Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics DOI

L. Li,

Jens Nielsen, Yu Chen

и другие.

Current Opinion in Biotechnology, Год журнала: 2024, Номер 91, С. 103248 - 103248

Опубликована: Дек. 31, 2024

Язык: Английский

BN-BacArena: Bayesian network extension of BacArena for the dynamic simulation of microbial communities DOI Creative Commons
Telmo Blasco, Francesco Balzerani, Luis V. Valcárcel

и другие.

Bioinformatics, Год журнала: 2024, Номер 40(5)

Опубликована: Апрель 29, 2024

Simulating gut microbial dynamics is extremely challenging. Several computational tools, notably the widely used BacArena, enable modeling of dynamic changes in environment. These methods, however, do not comprehensively account for microbe-microbe stimulant or inhibitory effects nutrient-microbe effects, typically observed different compounds present daily diet.

Язык: Английский

Процитировано

1

Refining microbial community metabolic models derived from metagenomics using reference-based taxonomic profiling DOI Creative Commons
Marwan E. Majzoub, Laurence Don Wai Luu, Craig Haifer

и другие.

mSystems, Год журнала: 2024, Номер 9(9)

Опубликована: Авг. 13, 2024

Characterization of microbial community metabolic output is crucial to understanding their functions. Construction genome-scale models from metagenome-assembled genomes (MAG) has enabled prediction metabolite production by communities, yet little known about accuracy. Here, we examined the performance two approaches for metagenomes, one that MAG-guided and another taxonomic reference-guided. We applied both on shotgun metagenomics data human environmental samples, validated findings in samples using untargeted metabolomics. found where profiling optimized reference are readily available, when number input taxa was normalized, reference-guided approach predicted more metabolites than approach. The showed significant overlap but each identified not other. Pathway enrichment analyses differences inferences derived based approach, highlighting need caution interpretation. In total sample types non-redundant seawater samples. Nonetheless, as observed overlapped substantially also Our report utility a complementary model construction less computationally intensive forgoing MAG assembly refinement, can be shallow sequencing MAGs cannot generated.IMPORTANCELittle accuracy (GEMs) communities despite influence inferring outputs culture conditions. GEMs metagenomes assessed applying validating dependent type, collectively, Despite differences, predictions biological with some examples uniquely enriched pathways group being invalidated alternative interpretation GEMs.

Язык: Английский

Процитировано

1

Metabolic modeling to study bacterial composition for probiotic and prebiotic production DOI
Alejandra Rojas López, Matteo Barberis

Chemical Engineering Journal, Год журнала: 2024, Номер unknown, С. 157852 - 157852

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

1

NovoLign: metaproteomics by sequence alignment DOI Creative Commons
Hugo B.C. Kleikamp,

Ramon van der Zwaan,

Ramon van Valderen

и другие.

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

Опубликована: Апрель 6, 2024

ABSTRACT Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these is complex, time-consuming, prone to errors, potentially biasing experimental outcomes conclusions. This asks alternative that can provide rapid orthogonal insights data. Here we present NovoLign, a de novo pipeline performs sequence alignment sequences complete enables taxonomic profiling complex communities evaluates coverage searches. Furthermore, NovoLign supports creation reference searching ensure comprehensive coverage. publicly available via: https://github.com/hbckleikamp/NovoLign .

Язык: Английский

Процитировано

0

Microbiome-microbolome and Disease: A Covert Nexus DOI Creative Commons

Kalyan Bhattacharjee -

International Journal For Multidisciplinary Research, Год журнала: 2024, Номер 6(2)

Опубликована: Апрель 12, 2024

The microbial diversity associated with all life forms, including humans, plants, and animals, is magnificent. Different regions of the body are inhabited by different species forms microbes. Such transient interaction microbiome affects organisms concerning their physiological functioning as well disease. Inherent functions body, like immune reactions, developmental pathways, metabolic endocrinological attributes coupled genetics, lifestyle factors, diet, introduction antibiotics in system, other metabolites, play an important integrative role maintenance or loss health. Research indicates that occurrence diverse types health conditions, autoimmune disorders, neurological problems Alzheimer’s, mood alterations, cancer, even social behavior, related to changes population human gastrointestinal tract. This paper highlights effects gut microbiome-metabolome conjugate its effect on a brief reflection modes advanced treatment future research using nanotechnology artificial intelligence.

Язык: Английский

Процитировано

0

Ai-Driven Microbiome-Based Disease Prediction: A Systematic Literature Review DOI

Tallat Jabeen,

Faezeh Karimi, Ali R. Zomorrodi

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Emerging Alternative Therapies: Health-Promoting Microbes DOI
Birbal Singh, Gorakh Mal, Rajkumar Singh Kalra

и другие.

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0

Personalized gut microbial community modeling by leveraging genome-scale metabolic models and metagenomics DOI

L. Li,

Jens Nielsen, Yu Chen

и другие.

Current Opinion in Biotechnology, Год журнала: 2024, Номер 91, С. 103248 - 103248

Опубликована: Дек. 31, 2024

Язык: Английский

Процитировано

0