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Lee S, Tejesvi MV, Hurskainen E, Aasmets O, Plaza-Díaz J, Franks S, Morin-Papunen L, Tapanainen JS, Ruuska TS, Altmäe S, Org E, Salumets A, Arffman RK, Piltonen TT. Gut bacteriome and mood disorders in women with PCOS. Hum Reprod 2024; 39:1291-1302. [PMID: 38614956 PMCID: PMC11145006 DOI: 10.1093/humrep/deae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/19/2024] [Indexed: 04/15/2024] Open
Abstract
STUDY QUESTION How does the gut bacteriome differ based on mood disorders (MDs) in women with polycystic ovary syndrome (PCOS), and how can the gut bacteriome contribute to the associations between these two conditions? SUMMARY ANSWER Women with PCOS who also have MDs exhibited a distinct gut bacteriome with reduced alpha diversity and a significantly lower abundance of Butyricicoccus compared to women with PCOS but without MDs. WHAT IS KNOWN ALREADY Women with PCOS have a 4- to 5-fold higher risk of having MDs compared to women without PCOS. The gut bacteriome has been suggested to influence the pathophysiology of both PCOS and MDs. STUDY DESIGN, SIZE, DURATION This population-based cohort study was derived from the Northern Finland Birth Cohort 1966 (NFBC1966), which includes all women born in Northern Finland in 1966. Women with PCOS who donated a stool sample at age 46 years (n = 102) and two BMI-matched controls for each case (n = 205), who also responded properly to the MD criteria scales, were included. PARTICIPANTS/MATERIALS, SETTING, METHODS A total of 102 women with PCOS and 205 age- and BMI-matched women without PCOS were included. Based on the validated MD criteria, the subjects were categorized into MD or no-MD groups, resulting in the following subgroups: PCOS no-MD (n = 84), PCOS MD (n = 18), control no-MD (n = 180), and control MD (n = 25). Clinical characteristics were assessed at age 31 years and age 46 years, and stool samples were collected from the women at age 46 years, followed by the gut bacteriome analysis using 16 s rRNA sequencing. Alpha diversity was assessed using observed features and Shannon's index, with a focus on genera, and beta diversity was characterized using principal components analysis (PCA) with Bray-Curtis Dissimilarity at the genus level. Associations between the gut bacteriome and PCOS-related clinical features were explored by Spearman's correlation coefficient. A P-value for multiple testing was adjusted with the Benjamini-Hochberg false discovery rate (FDR) method. MAIN RESULTS AND THE ROLE OF CHANCE We observed changes in the gut bacteriome associated with MDs, irrespective of whether the women also had PCOS. Similarly, PCOS MD cases showed a lower alpha diversity (Observed feature, PCOS no-MD, median 272; PCOS MD, median 208, FDR = 0.01; Shannon, PCOS no-MD, median 5.95; PCOS MD, median 5.57, FDR = 0.01) but also a lower abundance of Butyricicoccus (log-fold changeAnalysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC)=-0.90, FDRANCOM-BC=0.04) compared to PCOS no-MD cases. In contrast, in the controls, the gut bacteriome did not differ based on MDs. Furthermore, in the PCOS group, Sutterella showed positive correlations with PCOS-related clinical parameters linked to obesity (BMI, r2=0.31, FDR = 0.01; waist circumference, r2=0.29, FDR = 0.02), glucose metabolism (fasting glucose, r2=0.46, FDR < 0.001; fasting insulin, r2=0.24, FDR = 0.05), and gut barrier integrity (zonulin, r2=0.25, FDR = 0.03). LIMITATIONS, REASONS FOR CAUTION Although this was the first study to assess the link between the gut bacteriome and MDs in PCOS and included the largest PCOS dataset for the gut microbiome analysis, the number of subjects stratified by the presence of MDs was limited when contrasted with previous studies that focused on MDs in a non-selected population. WIDER IMPLICATIONS OF THE FINDINGS The main finding is that gut bacteriome is associated with MDs irrespective of the PCOS status, but PCOS may also modulate further the connection between the gut bacteriome and MDs. STUDY FUNDING/COMPETING INTEREST(S) This research was funded by the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement (MATER, No. 813707), the Academy of Finland (project grants 315921, 321763, 336449), the Sigrid Jusélius Foundation, Novo Nordisk Foundation (NNF21OC0070372), grant numbers PID2021-12728OB-100 (Endo-Map) and CNS2022-135999 (ROSY) funded by MCIN/AEI/10.13039/501100011033 and ERFD A Way of Making Europe. The study was also supported by EU QLG1-CT-2000-01643 (EUROBLCS) (E51560), NorFA (731, 20056, 30167), USA/NIH 2000 G DF682 (50945), the Estonian Research Council (PRG1076, PRG1414), EMBO Installation (3573), and Horizon 2020 Innovation Grant (ERIN, No. EU952516). The funders did not participate in any process of the study. We have no conflicts of interest to declare. TRIAL REGISTRATION NUMBER N/A.
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Affiliation(s)
- S Lee
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
| | - M V Tejesvi
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
- Ecology and Genetics, University of Oulu, Oulu, Finland
| | - E Hurskainen
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - O Aasmets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - J Plaza-Díaz
- Faculty of Pharmacy, Department of Biochemistry and Molecular Biology II, University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Canada
| | - S Franks
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - L Morin-Papunen
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - J S Tapanainen
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Obstetrics and Gynaecology, HFR—Cantonal Hospital of and University of Fribourg, Fribourg, Switzerland
| | - T S Ruuska
- Research Unit of Clinical Medicine, University of Oulu, Oulu, Finland
- Department of Pediatrics and Adolescent Medicine and Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - S Altmäe
- Faculty of Pharmacy, Department of Biochemistry and Molecular Biology II, University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
| | - E Org
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - A Salumets
- Department of Obstetrics and Gynaecology, Institute of Clinical Medicine, University of Tartu, Tartu, Estonia
- Division of Obstetrics and Gynaecology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Competence Centre on Health Technologies, Tartu, Estonia
| | - R K Arffman
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - T T Piltonen
- Department of Obstetrics and Gynecology, Research Unit of Clinical Medicine, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
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Liu K, Geng S, Shen P, Zhao L, Zhou P, Liu W. Development and application of a machine learning-based predictive model for obstructive sleep apnea screening. Front Big Data 2024; 7:1353469. [PMID: 38817683 PMCID: PMC11137315 DOI: 10.3389/fdata.2024.1353469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/29/2024] [Indexed: 06/01/2024] Open
Abstract
Objective To develop a robust machine learning prediction model for the automatic screening and diagnosis of obstructive sleep apnea (OSA) using five advanced algorithms, namely Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) to provide substantial support for early clinical diagnosis and intervention. Methods We conducted a retrospective analysis of clinical data from 439 patients who underwent polysomnography at the Affiliated Hospital of Xuzhou Medical University between October 2019 and October 2022. Predictor variables such as demographic information [age, sex, height, weight, body mass index (BMI)], medical history, and Epworth Sleepiness Scale (ESS) were used. Univariate analysis was used to identify variables with significant differences, and the dataset was then divided into training and validation sets in a 4:1 ratio. The training set was established to predict OSA severity grading. The validation set was used to assess model performance using the area under the curve (AUC). Additionally, a separate analysis was conducted, categorizing the normal population as one group and patients with moderate-to-severe OSA as another. The same univariate analysis was applied, and the dataset was divided into training and validation sets in a 4:1 ratio. The training set was used to build a prediction model for screening moderate-to-severe OSA, while the validation set was used to verify the model's performance. Results Among the four groups, the LightGBM model outperformed others, with the top five feature importance rankings of ESS total score, BMI, sex, hypertension, and gastroesophageal reflux (GERD), where Age, ESS total score and BMI played the most significant roles. In the dichotomous model, RF is the best performer of the five models respectively. The top five ranked feature importance of the best-performing RF models were ESS total score, BMI, GERD, age and Dry mouth, with ESS total score and BMI being particularly pivotal. Conclusion Machine learning-based prediction models for OSA disease grading and screening prove instrumental in the early identification of patients with moderate-to-severe OSA, revealing pertinent risk factors and facilitating timely interventions to counter pathological changes induced by OSA. Notably, ESS total score and BMI emerge as the most critical features for predicting OSA, emphasizing their significance in clinical assessments. The dataset will be publicly available on my Github.
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Affiliation(s)
- Kang Liu
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Ping Shen
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Lei Zhao
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Peng Zhou
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Wen Liu
- Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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Shtossel O, Finkelstein S, Louzoun Y. mi-Mic: a novel multi-layer statistical test for microbiota-disease associations. Genome Biol 2024; 25:113. [PMID: 38693546 PMCID: PMC11064322 DOI: 10.1186/s13059-024-03256-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/22/2024] [Indexed: 05/03/2024] Open
Abstract
mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.
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Affiliation(s)
- Oshrit Shtossel
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Shani Finkelstein
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel
| | - Yoram Louzoun
- Department of Mathematics, Bar-Ilan University, Ramat Gan, 52900, Israel.
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Baddal B, Taner F, Uzun Ozsahin D. Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review. Diagnostics (Basel) 2024; 14:484. [PMID: 38472956 DOI: 10.3390/diagnostics14050484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/23/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.
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Affiliation(s)
- Buket Baddal
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Ferdiye Taner
- Department of Medical Microbiology and Clinical Microbiology, Faculty of Medicine, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
- DESAM Research Institute, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
| | - Dilber Uzun Ozsahin
- Department of Medical Diagnostic Imaging, College of Health Science, University of Sharjah, Sharjah 27272, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
- Operational Research Centre in Healthcare, Near East University, North Cyprus, Mersin 10, 99138 Nicosia, Turkey
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Rojas-Velazquez D, Kidwai S, Kraneveld AD, Tonda A, Oberski D, Garssen J, Lopez-Rincon A. Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinformatics 2024; 25:26. [PMID: 38225565 PMCID: PMC10789030 DOI: 10.1186/s12859-024-05639-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 01/04/2024] [Indexed: 01/17/2024] Open
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 the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research. RESULTS Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods. CONCLUSIONS We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.
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Affiliation(s)
- David Rojas-Velazquez
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht, Utrecht, The Netherlands.
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Sarah Kidwai
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht, Utrecht, The Netherlands
| | - Aletta D Kraneveld
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht, Utrecht, The Netherlands
- Department of Neuroscience, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alberto Tonda
- UMR 518 MIA - PS, INRAE, Institut des Systèmes Complexes de Paris, Île - de - France (ISC-PIF) - UAR 3611 CNRS, Université Paris-Saclay, Paris, France
| | - Daniel Oberski
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johan Garssen
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht, Utrecht, The Netherlands
- Global Centre of Excellence Immunology, Danone Nutricia Research, Utrecht, The Netherlands
| | - Alejandro Lopez-Rincon
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, University of Utrecht, Utrecht, The Netherlands
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Afonso CL, Afonso AM. Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples. Vet Sci 2023; 10:690. [PMID: 38133241 PMCID: PMC10747646 DOI: 10.3390/vetsci10120690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/24/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.
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Huang G, Khan R, Zheng Y, Lee PC, Li Q, Khan I. Exploring the role of gut microbiota in advancing personalized medicine. Front Microbiol 2023; 14:1274925. [PMID: 38098666 PMCID: PMC10720646 DOI: 10.3389/fmicb.2023.1274925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/15/2023] [Indexed: 12/17/2023] Open
Abstract
Ongoing extensive research in the field of gut microbiota (GM) has highlighted the crucial role of gut-dwelling microbes in human health. These microbes possess 100 times more genes than the human genome and offer significant biochemical advantages to the host in nutrient and drug absorption, metabolism, and excretion. It is increasingly clear that GM modulates the efficacy and toxicity of drugs, especially those taken orally. In addition, intra-individual variability of GM has been shown to contribute to drug response biases for certain therapeutics. For instance, the efficacy of cyclophosphamide depends on the presence of Enterococcus hirae and Barnesiella intestinihominis in the host intestine. Conversely, the presence of inappropriate or unwanted gut bacteria can inactivate a drug. For example, dehydroxylase of Enterococcus faecalis and Eggerthella lenta A2 can metabolize L-dopa before it converts into the active form (dopamine) and crosses the blood-brain barrier to treat Parkinson's disease patients. Moreover, GM is emerging as a new player in personalized medicine, and various methods are being developed to treat diseases by remodeling patients' GM composition, such as prebiotic and probiotic interventions, microbiota transplants, and the introduction of synthetic GM. This review aims to highlight how the host's GM can improve drug efficacy and discuss how an unwanted bug can cause the inactivation of medicine.
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Affiliation(s)
- Gouxin Huang
- Clinical Research Center, Shantou Central Hospital, Shantou, China
| | - Raees Khan
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Yilin Zheng
- Clinical Research Center, Shantou Central Hospital, Shantou, China
| | - Ping-Chin Lee
- Biotechnology Research Institute, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
- Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
| | - Qingnan Li
- Clinical Research Center, Shantou Central Hospital, Shantou, China
- Department of Pharmacy, Shantou Central Hospital, Shantou, China
| | - Imran Khan
- Department of Biotechnology, Faculty of Chemical and Life Sciences, Abdul Wali Khan University Mardan, Mardan, Pakistan
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Willemsen Y, Ou Y, Belzer C, Arias Vásquez A, Smidt H, Beijers R, de Weerth C. A longitudinal study of the gut microbiota during the first three years of life: Links with problem behavior and executive functions at preschool age. Dev Psychopathol 2023:1-17. [PMID: 37994488 DOI: 10.1017/s0954579423001402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Early life is a sensitive period when microbiota-gut-brain interactions may have important impact on development. This study investigated the associations of the gut microbiota in the first three years of life (two, six, and 12 weeks, and one and three years) with problem behavior and executive functions in N = 64 three-year-old children. Higher relative abundance of Streptococcus at the age of two weeks, as well as its trajectory over time (including ages two, six and 12 weeks, and one and three years), was related to worse executive functions. Higher relative abundance of [Ruminococcus] torques group at the age of three years, as well as its trajectory from one to three years, was associated with less internalizing behavior. Besides, several robust age-specific associations were identified: higher Bifidobacterium relative abundance (age three years) was associated with more internalizing and externalizing issues; higher Blautia relative abundance (age three years) was linked to less internalizing behavior; and increased relative abundance of an unidentified Enterobacteriaceae genus (age two weeks) was related to more externalizing behavior. Our findings provide important longitudinal evidence that early-life gut microbiota may be linked to behavioral and cognitive development in low-risk children.
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Affiliation(s)
- Yvonne Willemsen
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yangwenshan Ou
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Clara Belzer
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Alejandro Arias Vásquez
- Department of Psychiatry and Human Genetics, Donders Center for Medical Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hauke Smidt
- Laboratory of Microbiology, Wageningen University & Research, Wageningen, The Netherlands
| | - Roseriet Beijers
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- Behavioral Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Carolina de Weerth
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
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Cambon MC, Trillat M, Lesur-Kupin I, Burlett R, Chancerel E, Guichoux E, Piouceau L, Castagneyrol B, Le Provost G, Robin S, Ritter Y, Van Halder I, Delzon S, Bohan DA, Vacher C. Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems. Mol Ecol 2023; 32:5944-5958. [PMID: 37815414 DOI: 10.1111/mec.17149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 09/14/2023] [Accepted: 09/19/2023] [Indexed: 10/11/2023]
Abstract
Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.
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Affiliation(s)
- Marine C Cambon
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- School of Natural Sciences, Bangor University, Bangor, UK
| | | | - Isabelle Lesur-Kupin
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
- HelixVenture, Mérignac, France
| | - Régis Burlett
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | | | | | | | | | - Yves Ritter
- INRAE, University of Bordeaux, BIOGECO, Pessac, France
| | | | | | - David A Bohan
- Agroécologie, INRAE, Université Bourgogne Franche-Comté, Dijon, France
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10
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Feng J, Yang K, Liu X, Song M, Zhan P, Zhang M, Chen J, Liu J. Machine learning: a powerful tool for identifying key microbial agents associated with specific cancer types. PeerJ 2023; 11:e16304. [PMID: 37901464 PMCID: PMC10601900 DOI: 10.7717/peerj.16304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023] Open
Abstract
Machine learning (ML) includes a broad class of computer programs that improve with experience and shows unique strengths in performing tasks such as clustering, classification and regression. Over the past decade, microbial communities have been implicated in influencing the onset, progression, metastasis, and therapeutic response of multiple cancers. Host-microbe interaction may be a physiological pathway contributing to cancer development. With the accumulation of a large number of high-throughput data, ML has been successfully applied to the study of human cancer microbiomics in an attempt to reveal the complex mechanism behind cancer. In this review, we begin with a brief overview of the data sources included in cancer microbiomics studies. Then, the characteristics of the ML algorithm are briefly introduced. Secondly, the application progress of ML in cancer microbiomics is also reviewed. Finally, we highlight the challenges and future prospects facing ML in cancer microbiomics. On this basis, we conclude that the development of cancer microbiomics can not be achieved without ML, and that ML can be used to develop tumor-targeting microbial therapies, ultimately contributing to personalized and precision medicine.
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Affiliation(s)
- Jia Feng
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Kailan Yang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Xuexue Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Min Song
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Ping Zhan
- Department of Obstetrics, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Mi Zhang
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinsong Chen
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
| | - Jinbo Liu
- Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan Province Engineering Technology Research Center of Molecular Diagnosis of Clinical Diseases, Molecular Diagnosis of Clinical Diseases Key Laboratory of Luzhou, Sichuan, China
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11
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Wang M, Cernava T. Soterobionts: disease-preventing microorganisms and proposed strategies to facilitate their discovery. Curr Opin Microbiol 2023; 75:102349. [PMID: 37369150 DOI: 10.1016/j.mib.2023.102349] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/25/2023] [Accepted: 05/26/2023] [Indexed: 06/29/2023]
Abstract
Crop production and the food security that it provides are currently threatened worldwide by plant pathogens. Conventional control measures, such as breeding for resistant plants, are progressively losing their efficacy due to rapidly evolving pathogens. The plant microbiota contributes to essential functions of host plants, among which is protection against pathogens. Only recently, microorganisms that provide holistic protection against certain plant diseases were identified. They were termed as 'soterobionts' and extend their host's immune system, which results in disease-resistant phenotypes. Further exploration of such microorganisms could not only provide answers to better understand the implications of the plant microbiota in health and disease, but also contribute to new developments in agriculture and beyond. The aim of this work is to point out how the identification of plant-associated soterobionts can be facilitated, and to discuss technologies that will be required to enable this.
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Affiliation(s)
- Mengcen Wang
- College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310058, China; State Key Laboratory of Rice Biology & Ministry of Agricultural and Rural Affairs Laboratory of Molecular Biology of Crop Pathogens and Insects, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Pesticide and Environmental Toxicology, Zhejiang University, Hangzhou 310058, China; Global Education Program for AgriScience Frontiers, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8589, Japan
| | - Tomislav Cernava
- School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton SO17 1BJ, United Kingdom.
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12
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Olaguez-Gonzalez JM, Chairez I, Breton-Deval L, Alfaro-Ponce M. Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition. Biomedicines 2023; 11:2633. [PMID: 37893007 PMCID: PMC10604849 DOI: 10.3390/biomedicines11102633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/01/2023] [Accepted: 09/18/2023] [Indexed: 10/29/2023] Open
Abstract
The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome's composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome's composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome's influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host-microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome's composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.
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Affiliation(s)
- Juan M. Olaguez-Gonzalez
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico; (J.M.O.-G.); (I.C.)
| | - Isaac Chairez
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico; (J.M.O.-G.); (I.C.)
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico
| | - Luz Breton-Deval
- Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca 62210, Mexico;
- Consejo Nacional de Ciencia y Tecnologia, Mexico City 03940, Mexico
| | - Mariel Alfaro-Ponce
- School of Engineering and Science, Tecnologico de Monterrey, Monterrey 64849, Mexico; (J.M.O.-G.); (I.C.)
- Institute of Advanced Materials for Sustainable Manufacturing, Tecnologico de Monterrey, Monterrey 64849, Mexico
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13
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Liu L, Deng Z, Liu W, Liu R, Ma T, Zhou Y, Wang E, Tang Y. The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets. Front Cell Infect Microbiol 2023; 13:1257073. [PMID: 37790913 PMCID: PMC10543748 DOI: 10.3389/fcimb.2023.1257073] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/29/2023] [Indexed: 10/05/2023] Open
Abstract
Background Methamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients. Method Fecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features. Results MUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients. Conclusion In conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.
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Affiliation(s)
- Linzi Liu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Zijing Deng
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Wen Liu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ruina Liu
- Department of Psychiatry, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Tao Ma
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yifang Zhou
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Enhui Wang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yanqing Tang
- Department of Psychiatry, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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14
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Montenegro J, Armet AM, Willing BP, Deehan EC, Fassini PG, Mota JF, Walter J, Prado CM. Exploring the Influence of Gut Microbiome on Energy Metabolism in Humans. Adv Nutr 2023; 14:840-857. [PMID: 37031749 PMCID: PMC10334151 DOI: 10.1016/j.advnut.2023.03.015] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 03/13/2023] [Accepted: 03/30/2023] [Indexed: 04/11/2023] Open
Abstract
The gut microbiome has a profound influence on host physiology, including energy metabolism, which is the process by which energy from nutrients is transformed into other forms of energy to be used by the body. However, mechanistic evidence for how the microbiome influences energy metabolism is derived from animal models. In this narrative review, we included human studies investigating the relationship between gut microbiome and energy metabolism -i.e., energy expenditure in humans and energy harvest by the gut microbiome. Studies have found no consistent gut microbiome patterns associated with energy metabolism, and most interventions were not effective in modulating the gut microbiome to influence energy metabolism. To date, cause-and-effect relationships and mechanistic evidence on the impact of the gut microbiome on energy expenditure have not been established in humans. Future longitudinal observational studies and randomized controlled trials utilizing robust methodologies and advanced statistical analysis are needed. Such knowledge would potentially inform the design of therapeutic avenues and specific dietary recommendations to improve energy metabolism through gut microbiome modulation.
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Affiliation(s)
- Julia Montenegro
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Anissa M Armet
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Benjamin P Willing
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada
| | - Edward C Deehan
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada; Department of Food Science and Technology, University of Nebraska, Lincoln, Nebraska, United States; Nebraska Food for Health Center, University of Nebraska, Lincoln, Nebraska, United States
| | - Priscila G Fassini
- Department of Internal Medicine, Division of Nutrology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - João F Mota
- School of Nutrition, Federal University of Goiás, Goiânia, Goiás, Brazil; APC Microbiome Ireland, School of Microbiology, and Department of Medicine, University College Cork - National University of Ireland, Cork, Ireland
| | - Jens Walter
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada; APC Microbiome Ireland, School of Microbiology, and Department of Medicine, University College Cork - National University of Ireland, Cork, Ireland.
| | - Carla M Prado
- Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta, Canada.
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15
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Hampton-Marcell JT, Ghosh A, Gukeh MJ, Megaridis CM. A new approach of microbiome monitoring in the built environment: feasibility analysis of condensation capture. MICROBIOME 2023; 11:129. [PMID: 37291673 DOI: 10.1186/s40168-023-01555-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/19/2023] [Indexed: 06/10/2023]
Abstract
BACKGROUND Humans emit approximately 30 million microbial cells per hour into their immediate vicinity. However, sampling of aerosolized microbial taxa (aerobiome) remains largely uncharacterized due to the complexity and limitations of sampling techniques, which are highly susceptible to low biomass and rapid sample degradation. Recently, there has been an interest in developing technology that collects naturally occurring water from the atmosphere, even within the built environment. Here, we analyze the feasibility of indoor aerosol condensation collection as a method to capture and analyze the aerobiome. METHODS Aerosols were collected via condensation or active impingement in a laboratory setting over the course of 8 h. Microbial DNA was extracted from collected samples and sequenced (16S rRNA) to analyze microbial diversity and community composition. Dimensional reduction and multivariate statistics were employed to identify significant (p < 0.05) differences in relative abundances of specific microbial taxa observed between the two sampling platforms. RESULTS Aerosol condensation capture is highly efficient with a yield greater than 95% when compared to expected values. Compared to air impingement, aerosol condensation showed no significant difference (ANOVA, p > 0.05) in microbial diversity. Among identified taxa, Streptophyta and Pseudomonadales comprised approximately 70% of the microbial community composition. CONCLUSION The results suggest that condensation of atmospheric humidity is a suitable method for the capture of airborne microbial taxa reflected by microbial community similarity between devices. Future investigation of aerosol condensation may provide insight into the efficacy and viability of this new tool to investigate airborne microorganisms. IMPORTANCE On average, humans shed approximately 30 million microbial cells each hour into their immediate environment making humans the primary contributor to shaping the microbiome found within the built environment. In addition, recent events have highlighted the importance of understanding how microorganisms within the built environment are aerosolized and dispersed, but more importantly, the lack in development of technology that is capable of actively sampling the ever-changing aerosolized microbiome, i.e., aerobiome. This research highlights the capability of sampling the aerobiome by taking advantage of naturally occurring atmospheric humidity. Our novel approach reproduces the biological content in the atmosphere and can provide insight into the environmental microbiology of indoor spaces. Video Abstract.
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Affiliation(s)
- Jarrad T Hampton-Marcell
- Biosciences Division, Argonne National Laboratory, Lemont, IL, USA.
- Department of Biological Sciences, University of Illinois at Chicago, 845 W. Taylor St, Chicago, IL, 60607, USA.
| | - Aritra Ghosh
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Mohamad Jafari Gukeh
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
| | - Constantine M Megaridis
- Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA
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Ekundayo TC, Adewoyin MA, Ijabadeniyi OA, Igbinosa EO, Okoh AI. Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents. Sci Rep 2023; 13:7749. [PMID: 37173379 PMCID: PMC10177717 DOI: 10.1038/s41598-023-34963-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 05/15/2023] Open
Abstract
A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models' performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040-4.5828)] and Cubist [3.1736 (1.1012-4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00-83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.
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Affiliation(s)
- Temitope C Ekundayo
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa.
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa.
- Department of Microbiology, University of Medical Sciences Ondo, Ondo, Nigeria.
| | - Mary A Adewoyin
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Biological Sciences, Faculty of Natural, Applied and Health Sciences, Anchor University, Ayobo Road, Ipaja, P. M. B. 001, Lagos, Nigeria
| | - Oluwatosin A Ijabadeniyi
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa
| | - Etinosa O Igbinosa
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Microbiology, Faculty of Life Sciences, University of Benin, Private Mail Bag 1154, Benin City, 300283, Nigeria
| | - Anthony I Okoh
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Environmental Health Sciences, College of Health Sciences, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates
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Zhao L, Walkowiak S, Fernando WGD. Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091852. [PMID: 37176910 PMCID: PMC10180744 DOI: 10.3390/plants12091852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023]
Abstract
There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.
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Affiliation(s)
- Liang Zhao
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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18
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Chung T, Yan R, Weller DL, Kovac J. Conditional Forest Models Built Using Metagenomic Data Accurately Predicted Salmonella Contamination in Northeastern Streams. Microbiol Spectr 2023; 11:e0038123. [PMID: 36946722 PMCID: PMC10100987 DOI: 10.1128/spectrum.00381-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 02/27/2023] [Indexed: 03/23/2023] Open
Abstract
The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was assessed using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. CF models outperformed the other two algorithms based on AUC (0.86, CF; 0.81, RRF; 0.65, SVM) and Kappa score (0.53, CF; 0.41, RRF; 0.12, SVM). The taxa that were most informative for accurately predicting Salmonella contamination based on CF were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and -negative samples. CF and differential abundance tests both identified Aeromonas salmonicida (variable importance [VI] = 0.012) and Aeromonas sp. strain CA23 (VI = 0.025) as the two most informative taxa for predicting Salmonella contamination. Our findings suggest that microbiome-based models may provide an alternative to or complement existing water monitoring strategies. Similarly, the informative taxa identified in this study warrant further investigation as potential indicators of Salmonella contamination of agricultural water. IMPORTANCE Understanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas species) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella contamination across regions, waterways, and years not represented in the data set used in this study. Validated indicators identified using microbiome data may be used as targets in the development of rapid (e.g., PCR-based) detection assays for the assessment of microbial safety of agricultural surface waters.
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Affiliation(s)
- Taejung Chung
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Runan Yan
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Daniel L. Weller
- Department of Statistics and Computational Biology, University of Rochester Medical Center, Rochester, New York, USA
| | - Jasna Kovac
- Department of Food Science, The Pennsylvania State University, University Park, Pennsylvania, USA
- Microbiome Center, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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19
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Johnson D, Letchumanan V, Thum CC, Thurairajasingam S, Lee LH. A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression. Nutrients 2023; 15:nu15061382. [PMID: 36986112 PMCID: PMC10053794 DOI: 10.3390/nu15061382] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/16/2023] Open
Abstract
Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.
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Affiliation(s)
- Dinyadarshini Johnson
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Vengadesh Letchumanan
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
| | - Chern Choong Thum
- Department of Psychiatry, Hospital Sultan Abdul Aziz Shah, Persiaran Mardi-UPM, Serdang 43400, Malaysia
| | - Sivakumar Thurairajasingam
- Clinical School Johor Bahru, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Johor Bahru 80100, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
| | - Learn-Han Lee
- Novel Bacteria and Drug Discovery Research Group (NBDD), Microbiome and Bioresource Research Strength (MBRS), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Pathogen Resistome Virulome and Diagnostic Research Group (PathRiD), Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Malaysia
- Correspondence: (S.T.); or (L.-H.L.)
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20
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Shen Y, Zhu J, Deng Z, Lu W, Wang H. EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:986-998. [PMID: 36001521 DOI: 10.1109/tcbb.2022.3201295] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimensional features, which pose a great challenge for machine learning methods. Therefore, this paper proposes a novel ensemble deep learning disease prediction method that combines unsupervised and supervised learning paradigms. First, unsupervised deep learning methods are used to learn the potential representation of the sample. Afterwards, the disease scoring strategy is developed based on the deep representations as the informative features for ensemble analysis. To ensure the optimal ensemble, a score selection mechanism is constructed, and performance boosting features are engaged with the original sample. Finally, the composite features are trained with gradient boosting classifier for health status decision. For case study, the ensemble deep learning flowchart has been demonstrated on six public datasets extracted from the human microbiome profiling. The results show that compared with the existing algorithms, our framework achieves better performance on disease prediction.
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21
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Impact of Aging Microbiome on Metabolic Profile of Natural Aging Huangjiu through Machine Learning. Foods 2023; 12:foods12040906. [PMID: 36832981 PMCID: PMC9956941 DOI: 10.3390/foods12040906] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 02/25/2023] Open
Abstract
Aging is a time-consuming step in the manufacturing of fermented alcoholic beverages. Natural-aging huangjiu sealed in pottery jars was taken as an example to investigate the changes of physiochemical indexes during aging and to quantify intercorrelations between aging-related factors and metabolites through machine learning methods. Machine learning models provided significant predictions for 86% of metabolites. Physiochemical indexes well reflected the metabolic profile, and total acid was the most important index that needed to be controlled. For aging-related factors, several aging biomarkers of huangjiu were also well predicted. Feature attribution analysis showed aging year was the most powerful predictive factor, and several microbial species were significantly associated with aging biomarkers. Some of the correlations, mostly connected to environmental microorganisms, were newly found, showing considerable microbial influence on aging. Overall, our results reveal the potential determinants that affect the metabolic profile of aged huangjiu, paving the way for a systematical understanding of changes in metabolites of fermented alcoholic beverages.
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22
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Blankestijn JM, Lopez-Rincon A, Neerincx AH, Vijverberg SJH, Hashimoto S, Gorenjak M, Sardón Prado O, Corcuera-Elosegui P, Korta-Murua J, Pino-Yanes M, Potočnik U, Bang C, Franke A, Wolff C, Brandstetter S, Toncheva AA, Kheiroddin P, Harner S, Kabesch M, Kraneveld AD, Abdel-Aziz MI, Maitland-van der Zee AH. Classifying asthma control using salivary and fecal bacterial microbiome in children with moderate-to-severe asthma. Pediatr Allergy Immunol 2023; 34:e13919. [PMID: 36825736 DOI: 10.1111/pai.13919] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/23/2023] [Accepted: 01/24/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics; however, its association with asthma control has not been explored. We aimed to investigate whether the gastrointestinal microbiome can be used to discriminate between uncontrolled and controlled asthma in children. METHODS 143 and 103 feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study. Patients were classified as controlled or uncontrolled asthmatics, and their microbiome at species level was compared using global (alpha/beta) diversity, conventional differential abundance analysis (DAA, analysis of compositions of microbiomes with bias correction), and machine learning [Recursive Ensemble Feature Selection (REFS)]. RESULTS Global diversity and DAA did not find significant differences between controlled and uncontrolled pediatric asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 86% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples. CONCLUSION Controlled and uncontrolled children with asthma can be differentiated based on their gastrointestinal microbiome using machine learning, specifically REFS. Our results show an association between asthma control and the gastrointestinal microbiome. This suggests that the gastrointestinal microbiome may be a potential biomarker for treatment responsiveness and thereby help to improve asthma control in children.
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Affiliation(s)
- Jelle M Blankestijn
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
| | - Alejandro Lopez-Rincon
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
- Department of Data Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anne H Neerincx
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Susanne J H Vijverberg
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Simone Hashimoto
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Pediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Gorenjak
- Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia
| | - Olaia Sardón Prado
- Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain
- Department of Pediatrics, University of the Basque Country (UPV/EHU), San Sebastián, Spain
| | - Paula Corcuera-Elosegui
- Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain
| | - Javier Korta-Murua
- Division of Pediatric Respiratory Medicine, Hospital Universitario Donostia, San Sebastián, Spain
| | - Maria Pino-Yanes
- Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna (ULL), San Cristóbal de La Laguna, Spain
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- Instituto de Tecnologías Biomédicas (ITB), Universidad de La Laguna, La Laguna, Spain
| | - Uroš Potočnik
- Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia
- Laboratory for Biochemistry, Molecular Biology and Genomics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christine Wolff
- Science and Development Campus Regensburg (WECARE), University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Susanne Brandstetter
- Science and Development Campus Regensburg (WECARE), University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Antoaneta A Toncheva
- Science and Development Campus Regensburg (WECARE), University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Parastoo Kheiroddin
- Science and Development Campus Regensburg (WECARE), University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Susanne Harner
- Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Michael Kabesch
- Science and Development Campus Regensburg (WECARE), University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
- Department of Pediatric Pneumology and Allergy, University Children's Hospital Regensburg (KUNO) at the Hospital St. Hedwig of the Order of St. John, University of Regensburg, Regensburg, Germany
| | - Aletta D Kraneveld
- Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Mahmoud I Abdel-Aziz
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
- Department of Clinical Pharmacy, Faculty of Pharmacy, Assiut University, Assiut, Egypt
| | - Anke H Maitland-van der Zee
- Department of Pulmonary Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Institute for Infection and Immunity, Amsterdam, The Netherlands
- Amsterdam Public Health, Amsterdam, The Netherlands
- Department of Pediatric Respiratory Medicine, Emma Children's Hospital, Amsterdam UMC, Amsterdam, The Netherlands
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23
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Ekundayo TC, Ijabadeniyi OA, Igbinosa EO, Okoh AI. Using machine learning models to predict the effects of seasonal fluxes on Plesiomonas shigelloides population density. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120734. [PMID: 36455774 DOI: 10.1016/j.envpol.2022.120734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Seasonal variations (SVs) affect the population density (PD), fate, and fitness of pathogens in environmental water resources and the public health impacts. Therefore, this study is aimed at applying machine learning intelligence (MLI) to predict the impacts of SVs on P. shigelloides population density (PDP) in the aquatic milieu. Physicochemical events (PEs) and PDP from three rivers acquired via standard microbiological and instrumental techniques across seasons were fitted to MLI algorithms (linear regression (LR), multiple linear regression (MR), random forest (RF), gradient boosted machine (GBM), neural network (NN), K-nearest neighbour (KNN), boosted regression tree (BRT), extreme gradient boosting (XGB) regression, support vector regression (SVR), decision tree regression (DTR), M5 pruned regression (M5P), artificial neural network (ANN) regression (with one 10-node hidden layer (ANN10), two 6- and 4-node hidden layers (ANN64), and two 5- and 5-node hidden layers (ANN55)), and elastic net regression (ENR)) to assess the implications of the SVs of PEs on aquatic PDP. The results showed that SVs significantly influenced PDP and PEs in the water (p < 0.0001), exhibiting a site-specific pattern. While MLI algorithms predicted PDP with differing absolute flux magnitudes for the contributing variables, DTR predicted the highest PDP value of 1.707 log unit, followed by XGB (1.637 log unit), but XGB (mean-squared-error (MSE) = 0.0025; root-mean-squared-error (RMSE) = 0.0501; R2 =0.998; medium absolute deviation (MAD) = 0.0275) outperformed other models in terms of regression metrics. Temperature and total suspended solids (TSS) ranked first and second as significant factors in predicting PDP in 53.3% (8/15) and 40% (6/15), respectively, of the models, based on the RMSE loss after permutations. Additionally, season ranked third among the 7 models, and turbidity (TBS) ranked fourth at 26.7% (4/15), as the primary significant factor for predicting PDP in the aquatic milieu. The results of this investigation demonstrated that MLI predictive modelling techniques can promisingly be exploited to complement the repetitive laboratory-based monitoring of PDP and other pathogens, especially in low-resource settings, in response to seasonal fluxes and can provide insights into the potential public health risks of emerging pathogens and TSS pollution (e.g., nanoparticles and micro- and nanoplastics) in the aquatic milieu. The model outputs provide low-cost and effective early warning information to assist watershed managers and fish farmers in making appropriate decisions about water resource protection, aquaculture management, and sustainable public health protection.
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Affiliation(s)
- Temitope C Ekundayo
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa; Department of Microbiology, University of Medical Sciences, Ondo City, Ondo State, Nigeria.
| | - Oluwatosin A Ijabadeniyi
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa
| | - Etinosa O Igbinosa
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Microbiology, Faculty of Life Sciences University of Benin, Private Mail Bag 1154, Benin City, 300283, Nigeria
| | - Anthony I Okoh
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa; Department of Environmental Health Sciences, College of Health Sciences, University of Sharjah, Sharjah, P.O. Box 27272, United Arab Emirates
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24
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Kirk D, Kok E, Tufano M, Tekinerdogan B, Feskens EJM, Camps G. Machine Learning in Nutrition Research. Adv Nutr 2022; 13:2573-2589. [PMID: 36166846 PMCID: PMC9776646 DOI: 10.1093/advances/nmac103] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/02/2022] [Accepted: 09/22/2022] [Indexed: 01/29/2023] Open
Abstract
Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.
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Affiliation(s)
- Daniel Kirk
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Esther Kok
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Michele Tufano
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Edith J M Feskens
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Guido Camps
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands.,OnePlanet Research Center, Wageningen, The Netherlands
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25
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Acharjee A, Singh U, Choudhury SP, Gkoutos GV. The diagnostic potential and barriers of microbiome based therapeutics. Diagnosis (Berl) 2022; 9:411-420. [PMID: 36000189 DOI: 10.1515/dx-2022-0052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/03/2022] [Indexed: 02/07/2023]
Abstract
High throughput technological innovations in the past decade have accelerated research into the trillions of commensal microbes in the gut. The 'omics' technologies used for microbiome analysis are constantly evolving, and large-scale datasets are being produced. Despite of the fact that much of the research is still in its early stages, specific microbial signatures have been associated with the promotion of cancer, as well as other diseases such as inflammatory bowel disease, neurogenerative diareses etc. It has been also reported that the diversity of the gut microbiome influences the safety and efficacy of medicines. The availability and declining sequencing costs has rendered the employment of RNA-based diagnostics more common in the microbiome field necessitating improved data-analytical techniques so as to fully exploit all the resulting rich biological datasets, while accounting for their unique characteristics, such as their compositional nature as well their heterogeneity and sparsity. As a result, the gut microbiome is increasingly being demonstrating as an important component of personalised medicine since it not only plays a role in inter-individual variability in health and disease, but it also represents a potentially modifiable entity or feature that may be addressed by treatments in a personalised way. In this context, machine learning and artificial intelligence-based methods may be able to unveil new insights into biomedical analyses through the generation of models that may be used to predict category labels, and continuous values. Furthermore, diagnostic aspects will add value in the identification of the non invasive markers in the critical diseases like cancer.
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Affiliation(s)
- Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University of Birmingham, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, UK.,MRC Health Data Research UK (HDR UK), Birmingham, UK
| | - Utpreksha Singh
- Department of Health and Life Sciences, Coventry University, Coventry, UK
| | | | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK.,Institute of Translational Medicine, University of Birmingham, Birmingham, UK.,NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, UK.,MRC Health Data Research UK (HDR UK), Birmingham, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, UK
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26
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Yeşilyurt N, Yılmaz B, Ağagündüz D, Capasso R. Microbiome-based personalized nutrition as a result of the 4.0 technological revolution: A mini literature review. Process Biochem 2022. [DOI: 10.1016/j.procbio.2022.07.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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27
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Wojciechowski S, Majchrzak-Górecka M, Biernat P, Odrzywołek K, Pruss Ł, Zych K, Jan Majta, Milanowska-Zabel K. Machine learning on the road to unlocking microbiota's potential for boosting immune checkpoint therapy. Int J Med Microbiol 2022; 312:151560. [PMID: 36113358 DOI: 10.1016/j.ijmm.2022.151560] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 07/15/2022] [Accepted: 08/31/2022] [Indexed: 10/14/2022] Open
Abstract
The intestinal microbiota is a complex and diverse ecological community that fulfills multiple functions and substantially impacts human health. Despite its plasticity, unfavorable conditions can cause perturbations leading to so-called dysbiosis, which have been connected to multiple diseases. Unfortunately, understanding the mechanisms underlying the crosstalk between those microorganisms and their host is proving to be difficult. Traditionally used bioinformatic tools have difficulties to fully exploit big data generated for this purpose by modern high throughput screens. Machine Learning (ML) may be a potential means of solving such problems, but it requires diligent application to allow for drawing valid conclusions. This is especially crucial as gaining insight into the mechanistic basis of microbial impact on human health is highly anticipated in numerous fields of study. This includes oncology, where growing amounts of studies implicate the gut ecosystems in both cancerogenesis and antineoplastic treatment outcomes. Based on these reports and first signs of clinical benefits related to microbiota modulation in human trials, hopes are rising for the development of microbiome-derived diagnostics and therapeutics. In this mini-review, we're inspecting analytical approaches used to uncover the role of gut microbiome in immune checkpoint therapy (ICT) with the use of shotgun metagenomic sequencing (SMS) data.
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Affiliation(s)
| | | | | | - Krzysztof Odrzywołek
- Ardigen, Podole 76, 30-394 Kraków, Poland; Institute of Computer Science, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland
| | - Łukasz Pruss
- Ardigen, Podole 76, 30-394 Kraków, Poland; Department of Biochemistry, Molecular Biology and Biotechnology, Faculty of Chemistry, Wroclaw University of Science and Technology, 50-373 Wroclaw, Poland
| | | | - Jan Majta
- Ardigen, Podole 76, 30-394 Kraków, Poland; Department of Computational Biophysics and Bioinformatics, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland
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28
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Jiang L, Haiminen N, Carrieri A, Huang S, Vázquez‐Baeza Y, Parida L, Kim H, Swafford AD, Knight R, Natarajan L. Utilizing stability criteria in choosing feature selection methods yields reproducible results in microbiome data. Biometrics 2022; 78:1155-1167. [PMID: 33914902 PMCID: PMC9787628 DOI: 10.1111/biom.13481] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 02/25/2021] [Accepted: 04/14/2021] [Indexed: 12/31/2022]
Abstract
Feature selection is indispensable in microbiome data analysis, but it can be particularly challenging as microbiome data sets are high dimensional, underdetermined, sparse and compositional. Great efforts have recently been made on developing new methods for feature selection that handle the above data characteristics, but almost all methods were evaluated based on performance of model predictions. However, little attention has been paid to address a fundamental question: how appropriate are those evaluation criteria? Most feature selection methods often control the model fit, but the ability to identify meaningful subsets of features cannot be evaluated simply based on the prediction accuracy. If tiny changes to the data would lead to large changes in the chosen feature subset, then many selected features are likely to be a data artifact rather than real biological signal. This crucial need of identifying relevant and reproducible features motivated the reproducibility evaluation criterion such as Stability, which quantifies how robust a method is to perturbations in the data. In our paper, we compare the performance of popular model prediction metrics (MSE or AUC) with proposed reproducibility criterion Stability in evaluating four widely used feature selection methods in both simulations and experimental microbiome applications with continuous or binary outcomes. We conclude that Stability is a preferred feature selection criterion over model prediction metrics because it better quantifies the reproducibility of the feature selection method.
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Affiliation(s)
- Lingjing Jiang
- Division of BiostatisticsUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Niina Haiminen
- IBM T. J. Watson Research CenterYorktown HeightsNew YorkUSA
| | | | - Shi Huang
- Center for Microbiome InnovationJacobs School of EngineeringUC San DiegoLa JollaCaliforniaUSA,Department of PediatricsUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Yoshiki Vázquez‐Baeza
- Center for Microbiome InnovationJacobs School of EngineeringUC San DiegoLa JollaCaliforniaUSA,Department of PediatricsUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Laxmi Parida
- IBM T. J. Watson Research CenterYorktown HeightsNew YorkUSA
| | - Ho‐Cheol Kim
- Scalable Knowledge IntelligenceIBM Research‐AlmadenSan JoseCaliforniaUSA
| | - Austin D. Swafford
- Center for Microbiome InnovationJacobs School of EngineeringUC San DiegoLa JollaCaliforniaUSA
| | - Rob Knight
- Center for Microbiome InnovationJacobs School of EngineeringUC San DiegoLa JollaCaliforniaUSA,Department of PediatricsUniversity of California San DiegoLa JollaCaliforniaUSA,Department of Computer Science and EngineeringUniversity of California San DiegoLa JollaCaliforniaUSA,Department of BioengineeringUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Loki Natarajan
- Division of BiostatisticsUniversity of California San DiegoLa JollaCaliforniaUSA
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29
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New-Generation Sequencing Technology in Diagnosis of Fungal Plant Pathogens: A Dream Comes True? J Fungi (Basel) 2022; 8:jof8070737. [PMID: 35887492 PMCID: PMC9320658 DOI: 10.3390/jof8070737] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 07/01/2022] [Accepted: 07/11/2022] [Indexed: 02/01/2023] Open
Abstract
The fast and continued progress of high-throughput sequencing (HTS) and the drastic reduction of its costs have boosted new and unpredictable developments in the field of plant pathology. The cost of whole-genome sequencing, which, until few years ago, was prohibitive for many projects, is now so affordable that a new branch, phylogenomics, is being developed. Fungal taxonomy is being deeply influenced by genome comparison, too. It is now easier to discover new genes as potential targets for an accurate diagnosis of new or emerging pathogens, notably those of quarantine concern. Similarly, with the development of metabarcoding and metagenomics techniques, it is now possible to unravel complex diseases or answer crucial questions, such as "What's in my soil?", to a good approximation, including fungi, bacteria, nematodes, etc. The new technologies allow to redraw the approach for disease control strategies considering the pathogens within their environment and deciphering the complex interactions between microorganisms and the cultivated crops. This kind of analysis usually generates big data that need sophisticated bioinformatic tools (machine learning, artificial intelligence) for their management. Herein, examples of the use of new technologies for research in fungal diversity and diagnosis of some fungal pathogens are reported.
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30
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Jiang J, Zhou H, Zhang T, Yao C, Du D, Zhao L, Cai W, Che L, Cao Z, Wu XE. Machine learning to predict dynamic changes of pathogenic Vibrio spp. abundance on microplastics in marine environment. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 305:119257. [PMID: 35398156 DOI: 10.1016/j.envpol.2022.119257] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 03/14/2022] [Accepted: 04/01/2022] [Indexed: 05/27/2023]
Abstract
Microplastics are widely found in the marine environment. Recent studies have shown that pathogenic microorganisms can hitchhike on microplastics, which might act as a vector for the spread of pathogens. Vibrio spp. are known to be pathogenic to humans and can cause serious foodborne diseases. In this study, using datasets from an estuary and a mariculture zone in China, five machine learning models were established to predict the relative abundance of Vibrio spp. on microplastics. The results showed that deep neural network (DNN) model and RandomForest algorithm achieved the best predictive performance. Different data sources, data sampling, and processing methods had a little impact on the prediction performance of DNN and RandomForest models. SHapley Additive exPlanations (SHAP) indicated that salinity and temperature are the primary factors affecting the relative abundance of Vibrio spp. The prediction performances of the five machine learning models were further improved by feature selection, providing information to support future experimental research. The results of this study could help establish a long-term and dynamic monitoring system for the relative abundance of Vibrio spp. on microplastics in response to environmental factors as well as provide useful information for assessing the potential health impacts of microplastics on marine ecology and humans.
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Affiliation(s)
- Jiawen Jiang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Hua Zhou
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Ting Zhang
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chuanyi Yao
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Delin Du
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Liang Zhao
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Wenfang Cai
- School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Liming Che
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Zhikai Cao
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xue E Wu
- Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
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31
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Morgan EW, Perdew GH, Patterson AD. Multi-Omics Strategies for Investigating the Microbiome in Toxicology Research. Toxicol Sci 2022; 187:189-213. [PMID: 35285497 PMCID: PMC9154275 DOI: 10.1093/toxsci/kfac029] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Microbial communities on and within the host contact environmental pollutants, toxic compounds, and other xenobiotic compounds. These communities of bacteria, fungi, viruses, and archaea possess diverse metabolic potential to catabolize compounds and produce new metabolites. Microbes alter chemical disposition thus making the microbiome a natural subject of interest for toxicology. Sequencing and metabolomics technologies permit the study of microbiomes altered by acute or long-term exposure to xenobiotics. These investigations have already contributed to and are helping to re-interpret traditional understandings of toxicology. The purpose of this review is to provide a survey of the current methods used to characterize microbes within the context of toxicology. This will include discussion of commonly used techniques for conducting omic-based experiments, their respective strengths and deficiencies, and how forward-looking techniques may address present shortcomings. Finally, a perspective will be provided regarding common assumptions that currently impede microbiome studies from producing causal explanations of toxicologic mechanisms.
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Affiliation(s)
- Ethan W Morgan
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Gary H Perdew
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
| | - Andrew D Patterson
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania 16802, USA.,Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, Pennsylvania 16802, USA
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Li J, Sang Y, Zeng S, Mo S, Zhang Z, He S, Li X, Su G, Liao J, Jiang C. MicrobioSee: A Web-Based Visualization Toolkit for Multi-Omics of Microbiology. Front Genet 2022; 13:853612. [PMID: 35464838 PMCID: PMC9024144 DOI: 10.3389/fgene.2022.853612] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 03/08/2022] [Indexed: 11/13/2022] Open
Abstract
With the upgrade and development of the high-throughput sequencing technology, multi-omics data can be obtained at a low cost. However, mapping tools that existed for microbial multi-omics data analysis cannot satisfy the needs of data description and result in high learning costs, complex dependencies, and high fees for researchers in experimental biology fields. Therefore, developing a toolkit for multi-omics data is essential for microbiologists to save effort. In this work, we developed MicrobioSee, a real-time interactive visualization tool based on web technologies, which could visualize microbial multi-omics data. It includes 17 modules surrounding the major omics data of microorganisms such as the transcriptome, metagenome, and proteome. With MicrobioSee, methods for plotting are simplified in multi-omics studies, such as visualization of diversity, ROC, and enrichment pathways for DEGs. Subsequently, three case studies were chosen to represent the functional application of MicrobioSee. Overall, we provided a concise toolkit along with user-friendly, time-saving, cross-platform, and source-opening for researchers, especially microbiologists without coding experience. MicrobioSee is freely available at https://microbiosee.gxu.edu.cn.
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Affiliation(s)
- JinHui Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Yimeng Sang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Sen Zeng
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Shuming Mo
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
- National Engineering Research Center for Non-Food Biorefinery, State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning, China
| | - Zufan Zhang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Sheng He
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
- Guangxi Birth Defects Prevention and Control Institute, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Xinying Li
- Key Laboratory of Industrial Biotechnology, School of Biotechnology, Ministry of Education, Jiangnan University, Wuxi, China
| | - Guijiao Su
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
| | - Jianping Liao
- School of Computer and Information Engineering, Nanning Normal University, Nanning, China
- *Correspondence: Chengjian Jiang, ; Jianping Liao,
| | - Chengjian Jiang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi Research Center for Microbial and Enzyme Engineering Technology, College of Life Science and Technology, Guangxi University, Nanning, China
- National Engineering Research Center for Non-Food Biorefinery, State Key Laboratory of Non-Food Biomass and Enzyme Technology, Guangxi Key Laboratory of Bio-Refinery, Guangxi Research Center for Biological Science and Technology, Guangxi Academy of Sciences, Nanning, China
- *Correspondence: Chengjian Jiang, ; Jianping Liao,
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Eckermann HA, Ou Y, Lahti L, Weerth C. Can gut microbiota throughout the first 10 years of life predict executive functioning in childhood? Dev Psychobiol 2022; 64:e22226. [DOI: 10.1002/dev.22226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 11/08/2022]
Affiliation(s)
- Henrik Andreas Eckermann
- Department of Cognitive Neuroscience Cognition and Behavior Radboud University Medical Center Donders Institute for Brain Nijmegen The Netherlands
| | - Yangwenshan Ou
- Laboratory of Microbiology Wageningen University Wageningen The Netherlands
| | - Leo Lahti
- Department of Computing University of Turku Turku Finland
| | - Carolina Weerth
- Department of Cognitive Neuroscience Cognition and Behavior Radboud University Medical Center Donders Institute for Brain Nijmegen The Netherlands
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34
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Fecal Microbiota Transplants for Inflammatory Bowel Disease Treatment: Synthetic- and Engineered Communities-Based Microbiota Transplants Are the Future. Gastroenterol Res Pract 2022; 2022:9999925. [PMID: 35140783 PMCID: PMC8820897 DOI: 10.1155/2022/9999925] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 01/12/2022] [Indexed: 12/21/2022] Open
Abstract
The human intestine harbors a huge number of diverse microorganisms where a variety of complex interactions take place between the microbes as well as the host and gut microbiota. Significant long-term variations in the gut microbiota (dysbiosis) have been associated with a variety of health conditions including inflammatory bowel disease (IBD). Conventional fecal microbiota transplantations (FMTs) have been utilized to treat IBD and have been proved promising. However, various limitations such as transient results, pathogen transfer, storage, and reproducibility render conventional FMT less safe and less sustainable. Defined synthetic microbial communities (SynCom) have been used to dissect the host-microbiota-associated functions using gnotobiotic animals or in vitro cell models. This review focuses on the potential use of SynCom in IBD and its advantages and relative safety over conventional FMT. Additionally, this review reinforces how various technological advances could be combined with SynCom to have a better understanding of the complex microbial interactions in various gut inflammatory diseases including IBD. Some technological advances including the availability of a gut-on-a-chip system, intestinal organoids, ex vivo intestinal cultures, AI-based refining of the microbiome structural and functional data, and multiomic approaches may help in making more practical in vitro models of the human host. Additionally, an increase in the cultured diversity from gut microbiota and the availability of their genomic information would further make the design and utilization of SynCom more feasible. Taken together, the combined use of the available knowledge of the gut microbiota in health and disease and recent technological advances and the development of defined SynCom seem to be a promising, safe, and sustainable alternative to conventional FMT in treating IBD.
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Zhu J, Li H, Jing ZZ, Zheng W, Luo YR, Chen SX, Guo F. Robust host source tracking building on the divergent and non-stochastic assembly of gut microbiomes in wild and farmed large yellow croaker. MICROBIOME 2022; 10:18. [PMID: 35081990 PMCID: PMC8790850 DOI: 10.1186/s40168-021-01214-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 12/12/2021] [Indexed: 05/08/2023]
Abstract
BACKGROUND Given the lack of genetic background, the source tracking unknown individuals of fish species with both farmed and wild populations often cannot be robustly achieved. The gut microbiome, which is shaped by both deterministic and stochastic processes, can serve as a molecular marker of fish host source tracking, particularly as an alternative to the yet-to-be-established host genetic marker. A candidate for testing the feasibility is the large yellow croaker, Larimichthys crocea, which is carnivorous and ranks the top mariculture fish in China. Wild resource of this fish was depleted decades ago and might have potential problematic estimation because of escaping of farmed individuals. RESULTS The rectums of wild (n = 212) and farmed (n = 79) croakers from multiple batches were collected for the profiling of their gut bacterial communities. The farmed individuals had a higher alpha diversity and lower bacterial load than the wild individuals. The gut microbiota of the two sources exhibited divergence and high inter-batch variation, as featured by the dominance of Psychrobacter spp. in the wild group. Predicted functional capacity of the gut microbiome and representative isolates showed differences in terms of host source. This difference can be linked to the potential diet divergence between farmed and wild fishes. The non-stochastic distribution pattern of the core gut microbiota of the wild and farmed individuals supports the feasibility of microbiota-based host source tracking via the machine learning algorithm. A random forest classifier based on the divergence and non-stochastic assembly of the gut microbiome was robust in terms of host source tracking the individuals from all batches of croaker, including a newly introduced batch. CONCLUSIONS Our study revealed the divergence of gut microbiota and related functional profiles between wild and farmed croakers. For the first time, with representative datasets and non-stochastic patterns, we have verified that gut microbiota can be robustly applied to the tracking of host source even in carnivorous fish. Video abstract.
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Affiliation(s)
- Jun Zhu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Hao Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Ze Zhou Jing
- State-Province Joint Engineering Laboratory of Marine Bioproducts and Technology, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China
| | - Wei Zheng
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China
| | - Yuan Rong Luo
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, Xiamen University, Xiamen, China
| | - Shi Xi Chen
- State-Province Joint Engineering Laboratory of Marine Bioproducts and Technology, College of Ocean and Earth Sciences, Xiamen University, Xiamen, China.
| | - Feng Guo
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China.
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China.
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36
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Asselstine V, Lam S, Miglior F, Brito LF, Sweett H, Guan L, Waters SM, Plastow G, Cánovas A. The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies. J Anim Sci 2021; 99:6377879. [PMID: 34586400 PMCID: PMC8480417 DOI: 10.1093/jas/skab193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 07/21/2021] [Indexed: 12/14/2022] Open
Abstract
Ruminant supply chains contribute 5.7 gigatons of CO2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH4), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH4 production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH4 yield in ruminants indicates that genomic selection for reduced CH4 emissions is possible. Although the microbiology of CH4 production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH4 production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH4 emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH4 production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH4 emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH4 production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.
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Affiliation(s)
- Victoria Asselstine
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Stephanie Lam
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Filippo Miglior
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada.,Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Hannah Sweett
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Leluo Guan
- Livestock Gentec, Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Alberta, T6G 2C8, Canada
| | - Sinead M Waters
- Animal and Bioscience Research Department, Teagasc Grange, Dunsany, Co. Meath, C15 PW93, Ireland
| | - Graham Plastow
- Livestock Gentec, Department of Agricultural, Food and Nutritional Sciences, University of Alberta, Edmonton, Alberta, T6G 2C8, Canada
| | - Angela Cánovas
- Centre for Genetic Improvement of Livestock (CGIL), Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
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Leggieri PA, Liu Y, Hayes M, Connors B, Seppälä S, O'Malley MA, Venturelli OS. Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes. Annu Rev Biomed Eng 2021; 23:169-201. [PMID: 33781078 PMCID: PMC8277735 DOI: 10.1146/annurev-bioeng-082120-022836] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.
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Affiliation(s)
- Patrick A Leggieri
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Yiyi Liu
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Madeline Hayes
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
| | - Bryce Connors
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
| | - Susanna Seppälä
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Michelle A O'Malley
- Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA;
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA;
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, Wisconsin 53706, USA
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Abstract
Microbial communities in the airways of persons with CF (pwCF) are variable, may include genera that are not typically associated with CF, and their composition can be difficult to correlate with long-term disease outcomes. Leveraging two large data sets characterizing sputum communities of 167 pwCF and associated metadata, we identified five bacterial community types. These communities explain 24% of the variability in lung function in this cohort, far more than single factors like Simpson diversity, which explains only 4%. Subjects with Pseudomonas-dominated communities tended to be older and have reduced percent predicted FEV1 (ppFEV1) compared to subjects with Streptococcus-dominated communities, consistent with previous findings. To assess the predictive power of these five communities in a longitudinal setting, we used random forests to classify 346 additional samples from 24 subjects observed 8 years on average in a range of clinical states. Subjects with mild disease were more likely to be observed at baseline, that is, not in the context of a pulmonary exacerbation, and community structure in these subjects was more self-similar over time, as measured by Bray-Curtis distance. Interestingly, we found that subjects with mild disease were more likely to remain in a mixed Pseudomonas community, providing some support for the climax-attack model of the CF airway. In contrast, patients with worse outcomes were more likely to show shifts among community types. Our results suggest that bacterial community instability may be a risk factor for lung function decline and indicates the need to understand factors that drive shifts in community composition. IMPORTANCE While much research supports a polymicrobial view of the CF airway, one in which the community is seen as the pathogenic unit, only controlled experiments using model bacterial communities can unravel the mechanistic role played by different communities. This report uses a large data set to identify a small number of communities as a starting point in the development of tractable model systems. We describe a set of five communities that explain 24% of the variability in lung function in our data set, far more than single factors like Simpson diversity, which explained only 4%. In addition, we report that patients with severe disease experienced more shifts among community types, suggesting that bacterial community instability may be a risk factor for lung function decline. Together, these findings provide a proof of principle for selecting bacterial community model systems.
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Young RB, Marcelino VR, Chonwerawong M, Gulliver EL, Forster SC. Key Technologies for Progressing Discovery of Microbiome-Based Medicines. Front Microbiol 2021; 12:685935. [PMID: 34239510 PMCID: PMC8258393 DOI: 10.3389/fmicb.2021.685935] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 05/25/2021] [Indexed: 12/22/2022] Open
Abstract
A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.
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Affiliation(s)
- Remy B Young
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia
| | - Vanessa R Marcelino
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Michelle Chonwerawong
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Emily L Gulliver
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
| | - Samuel C Forster
- Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Clayton, VIC, Australia.,Infection and Immunity Program, Monash Biomedicine Discovery Institute and Department of Microbiology, Monash University, Clayton, VIC, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, VIC, Australia
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40
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The Effect of Immunobiotic/Psychobiotic Lactobacillus acidophilus Strain INMIA 9602 Er 317/402 Narine on Gut Prevotella in Familial Mediterranean Fever: Gender-Associated Effects. Probiotics Antimicrob Proteins 2021; 13:1306-1315. [PMID: 34132998 DOI: 10.1007/s12602-021-09779-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2021] [Indexed: 10/21/2022]
Abstract
Possible mechanisms involved in sex-dependent differences in the gut microbiota have a growing interest worldwide, but the effects of probiotics dependence on the gender of the host have remained outside of researchers' attention until now. Previously, our research data described gender-specific differences in the gut microbiota of Armenian Familial Mediterranean fever (FMF) patients. Taking into account the possible association of Prevotella spp. with depressive disorders, the aim of the current investigations was an evaluation of changes in the abundance of gut Prevotella of FMF patients in association with the patient's depression and gender. The differences between healthy and FMF diseased gut microbiota in terms of Prevotella abundance were revealed. In addition, the gender-dependent effects of immunobiotic/psychobiotic Narine on the abundance of gut Prevotella of FMF patients and patients' depression scores were shown by us in this study.
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Ma H, Yang J, Chen X, Jiang X, Su Y, Qiao S, Zhong G. Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy. J Microbiol 2021; 59:563-572. [PMID: 33779956 DOI: 10.1007/s12275-021-1013-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/10/2021] [Accepted: 02/10/2021] [Indexed: 12/14/2022]
Abstract
Fungi of the genus Aspergillus are ubiquitously distributed in nature, and some cause invasive aspergillosis (IA) infections in immunosuppressed individuals and contamination in agricultural products. Because microscopic observation and molecular detection of Aspergillus species represent the most operator-dependent and time-intensive activities, automated and cost-effective approaches are needed. To address this challenge, a deep convolutional neural network (CNN) was used to investigate the ability to classify various Aspergillus species. Using a dissecting microscopy (DM)/stereomicroscopy platform, colonies on plates were scanned with a 35× objective, generating images of sufficient resolution for classification. A total of 8,995 original colony images from seven Aspergillus species cultured in enrichment medium were gathered and autocut to generate 17,142 image crops as training and test datasets containing the typical representative morphology of conidiophores or colonies of each strain. Encouragingly, the Xception model exhibited a classification accuracy of 99.8% on the training image set. After training, our CNN model achieved a classification accuracy of 99.7% on the test image set. Based on the Xception performance during training and testing, this classification algorithm was further applied to recognize and validate a new set of raw images of these strains, showing a detection accuracy of 98.2%. Thus, our study demonstrated a novel concept for an artificial-intelligence-based and cost-effective detection methodology for Aspergillus organisms, which also has the potential to improve the public's understanding of the fungal kingdom.
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Affiliation(s)
- Haozhong Ma
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jinshan Yang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xiaolu Chen
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Xinyu Jiang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yimin Su
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Shanlei Qiao
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
| | - Guowei Zhong
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
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42
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Wu S, Chen Y, Li Z, Li J, Zhao F, Su X. Towards multi-label classification: Next step of machine learning for microbiome research. Comput Struct Biotechnol J 2021; 19:2742-2749. [PMID: 34093989 PMCID: PMC8131981 DOI: 10.1016/j.csbj.2021.04.054] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/21/2021] [Accepted: 04/22/2021] [Indexed: 11/22/2022] Open
Abstract
Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.
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Affiliation(s)
- Shunyao Wu
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Yuzhu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Zhiruo Li
- School of Mathematics and Statistics, Qingdao University, Qingdao, Shandong 266071, China
| | - Jian Li
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Fengyang Zhao
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
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43
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Zhang W, Chen X, Wong KC. Noninvasive early diagnosis of intestinal diseases based on artificial intelligence in genomics and microbiome. J Gastroenterol Hepatol 2021; 36:823-831. [PMID: 33880763 DOI: 10.1111/jgh.15500] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/15/2022]
Abstract
The maturing development in artificial intelligence (AI) and genomics has propelled the advances in intestinal diseases including intestinal cancer, inflammatory bowel disease (IBD), and irritable bowel syndrome (IBS). On the other hand, colorectal cancer is the second most deadly and the third most common type of cancer in the world according to GLOBOCAN 2020 data. The mechanisms behind IBD and IBS are still speculative. The conventional methods to identify colorectal cancer, IBD, and IBS are based on endoscopy or colonoscopy to identify lesions. However, it is invasive, demanding, and time-consuming for early-stage intestinal diseases. To address those problems, new strategies based on blood and/or human microbiome in gut, colon, or even feces were developed; those methods took advantage of high-throughput sequencing and machine learning approaches. In this review, we summarize the recent research and methods to diagnose intestinal diseases with machine learning technologies based on cell-free DNA and microbiome data generated by amplicon sequencing or whole-genome sequencing. Those methods play an important role in not only intestinal disease diagnosis but also therapy development in the near future.
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Affiliation(s)
- Weitong Zhang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR.,Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
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Abstract
Microbial ecology is the study of microorganisms present in nature. It particularly focuses on microbial interactions with any biota and with surrounding environments. Microbial ecology is entering its golden age with innovative multi-omics methods triggered by next-generation sequencing technologies. However, the extraction of ecologically relevant information from ever-increasing omics data remains one of the most challenging tasks in microbial ecology. This special issue includes 11 review articles that provide an overview of the state of the art of omics-based approaches in the field of microbial ecology, with particular emphasis on the interpretation of omics data, environmental pollution tracking, interactions in microbiomes, and viral ecology.
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45
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Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau ECD, D’Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol 2021; 12:635781. [PMID: 33692771 PMCID: PMC7937616 DOI: 10.3389/fmicb.2021.635781] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/28/2021] [Indexed: 12/23/2022] Open
Abstract
The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.
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Affiliation(s)
- Isabel Moreno-Indias
- Instituto de Investigación Biomédica de Málaga (IBIMA), Unidad de Gestión Clìnica de Endocrinologìa y Nutrición, Hospital Clìnico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomeìdica en Red de Fisiopatologtìa de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Miroslava Nedyalkova
- Human Genetics and Disease Mechanisms, Latvian Biomedical Research and Study Centre, Riga, Latvia
| | - Ilze Elbere
- Latvian Biomedical Research and Study Centre, Riga, Latvia
| | | | - Muhamed Adilovic
- Department of Genetics and Bioengineering, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Onder Aydemir
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Abdullah Gul University, Kayseri, Turkey
| | | | - Domenica D’Elia
- Department for Biomedical Sciences, Institute for Biomedical Technologies, National Research Council, Bari, Italy
| | - Mahesh S. Desai
- Department of Infection and Immunity, Luxembourg Institute of Health, Esch-sur-Alzette, Luxembourg
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Laurent Falquet
- Department of Biology, University of Fribourg, Fribourg, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Aycan Gundogdu
- Department of Microbiology and Clinical Microbiology, Faculty of Medicine, Erciyes University, Kayseri, Turkey
- Metagenomics Laboratory, Genome and Stem Cell Center (GenKök), Erciyes University, Kayseri, Turkey
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Marta B. Lopes
- NOVA Laboratory for Computer Science and Informatics (NOVA LINCS), FCT, UNL, Caparica, Portugal
- Centro de Matemática e Aplicações (CMA), FCT, UNL, Caparica, Portugal
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | - Cláudia Marques
- CINTESIS, NOVA Medical School, NMS, Universidade Nova de Lisboa, Lisbon, Portugal
| | - Michael Mason
- Computational Oncology, Sage Bionetworks, Seattle, WA, United States
| | - Patrick May
- Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lejla Pašić
- Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Sándor Pongor
- Faculty of Information Tehnology and Bionics, Pázmány University, Budapest, Hungary
| | - Vasilis J. Promponas
- Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruñ, Poland
| | - Julio Saez-Rodriguez
- Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine and Heidelberg University Hospital, Heidelberg, Germany
| | - Alexia Sampri
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, University of Manchester, Manchester, United Kingdom
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Jozef Stefan Institute, Ljubljana, Slovenia
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Lab, Faculty of the Food Science and Technology, Institute of Life Sciences, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Ciprian-Octavian Truică
- Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Dimitrios Vlachakis
- Laboratory of Genetics, Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, Athens, Greece
| | - Ercument Yilmaz
- Department of Computer Technologies, Karadeniz Technical University, Trabzon, Turkey
| | - Georg Zeller
- European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
| | - Aldert L. Zomer
- Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - David Gómez-Cabrero
- Navarrabiomed, Complejo Hospitalario de Navarra (CHN), IdiSNA, Universidad Pública de Navarra (UPNA), Pamplona, Spain
| | - Marcus J. Claesson
- School of Microbiology and APC Microbiome Ireland, University College Cork, Cork, Ireland
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Ramon E, Belanche-Muñoz L, Molist F, Quintanilla R, Perez-Enciso M, Ramayo-Caldas Y. kernInt: A Kernel Framework for Integrating Supervised and Unsupervised Analyses in Spatio-Temporal Metagenomic Datasets. Front Microbiol 2021; 12:609048. [PMID: 33584612 PMCID: PMC7876079 DOI: 10.3389/fmicb.2021.609048] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 01/07/2021] [Indexed: 12/20/2022] Open
Abstract
The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i.e., prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github.com/elies-ramon/kernInt.
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Affiliation(s)
- Elies Ramon
- Plant and Animal Genomics, Statistical and Population Genomics Group, CSIC-IRTA-UAB-UB Consortium, Centre for Research in Agricultural Genomics (CRAG), Bellaterra, Spain
| | - Lluís Belanche-Muñoz
- Department of Computer Science, Polytechnic University of Catalonia, Barcelona, Spain
| | | | | | - Miguel Perez-Enciso
- Plant and Animal Genomics, Statistical and Population Genomics Group, CSIC-IRTA-UAB-UB Consortium, Centre for Research in Agricultural Genomics (CRAG), Bellaterra, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
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47
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McCoubrey LE, Elbadawi M, Orlu M, Gaisford S, Basit AW. Harnessing machine learning for development of microbiome therapeutics. Gut Microbes 2021; 13:1-20. [PMID: 33522391 PMCID: PMC7872042 DOI: 10.1080/19490976.2021.1872323] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 12/20/2020] [Indexed: 02/06/2023] Open
Abstract
The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.
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Affiliation(s)
| | - Moe Elbadawi
- UCL School of Pharmacy, University College London, London, UK
| | - Mine Orlu
- UCL School of Pharmacy, University College London, London, UK
| | - Simon Gaisford
- UCL School of Pharmacy, University College London, London, UK
- FabRx Ltd., Ashford, Kent, UK
| | - Abdul W. Basit
- UCL School of Pharmacy, University College London, London, UK
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48
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Walker LP, Buhler D. Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology. Ind Biotechnol (New Rochelle N Y) 2020. [DOI: 10.1089/ind.2020.29222.lpw] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Affiliation(s)
- Larry P. Walker
- Biosystems and Agricultural Engineering Department, Michigan State University, East Lansing, Michigan, USA
- Somaiya Vidyavihar University, Mumbai, India
- Biological and Environmental Engineering Department, Cornell University, Ithaca, New York, USA
| | - Douglas Buhler
- Michigan State University AgBioResearch and Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA
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49
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Na D. User guides for biologists to learn computational methods. J Microbiol 2020; 58:173-175. [DOI: 10.1007/s12275-020-9723-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 02/10/2020] [Accepted: 02/11/2020] [Indexed: 12/12/2022]
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