Biology subjects

Fodor, A. A.

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

8 recordsLinked to original sources

bioRxiv2022

An integrative analysis of the biological clock hypothesis in human gut microbiome

BackgroundWhile previous studies have explored the relationship between aging and the gut microbiome, it remains unclear how consistent and reproducible this association is across different cultures and groups. We performed an integrative analysis with 11 independent datasets from nine different countries to test the idea that the aging gut microbiome can be viewed as a biological clock, in which microbial changes associated with age are consistent and measurable across distinct datasets. ResultsAs expected, our Principal Coordinate Analysis found strong batch effects with study ID by far the strongest signal across datasets. Despite this large batch effect, we found a consistent signal across studies that was largely driven by sample size with only our larger cohorts showing taxa in common associated with age. Likewise, Shannon diversity and richness were not consistently associated with age across the 11 datasets, but some positive correlations with richness and host age were observed among the four largest cohorts. The taxon with the most potential as a biomarker for the aging human gut microbiome is genus Bifidobacterium, with significantly negative associations with host age in three out of the four datasets that had more than 200 samples. ConclusionThe driving force behind low reproducibility of association of age with the microbiome in previous studies appears to be inadequate sample size rather than structural differences in the microbial community based on cohort characteristics. Results from a power analysis suggest that future studies on the aging human gut microbiome consider on the order of 100-300 samples to consistently observe an age signal. With these larger sample sizes, parametric and non-parametric model yield broadly similar power.

bioinformatics↗

bioRxiv2021

Microbial associations with Microscopic Colitis

BACKGROUND AND OBJECTIVEMicroscopic colitis is a relatively common cause of chronic diarrhea and may be linked to luminal factors. Given the essential role of the microbiome in human gut health, analysis of microbiome changes associated with microscopic colitis could provide insights into the development of the disease. METHODSWe enrolled patients who underwent colonoscopy for diarrhea. An experienced pathologist classified patients as having microscopic colitis (n=52) or controls (n=153). Research biopsies were taken from the ascending and descending colon, and the microbiome was characterized with Illumina sequencing. We analyzed the associations between microscopic colitis and microbiome with a series of increasingly complex models adjusted for a range of demographic and health factors. RESULTSWe found that alpha-diversity was significantly lower in microscopic colitis cases compared to controls in the descending colon microbiome. In the descending colon, a series of models that adjusted for an increasing number of co-variates found taxa significantly associated with microscopic colitis, including Proteobacteria that was enriched in cases and Collinsella enriched in controls. While the alpha-diversity and taxa were not significantly associated with microscopic colitis in the ascending colon microbiome, the inference p-values based on ascending and descending microbiomes were highly correlated. CONCLUSIONOur study demonstrates an altered microbiome in microscopic colitis cases compared to controls. Because both the cases and controls had diarrhea, we have identified candidate taxa that could be mechanistically responsible for the development of microscopic colitis independent of changes to the microbial community caused by diarrhea. Significance of this studyO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIMicroscopic colitis is a common cause of chronic diarrhea. The exact etiology of microscopic colitis is unknown, but the gut microbiome is considered to play an important role. C_LIO_LIPrior studies have reported microbial changes associated with microscopic colitis, but the results are not consistent. Prior studies have generally involved small numbers of patients, fecal samples, and comparison to healthy controls. C_LI What are the new findings?O_LIThe microbiome was altered in microscopic colitis patients. C_LIO_LIMicroscopic colitis was associated with lower alpha-diversity, increase of inflammation related taxa and decrease of Collinsella in the descending colon microbiome independent of diarrhea status. C_LIO_LIThe changes of microbial features were consistent between ascending and descending colon microbiomes, but with smaller changes in the ascending colon microbiome. C_LI How might it impact on clinical practice in the foreseeable future?O_LIThe altered microbial communities in patients with microscopic colitis suggest that the patients might benefit from prevention or treatment with live biotherapeutic products. C_LI

microbiology↗

bioRxiv2021

Lasting effects of low-calorie sweeteners on glucose regulation, sugar intake, and memory

Low-calorie sweetener (LCS) consumption in children has increased due to widespread LCS presence in the food environment and efforts to mitigate obesity through sugar replacement. However, mechanistic studies on the impact of early-life LCS consumption are lacking. Therefore, we developed a rodent model to evaluate the effects of daily LCS consumption (acesulfame potassium, saccharin, or stevia) during adolescence on adult metabolic, gut microbiome, neural, and behavioral outcomes. Results reveal that habitual early-life LCS consumption disrupts post-oral glucose tolerance and impairs hippocampal-dependent memory in the absence of weight gain. Furthermore, LCS consumption reduces lingual sweet taste receptor expression and alters sugar-motivated appetitive and consummatory responses. RNA sequencing analyses reveal that LCS also impacts collagen- and synaptic signaling-related gene pathways in the hippocampus and nucleus accumbens, respectively, in a sex-dependent manner. Collectively, these results suggest that regular early-life LCS consumption yields long-lasting impairments in metabolism, sugar-motivated behavior, and hippocampal-dependent memory.

neuroscience↗

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↗