Robust host source tracking building on the divergent and non-stochastic assembly of gut microbiomes in wild and farmed large yellow croaker

Microbiome, Journal Year: 2022, Volume and Issue: 10(1)

Published: Jan. 26, 2022

Abstract Background Given the lack of genetic background, source tracking unknown individuals fish species with both farmed and wild populations often cannot be robustly achieved. The gut microbiome, which is shaped by deterministic stochastic processes, can serve as a molecular marker host tracking, particularly an alternative to yet-to-be-established marker. A candidate for testing feasibility large yellow croaker, Larimichthys crocea , carnivorous ranks top mariculture in China. Wild resource this was depleted decades ago might have potential problematic estimation because escaping individuals. Results rectums ( n = 212) 79) croakers from multiple batches were collected profiling their bacterial communities. had higher alpha diversity lower load than microbiota two sources exhibited divergence high inter-batch variation, featured dominance Psychrobacter spp. group. Predicted functional capacity microbiome representative isolates showed differences terms source. This difference linked diet between fishes. non-stochastic distribution pattern core supports microbiota-based via machine learning algorithm. random forest classifier based on assembly robust all including newly introduced batch. Conclusions Our study revealed related profiles croakers. For first time, datasets patterns, we verified that applied even fish.

Language: Английский

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Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Annual Review of Biomedical Engineering, Journal Year: 2021, Volume and Issue: 23(1), P. 169 - 201

Published: March 30, 2021

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, biotechnology. The spatial temporal changes in microbiome composition function influenced by a multitude molecular ecological factors. This complexity yields both versatility challenges designing synthetic microbiomes perturbing natural controlled, predictable ways. In this review, we describe factors that give rise emergent properties the meta-omics computational modeling tools can used understand at cellular system levels. We also strategies for engineering enhance or build novel functions. Throughout discuss key knowledge technology gaps elucidating deciphering control points engineering, highlight examples where multiple omics approaches integrated address these gaps.

Language: Английский

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Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 23565 - 23578

Published: Jan. 1, 2021

Colorectal cancer (CRC) is the third most deadly worldwide. The use of gut microbiome in early detection disease has attracted much attention from research community, mainly because its noninvasive nature. Recent achievements next generation sequencing technology have led to increased availability sequence data and enabled an environment for growth research. conventional machine learning algorithms automatic CRC based on limited by factors such as low accuracy need manual selection features. Despite their success other fields, Deep Neural Network (DNN) limitations microbiome-based classification. These include high dimensionality characteristics associated with feature dominance. In this paper, we propose a augmentation approach that aggregates normalization methods extend existing features dataset. proposed method combines extension improve classification performance DNN model. model obtained area under curve (AUC) scores 0.96 0.89 two publicly available datasets.

Language: Английский

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Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment

Environmental Pollution, Journal Year: 2022, Volume and Issue: 305, P. 119257 - 119257

Published: April 6, 2022

Language: Английский

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Fecal Microbiota Transplants for Inflammatory Bowel Disease Treatment: Synthetic- and Engineered Communities-Based Microbiota Transplants Are the Future

Gastroenterology Research and Practice, Journal Year: 2022, Volume and Issue: 2022, P. 1 - 9

Published: Jan. 31, 2022

The human intestine harbors a huge number of diverse microorganisms where variety complex interactions take place between the microbes as well host and gut microbiota. Significant long-term variations in microbiota (dysbiosis) have been associated with health conditions including inflammatory bowel disease (IBD). Conventional fecal transplantations (FMTs) utilized to treat IBD proved promising. However, various limitations such transient results, pathogen transfer, storage, reproducibility render conventional FMT less safe sustainable. Defined synthetic microbial communities (SynCom) used dissect host-microbiota-associated functions using gnotobiotic animals or vitro cell models. This review focuses on potential use SynCom its advantages relative safety over FMT. Additionally, this reinforces how technological advances could be combined better understanding diseases IBD. Some availability gut-on-a-chip system, intestinal organoids, ex vivo cultures, AI-based refining microbiome structural functional data, multiomic approaches may help making more practical models host. an increase cultured diversity from their genomic information would further make design utilization feasible. Taken together, available knowledge recent development defined seem promising, safe, sustainable alternative treating

Language: Английский

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Predicting Personalized Responses to Dietary Fiber Interventions: Opportunities for Modulation of the Gut Microbiome to Improve Health

Annual Review of Food Science and Technology, Journal Year: 2022, Volume and Issue: 14(1), P. 157 - 182

Published: Nov. 29, 2022

Inadequate dietary fiber consumption has become common across industrialized nations, accompanied by changes in gut microbial composition and a dramatic increase chronic metabolic diseases. The human microbiome harbors genes that are required for the digestion of fiber, resulting production end products mediate gastrointestinal systemic benefits to host. Thus, use interventions attracted increasing interest as strategy modulate improve health. However, considerable interindividual differences have resulted variable responses toward interventions. This variability led observed nonresponder individuals highlights need personalized approaches effectively redirect ecosystem. In this review, we summarize strategies used address responder phenomenon propose targeted approach identify predictive features based on knowledge metabolism machine learning approaches.

Language: Английский

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Exploring the role of gut microbiota in advancing personalized medicine

Frontiers in Microbiology, Journal Year: 2023, Volume and Issue: 14

Published: Nov. 30, 2023

Ongoing extensive research in the field of gut microbiota (GM) has highlighted crucial role gut-dwelling microbes human health. These possess 100 times more genes than genome and offer significant biochemical advantages to host nutrient drug absorption, metabolism, excretion. It is increasingly clear that GM modulates efficacy toxicity drugs, especially those taken orally. In addition, intra-individual variability been shown contribute response biases for certain therapeutics. For instance, cyclophosphamide depends on presence Enterococcus hirae Barnesiella intestinihominis intestine. Conversely, inappropriate or unwanted bacteria can inactivate a drug. example, dehydroxylase faecalis Eggerthella lenta A2 metabolize L-dopa before it converts into active form (dopamine) crosses blood-brain barrier treat Parkinson's disease patients. Moreover, emerging as new player personalized medicine, various methods are being developed diseases by remodeling patients' composition, such prebiotic probiotic interventions, transplants, introduction synthetic GM. This review aims highlight how host's improve discuss an bug cause inactivation medicine.

Language: Английский

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Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

Plants, Journal Year: 2023, Volume and Issue: 12(9), P. 1852 - 1852

Published: April 30, 2023

There is increasing interest in harnessing the microbiome to improve cropping systems. With availability of high—throughput and low—cost sequencing technologies, gathering data becoming more routine. However, analysis challenged by size complexity data, incomplete nature many databases. Further, bring value, it often needs be analyzed conjunction with other complex that impact on crop health disease management, such as plant genotype environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs a powerful tool for managing large datasets interplay between microbiome, plants, their environment. In this review, we aim provide readers brief introduction AI techniques, introduce how been applied areas taxonomy, functional annotation sequences, associating community host traits, designing synthetic communities, genomic selection, field phenotyping, forecasting. At end proposed further efforts are required fully exploit power studying phytomicrobiomes.

Language: Английский

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Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Jan. 15, 2024

Abstract Background In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on discovery biomarkers using machine learning tools has produced positive outcomes. Despite promising results, several issues can still be found these such datasets with small number of samples, inconsistent lack uniform processing methodologies, other additional factors lead to reproducibility biomedical research. work, we propose methodology that combines DADA2 pipeline 16s rRNA sequences Recursive Ensemble Feature Selection (REFS) multiple increase obtain robust reliable results Results Three experiments were performed analyzing data from patients/cases Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), Type 2 Diabetes (T2D). each experiment, biomarker signature one dataset applied further validation. The effectiveness proposed was compared feature selection methods K-Best F-score random base line. Area Under Curve (AUC) employed measure diagnostic accuracy used metric comparing methods. Additionally, use Matthews Correlation Coefficient (MCC) evaluate performance well comparison Conclusions We developed reproducible sequence analysis, addressing related dimensionality, validation across independent datasets. findings three experiments, 9 different datasets, show achieved higher This first approach reproducibility, provide results.

Language: Английский

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Design optimization of groundwater circulation well based on numerical simulation and machine learning

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: May 20, 2024

Abstract The optimal design of groundwater circulation wells (GCWs) is challenging. key to purifying using this technique its proficiency and productivity. However, traditional numerical simulation methods are limited by long modeling times, random optimization schemes, results that not comprehensive. To address these issues, study introduced an innovative approach for the a GCW machine learning methods. FloPy package was used create implement MODFLOW MODPATH models. Subsequently, formulated models were employed calculate characteristic indicators effectiveness operation, including radius influence (R) ratio particle recovery (Pr). A detailed collection 3000 datasets, measures operational efficiency elements in learning, meticulously compiled into documents through model execution. trained evaluated multiple linear regression (MLR), artificial neural networks (ANN), support vector machines (SVM). produced three approaches exhibited notable correlations between anticipated outcomes datasets. For circulating well parameters, only improve speed, but also expand scope parameter optimization. Consequently, applied optimize configuration at site Xi’an. scheme R (Q = 293.17 m 3 /d, 6.09 m, L 7.28 m) Pr 300 3.64 1 obtained. combination simulations effective tool optimizing predicting remediation effect.

Language: Английский

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