Hybrid-denovo: a de novo OTU-picking pipeline integrating single-end and paired-end 16S sequence tags

Don't let valuable microbiome data go to waste: combined usage of merging and direct-joining of sequencing reads for low-quality paired-end amplicon data

The pernicious nature of low-quality sequencing data warrants improvement in the bioinformatics workflow for profiling microbial diversity. The conventional merging approach, which drops a copious amount of sequencing reads when processing low-quality amplicon data, requires alternative methods. In this study, a computational workflow, a combination of merging and direct-joining where the paired-end reads lacking overlaps are concatenated and pooled with the merged sequences, is proposed to handle the low-quality amplicon data. The proposed computational strategy was compared with two workflows; the merging approach where the paired-end reads are merged, and the direct-joining approach where the reads are concatenated. The results showed that the merging approach generates a significantly low number of amplicon sequences, limits the microbiome inference, and obscures some microbial associations. In comparison to other workflows, the combination of merging and direct-joining strategy reduces the loss of amplicon data, improves the taxonomy classification, and importantly, abates the misleading results associated with the merging approach when analysing the low-quality amplicon data. The mock community analysis also supports the findings. In summary, the researchers are suggested to follow the merging and direct-joining workflow to avoid problems associated with low-quality data while profiling the microbial community structure.

Preoperative Vaginal Microbiome as a Predictor of Postoperative Urinary Tract Infection

This is a single Institute, prospective cohort study. We collected twenty- two postmenopausal women with pelvic organ prolapse planning to undergo vaginal hysterectomy with transvaginal pelvic reconstructive surgery, with or without a concomitant anti-incontinence procedure. Vaginal swabs and urine samples were longitudinally collected at five time points: preoperative consult visit (T1), day of surgery prior to surgical scrub (T2), immediately postoperative (T3), day of hospital discharge (T4), and at the postoperative exam visit (T5). Women experiencing urinary tract infection symptoms provided a sample set prior to antibiotic administration (T6). Microbiome analysis on vaginal and urinary specimens at each time point. Region V3-V5 of the 16S ribosomal RNA gene was amplified and sequenced. Sample DNA was analyzed with visit T1, T2, T5 and T6. Six (27.3%) participants developed postoperative urinary tract infection whose vaginal sample at first clinical visit (T1) revealed beta-diversity analysis with significant differences in microbiome structure and composition. Women diagnosed with a postoperative urinary tract infection had a vaginal microbiome characterized by low abundance of Lactobacillus and high prevalence of Prevotella and Gardnerella species. In our cohort, preoperative vaginal swabs can predict who will develop a urinary tract infection following transvaginal surgery for pelvic organ prolapse.

Host habitat shapes the core gut bacteria of decapod crustaceans: A meta-analysis

Gut microbiota is an essential determinant factor that drives the physiological, immunological, and metabolic functions of animals. A few meta-analysis studies identified crucial information about the gut microbiota of vertebrate animals in different habitats including fish while no report is yet available for the commercially cultured decapod crustaceans (DC). This meta-analysis investigated the gut microbiota of 11 commercially cultured DC species from five different groups-crab, crayfish, lobster, prawn, and shrimp to gain an overview of microbial diversity and composition and to find out core genera under two different host habitats: freshwater and saltwater. The analysis of 627 Illumina datasets from 25 published studies revealed selective patterns of diversity and compositional differences among groups and between freshwater and saltwater culture systems. The study found a salinity-dependent heterogeneous response of gut microbiota, specifically Vibrio in saltwater for white shrimp, a species that can be cultured with and without salt. Overall, the genera reared in freshwater showed higher diversity in the gut microbial communities than those reared in saltwater. An overwhelming abundance of Candidatus Bacilloplasma and Vibrio were identified for species cultured in freshwater and saltwater system, respectively and these two species were identified as the main core genera for nine out of 11 DC species, except freshwater prawn and river prawn. Together, these results demonstrate the effectiveness of the meta-analysis in identifying the robust and reproducible features of DC gut microbiota for different groups and host habitats. The diversity information curated here could be used as a reference for future studies to differentiate various DC species under two different rearing environments.

Editing efficiencies with Cas9 orthologs, Cas12a endonucleases, and temperature in rice

The advent of CRISPR-Cas technology has made it the genome editing tool of choice in all kingdoms of life, including plants, which can have large, highly duplicated genomes. As a result, finding adequate target sequences that meet the specificities of a given Cas nuclease on any gene of interest remains challenging in many cases. To assess target site flexibility, we tested five different Cas9/Cas12a endonucleases (SpCas9, SaCas9, St1Cas9, Mb3Cas12a, and AsCas12a) in embryogenic rice calli from Taipei 309 at 37°C (optimal temperature for most Cas9/Cas12a proteins) and 27°C (optimal temperature for tissue culture) and measured their editing rates under regular tissue culture conditions using Illumina sequencing. StCas9 and AsCas12 were not functional as tested, regardless of the temperature used. SpCas9 was the most efficient endonuclease at either temperature, regardless of whether monoallelic or biallelic edits were considered. Mb3Cas12a at 37°C was the next most efficient endonuclease. Monoallelic edits prevailed for both SaCas9 and Mb3Cas12a at 27°C, but biallelic edits prevailed at 37°C. Overall, the use of other Cas9 orthologs, the use of Cas12a endonucleases, and the optimal temperature can expand the range of targetable sequences.

Potential Role of Inflammation-Promoting Biliary Microbiome in Primary Sclerosing Cholangitis and Cholangiocarcinoma

Background: Primary sclerosing cholangitis (PSC) is a major risk factor for cholangiocarcinoma (CCA). We investigated biliary and fecal microbiota to determine whether specific microbes in the bile or stool are associated with PSC or CCA. Methods: Bile was obtained from 32 patients with PSC, 23 with CCA with PSC, 26 with CCA without PSC, and 17 controls. Over 90% of bile samples were from patients with perihilar CCA. Stool was obtained from 31 patients with PSC (11 were matched to bile), 16 with CCA with PSC (10 matched to bile), and 11 with CCA without PSC (6 matched to bile). Microbiota composition was assessed using 16SrRNA-marker-based sequencing and was compared between groups. Results: Bile has a unique microbiota distinguished from negative DNA controls and stool. Increased species richness and abundance of Fusobacteria correlated with duration of PSC and characterized the biliary microbiota in CCA. Stool microbiota composition showed no significant differences between groups. Conclusions: We identified a unique microbial signature in the bile of patients with increased duration of PSC or with CCA, suggesting a role for microbiota-driven inflammation in the pathogenesis and or progression to perihilar CCA. Further studies are needed to test this hypothesis.

The breast tissue microbiome, stroma, immune cells and breast cancer

BACKGROUND

Stromal and immune cell composition alterations in benign breast tissue associate with future cancer risk. Pilot data suggest the innate microbiome of normal breast tissue differs between women with and without breast cancer. Microbiome alterations might explain tissue microenvironment variations associated with disease status.

METHODS

Prospectively-collected sterile normal breast tissues from women with benign (n=16) or malignant (n=17) disease underwent 16SrRNA sequencing with Illumina MiSeq and Hybrid-denovo pipeline processing. Breast tissue was scored for fibrosis and fat percentages and immune cell infiltrates (lobulitis) classified as absent/mild/moderate/severe. Alpha and beta diversity were calculated on rarefied OTU data and associations analyzed with multiple linear regression and PERMANOVA.

RESULTS

Breast tissue stromal fat% was lower and fibrosis% higher in benign disease versus cancer (median 30% versus 60%, p=0.01, 70% versus 30%, p=0.002, respectively). The microbiome varied with stromal composition. Alpha diversity (Chao1) correlated with fat% (r=0.38, p=0.02) and fibrosis% (r=-0.32, p=0.05) and associated with different microbial populations as indicated by beta diversity metrics (weighted UniFrac, p=0.08, fat%, p=0.07, fibrosis%). Permutation testing with FDR control revealed taxa differences for fat% in Firmicutes, Bacilli, Bacillales, Staphylococcaceae and genus Staphylococcus, and fibrosis% in Firmicutes, Spirochaetes, Bacilli, Bacillales, Spirochaetales, Proteobacteria RF32, Sphingomonadales, Staphylococcaceae, and genera Clostridium, Staphylococcus, Spirochaetes, Actinobacteria Adlercreutzia. Moderate/severe lobulitis was more common in cancer (73%) than benign disease (13%), p=0.003, but no significant microbial associations were seen.

CONCLUSION

These data suggest a link between breast tissue stromal alterations and its microbiome, further supporting a connection between the breast tissue microenvironment and breast cancer.

Blood Bacterial 16S rRNA Gene Alterations in Women With Polycystic Ovary Syndrome

BACKGROUND

Evidence proved the association between gut microbiome dysbiosis and polycystic ovary syndrome (PCOS) in metabolic disorder, decreased fertility, and hyperandrogenism. However, alterations in blood microbiome of PCOS remained unknown.

OBJECTIVE

This study aims to measure the blood microbiome profile of PCOS patients compared with healthy controls by 16S rRNA sequencing and to investigate its association with PCOS.

METHODS

In this case-control study, bacterial DNA in blood of 24 PCOS patients and 24 healthy controls was investigated by 16S rRNA gene sequencing using the MiSeq technology. Alpha and beta diversity were used to analyze within-sample biodiversity and similarity of one group to another, respectively. Linear discriminant analysis effect size (LEfSe) was calculated to determine biomarkers between groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) functional prediction was performed at genera level.

RESULT

Alpha diversity of blood microbiome decreased significantly in women with PCOS, and beta diversity analysis demonstrated a major separation between the two groups. In the PCOS group, the relative abundance of Proteobacteria, Firmicutes, and Bacteroidetes decreased significantly, while Actinobacteria increased significantly. Cladogram demonstrated the microbiome differences between the two groups at various phylogenic levels. Meanwhile, linear discriminant analysis (LDA) presented significant decreases in Burkholderiaceae, Lachnospiraceae, Bacteroidaceae, Ruminococcaceae, and S24-7 and significant increases in Nocardioidaceae and Oxalobacteraceae of the PCOS group. KEGG pathway analysis at genera level suggested that 14 pathways had significant differences between the two groups.

CONCLUSION

Our findings demonstrated that blood microbiome had a significantly lower alpha diversity, different beta diversity, and significant taxonomic variations in PCOS patients compared with healthy controls.

A comprehensive evaluation of single-end sequencing data analyses for environmental microbiome research

Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3' end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low-quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Background

Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases.

Results

The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes.

Conclusions

Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04410-2.

Distinct Changes in Gut Microbiota Are Associated with Estradiol-Mediated Protection from Diet-Induced Obesity in Female Mice

A decrease in ovarian estrogens in postmenopausal women increases the risk of weight gain, cardiovascular disease, type 2 diabetes, and chronic inflammation. While it is known that gut microbiota regulates energy homeostasis, it is unclear if gut microbiota is associated with estradiol regulation of metabolism. In this study, we tested if estradiol-mediated protection from high-fat diet (HFD)-induced obesity and metabolic changes are associated with longitudinal alterations in gut microbiota in female mice. Ovariectomized adult mice with vehicle or estradiol (E2) implants were fed chow for two weeks and HFD for four weeks. As reported previously, E2 increased energy expenditure, physical activity, insulin sensitivity, and whole-body glucose turnover. Interestingly, E2 decreased the tight junction protein occludin, suggesting E2 affects gut epithelial integrity. Moreover, E2 increased Akkermansia and decreased Erysipleotrichaceae and Streptococcaceae. Furthermore, Coprobacillus and Lactococcus were positively correlated, while Akkermansia was negatively correlated, with body weight and fat mass. These results suggest that changes in gut epithelial barrier and specific gut microbiota contribute to E2-mediated protection against diet-induced obesity and metabolic dysregulation. These findings provide support for the gut microbiota as a therapeutic target for treating estrogen-dependent metabolic disorders in women.

Prevotella histicola Protects From Arthritis by Expansion of Allobaculum and Augmenting Butyrate Production in Humanized Mice

Bacterial therapeutics are the emergent alternatives in treating autoimmune diseases such as Rheumatoid Arthritis [RA]. P. histicola MCI 001 is one such therapeutic bacterium that has been proven to treat autoimmune diseases such as RA and multiple sclerosis [MS] in animal models. The present study characterized P. histicola MCI 001 isolated from a human duodenal biopsy, and evaluated its impact on the gut microbial and metabolic profile in a longitudinal study using the collagen-induced arthritis model in HLA-DQ8.AEo transgenic mice. P. histicola MCI 001 though closely related to the type strain of P. histicola, DSM 19854, differed in utilizing glycerol. In culture, P. histicola MCI 001 produced vitamins such as biotin and folate, and was involved in digesting complex carbohydrates and production of acetate. Colonization study showed that duodenum was the predominant niche for the gavaged MCI 001. A longitudinal follow-up of gut microbial profile in arthritic mice treated with MCI 001 suggested that dysbiosis caused due to arthritis was partially restored to the profile of naïve mice after treatment. A taxon-level analysis suggested an expansion of intestinal genus Allobaculum in MCI001 treated arthritic mice. Eubiosis achieved post treatment with P. histicola MCI 001 was also reflected in the increased production of short-chain fatty acids [SCFAs]. Present study suggests that the treatment with P. histicola MCI 001 leads to an expansion of Allobaculum by increasing the availability of simple carbohydrates and acetate. Restoration of microbial profile and metabolites like butyrate induce immune and gut homeostasis.

Mining microbes for mental health: Determining the role of microbial metabolic pathways in human brain health and disease

There is increasing knowledge regarding the role of the microbiome in modulating the brain and behaviour. Indeed, the actions of microbial metabolites are key for appropriate gut-brain communication in humans. Among these metabolites, short-chain fatty acids, tryptophan, and bile acid metabolites/pathways show strong preclinical evidence for involvement in various aspects of brain function and behaviour. With the identification of neuroactive gut-brain modules, new predictive tools can be applied to existing datasets. We identified 278 studies relating to the human microbiota-gut-brain axis which included sequencing data. This spanned across psychiatric and neurological disorders with a small number also focused on normal behavioural development. With a consistent bioinformatics pipeline, thirty-five of these datasets were reanalysed from publicly available raw sequencing files and the remainder summarised and collated. Among the reanalysed studies, we uncovered evidence of disease-related alterations in microbial metabolic pathways in Alzheimer's Disease, schizophrenia, anxiety and depression. Amongst studies that could not be reanalysed, many sequencing and technical limitations hindered the discovery of specific biomarkers of microbes or metabolites conserved across studies. Future studies are warranted to confirm our findings. We also propose guidelines for future human microbiome analysis to increase reproducibility and consistency within the field.

A decreased abundance of clostridia characterizes the gut microbiota in eosinophilic esophagitis

Abnormalities in the gut microbiome are associated with suppressed Th2 response (Belizario et al., 2018 Mediators Inflamm. 2018:2037838) and predisposition to atopic disease such as asthma and eczema. We investigated if this applies to eosinophilic esophagitis (EoE). Stool bacterial DNA was extracted and followed by 16S rRNA amplification from 12 patients with eosinophilic esophagitis and 12 controls. Alpha‐ and beta‐diversity were analyzed. Only two patients had asthma or atopy and one patient was on budesonide. No patients were on PPIs. Patients with EoE had lower gut microbiota alpha diversity (species richness, P = 0.09; Shannon index, P = 0.01). The microbial composition was distinct as evidenced by significantly different beta diversity (P = 0.03) when compared to healthy controls. There were also significant differences in relative abundance at multiple taxonomic levels when comparing the two communities; at the phylum level, we observed a marked decrease in Firmicutes and increase in Bacteroidetes and at the order and family level there were significant decreases in Clostridia and Clostridiales in patients with EoE (q ≤ 0.1). We conclude that there are significant differences in microbial community structure, microbial richness, and evenness and a significant decrease in taxa within the Clostridia in patients with EoE. Our data suggest that Clostridia based interventions could be tested as adjuncts to current therapeutic strategies in EoE.

Estradiol and high fat diet associate with changes in gut microbiota in female ob/ob mice

Estrogens protect against diet-induced obesity in women and female rodents. For example, a lack of estrogens in postmenopausal women is associated with an increased risk of weight gain, cardiovascular diseases, low-grade inflammation, and cancer. Estrogens act with leptin to regulate energy homeostasis in females. Leptin-deficient mice (ob/ob) exhibit morbid obesity and insulin resistance. The gut microbiome is also critical in regulating metabolism. The present study investigates whether estrogens and leptin modulate gut microbiota in ovariectomized ob/ob (obese) or heterozygote (lean) mice fed high-fat diet (HFD) that received either 17β-Estradiol (E2) or vehicle implants. E2 attenuated weight gain in both genotypes. Moreover, both obesity (ob/ob mice) and E2 were associated with reduced gut microbial diversity. ob/ob mice exhibited lower species richness than control mice, while E2-treated mice had reduced evenness compared with vehicle mice. Regarding taxa, E2 was associated with an increased abundance of the S24-7 family, while leptin was associated with increases in Coriobacteriaceae, Clostridium and Lactobacillus. Some taxa were affected by both E2 and leptin, suggesting these hormones alter gut microbiota of HFD-fed female mice. Understanding the role of E2 and leptin in regulating gut microbiota will provide important insights into hormone-dependent metabolic disorders in women.

A Phylogeny-Regularized Sparse Regression Model for Predictive Modeling of Microbial Community Data

Fueled by technological advancement, there has been a surge of human microbiome studies surveying the microbial communities associated with the human body and their links with health and disease. As a complement to the human genome, the human microbiome holds great potential for precision medicine. Efficient predictive models based on microbiome data could be potentially used in various clinical applications such as disease diagnosis, patient stratification and drug response prediction. One important characteristic of the microbial community data is the phylogenetic tree that relates all the microbial taxa based on their evolutionary history. The phylogenetic tree is an informative prior for more efficient prediction since the microbial community changes are usually not randomly distributed on the tree but tend to occur in clades at varying phylogenetic depths (clustered signal). Although community-wide changes are possible for some conditions, it is also likely that the community changes are only associated with a small subset of "marker" taxa (sparse signal). Unfortunately, predictive models of microbial community data taking into account both the sparsity and the tree structure remain under-developed. In this paper, we propose a predictive framework to exploit sparse and clustered microbiome signals using a phylogeny-regularized sparse regression model. Our approach is motivated by evolutionary theory, where a natural correlation structure among microbial taxa exists according to the phylogenetic relationship. A novel phylogeny-based smoothness penalty is proposed to smooth the coefficients of the microbial taxa with respect to the phylogenetic tree. Using simulated and real datasets, we show that our method achieves better prediction performance than competing sparse regression methods for sparse and clustered microbiome signals.

Hybrid-denovo: a de novo OTU-picking pipeline integrating single-end and paired-end 16S sequence tags

Background

Illumina paired-end sequencing has been increasingly popular for 16S rRNA gene-based microbiota profiling. It provides higher phylogenetic resolution than single-end reads due to a longer read length. However, the reverse read (R2) often has significant low base quality, and a large proportion of R2s will be discarded after quality control, resulting in a mixture of paired-end and single-end reads. A typical 16S analysis pipeline usually processes either paired-end or single-end reads but not a mixture. Thus, the quantification accuracy and statistical power will be reduced due to the loss of a large amount of reads. As a result, rare taxa may not be detectable with the paired-end approach, or low taxonomic resolution will result in a single-end approach.

Results

To have both the higher phylogenetic resolution provided by paired-end reads and the higher sequence coverage by single-end reads, we propose a novel OTU-picking pipeline, hybrid-denovo, that can process a hybrid of single-end and paired-end reads. Using high-quality paired-end reads as a gold standard, we show that hybrid-denovo achieved the highest correlation with the gold standard and performed better than the approaches based on paired-end or single-end reads in terms of quantifying the microbial diversity and taxonomic abundances. By applying our method to a rheumatoid arthritis (RA) data set, we demonstrated that hybrid-denovo captured more microbial diversity and identified more RA-associated taxa than a paired-end or single-end approach.

Conclusions

Hybrid-denovo utilizes both paired-end and single-end 16S sequencing reads and is recommended for 16S rRNA gene targeted paired-end sequencing data.

Impact of Agaricus bisporus Mushroom Consumption on Gut Health Markers in Healthy Adults

Eating Agaricus bisporus mushrooms may impact gut health, because they contain known prebiotics. This study assessed mushroom consumption compared to meat on gastrointestinal tolerance, short chain fatty acid (SCFA) production, laxation, and fecal microbiota. A randomized open-label crossover study was conducted in healthy adults (n = 32) consuming protein-matched amounts of mushrooms or meat twice daily for ten days. Breath hydrogen measures were taken on day one, and gastrointestinal tolerance was evaluated throughout treatments. Fecal sample collection was completed days 6⁻10, and samples were assessed for bacterial composition, SCFA concentrations, weight, pH, and consistency. There were no differences in breath hydrogen, stool frequency, consistency, fecal pH, or SCFA concentrations between the two diets. The mushroom diet led to greater overall gastrointestinal symptoms than the meat diet on days one and two. The mushroom-rich diet resulted in higher average stool weight (p = 0.002) and a different fecal microbiota composition compared to the meat diet, with greater abundance of Bacteroidetes (p = 0.0002) and lower abundance of Firmicutes (p = 0.0009). The increase in stool weight and presence of undigested mushrooms in stool suggests that mushroom consumption may impact laxation in healthy adults. Additional research is needed to interpret the health implications of fecal microbiota shifts with mushroom feeding.

Predictive Modeling of Microbiome Data Using a Phylogeny-Regularized Generalized Linear Mixed Model

Recent human microbiome studies have revealed an essential role of the human microbiome in health and disease, opening up the possibility of building microbiome-based predictive models for individualized medicine. One unique characteristic of microbiome data is the existence of a phylogenetic tree that relates all the microbial species. It has frequently been observed that a cluster or clusters of bacteria at varying phylogenetic depths are associated with some clinical or biological outcome due to shared biological function (clustered signal). Moreover, in many cases, we observe a community-level change, where a large number of functionally interdependent species are associated with the outcome (dense signal). We thus develop "glmmTree," a prediction method based on a generalized linear mixed model framework, for capturing clustered and dense microbiome signals. glmmTree uses the similarity between microbiomes, which is defined based on the microbiome composition and the phylogenetic tree, to predict the outcome. The effects of other predictive variables (e.g., age, sex) can be incorporated readily in the regression framework. Additional tuning parameters enable a data-adaptive approach to capture signals at different phylogenetic depth and abundance level. Simulation studies and real data applications demonstrated that "glmmTree" outperformed existing methods in the dense and clustered signal scenarios.

Systematic Bias Introduced by Genomic DNA Template Dilution in 16S rRNA Gene-Targeted Microbiota Profiling in Human Stool Homogenates

The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.

Variability in representation of microbial communities can be caused by differences in microbial composition or artifacts introduced at sample collection or processing. Alterations in community representation introduced by variations in starting DNA concentrations have not been systematically investigated in stool samples. The goal of this study was to evaluate the effect of the genomic DNA (gDNA) concentration in the resulting 16S rRNA gene library composition and compare its effect to other sample processing variables in homogenized human fecal material. Compared to a gDNA input of 1 ng/μl, inputs of ≤1.6 × 10⁻³ ng/μl resulted in a marked decrease in the concentration of the 16S rRNA gene amplicon (P < 0.001). Low gDNA concentrations (≤1.6 × 10⁻³ ng/μl) were also associated with a decrease (P < 0.001) in the number of operational taxonomic units and significant divergence in β-diversity profiles (unweighted UniFrac distance, P < 0.001), as characterized by an overestimation of Proteobacteria and underestimation of Firmicutes. Even a gDNA concentration of 4 × 10⁻² ng/μl showed a significant impact on the β-diversity profile (unweighted UniFrac distance, P = 0.03). Overall, the gDNA concentration explained 22.4% to 38.1% of the microbiota variation based on various β-diversity measures (P < 0.001). By comparison, the DNA extraction methods and PCR volumes tested did not significantly affect the microbial composition profile, and the PCR cycling method explained less than 3.7% of the microbiota variation (weighted UniFrac distance, P = 0.03). The 16S rRNA gene yield and the microbial community representation of human homogenized stool samples are significantly altered by gDNA template concentrations of ≤1.6 × 10⁻³ ng/μl. In addition, data from studies with a gDNA input of ≤4 × 10⁻² ng/μl should be interpreted with caution.

IMPORTANCE The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.