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Mora-Flores LP, Moreno-Terrazas Casildo R, Fuentes-Cabrera J, Pérez-Vicente HA, de Anda-Jáuregui G, Neri-Torres EE. The Role of Carbohydrate Intake on the Gut Microbiome: A Weight of Evidence Systematic Review. Microorganisms 2023; 11:1728. [PMID: 37512899 PMCID: PMC10385781 DOI: 10.3390/microorganisms11071728] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/12/2023] [Accepted: 06/23/2023] [Indexed: 07/30/2023] Open
Abstract
(1) Background: Carbohydrates are the most important source of nutritional energy for the human body. Carbohydrate digestion, metabolism, and their role in the gut microbiota modulation are the focus of multiple studies. The objective of this weight of evidence systematic review is to investigate the potential relationship between ingested carbohydrates and the gut microbiota composition at different taxonomic levels. (2) Methods: Weight of evidence and information value techniques were used to evaluate the relationship between dietary carbohydrates and the relative abundance of different bacterial taxa in the gut microbiota. (3) Results: The obtained results show that the types of carbohydrates that have a high information value are: soluble fiber with Bacteroides increase, insoluble fiber with Bacteroides and Actinobacteria increase, and Firmicutes decrease. Oligosaccharides with Lactobacillus increase and Enterococcus decrease. Gelatinized starches with Prevotella increase. Starches and resistant starches with Blautia decrease and Firmicutes increase. (4) Conclusions: This work provides, for the first time, an integrative review of the subject by using statistical techniques that have not been previously employed in microbiota reviews.
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Affiliation(s)
- Lorena P Mora-Flores
- Laboratorio de Biopolímeros, Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
| | - Rubén Moreno-Terrazas Casildo
- Laboratorio de Microbiología, Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
| | - José Fuentes-Cabrera
- Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
| | - Hugo Alexer Pérez-Vicente
- Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
| | - Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Ciudad de México 14610, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Ciudad de México 04510, Mexico
- Programa de Cátedras CONACYT, Consejo Nacional de Ciencia y Tecnología, Ciudad de México 03940, Mexico
| | - Elier Ekberg Neri-Torres
- Laboratorio de Biopolímeros, Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
- Laboratorio de Microbiología, Departamento de Ingeniería Química, Industrial y de Alimentos-Universidad Iberoamericana Ciudad de México, Ciudad de México 01219, Mexico
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Martínez MJ, Sabando MV, Soto AJ, Roca C, Requena-Triguero C, Campillo NE, Páez JA, Ponzoni I. Multitask Deep Neural Networks for Ames Mutagenicity Prediction. J Chem Inf Model 2022; 62:6342-6351. [PMID: 36066065 DOI: 10.1021/acs.jcim.2c00532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
The Ames mutagenicity test constitutes the most frequently used assay to estimate the mutagenic potential of drug candidates. While this test employs experimental results using various strains of Salmonella typhimurium, the vast majority of the published in silico models for predicting mutagenicity do not take into account the test results of the individual experiments conducted for each strain. Instead, such QSAR models are generally trained employing overall labels (i.e., mutagenic and nonmutagenic). Recently, neural-based models combined with multitask learning strategies have yielded interesting results in different domains, given their capabilities to model multitarget functions. In this scenario, we propose a novel neural-based QSAR model to predict mutagenicity that leverages experimental results from different strains involved in the Ames test by means of a multitask learning approach. To the best of our knowledge, the modeling strategy hereby proposed has not been applied to model Ames mutagenicity previously. The results yielded by our model surpass those obtained by single-task modeling strategies, such as models that predict the overall Ames label or ensemble models built from individual strains. For reproducibility and accessibility purposes, all source code and datasets used in our experiments are publicly available.
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Affiliation(s)
- María Jimena Martínez
- ISISTAN (CONICET - UNCPBA) Campus Universitario - Paraje Arroyo Seco, 7000, Tandil, Argentina
| | - María Virginia Sabando
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
| | - Axel J Soto
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
| | - Carlos Roca
- CIB Margarita Salas (CSIC) Ramiro de Maeztu, 9. 28740, Madrid, Spain
| | | | - Nuria E Campillo
- CIB Margarita Salas (CSIC) Ramiro de Maeztu, 9. 28740, Madrid, Spain.,Instituto de Ciencias Matemáticas (CSIC), Nicolás Cabrera, no13-15, Campus de Cantoblanco, UAM, CP 28049, Madrid, Spain
| | - Juan A Páez
- Instituto de Química Médica. Consejo Superior de Investigaciones Científicas (CSIC), Juan de la Cierva 3, 28006, Madrid, Spain
| | - Ignacio Ponzoni
- Institute for Computer Science and Engineering, UNS-CONICET, 8000, Bahía Blanca, Argentina.,Department of Computer Science and Engineering, Universidad Nacional del Sur, 8000, Bahía Blanca, Argentina
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Kim KB, Kwack SJ, Lee JY, Kacew S, Lee BM. Current opinion on risk assessment of cosmetics. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART B, CRITICAL REVIEWS 2021; 24:137-161. [PMID: 33832410 DOI: 10.1080/10937404.2021.1907264] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Risk assessment of cosmetic ingredients is a useful scientific method to characterize potential adverse effects resulting from using cosmetics. The process of risk assessment consists of four steps: hazard identification, dose-response assessment, exposure assessment, and risk characterization. Hazard identification of chemicals refers to the initial stage of risk assessment and generally utilizes animal studies to evaluate toxicity. Since 2013, however, toxicity studies of cosmetic ingredients using animals have not been permitted in the EU and alternative toxicity test methods for animal studies have momentum to be developed for cosmetic ingredients. In this paper, we briefly review the alternative test methods that are available for cosmetic ingredients including read-across, in silico, in chemico, and invitro methods. In addition, new technologies such as omics and artificial intelligence (AI) have been discussed to expand or improve the knowledge and hazard identification of cosmetic ingredients. Aggregate exposure of cosmetic ingredients is another safety issue and methods for its improvement were reviewed. There have been concerns over the safety of nano-cosmetics for a long time, but the risk of nano-cosmetics remains unclear. Therefore, current issues of cosmetic risk assessment are discussed and expert opinion will be provided for the safety of cosmetics.
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Affiliation(s)
- Kyu-Bong Kim
- College of Pharmacy, Dankook University, Cheonan, Chungnam, South Korea
| | - Seung Jun Kwack
- Department of Bio Health Science, College of Natural Science, Changwon National University, Changwon, Gyeongnam, Suwon, Gyeonggi-Do, South Korea
| | - Joo Young Lee
- College of Pharmacy, The Catholic University of Korea, Bucheon, South Korea
| | - Sam Kacew
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, ON, Canada
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, Republic of Korea
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