Reducing bias in microbiome research: Comparing methods from sample collection to sequencing

De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

INTRODUCTION

Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties.

METHODS

We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks.

RESULTS

Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions.

CONCLUSIONS

Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract.

Impact of antibiotic prophylaxis on gut microbiota in colorectal surgery: insights from an Eastern European stewardship study

Introduction

Antibiotic overuse is driving a global rise in antibiotic resistance, highlighting the need for robust antimicrobial stewardship (AMS) initiatives to improve prescription practices. While antimicrobials are essential for treating sepsis and preventing surgical site infections (SSIs), they can inadvertently disrupt the gut microbiota, leading to postoperative complications. Treatment methods vary widely across nations due to differences in drug choice, dosage, and therapy duration, affecting antibiotic resistance rates, which can reach up to 51% in some countries. In Romania and the Republic of Moldova, healthcare practices for surgical antibiotic prophylaxis differ significantly despite similarities in genetics, culture, and diet. Romania's stricter healthcare regulations result in more standardized antibiotic protocols, whereas Moldova's limited healthcare funding leads to less consistent practices and greater variability in treatment outcomes.

Methods

This study presents the results of a prospective cross-border investigation involving 86 colorectal cancer patients from major oncological hospitals in Romania and Moldova. We analyzed fecal samples collected from patients before and 7 days post-antibiotic treatment, focusing on the V3-V4 region of the 16S rRNA gene.

Results

Our findings indicate that inconsistent antibiotic prophylaxis policies-varying in type, dosage, or therapy duration-significantly impacted the gut microbiota and led to more frequent dysbiosis compared to stricter prophylactic antibiotic practices (single dose, single product, limited time).

Discussion

We emphasize the need for standardized antibiotic prophylaxis protocols to minimize dysbiosis and its associated risks, promoting more effective antimicrobial use, particularly in low- and middle-income countries (LMICs).

Profiling the fecal microbiome and its modulators across the lifespan in the Netherlands

Defining what constitutes a healthy microbiome throughout our lives remains an ongoing challenge. Understanding to what extent host and environmental factors can influence it has been the primary motivation for large population studies worldwide. Here, we describe the fecal microbiome of 3,746 individuals (0-87 years of age) in a nationwide study in the Netherlands, in association with extensive questionnaires. We validate previous findings, such as infant-adult trajectories, and explore the collective impact of our variables, which explain over 40% of the variation in microbiome composition. We identify associations with less explored factors, particularly those ethnic related, which show the largest impact on the adult microbiome composition, diversity, metabolic profiles, and CAZy (carbohydrate-active enzyme) repertoires. Understanding the sources of microbiome variability is crucial, given its potential as a modifiable target with therapeutic possibilities. With this work, we aim to serve as a foundational element for the design of health interventions and fundamental research.

Changes in the Gastrointestinal Microbiota Induced by Proton Pump Inhibitors-A Review of Findings from Experimental Trials

The use of proton pump inhibitors (PPIs) has increased considerably in many Western countries, and there is concern that numerous conditions and diseases associated with PPI use may be adverse events. The main function of gastric acid is to defend the organism against orally ingested microorganisms, and there is also concern that alterations not only in the gastric microbiome but also the downstream intestinal microbiome may increase the risk of disease or alter the course of preexisting disease. The current study is a systematic review of the available evidence from experimental trials investigating the effects of PPIs on the gastrointestinal microbiota by next-generation sequencing. Thirteen studies were identified. The effects of PPIs were seen on alterations in diversity and richness in some of the studies, while a larger proportion of the studies detected alterations at various taxonomic levels. The general finding was that PPI use caused an increase in bacteria normally found in the oral microbiota in both the upper and lower GI tract. The most consistent taxonomic alterations seemed to be increases in oral flora along the axis Streptococcaceae and Streptococcus at genus level and various Streptococcus spp., as well as Veillonellaceae, Veillonella and Haemophilus.

The past, present and future of polymicrobial infection research: Modelling, eavesdropping, terraforming and other stories

Over the last two centuries, great advances have been made in microbiology as a discipline. Much of this progress has come about as a consequence of studying the growth and physiology of individual microbial species in well-defined laboratory media; so-called "axenic growth". However, in the real world, microbes rarely live in such "splendid isolation" (to paraphrase Foster) and more often-than-not, share the niche with a plethora of co-habitants. The resulting interactions between species (and even between kingdoms) are only very poorly understood, both on a theoretical and experimental level. Nevertheless, the last few years have seen significant progress, and in this review, we assess the importance of polymicrobial infections, and show how improved experimental traction is advancing our understanding of these. A particular focus is on developments that are allowing us to capture the key features of polymicrobial infection scenarios, especially as those associated with the human airways (both healthy and diseased).

Current Trends and Challenges of Microbiome Research in Prostate Cancer

PURPOSE OF REVIEW

The role of the gut microbiome in prostate cancer is an emerging area of research interest. However, no single causative organism has yet been identified. The goal of this paper is to examine the role of the microbiome in prostate cancer and summarize the challenges relating to methodology in specimen collection, sequencing technology, and interpretation of results.

RECENT FINDINGS

Significant heterogeneity still exists in methodology for stool sampling/storage, preservative options, DNA extraction, and sequencing database selection/in silico processing. Debate persists over primer choice in amplicon sequencing as well as optimal methods for data normalization. Statistical methods for longitudinal microbiome analysis continue to undergo refinement. While standardization of methodology may help yield more consistent results for organism identification in prostate cancer, this is a difficult task due to considerable procedural variation at each step in the process. Further reproducibility and methodology research is required.

A streamlined pipeline based on HmmUFOtu for microbial community profiling using 16S rRNA amplicon sequencing

Microbial community profiling using 16S rRNA amplicon sequencing allows for taxonomic characterization of diverse microorganisms. While amplicon sequence variant (ASV) methods are increasingly favored for their fine-grained resolution of sequence variants, they often discard substantial portions of sequencing reads during quality control, particularly in datasets with large number samples. We present a streamlined pipeline that integrates FastP for read trimming, HmmUFOtu for operational taxonomic units (OTU) clustering, Vsearch for chimera checking, and Kraken2 for taxonomic assignment. To assess the pipeline's performance, we reprocessed two published stool datasets of normal Korean populations: one with 890 and the other with 1,462 independent samples. In the first dataset, HmmUFOtu retained 93.2% of over 104 million read pairs after quality trimming, discarding chimeric or unclassifiable reads, while DADA2, a commonly used ASV method, retained only 44.6% of the reads. Nonetheless, both methods yielded qualitatively similar β-diversity plots. For the second dataset, HmmUFOtu retained 89.2% of read pairs, while DADA2 retained a mere 18.4% of the reads. HmmUFOtu, being a closed-reference clustering method, facilitates merging separately processed datasets, with shared OTUs between the two datasets exhibiting a correlation coefficient of 0.92 in total abundance (log scale). While the first two dimensions of the β-diversity plot exhibited a cohesive mixture of the two datasets, the third dimension revealed the presence of a batch effect. Our comparative evaluation of ASV and OTU methods within this streamlined pipeline provides valuable insights into their performance when processing large-scale microbial 16S rRNA amplicon sequencing data. The strengths of HmmUFOtu and its potential for dataset merging are highlighted.