Modulation of the Neuro–Cancer Connection by Metabolites of Gut Microbiota

Biomolecules, Journal Year: 2025, Volume and Issue: 15(2), P. 270 - 270

Published: Feb. 12, 2025

The gut-brain-cancer axis represents a novel and intricate connection between the gut microbiota, neurobiology, cancer progression. Recent advances have accentuated significant role of microbiota metabolites in modulating systemic processes that influence both brain health tumorigenesis. This paper explores emerging concept metabolite-mediated modulation within connection, focusing on key such as short-chain fatty acids (SCFAs), tryptophan derivatives, secondary bile acids, lipopolysaccharides (LPS). While microbiota's impact immune regulation, neuroinflammation, tumor development is well established, gaps remain grasping how specific contribute to neuro-cancer interactions. We discuss with potential implications for neurobiology cancer, indoles polyamines, which yet be extensively studied. Furthermore, we review preclinical clinical evidence linking dysbiosis, altered metabolite profiles, tumors, showcasing limitations research gaps, particularly human longitudinal studies. Case studies investigating microbiota-based interventions, including dietary changes, fecal transplantation, probiotics, demonstrate promise but also indicate hurdles translating these findings therapies. concludes call standardized multi-omics approaches bi-directional frameworks integrating microbiome, neuroscience, oncology develop personalized therapeutic strategies patients.

Language: Английский

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The impact of perinatal maternal stress on the maternal and infant gut and human milk microbiomes: A scoping review

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(2), P. e0318237 - e0318237

Published: Feb. 28, 2025

Background Perinatal maternal stress, which includes both psychological and physiological stress experienced by healthy women during pregnancy the postpartum period, is becoming increasingly prevalent. Infant early exposure to adverse environments such as perinatal has been shown increase long-term risk metabolic, immunologic neurobehavioral disorders. Evidence suggests that human microbiome facilitates transmission of factors infants via vaginal, gut, milk microbiomes. The colonization aberrant microorganisms in mother’s microbiome, influenced microbiome-brain-gut axis, may be transferred a critical developmental period. This transfer predispose more inflammatory-prone associated with dysregulated metabolic process leading health outcomes. Given prevalence potential impact on infant health, no systematic mapping or review data date, aim this scoping gather evidence relationship between milk, maternal, gut Methods an exploratory review, guided Joanna Briggs Institute’s methodology along use Prisma Scr reporting guideline. A comprehensive search was conducted using following databases, CINAHL Complete; MEDLINE; PsycINFO, Web Science Scopus protocol registered Open Framework DOI 10.17605/OSF.IO/5SRMV. Results After screening 1145 papers there were 7 paper met inclusion criteria. Statistically significant associations found five studies identify higher abundance potentially pathogenic bacteria Erwinia, Serratia, T mayombie, Bacteroides lower levels linked beneficial Lactococcus, Lactobacillus, Akkermansia. However, one study presents conflicting results where it reported bacteria. Conclusion does have alteration diversity influential however, can affect colonisation different ways. These bacterial changes capacity influence long term disease. analyses collection tools methods, offers reasons for these findings well suggestions future research.

Language: Английский

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Microbial network inference for longitudinal microbiome studies with LUPINE

Microbiome, Journal Year: 2025, Volume and Issue: 13(1)

Published: March 3, 2025

Abstract Background The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal studies are becoming increasingly popular. These enable researchers to infer associations towards the understanding coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, limited due data characteristics (sparse, compositional, multivariate). Several network inference methods have proposed, but largely unexplored in setting. Results We introduce LUPINE (LongitUdinal modelling with Partial least squares regression NEtwork inference), novel approach leverages on conditional independence low-dimensional representation. This method specifically designed handle scenarios small sample sizes number time points. first its kind microbial networks time, while considering information from all past points thus able capture dynamic interactions evolve over validate variant, LUPINE_single (for single point analysis) simulated four case studies, where we highlight LUPINE’s ability identify relevant each study context, different experimental designs (mouse human or without interventions, short long courses). To detect changes groups response external disturbances, used compare inferred networks. Conclusions simple yet innovative methodology suitable for, not to, analysing data. R code publicly available readers interested applying these new their

Language: Английский

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Influence of Microbiome Interactions on Antibiotic Resistance Development in the ICU Environment: Insights and Opportunities with Machine Learning

Deleted Journal, Journal Year: 2025, Volume and Issue: 70(2), P. 14 - 14

Published: April 9, 2025

Antibiotic resistance is a global health crisis exacerbated by the misuse of antibiotics in healthcare, agriculture, and environment. In an intensive care unit (ICU), where high antibiotic usage, invasive procedures, immunocompromised patients converge, risks are amplified, leading to multidrug-resistant organisms (MDROs) poor patient outcomes. The human microbiome plays crucial role development dissemination genes (ARGs) through mechanisms like horizontal gene transfer, biofilm formation, quorum sensing. Disruptions balance, or dysbiosis, further exacerbate resistance, particularly high-risk ICU environments. This study explores interactions ICU, highlighting machine learning (ML) as transformative tool. Machine algorithms analyze high-dimensional data, predict patterns, identify novel therapeutic targets. By integrating genomic, microbiome, clinical these models support personalized treatment strategies enhance infection control measures. results demonstrate potential improve stewardship outcomes, emphasizing its utility ICU-specific interventions. conclusion, addressing requires multidisciplinary approach combining advanced computational methods, research, expertise. Enhanced surveillance, targeted interventions, collaboration essential mitigate care.

Language: Английский

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From the Gut to the Brain: Is Microbiota a New Paradigm in Parkinson’s Disease Treatment?

Cells, Journal Year: 2024, Volume and Issue: 13(9), P. 770 - 770

Published: April 30, 2024

Parkinson’s disease (PD) is recognized as the second most prevalent primary chronic neurodegenerative disorder of central nervous system. Clinically, PD characterized a movement disorder, exhibiting an incidence and mortality rate that increasing faster than any other neurological condition. In recent years, there has been growing interest concerning role gut microbiota in etiology pathophysiology PD. The establishment brain–gut axis now real, with evidence denoting bidirectional communication between brain through metabolic, immune, neuronal, endocrine mechanisms pathways. Among these, vagus nerve represents direct form gut. Given potential interactions bacteria drugs, it observed therapies for can have impact on composition microbiota. Therefore, scope present review, we will discuss current understanding whether this may be new paradigm treating devastating disease.

Language: Английский

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From the Gut to the Brain: Is Microbiome a New Paradigm in Parkinson’s Disease Treatment?

Published: April 19, 2024

Parkinson's disease (PD) is recognized as the second most prevalent primary chronic neurodegenerative disorder of central nervous system. Clinically, PD characterized a movement disorder, exhibiting an incidence and mortality rate that increasing faster than any other neurological condition. In recent years, there has been growing interest concerning role gut microbiome in etiology pathophysiology PD. The establishment brain-gut axis now real, with evidence denoting bidirectional communication between brain microbiota through metabolic, immune, neuronal, endocrine mechanisms pathways. Among these, vagus nerve represents direct form gut. Given potential interactions bacteria drugs, it observed therapies for can have impact on composition microbiome. Therefore, scope present review, we will discuss current understanding whether this may be new paradigm treating devastating disease.

Language: Английский

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LP-Micro Offers Interpretable Disease Outcome Prediction by Leveraging Microbial Biomarkers and Their Time-Varying Effects

Published: Oct. 22, 2024

Information generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions diseases. Identifying biomarkers their time-varying effects can not only advance our understanding pathogenetic mechanisms, but also facilitate early diagnosis guide optimal timing interventions. However, longitudinal predictive modeling highly noisy dynamic (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust interpretable machine-learning-based microbiome analysis framework, LP-Micro, that encompasses: (i) feature screening via polynomial group lasso, (ii) disease outcome prediction implemented machine learning methods XGBoost, deep neural networks), (iii) association testing between time points, features, outcomes permutation importance. We demonstrate in simulations LP-Micro identify incident disease-related taxa offers improved accuracy compared existing approaches. Applications two studies with clinical childhood dental weight loss following bariatric surgery yield consistently high accuracy. The identified critical points are informative aligned expectations.

Language: Английский

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Oral Microbiome Research in Biopsy Samples of Oral Potentially Malignant Disorders and Oral Squamous Cell Carcinoma and Its Challenges

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(23), P. 11405 - 11405

Published: Dec. 7, 2024

This study aims to evaluate the potential benefits and challenges of integrating oral microbiome research into clinical management potentially malignant disorders (OPMD) squamous cell carcinoma (OSCC). The has gained significant attention for its role in pathogenesis progression these conditions, with emerging evidence suggesting value as a diagnostic prognostic tool. By critically analyzing current methodological considerations, this manuscript examines whether analysis biopsy samples can aid early detection, prognosis, OPMD OSCC. complexity dynamic nature require multifaceted approach fully understand utility. Based on review, we conclude that studying context holds promise but also faces notable challenges, including variability need standardization. Ultimately, addresses question, “Should such be undertaken, given intricate interactions various factors inherent obstacles involved?”, emphasizes importance further optimize applications improve patient outcomes.

Language: Английский

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Unlocking the secrets of the human gut microbiota: Comprehensive review on its role in different diseases

World Journal of Gastroenterology, Journal Year: 2024, Volume and Issue: 31(5)

Published: Dec. 30, 2024

The human gut microbiota, a complex and diverse community of microorganisms, plays crucial role in maintaining overall health by influencing various physiological processes, including digestion, immune function, disease susceptibility. balance between beneficial harmful bacteria is essential for health, with dysbiosis - disruption this linked to numerous conditions such as metabolic disorders, autoimmune diseases, cancers. This review highlights key genera Enterococcus, Ruminococcus, Bacteroides, Bifidobacterium, Escherichia coli, Akkermansia muciniphila, Firmicutes (including Clostridium Lactobacillus), Roseburia due their well-established roles regulation but other bacteria, Clostridioides difficile, Salmonella, Helicobacter pylori, Fusobacterium nucleatum, are also implicated diseases. Pathogenic coli Bacteroides fragilis, contribute inflammation cancer progression disrupting responses damaging tissues. potential microbiota-based therapies, probiotics, prebiotics, fecal microbiota transplantation, dietary interventions, improve outcomes examined. Future research directions the integration multi-omics, impact diet lifestyle on composition, advancing engineering techniques discussed. Understanding microbiota's formulating personalized, efficacious treatments preventive strategies, thereby enhancing progressing microbiome research.

Language: Английский

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Microbial network inference for longitudinal microbiome studies with LUPINE

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

Published: May 10, 2024

Abstract The microbiome is a complex ecosystem of interdependent taxa that has traditionally been studied through cross-sectional studies. However, longitudinal studies are becoming increasingly popular. These enable researchers to infer associations towards the understanding coexistence, competition, and collaboration between microbes across time. Traditional metrics for association analysis, such as correlation, limited due data characteristics (sparse, compositional, multivariate). Several network inference methods have proposed, but largely unexplored in setting. We introduce LUPINE (LongitUdinal modelling with Partial least squares regression NEtwork inference), novel approach leverages on conditional independence low-dimensional representation. This method specifically designed handle scenarios small sample sizes number time points. first its kind microbial networks time, while considering information from all past points thus able capture dynamic interactions evolve over validate variant, single (for point analysis) simulated four case studies, where we highlight LUPINE’s ability identify relevant each study context, different experimental designs (mouse human or without interventions, short long courses). propose compare inferred detect changes groups response external disturbances. simple yet innovative methodology suitable for, not to, analysing data. R code publicly available readers interested applying these new their

Language: Английский

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