Biology subjects

Bokulich, N.

Publications and source records attributed to Bokulich, N..

13 recordsLinked to original sources

bioRxiv2026

Decoding the microbiota of bors: multifunctional potential of a traditional Romanian beverage fermentation

Romanian bors, a traditional fermented wheat bran beverage, is produced through spontaneous fermentation and represents a complex microbial ecosystem. Despite its cultural importance and presumed health benefits, its microbial ecology and functional potential remain poorly characterized. The present study aimed to elucidate the microbial community structure of bors and link it to functional traits relevant to fermentation performance and food functionality by integrating culture-independent sequencing with culture-dependent isolation and functional characterization. A total of 32 bors samples (12 commercial and 20 homemade) were analyzed. Amplicon-based sequencing revealed a microbiome dominated by lactic acid bacteria (LAB), with lactobacilli accounting for the majority of the bacterial communities and Lactobacillus amylolyticus being identified as the most prevalent and abundant species. The yeast communities were mainly composed of fermentative taxa, including Pichia kudriavzevii and Kluyveromyces marxianus. Lactobacillus amylolyticus and P. kudriavzevii were also the most frequently isolated species among bacteria and yeasts, respectively. These results highlighted a strong adaptation of the microbial isolates to starch-rich cereal substrates and underscored the central role of these microorganisms in wheat bran fermentation for bors production. Whereas the sequencing-based analyses showed no significant differences in overall diversity between the commercial and homemade bors samples, the cultivation-based results indicated a higher bacterial richness in the commercial products. Notably, the culture-dependent method captured substantially fewer taxa, highlighting the complementary nature of the two approaches. Of a total of 101 bacterial strains (88 LAB and 13 acetic acid bacteria) isolated, many exhibited rapid growth and strong acidification capacity, reaching pH values below 4.5 within 12 h. A functional screening revealed that 21 % of these strains displayed -amylase activity, 65 % phytase activity, and 50 % {beta}-glucosidase activity, highlighting their capacity to metabolize cereal substrates and enhance the nutrient availability of bors. All strains showed antibacterial activity against at least one indicator bacterium tested, with a universal inhibition of Listeria monocytogenes. Overall, Romanian bors harbored a lactic acid bacteria-dominated core microbiome with a significant functional diversity. These findings underscored its potential as a rich source of functional and technologically important strains for application in starter and protective culture development.

microbiology↗

bioRxiv2025

Target-driven optimization of feature representation and model selection for microbiome sequencing data with ritme

Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured, and predictive modeling from them typically relies on ad hoc feature representation choices that obscure their impact on performance and interpretation. We present ritme, an open-source Python package that jointly optimizes microbiome-specific feature representation and model selection - combined algorithm selection and hyperparameter optimization - tailored to these data. ritme systematically searches taxonomic aggregation, sparsity-aware selection, compositional transforms, and metadata enrichment together with model class and hyperparameters, using state-of-the-art optimizers that scale from a laptop to a compute cluster. Across three real-world use cases, ritme outperformed the original study pipelines by 7-29% on the primary task metric and surpassed three AutoML baselines in six of seven comparisons, while selecting substantially fewer features and exposing how feature and model choices drive performance. Open-source and modular, ritme supports reproducible, parsimonious predictive modeling, downstream biological investigation, and extension to other multi-omics modalities.

bioinformatics↗

bioRxiv2025

Shipped and shifted: modeling collection-induced bias in microbiome multi-omics using a tractable fermentation system

Large-scale, decentralized microbiome sampling surveys and citizen science initiatives often require periods of storage at ambient temperature, potentially altering sample composition during collection and transport. We developed a generalizable framework to quantify and model these biases using sourdough as a tractable fermentation system, with samples subjected to controlled storage conditions (4 {degrees}C, 17 {degrees}C, 30 {degrees}C, regularly sampled up to 28 days). Machine-learning models paired with multi-omics profiling -- including microbiome, targeted and untargeted metabolome profiling, and cultivation -- revealed temperature-dependent shifts in bacterial community structure and metabolic profiles, while fungal communities remained stable. Storage induced ecological restructuring, marked by reduced network modularity and increased centrality of dominant taxa at higher temperatures. Notably, storage duration and temperature were strongly encoded in the multi-omics data, with temperature exerting a more pronounced influence than time. 24 of the top 25 predictors of storage condition were metabolites, underscoring functional layers as both sensitive to and informative of environmental exposure. These findings demonstrate that even short-term ambient storage (< 2 days) can substantially reshape microbiome, metabolome, and biochemical profiles, posing risks to data comparability in decentralized studies and emphasizing the need to recognize and address such biases. Critically, the high predictability of storage history offers a path toward bias detection and correction -- particularly when standardized collection protocols are infeasible, as is common in decentralized sampling contexts. Our approach enables robust quantification and modeling of such storage effects across multi-omics datasets, unlocking more accurate interpretation of large-scale microbiome surveys.

microbiology↗

bioRxiv2025

Evaluating long-term stool preservation methods for maximizing the recovery of viable human fecal microbiota

The gut microbiome plays a fundamental role in human health, prompting efforts to catalogue and preserve its diversity across human populations. While DNA sequencing dominates microbiome research, cultivation remains essential for mechanistic studies and therapeutic development. Yet, best practices for long-term stool preservation remain limited. Here, we compared the stability of eight cryopreservation treatments for maintaining viable stool microbiota over a 1-year storage period at -80{degrees}C (freezer) or at -196{degrees}C (liquid nitrogen), using samples from infants, children, and adults. Combining cultivation on six media with 16S rRNA sequencing, we show that ultralow temperature cryopreservation has minimal impact on microbiota diversity compared to fresh cultures. Standard glycerol preservation and simple snap-freezing performed comparably to more complex and costly protocols, with all cultured samples retaining donor-specific microbiota profiles also after long-term cryopreservation. The lack of strong treatment-specific effects on microbiota composition suggest a shared microbial response to freeze-thaw stress favoring fast-growing taxa. Our findings offer practical, low-cost strategies for stool biobanking. ImportanceThe cultivation of bacterial taxa from complex communities, such as those in fecal samples, is essential for mechanistic studies and the development of microbiota-based therapeutics, including defined consortia and individual probiotic strains. Such cultivation efforts typically rely on previously stored samples; however, systematic knowledge regarding long-term preservation strategies that ensure viability and regrowth of constituent bacterial taxa remains limited. In this study, we systematically evaluated 16 distinct cryopreservation conditions to assess their efficacy in maintaining bacterial viability. Our results show that conventional glycerol-based preservation and simple snap-freezing are comparable in performance to more elaborate and cost-intensive protocols. Moreover, we identified the duration of sample transport prior to freezing as a critical determinant of post-thaw bacterial recovery. These findings provide valuable data on the relative effectiveness of various preservation methods and support the use of low-cost, easily implementable strategies that are particularly suitable for application in resource-limited settings.

microbiology↗

bioRxiv2025

Tier-based standards for FAIR sequence data and metadata sharing in microbiome research

Microbiome research is a growing, data-driven field within the life sciences. While policies exist for sharing microbiome sequence data and using standardized metadata schemes, compliance among researchers varies. To promote open research data best practices in microbiome research and adjacent communities, we (1) propose two tiered badge systems to evaluate data/metadata sharing compliance, and (2) developed an automated evaluation tool to determine adherence to data reporting standards in publications with amplicon and metagenome sequence data. In a systematic evaluation of publications (n = 2929) spanning human gut microbiome research, and in three case studies of soil and gut microbiota used to manually validate the evaluation tool (n = 370), we found nearly half of publications do not meet minimum standards for sequence data availability. Moreover, poor standardization of metadata creates a high barrier to harmonization and cross-study comparison. Using this badge system and evaluation tool, our proof-of-concept work exposes the (i) ineffectiveness of sequence data availability statements, and (ii) lack of consistent metadata reports used for annotation of microbial data. We highlight the need for improved practices and infrastructure that reduce barriers to data submission and maximize reproducibility in microbiome research. We anticipate that our tiered badge framework will promote dialogue regarding data sharing practices and facilitate microbiome data reuse, supporting best practices that make microbiome data FAIR. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=86 SRC="FIGDIR/small/636914v3_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@aaa4e8org.highwire.dtl.DTLVardef@130a3b1org.highwire.dtl.DTLVardef@4acc8aorg.highwire.dtl.DTLVardef@ba8688_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2024

HighALPS: Ultra-High-Throughput Marker-Gene Amplicon Library Preparation and Sequencing on the Illumina NextSeq and NovaSeq Platforms

Microbiome research using amplicon sequencing of microbial marker genes has surged over the past decade, propelled by protocols for highly multiplexed sequencing with barcoded primer constructs. Newer Illumina platforms like the NovaSeq and NextSeq series significantly outperform older sequencers in terms of reads, output, and runtime. However these platforms are more prone to index-hopping, which limits the application of protocols designed for older platforms such as the Earth Microbiome Project (EMP) protocols, hence there is a need to adapt these established protocols. Here, we present an ultra-High-throughput Amplicon Library Preparation and Sequencing protocol (HighALPS) incorporating the capabilities of these newer sequencing platforms, designed for both 16S rRNA gene and fungal internal transcribed spacer (ITS) domain sequencing. Our results demonstrate good run performance across different sequencing platforms and flow cells, with successful sequencing of mock communities, validating the protocols effectiveness. The new HighALPS library preparation method offers a robust, cost effective, and ultra-high-throughput solution for microbiome research, compatible with the latest sequencing technologies. This protocol allows multiplexing thousands of samples in a single run at a read depth of tens of millions of sequences per sample.

microbiology↗

bioRxiv2024

Domestic freezer storage: a solution for preserving stool microbiota integrity for at-home collection

The gut microbiome is crucial for host health. Early childhood is a critical period for the development of a healthy gut microbiome, but it is particularly sensitive to external influences. Recent research has focused on using advanced techniques like shotgun metagenome sequencing to identify key microbial signatures and disruptions linked to disease. For accurate microbiome analysis, samples need to be collected and stored under specific conditions to preserve microbial integrity and composition, with -80{degrees}C storage considered the gold standard for stabilization. This study investigates the effect of domestic freezer storage on the microbial composition of stool samples from 20 children under 4 years with the use of shotgun metagenomic sequencing. Fresh stool samples were aliquoted into sterile tubes, with one aliquot stored at 4{degrees}C and analyzed within 24 hours, while others were frozen in domestic freezers (below -18{degrees}C) and analyzed after 1 week, 2 months, and 6 months. Assessments of contig assembly quality, microbial diversity, and antimicrobial resistance genes revealed no significant degradation or variation in microbial composition. ImportanceMost previous studies on sample storage have used amplicon sequencing, which limits relevance to metagenome sequencing, in which contig quality and functional gene detection are additional concerns. Moreover, the effects of domestic freezer storage for at-home stool collection on microbiome profiles, contig quality, and antimicrobial resistance gene profiles have not been tested previously. Our findings suggest that stool samples stored in domestic freezers for up to six months maintain the integrity of metagenomic data. These findings indicate that domestic freezer storage does not compromise the integrity or reproducibility of metagenomic data, offering a reliable and accessible alternative for temporary sample storage. This approach enhances the feasibility of large-scale at-home stool collection and citizen science projects, even those focused on the more easily perturbed early life microbiome. This advancement enables more inclusive research into the gut microbiome, enhancing our understanding of its role in human health.

microbiology↗

bioRxiv2024

Standardising a microbiome pipeline for body fluid identification from complex crime scene stains

BackgroundRecent advances in next-generation sequencing have opened up new possibilities for utilizing the human microbiome in various fields, including forensics. Researchers have capitalized on the site-specific microbial communities found in different parts of the body to identify body fluids from biological evidence. Despite promising results, microbiome-based methods have not yet been fully integrated into forensic practice due to the lack of standardized protocols and systematic testing of methods on forensically relevant samples. Our study addresses critical decisions in establishing these protocols, focusing on bioinformatics choices and the use of machine learning to present microbiome results in court for forensically relevant and challenging samples. ResultsWe propose using Operational Taxonomic Units (OTUs) for read data processing and creating heterogeneous training datasets for training a random forest classifier. Our classifier incorporates six forensically relevant classes: saliva, semen, hand skin, penile skin, urine, and vaginal/menstrual fluid. Across these classes, our classifier achieved a high weighted average F1 score of 0.89. Systematic testing on mixed-source samples and underwear revealed reliable detection of at least one component of the mixture and the identification of vaginal fluid from underwear substrates. Additionally, when investigating the sexually shared microbiome (sexome) of heterosexual couples, our classifier shows promising results for the inference of sexual activity. ConclusionIn our study, we recommend the use of a novel random forest classifier trained on a heterogenous dataset for obtaining predictions from samples mimicking forensic evidence. We also highlight the potential of the sexome for assessing the nature of sexual activities in forensic investigations, while delineating areas that warrant further research. Furthermore, we underscore key considerations when presenting machine learning results for classifying mixed-source samples.

bioinformatics↗

bioRxiv2020

Soluble corn fiber reduces ovalbumin-induced sinonasal inflammation via the gut microbiota-airway axis

Asthma is a chronic airway inflammatory disease that affects approximately 300 million people worldwide, causing a substantial healthcare burden. Although there is a large degree of heterogeneity in the inflammatory response of asthmatics, a subset of patients are characterized by type-2 inflammation, which is in part mediated by TH2 cells in both the upper and lower airways. Asthma prevalence is increased in low-socioeconomic-status populations, where disparities in health behavior exist, including a shift toward a western diet characterized by low dietary fiber. Gut microbes metabolize fiber into short chain fatty acids that can reduce type-2 inflammation in peripheral organs, such as the airways. We hypothesized that soluble fiber can reduce ovalbumin (OVA)-induced upper airway inflammation in the context of the unified airway hypothesis, in mice maintained on ingredient-matched western (WD) and control diets (CD) through production of short chain fatty acids. Our results show that soluble fiber reduces IL-4 and IL-13 gene expression (p<0.05, Mann Whitney) in the sinonasal cavity of CD-fed mice, but this effect was lost in WD-fed mice. This loss of protection in WD-fed mice parallels compositional changes of the cecal and fecal microbiota. Mice fed a soluble fiber supplement while being maintained on a WD had altered microbial communities characterized by lower abundance of fiber fermentering bacteria. This work can be used to develop effective microbiome-based therapeutics as a low-cost method to reduce asthma morbidity. IMPORTANCEPrevious research has supported that western-style diets, typically high-fat and low-fiber, are associated with changes in the gut microbiome and increased inflammation. Western diets are accessible and prominent in low-socioeconomic-status populations, where asthma rates are highest; however, there has yet to be a low-cost asthma therapeutic. For the first time, we investigated whether supplementation with a physiologically relevant quantity of soluble corn fiber can reduce allergic airway inflammation. Our study supports that soluble corn fiber supplementation is associated with compositional shifts of the gut microbiota and reduced airway inflammation, promoting the use of fiber as a low-cost microbiome modifying therapy to reduce asthma-associated inflammation. However, soluble corn fiber in conjunction with a western diet resulted in an alternate gut microbiome composition and loss of protection against allergic airway inflammation. These findings further support the importance of the gut microbiota in host health.

microbiology↗

bioRxiv2019

Revisiting microbe-metabolite interactions: doing better than random

I.Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.

bioinformatics↗

bioRxiv2018

Species-level microbial sequence classification is improved by source-environment information

Popular naive Bayes taxonomic classifiers for amplicon sequences assume that all species in the reference database are equally likely to be observed. We demonstrate that classification accuracy degrades linearly with the degree to which that assumption is violated, and in practice it is always violated. By incorporating environment-specific taxonomic abundance information, we demonstrate that species-level resolution is attainable.

bioinformatics↗

bioRxiv2018

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning library. We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. Automated feature selection and hyperparameter tuning enhance performance of most methods but may not be necessary under all circumstances. The q2-sample-classifier plugin makes these methods more accessible and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition. The q2-sample-classifier source code is available at https://github.com/qiime2/q2-sample-classifier. It is released under a BSD-3-Clause license, and is freely available including for commercial use.

bioinformatics↗

bioRxiv2017

q2-longitudinal: a QIIME 2 plugin for longitudinal and paired-sample analyses of microbiome data

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre/post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including paired differences and distances, linear mixed effects models, microbial interdependence test, first differencing, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open source software released under a BSD-3-Clause license and is freely available, including for commercial use.

bioinformatics↗