1
|
Kundu P, Beura S, Mondal S, Das AK, Ghosh A. Machine learning for the advancement of genome-scale metabolic modeling. Biotechnol Adv 2024:108400. [PMID: 38944218 DOI: 10.1016/j.biotechadv.2024.108400] [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: 10/25/2023] [Revised: 05/13/2024] [Accepted: 06/23/2024] [Indexed: 07/01/2024]
Abstract
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.
Collapse
Affiliation(s)
- Pritam Kundu
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Satyajit Beura
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Suman Mondal
- P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India
| | - Amit Kumar Das
- Department of Bioscience and Biotechnology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
| | - Amit Ghosh
- School School of Energy Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India; P.K. Sinha Centre for Bioenergy and Renewables, Indian Institute of Technology Kharagpur, West Bengal 721302, India.
| |
Collapse
|
2
|
Sadeghi P, Karimi H, Lavafian A, Rashedi R, Samieefar N, Shafiekhani S, Rezaei N. Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective. Expert Rev Clin Immunol 2024:1-18. [PMID: 38771915 DOI: 10.1080/1744666x.2024.2359019] [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: 11/19/2023] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
INTRODUCTION Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoimmune diseases are uncertain because of the variety of onset and development. Machine learning can identify clinically relevant patterns from vast amounts of data. Hence, its introduction has been beneficial in the diagnosis and management of patients. AREAS COVERED This narrative review was conducted through searching various electronic databases, including PubMed, Scopus, and Web of Science. This study thoroughly explores the current knowledge and identifies the remaining gaps in the applications of machine learning specifically in the context of pediatric autoimmune and related diseases. EXPERT OPINION Machine learning algorithms have the potential to completely change how pediatric autoimmune disorders are identified, treated, and managed. Machine learning can assist physicians in making more precise and fast judgments, identifying new biomarkers and therapeutic targets, and personalizing treatment strategies for each patient by utilizing massive datasets and powerful analytics.
Collapse
Affiliation(s)
- Parniyan Sadeghi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hanie Karimi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Atiye Lavafian
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- School of Medicine, Semnan University of Medical Science, Semnan, Iran
| | - Ronak Rashedi
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Noosha Samieefar
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- USERN Office, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sajad Shafiekhani
- Department of Biomedical Engineering, Buein Zahra Technical University, Qazvin, Iran
| | - Nima Rezaei
- Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
3
|
Meier TA, Refahi MS, Hearne G, Restifo DS, Munoz-Acuna R, Rosen GL, Woloszynek S. The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain. Curr Pain Headache Rep 2024:10.1007/s11916-024-01264-0. [PMID: 38822995 DOI: 10.1007/s11916-024-01264-0] [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: 04/28/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE OF REVIEW This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies. RECENT FINDINGS In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.
Collapse
Affiliation(s)
| | - Mohammad S Refahi
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Gavin Hearne
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | | | - Ricardo Munoz-Acuna
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Gail L Rosen
- Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
| | - Stephen Woloszynek
- Anesthesia, Critical Care, and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| |
Collapse
|
4
|
Nichols B, Briola A, Logan M, Havlik J, Mascellani A, Gkikas K, Milling S, Ijaz UZ, Quince C, Svolos V, Russell RK, Hansen R, Gerasimidis K. Gut metabolome and microbiota signatures predict response to treatment with exclusive enteral nutrition in a prospective study in children with active Crohn's disease. Am J Clin Nutr 2024; 119:885-895. [PMID: 38569785 PMCID: PMC11007740 DOI: 10.1016/j.ajcnut.2023.12.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/06/2023] [Accepted: 12/22/2023] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND Predicting response to exclusive enteral nutrition (EEN) in active Crohn's disease (CD) could lead to therapy personalization and pretreatment optimization. OBJECTIVES This study aimed to explore the ability of pretreatment parameters to predict fecal calprotectin (FCal) levels at EEN completion in a prospective study in children with CD. METHODS In children with active CD, clinical parameters, dietary intake, cytokines, inflammation-related blood proteomics, and diet-related metabolites, metabolomics and microbiota in feces, were measured before initiation of 8 wk of EEN. Prediction of FCal levels at EEN completion was performed using machine learning. Data are presented with medians (IQR). RESULTS Of 37 patients recruited, 15 responded (FCal < 250 μg/g) to EEN (responders) and 22 did not (nonresponders). Clinical and immunological parameters were not associated with response to EEN. Responders had lesser (μmol/g) butyrate [responders: 13.2 (8.63-18.4) compared with nonresponders: 22.3 (12.0-32.0); P = 0.03], acetate [responders: 49.9 (46.4-68.4) compared with nonresponders: 70.4 (57.0-95.5); P = 0.027], phenylacetate [responders: 0.175 (0.013-0.611) compared with nonresponders: 0.943 (0.438-1.35); P = 0.021], and a higher microbiota richness [315 (269-347) compared with nonresponders: 243 (205-297); P = 0.015] in feces than nonresponders. Responders consumed (portions/1000 kcal/d) more confectionery products [responders: 0.55 (0.38-0.72) compared with nonresponders: 0.19 (0.01-0.38); P = 0.045]. A multicomponent model using fecal parameters, dietary data, and clinical and immunological parameters predicted response to EEN with 78% accuracy (sensitivity: 80%; specificity: 77%; positive predictive value: 71%; negative predictive value: 85%). Higher taxon abundance from Ruminococcaceae, Lachnospiraceae, and Bacteroides and phenylacetate, butyrate, and acetate were the most influential variables in predicting lack of response to EEN. CONCLUSIONS We identify microbial signals and diet-related metabolites in feces, which could comprise targets for pretreatment optimization and personalized nutritional therapy in pediatric CD.
Collapse
Affiliation(s)
- Ben Nichols
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - Anny Briola
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - Michael Logan
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - Jaroslav Havlik
- Department of Food Science, Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Anna Mascellani
- Department of Food Science, Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Konstantinos Gkikas
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - Simon Milling
- School of Infection and Inflammation, University of Glasgow, Glasgow, United Kingdom
| | - Umer Zeeshan Ijaz
- Civil Engineering, School of Engineering, University of Glasgow, Glasgow, United Kingdom
| | | | - Vaios Svolos
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom
| | - Richard K Russell
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Royal Hospital for Children and Young People, Edinburgh, United Kingdom
| | - Richard Hansen
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Royal Hospital for Children, Glasgow, United Kingdom; Department of Child Health, Division of Clinical and Molecular Medicine, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Konstantinos Gerasimidis
- Human Nutrition, School of Medicine, University of Glasgow, Glasgow Royal Infirmary, Glasgow, United Kingdom.
| |
Collapse
|
5
|
Fonseca DC, Marques Gomes da Rocha I, Depieri Balmant B, Callado L, Aguiar Prudêncio AP, Tepedino Martins Alves J, Torrinhas RS, da Rocha Fernandes G, Linetzky Waitzberg D. Evaluation of gut microbiota predictive potential associated with phenotypic characteristics to identify multifactorial diseases. Gut Microbes 2024; 16:2297815. [PMID: 38235595 PMCID: PMC10798365 DOI: 10.1080/19490976.2023.2297815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 12/18/2023] [Indexed: 01/19/2024] Open
Abstract
Gut microbiota has been implicated in various clinical conditions, yet the substantial heterogeneity in gut microbiota research results necessitates a more sophisticated approach than merely identifying statistically different microbial taxa between healthy and unhealthy individuals. Our study seeks to not only select microbial taxa but also explore their synergy with phenotypic host variables to develop novel predictive models for specific clinical conditions. DESIGN We assessed 50 healthy and 152 unhealthy individuals for phenotypic variables (PV) and gut microbiota (GM) composition by 16S rRNA gene sequencing. The entire modeling process was conducted in the R environment using the Random Forest algorithm. Model performance was assessed through ROC curve construction. RESULTS We evaluated 52 bacterial taxa and pre-selected PV (p < 0.05) for their contribution to the final models. Across all diseases, the models achieved their best performance when GM and PV data were integrated. Notably, the integrated predictive models demonstrated exceptional performance for rheumatoid arthritis (AUC = 88.03%), type 2 diabetes (AUC = 96.96%), systemic lupus erythematosus (AUC = 98.4%), and type 1 diabetes (AUC = 86.19%). CONCLUSION Our findings underscore that the selection of bacterial taxa based solely on differences in relative abundance between groups is insufficient to serve as clinical markers. Machine learning techniques are essential for mitigating the considerable variability observed within gut microbiota. In our study, the use of microbial taxa alone exhibited limited predictive power for health outcomes, while the integration of phenotypic variables into predictive models substantially enhanced their predictive capabilities.
Collapse
Affiliation(s)
- Danielle Cristina Fonseca
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Ilanna Marques Gomes da Rocha
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Bianca Depieri Balmant
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Leticia Callado
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Ana Paula Aguiar Prudêncio
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Juliana Tepedino Martins Alves
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Raquel Susana Torrinhas
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Gabriel da Rocha Fernandes
- Biosystems Informatics and Genomics Group, Instituto René Rachou - Fiocruz Minas, Belo Horizonte, Brazil
| | - Dan Linetzky Waitzberg
- Laboratory of Nutrition and Metabolic Surgery of the Digestive System, LIM 35, Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Gastroenterology, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| |
Collapse
|
6
|
Rimmer P, Iqbal T. Prognostic modelling in IBD. Best Pract Res Clin Gastroenterol 2023; 67:101877. [PMID: 38103929 DOI: 10.1016/j.bpg.2023.101877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 11/24/2023] [Indexed: 12/19/2023]
Abstract
In the ideal world prognostication or predicting disease course in any chronic condition would allow the clinician to anticipate disease behaviour, providing crucial information for the patient and data regarding best use of resources. Prognostication also allows an understanding of likely response to treatment and the risk of adverse effects of a treatment leading to withdrawal in any individual patient. Therefore, the ability to predict outcomes from the onset of disease is the key step to developing precision personalised medicine, which is the design of medical care to optimise efficiency or therapeutic benefit based on careful profiling of patients. An important corollary is to prevent unnecessary healthcare costs. This paper outlines currently available predictors of disease outcome in IBD and looks to the future which will involve the use of artificial intelligence to interrogate big data derived from various important 'omes' to tease out a more holistic approach to IBD.
Collapse
Affiliation(s)
- Peter Rimmer
- Queen Elizabeth Hospital Birmingham, B15 2TH, UK; University of Birmingham, College of Medical and Dental Science, UK.
| | - Tariq Iqbal
- Queen Elizabeth Hospital Birmingham, B15 2TH, UK; University of Birmingham, College of Medical and Dental Science, UK.
| |
Collapse
|
7
|
Kang DY, Park JL, Yeo MK, Kang SB, Kim JM, Kim JS, Kim SY. Diagnosis of Crohn's disease and ulcerative colitis using the microbiome. BMC Microbiol 2023; 23:336. [PMID: 37951857 PMCID: PMC10640746 DOI: 10.1186/s12866-023-03084-5] [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: 03/20/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a multifactorial chronic inflammatory disease resulting from dysregulation of the mucosal immune response and gut microbiota. Crohn's disease (CD) and ulcerative colitis (UC) are difficult to distinguish, and differential diagnosis is essential for establishing a long-term treatment plan for patients. Furthermore, the abundance of mucosal bacteria is associated with the severity of the disease. This study aimed to differentiate and diagnose these two diseases using the microbiome and identify specific biomarkers associated with disease activity. RESULTS Differences in the abundance and composition of the microbiome between IBD patients and healthy controls (HC) were observed. Compared to HC, the diversity of the gut microbiome in patients with IBD decreased; the diversity of the gut microbiome in patients with CD was significantly lower. Sixty-eight microbiota members (28 for CD and 40 for UC) associated with these diseases were identified. Additionally, as the disease progressed through different stages, the diversity of the bacteria decreased. The abundances of Alistipes shahii and Pseudodesulfovibrio aespoeensis were negatively correlated with the severity of CD, whereas the abundance of Polynucleobacter wianus was positively correlated. The severity of UC was negatively correlated with the abundance of A. shahii, Porphyromonas asaccharolytica and Akkermansia muciniphilla, while it was positively correlated with the abundance of Pantoea candidatus pantoea carbekii. A regularized logistic regression model was used for the differential diagnosis of the two diseases. The area under the curve (AUC) was used to examine the performance of the model. The model discriminated UC and CD at an AUC of 0.873 (train set), 0.778 (test set), and 0.633 (validation set) and an area under the precision-recall curve (PRAUC) of 0.888 (train set), 0.806 (test set), and 0.474 (validation set). CONCLUSIONS Based on fecal whole-metagenome shotgun (WMS) sequencing, CD and UC were diagnosed using a machine-learning predictive model. Microbiome biomarkers associated with disease activity (UC and CD) are also proposed.
Collapse
Affiliation(s)
- Da-Yeon Kang
- Department of New Drug Development, Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon, Korea
- Disease Target Structure Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Korea
| | - Jong-Lyul Park
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Korea
| | - Min-Kyung Yeo
- Department of Pathology, Chungnam National University School of Medicine, Munwha-Ro 266, Daejeon, 35015, Korea
| | - Sang-Bum Kang
- Department of Internal Medicine, Division of Gastroenterology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Daejeon, Korea
| | - Jin-Man Kim
- Department of Pathology, Chungnam National University School of Medicine, Munwha-Ro 266, Daejeon, 35015, Korea
| | - Ju Seok Kim
- Departments of Internal Medicine, Chungnam National University School of Medicine, Daejeon, Korea.
| | - Seon-Young Kim
- Korea Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, Korea.
| |
Collapse
|
8
|
He X, Ye H, Zhao R, Lu M, Chen Q, Bao L, Lv T, Li Q, Wu F. Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization. Comput Biol Med 2023; 163:107216. [PMID: 37399742 DOI: 10.1016/j.compbiomed.2023.107216] [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: 05/21/2023] [Revised: 06/13/2023] [Accepted: 06/25/2023] [Indexed: 07/05/2023]
Abstract
Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.
Collapse
Affiliation(s)
- Xixi He
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Huajun Ye
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Rui Zhao
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Mengmeng Lu
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Qiwen Chen
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Lishimeng Bao
- The Second Clinical College, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| | - Tianmin Lv
- Department of Nursing Wenzhou Heping International Hospital, Wenzhou, Zhejiang, 325000, China.
| | - Qiang Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Fang Wu
- Department of Gastroenterology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
| |
Collapse
|
9
|
Ahmad HA, East JE, Panaccione R, Travis S, Canavan JB, Usiskin K, Byrne MF. Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials. J Crohns Colitis 2023; 17:1342-1353. [PMID: 36812142 PMCID: PMC10441563 DOI: 10.1093/ecco-jcc/jjad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Indexed: 02/24/2023]
Abstract
Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.
Collapse
Affiliation(s)
| | - James E East
- Translational Gastroenterology Unit, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Remo Panaccione
- Inflammatory Bowel Disease Clinic, University of Calgary, Calgary, AB, Canada
| | - Simon Travis
- Translational Gastroenterology Unit, Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | | | | | - Michael F Byrne
- University of British Columbia, Division of Gastroenterology, Department of Medicine, Vancouver, BC, Canada
- Satisfai Health, Vancouver, BC, Canada
| |
Collapse
|
10
|
Ashton JJ, Gurung A, Davis C, Seaby EG, Coelho T, Batra A, Afzal NA, Ennis S, Beattie RM. The Pediatric Crohn Disease Morbidity Index (PCD-MI): Development of a Tool to Assess Long-Term Disease Burden Using a Data-Driven Approach. J Pediatr Gastroenterol Nutr 2023; 77:70-78. [PMID: 37079872 PMCID: PMC10259218 DOI: 10.1097/mpg.0000000000003793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/05/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND/OBJECTIVE Heterogeneity and chronicity of Crohn disease (CD) make prediction of outcomes difficult. To date, no longitudinal measure can quantify burden over a patient's disease course, preventing assessment and integration into predictive modeling. Here, we aimed to demonstrate the feasibility of constructing a data driven, longitudinal disease burden score. METHODS Literature was reviewed for tools used in assessment of CD activity. Themes were identified to construct a pediatric CD morbidity index (PCD-MI). Scores were assigned to variables. Data were extracted automatically from the electronic patient records at Southampton Children's Hospital, diagnosed from 2012 to 2019 (inclusive). PCD-MI scores were calculated, adjusted for duration of follow up and assessed for variation (ANOVA) and distribution (Kolmogorov-Smirnov). RESULTS Nineteen clinical/biological features across five themes were included in the PCD-MI including blood/fecal/radiological/endoscopic results, medication usage, surgery, growth parameters, and extraintestinal manifestations. Maximal score was 100 after accounting for follow-up duration. PCD-MI was assessed in 66 patients, mean age 12.5 years. Following quality filtering, 9528 blood/fecal test results and 1309 growth measures were included. Mean PCD-MI score was 14.95 (range 2.2-32.5); data were normally distributed ( P = 0.2) with 25% of patients having a PCD-MI < 10. There was no difference in the mean PCD-MI when split by year of diagnosis, F -statistic 1.625, P = 0.147. CONCLUSIONS PCD-MI is a calculatable measure for a cohort of patients diagnosed over an 8-year period, integrating a wide-range of data with potential to determine high or low disease burden. Future iterations of the PCD-MI require refinement of included features, optimized scores, and validation on external cohorts.
Collapse
Affiliation(s)
- James J. Ashton
- From the Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - Abhilasha Gurung
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - Cai Davis
- NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Eleanor G. Seaby
- From the Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Tracy Coelho
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - Akshay Batra
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - Nadeem A. Afzal
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| | - Sarah Ennis
- From the Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - R. Mark Beattie
- the Department of Paediatric Gastroenterology, Southampton Children’s Hospital, Southampton, UK
| |
Collapse
|
11
|
Li L, Wang H, Dong S, Ma Y. Supplementation with alpha-glycerol monolaurate during late gestation and lactation enhances sow performance, ameliorates milk composition, and improves growth of suckling piglets. J Anim Sci Biotechnol 2023; 14:47. [PMID: 37016429 PMCID: PMC10074715 DOI: 10.1186/s40104-023-00848-x] [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: 09/29/2022] [Accepted: 02/05/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Physiological changes during lactation cause oxidative stress in sows, reduce immunity, and hamper the growth capacity of piglets. Alpha-glycerol monolaurate (α-GML) has potential for enhancing the antimicrobial activity of sows and the growth of suckling piglets. METHODS Eighty sows were allocated randomly to four groups: basal diet and basal diets supplemented with 500, 1000, or 2000 mg/kg α-GML. The experiment started on d 85 of gestation and lasted until piglets were weaned on d 21 of lactation. The number of live-born piglets was standardized to 12 ± 1 per sow on day of parturition. On d 0 and 21 of lactation, body weight of piglets was measured and milk samples were obtained from sows, and serum samples and feces from piglets were obtained on d 21. RESULTS Feed intake, backfat loss, and weaning estrus interval did not differ among the four groups of sows. Maternal α-GML supplementation increased (P < 0.05) the body weight of piglets at weaning and the apparent total tract digestibility of crude fat of sows. The immunoglobulin A and immunoglobulin G levels were greater (P < 0.05) in a quadratic manner in the milk of sows as dietary α-GML increased. Concerning fatty acid profile, C12:0, C15:0, C17:0, C18:2n6c, C18:3n3, C24:0, and C22:6n3 were higher (P < 0.05) in linear and quadratic manners in colostrum of sows-fed α-GML diets compared with the control sows. There was lower (P < 0.05) n-6:n-3 polyunsaturated fatty acid ratio in milk than in the control sows. Maternal α-GML increased the abundance of Firmicutes (P < 0.05) and decreased the abundance of Proteobacteria (P < 0.05) of piglet fecal microbiota. CONCLUSIONS Dietary supplementation with α-GML improved milk immunoglobulins and altered fatty acids of sows, thereby improving the health of piglets.
Collapse
Affiliation(s)
- Longxian Li
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Huakai Wang
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shuang Dong
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yongxi Ma
- State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing, China.
| |
Collapse
|
12
|
Xu S, Zhan L, Tang W, Wang Q, Dai Z, Zhou L, Feng T, Chen M, Wu T, Hu E, Yu G. MicrobiotaProcess: A comprehensive R package for deep mining microbiome. Innovation (N Y) 2023; 4:100388. [PMID: 36895758 PMCID: PMC9988672 DOI: 10.1016/j.xinn.2023.100388] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 01/30/2023] [Indexed: 02/05/2023] Open
Abstract
The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of an effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under a tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analyses, and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results.
Collapse
Affiliation(s)
- Shuangbin Xu
- Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Li Zhan
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenli Tang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Qianwen Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Zehan Dai
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lang Zhou
- Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tingze Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Meijun Chen
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianzhi Wu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Erqiang Hu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Guangchuang Yu
- Division of Laboratory Medicine, Microbiome Medicine Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China.,Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
13
|
Using machine learning to impact on long-term clinical care: principles, challenges, and practicalities. Pediatr Res 2023; 93:324-333. [PMID: 35906306 PMCID: PMC9937918 DOI: 10.1038/s41390-022-02194-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/10/2022] [Accepted: 06/22/2022] [Indexed: 11/08/2022]
Abstract
The rise of machine learning in healthcare has significant implications for paediatrics. Long-term conditions with significant disease heterogeneity comprise large portions of the routine work performed by paediatricians. Improving outcomes through discovery of disease and treatment prediction models, alongside novel subgroup clustering of patients, are some of the areas in which machine learning holds significant promise. While artificial intelligence has percolated into routine use in our day to day lives through advertising algorithms, song or movie selections and sifting of spam emails, the ability of machine learning to utilise highly complex and dimensional data has not yet reached its full potential in healthcare. In this review article, we discuss some of the foundations of machine learning, including some of the basic algorithms. We emphasise the importance of correct utilisation of machine learning, including adequate data preparation and external validation. Using nutrition in preterm infants and paediatric inflammatory bowel disease as examples, we discuss the evidence and potential utility of machine learning in paediatrics. Finally, we review some of the future applications, alongside challenges and ethical considerations related to application of artificial intelligence. IMPACT: Machine learning is a widely used term; however, understanding of the process and application to healthcare is lacking. This article uses clinical examples to explore complex machine learning terms and algorithms. We discuss limitations and potential future applications within paediatrics and neonatal medicine.
Collapse
|
14
|
Jurburg SD, Buscot F, Chatzinotas A, Chaudhari NM, Clark AT, Garbowski M, Grenié M, Hom EFY, Karakoç C, Marr S, Neumann S, Tarkka M, van Dam NM, Weinhold A, Heintz-Buschart A. The community ecology perspective of omics data. MICROBIOME 2022; 10:225. [PMID: 36510248 PMCID: PMC9746134 DOI: 10.1186/s40168-022-01423-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
The measurement of uncharacterized pools of biological molecules through techniques such as metabarcoding, metagenomics, metatranscriptomics, metabolomics, and metaproteomics produces large, multivariate datasets. Analyses of these datasets have successfully been borrowed from community ecology to characterize the molecular diversity of samples (ɑ-diversity) and to assess how these profiles change in response to experimental treatments or across gradients (β-diversity). However, sample preparation and data collection methods generate biases and noise which confound molecular diversity estimates and require special attention. Here, we examine how technical biases and noise that are introduced into multivariate molecular data affect the estimation of the components of diversity (i.e., total number of different molecular species, or entities; total number of molecules; and the abundance distribution of molecular entities). We then explore under which conditions these biases affect the measurement of ɑ- and β-diversity and highlight how novel methods commonly used in community ecology can be adopted to improve the interpretation and integration of multivariate molecular data. Video Abstract.
Collapse
Affiliation(s)
- Stephanie D Jurburg
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
- Institute of Biology, Leipzig University, Leipzig, Germany.
| | - François Buscot
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Soil Ecology, Helmholtz Centre for Environmental Research- UFZ, Halle, Germany
| | - Antonis Chatzinotas
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Leipzig University, Leipzig, Germany
| | - Narendrakumar M Chaudhari
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
| | - Adam T Clark
- Institute of Biology, University of Graz, Graz, Austria
| | - Magda Garbowski
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Botany, University of Wyoming, Wyoming, USA
| | - Matthias Grenié
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Leipzig University, Leipzig, Germany
| | - Erik F Y Hom
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Biology and Center for Biodiversity and Conservation Research, University of Mississippi, Oxford, Mississippi, USA
| | - Canan Karakoç
- Department of Environmental Microbiology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Biology, Indiana University, Indiana, USA
| | - Susanne Marr
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biology, Geobotany and Botanical Garden, Martin Luther University Halle Wittenberg, Halle, Germany
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, Halle, Germany
| | - Steffen Neumann
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, Halle, Germany
| | - Mika Tarkka
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Department of Soil Ecology, Helmholtz Centre for Environmental Research- UFZ, Halle, Germany
| | - Nicole M van Dam
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
- Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, Germany
| | - Alexander Weinhold
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Institute of Biodiversity, Friedrich Schiller University, Jena, Germany
| | - Anna Heintz-Buschart
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
15
|
Wu P, Wu B, Zhuang Z, Liu J, Hong L, Ma B, Lin B, Wang J, Lin C, Chen J, Chen S. Intestinal mucosal and fecal microbiota profiles in Crohn's disease in Chinese children. MEDICINE IN MICROECOLOGY 2022. [DOI: 10.1016/j.medmic.2022.100071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
|
16
|
Fernandez ME, Nazar FN, Moine LB, Jaime CE, Kembro JM, Correa SG. Network Analysis of Inflammatory Bowel Disease Research: Towards the Interactome. J Crohns Colitis 2022; 16:1651-1662. [PMID: 35439301 DOI: 10.1093/ecco-jcc/jjac059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Modern views accept that inflammatory bowel diseases [IBD] emerge from complex interactions among the multiple components of a biological network known as the 'IBD interactome'. These diverse components belong to different functional levels including cells, molecules, genes and biological processes. This diversity can make it difficult to integrate available empirical information from human patients into a collective view of aetiopathogenesis, a necessary step to understand the interactome. Herein, we quantitatively analyse how the representativeness of components involved in human IBD and their relationships ha ve changed over time. METHODS A bibliographic search in PubMed retrieved 25 971 abstracts of experimental studies on IBD in humans, published between 1990 and 2020. Abstracts were scanned automatically for 1218 IBD interactome components proposed in recent reviews. The resulting databases are freely available and were visualized as networks indicating the frequency at which different components are referenced together within each abstract. RESULTS As expected, over time there was an increase in components added to the IBD network and heightened connectivity within and across functional levels. However, certain components were consistently studied together, forming preserved motifs in the networks. These overrepresented and highly linked components reflect main 'hypotheses' in IBD research in humans. Interestingly, 82% of the components cited in reviews were absent or showed low frequency, suggesting that many aspects of the proposed IBD interactome still have weak experimental support in humans. CONCLUSIONS A reductionist and fragmented approach to the study of IBD has prevailed in previous decades, highlighting the importance of transitioning towards a more integrated interactome framework.
Collapse
Affiliation(s)
- M Emilia Fernandez
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Córdoba, Argentina
| | - F Nicolas Nazar
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Biológicas y Tecnológicas (IIByT), Córdoba, Argentina.,Universidad Nacional de Córdoba, Facultad de Ciencias Exactas, Físicas y Naturales, Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Argentina
| | - Luciana B Moine
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Córdoba, Argentina
| | - Cristian E Jaime
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Córdoba, Argentina
| | - Jackelyn M Kembro
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Instituto de Investigaciones Biológicas y Tecnológicas (IIByT), Córdoba, Argentina.,Universidad Nacional de Córdoba, Facultad de Ciencias Exactas, Físicas y Naturales, Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Córdoba, Argentina.,Universidad Nacional de Córdoba, Facultad de Ciencias Exactas, Físicas y Naturales, Cátedra de Química Biológica, Córdoba, Argentina
| | - Silvia G Correa
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Centro de Investigaciones en Bioquímica Clínica e Inmunología (CIBICI), Córdoba, Argentina.,Universidad Nacional de Córdoba, Facultad de Ciencias Químicas, Departamento de Bioquímica Clínica, Inmunología, Córdoba, Argentina
| |
Collapse
|
17
|
Kawamoto A, Takenaka K, Okamoto R, Watanabe M, Ohtsuka K. Systematic review of artificial intelligence-based image diagnosis for inflammatory bowel disease. Dig Endosc 2022; 34:1311-1319. [PMID: 35441381 DOI: 10.1111/den.14334] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/18/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES Diagnosis of inflammatory bowel diseases (IBD) involves combining clinical, laboratory, endoscopic, histologic, and radiographic data. Artificial intelligence (AI) is rapidly being developed in various fields of medicine, including IBD. Because a key part in the diagnosis of IBD involves evaluating imaging data, AI is expected to play an important role in this aspect in the coming decades. We conducted a systematic literature review to highlight the current advancement of AI in diagnosing IBD from imaging data. METHODS We performed an electronic PubMed search of the MEDLINE database for studies up to January 2022 involving IBD and AI. Studies using imaging data as input were included, and nonimaging data were excluded. RESULTS A total of 27 studies are reviewed, including 18 studies involving endoscopic images and nine studies involving other imaging data. CONCLUSION We highlight in this review the recent advancement of AI in diagnosing IBD from imaging data by summarizing the relevant studies, and discuss the future role of AI in clinical practice.
Collapse
Affiliation(s)
- Ami Kawamoto
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kento Takenaka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Ryuichi Okamoto
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mamoru Watanabe
- TMDU Advanced Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Kazuo Ohtsuka
- Department of Gastroenterology and Hepatology, Tokyo Medical and Dental University, Tokyo, Japan.,Endoscopic Unit, Tokyo Medical and Dental University, Tokyo, Japan
| |
Collapse
|
18
|
Imangaliyev S, Schlötterer J, Meyer F, Seifert C. Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data. Diagnostics (Basel) 2022; 12:diagnostics12102514. [PMID: 36292203 PMCID: PMC9600435 DOI: 10.3390/diagnostics12102514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/08/2022] [Accepted: 10/11/2022] [Indexed: 12/02/2022] Open
Abstract
Most of the microbiome studies suggest that using ensemble models such as Random Forest results in best predictive power. In this study, we empirically evaluate a more powerful ensemble learning algorithm, multi-view stacked generalization, on pediatric inflammatory bowel disease and adult colorectal cancer patients’ cohorts. We aim to check whether stacking would lead to better results compared to using a single best machine learning algorithm. Stacking achieves the best test set Average Precision (AP) on inflammatory bowel disease dataset reaching AP = 0.69, outperforming both the best base classifier (AP = 0.61) and the baseline meta learner built on top of base classifiers (AP = 0.63). On colorectal cancer dataset, the stacked classifier also outperforms (AP = 0.81) both the best base classifier (AP = 0.79) and the baseline meta learner (AP = 0.75). Stacking achieves best predictive performance on test set outperforming the best classifiers on both patient cohorts. Application of the stacking solves the issue of choosing the most appropriate machine learning algorithm by automating the model selection procedure. Clinical application of such a model is not limited to diagnosis task only, but it also can be extended to biomarker selection thanks to feature selection procedure.
Collapse
Affiliation(s)
- Sultan Imangaliyev
- Institute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), 45147 Essen, Germany
| | - Jörg Schlötterer
- Institute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), 45147 Essen, Germany
| | - Folker Meyer
- Institute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany
| | - Christin Seifert
- Institute for Artificial Intelligence in Medicine, University of Duisburg-Essen, 45131 Essen, Germany
- Cancer Research Center Cologne Essen (CCCE), 45147 Essen, Germany
- Correspondence:
| |
Collapse
|
19
|
Ashton JJ, Cheng G, Stafford IS, Kellermann M, Seaby EG, Cummings JRF, Coelho TAF, Batra A, Afzal NA, Beattie RM, Ennis S. Prediction of Crohn's Disease Stricturing Phenotype Using a NOD2-derived Genomic Biomarker. Inflamm Bowel Dis 2022; 29:511-521. [PMID: 36161322 PMCID: PMC10069659 DOI: 10.1093/ibd/izac205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Indexed: 12/09/2022]
Abstract
BACKGROUND Crohn's disease (CD) is highly heterogenous and may be complicated by stricturing behavior. Personalized prediction of stricturing will inform management. We aimed to create a stricturing risk stratification model using genomic/clinical data. METHODS Exome sequencing was performed on CD patients, and phenotype data retrieved. Biallelic variants in NOD2 were identified. NOD2 was converted into a per-patient deleteriousness metric ("GenePy"). Using training data, patients were stratified into risk groups for fibrotic stricturing using NOD2. Findings were validated in a testing data set. Models were modified to include disease location at diagnosis. Cox proportional hazards assessed performance. RESULTS Six hundred forty-five patients were included (373 children and 272 adults); 48 patients fulfilled criteria for monogenic NOD2-related disease (7.4%), 24 of whom had strictures. NOD2 GenePy scores stratified patients in training data into 2 risk groups. Within testing data, 30 of 161 patients (18.6%) were classified as high-risk based on the NOD2 biomarker, with stricturing in 17 of 30 (56.7%). In the low-risk group, 28 of 131 (21.4%) had stricturing behavior. Cox proportional hazards using the NOD2 risk groups demonstrated a hazard ratio (HR) of 2.092 (P = 2.4 × 10-5), between risk groups. Limiting analysis to patients diagnosed aged < 18-years improved performance (HR-3.164, P = 1 × 10-6). Models were modified to include disease location, such as terminal ileal (TI) disease or not. Inclusion of NOD2 risk groups added significant additional utility to prediction models. High-risk group pediatric patients presenting with TI disease had a HR of 4.89 (P = 2.3 × 10-5) compared with the low-risk group patients without TI disease. CONCLUSIONS A NOD2 genomic biomarker predicts stricturing risk, with prognostic power improved in pediatric-onset CD. Implementation into a clinical setting can help personalize management.
Collapse
Affiliation(s)
- James J Ashton
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.,Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK
| | - Guo Cheng
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.,NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
| | - Imogen S Stafford
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.,Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Melina Kellermann
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Eleanor G Seaby
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - J R Fraser Cummings
- Department of Gastroenterology, University Hospital Southampton, Southampton, UK
| | - Tracy A F Coelho
- Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK
| | - Akshay Batra
- Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK
| | - Nadeem A Afzal
- Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK
| | - R Mark Beattie
- Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK
| | - Sarah Ennis
- Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| |
Collapse
|
20
|
Verburgt CM, Dunn KA, Ghiboub M, Lewis JD, Wine E, Sigall Boneh R, Gerasimidis K, Shamir R, Penny S, Pinto DM, Cohen A, Bjorndahl P, Svolos V, Bielawski JP, Benninga MA, de Jonge WJ, Van Limbergen JE. Successful Dietary Therapy in Paediatric Crohn's Disease is Associated with Shifts in Bacterial Dysbiosis and Inflammatory Metabotype Towards Healthy Controls. J Crohns Colitis 2022; 17:61-72. [PMID: 36106847 PMCID: PMC9880954 DOI: 10.1093/ecco-jcc/jjac105] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/02/2022] [Accepted: 07/28/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND AND AIMS Nutritional therapy with the Crohn's Disease Exclusion Diet + Partial Enteral Nutrition [CDED+PEN] or Exclusive Enteral Nutrition [EEN] induces remission and reduces inflammation in mild-to-moderate paediatric Crohn's disease [CD]. We aimed to assess if reaching remission with nutritional therapy is mediated by correcting compositional or functional dysbiosis. METHODS We assessed metagenome sequences, short chain fatty acids [SCFA] and bile acids [BA] in 54 paediatric CD patients reaching remission after nutritional therapy [with CDED + PEN or EEN] [NCT01728870], compared to 26 paediatric healthy controls. RESULTS Successful dietary therapy decreased the relative abundance of Proteobacteria and increased Firmicutes towards healthy controls. CD patients possessed a mixture of two metabotypes [M1 and M2], whereas all healthy controls had metabotype M1. M1 was characterised by high Bacteroidetes and Firmicutes, low Proteobacteria, and higher SCFA synthesis pathways, and M2 was associated with high Proteobacteria and genes involved in SCFA degradation. M1 contribution increased during diet: 48%, 63%, up to 74% [Weeks 0, 6, 12, respectively.]. By Week 12, genera from Proteobacteria reached relative abundance levels of healthy controls with the exception of E. coli. Despite an increase in SCFA synthesis pathways, remission was not associated with increased SCFAs. Primary BA decreased with EEN but not with CDED+PEN, and secondary BA did not change during diet. CONCLUSION Successful dietary therapy induced correction of both compositional and functional dysbiosis. However, 12 weeks of diet was not enough to achieve complete correction of dysbiosis. Our data suggests that composition and metabotype are important and change quickly during the early clinical response to dietary intervention. Correction of dysbiosis may therefore be an important future treatment goal for CD.
Collapse
Affiliation(s)
| | | | - Mohammed Ghiboub
- Department of Paediatric Gastroenterology and Nutrition, Amsterdam University Medical Centers, University of Amsterdam, Emma Children’s Hospital, Amsterdam, The Netherlands,Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, University of Amsterdam, Amsterdam, The Netherlands
| | - James D Lewis
- Centre for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Division of Gastroenterology and Hepatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eytan Wine
- Division of Paediatric Gastroenterology, Stollery Children’s Hospital, University of Alberta, Edmonton, AB, Canada
| | - Rotem Sigall Boneh
- Wolfson Medical Centre, Holon, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Konstantinos Gerasimidis
- Department of Human Nutrition, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK
| | - Raanan Shamir
- Institute of Gastroenterology, Nutrition and Liver Diseases, Schneider Children’s Medical Centre, Petach-Tikva, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Susanne Penny
- Human Health Therapeutics, National Research Council, Halifax, NS, Canada
| | - Devanand M Pinto
- Human Health Therapeutics, National Research Council, Halifax, NS, Canada
| | - Alejandro Cohen
- Proteomics and Mass Spectrometry Core Facility, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Paul Bjorndahl
- Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada
| | - Vaios Svolos
- Department of Human Nutrition, School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, UK
| | - Joseph P Bielawski
- Centre for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA,Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada
| | - Marc A Benninga
- Department of Paediatric Gastroenterology and Nutrition, Amsterdam University Medical Centers, University of Amsterdam, Emma Children’s Hospital, Amsterdam, The Netherlands
| | - Wouter J de Jonge
- Tytgat Institute for Liver and Intestinal Research, Amsterdam Gastroenterology Endocrinology Metabolism, University of Amsterdam, Amsterdam, The Netherlands,Department of Mathematics & Statistics, Dalhousie University, Halifax, NS, Canada,Department of Surgery, University of Bonn, Bonn, Germany
| | - Johan E Van Limbergen
- Corresponding author: Dr Johan Van Limbergen, MD, PhD, Department of Paediatric Gastroenterology and Nutrition, Emma Children’s Hospital, Amsterdam University Medical Centers, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands. Tel.: +31-20 566 3053;
| |
Collapse
|
21
|
Zhou H, Yang Y, Wang L, Ye S, Liu J, Gong P, Qian Y, Zeng H, Chen X. Integrated multi-omic data reveal the potential molecular mechanisms of the nutrition and flavor in Liancheng white duck meat. Front Genet 2022; 13:939585. [PMID: 36046229 PMCID: PMC9421069 DOI: 10.3389/fgene.2022.939585] [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: 05/11/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
The Liancheng white (LW) duck is one of the most valued Chinese indigenous poultry breeds. Its meat is rich in nutrients and has distinct flavors, but the molecular mechanisms behind them are unknown. To address this issue, we measured and compared multi-omic data (genome, transcriptome, and metabolome) of breast meat from LW ducks and the Mianyang Shelduck (MS) ducks. We found that the LW duck has distinct breed-specific genetic features, including numerous mutant genes with differential expressions associated with amino acid metabolism and transport activities. The metabolome driven by genetic materials was also seen to differ between the two breeds. For example, several amino acids that are beneficial for human health, such as L-Arginine, L-Ornithine, and L-lysine, were found in considerably higher concentrations in LW muscle than in MS duck muscle (p < 0.05). SLC7A6, a mutant gene, was substantially upregulated in the LW group (p < 0.05), which may lead to excessive L-arginine and L-ornithine accumulation in LW duck meat through transport regulation. Further, guanosine monophosphate (GMP), an umami-tasting molecule, was considerably higher in LW muscle (p < 0.05), while L-Aspartic acid was significantly abundant in MS duck meat (p < 0.05), showing that the LW duck has a different umami formation. Overall, this study contributed to our understanding of the molecular mechanisms driving the enriched nutrients and distinct umami of LW duck meat, which will provide a useful reference for duck breeding.
Collapse
Affiliation(s)
- Hao Zhou
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Yang
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Lixia Wang
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Shengqiang Ye
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Jiajia Liu
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Gong
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Yunguo Qian
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
| | - Huijun Zeng
- Wuhan Institute for Food and Cosmetic Control, Wuhan, China
- Key Laboratory of Edible Oil Quality and Safety for State Market Regulation, Wuhan, China
- *Correspondence: Huijun Zeng, ; Xing Chen,
| | - Xing Chen
- Insitute of Animal Husbandry and Veterinary, Wuhan Academy of Agricultural Science, Wuhan, China
- *Correspondence: Huijun Zeng, ; Xing Chen,
| |
Collapse
|
22
|
Yoo BS, Houston KV, D'Souza SM, Elmahdi A, Davis I, Vilela A, Parekh PJ, Johnson DA. Advances and horizons for artificial intelligence of endoscopic screening and surveillance of gastric and esophageal disease. Artif Intell Med Imaging 2022; 3:70-86. [DOI: 10.35711/aimi.v3.i3.70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/18/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence in endoscopic assessment of the gastrointestinal tract has shown progressive enhancement in diagnostic acuity. This review discusses the expanding applications for gastric and esophageal diseases. The gastric section covers the utility of AI in detecting and characterizing gastric polyps and further explores prevention, detection, and classification of gastric cancer. The esophageal discussion highlights applications for use in screening and surveillance in Barrett's esophagus and in high-risk conditions for esophageal squamous cell carcinoma. Additionally, these discussions highlight applications for use in assessing eosinophilic esophagitis and future potential in assessing esophageal microbiome changes.
Collapse
Affiliation(s)
- Byung Soo Yoo
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin V Houston
- Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA 23298, United States
| | - Steve M D'Souza
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Isaac Davis
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ana Vilela
- Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - David A Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| |
Collapse
|
23
|
Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation. Inflamm Bowel Dis 2022; 28:1573-1583. [PMID: 35699597 PMCID: PMC9527612 DOI: 10.1093/ibd/izac115] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.
Collapse
Affiliation(s)
| | | | - Enrico Mossotto
- Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK
| | - Sarah Ennis
- Address correspondence to: Sarah Ennis, Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK ()
| | | |
Collapse
|
24
|
De Gregorio V, Sgambato C, Urciuolo F, Vecchione R, Netti PA, Imparato G. Immunoresponsive microbiota-gut-on-chip reproduces barrier dysfunction, stromal reshaping and probiotics translocation under inflammation. Biomaterials 2022; 286:121573. [DOI: 10.1016/j.biomaterials.2022.121573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 01/21/2022] [Accepted: 05/07/2022] [Indexed: 11/25/2022]
|
25
|
Bill Kaelle GC, Menezes Souza CM, Bastos TS, Vasconcellos RS, Oliveira SGD, Félix AP. Diet digestibility and palatability and intestinal fermentative products in dogs fed yeast extract. ITALIAN JOURNAL OF ANIMAL SCIENCE 2022. [DOI: 10.1080/1828051x.2022.2054733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
| | | | - Taís Silvino Bastos
- Department of Animal Science, Federal University of Paraná, Curitiba, Brazil
| | | | | | | |
Collapse
|
26
|
Cheng Y, Straube R, Alnaif AE, Huang L, Leil TA, Schmidt BJ. Virtual Populations for Quantitative Systems Pharmacology Models. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2486:129-179. [PMID: 35437722 DOI: 10.1007/978-1-0716-2265-0_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.
Collapse
Affiliation(s)
- Yougan Cheng
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | - Ronny Straube
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Abed E Alnaif
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,EMD Serono, Billerica, MA, USA
| | - Lu Huang
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA
| | - Tarek A Leil
- QSP and PBPK, Bristol Myers Squibb, Princeton, NJ, USA.,Daiichi Sankyo, Inc., Pennington, NJ, USA
| | | |
Collapse
|
27
|
Sudhakar P, Alsoud D, Wellens J, Verstockt S, Arnauts K, Verstockt B, Vermeire S. Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All. J Crohns Colitis 2022; 16:1306-1320. [PMID: 35150242 PMCID: PMC9426669 DOI: 10.1093/ecco-jcc/jjac027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/02/2022] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
Abstract
Inflammatory bowel disease [IBD] has a multifactorial origin and originates from a complex interplay of environmental factors with the innate immune system at the intestinal epithelial interface in a genetically susceptible individual. All these factors make its aetiology intricate and largely unknown. Multi-omic datasets obtained from IBD patients are required to gain further insights into IBD biology. We here review the landscape of multi-omic data availability in IBD and identify barriers and gaps for future research. We also outline the various technical and non-technical factors that influence the utility and interpretability of multi-omic datasets and thereby the study design of any research project generating such datasets. Coordinated generation of multi-omic datasets and their systemic integration with clinical phenotypes and environmental exposures will not only enhance understanding of the fundamental mechanisms of IBD but also improve therapeutic strategies. Finally, we provide recommendations to enable and facilitate generation of multi-omic datasets.
Collapse
Affiliation(s)
- Padhmanand Sudhakar
- Corresponding author: Padhmanand Sudhakar, Translational Research in Gastrointestinal Disorders [TARGID], ON I, Herestraat 49, box 701, 3000 Leuven, Belgium. Tel.: 0032 [0]16 19 49 40;
| | - Dahham Alsoud
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium
| | - Judith Wellens
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium
| | - Sare Verstockt
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium
| | - Kaline Arnauts
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium
| | - Bram Verstockt
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium,Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Severine Vermeire
- KU Leuven Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders [TARGID], Leuven, Belgium,Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| |
Collapse
|
28
|
Nearing JT, Douglas GM, Hayes MG, MacDonald J, Desai DK, Allward N, Jones CMA, Wright RJ, Dhanani AS, Comeau AM, Langille MGI. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun 2022; 13:342. [PMID: 35039521 PMCID: PMC8763921 DOI: 10.1038/s41467-022-28034-z] [Citation(s) in RCA: 237] [Impact Index Per Article: 118.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 01/04/2022] [Indexed: 12/12/2022] Open
Abstract
Identifying differentially abundant microbes is a common goal of microbiome studies. Multiple methods are used interchangeably for this purpose in the literature. Yet, there are few large-scale studies systematically exploring the appropriateness of using these tools interchangeably, and the scale and significance of the differences between them. Here, we compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups. We test for differences in amplicon sequence variants and operational taxonomic units (ASVs) between these groups. Our findings confirm that these tools identified drastically different numbers and sets of significant ASVs, and that results depend on data pre-processing. For many tools the number of features identified correlate with aspects of the data, such as sample size, sequencing depth, and effect size of community differences. ALDEx2 and ANCOM-II produce the most consistent results across studies and agree best with the intersect of results from different approaches. Nevertheless, we recommend that researchers should use a consensus approach based on multiple differential abundance methods to help ensure robust biological interpretations. Many microbiome differential abundance methods are available, but it lacks systematic comparison among them. Here, the authors compare the performance of 14 differential abundance testing methods on 38 16S rRNA gene datasets with two sample groups, and show ALDEx2 and ANCOM-II produce the most consistent results.
Collapse
Affiliation(s)
- Jacob T Nearing
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada.
| | - Gavin M Douglas
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Molly G Hayes
- Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada
| | - Jocelyn MacDonald
- Department of Computer Science, Dalhousie University, Halifax, NS, Canada
| | - Dhwani K Desai
- Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada
| | - Nicole Allward
- Department of Civil and Resource Engineering, Dalhousie University, Halifax, NS, Canada
| | - Casey M A Jones
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Robyn J Wright
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Akhilesh S Dhanani
- Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada
| | - André M Comeau
- Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada
| | - Morgan G I Langille
- Integrated Microbiome Resource, Dalhousie University, Halifax, NS, Canada.,Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| |
Collapse
|
29
|
Artificial Intelligence in Clinical Immunology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_83] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
30
|
Kubinski R, Djamen-Kepaou JY, Zhanabaev T, Hernandez-Garcia A, Bauer S, Hildebrand F, Korcsmaros T, Karam S, Jantchou P, Kafi K, Martin RD. Benchmark of Data Processing Methods and Machine Learning Models for Gut Microbiome-Based Diagnosis of Inflammatory Bowel Disease. Front Genet 2022; 13:784397. [PMID: 35251123 PMCID: PMC8895431 DOI: 10.3389/fgene.2022.784397] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/13/2022] [Indexed: 12/14/2022] Open
Abstract
Patients with inflammatory bowel disease (IBD) wait months and undergo numerous invasive procedures between the initial appearance of symptoms and receiving a diagnosis. In order to reduce time until diagnosis and improve patient wellbeing, machine learning algorithms capable of diagnosing IBD from the gut microbiome's composition are currently being explored. To date, these models have had limited clinical application due to decreased performance when applied to a new cohort of patient samples. Various methods have been developed to analyze microbiome data which may improve the generalizability of machine learning IBD diagnostic tests. With an abundance of methods, there is a need to benchmark the performance and generalizability of various machine learning pipelines (from data processing to training a machine learning model) for microbiome-based IBD diagnostic tools. We collected fifteen 16S rRNA microbiome datasets (7,707 samples) from North America to benchmark combinations of gut microbiome features, data normalization and transformation methods, batch effect correction methods, and machine learning models. Pipeline generalizability to new cohorts of patients was evaluated with two binary classification metrics following leave-one-dataset-out cross (LODO) validation, where all samples from one study were left out of the training set and tested upon. We demonstrate that taxonomic features processed with a compositional transformation method and batch effect correction with the naive zero-centering method attain the best classification performance. In addition, machine learning models that identify non-linear decision boundaries between labels are more generalizable than those that are linearly constrained. Lastly, we illustrate the importance of generating a curated training dataset to ensure similar performance across patient demographics. These findings will help improve the generalizability of machine learning models as we move towards non-invasive diagnostic and disease management tools for patients with IBD.
Collapse
Affiliation(s)
- Ryszard Kubinski
- Phyla Technologies Inc, Montréal, QC, Canada
- *Correspondence: Ryszard Kubinski, ; Ryan D. Martin,
| | | | | | - Alex Hernandez-Garcia
- Mila, Quebec Artificial Intelligence Institute, University of Montreal, Montréal, QC, Canada
| | - Stefan Bauer
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, United Kingdom
- Earlham Institute, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, United Kingdom
- Earlham Institute, Norwich, United Kingdom
| | - Sani Karam
- Phyla Technologies Inc, Montréal, QC, Canada
| | - Prévost Jantchou
- Centre Hospitalier Universitaire Sainte-Justine, Montréal, QC, Canada
| | - Kamran Kafi
- Phyla Technologies Inc, Montréal, QC, Canada
| | - Ryan D. Martin
- Phyla Technologies Inc, Montréal, QC, Canada
- *Correspondence: Ryszard Kubinski, ; Ryan D. Martin,
| |
Collapse
|
31
|
The Archaeal Transcription Termination Factor aCPSF1 is a Robust Phylogenetic Marker for Archaeal Taxonomy. Microbiol Spectr 2021; 9:e0153921. [PMID: 34878325 PMCID: PMC8653824 DOI: 10.1128/spectrum.01539-21] [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] [Indexed: 11/20/2022] Open
Abstract
Archaea are highly diverse and represent a primary life domain, but the majority of them remain uncultured. Currently, 16S rRNA phylogeny is widely used in archaeal taxonomy and diversity surveys. However, highly conserved sequence of 16S rRNA possibly results in generation of chimera in the amplicons and metagenome-assembled genomes (MAGs) and therefore limits its application. The newly developed phylogenomic approach has overcome these flaws, but it demands high-quality MAGs and intensive computation. In this study, we investigated the use of the archaeal transcription termination factor aCPSF1 in archaeal classification and diversity surveys. The phylogenetic analysis of 1,964 aCPSF1 orthologs retrieved from the available archaeal (meta)genomes resulted in convergent clustering patterns with those of archaeal phylogenomics and 16S rRNA phylogeny. The aCPSF1 phylogeny also displayed comparable clustering with the methanoarchaeal McrABG phylogeny and the haloarchaeal phylogenomics. Normalization of 779 aCPSF1 sequences including 261 from cultured archaeal species yielded a taxonomic ranking system with higher resolutions than that obtained with 16S rRNA for genus and species. Using the aCPSF1 taxonomy, 144 unclassified archaea in NCBI database were identified to various taxonomic ranks. Moreover, aCPSF1- and 16S rRNA-based surveys of the archaeal diversity in a sample from a South China Sea cold seep produced similar results. Our results demonstrate that aCPSF1 is an alternative archaeal phylogenetic marker, which exhibits higher resolution than 16S rRNA, and is more readily usable than phylogenomics in the taxonomic study of archaea. IMPORTANCE Archaea represent a unique type of prokaryote, which inhabit in various environments including extreme environments, and so define the boundary of biosphere, and play pivotal ecological roles, particularly in extreme environments. Since their discovery over 40 years ago, environmental archaea have been widely investigated using the 16S rRNA sequence comparison, and the recently developed phylogenomic approach because the majority of archaea are recalcitrant to laboratory cultivation. However, the highly conserved sequence of 16S rRNA and intensive bioinformatic computation of phylogenomics limit their applications in archaeal species delineation and diversity investigations. aCPSF1 is a ubiquitously distributed and vertically inherited transcription termination factor in archaea. In this study, we developed an aCPSF1-based archaeal taxonomic system which exhibits congruent phylogenic clustering patterns with archaeal phylogenomics and higher resolution than 16S rRNA in distinguishing archaea at lower taxonomic ranks. Therefore, aCPSF1 is a new phylogenetic marker in the taxonomic and diversity studies of archaea.
Collapse
|
32
|
Taguer M, Darbinian E, Wark K, Ter-Cheam A, Stephens DA, Maurice CF. Changes in Gut Bacterial Translation Occur before Symptom Onset and Dysbiosis in Dextran Sodium Sulfate-Induced Murine Colitis. mSystems 2021; 6:e0050721. [PMID: 34874778 PMCID: PMC8651081 DOI: 10.1128/msystems.00507-21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 10/20/2021] [Indexed: 11/30/2022] Open
Abstract
Longitudinal studies on the gut microbiome that follow the effect of a perturbation are critical in understanding the microbiome's response and succession to disease. Here, we use a dextran sodium sulfate (DSS) mouse model of colitis as a tractable perturbation to study how gut bacteria change their physiology over the course of a perturbation. Using single-cell methods such as flow cytometry, bioorthogonal noncanonical amino acid tagging (BONCAT), and population-based cell sorting combined with 16S rRNA sequencing, we determine the diversity of physiologically distinct fractions of the gut microbiota and how they respond to a controlled perturbation. The physiological markers of bacterial activity studied here include relative nucleic acid content, membrane damage, and protein production. There is a distinct and reproducible succession in bacterial physiology, with an increase in bacteria with membrane damage and diversity changes in the translationally active fraction, both, critically, occurring before symptom onset. Large increases in the relative abundance of Akkermansia were seen in all physiological fractions, most notably in the translationally active bacteria. Performing these analyses within a detailed, longitudinal framework determines which bacteria change their physiology early on, focusing therapeutic efforts in the future to predict or even mitigate relapse in diseases like inflammatory bowel diseases. IMPORTANCE Most studies on the gut microbiome focus on the composition of this community and how it changes in disease. However, how the community transitions from a healthy state to one associated with disease is currently unknown. Additionally, common diversity metrics do not provide functional information on bacterial activity. We begin to address these two unknowns by following bacterial activity over the course of disease progression, using a tractable mouse model of colitis. We find reproducible changes in gut bacterial physiology that occur before symptom onset, with increases in the proportion of bacteria with membrane damage, and changes in community composition of the translationally active bacteria. Our data provide a framework to identify possible windows of intervention and which bacteria to target in microbiome-based therapeutics.
Collapse
Affiliation(s)
- M. Taguer
- Department of Microbiology & Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - E. Darbinian
- Department of Microbiology & Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - K. Wark
- Department of Microbiology & Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - A. Ter-Cheam
- Department of Mathematics and Statistics, Faculty of Science, McGill University, Montreal, Quebec, Canada
| | - D. A. Stephens
- Department of Mathematics and Statistics, Faculty of Science, McGill University, Montreal, Quebec, Canada
| | - C. F. Maurice
- Department of Microbiology & Immunology, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
33
|
Teh JJ, Berendsen EM, Hoedt EC, Kang S, Zhang J, Zhang F, Liu Q, Hamilton AL, Wilson-O’Brien A, Ching J, Sung JJY, Yu J, Ng SC, Kamm MA, Morrison M. Novel strain-level resolution of Crohn's disease mucosa-associated microbiota via an ex vivo combination of microbe culture and metagenomic sequencing. THE ISME JOURNAL 2021; 15:3326-3338. [PMID: 34035441 PMCID: PMC8528831 DOI: 10.1038/s41396-021-00991-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 04/07/2021] [Accepted: 04/15/2021] [Indexed: 02/03/2023]
Abstract
The mucosa-associated microbiota is widely recognized as a potential trigger for Crohn's disease pathophysiology but remains largely uncharacterised beyond its taxonomic composition. Unlike stool microbiota, the functional characterisation of these communities using current DNA/RNA sequencing approaches remains constrained by the relatively small microbial density on tissue, and the overwhelming amount of human DNA recovered during sample preparation. Here, we have used a novel ex vivo approach that combines microbe culture from anaerobically preserved tissue with metagenome sequencing (MC-MGS) to reveal patient-specific and strain-level differences among these communities in post-operative Crohn's disease patients. The 16 S rRNA gene amplicon profiles showed these cultures provide a representative and holistic representation of the mucosa-associated microbiota, and MC-MGS produced both high quality metagenome-assembled genomes of recovered novel bacterial lineages. The MC-MGS approach also produced a strain-level resolution of key Enterobacteriacea and their associated virulence factors and revealed that urease activity underpins a key and diverse metabolic guild in these communities, which was confirmed by culture-based studies with axenic cultures. Collectively, these findings using MC-MGS show that the Crohn's disease mucosa-associated microbiota possesses taxonomic and functional attributes that are highly individualistic, borne at least in part by novel bacterial lineages not readily isolated or characterised from stool samples using current sequencing approaches.
Collapse
Affiliation(s)
- J. J. Teh
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, QLD Australia
| | - E. M. Berendsen
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, QLD Australia ,Present Address: Wacker Biotech B.V., Amsterdam, The Netherlands
| | - E. C. Hoedt
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, QLD Australia ,grid.413648.cPresent Address: NHMRC Centre of Research Excellence (CRE) in Digestive Health, Hunter Medical Research Institute (HMRI), Newcastle, NSW Australia
| | - S. Kang
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, QLD Australia
| | - J. Zhang
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - F. Zhang
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - Q. Liu
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - A. L. Hamilton
- grid.413105.20000 0000 8606 2560Department of Gastroenterology, St Vincent’s Hospital, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Medicine, The University of Melbourne, Melbourne, VIC Australia
| | - A. Wilson-O’Brien
- grid.413105.20000 0000 8606 2560Department of Gastroenterology, St Vincent’s Hospital, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Medicine, The University of Melbourne, Melbourne, VIC Australia
| | - J. Ching
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China
| | - J. J. Y. Sung
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China ,grid.59025.3b0000 0001 2224 0361Present Address: Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - J. Yu
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China ,grid.10784.3a0000 0004 1937 0482Center for Gut Microbiota Research, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - S. C. Ng
- grid.10784.3a0000 0004 1937 0482Department of Medicine and Therapeutics, Institute of Digestive Disease, State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Science, The Chinese University of Hong Kong, Hong Kong, China ,grid.10784.3a0000 0004 1937 0482Center for Gut Microbiota Research, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
| | - M. A. Kamm
- grid.413105.20000 0000 8606 2560Department of Gastroenterology, St Vincent’s Hospital, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Medicine, The University of Melbourne, Melbourne, VIC Australia
| | - M. Morrison
- grid.1003.20000 0000 9320 7537The University of Queensland Diamantina Institute, Faculty of Medicine, University of Queensland, Woolloongabba, QLD Australia
| |
Collapse
|
34
|
Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
Collapse
Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
| |
Collapse
|
35
|
Kwong GA, Ghosh S, Gamboa L, Patriotis C, Srivastava S, Bhatia SN. Synthetic biomarkers: a twenty-first century path to early cancer detection. Nat Rev Cancer 2021; 21:655-668. [PMID: 34489588 PMCID: PMC8791024 DOI: 10.1038/s41568-021-00389-3] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/14/2021] [Indexed: 02/08/2023]
Abstract
Detection of cancer at an early stage when it is still localized improves patient response to medical interventions for most cancer types. The success of screening tools such as cervical cytology to reduce mortality has spurred significant interest in new methods for early detection (for example, using non-invasive blood-based or biofluid-based biomarkers). Yet biomarkers shed from early lesions are limited by fundamental biological and mass transport barriers - such as short circulation times and blood dilution - that limit early detection. To address this issue, synthetic biomarkers are being developed. These represent an emerging class of diagnostics that deploy bioengineered sensors inside the body to query early-stage tumours and amplify disease signals to levels that could potentially exceed those of shed biomarkers. These strategies leverage design principles and advances from chemistry, synthetic biology and cell engineering. In this Review, we discuss the rationale for development of biofluid-based synthetic biomarkers. We examine how these strategies harness dysregulated features of tumours to amplify detection signals, use tumour-selective activation to increase specificity and leverage natural processing of bodily fluids (for example, blood, urine and proximal fluids) for easy detection. Finally, we highlight the challenges that exist for preclinical development and clinical translation of synthetic biomarker diagnostics.
Collapse
Affiliation(s)
- Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA.
- Parker H. Petit Institute of Bioengineering and Bioscience, Atlanta, GA, USA.
- Institute for Electronics and Nanotechnology, Georgia Tech, Atlanta, GA, USA.
- The Georgia Immunoengineering Consortium, Emory University and Georgia Tech, Atlanta, GA, USA.
- Winship Cancer Institute, Emory University, Atlanta, GA, USA.
| | - Sharmistha Ghosh
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Lena Gamboa
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA
| | - Christos Patriotis
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Sangeeta N Bhatia
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Howard Hughes Medical Institute, Chevy Chase, MD, USA.
| |
Collapse
|
36
|
Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications. Genes (Basel) 2021; 12:genes12091438. [PMID: 34573420 PMCID: PMC8466305 DOI: 10.3390/genes12091438] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/21/2021] [Accepted: 09/14/2021] [Indexed: 12/14/2022] Open
Abstract
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.
Collapse
|
37
|
Lo B, Burisch J. Artificial intelligence assisted assessment of endoscopic disease activity in inflammatory bowel disease. Artif Intell Gastrointest Endosc 2021; 2:95-102. [DOI: 10.37126/aige.v2.i4.95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/27/2021] [Accepted: 08/16/2021] [Indexed: 02/06/2023] Open
Abstract
Assessment of endoscopic disease activity can be difficult in patients with inflammatory bowel disease (IBD) [comprises Crohn's disease (CD) and ulcerative colitis (UC)]. Endoscopic assessment is currently the foundation of disease evaluation and the grading is pivotal for the initiation of certain treatments. Yet, disharmony is found among experts; even when reassessed by the same expert. Some studies have demonstrated that the evaluation is no better than flipping a coin. In UC, the greatest achieved consensus between physicians when assessing endoscopic disease activity only reached a Kappa value of 0.77 (or 77% agreement adjustment for chance/accident). This is unsatisfactory when dealing with patients at risk of surgery or disease progression without proper care. Lately, across all medical specialities, computer assistance has become increasingly interesting. Especially after the emanation of machine learning – colloquially referred to as artificial intelligence (AI). Compared to other data analysis methods, the strengths of AI lie in its capability to derive complex models from a relatively small dataset and its ability to learn and optimise its predictions from new inputs. It is therefore evident that with such a model, one hopes to be able to remove inconsistency among humans and standardise the results across educational levels, nationalities and resources. This has manifested in a handful of studies where AI is mainly applied to capsule endoscopy in CD and colonoscopy in UC. However, due to its recent place in IBD, there is a great inconsistency between the results, as well as the reporting of the same. In this opinion review, we will explore and evaluate the method and results of the published studies utilising AI within IBD (with examples), and discuss the future possibilities AI can offer within IBD.
Collapse
Affiliation(s)
- Bobby Lo
- Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
- Copenhagen Centre for Inflammatory Bowel Disease in Children, Adolescents and Adults, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
| | - Johan Burisch
- Gastrounit, Medical Section, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
- Copenhagen Centre for Inflammatory Bowel Disease in Children, Adolescents and Adults, Copenhagen University Hospital Hvidovre, Hvidovre 2650, Denmark
| |
Collapse
|
38
|
Chen G, Shen J. Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease. Front Bioeng Biotechnol 2021; 9:635764. [PMID: 34307315 PMCID: PMC8297505 DOI: 10.3389/fbioe.2021.635764] [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: 01/18/2021] [Accepted: 06/09/2021] [Indexed: 12/18/2022] Open
Abstract
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.
Collapse
Affiliation(s)
- Guihua Chen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Shen
- Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Inflammatory Bowel Disease Research Center, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| |
Collapse
|
39
|
He C, Wang H, Yu C, Peng C, Shu X, Liao W, Zhu Z. Alterations of Gut Microbiota in Patients With Intestinal Tuberculosis That Different From Crohn's Disease. Front Bioeng Biotechnol 2021; 9:673691. [PMID: 34295880 PMCID: PMC8290844 DOI: 10.3389/fbioe.2021.673691] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/11/2021] [Indexed: 01/01/2023] Open
Abstract
Intestinal tuberculosis (ITB) and Crohn's disease (CD) are chronic inflammatory bowel disorders that are associated with dysregulated mucosal immunity. The gut microbiota plays an important role in the regulation of host immunity and inflammatory response. Although mounting evidence has linked CD with the dysbiosis of gut microbiota, the characteristic profiles of mucosal bacteria in ITB remain unclear. The aim of this study was to assess the alterations of the gut microbiota in ITB and compare the microbial structure of ITB with CD. A total of 71 mucosal samples were collected from patients with ITB, CD, and healthy controls (HC), and then, 16S rRNA gene sequencing was performed. The overall composition of gut microbiota in ITB was strikingly different from HC, with the dominance of Proteobacteria and reduction of Firmicutes. Of note, the short-chain fatty acids (SCFAs)-producing bacteria such as Faecalibacterium, Roseburia, and Ruminococcus were decreased in ITB relative to HC, while Klebsiella and Pseudomonas were enriched. Multiple predictive functional modules were altered in ITB, including the over-representation of lipopolysaccharide biosynthesis, bacterial invasion of epithelial cells, and pathogenic Escherichia coli infection that can promote inflammation. Additionally, the microbial structure in CD was distinctly different from ITB, characterized by lower alpha diversity and increased abundance of Bacteroides, Faecalibacterium, Collinsella, and Klebsiella. These four bacterial markers distinguished ITB from CD with an area under the curve of 97.6%. This study established the compositional and functional perturbation of the gut microbiome in ITB and suggested the potential for using gut microbiota as biomarkers to differentiate ITB from CD.
Collapse
Affiliation(s)
- Cong He
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Huan Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chen Yu
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chao Peng
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xu Shu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wangdi Liao
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhenhua Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| |
Collapse
|
40
|
Abstract
BACKGROUND Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results. METHODS Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed. RESULTS The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies. CONCLUSIONS The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.
Collapse
Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | |
Collapse
|
41
|
Gubatan J, Levitte S, Patel A, Balabanis T, Wei MT, Sinha SR. Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions. World J Gastroenterol 2021; 27:1920-1935. [PMID: 34007130 PMCID: PMC8108036 DOI: 10.3748/wjg.v27.i17.1920] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/04/2021] [Accepted: 04/13/2021] [Indexed: 02/06/2023] Open
Abstract
Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.
Collapse
Affiliation(s)
- John Gubatan
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Steven Levitte
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Akshar Patel
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Tatiana Balabanis
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Mike T Wei
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| | - Sidhartha R Sinha
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Redwood City, CA 94063, United States
| |
Collapse
|
42
|
Beck LC, Granger CL, Masi AC, Stewart CJ. Use of omic technologies in early life gastrointestinal health and disease: from bench to bedside. Expert Rev Proteomics 2021; 18:247-259. [PMID: 33896313 DOI: 10.1080/14789450.2021.1922278] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: At birth, the gastrointestinal (GI) tract is colonized by a complex community of microorganisms, forming the basis of the gut microbiome. The gut microbiome plays a fundamental role in host health, disorders of which can lead to an array of GI diseases, both short and long term. Pediatric GI diseases are responsible for significant morbidity and mortality, but many remain poorly understood. Recent advancements in high-throughput technologies have enabled deeper profiling of GI morbidities. Technologies, such as metagenomics, transcriptomics, proteomics and metabolomics, have already been used to identify associations with specific pathologies, and highlight an exciting area of research. However, since these diseases are often complex and multifactorial by nature, reliance on a single experimental approach may not capture the true biological complexity. Therefore, multi-omics aims to integrate singular omic data to further enhance our understanding of disease.Areas covered: This review will discuss and provide an overview of the main omic technologies that are used to study complex GI pathologies in early life.Expert opinion: Multi-omic technologies can help to unravel the complexities of several diseases during early life, aiding in biomarker discovery and enabling the development of novel therapeutics and augment predictive models.
Collapse
Affiliation(s)
- Lauren C Beck
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Claire L Granger
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK.,Newcastle Neonatal Service, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne, UK
| | - Andrea C Masi
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| | - Christopher J Stewart
- Translational and Clinical Research Institute, Newcastle University, Newcastle Upon Tyne, UK
| |
Collapse
|
43
|
Manandhar I, Alimadadi A, Aryal S, Munroe PB, Joe B, Cheng X. Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. Am J Physiol Gastrointest Liver Physiol 2021; 320:G328-G337. [PMID: 33439104 PMCID: PMC8828266 DOI: 10.1152/ajpgi.00360.2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Despite the availability of various diagnostic tests for inflammatory bowel diseases (IBD), misdiagnosis of IBD occurs frequently, and thus, there is a clinical need to further improve the diagnosis of IBD. As gut dysbiosis is reported in patients with IBD, we hypothesized that supervised machine learning (ML) could be used to analyze gut microbiome data for predictive diagnostics of IBD. To test our hypothesis, fecal 16S metagenomic data of 729 subjects with IBD and 700 subjects without IBD from the American Gut Project were analyzed using five different ML algorithms. Fifty differential bacterial taxa were identified [linear discriminant analysis effect size (LEfSe): linear discriminant analysis (LDA) score > 3] between the IBD and non-IBD groups, and ML classifications trained with these taxonomic features using random forest (RF) achieved a testing area under the receiver operating characteristic curves (AUC) of ∼0.80. Next, we tested if operational taxonomic units (OTUs), instead of bacterial taxa, could be used as ML features for diagnostic classification of IBD. Top 500 high-variance OTUs were used for ML training, and an improved testing AUC of ∼0.82 (RF) was achieved. Lastly, we tested if supervised ML could be used for differentiating Crohn's disease (CD) and ulcerative colitis (UC). Using 331 CD and 141 UC samples, 117 differential bacterial taxa (LEfSe: LDA score > 3) were identified, and the RF model trained with differential taxonomic features or high-variance OTU features achieved a testing AUC > 0.90. In summary, our study demonstrates the promising potential of artificial intelligence via supervised ML modeling for predictive diagnostics of IBD using gut microbiome data.NEW & NOTEWORTHY Our study demonstrates the promising potential of artificial intelligence via supervised machine learning modeling for predictive diagnostics of different types of inflammatory bowel diseases using fecal gut microbiome data.
Collapse
Affiliation(s)
- Ishan Manandhar
- 1Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Ahmad Alimadadi
- 1Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Sachin Aryal
- 1Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Patricia B. Munroe
- 2Clinical Pharmacology, William Harvey Research Institute &
National Institute of Health Research Barts Cardiovascular Biomedical Research Centre, Barts
and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Bina Joe
- 1Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | - Xi Cheng
- 1Bioinformatics & Artificial Intelligence Laboratory, Center for Hypertension and Precision Medicine, Program in Physiological Genomics, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| |
Collapse
|
44
|
Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol 2021; 12:634511. [PMID: 33737920 PMCID: PMC7962872 DOI: 10.3389/fmicb.2021.634511] [Citation(s) in RCA: 113] [Impact Index Per Article: 37.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/01/2021] [Indexed: 12/19/2022] Open
Abstract
The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
Collapse
Affiliation(s)
- Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, Madrid, Spain
| | | | | | - Piotr Przymus
- Faculty of Mathematics and Computer Science, Nicolaus Copernicus University, Toruń, Poland
| | - Vladimir Trajkovik
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | - Oliver Aasmets
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
- Department of Biotechnology, Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Magali Berland
- Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Jasminka Hasic
- University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Palacký University, Olomouc, Czechia
| | | | - Mikhail Kolev
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - 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
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology (ICO)Barcelona, Spain
- Colorectal Cancer Group, Institut de Recerca Biomedica de Bellvitge (IDIBELL), Barcelona, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - Irina Naskinova
- South West University “Neofit Rilski”, Blagoevgrad, Bulgaria
| | - Elin Org
- Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia
| | - Inês Paciência
- EPIUnit – Instituto de Saúde Pública da Universidade do Porto, Porto, Portugal
| | | | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Blaz Stres
- Group for Microbiology and Microbial Biotechnology, Department of Animal Science, University of Ljubljana, Ljubljana, Slovenia
| | - Baiba Vilne
- Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- Galilee Digital Health Research Center (GDH), Zefat Academic College, Zefat, Israel
| | - Eftim Zdravevski
- Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia
| | | | | | - Marcus J. Claesson
- School of Microbiology & APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Isabel Moreno-Indias
- Unidad de Gestión Clínica de Endocrinología y Nutrición, Instituto de Investigación Biomédica de Málaga (IBIMA), Hospital Clínico Universitario Virgen de la Victoria, Universidad de Málaga, Málaga, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| |
Collapse
|
45
|
Ahlawat S, Kumar P, Mohan H, Goyal S, Sharma KK. Inflammatory bowel disease: tri-directional relationship between microbiota, immune system and intestinal epithelium. Crit Rev Microbiol 2021; 47:254-273. [PMID: 33576711 DOI: 10.1080/1040841x.2021.1876631] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Human gut microbiota contributes to host nutrition and metabolism, sustains intestinal cell proliferation and differentiation, and modulates host immune system. The alterations in their composition lead to severe gut disorders, including inflammatory bowel disease (IBD) or inflammatory bowel syndrome (IBS). IBD including ulcerative colitis (UC) and Crohn's disease (CD) are gamut of chronic inflammatory disorders of gut, mediated by complex interrelations among genetic, environmental, and internal factors. IBD has debateable aetiology, however in recent years, exploring the central role of a tri-directional relationship between gut microbiota, mucosal immune system, and intestinal epithelium in pathogenesis is getting the most attention. Increasing incidences and early onset explains the exponential rise in IBD burden on health-care systems. Industrialization, hypersensitivity to allergens, lifestyle, hygiene hypothesis, loss of intestinal worms, and gut microbial composition, explains this shifted rise. Hitherto, the interventions modulating gut microbiota composition, microfluidics-based in vitro gastrointestinal models, non-allergic functional foods, nutraceuticals, and faecal microbiota transplantation (FMT) from healthy donors are some of the futuristic approaches for the disease management.
Collapse
Affiliation(s)
- Shruti Ahlawat
- Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Pramod Kumar
- Ministry of Health and Family Welfare, Government of India, Indian Council of Medical Research, New Delhi, India
| | - Hari Mohan
- Centre for Medical Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Sandeep Goyal
- Department of Medicine, Pt. BD Sharma Post-graduate Institute of Medical Sciences, Rohtak, Haryana, India
| | - Krishna Kant Sharma
- Department of Microbiology, Maharshi Dayanand University, Rohtak, Haryana, India
| |
Collapse
|
46
|
Advances in the understanding of the intestinal micro-environment and inflammatory bowel disease. Chin Med J (Engl) 2021; 133:834-841. [PMID: 32106123 PMCID: PMC7147659 DOI: 10.1097/cm9.0000000000000718] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The human gastrointestinal tract accommodates an entire micro-environment for divergent physiologic processes, the dysbiosis of this micro-ecology has a strong inter-action with the pathogenesis of inflammatory bowel disease (IBD). In the past few years, with the advances in the understanding of microbiome, its metabolites and further application of next generation sequencing, analysis of dynamic alteration of gut micro-environment was realized, which provides numerous information beyond simple microbiota structure or metabolites differences under chronic colitis status. The subsequent intervention strategies targeting the modulation of intestinal micro-environment have been explored as a potential therapy. In this review, we will summarize the recent knowledge about multi-dimensional dysbiosis, the inter-action between fungus and bacteria under inflamed mucosa, and the clinical application of probiotics and fecal microbiota transplantation as a promising therapeutic approach in IBD.
Collapse
|
47
|
Artificial intelligence in inflammatory bowel disease: current status and opportunities. Chin Med J (Engl) 2021; 133:757-759. [PMID: 32132365 PMCID: PMC7147662 DOI: 10.1097/cm9.0000000000000714] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
|
48
|
Longitudinal evaluation of fecal microbiota transplantation for ameliorating calf diarrhea and improving growth performance. Nat Commun 2021; 12:161. [PMID: 33420064 PMCID: PMC7794225 DOI: 10.1038/s41467-020-20389-5] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 12/01/2020] [Indexed: 12/26/2022] Open
Abstract
Calf diarrhea is associated with enteric infections, and also provokes the overuse of antibiotics. Therefore, proper treatment of diarrhea represents a therapeutic challenge in livestock production and public health concerns. Here, we describe the ability of a fecal microbiota transplantation (FMT), to ameliorate diarrhea and restore gut microbial composition in 57 growing calves. We conduct multi-omics analysis of 450 longitudinally collected fecal samples and find that FMT-induced alterations in the gut microbiota (an increase in the family Porphyromonadaceae) and metabolomic profile (a reduction in fecal amino acid concentration) strongly correlate with the remission of diarrhea. During the continuous follow-up study over 24 months, we find that FMT improves the growth performance of the cattle. This first FMT trial in ruminants suggest that FMT is capable of ameliorating diarrhea in pre-weaning calves with alterations in their gut microbiota, and that FMT may have a potential role in the improvement of growth performance. Here, the authors report the results of a longitudinal multi-omics trial of the use of fecal microbiota transplantation (FMT) to ameliorate diarrhea and restore gut microbial composition in 57 growing calves, and find that oral FMT induces alterations in the gut microbiota correlate with the remission of diarrhea and improves the growth performance of the cattle.
Collapse
|
49
|
Artificial Intelligence in Clinical Immunology. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_83-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
50
|
A Machine Learning Model Accurately Predicts Ulcerative Colitis Activity at One Year in Patients Treated with Anti-Tumour Necrosis Factor α Agents. ACTA ACUST UNITED AC 2020; 56:medicina56110628. [PMID: 33233514 PMCID: PMC7699478 DOI: 10.3390/medicina56110628] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/17/2020] [Accepted: 11/18/2020] [Indexed: 02/06/2023]
Abstract
Background and objectives: The biological treatment is a promising therapeutic option for ulcerative colitis (UC) patients, being able to induce subclinical and long-term remission. However, the relatively high costs and the potential toxicity have led to intense debates over the most appropriate criteria for starting, stopping, and managing biologics in UC. Our aim was to build a machine learning (ML) model for predicting disease activity at one year in UC patients treated with anti-Tumour necrosis factor α agents as a useful tool to assist the clinician in the therapeutic decisions. Materials and Methods: Clinical and biological parameters and the endoscopic Mayo score were collected from 55 UC patients at the baseline and one year follow-up. A neural network model was built using the baseline endoscopic activity and four selected variables as inputs to predict whether a UC patient will have an active or inactive endoscopic disease at one year, under the same therapeutic regimen. Results: The classifier achieved an excellent performance predicting the disease activity at one year with an accuracy of 90% and area under curve (AUC) of 0.92 on the test set and an accuracy of 100% and an AUC of 1 on the validation set. Conclusions: Our proposed ML solution may prove to be a useful tool in assisting the clinicians’ decisions to increase the dose or switch to other biologic agents after the model’s validation on independent, external cohorts of patients.
Collapse
|