scholarly journals Using data science for medical decision making case: role of gut microbiome in multiple sclerosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jasminka Hasic Telalovic ◽  
Azra Music
Keyword(s):  
Multiple Sclerosis ◽  
Gut Microbiome ◽  
Data Science ◽  
Classification Model ◽  
Medical Decision ◽  
Hierarchical Level ◽  
Kegg Pathways ◽  
Genus Level ◽  
Microbiome Data ◽  
Predictive Functions

Abstract Background A decade ago, the advancements in the microbiome data sequencing techniques initiated the development of research of the microbiome and its relationship with the host organism. The development of sophisticated bioinformatics and data science tools for the analysis of large amounts of data followed. Since then, the analyzed gut microbiome data, where microbiome is defined as a network of microorganisms inhabiting the human intestinal system, has been associated with several conditions such as irritable bowel syndrome - IBS, colorectal cancer, diabetes, obesity, and metabolic syndrome, and lately in the study of Parkinson’s and Alzheimer’s diseases as well. This paper aims to provide an understanding of differences between microbial data of individuals who have been diagnosed with multiple sclerosis and those who were not by exploiting data science techniques on publicly available data. Methods This study examines the relationship between multiple sclerosis (MS), an autoimmune central nervous system disease, and gut microbial community composition, using the samples acquired by 16s rRNA sequencing technique. We have used three different sets of MS samples sequenced during three independent studies (Jangi et al, Nat Commun 7:1–11, 2016), (Miyake et al, PLoS ONE 10:0137429, 2015), (McDonald et al, Msystems 3:00031–18, 2018) and this approach strengthens our results. Analyzed sequences were from healthy control and MS groups of sequences. The extracted set of statistically significant bacteria from the (Jangi et al, Nat Commun 7:1–11, 2016) dataset samples and their statistically significant predictive functions were used to develop a Random Forest classifier. In total, 8 models based on two criteria: bacteria abundance (at six taxonomic levels) and predictive functions (at two levels), were constructed and evaluated. These include using taxa abundances at different taxonomy levels as well as predictive function analysis at different hierarchical levels of KEGG pathways. Results The highest accuracy of the classification model was obtained at the genus level of taxonomy (76.82%) and the third hierarchical level of KEGG pathways (70.95%). The second dataset’s 18 MS samples (Miyake et al, PLoS ONE 10:0137429, 2015) and 18 self-reported healthy samples from the (McDonald et al, Msystems 3:00031–18, 2018) dataset were used to validate the developed classification model. The significance of this step is to show that the model is not overtrained for a specific dataset but can also be used on other independent datasets. Again, the highest classification model accuracy for both validating datasets combined was obtained at the genus level of taxonomy (70.98%) and third hierarchical level of KEGG pathways (67.24%). The accuracy of the independent set remained very relevant. Conclusions Our results demonstrate that the developed classification model provides a good tool that can be used to suggest the presence or absence of MS condition by collecting and analyzing gut microbiome samples. The accuracy of the model can be further increased by using sequencing methods that allow higher taxa resolution (i.e. shotgun metagenomic sequencing).

scholarly journals Host Genetics and Gut Microbiome: Perspectives for Multiple Sclerosis

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1181
Author(s):  
Alessandro Maglione ◽  
Miriam Zuccalà ◽  
Martina Tosi ◽  
Marinella Clerico ◽  
Simona Rolla
Keyword(s):  
Multiple Sclerosis ◽  
Environmental Factors ◽  
Lupus Erythematosus ◽  
Gut Microbiome ◽  
Complex Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Host Genetics

As a complex disease, Multiple Sclerosis (MS)’s etiology is determined by both genetic and environmental factors. In the last decade, the gut microbiome has emerged as an important environmental factor, but its interaction with host genetics is still unknown. In this review, we focus on these dual aspects of MS pathogenesis: we describe the current knowledge on genetic factors related to MS, based on genome-wide association studies, and then illustrate the interactions between the immune system, gut microbiome and central nervous system in MS, summarizing the evidence available from Experimental Autoimmune Encephalomyelitis mouse models and studies in patients. Finally, as the understanding of influence of host genetics on the gut microbiome composition in MS is in its infancy, we explore this issue based on the evidence currently available from other autoimmune diseases that share with MS the interplay of genetic with environmental factors (Inflammatory Bowel Disease, Rheumatoid Arthritis and Systemic Lupus Erythematosus), and discuss avenues for future research.

scholarly journals QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science

2018 ◽  
Author(s):  
Evan Bolyen ◽  
Jai Ram Rideout ◽  
Matthew R Dillon ◽  
Nicholas A Bokulich ◽  
Christian Abnet ◽  
...  
Keyword(s):  
Data Science ◽  
Temporal Analysis ◽  
Data Provenance ◽  
Policy Makers ◽  
Shotgun Metagenomics ◽  
Spatial And Temporal Analysis ◽  
Microbiome Research ◽  
Scientists And Engineers ◽  
Visualization Tools ◽  
Microbiome Data

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

scholarly journals Comparison study of sixteen differential abundance testing methods using two large Parkinson disease gut microbiome datasets

2021 ◽  
Author(s):  
Zachary D Wallen
Keyword(s):  
Parkinson Disease ◽  
Gut Microbiome ◽  
Complex Disease ◽  
Statistical Tests ◽  
Testing Methods ◽  
Differential Abundance ◽  
Healthy State ◽  
Microbiome Data ◽  
Log Ratio ◽  
The Relationship

Background: When studying the relationship between the microbiome and a disease, a common question asked is what individual microbes are differentially abundant between a disease and healthy state. Numerous differential abundance (DA) testing methods exist and range from standard statistical tests to methods specifically designed for microbiome data. Comparison studies of DA testing methods have been performed, but none were performed on microbiome datasets collected for the study of real, complex disease. Due to this, we performed DA testing of microbial genera using 16 DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and compared their results. Results: Pairwise concordances between methods ranged from 46%-99% similarity. Average pairwise concordance per dataset was 76%, and dropped to 62% when taking replication of signals across datasets into account. Certain methods consistently resulted in above average concordances (e.g. Kruskal-Wallis, ALDEx2, GLM with centered-log-ratio transform), while others consistently resulted in lower than average concordances (e.g. edgeR, fitZIG). Overall, ~80% of genera tested were detected as differentially abundant by at least one method in each dataset. Requiring associations to replicate across datasets reduced significant signals by almost half. Further requirement of signals to be replicated by the majority of methods (≥8) yielded 19 associations. Only one genus (Agathobacter) was replicated by all methods. Use of hierarchical clustering revealed three groups of DA signatures that were (1) replicated by the majority of methods and included genera previously associated with PD, (2) replicated by few or no methods, and (3) replicated by a subset of methods and included rarer genera, all enriched in PD. Conclusions: Differential abundance tests yielded varied results. Using one method on one dataset may find true associations, but may also detect non-reproducible signals, adding to inconsistency in the literature. To help lower false positives, one might analyze data with two or more DA methods to gauge concordance, and use a built-in replication dataset to show reproducibility. This study corroborated previously reported microorganism associations in PD, and revealed a potential new group of microorganisms whose abundance is significantly elevated in PD, and might be worth pursuing in future investigations.

scholarly journals Fully Automated Detection of Paramagnetic Rims in Multiple Sclerosis Lesions on 3T Susceptibility-Based MR Imaging

2020 ◽  
Author(s):  
Carolyn Lou ◽  
Pascal Sati ◽  
Martina Absinta ◽  
Kelly Clark ◽  
Jordan D. Dworkin ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Severe Disease ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
List Type ◽  
Training Set ◽  
Random Forest Classification ◽  
Automated Method ◽  
Forest Classification

AbstractBackground and PurposeThe presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis (MS) lesion. Increased prevalence of these paramagnetic rim lesions (PRLs) is associated with a more severe disease course in MS. The identification of these lesions is time-consuming to perform manually. We present a method to automatically detect PRLs on 3T T2*-phase images.MethodsT1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 19 subjects with MS. The images were then processed with lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 877 lesions were identified, 118 (13%) of which contained a paramagnetic rim. We divided our data into a training set (15 patients, 673 lesions) and a testing set (4 patients, 204 lesions). We fit a random forest classification model on the training set and assessed our ability to classify lesions as PRL on the test set.ResultsThe number of PRLs per subject identified via our automated lesion labelling method was highly correlated with the gold standard count of PRLs per subject, r = 0.91 (95% CI [0.79, 0.97]). The classification algorithm using radiomic features can classify a lesion as PRL or not with an area under the curve of 0.80 (95% CI [0.67, 0.86]).ConclusionThis study develops a fully automated technique for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.HighlightsA fully automated method for both the identification and classification of paramagnetic rim lesions is proposed.Radiomic features in conjunction with machine learning algorithms can accurately classify paramagnetic rim lesions.Challenges for classification are largely driven by heterogeneity between lesions, including equivocal rim signatures and lesion location.

Neonatal metabolome of cesarean section and risk of childhood asthma

2021 ◽  
pp. 2102406
Author(s):  
Gözde Gürdeniz ◽  
Madeleine Ernst ◽  
Daniela Rago ◽  
Min Kim ◽  
Julie Courraud ◽  
...  
Keyword(s):  
Cesarean Section ◽  
Cord Blood ◽  
Gut Microbiome ◽  
Childhood Asthma ◽  
Metabolic Profile ◽  
Partial Least Square ◽  
Least Square ◽  
Microbial Colonization ◽  
Increased Risk ◽  
Microbiome Data

BackgroundBirth by cesarean section (CS) is linked to an increased risk of developing asthma, but the underlying mechanisms are unclear.ObjectiveTo elucidate the link between birth by CS and asthma using newborn metabolomic profiles and integrating early life gut microbiome data and cord blood immunology.MethodsWe investigated the influence of CS on liquid chromatography mass spectrometry (LC-MS) metabolomic profiles of dried blood spots from newborns of the two independent Copenhagen Prospective Studies on Asthma in Childhood cohorts, i.e. COPSAC2010 (n=677) and COPSAC2000 (n=387). We assessed the associations between the CS metabolic profile, age one-week gut microbiome data and frequency of cord blood Tregs.ResultsIn COPSAC2010, a partial least square-discriminant analysis (PLS-DA) model showed that children born by CS versus natural delivery had different metabolic profiles (AUC=0.77, p=2.2e-16), which was replicated in COPSAC2000 (AUC=0.66, p=1.2e-5). The metabolic profile of CS was significantly associated with an increased risk of asthma at school-age in both COPSAC2010 (p=0.03) and COPSAC2000 (p=0.005). CS was associated with lower abundance of tryptophan, bile acid and phenylalanine metabolites, indicative of a perturbed gut microbiota. Further, gut bacteria dominating after natural delivery, i.e. Bifidobacterium and Bacteroides were correlated with CS-discriminative microbial metabolites, suggesting maternal microbial transmission during birth regulating the newborn's metabolism. Finally, the CS metabolic profile was associated with frequency of cord blood Tregs.ConclusionsThese findings propose that CS is programming the risk of childhood asthma through perturbed immune responses and gut microbial colonization patterns reflected in the blood metabolome at birth.

scholarly journals Changes in Gut Microbiome Associated With Co-Occurring Symptoms Development During Chemo-Radiation for Rectal Cancer: A Proof of Concept Study

2020 ◽  
Vol 23 (1) ◽  
pp. 31-41
Author(s):  
Velda J. González-Mercado ◽  
Wendy A. Henderson ◽  
Anujit Sarkar ◽  
Jean Lim ◽  
Leorey N. Saligan ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Gut Microbiome ◽  
Learning Algorithm ◽  
Descriptive Statistics ◽  
Proof Of Concept ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Symptom Ratings ◽  
Stool Samples ◽  
Microbiome Data

Purpose: To examine a) whether there are significant differences in the severity of symptoms of fatigue, sleep disturbance, or depression between patients with rectal cancer who develop co-occurring symptoms and those with no symptoms before and at the end of chemotherapy and radiation therapy (CRT); b) differences in gut microbial diversity between those with co-occurring symptoms and those with no symptoms; and c) whether before-treatment diversity measurements and taxa abundances can predict co-occurrence of symptoms. Methods: Stool samples and symptom ratings were collected from 31 patients with rectal cancer prior to and at the end of (24–28 treatments) CRT. Descriptive statistics were computed and the Mann-Whitney U test was performed for symptoms. Gut microbiome data were analyzed using R’s vegan package software. Results: Participants with co-occurring symptoms reported greater severity of fatigue at the end of CRT than those with no symptoms. Bacteroides and Blautia2 abundances differed between participants with co-occurring symptoms and those with no symptoms. Our random forest classification (unsupervised learning algorithm) predicted participants who developed co-occurring symptoms with 74% accuracy, using specific phylum, family, and genera abundances as predictors. Conclusion: Our preliminary results point to an association between the gut microbiota and co-occurring symptoms in rectal cancer patients and serves as a first step in potential identification of a microbiota-based classifier.

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