Assessing and removing the effect of unwanted technical variations in microbiome data

Nepali oral microbiomes reflect a gradient of lifestyles from traditional to industrialized

BACKGROUND

Lifestyle plays an important role in shaping the gut microbiome. However, its contributions to the oral microbiome remain less clear, due to the confounding effects of geography and methodology in investigations of populations studied to date. Furthermore, while the oral microbiome seems to differ between foraging and industrialized populations, we lack insight into whether transitions to and away from agrarian lifestyles shape the oral microbiota. Given the growing interest in so-called "vanishing microbiomes" potentially being a risk factor for increased disease prevalence in industrialized populations, it is important that we distinguish lifestyle from geography in the study of microbiomes across populations.

RESULTS

Here, we investigate salivary microbiomes of 63 Nepali individuals representing a spectrum of lifestyles: foraging, subsistence farming (individuals that transitioned from foraging to farming within the last 50 years), agriculturalists (individuals that have transitioned to farming for at least 300 years), and industrialists (expatriates that immigrated to the USA within the last 20 years). We characterize the role of lifestyle in microbial diversity, identify microbes that differ between lifestyles, and pinpoint specific lifestyle factors that may be contributing to differences in the microbiomes across populations. Contrary to prevailing views, when geography is controlled for, oral microbiome alpha diversity does not differ significantly across lifestyles. Microbiome composition, however, follows the gradient of lifestyles from foraging through agrarianism to industrialism, supporting the notion that lifestyle indeed plays a role in the oral microbiome. Relative abundances of several individual taxa, including Streptobacillus and an unclassified Porphyromonadaceae genus, also mirror lifestyle. Finally, we identify specific lifestyle factors associated with microbiome composition across the gradient of lifestyles, including smoking and grain sources.

CONCLUSION

Our findings demonstrate that by studying populations within Nepal, we can isolate an important role of lifestyle in determining oral microbiome composition. In doing so, we highlight the potential contributions of several lifestyle factors, underlining the importance of carefully examining the oral microbiome across lifestyles to improve our understanding of global microbiomes. Video Abstract.

Assessing and mitigating batch effects in large-scale omics studies

Batch effects in omics data are notoriously common technical variations unrelated to study objectives, and may result in misleading outcomes if uncorrected, or hinder biomedical discovery if over-corrected. Assessing and mitigating batch effects is crucial for ensuring the reliability and reproducibility of omics data and minimizing the impact of technical variations on biological interpretation. In this review, we highlight the profound negative impact of batch effects and the urgent need to address this challenging problem in large-scale omics studies. We summarize potential sources of batch effects, current progress in evaluating and correcting them, and consortium efforts aiming to tackle them.

Nepali oral microbiomes reflect a gradient of lifestyles from traditional to industrialized

Background

Lifestyle plays an important role in shaping the gut microbiome. However, its contributions to the oral microbiome remains less clear, due to the confounding effects of geography and methodology in investigations of populations studied to date. Furthermore, while the oral microbiome seems to differ between foraging and industrialized populations, we lack insight into whether transitions to and away from agrarian lifestyles shape the oral microbiota. Given the growing interest in so-called 'vanishing microbiomes' potentially being a risk factor for increased disease prevalence in industrialized populations, it is important that we distinguish lifestyle from geography in the study of microbiomes across populations.

Results

Here, we investigate salivary microbiomes of 63 Nepali individuals representing a spectrum of lifestyles: foraging, subsistence farming (individuals that transitioned from foraging to farming within the last 50 years), agriculturalists (individuals that have transitioned to farming for at least 300 years), and industrialists (expatriates that immigrated to the United States within the last 20 years). We characterize the role of lifestyle in microbial diversity, identify microbes that differ between lifestyles, and pinpoint specific lifestyle factors that may be contributing to differences in the microbiomes across populations. Contrary to prevailing views, when geography is controlled for, oral microbiome alpha diversity does not differ significantly across lifestyles. Microbiome composition, however, follows the gradient of lifestyles from foraging through agrarianism to industrialism, supporting the notion that lifestyle indeed plays a role in the oral microbiome. Relative abundances of several individual taxa, including Streptobacillus and an unclassified Porphyromonadaceae genus, also mirror lifestyle. Finally, we identify specific lifestyle factors associated with microbiome composition across the gradient of lifestyles, including smoking and grain source.

Conclusion

Our findings demonstrate that by controlling for geography, we can isolate an important role for lifestyle in determining oral microbiome composition. In doing so, we highlight the potential contributions of several lifestyle factors, underlining the importance of carefully examining the oral microbiome across lifestyles to improve our understanding of global microbiomes.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

An improved and extended dual-index multiplexed 16S rRNA sequencing for the Illumina HiSeq and MiSeq platform

BACKGROUND

Recent advancements in next-generation sequencing (NGS) technology have ushered in significant improvements in sequencing speed and data throughput, thereby enabling the simultaneous analysis of a greater number of samples within a single sequencing run. This technology has proven particularly valuable in the context of microbial community profiling, offering a powerful tool for characterizing the microbial composition at the species level within a given sample. This profiling process typically involves the sequencing of 16S ribosomal RNA (rRNA) gene fragments. By scaling up the analysis to accommodate a substantial number of samples, sometimes as many as 2,000, it becomes possible to achieve cost-efficiency and minimize the introduction of potential batch effects. Our study was designed with the primary objective of devising an approach capable of facilitating the comprehensive analysis of 1,711 samples sourced from diverse origins, including oropharyngeal swabs, mouth cavity swabs, dental swabs, and human fecal samples. This analysis was based on data obtained from 16S rRNA metagenomic sequencing conducted on the Illumina MiSeq and HiSeq sequencing platforms.

RESULTS

We have designed a custom set of 10-base pair indices specifically tailored for the preparation of libraries from amplicons derived from the V3-V4 region of the 16S rRNA gene. These indices are instrumental in the analysis of the microbial composition in clinical samples through sequencing on the Illumina MiSeq and HiSeq platforms. The utilization of our custom index set enables the consolidation of a significant number of libraries, enabling the efficient sequencing of these libraries in a single run.

CONCLUSIONS

The unique array of 10-base pair indices that we have developed, in conjunction with our sequencing methodology, will prove highly valuable to laboratories engaged in sequencing on Illumina platforms or utilizing Illumina-compatible kits.

Commercial farmed swine harbour a variety of pathogenic bacteria and antimicrobial resistance genes

Introduction. The northern region of Thailand serves as a crucial area for swine production, contributing to the Thai community food supply. Previous studies have highlighted the presence of foodborne bacterial pathogens originating from swine farms in this region, posing a threat to both human and animal health.Gap statement. Multiple swine bacterial pathogens have been studied at a species level, but the distribution and co-occurrence of bacterial pathogens in agricultural swine has not been well established.Aim. Our study employed the intestinal scraping technique to directly examine the bacterial micro-organisms interacting with the swine host.Methodology. We used shotgun metagenomic sequencing to analyse the bacterial pathogens inhabiting the caecal microbiome of swine from five commercial farms in northern Thailand.Results. A variety of pathogenic and opportunistic bacteria were identified, including Escherichia coli, Clostridium botulinum, Staphylococcus aureus and the Corynebacterium genus. From a One Health perspective, these species are important foodborne and opportunistic pathogens in both humans and agricultural animals, making swine a critical pathogen reservoir that can cause illness in humans, especially farm workers. Additionally, the swine caecal microbiome contains commensal bacteria such as Bifidobacterium, Lactobacillus and Faecalibacterium, which are associated with normal physiology and feed utilization in healthy swine. Antimicrobial resistance genes were also detected in all samples, specifically conferring resistance to tetracycline and aminoglycosides, which have historically been used extensively in swine farming.Conclusion. The findings further support the need for improved sanitation standards in swine farms, and additional monitoring of agricultural animals and farm workers to reduce contamination and improved produce safety for human consumption.

Guidelines for microbiome studies in renal physiology

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.