Ecology of the respiratory tract microbiome

Trends in Microbiology, Год журнала: 2023, Номер 31(9), С. 972 - 984

Опубликована: Май 10, 2023

Язык: Английский

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A robust microbiome signature for autism spectrum disorder across different studies using machine learning

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 8, 2024

Abstract Autism spectrum disorder (ASD) is a highly complex neurodevelopmental characterized by deficits in sociability and repetitive behaviour, however there great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as plausible contributing factor for ASD development individuals diagnosed with often suffer from intestinal problems show differentiated microbial composition. Nevertheless, studies rarely agree on the specific bacterial taxa involved this disorder. Regarding potential role of pathophysiology, our aim to investigate whether set relevant classification using sibling-controlled dataset. Additionally, we validate these results across two independent cohorts several confounding factors, such lifestyle, influence both studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied 16S rRNA gene sequencing data 117 subjects (60 cases 57 siblings) identifying 26 discriminate controls. The average area under curve (AUC) bacteria dataset 81.6%. Moreover, selected tenfold cross-validation scheme (a total 223 samples—125 98 controls). We obtained AUCs 74.8% 74%, respectively. Analysis REFS identified can be used predict status children three distinct AUC over 80% best-performing classifiers. Our indicate strong association should not disregarded target therapeutic interventions. Furthermore, work contribute use proposed approach signatures datasets.

Язык: Английский

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Deciphering the gut microbiome: The revolution of artificial intelligence in microbiota analysis and intervention

Current Research in Biotechnology, Год журнала: 2024, Номер 7, С. 100211 - 100211

Опубликована: Янв. 1, 2024

The human gut microbiome is an intricate ecosystem with profound implications for host metabolism, immune function, and neuroendocrine activity. Over the years, studies have strived to decode this microbial universe, especially its interactions health underlying metabolic processes. Traditional analyses often struggle complex interplay within due presumptions of independence. In response, machine learning (ML) deep (DL) provide advanced multivariate non-linear analytical tools that adeptly capture microbiota. With influx data from metagenomic next-generation sequencing (mNGS), there's increasing reliance on these artificial intelligence (AI) subsets derive actionable insights. This review delves into cutting-edge ML techniques tailored microbiota research. It further underscores potential in shaping clinical diagnostics, prognosis, intervention strategies, pointing a future where computational methods bridge gap between knowledge targeted interventions.

Язык: Английский

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Deep learning methods in metagenomics: a review

Microbial Genomics, Год журнала: 2024, Номер 10(4)

Опубликована: Апрель 17, 2024

The ever-decreasing cost of sequencing and the growing potential applications metagenomics have led to an unprecedented surge in data generation. One most prevalent is study microbial environments, such as human gut. gut microbiome plays a crucial role health, providing vital information for patient diagnosis prognosis. However, analysing metagenomic remains challenging due several factors, including reference catalogues, sparsity compositionality. Deep learning (DL) enables novel promising approaches that complement state-of-the-art pipelines. DL-based methods can address almost all aspects analysis, pathogen detection, sequence classification, stratification disease prediction. Beyond generating predictive models, key aspect these also their interpretability. This article reviews DL metagenomics, convolutional networks, autoencoders attention-based models. These aggregate contextualized pave way improved care better understanding microbiome's our health.

Язык: Английский

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Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data

Ecotoxicology and Environmental Safety, Год журнала: 2025, Номер 291, С. 117609 - 117609

Опубликована: Фев. 1, 2025

Groundwater pollution, particularly in retired pesticide sites, is a significant environmental concern due to the presence of chlorinated aliphatic hydrocarbons (CAHs) and benzene, toluene, ethylbenzene, xylene (BTEX). These contaminants pose serious risks ecosystems human health. Natural attenuation (NA) has emerged as sustainable solution, with microorganisms playing crucial role pollutant biodegradation. However, interpretation diverse microbial communities relation complex pollutants still challenging, there limited research multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key indicators for different pollution types (CAHs, BTEX plumes, mixed plumes). The accuracy Area Under Curve (AUC) achieved by Support Vector Machines (SVM) were impressive, values 0.87 0.99, respectively. With assistance model explanation methods, we identified bioindicators which then analyzed using co-occurrence network analysis better understand their potential roles degradation. genera indicate that oxidation co-metabolism predominantly drive dechlorination processes within CAHs group. In group, primary mechanism degradation was observed be anaerobic under sulfate-reducing conditions. CAHs&BTEX groups, indicative suggested occurred iron-reducing conditions reductive existed. Overall, this study establishes framework harnessing power ML alongside based on microbiome data enhance understanding provide robust assessment natural process at sites.

Язык: Английский

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Synergizing Artificial Intelligence and Probiotics: A Comprehensive Review of Emerging Applications in Health Promotion and Industrial Innovation

Trends in Food Science & Technology, Год журнала: 2025, Номер unknown, С. 104938 - 104938

Опубликована: Фев. 1, 2025

Язык: Английский

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Disentangling microbial interaction networks

Trends in Microbiology, Год журнала: 2025, Номер unknown

Опубликована: Март 1, 2025

The structure and function of microbial communities is shaped by intricate ecological interactions amongst the constituent microorganisms. Thus, a mechanistic understanding emergent community-level functions requires knowledge on how architecture underlying interaction networks affects these properties. To address this, researchers employ different sequencing-based experimental approaches to infer topology given network. However, it remains generally unclear which method best suited for quantifying critical network parameters. Here we provide comparative overview serving this purpose, with particular emphasis their strengths weaknesses. In way, our work can help guide design studies that aim at unraveling structure-function relationships in communities.

Язык: Английский

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Perspective on the development of synthetic microbial community (SynCom) biosensors

Trends in biotechnology, Год журнала: 2023, Номер 41(10), С. 1227 - 1236

Опубликована: Май 13, 2023

Язык: Английский

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Targeting the gut microbiome to control drug pharmacomicrobiomics: the next frontier in oral drug delivery

Expert Opinion on Drug Delivery, Год журнала: 2023, Номер 20(10), С. 1315 - 1331

Опубликована: Июль 5, 2023

Introduction The trillions of microorganisms that comprise the gut microbiome form dynamic bidirectional interactions with orally administered drugs and host health. These relationships can alter all aspects drug pharmacokinetics pharmacodynamics (PK/PD); thus, there is a desire to control these maximize therapeutic efficacy. Attempts modulate drug-gut have spurred advancements within field 'pharmacomicrobiomics' are poised become next frontier oral delivery.Areas covered This review details exist between microbiome, clinically relevant case examples outlining clear motive for controlling pharmacomicrobiomic interactions. Specific focus attributed novel advanced strategies demonstrated success in mediating interactions.Expert opinion Co-administration gut-active supplements (e.g. pro- pre-biotics), innovative delivery vehicles, strategic polypharmacy serve as most promising viable approaches Targeting through presents new opportunities improving efficacy by precisely PK/PD, while mitigating metabolic disturbances caused drug-induced dysbiosis. However, successfully translating preclinical potential into clinical outcomes relies on overcoming key challenges related interindividual variability composition study design parameters.

Язык: Английский

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Unraveling plant–microbe interactions: can integrated omics approaches offer concrete answers?

Journal of Experimental Botany, Год журнала: 2023, Номер 75(5), С. 1289 - 1313

Опубликована: Ноя. 9, 2023

Abstract Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help deeper insights into plant–microbe interactions a concerted way. Integrating multi-omics datasets transform our understanding of the microbiome from unspecified genetic influences interacting species specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies crop research review key aspects integration host microbial omics-based be used comprehensive outline complex crop–microbe We describe these technological advances have helped unravel crucial genes pathways that control beneficial, pathogenic, commensal identify knowledge gaps synthesize current limitations approaches. studies which multi-omics-based approaches led improved models community structure function. Finally, recommend holistic integrating achieve precision efficiency data analysis, for biotic abiotic stress contribution microbiota shaping fitness.

Язык: Английский

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