Biology subjects

Fodor, A. A.

Publications and source records attributed to Fodor, A. A..

5 recordsLinked to original sources

bioRxiv2021

Batch effects removal for microbiome data via conditional quantile regression (ConQuR)

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Most existing strategies for mitigating batch effects rely on approaches designed for genomic analysis, failing to address the zero-inflated and over-dispersed microbiome data. Strategies tailored for microbiome data are restricted to association testing, failing to allow other analytic goals such as visualization. We develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. It is a fundamental advancement in the field because it is the first comprehensive method that accommodates the complex distributions of microbial read counts, and it generates batch-removed zero-inflated read counts that can be used in and benefit all usual subsequent analyses. We apply ConQuR to real microbiome data sets and demonstrate its state-of-the-art performance in removing batch effects while preserving or even amplifying the signals of interest.

bioinformatics↗

bioRxiv2021

Early life Western diet-induced memory impairments and gut microbiome changes in female rats are long-lasting despite healthy dietary intervention

ObjectiveConsumption of a Western diet during adolescence results in hippocampus (HPC)-dependent memory impairments and gut microbiome dysbiosis. Whether these adverse outcomes are reversible in adulthood following intervention with a healthy diet is unknown. Here we assessed the short- and long-term effects of adolescent consumption of a Western diet enriched with either sugar alone, or sugar and fat on metabolic outcomes, HPC-dependent memory, and gut microbiota. MethodsAdolescent female rats (PN 26) were fed a standard chow diet (CTL), a chow diet with access to 11% sugar solution (SUG), or a junk food cafeteria-style diet (CAF) containing a variety of fat- and/or sugar-enriched foods. During adulthood (PN 65+), metabolic outcomes, HPC-dependent memory, and gut microbial populations were evaluated both before and after a 5-week dietary intervention period where all groups were fed a diet of water standard chow. ResultsPrior to the dietary intervention both the CAF and SUG groups demonstrated impaired HPC-dependent memory, increased adiposity, and altered gut microbial populations relative to controls. However, impaired peripheral glucose regulation was only observed in the SUG group. The dietary intervention reversed the metabolic dysfunction in both the CAF and SUG groups, whereas HPC-dependent memory impairments were reversed in the SUG, but not the CAF group. The composition of the gut microbiota remained distinct from controls in both groups after dietary intervention. ConclusionsWhile the metabolic impairments associated with adolescent cafeteria diet consumption are reversible in adulthood with dietary intervention, the HPC-dependent memory impairments and the gut microbiome dysbiosis persist.

developmental biology↗

bioRxiv2021

On the robustness of inference of association with the gut microbiota in stool, swab and mucosal tissue samples

The gut microbiota plays an important role in human health and disease. Stool, swab and mucosal tissue samples have been used in individual studies to survey the microbial community but the consequences of using these different sample types are not completely understood. We previously reported differences in microbial community composition with 16S rRNA amplicon sequencing between stool, swab and mucosal tissue samples. Here, we extended the previous study to a larger cohort and performed shotgun metagenome sequencing of 1,397 stool, swab and mucosal tissue samples from 240 participants. Consistent with previous results, taxonomic composition of stool and swab samples was distinct, but still more similar to each other than mucosal tissue samples, which had a substantially different community composition, characterized by a high relative abundance of the mucus metabolizers Bacteroides and Subdoligranulum, as well as bacteria with higher tolerance for oxidative stress such as Escherichia. As has been previously reported, functional profiles were more uniform across sample types than taxonomic profiles with differences between stool and swab samples smaller, but mucosal tissue samples remained distinct from the other two types. When the taxonomic and functional profiles of different sample types were used for inference in association with host phenotypes of age, sex, body mass index (BMI), antibiotics or non-steroidal anti-inflammatory drugs (NSAIDs) use, hypothesis testing using either stool or swab gave broadly similar results, but inference performed on mucosal tissue samples gave results that were generally less consistent with either stool or swab. Our study represents an important resource for the experimental design of studies aimed to understand microbiota perturbations specific to defined micro niches within the human intestinal tract.

microbiology↗

bioRxiv2021

HashSeq: A Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Datasets

16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this manuscript, we utilize a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene datasets. We then use the normal distribution combined with LOESS regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, simple, scalable to large datasets, and provides a high-resolution set of sequence variants which are less likely to be the result of sequencing error.

bioinformatics↗

bioRxiv2019

Inference based PICRUSt accuracy varies across sample types and functional categories

BackgroundDespite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition, and PICRUSt is the most widely used of these techniques. In this study, we evaluated the performance of PICRUSt by comparing the significance of the differential abundance of functional gene profiles predicted with PICRUSt to those from shotgun metagenome sequencing across different environments.\n\nResultsWe selected 7 datasets of human, non-human animal and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between gene compositions predicted with PICRUSt and measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the sample labels were shuffled. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of algorithms like PICRUSt. As an alternative, we compared the performance of PICRUSt predicted genes to metagenome genes in inference models associated with metadata within each dataset. With this method, we found reasonable performance for human datasets, with PICRUSt performing better for inference on genes related to \"house-keeping\" functions. However, the performance of PICRUSt degraded sharply outside of human datasets when used for inference.\n\nConclusionWe conclude that the utility of PICRUSt for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

bioinformatics↗