Library preparation methodology can influence genomic and functional predictions in human microbiome research

Comparative analysis of commercially available kits for optimal DNA extraction from bovine fecal samples

In the field of metagenomic research, the choice of DNA extraction methods plays a pivotal yet often underestimated role in shaping the reliability and interpretability of microbial community data. This study delves into the impact of five commercially available DNA extraction kits on the analysis of bovine fecal microbiota. Recognizing the importance of accurate DNA extraction in elucidating microbial community dynamics, we systematically assessed DNA yield, quality, and microbial composition across these kits using 16S rRNA gene sequencing. Notably, the FastDNA spin soil kit yielded the highest DNA concentration, while significant variations in quality were observed across kits. Furthermore, differential abundance analysis revealed kit-specific biases that impacted taxa representation. Microbial richness and diversity were significantly influenced by the choice of extraction kit, with QIAamp DNA stool minikit, QIAamp Power Pro, and DNeasy PowerSoil outperforming the Stool DNA Kit. Principal-coordinate analysis revealed distinct clustering based on DNA isolation procedures, particularly highlighting the unique microbial community composition derived from the Stool DNA Kit. This study also addressed practical implications, demonstrating how kit selection influences the concentration of Gram-positive and Gram-negative bacterial taxa in samples. This research highlights the need for consideration of DNA extraction kits in metagenomic studies, offering valuable insights for researchers striving to advance the precision and depth of microbiota analyses in ruminants.

A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1-2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

Variability and bias in microbiome metagenomic sequencing: an interlaboratory study comparing experimental protocols

Several studies have documented the significant impact of methodological choices in microbiome analyses. The myriad of methodological options available complicate the replication of results and generally limit the comparability of findings between independent studies that use differing techniques and measurement pipelines. Here we describe the Mosaic Standards Challenge (MSC), an international interlaboratory study designed to assess the impact of methodological variables on the results. The MSC did not prescribe methods but rather asked participating labs to analyze 7 shared reference samples (5 × human stool samples and 2 × mock communities) using their standard laboratory methods. To capture the array of methodological variables, each participating lab completed a metadata reporting sheet that included 100 different questions regarding the details of their protocol. The goal of this study was to survey the methodological landscape for microbiome metagenomic sequencing (MGS) analyses and the impact of methodological decisions on metagenomic sequencing results. A total of 44 labs participated in the MSC by submitting results (16S or WGS) along with accompanying metadata; thirty 16S rRNA gene amplicon datasets and 14 WGS datasets were collected. The inclusion of two types of reference materials (human stool and mock communities) enabled analysis of both MGS measurement variability between different protocols using the biologically-relevant stool samples, and MGS bias with respect to ground truth values using the DNA mixtures. Owing to the compositional nature of MGS measurements, analyses were conducted on the ratio of Firmicutes: Bacteroidetes allowing us to directly apply common statistical methods. The resulting analysis demonstrated that protocol choices have significant effects, including both bias of the MGS measurement associated with a particular methodological choices, as well as effects on measurement robustness as observed through the spread of results between labs making similar methodological choices. In the analysis of the DNA mock communities, MGS measurement bias was observed even when there was general consensus among the participating laboratories. This study was the result of a collaborative effort that included academic, commercial, and government labs. In addition to highlighting the impact of different methodological decisions on MGS result comparability, this work also provides insights for consideration in future microbiome measurement study design.

A user's guide to the bioinformatic analysis of shotgun metagenomic sequence data for bacterial pathogen detection

Metagenomics, i.e., shotgun sequencing of the total microbial community DNA from a sample, has become a mature technique but its application to pathogen detection in clinical, environmental, and food samples is far from common or standardized. In this review, we summarize ongoing developments in metagenomic sequence analysis that facilitate its wider application to pathogen detection. We examine theoretical frameworks for estimating the limit of detection for a particular level of sequencing effort, current approaches for achieving species and strain analytical resolution, and discuss some relevant modern tools for these tasks. While these recent advances are significant and establish metagenomics as a powerful tool to provide insights not easily attained by culture-based approaches, metagenomics is unlikely to emerge as a widespread, routine monitoring tool in the near future due to its inherently high detection limits, cost, and inability to easily distinguish between viable and non-viable cells. Instead, metagenomics seems best poised for applications involving special circumstances otherwise challenging for culture-based and molecular (e.g., PCR-based) approaches such as the de novo detection of novel pathogens, cases of co-infection by more than one pathogen, and situations where it is important to assess the genomic composition of the pathogenic population(s) and/or its impact on the indigenous microbiome.

Automated centrifugal microfluidic system for the preparation of adaptor-ligated sequencing libraries

The intensive workload associated with the preparation of high-quality DNA libraries remains a key obstacle toward widespread deployment of sequencing technologies in remote and resource-limited areas. We describe the development of single-use microfluidic devices driven by an advanced pneumatic centrifugal microfluidic platform, the PowerBlade, to automate the preparation of Illumina-compatible libraries based on adaptor ligation methodology. The developed on-chip workflow includes enzymatic DNA fragmentation coupled to end-repair, adaptor ligation, first DNA cleanup, PCR amplification, and second DNA cleanup. This complex workflow was successfully integrated into simple thermoplastic microfluidic devices that are amenable to mass production with injection molding. The system was validated by preparing, on chip, libraries from a mixture of genomic DNA extracted from three common foodborne pathogens (Listeria monocytogenes, Escherichia coli and Salmonella enterica serovar Typhimurium) and comparing them with libraries made via a manual procedure. The two types of libraries were found to exhibit similar quality control metrics (including genome coverage, assembly, and relative abundances) and led to nearly uniform coverage independent of GC content. This microfluidic technology offers a time-saving and cost-effective alternative to manual procedures and robotic-based automation, making it suitable for deployment in remote environments where technical expertise and resources might be scarce. Specifically, it facilitates field practices that involve mid- to low-throughput sequencing, such as tasks related to foodborne pathogen detection, characterization, and microbial profiling.

Mock microbial community meta-analysis using different trimming of amplicon read lengths

Trimming of sequencing reads is a pre-processing step that aims to discard sequence segments such as primers, adapters and low quality nucleotides that will interfere with clustering and classification steps. We evaluated the impact of trimming length of paired-end 16S and 18S rRNA amplicon reads on the ability to reconstruct the taxonomic composition and relative abundances of communities with a known composition in both even and uneven proportions. We found that maximizing read retention maximizes recall but reduces precision by increasing false positives. The presence of expected taxa was accurately predicted across broad trim length ranges but recovering original relative proportions remains a difficult challenge. We show that parameters that maximize taxonomic recovery do not simultaneously maximize relative abundance accuracy. Trim length represents one of several experimental parameters that have non-uniform impact across microbial clades, making it a difficult parameter to optimize. This study offers insights, guidelines, and helps researchers assess the significance of their decisions when trimming raw reads in a microbiome analysis based on overlapping or non-overlapping paired-end amplicons.

Multicenter evaluation of gut microbiome profiling by next-generation sequencing reveals major biases in partial-length metabarcoding approach

Next-generation sequencing workflows, using either metabarcoding or metagenomic approaches, have massively contributed to expanding knowledge of the human gut microbiota, but methodological bias compromises reproducibility across studies. Where these biases have been quantified within several comparative analyses on their own, none have measured inter-laboratory reproducibility using similar DNA material. Here, we designed a multicenter study involving seven participating laboratories dedicated to partial- (P1 to P5), full-length (P6) metabarcoding, or metagenomic profiling (MGP) using DNA from a mock microbial community or extracted from 10 fecal samples collected at two time points from five donors. Fecal material was collected, and the DNA was extracted according to the IHMS protocols. The mock and isolated DNA were then provided to the participating laboratories for sequencing. Following sequencing analysis according to the laboratories' routine pipelines, relative taxonomic-count tables defined at the genus level were provided and analyzed. Large variations in alpha-diversity between laboratories, uncorrelated with sequencing depth, were detected among the profiles. Half of the genera identified by P1 were unique to this partner and two-thirds of the genera identified by MGP were not detected by P3. Analysis of beta-diversity revealed lower inter-individual variance than inter-laboratory variances. The taxonomic profiles of P5 and P6 were more similar to those of MGP than those obtained by P1, P2, P3, and P4. Reanalysis of the raw sequences obtained by partial-length metabarcoding profiling, using a single bioinformatic pipeline, harmonized the description of the bacterial profiles, which were more similar to each other, except for P3, and closer to the profiles obtained by MGP. This study highlights the major impact of the bioinformatics pipeline, and primarily the database used for taxonomic annotation. Laboratories need to benchmark and optimize their bioinformatic pipelines using standards to monitor their effectiveness in accurately detecting taxa present in gut microbiota.

A metagenomics method for the quantitative detection of bacterial pathogens causing hospital-associated and ventilator-associated pneumonia

IMPORTANCE

The management of ventilator-associated pneumonia and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen. To this end, we propose a metagenomic sequencing assay that includes the use of an internal sample processing control for the quantitative detection of 20 relevant bacterial species from bronchoalveolar lavage samples.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Field and laboratory guidelines for reliable bioinformatic and statistical analysis of bacterial shotgun metagenomic data

Shotgun metagenomics is an increasingly cost-effective approach for profiling environmental and host-associated microbial communities. However, due to the complexity of both microbiomes and the molecular techniques required to analyze them, the reliability and representativeness of the results are contingent upon the field, laboratory, and bioinformatic procedures employed. Here, we consider 15 field and laboratory issues that critically impact downstream bioinformatic and statistical data processing, as well as result interpretation, in bacterial shotgun metagenomic studies. The issues we consider encompass intrinsic properties of samples, study design, and laboratory-processing strategies. We identify the links of field and laboratory steps with downstream analytical procedures, explain the means for detecting potential pitfalls, and propose mitigation measures to overcome or minimize their impact in metagenomic studies. We anticipate that our guidelines will assist data scientists in appropriately processing and interpreting their data, while aiding field and laboratory researchers to implement strategies for improving the quality of the generated results.

Challenges and insights in the exploration of the low abundance human ocular surface microbiome

Purpose

The low microbial abundance on the ocular surface results in challenges in the characterization of its microbiome. The purpose of this study was to reveal factors introducing bias in the pipeline from sample collection to data analysis of low-abundant microbiomes.

Methods

Lower conjunctiva and lower lid swabs were collected from six participants using either standard cotton or flocked nylon swabs. Microbial DNA was isolated with two different kits (with or without prior host DNA depletion and mechanical lysis), followed by whole-metagenome shotgun sequencing with a high sequencing depth set at 60 million reads per sample. The relative microbial compositions were generated using the two different tools MetaPhlan3 and Kraken2.

Results

The total amount of extracted DNA was increased by using nylon flocked swabs on the lower conjunctiva. In total, 269 microbial species were detected. The most abundant bacterial phyla were Actinobacteria, Firmicutes and Proteobacteria. Depending on the DNA extraction kit and tool used for profiling, the microbial composition and the relative abundance of viruses varied.

Conclusion

The microbial composition on the ocular surface is not dependent on the swab type, but on the DNA extraction method and profiling tool. These factors have to be considered in further studies about the ocular surface microbiome and other sparsely colonized microbiomes in order to improve data reproducibility. Understanding challenges and biases in the characterization of the ocular surface microbiome may set the basis for microbiome-altering interventions for treatment of ocular surface associated diseases.

Salivary and fecal microbiota: potential new biomarkers for early screening of colorectal polyps

Objective

Gut microbiota plays an important role in colorectal cancer (CRC) pathogenesis through microbes and their metabolites, while oral pathogens are the major components of CRC-associated microbes. Multiple studies have identified gut and fecal microbiome-derived biomarkers for precursors lesions of CRC detection. However, few studies have used salivary samples to predict colorectal polyps. Therefore, in order to find new noninvasive colorectal polyp biomarkers, we searched into the differences in fecal and salivary microbiota between patients with colorectal polyps and healthy controls.

Methods

In this case-control study, we collected salivary and fecal samples from 33 patients with colorectal polyps (CP) and 22 healthy controls (HC) between May 2021 and November 2022. All samples were sequenced using full-length 16S rRNA sequencing and compared with the Nucleotide Sequence Database. The salivary and fecal microbiota signature of colorectal polyps was established by alpha and beta diversity, Linear discriminant analysis Effect Size (LEfSe) and random forest model analysis. In addition, the possibility of microbiota in identifying colorectal polyps was assessed by Receiver Operating Characteristic Curve (ROC).

Results

In comparison to the HC group, the CP group's microbial diversity increased in saliva and decreased in feces (p < 0.05), but there was no significantly difference in microbiota richness (p > 0.05). The principal coordinate analysis revealed significant differences in β-diversity of salivary and fecal microbiota between the CP and HC groups. Moreover, LEfSe analysis at the species level identified Porphyromonas gingivalis, Fusobacterium nucleatum, Leptotrichia wadei, Prevotella intermedia, and Megasphaera micronuciformis as the major contributors to the salivary microbiota, and Ruminococcus gnavus, Bacteroides ovatus, Parabacteroides distasonis, Citrobacter freundii, and Clostridium symbiosum to the fecal microbiota of patients with polyps. Salivary and fecal bacterial biomarkers showed Area Under ROC Curve of 0.8167 and 0.8051, respectively, which determined the potential of diagnostic markers in distinguishing patients with colorectal polyps from controls, and it increased to 0.8217 when salivary and fecal biomarkers were combined.

Conclusion

The composition and diversity of the salivary and fecal microbiota were significantly different in colorectal polyp patients compared to healthy controls, with an increased abundance of harmful bacteria and a decreased abundance of beneficial bacteria. A promising non-invasive tool for the detection of colorectal polyps can be provided by potential biomarkers based on the microbiota of the saliva and feces.

Sources of gut microbiota variation in a large longitudinal Finnish infant cohort

BACKGROUND

Although the infant gut microbiota has been extensively studied, comprehensive assessment on the microbiota determinants including technical variables has not been performed in large infant cohorts.

METHODS

We studied the effect of 109 variables on the 16S rRNA gene amplicon-based gut microbiota profiles of infants sampled longitudinally from three weeks to two years of life in the Finnish HELMi birth cohort. Spot faecal samples from both parents were included for intra-family analyses, totalling to 7657 samples from 985 families that were evaluated for beta-diversity patterns using permutational multivariate analysis on Bray-Curtis distances, and differential abundance testing and alpha-diversity for variables of interest. We also assessed the effect of different taxonomic levels and distance methods.

FINDINGS

In time point-specific models, the largest share of variation explained, up to 2-6%, were seen in decreasing order for the DNA extraction batch, delivery mode and related perinatal exposures, defecation frequency and parity/siblings. Variables describing the infant gastrointestinal function were continuously important during the first two years, reflecting changes in e.g., feeding habits. The effect of parity/siblings on infant microbiota was modified by birth mode and exposure to intrapartum antibiotics, exemplifying the tight interlinkage of perinatal factors relevant for infant microbiota research. In total, up to 19% of the biological microbiota variation in the infant gut could be explained. Our results highlight the need to interpret variance partitioning results in the context of each cohort's characteristics and microbiota processing.

INTERPRETATION

Our study provides a comprehensive report of key factors associated with infant gut microbiota composition across the two first years of life in a homogenous cohort. The study highlights possible important future research areas and confounding factors to be considered.

FUNDING

This research was supported by Business Finland, Academy of Finland, Foundation for Nutrition Research and the Doctoral Program in Microbiology and Biotechnology, University of Helsinki, Finland.

Realising respiratory microbiomic meta-analyses: time for a standardised framework

In microbiome fields of study, meta-analyses have proven to be a valuable tool for identifying the technical drivers of variation among studies and results of investigations in several diseases, such as those of the gut and sinuses. Meta-analyses also represent a powerful and efficient approach to leverage existing scientific data to both reaffirm existing findings and generate new hypotheses within the field. However, there are currently limited data in other fields, such as the paediatric respiratory tract, where extension of original data becomes even more critical due to samples often being difficult to obtain and process for a range of both technical and ethical reasons. Performing such analyses in an evolving field comes with challenges related to data accessibility and heterogeneity. This is particularly the case in paediatric respiratory microbiomics - a field in which best microbiome-related practices are not yet firmly established, clinical heterogeneity abounds and ethical challenges can complicate sharing of patient data. Having recently conducted a large-scale, individual participant data meta-analysis of the paediatric respiratory microbiota (n = 2624 children from 20 studies), we discuss here some of the unique barriers facing these studies and open and invite a dialogue towards future opportunities. Video Abstract.

Noninvasive Prenatal Screening for Common Fetal Aneuploidies Using Single-Molecule Sequencing

Amplification biases caused by next-generation sequencing (NGS) for noninvasive prenatal screening (NIPS) may be reduced using single-molecule sequencing (SMS), during which PCR is omitted. Therefore, the performance of SMS-based NIPS was evaluated. We used SMS-based NIPS to screen for common fetal aneuploidies in 477 pregnant women. The sensitivity, specificity, positive predictive value, and negative predictive value were estimated. The GC-induced bias was compared between the SMS- and NGS-based NIPS methods. Notably, a sensitivity of 100% was achieved for fetal trisomy 13 (T13), trisomy 18 (T18), and trisomy 21 (T21). The positive predictive value was 46.15% for T13, 96.77% for T18, and 99.07% for T21. The overall specificity was 100% (334/334). Compared with NGS, SMS (without PCR) had less GC bias, a better distinction between T21 or T18 and euploidies, and better diagnostic performance. Overall, our results suggest that SMS improves the performance of NIPS for common fetal aneuploidies by reducing the GC bias introduced during library preparation and sequencing.

Relationship between hindgut microbes and feed conversion ratio in Hu sheep and microbial longitudinal development

Feed efficiency is an important indicator in the sheep production process, which plays an important role in improving economic benefits and strengthening energy conservation and emission reduction. Compared with the rumen, the fermentation of the hindgut microorganisms can also provide part of the energy for the host, and the composition of the hindgut microorganisms will affect the feed efficiency. Therefore, we hope to find new ways to regulate sheep feed efficiency by studying the sheep gut microbes. In this study, male Hu sheep with the same birth date were raised under the same conditions until 180 d old. The sheep were divided into high and low groups according to the feed conversion ratio (FCR) at 80 to 180 d old, and the differences in rectal microorganisms between the two groups were compared. The permutational multivariate analysis (PERMANOVA) test showed that there were differences in microorganisms between the two groups (P < 0.05). Combined with linear fitting analysis, a total of six biomarkers were identified, including Ruminobacter, Eubacterium_xylanophilum_group, Romboutsia, etc. Functional enrichment analysis showed that microorganisms may affect FCR through volatile fatty acids synthesis and inflammatory response. At the same time, we conducted a longitudinal analysis of the hindgut microbes, sampling nine-time points throughout the sheep birth to market stages. The microbiota is clearly divided into two parts: before weaning and after weaning, and after weaning microbes are less affected by before weaning microbial composition.

Dual Domestication, Diversity, and Differential Introgression in Old World Cotton Diploids

Domestication in the cotton genus is remarkable in that it has occurred independently four different times at two different ploidy levels. Relatively little is known about genome evolution and domestication in the cultivated diploid species Gossypium herbaceum and Gossypium arboreum, due to the absence of wild representatives for the latter species, their ancient domestication, and their joint history of human-mediated dispersal and interspecific gene flow. Using in-depth resequencing of a broad sampling from both species, we provide support for their independent domestication, as opposed to a progenitor-derivative relationship, showing that diversity (mean π = 6 × 10-3) within species is similar, and that divergence between species is modest (FST = 0.413). Individual accessions were homozygous for ancestral single-nucleotide polymorphisms at over half of variable sites, while fixed, derived sites were at modest frequencies. Notably, two chromosomes with a paucity of fixed, derived sites (i.e., chromosomes 7 and 10) were also strongly implicated as having experienced high levels of introgression. Collectively, these data demonstrate variable permeability to introgression among chromosomes, which we propose is due to divergent selection under domestication and/or the phenomenon of F2 breakdown in interspecific crosses. Our analyses provide insight into the evolutionary forces that shape diversity and divergence in the diploid cultivated species and establish a foundation for understanding the contribution of introgression and/or strong parallel selection to the extensive morphological similarities shared between species.

Predominance of Methanomicrobiales and diverse hydrocarbon-degrading taxa in the Appalachian coalbed biosphere revealed through metagenomics and genome-resolved metabolisms

Coalbed deposits are a unique subsurface environment and represent an underutilized resource for methane generation. Microbial communities extant in coalbed deposits are responsible for key subsurface biogeochemical cycling and could be utilized to enhance methane production in areas where existing gas wells have depleted methane stores, or in coalbeds that are unmined, or conversely be utilized for mitigation of methane release. Here we utilize metagenomics and metagenome-assembled genomes (MAGs) to identify extant microbial lineages and genome-resolved microbial metabolisms of coalbed produced water, which has not yet been explored in the Appalachian Basin (AppB). Our analyses resulted in the recovery of over 40 MAGs from 8 coalbed methane wells. The most commonly identified taxa among samples were hydrogenotrophic methanogens from the order Methanomicrobiales and these dominant MAGs were highly similar to one another. Conversely, low-abundance coalbed bacterial populations were taxonomically and functionally diverse, mostly belonging to a variety of Proteobacteria classes, and encoding various hydrocarbon solubilization and degradation pathways. The data presented herein provides novel insights into AppB coalbed microbial ecology, and our findings provide new perspectives on underrepresented Methanocalculus species and low-relative abundance bacterial assemblages in coalbed environments, and their potential roles in stimulation or mitigation of methane release.

Comparative analysis of two next-generation sequencing platforms for analysis of antimicrobial resistance genes

OBJECTIVES

The use of antibiotics in human medicine and livestock production has contributed to the widespread occurrence of Antimicrobial Resistance (AMR). Recognizing the relevance of AMR to human and livestock health, it is important to assess the occurrence of genetic determinants of resistance in medical, veterinary, and public health settings in order to understand risks of transmission and treatment failure. Advances in next-generation sequencing technologies have had a significant impact on research in microbial genetics and microbiome analyses. The aim of the present study was to compare the Illumina MiSeq and Ion Torrent S5 Plus sequencing platforms for the analysis of AMR genes in a veterinary/public health setting.

METHODS

All samples were processed in parallel for the two sequencing technologies, subsequently following a common bioinformatics workflow to define the occurrence and abundance of AMR gene sequences. The Comprehensive Antibiotic Resistance Database (CARD), QIAGEN Microbial Insight - Antimicrobial Resistance, Antimicrobial resistance database, and Comprehensive Antibiotic Resistance Database developed by CLC bio (CARD-CLC) databases were compared for analysis, with the most genes identified using CARD.

RESULTS

Drawing on these results, we described an end-to-end workflow for the analysis of AMR genes a using advances in next-generation sequencing. No statistically significant differences were observed among any other genes except the tet-(40) gene between two sequencing platforms, which may be due to the short amplicon length.

CONCLUSIONS

Irrespective of sequencing chemistry and platform used, comparative analysis of AMR genes and candidate host organism suggest that the Illumina MiSeq and Ion Torrent platforms performed almost equally. Regardless of sequencing platform, the results were closely comparable with minor differences.

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

Background Filtering of Clinical Metagenomic Sequencing with a Library Concentration-Normalized Model

Metagenomic next-generation sequencing (mNGS) can accurately detect pathogens in clinical samples. However, wet-lab contamination constrains mNGS analysis and may result in erroneous interpretation of results. Many existing methods rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS tests. By generation of a pretrained profile of common laboratory contaminants, we developed an mNGS noise-filtering model based on the inverse linear relationship between microbial sequencing reads and sample library concentration, named the background elimination and correction by library concentration-normalized (BECLEAN) model. Its efficacy was evaluated with bacteria- and yeast-spiked samples and 28 cerebrospinal fluid (CSF) specimens. The diagnostic accuracy, precision, sensitivity, and specificity of BECLEAN with reference to conventional methods and diagnosis were 92.9%, 86.7%, 100%, and 86.7%, respectively. BECLEAN led to a dramatic reduction of background noise without affecting the true-positive rate and thus can provide a time-saving and convenient tool in various clinical settings. IMPORTANCE Most of the existing methods to remove wet-lab contamination rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS testing in individual cases. In clinical settings, only a handful of samples might be sequenced in a run. The lab-specific microbiome can complicate existing statistical approaches for removing contamination from small-scale clinical metagenomic sequencing data sets; thus, use of a preliminary lab-specific training set is necessary. Our study provides a rapid and accurate background-filtering tool for clinical metagenomic sequencing by generation of a pretrained profile of common laboratory contaminants. Notably, our work demonstrates that the inverse linear relationship between microbial sequencing reads and library concentration can serve to identify true contaminants and evaluate the relative abundance of a taxon in samples by comparing the observed microbial reads to the model-predicted value. Our findings extend the previously published research and demonstrate confirmatory results in clinical settings.

Altered Mycobiota Signatures and Enriched Pathogenic Aspergillus rambellii Are Associated With Colorectal Cancer Based on Multicohort Fecal Metagenomic Analyses

BACKGROUND & AIMS

The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC.

METHODS

We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo.

RESULTS

Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%).

CONCLUSIONS

This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC.

Be aware of the allele-specific bias and compositional effects in multi-template PCR

High-throughput sequencing of amplicon libraries is the most widespread and one of the most effective ways to study the taxonomic structure of microbial communities, even despite growing accessibility of whole metagenome sequencing. Due to the targeted amplification, the method provides unparalleled resolution of communities, but at the same time perturbs initial community structure thereby reducing data robustness and compromising downstream analyses. Experimental research of the perturbations is largely limited to comparative studies on different PCR protocols without considering other sources of experimental variation related to characteristics of the initial microbial composition itself. Here we analyse these sources and demonstrate how dramatically they effect the relative abundances of taxa during the PCR cycles. We developed the mathematical model of the PCR amplification assuming the heterogeneity of amplification efficiencies and considering the compositional nature of data. We designed the experiment-five consecutive amplicon cycles (22-26) with 12 replicates for one real human stool microbial sample-and estimated the dynamics of the microbial community in line with the model. We found the high heterogeneity in amplicon efficiencies of taxa that leads to the non-linear and substantial (up to fivefold) changes in relative abundances during PCR. The analysis of possible sources of heterogeneity revealed the significant association between amplicon efficiencies and the energy of secondary structures of the DNA templates. The result of our work highlights non-trivial changes in the dynamics of real-life microbial communities due to their compositional nature. Obtained effects are specific not only for amplicon libraries, but also for any studies of metagenome dynamics.

Precision digital mapping of endogenous and induced genomic DNA breaks by INDUCE-seq

Understanding how breaks form and are repaired in the genome depends on the accurate measurement of the frequency and position of DNA double strand breaks (DSBs). This is crucial for identification of a chemical’s DNA damage potential and for safe development of therapies, including genome editing technologies. Current DSB sequencing methods suffer from high background levels, the inability to accurately measure low frequency endogenous breaks and high sequencing costs. Here we describe INDUCE-seq, which overcomes these problems, detecting simultaneously the presence of low-level endogenous DSBs caused by physiological processes, and higher-level recurrent breaks induced by restriction enzymes or CRISPR-Cas nucleases. INDUCE-seq exploits an innovative NGS flow cell enrichment method, permitting the digital detection of breaks. It can therefore be used to determine the mechanism of DSB repair and to facilitate safe development of therapeutic genome editing. We further discuss how the method can be adapted to detect other genomic features.

Understanding how DNA double strand breaks (DSBs) form and are repaired in the genome depends on their accurate measurement. Here the authors describe INDUCE-seq; a DSB-detection method that simultaneously measures physiological and induced breaks throughout the genome.

The standardisation of the approach to metagenomic human gut analysis: from sample collection to microbiome profiling

In recent years, the number of metagenomic studies increased significantly. Wide range of factors, including the tremendous community complexity and variability, is contributing to the challenge in reliable microbiome community profiling. Many approaches have been proposed to overcome these problems making hardly possible to compare results of different studies. The significant differences between procedures used in metagenomic research are reflected in a variation of the obtained results. This calls for the need for standardisation of the procedure, to reduce the confounding factors originating from DNA isolation, sequencing and bioinformatics analyses in order to ensure that the differences in microbiome composition are of a true biological origin. Although the best practices for metagenomics studies have been the topic of several publications and the main aim of the International Human Microbiome Standard (IHMS) project, standardisation of the procedure for generating and analysing metagenomic data is still far from being achieved. To highlight the difficulties in the standardisation of metagenomics methods, we thoroughly examined each step of the analysis of the human gut microbiome. We tested the DNA isolation procedure, preparation of NGS libraries for next-generation sequencing, and bioinformatics analysis, aimed at identifying microbial taxa. We showed that the homogenisation time is the leading factor impacting sample diversity, with the recommendation for a shorter homogenisation time (10 min). Ten minutes of homogenisation allows for better reflection of the bacteria gram-positive/gram-negative ratio, and the obtained results are the least heterogenous in terms of beta-diversity of samples microbial composition. Besides increasing the homogenisation time, we observed further potential impact of the library preparation kit on the gut microbiome profiling. Moreover, our analysis revealed that the choice of the library preparation kit influences the reproducibility of the results, which is an important factor that has to be taken into account in every experiment. In this study, a tagmentation-based kit allowed for obtaining the most reproducible results. We also considered the choice of the computational tool for determining the composition of intestinal microbiota, with Kraken2/Bracken pipeline outperforming MetaPhlAn2 in our in silico experiments. The design of an experiment and a detailed establishment of an experimental protocol may have a serious impact on determining the taxonomic profile of the intestinal microbiome community. Results of our experiment can be helpful for a wide range of studies that aim to better understand the role of the gut microbiome, as well as for clinical purposes.

Library Preparation and Sequencing Platform Introduce Bias in Metagenomic-Based Characterizations of Microbiomes

Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here, we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a PCR step (Nextera), and two were PCR free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at -80°C) were assessed. All DNA isolations were performed in duplicate, totaling 80 samples, excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generating comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously. IMPORTANCE Previous research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies

DNA methylation is associated with transcriptional repression, genomic imprinting, stem cell differentiation, embryonic development, and inflammation. Aberrant DNA methylation can indicate disease states, including cancer and neurological disorders. Therefore, the prevalence and location of 5-methylcytosine in the human genome is a topic of interest. Whole-genome bisulfite sequencing (WGBS) is a high-throughput method for analyzing DNA methylation. This technique involves library preparation, alignment, and quality control. Advancements in epigenetic technology have led to an increase in DNA methylation studies. This review compares the detailed experimental methodology of WGBS using accessible and up-to-date analysis tools. Practical codes for WGBS data processing are included as a general guide to assist progress in DNA methylation studies through a comprehensive case study.

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

AGAMEMNON: an Accurate metaGenomics And MEtatranscriptoMics quaNtificatiON analysis suite

We introduce AGAMEMNON ( https://github.com/ivlachos/agamemnon ) for the acquisition of microbial abundances from shotgun metagenomics and metatranscriptomic samples, single-microbe sequencing experiments, or sequenced host samples. AGAMEMNON delivers accurate abundances at genus, species, and strain resolution. It incorporates a time and space-efficient indexing scheme for fast pattern matching, enabling indexing and analysis of vast datasets with widely available computational resources. Host-specific modules provide exceptional accuracy for microbial abundance quantification from tissue RNA/DNA sequencing, enabling the expansion of experiments lacking metagenomic/metatranscriptomic analyses. AGAMEMNON provides an R-Shiny application, permitting performance of investigations and visualizations from a graphics interface.

Evaluating whole-genome sequencing quality metrics for enteric pathogen outbreaks

Background

Whole genome sequencing (WGS) has gained increasing importance in responses to enteric bacterial outbreaks. Common analysis procedures for WGS, single nucleotide polymorphisms (SNPs) and genome assembly, are highly dependent upon WGS data quality.

Methods

Raw, unprocessed WGS reads from Escherichia coli, Salmonella enterica, and Shigella sonnei outbreak clusters were characterized for four quality metrics: PHRED score, read length, library insert size, and ambiguous nucleotide composition. PHRED scores were strongly correlated with improved SNPs analysis results in E. coli and S. enterica clusters.

Results

Assembly quality showed only moderate correlations with PHRED scores and library insert size, and then only for Salmonella. To improve SNP analyses and assemblies, we compared seven read-healing pipelines to improve these four quality metrics and to see how well they improved SNP analysis and genome assembly. The most effective read healing pipelines for SNPs analysis incorporated quality-based trimming, fixed-width trimming, or both. The Lyve-SET SNPs pipeline showed a more marked improvement than the CFSAN SNP Pipeline, but the latter performed better on raw, unhealed reads. For genome assembly, SPAdes enabled significant improvements in healed E. coli reads only, while Skesa yielded no significant improvements on healed reads.

Conclusions

PHRED scores will continue to be a crucial quality metric albeit not of equal impact across all types of analyses for all enteric bacteria. While trimming-based read healing performed well for SNPs analyses, different read healing approaches are likely needed for genome assembly or other, emerging WGS analysis methodologies.

Standard Sample Storage Conditions Have an Impact on Inferred Microbiome Composition and Antimicrobial Resistance Patterns

Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, -80°C; freezer, -20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.

Standard Sample Storage Conditions Have an Impact on Inferred Microbiome Composition and Antimicrobial Resistance Patterns

Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, -80°C; freezer, -20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.

Evaluation of the Effects of Library Preparation Procedure and Sample Characteristics on the Accuracy of Metagenomic Profiles

Shotgun metagenomic sequencing has transformed our understanding of microbial community ecology. However, preparing metagenomic libraries for high-throughput DNA sequencing remains a costly, labor-intensive, and time-consuming procedure, which in turn limits the utility of metagenomes. Several library preparation procedures have recently been developed to offset these costs, but it is unclear how these newer procedures compare to current standards in the field. In particular, it is not clear if all such procedures perform equally well across different types of microbial communities or if features of the biological samples being processed (e.g., DNA amount) impact the accuracy of the approach. To address these questions, we assessed how five different shotgun DNA sequence library preparation methods, including the commonly used Nextera Flex kit, perform when applied to metagenomic DNA. We measured each method's ability to produce metagenomic data that accurately represent the underlying taxonomic and genetic diversity of the community. We performed these analyses across a range of microbial community types (e.g., soil, coral associated, and mouse gut associated) and input DNA amounts. We find that the type of community and amount of input DNA influence each method's performance, indicating that careful consideration may be needed when selecting between methods, especially for low-complexity communities. However, the cost-effective preparation methods that we assessed are generally comparable to the current gold-standard Nextera DNA Flex kit for high-complexity communities. Overall, the results from this analysis will help expand and even facilitate access to metagenomic approaches in future studies. IMPORTANCE Metagenomic library preparation methods and sequencing technologies continue to advance rapidly, allowing researchers to characterize microbial communities in previously underexplored environmental samples and systems. However, widely accepted standardized library preparation methods can be cost-prohibitive. Newly available approaches may be less expensive, but their efficacy in comparison to standardized methods remains unknown. In this study, we compared five different metagenomic library preparation methods. We evaluated each method across a range of microbial communities varying in complexity and quantity of input DNA. Our findings demonstrate the importance of considering sample properties, including community type, composition, and DNA amount, when choosing the most appropriate metagenomic library preparation method.

Exploring Semi-Quantitative Metagenomic Studies Using Oxford Nanopore Sequencing: A Computational and Experimental Protocol

The gut microbiome plays a major role in chronic diseases, of which several are characterized by an altered composition and diversity of bacterial communities. Large-scale sequencing projects allowed for characterizing the perturbations of these communities. However, translating these discoveries into clinical applications remains a challenge. To facilitate routine implementation of microbiome profiling in clinical settings, portable, real-time, and low-cost sequencing technologies are needed. Here, we propose a computational and experimental protocol for whole-genome semi-quantitative metagenomic studies of human gut microbiome with Oxford Nanopore sequencing technology (ONT) that could be applied to other microbial ecosystems. We developed a bioinformatics protocol to analyze ONT sequences taxonomically and functionally and optimized preanalytic protocols, including stool collection and DNA extraction methods to maximize read length. This is a critical parameter for the sequence alignment and classification. Our protocol was evaluated using simulations of metagenomic communities, which reflect naturally occurring compositional variations. Next, we validated both protocols using stool samples from a bariatric surgery cohort, sequenced with ONT, Illumina, and SOLiD technologies. Results revealed similar diversity and microbial composition profiles. This protocol can be implemented in a clinical or research setting, bringing rapid personalized whole-genome profiling of target microbiome species.

Evaluation of a high-throughput, cost-effective Illumina library preparation kit

Library preparation for high-throughput sequencing applications is a critical step in producing representative, unbiased sequencing data. The iGenomX Riptide High Throughput Rapid Library Prep Kit purports to provide high-quality sequencing data with lower costs compared to other Illumina library kits. To test these claims, we compared sequence data quality of Riptide libraries to libraries constructed with KAPA Hyper and NEBNext Ultra. Across several single-source genome samples, mapping performance and de novo assembly of Riptide libraries were similar to conventional libraries prepared with the same DNA. Poor performance of some libraries resulted in low sequencing depth. In particular, degraded DNA samples may be challenging to sequence with Riptide. There was little cross-well plate contamination with the overwhelming majority of reads belong to the proper source genomes. The sequencing of metagenome samples using different Riptide primer sets resulted in variable taxonomic assignment of reads. Increased adoption of the Riptide kit will decrease library preparation costs. However, this method might not be suitable for degraded DNA.

Measuring the absolute abundance of the microbiome by adding yeast containing 16S rRNA gene from a hyperthermophile

High-throughput sequencing (HTS) of 16S rRNA gene amplicons provides compositional information regarding the microbial community, but not the absolute abundance of the bacteria. We aimed to develop a standardized method for quantifying the absolute abundance of bacteria in microbiome studies. To demonstrate the utility of our approach, we quantified the number of bacteria from the compositional data of the fecal and cecal microbiomes. The 16S rRNA gene of a hyperthermophile, Thermus aquaticus, was cloned into Pichia pastoris (yeast) genome, and an equivalent amount of the yeast was added to the stool and cecal samples of mice before DNA extraction. 16S rRNA gene library construction and HTS were performed after DNA extraction. The absolute abundances of bacteria were calculated using T. aquaticus reads. The average relative abundances of T. aquaticus in the five stool and five cecal samples were 0.95% and 0.33%, respectively, indicating that the number of bacteria in a cecum sample is 2.9 times higher than that in a stool sample. The method proposed for quantifying the absolute abundance of the bacterial population in this study is expected to overcome the limitation of showing only compositional data in most microbiome studies.

Evaluating coverage bias in next-generation sequencing of Escherichia coli

Whole-genome sequencing is essential to many facets of infectious disease research. However, technical limitations such as bias in coverage and tagmentation, and difficulties characterising genomic regions with extreme GC content have created significant obstacles in its use. Illumina has claimed that the recently released DNA Prep library preparation kit, formerly known as Nextera Flex, overcomes some of these limitations. This study aimed to assess bias in coverage, tagmentation, GC content, average fragment size distribution, and de novo assembly quality using both the Nextera XT and DNA Prep kits from Illumina. When performing whole-genome sequencing on Escherichia coli and where coverage bias is the main concern, the DNA Prep kit may provide higher quality results; though de novo assembly quality, tagmentation bias and GC content related bias are unlikely to improve. Based on these results, laboratories with existing workflows based on Nextera XT would see minor benefits in transitioning to the DNA Prep kit if they were primarily studying organisms with neutral GC content.

Choice of 16S ribosomal RNA primers affects the microbiome analysis in chicken ceca

We evaluated the effect of applying different sets of 16S rRNA primers on bacterial composition, diversity, and predicted function in chicken ceca. Cecal contents from Ross 708 birds at 1, 3, and 5 weeks of age were collected for DNA isolation. Eight different primer pairs targeting different variable regions of the 16S rRNA gene were employed. DNA sequences were analyzed using open-source platform QIIME2 and the Greengenes database. PICRUSt2 was used to determine the predicted function of bacterial communities. Changes in bacterial relative abundance due to 16S primers were determined by GLMs. The average PCR amplicon size ranged from 315 bp (V3) to 769 bp (V4–V6). Alpha- and beta-diversity, taxonomic composition, and predicted functions were significantly affected by the primer choice. Beta diversity analysis based on Unweighted UniFrac distance matrix showed separation of microbiota with four different clusters of bacterial communities. Based on the alpha- and beta-diversity and taxonomic composition, variable regions V1–V3(1) and (2), and V3–V4 and V3–V5 were in most consensus. Our data strongly suggest that selection of particular sets of the 16S rRNA primers can impact microbiota analysis and interpretation of results in chicken as was shown previously for humans and other animal species.

Investigation of Potential Brain Microbiome in Alzheimer's Disease: Implications of Study Bias

BACKGROUND

Dysbiotic microbiota in the gastrointestinal tract promotes and aggravates neurodegenerative disorders. Alzheimer's disease (AD) has been shown to correlate to dysbiotic bacteria and the immune, metabolic, and endocrine abnormalities associated with abnormal gut-brain-axis signaling. Recent reports also indicate that brain dysbacteriosis may play a role in AD pathogenesis.

OBJECTIVE

To evaluate the presence and differences of brain-region dependent microbiomes in control and AD subjects and the contribution of study bias.

METHODS

Two independent cohorts of postmortem AD brain samples were collected from separate locations, processed with different extraction protocols and investigated for the presence of bacterial DNA indicative of a brain microbiome with V4 16S next generation sequencing.

RESULTS

In both cohorts, few differences between the control and AD groups were observed in terms of alpha and beta diversities, phyla and genera proportions. Independent of study in both AD and control subjects the most abundant phyla were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Variations in beta diversity between hippocampal and cerebellum samples were observed indicating an impact of brain region on the presence of microbial DNA. Importantly, differences in alpha and beta diversities between the two independent cohorts were found indicating a significant cohort- and processing-dependent effect on the microbiome. Finally, there were cohort-specific correlations between the gut microbiome and subject demographics indicate that postmortem interval may have a significant impact on brain microbiome determination.

CONCLUSIONS

Regardless of the study bias, this study concludes that bacterial DNA can be isolated from the human brain suggesting that a brain microbiome may exist; however, more studies are required to understand the variation in AD.

Recent advances and health implications of dietary fasting regimens on the gut microbiome

Calorie restriction is a primary dietary intervention demonstrated over many decades in cellular and animal models to modulate aging pathways, positively affect age-associated diseases and, in clinical studies, to promote beneficial health outcomes. Because long-term compliance with daily calorie restriction has proven problematic in humans several intermittent fasting regimens, including alternate day fasting and time-restricted feeding, have evolved revealing similar clinical benefits as calorie restriction. Despite significant research on the cellular and physiological mechanisms contributing to, and responsible for, these observed benefits, relatively little research has investigated the impact of these various fasting protocols on the gut microbiome (GM). Reduced external nutrient supply to the gut may beneficially alter the composition and function of a "fed" gut microflora. Indeed, the prevalent, obesogenic Western diet can promote deleterious changes in the GM, signaling intermediates involved in lipid and glucose metabolism, and immune responses in the gastrointestinal tract. This review describes recent preclinical and clinical effects of varying fasting regimens on GM composition and associated physiology. Although the number of preclinical and clinical interventions are limited, significant data thus far suggest fasting interventions impact GM composition and physiology. However, there are considerable heterogeneities of study design, methodological considerations, and practical implications. Ongoing research on the health impact of fasting regimens on GM modulation is warranted.

Standardization of microbiome studies for urolithiasis: an international consensus agreement

Numerous metagenome-wide association studies (MWAS) for urolithiasis have been published, leading to the discovery of potential interactions between the microbiome and urolithiasis. However, questions remain about the reproducibility, applicability and physiological relevance of these data owing to discrepancies in experimental technique and a lack of standardization in the field. One barrier to interpreting MWAS is that experimental biases can be introduced at every step of the experimental pipeline, including sample collection, preservation, storage, processing, sequencing, data analysis and validation. Thus, the introduction of standardized protocols that maintain the flexibility to achieve study-specific objectives is urgently required. To address this need, the first international consortium for microbiome in urinary stone disease - MICROCOSM - was created and consensus panel members were asked to participate in a consensus meeting to develop standardized protocols for microbiome studies if they had published an MWAS on urolithiasis. Study-specific protocols were revised until a consensus was reached. This consensus group generated standardized protocols, which are publicly available via a secure online server, for each step in the typical clinical microbiome-urolithiasis study pipeline. This standardization creates the benchmark for future studies to facilitate consistent interpretation of results and, collectively, to lead to effective interventions to prevent the onset of urolithiasis, and will also be useful for investigators interested in microbiome research in other urological diseases.

Recovering prokaryotic genomes from host-associated, short-read shotgun metagenomic sequencing data

Recovering genomes from shotgun metagenomic sequence data allows detailed taxonomic and functional characterization of individual species or strains in a microbial community. Retrieving these metagenome-assembled genomes (MAGs) involves seven stages. First, low-quality bases, along with adapter and host sequences, are removed. Second, overlapping sequences are assembled to create longer contiguous fragments. Third, these fragments are clustered based on sequence composition and abundance. Fourth, these sequence clusters, or bins, undergo rounds of quality assessment and refinement to yield MAGs. The optional fifth stage is dereplication of MAGs to select representatives. Next, each MAG is taxonomically classified. The optional seventh stage is assessing the fraction of diversity that has been recovered. The output of this protocol is draft genomes, which can provide invaluable clues about uncultured organisms. This protocol takes ~1 week to run, depending on computational resources available, and requires prior experience with high-performance computing, shell script programming and Python.

Validation and standardization of DNA extraction and library construction methods for metagenomics-based human fecal microbiome measurements

BACKGROUND

Validation and standardization of methodologies for microbial community measurements by high-throughput sequencing are needed to support human microbiome research and its industrialization. This study set out to establish standards-based solutions to improve the accuracy and reproducibility of metagenomics-based microbiome profiling of human fecal samples.

RESULTS

In the first phase, we performed a head-to-head comparison of a wide range of protocols for DNA extraction and sequencing library construction using defined mock communities, to identify performant protocols and pinpoint sources of inaccuracy in quantification. In the second phase, we validated performant protocols with respect to their variability of measurement results within a single laboratory (that is, intermediate precision) as well as interlaboratory transferability and reproducibility through an industry-based collaborative study. We further ascertained the performance of our recommended protocols in the context of a community-wide interlaboratory study (that is, the MOSAIC Standards Challenge). Finally, we defined performance metrics to provide best practice guidance for improving measurement consistency across methods and laboratories.

CONCLUSIONS

The validated protocols and methodological guidance for DNA extraction and library construction provided in this study expand current best practices for metagenomic analyses of human fecal microbiota. Uptake of our protocols and guidelines will improve the accuracy and comparability of metagenomics-based studies of the human microbiome, thereby facilitating development and commercialization of human microbiome-based products. Video Abstract.

Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing

Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.

IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

Monitoring the microbiome for food safety and quality using deep shotgun sequencing

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species' viability from total RNA sequencing.

The Seagrass Holobiont: What We Know and What We Still Need to Disclose for Its Possible Use as an Ecological Indicator

Microbes and seagrass establish symbiotic relationships constituting a functional unit called the holobiont that reacts as a whole to environmental changes. Recent studies have shown that the seagrass microbial associated community varies according to host species, environmental conditions and the host’s health status, suggesting that the microbial communities respond rapidly to environmental disturbances and changes. These changes, dynamics of which are still far from being clear, could represent a sensitive monitoring tool and ecological indicator to detect early stages of seagrass stress. In this review, the state of art on seagrass holobiont is discussed in this perspective, with the aim of disentangling the influence of different factors in shaping it. As an example, we expand on the widely studied Halophila stipulacea’s associated microbial community, highlighting the changing and the constant components of the associated microbes, in different environmental conditions. These studies represent a pivotal contribution to understanding the holobiont’s dynamics and variability pattern, and to the potential development of ecological/ecotoxicological indices. The influences of the host’s physiological and environmental status in changing the seagrass holobiont, alongside the bioinformatic tools for data analysis, are key topics that need to be deepened, in order to use the seagrass-microbial interactions as a source of ecological information.

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array [GSA]) on 120 DNA samples derived from African- and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave-one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome-wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼0.5× and higher compared to the Illumina GSA.

Bacterial associations in the healthy human gut microbiome across populations

In a microbial community, associations between constituent members play an important role in determining the overall structure and function of the community. The human gut microbiome is believed to play an integral role in host health and disease. To understand the nature of bacterial associations at the species level in healthy human gut microbiomes, we analyzed previously published collections of whole-genome shotgun sequence data, totaling over 1.6 Tbp, generated from 606 fecal samples obtained from four different healthy human populations. Using a Random Forest Classifier, we identified 202 signature bacterial species that were prevalent in these populations and whose relative abundances could be used to accurately distinguish between the populations. Bacterial association networks were constructed with these signature species using an approach based on the graphical lasso. Network analysis revealed conserved bacterial associations across populations and a dominance of positive associations over negative associations, with this dominance being driven by associations between species that are closely related either taxonomically or functionally. Bacterial species that form network modules, and species that constitute hubs and bottlenecks, were also identified. Functional analysis using protein families suggests that much of the taxonomic variation across human populations does not foment substantial functional or structural differences.