be used to build an iterative computational-experimental workflow.

Language: Английский

Citations

9
Leveraging endophytic fungi and multiomics integration for targeted drug discovery DOI
Aleena James Chirayimmel, Gursharan Kaur, Swapnil Kajale

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 277 - 293

Published: Jan. 1, 2025

Language: Английский

Citations

0
Molecular docking technology drives multidimensional applications of microbial natural products DOI

Chan Zhang,

Qingjie Sun,

Arzugul Ablimit

et al.

Journal of Molecular Structure, Journal Year: 2025, Volume and Issue: unknown, P. 142044 - 142044

Published: March 1, 2025

Language: Английский

Citations

0
Molecular insights fast-tracked: AI in biosynthetic pathway research DOI
Lijuan Liao,

Mengjun Xie,

Xiaoshan Zheng

et al.

Natural Product Reports, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

This review explores how AI addresses challenges in biosynthetic pathway research, accelerating the development of bioactive natural products for pharmacology, agriculture, and biotechnology.

Language: Английский

Citations

0
Developing filamentous fungal chassis for natural product production DOI
Jie Fan, Peng‐Lin Wei, Yuanyuan Li

et al.

Bioresource Technology, Journal Year: 2024, Volume and Issue: unknown, P. 131703 - 131703

Published: Oct. 1, 2024

Language: Английский

Citations

3
Artificial intelligence-driven innovation in Ganoderma spp.: potentialities of their bioactive compounds as functional foods DOI Creative Commons
Sonali Khanal, Aman Sharma,

M. Radhakrishna Pillai

et al.

Sustainable Food Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

AI significantly transforms the food business by optimizing production processes of therapeutic Ganoderma spp. and improving quality safety control based functional food.

Language: Английский

Citations

0
Enhancing Antimicrobial Activity Predictors Based on Machine Learning Approaches DOI

Salah G. Abdelkhabir,

Seham S. Ezz-eldeen,

Alaa R. Gabr

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 257 - 276

Published: March 28, 2025

Recently, the prediction tools of antimicrobial activity revealed a promising avenue for novel peptide (AMP) sequence determination and discovery. Machine learning (ML) approaches can be utilized to offer AMP with great success, which explores alternative strategies combat resistance develop effective treatments infections. The main objective this chapter is study evaluate predictive ability modern ML methods accurately identify activities sequences previously described at protein level through in vitro studies. To formally confirm whether have significant enhancement, authors used dataset size 6623 instances both non-AMP classes. best performance was LGBM an accuracy 0.92%, MCC 0.83, recall 90%, Area Under Curve (AUC) 0.97%, precision 0.91%, F1-score 0.92%.

Language: Английский

Citations

0
Convergent reductive evolution in bee-associated lactic acid bacteria DOI Creative Commons
Ana Pontes, Marie‐Claire Harrison, Antonis Rokas

et al.

Applied and Environmental Microbiology, Journal Year: 2024, Volume and Issue: 90(11)

Published: Oct. 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

Language: Английский

Citations

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

Ana Pontes,

Marie‐Claire Harrison, Antonis Rokas

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 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

Language: Английский

Citations

0