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 Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zachary D. Wallen
Keyword(s):  
Parkinson Disease ◽  
Gut Microbiome ◽  
Complex Disease ◽  
Statistical Tests ◽  
Method Comparison ◽  
Amplicon Sequencing ◽  
False Positives ◽  
Testing Methods ◽  
Differential Abundance ◽  
16S Amplicon Sequencing

Abstract Background Testing for differential abundance of microbes in disease is a common practice in microbiome studies. Numerous differential abundance (DA) testing methods exist and range from traditional statistical tests to methods designed for microbiome data. Comparison studies of DA testing methods have been performed, but none performed on microbiome datasets collected for the study of real, complex disease. Due to this, DA testing was performed here using various DA methods in two large, uniformly collected gut microbiome datasets on Parkinson disease (PD), and their results compared. Results Overall, 78–92% of taxa tested were detected as differentially abundant by at least one method, while 5–22% were called differentially abundant by the majority of methods (depending on dataset and filtering of taxonomic data prior to testing). Concordances between method results ranged from 1 to 100%. Average concordance for datasets 1 and 2 were 24% and 28% respectively, and 27% for replicated DA signatures. Concordances increased when removing rarer taxa before testing, increasing average concordances by 2–32%. Certain methods consistently resulted in higher concordances (e.g. ANCOM-BC, LEfSe), while others consistently resulted in lower (e.g. edgeR, fitZIG). Hierarchical clustering revealed three groups of DA signatures that were (1) replicated by the majority of methods on average and included taxa previously associated with PD, (2) replicated by a subset of methods and included taxa largely enriched in PD, and (3) replicated by few to one method(s). Conclusions Differential abundance tests yielded varied concordances, and amounts of detected DA signatures. Some methods were more concordant than others on both filtered and unfiltered data, therefore, if consistency with other study methodology is a key goal, one might choose among these methods. Even still, using one method on one dataset may find true associations, but may also detect false positives. 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. This study will hopefully serve to complement previously reported DA method comparison studies by implementing and coalescing a large number of both previously and yet to be compared methods on two real gut microbiome datasets.

scholarly journals The Relationship between the Gut Microbiome and Metformin as a Key for Treating Type 2 Diabetes Mellitus

2021 ◽  
Vol 22 (7) ◽  
pp. 3566
Author(s):  
Chae Bin Lee ◽  
Soon Uk Chae ◽  
Seong Jun Jo ◽  
Ui Min Jerng ◽  
Soo Kyung Bae
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Glucose Metabolism ◽  
Gut Microbiome ◽  
Metabolic Disorders ◽  
Current Knowledge ◽  
Potential Target ◽  
The Relationship

Metformin is the first-line pharmacotherapy for treating type 2 diabetes mellitus (T2DM); however, its mechanism of modulating glucose metabolism is elusive. Recent advances have identified the gut as a potential target of metformin. As patients with metabolic disorders exhibit dysbiosis, the gut microbiome has garnered interest as a potential target for metabolic disease. Henceforth, studies have focused on unraveling the relationship of metabolic disorders with the human gut microbiome. According to various metagenome studies, gut dysbiosis is evident in T2DM patients. Besides this, alterations in the gut microbiome were also observed in the metformin-treated T2DM patients compared to the non-treated T2DM patients. Thus, several studies on rodents have suggested potential mechanisms interacting with the gut microbiome, including regulation of glucose metabolism, an increase in short-chain fatty acids, strengthening intestinal permeability against lipopolysaccharides, modulating the immune response, and interaction with bile acids. Furthermore, human studies have demonstrated evidence substantiating the hypotheses based on rodent studies. This review discusses the current knowledge of how metformin modulates T2DM with respect to the gut microbiome and discusses the prospect of harnessing this mechanism in treating T2DM.

scholarly journals Seasonal Shifts in Cold Tolerance and the Composition of the Gut Microbiome of Dendroctonus valens LeConte Occur Concurrently

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 888
Author(s):  
Zehai Hou ◽  
Yaxin Dong ◽  
Fengming Shi ◽  
Yabei Xu ◽  
Sixun Ge ◽  
...  
Keyword(s):  
Antioxidant Enzyme ◽  
Cold Tolerance ◽  
Gut Microbiome ◽  
Antioxidant Enzyme Activities ◽  
Severe Damage ◽  
Geographic Ranges ◽  
Dendroctonus Valens ◽  
Different Seasons ◽  
The Relationship ◽  
Seasonal Shifts

Dendroctonus valens LeConte, an invasive bark beetle, has caused severe damage in pine forests and has the potential to disperse into new geographic ranges in China. Although the gut microbiota of D. valens and its fundamental role in host fitness have been investigated widely, little is known about the relationship between the seasonal shifts of both cold tolerance and the gut microbiome of D. valens during overwintering, which occurs at the larval stage. In this study, to examine seasonal variations in the composition of the microbiome, we collected D. valens larvae in September (autumn), January (winter), and May (spring), and then analyzed the bacterial and fungal communities of the gut via sequencing of partial 16S rRNA and ITS genes. In addition, changes in the supercooling capacity and antioxidant enzyme activities of D. valens larvae collected in the different seasons were evaluated. Overwintering resulted in changes to microbial communities. In particular, the abundances of Enterobacter, Serratia, Erwinia, and Klebsiella decreased during overwintering. Concurrent with these changes, the cold tolerance of D. valens larvae was enhanced during overwintering, and the activities of the antioxidant enzymes catalase and peroxidase were reduced. We hypothesize that seasonal shifts in the gut microbiome may be connected to changes in cold tolerance and antioxidant enzyme activity in D. valens. It will be worthwhile to confirm whether seasonal changes in the microbiome contribute to the success of host overwintering.

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 Parkinson Disease: The Relationship Between Non-motor Symptoms and Motor Phenotype

2015 ◽  
Vol 43 (2) ◽  
pp. 261-267 ◽  
Author(s):  
Fang Ba ◽  
Mona Obaid ◽  
Marguerite Wieler ◽  
Richard Camicioli ◽  
W.R. Wayne Martin
Keyword(s):  
Parkinson Disease ◽  
Rating Scales ◽  
Advanced Disease ◽  
Axial Component ◽  
Motor Symptoms ◽  
Linear Regression Models ◽  
Cross Sectional ◽  
The Relationship ◽  
Non Motor Symptoms ◽  
Motor Phenotype

AbstractBackground: Parkinson disease (PD) presents with motor and non-motor symptoms (NMS). The NMS often precede the onset of motor symptoms, but may progress throughout the disease course. Tremor dominant, postural instability gait difficulty (PIGD), and indeterminate phenotypes can be distinguished using Unified PD Rating scales (UPDRS-III). We hypothesized that the PIGD phenotype would be more likely to develop NMS, and that the non-dopamine–responsive axial signs would correlate with NMS severity. Methods: We conducted a retrospective cross-sectional chart review to assess the relationship between NMS and PD motor phenotypes. PD patients were administered the NMS Questionnaire, the UPDRS-III, and the Mini-Mental State Examination score. The relationship between NMS burden and PD subtypes was examined using linear regression models. The prevalence of each NMS among difference PD motor subtypes was analyzed using chi-square test. Results: PD patients with more advanced disease based on their UPDRS-III had higher NMS Questionnaire scores. The axial component of UPDRS-III correlated with higher NMS. There was no correlation between NMS and tremor scores. There was a significant correlation between PIGD score and higher NMS burden. PIGD group had higher prevalence in most NMS domains when compared with tremor dominant and indeterminate groups independent of disease duration and severity. Conclusions: NMS profile and severity vary according to motor phenotype. We conclude that in the PD population, patients with a PIGD phenotype who have more axial involvement, associated with advanced disease and poor motor response, have a higher risk for a higher NMS burden.

MICROBIOMA: AMIGO O ENEMIGO EN PACIENTES CON COVID - 19

SCIENTIARVM ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 15-21
Author(s):  
Katherine Milagros Quispe Medina ◽  
◽  
Angel Sixto Mamani Ruelas ◽  
Brenda Jasmin Alvarez Vera ◽  
Yasmin Yessenia Silvestre Gutierrez ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Gastrointestinal Tract ◽  
Molecular Biology ◽  
Gut Microbiome ◽  
Next Generation ◽  
Multiple Factors ◽  
Lung Microbiome ◽  
The Relationship ◽  
Generation Sequencing ◽  
Clinical Cases

The research of the microbiome concerning various diseases has grown in the last ten years due to the advances in molecular biology and next-generation sequencing, finding interactions with various pathologies. The new coronavirus 19 (SARS-COV 2) pandemic has aroused interest in the study of multiple factors that could influence in the development of symptoms mainly due to the interaction of the microbiome whether of the respiratory or gastrointestinal tract finally in the prognosis. Therefore, in this study, we focus on reviewing and analyzing the current bibliography of research and clinical cases about the relationship between the lung and gut microbiome and COVID-19, highlighting its effect on infected patients, aiming to contribute to this new line of research. Keywords: Microbiome, COVID-19, SARS-COV 2, gut microbiome, lung microbiome.

Utilizing the Microbiota and Machine Learning Algorithms to Assess Risk of Salmonella Contamination in Poultry Rinsate

2021 ◽  
Author(s):  
Hannah Bolinger ◽  
David Tran ◽  
Kenneth Harary ◽  
George C. Paoli ◽  
Giselle Guron ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Diagnostic Tools ◽  
Sequencing Data ◽  
Testing Methods ◽  
16S Sequencing ◽  
Sequencing Technologies ◽  
Microbiological Testing ◽  
Microbiome Data ◽  
Larger Sample

Traditional microbiological testing methods are slow, and many molecular-based techniques rely on culture-based enrichment to overcome low limits of detection. Recent advancements in sequencing technologies may make it possible to utilize machine learning (ML) to identify patterns in microbiome data to potentially predict the presence or absence of pathogens. In this study, 299 poultry rinsate samples from various points in the processing chain were analyzed to determine if microbiota could inform about a sample’s risk for containing Salmonella . Samples were culture confirmed as Salmonella -positive or -negative following modified USDA MLG protocols. The culture confirmation result was used as a reference to compare with 16S sequencing data. Pre-chill samples tested positive (71/82) at a higher frequency than post-chill samples (30/217) and contained greater microbial diversity. Due to their larger sample size, post-chill samples were analyzed more deeply. Analysis of variance (ANOVA) identified a significant effect of chilling on the number of genera (p<0.001), but analysis of similarities (ANOSIM) failed to provide evidence for microbial dissimilarity between pre- and post-chill samples (p=0.001, R=0.443). Various ML models were trained using post-chill samples to predict if a sample contained Salmonella based on the samples’ microbiota pre-enrichment. The optimal model was a Random Forest-based model with a performance as follows: accuracy (88%), sensitivity (85%), specificity (90%). While the algorithms described in this paper are prototypes, these risk-based algorithms demonstrate the potential and need for further studies to provide insight alongside diagnostic tests. Combining risk-based information with diagnostic tools can help poultry processors make informed decisions to help identify and prevent the spread of Salmonella . These data add to the growing body of literature exploring novel ways to utilize microbiome data for predictive food safety.

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