Differential Abundance Testing Research Articles

2 Evaluation of the microbiome effects of easy and hard keeper horses with the application of the Equine Keeper Status Scale

Horses with 2 different metabolic tendencies are referred to as “easy keepers” (EK) or “hard keepers” (HK). These tendencies are anecdotal and often related to the Henneke Body Condition Scoring system. EK tends to be over-conditioned (BCS ≥6) and HK need extra feed to maintain a BCS of 5. “Medium keepers” (MK) can maintain a BCS of 5 easily. This lab group has developed the Equine Keeper Status Scale (EKSS) and the objective of this work is to compare the microbiome composition of the 3 keeper statuses between owner reported keeper statuses (ORKS) and EKSS assignments. Fresh fecal samples from 73 horses were collected for 16S rRNA profiling. ORKS were obtained by a verbal estimate of their horse's keeper status in regard to their metabolic tendencies with no influence of the research team (ORKS: EK, n = 20; MK, n = 26; HK, n = 27). Feed weights and BCS scores of each horse were collected and digestible energy intakes were estimated by FeedXL software (input variables of the EKSS) and EKSS assignments were determined at a later time (EK, n = 31; MK, n = 22; HK, n = 20). QIIME2 (Quantitative Insights Into Microbial Ecology, v. 2020.8) and R was used for data processing and analysis. α diversity Shannon-Weaver measures between ORKS groups were not found ( P = 0.34) but were found after EKSS evaluation ( P = 0.06) and Tukey's pairwise tests found the difference between EK and MK to be the most different ( P < 0.05). β diversity weighted Unifrac measures of ORKS found group centroids were different ( P = 0.07) but it wasn't due to ORKS ( P = 0.58); while EKSS evaluation found different centroids due to EKSS ( P = 0.02, P = 0.03, respectively). Spearman correlation testing identified bacteria correlated with every keeper status except ORKS MK which is likely due to the inconsistencies of ORKS. Differential abundance testing performed with ANCOM identified 4 phyla differences (Planctomycetes, Euryarchaeota, Spirochaetes, Proteobacteria) between EKSS groups but not between ORKS. Planctomycetes thrive in low nutrient environments and are most abundant in EK. HK appears to have optimized plant fermentation characteristics due to higher abundances of the methane producers (Euryarcheaota), and denitrifying (Spirochaetes), and N oxidizers (Proteobacteria). The EKSS removes the objectivity and reliance on ORKS and with the more clearly defined EKSS keeper groups we demonstrate we are able to identify structural and functional differences in the microbiomes of EK and HK horses and how it relates to metabolism.

Clustering based approach for population level identification of condition-associated T-cell receptor \u03b2-chain CDR3 sequences

BackgroundDeep immune receptor sequencing, RepSeq, provides unprecedented opportunities for identifying and studying condition-associated T-cell clonotypes, represented by T-cell receptor (TCR) CDR3 sequences. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has mostly been limited to either “public” CDR3 sequences or to comparisons of CDR3 frequencies observed in a single individual. A methodology for the identification of condition-associated TCR CDR3s by direct population level comparison of RepSeq samples is currently lacking.ResultsWe present a method for direct population level comparison of RepSeq samples using immune repertoire sub-units (or sub-repertoires) that are shared across individuals. The method first performs unsupervised clustering of CDR3s within each sample. It then finds matching clusters across samples, called immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. It finally ranks CDR3s in differentially abundant sub-repertoires for relevance to the condition. We applied the method on total TCR CDR3β RepSeq datasets of celiac disease patients, as well as on public datasets of yellow fever vaccination. The method successfully identified celiac disease associated CDR3β sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3β sequences with previously known CDR3βs specific to gluten in celiac disease. It also successfully recovered significantly high numbers of previously known CDR3β sequences relevant to each condition than would be expected by chance.ConclusionWe conclude that immune sub-repertoires of similar immuno-genomic features shared across unrelated individuals can serve as viable units of immune repertoire comparison, serving as proxy for identification of condition-associated CDR3s.

A Statistical Perspective on the Challenges in Molecular Microbial Biology.

High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.

The Impact of Sampling Season and Catching Site (Wild and Aquaculture) on Gut Microbiota Composition and Diversity of Nile Tilapia (Oreochromis niloticus).

Simple SummaryThe gut microbiota (all microbes in the intestine) of fishes is known to play an essential role in diverse aspects of their life. The gut microbiota of fish is affected by various environmental parameters, including temperature changes, salinity and diet. This study characterised the microbial composition in gut samples of Nile Tilapia collected from Lake Tana and the Bahir Dar aquaculture facility centre applying modern molecular techniques. The results show clear differences in the gut microbiota in fish from the Lake Tana and the ones from aquaculture. Further, also significant differences were observed on the composition of the gut microbiota across sampling months. Samples from the aquaculture centre displayed a higher diversity than the wild catch Nile tilapia from Lake Tana even though there is also an overlapping of the detected microbial groups. Overall, this is the first study on the effects of sampling season and catching site on the gut microbiota of Nile tilapia in Ethiopia. Future work will help to precisely explain the causes of these changes and their influence of the health and growth of Nile tilapia in Ethiopian lakes as well as under aquaculture conditions.The gut microbiota of fishes is known to play an essential role in diverse aspects of host biology. The gut microbiota of fish is affected by various environmental parameters, including temperature changes, salinity and diet. Studies of effect of environment on gut microbiota enables to have a further understanding of what comprises a healthy microbiota under different environmental conditions. However, there is insufficient understanding regarding the effects of sampling season and catching site (wild and aquaculture) on the gut microbiota of Nile tilapia. This study characterised gut microbial composition and diversity from samples collected from Lake Tana and the Bahir Dar aquaculture facility centre using 16S rDNA Illumina MiSeq platform sequencing. Firmicutes and Fusobacteria were the most dominant phyla in the Lake Tana samples, while Proteobacteria was the most dominant in the aquaculture samples. The results of differential abundance testing clearly indicated significant differences for Firmicutes, Fusobacteria, Bacteroidetes and Cyanobacteria across sampling months. However, Proteobacteria, Chloroflexi, Fusobacteria and Cyanobacteria were significantly enriched in the comparison of samples from the Lake Tana and aquaculture centre. Significant differences were observed in microbial diversity across sampling months and between wild and captive Nile tilapia. The alpha diversity clearly showed that samples from the aquaculture centre (captive) had a higher diversity than the wild Nile tilapia samples from Lake Tana. The core gut microbiota of all samples of Nile tilapia used in our study comprised Firmicutes, Proteobacteria and Fusobacteria. This study clearly showed the impact of sampling season and catching site (wild and aquaculture) on the diversity and composition of bacterial communities associated with the gut of Nile tilapia. Overall, this is the first study on the effects of sampling season and catching site on the gut microbiota of Nile tilapia in Ethiopia. Future work is recommended to precisely explain the causes of these changes using large representative samples of Nile tilapia from different lakes and aquaculture farms.

Data Analysis Strategies for Microbiome Studies in Human Populations-a Systematic Review of Current Practice.

ABSTRACTReproducibility is a major issue in microbiome studies, which is partly caused by missing consensus about data analysis strategies. The complex nature of microbiome data, which are high-dimensional, zero-inflated, and compositional, makes them challenging to analyze, as they often violate assumptions of classic statistical methods. With advances in human microbiome research, research questions and study designs increase in complexity so that more sophisticated data analysis concepts are applied. To improve current practice of the analysis of microbiome studies, it is important to understand what kind of research questions are asked and which tools are used to answer these questions. We conducted a systematic literature review considering all publications focusing on the analysis of human microbiome data from June 2018 to June 2019. Of 1,444 studies screened, 419 fulfilled the inclusion criteria. Information about research questions, study designs, and analysis strategies were extracted. The results confirmed the expected shift to more advanced research questions, as one-third of the studies analyzed clustered data. Although heterogeneity in the methods used was found at any stage of the analysis process, it was largest for differential abundance testing. Especially if the underlying data structure was clustered, we identified a lack of use of methods that appropriately addressed the underlying data structure while taking into account additional dependencies in the data. Our results confirm considerable heterogeneity in analysis strategies among microbiome studies; increasingly complex research questions require better guidance for analysis strategies.IMPORTANCE The human microbiome has emerged as an important factor in the development of health and disease. Growing interest in this topic has led to an increasing number of studies investigating the human microbiome using high-throughput sequencing methods. However, the development of suitable analytical methods for analyzing microbiome data has not kept pace with the rapid progression in the field. It is crucial to understand current practice to identify the scope for development. Our results highlight the need for an extensive evaluation of the strengths and shortcomings of existing methods in order to guide the choice of proper analysis strategies. We have identified where new methods could be designed to address more advanced research questions while taking into account the complex structure of the data.

SARS-CoV-2 infection and viral load are associated with the upper respiratory tract microbiome

BackgroundLittle is known about the relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the respiratory virus responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic, and the upper respiratory tract (URT) microbiome.ObjectiveWe sought to compare the URT microbiome between SARS-CoV-2–infected and –uninfected adults and to examine the association of SARS-CoV-2 viral load with the URT microbiome during COVID-19.MethodsWe characterized the URT microbiome using 16S ribosomal RNA sequencing in 59 adults (38 with confirmed, symptomatic, mild to moderate COVID-19 and 21 asymptomatic, uninfected controls). In those with COVID-19, we measured SARS-CoV-2 viral load using quantitative reverse transcription PCR. We then examined the association of SARS-CoV-2 infection status and its viral load with the ⍺-diversity, β-diversity, and abundance of bacterial taxa of the URT microbiome. Our main models were all adjusted for age and sex.ResultsThe observed species index was significantly higher in SARS-CoV-2–infected than in –uninfected adults (β linear regression coefficient = 7.53; 95% CI, 0.17-14.89; P = .045). In differential abundance testing, 9 amplicon sequence variants were significantly different in both of our comparisons, with Peptoniphilus lacrimalis, Campylobacter hominis, Prevotella 9 copri, and an Anaerococcus unclassified amplicon sequence variant being more abundant in those with SARS-CoV-2 infection and in those with high viral load during COVID-19, whereas Corynebacterium unclassified, Staphylococcus haemolyticus, Prevotella disiens, and 2 Corynebacterium_1 unclassified amplicon sequence variants were more abundant in those without SARS-CoV-2 infection and in those with low viral load during COVID-19.ConclusionsOur findings suggest complex associations between SARS-CoV-2 and the URT microbiome in adults. Future studies are needed to examine how these viral-bacterial interactions can impact the clinical progression, severity, and recovery of COVID-19.

Impact of long-term fertilizer and summer warming treatments on bulk soil and birch rhizosphere microbial communities in mesic arctic tundra

ABSTRACT Recent climate warming in the Arctic is enhancing microbial decomposition of soil organic matter, which may result in globally significant greenhouse gas releases to the atmosphere. To better predict future impacts, bacterial and fungal community structures in both the bulk soil and the rhizosphere of Arctic birch, Betula glandulosa, were determined in control, greenhouse summer warming, and annual factorial nitrogen (N) and phosphate (P) addition treatments twelve years after their establishment. DNA sequence analyses at multiple taxonomic levels consistently indicated substantial bulk soil and rhizosphere microbial community differences among the fertilization treatments but no significant greenhouse effects. These results suggest that climate warming will likely increase the activity rates of soil microbial decomposers but without substantially altering the structure of either the bacterial or fungal communities. Differential abundance testing revealed changes in ectomycorrhizal fungal species of the genus Thelephora in both bulk soil and rhizosphere, with increases in their relative abundance in P and N + P amended plots compared with warming and controls. Because birch is the principal low Arctic ectomycorrhizal host, our results suggest that these fungi may promote this shrub’s competitiveness where tundra soil nutrient availability is enhanced by warming or other means, ultimately contributing to arctic vegetation “greening.”

Microeco: an R package for data mining in microbial community ecology.

A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-'microeco' as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).

Revealing oral microbiota composition and functionality associated with heavy cigarette smoking

BackgroundHeavy tobacco smoking, a hallmark feature of lung cancer, is drastically predominant in Middle Eastern populations. The precise links between nicotine dependence and the functional contribution of the oral microbiota remain unknown in these populations.MethodsWe evaluated the composition and functional capabilities of oral microbiota with relation to cigarette smoking in 105 adults through shotgun metagenomics using buccal swabs.ResultsThe oral microbiota composition in our study subjects was dominated by the phyla Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes, in addition to the genera Prevotella and Veillonella, similar to previously described westernized cohorts. Furthermore, the smoker's oral microbiota represented a significant abundance of Veillonella dispar, Leptotrichia spp. and Prevotella pleuritidis when compared to non-smokers. Within the smoking groups, differential relative abundance testing unveiled relative abundance of Streptobacillus hongkongensis, Fusobacterium massiliense, Prevotella bivia in high nicotine dependent compared to low nicotine dependent profiles based on Fagerström Test for Nicotine Dependence. Functional profiling showed marked differences between smokers and non-smokers. Smokers exhibited an enrichment of Tricarballylate utilization and Lactate racemization when compared to the non-smokers. According to their nicotine dependence, enrichment of Xanthosine utilization, p-Aminobenzoyl-Glutamate utilization, and multidrug efflux pump in Campylobacter jejuni biosynthesis modules were detected in the high nicotine dependent group.ConclusionsThese compositional and functional differences may provide critical insight on how variations in the oral microbiota could predispose to respiratory illnesses and smoke cessation relapse in cigarette smokers. In particular, the observed enrichment of Fusobacterium and Prevotella in the oral microbiota possibly suggests an intriguing linkage to gut and lung cancers.

Cutaneous Bacteria in the Gut Microbiome as Biomarkers of Systemic Malodor and People Are Allergic to Me (PATM) Conditions: Insights From a Virtually Conducted Clinical Trial

Background The skin is a dynamic ecosystem of microbes and the source of many chemical compounds that affect human health. Skin-microbiome interactions can cause persistent, psychosocially devastating body smell despite good hygiene. Since odor production is often transient, malodors may not be perceptible during medical examinations. Therefore, having odor complaints can be diagnosed as body dysmorphic disorder and referred for psychological evaluations. Development of simple at-home tests and virtual care programs could improve the diagnosis and management of socially debilitating malodor conditions. Objective The aim of this study was to assess potential effectiveness of at-home gut microbiome testing in the diagnosis and management of idiopathic body and breath odor and in people are allergic to me (PATM) syndrome. Methods We contacted participants of prior metabolic body odor (MEBO) and PATM studies and online support groups by email or social media. Individuals who consented to participate were mailed test kits for at-home collection of gut microbiome samples. Participants completed an online survey (specially developed for this study) addressing their symptoms and other quality-of-life indicators at baseline and after sampling. Participants collected stool samples after flare-ups or symptom improvements and mailed them to the laboratory to be processed and analyzed. We evaluated between-group differences in symptom severity, as well as symptom improvement observations for the same individuals. For differential abundance testing of microbial taxa, we performed nonparametric statistical analyses using Mann-Whitney U tests for unpaired samples and Wilcoxon signed rank test for paired samples. Results A total of 112 individuals from 21 countries consented to participate. About half the participants had been tested for the metabolic disorder trimethylaminuria, and about half of those tested were diagnosed with the disorder. The levels of bacteria previously associated with cutaneous body odor were significantly elevated in gut samples. For the combination of species from Anaerococcus, Corynebacterium, Campylobacter, and Propionibacterium genera, the differences were P=.002 for active (73 participants, 182 samples) versus regression or remission groups (30 participants, 51 samples); P=.01 for those experiencing symptoms most or all of the time (46 participants, 88 samples) versus those who had symptoms sometimes, rarely, or never (25 participants, 74 samples); and P&lt;.001 for improvement of symptoms in the same individuals (22 participants, 43 sets of matched samples). Changes in microbial diversity were significant for between- but not within-participant comparisons. Conclusions Changes in the gut microbiome composition affect MEBO and PATM severity. In particular, an increase in intestinal bacteria producing odor when in skin flexures was associated with increased intensity of self-reported symptoms. The changes were consistent in the within-group and between-group analyses. Our findings support the feasibility of remote and decentralized clinical studies of malodor conditions. Supplementary sample collection procedures may help to meet established research quality standards. Trial Registration ClinicalTrials.gov NCT03582826; http://clinicaltrials.gov/ct2/show/NCT03582826 International Registered Report Identifier (IRRID) RR2-10.1101/2020.08.21.20179242

Altered respiratory microbiota composition and functionality associated with asthma early in life

BackgroundThe microbiota of the respiratory tract has an important role in maintaining respiratory health. However, little is known on the respiratory microbiota in asthmatic patients among Middle Eastern populations. This study investigated the respiratory microbiota composition and functionality associated with asthma in Emirati subjects.MethodsWe performed 16S rRNA and ITS2-gene based microbial profiling of 40 expectorated sputum samples from adult and pediatric Emirati individuals averaging 52 and 7 years of age, respectively with or without asthma.ResultsWe report bacterial difference belonging to Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria phyla between asthmatic and non-asthmatic controls. Similarly, fungal difference belonging to Ascomycota, Basidiomycota phyla and other unclassified fungi. Differential abundance testing among asthmatic individuals with relation to Asthma Control Test show a significant depletion of Penicillium aethiopicum and Alternaria spp., among poorly controlled asthmatics. Moreover, data suggest a significant expansion of Malassezia spp. and other unclassified fungi in the airways of those receiving steroids and leukotriene receptor antagonists’ combination therapy, in contrast to those receiving steroids alone. Functional profiling from 16S data showed marked differences between pediatric asthmatic and non-asthmatic controls, with pediatric asthmatic patients showing an increase in amino acid (p-value < 5.03 × 10− 7), carbohydrate (p-value < 4.76 × 10− 7), and fatty acid degradation (p-value < 6.65 × 10− 7) pathways, whereas non-asthmatic controls are associated with increase in amino acid (p-value < 8.34 × 10− 7), carbohydrate (p-value < 3.65 × 10− 7), and fatty acid (p-value < 2.18 × 10− 6) biosynthesis pathways in concordance with enterotype composition.ConclusionsThese differences provide an insight into respiratory microbiota composition in Emirati population and its possible role in the development of asthma early in life. This study provides important information that may eventually lead to the development of screening biomarkers to predict early asthma development and novel therapeutic approaches.

A Distinct Brain-Gut-Microbiome Profile Exists for Females with Obesity and Food Addiction.

Alterations in brain-gut-microbiome interactions have been implicated as an important factor in obesity. This study aimed to explore the relationship between food addiction (FA) and the brain-gut-microbiome axis, using a multi-omics approach involving microbiome data, metabolomics, and brain imaging. Brain magnetic resonance imaging was obtained in 105 females. FA was defined by using the Yale Food Addiction Scale. Fecal samples were collected for sequencing and metabolomics. Statistical analysis was done by using multivariate analyses and machine learning algorithms. Of the females with obesity, 33.3% exhibited FA as compared with 5.3% and 0.0% of females with overweight and normal BMI, respectively (P = 0.0001). Based on a multilevel sparse partial least square discriminant analysis, there was a difference in the gut microbiome of females with FA versus those without. Differential abundance testing showed Bacteroides, Megamonas, Eubacterium, and Akkermansia were statistically associated with FA (q < 0.05). Metabolomics showed that indolepropionic acid was inversely correlated with FA. FA was also correlated with increased connectivity within the brain's reward network, specifically between the intraparietal sulcus, brain stem, and putamen. This is the first study to examine FA along the brain-gut-microbiome axis and it supports the idea of targeting the brain-gut-microbiome axis for the treatment of FA and obesity.

An empirical Bayes approach to normalization and differential abundance testing for microbiome data

BackgroundAdvances in DNA sequencing have offered researchers an unprecedented opportunity to better study the variety of species living in and on the human body. However, the analysis of microbiome data is complicated by several challenges. First, the sequencing depth may vary by orders of magnitude across samples. Second, species are rare and the data often contain many zeros. Third, the specimen is a fraction of the microbial ecosystem, and so the data are compositional carrying only relative information. Other characteristics of microbiome data include pronounced over-dispersion in taxon abundances, and the existence of a phylogenetic tree that relates all bacterial species. To address some of these challenges, microbiome analysis workflows often normalize the read counts prior to downstream analysis. However, there are limitations in the current literature on the normalization of microbiome data.ResultsUnder the multinomial distribution for the read counts and a prior for the unknown proportions, we propose an empirical Bayes approach to microbiome data normalization. Using a tree-based extension of the Dirichlet prior, we further extend our method by incorporating the phylogenetic tree into the normalization process. We study the impact of normalization on differential abundance analysis. In the presence of tree structure, we propose a phylogeny-aware detection procedure.ConclusionsExtensive simulations and gut microbiome data applications are conducted to demonstrate the superior performance of our empirical Bayes method over other normalization methods, and over commonly-used methods for differential abundance testing. Original R scripts are available at GitHub (https://github.com/liudoubletian/eBay).

FRI0576 IDENTIFICATION OF SERUM PROTEIN BIOMARKERS ASSOCIATED WITH RA DISEASE SEVERITY

Background:RA is a systemic autoimmune disease with heterogeneous manifestation. Recent advances in serum proteomics, such as the SomaScan®platform (SomaLogic, Inc., Boulder, USA), allow for a deeper exploration of the protein biomarkers associated with RA and a better understanding of the molecular aetiology of the disease.Objectives:To characterise the differences in baseline serum proteome of patients with RA (enrolled in the Phase IIIb Abatacept vs adaliMumab comParison in bioLogic-naïvERA subjects with background MTX [AMPLE] study)1compared with a healthy population, and to identify serum protein biomarkers associated with disease severity and radiographic progression.Methods:Patients in the AMPLE study had an inadequate response to MTX and were naïve to biologic DMARDs. Protein abundance was assessed in baseline serum samples from 440 AMPLE study patients and 123 healthy individuals with matching demographics using the SomaScan®platform, with 5000+ slow off-rate modified aptamers and up to 8 log of dynamic range.2Differential abundance testing was performed using linear models to identify differences in protein abundance in patients with RA vs healthy individuals. A separate analysis using a linear model was conducted in only the patients with RA to identify the proteins associated with DAS28 (CRP) and TSS. Pathway analyses were performed for proteins significantly (false discovery rate-adjusted p value &lt;0.05) associated with RA and the disease severity measurements to identify over-representation of the molecular pathways.Results:Compared with healthy individuals, &gt;2000 serum proteins were significantly differentially expressed in patients with RA, including many proteins that have been associated with RA (e.g. serum amyloid A [SAA], CRP) and complement. Most of the protein expression differences were of small magnitude (fold change &lt;2). Proteins that were differentially expressed between patients with RA and healthy individuals were enriched in interleukin signalling, neutrophil degranulation, platelet activation/degranulation and extracellular matrix organisation pathways. DAS28 (CRP) was significantly associated with several biomarkers, including SAA, fibrinogen and CRP; in general, proteins associated with DAS28 (CRP) were most strongly enriched in the platelet activation/degranulation pathways (Figure 1), also seen in patients with RA vs healthy individuals. Additionally, many proteins were significantly associated with TSS, including SAA, matrix metalloproteinase-3 and cartilage acidic protein 1. Here, the proteins were most strongly enriched in the extracellular matrix remodelling pathways (Figure 2).Conclusion:Our study revealed that thousands of serum proteins are differentially expressed and several pathways are dysregulated between patients with RA and healthy individuals. Additional pathways were identified that reflect disease severity, including joint damage, distinct from those pathways associated with the disease. The SomaScan®platform provides a unique proteomic tool with a wide dynamic range for the identification of serum protein biomarkers associated with RA and disease severity. Proteomic signatures should be considered in clinical trials to better understand disease pathogenesis and predict risk in response to treatment.

Preliminary Comparison of Lactobacillus reuteri and Bifidobacterium longum Subspecies Infantis Probiotics Impact on Preterm Infant Gut Microbiome and Length Growth

Abstract Objectives The gut microbiome is associated with term infant growth. Here, we examine if use of a L. reuteri or a B. infantis probiotic in preterm infants is associated with changes in the gut microbiome and growth. Methods In this observational study, 26 preterm infants received L. reuteri and 13 infants received B. infantis. Infant length delta z-score was calculated by subtracting the birth length z-score (Fenton Growth Chart) from the discharge z-score value. Association between length delta z-score and probiotic was tested with a t-test. Gut microbiome was assessed by V4 sequencing using the first fecal sample collected in week 2 or 3 of life from each infant. Data analysis was done in QIIME2 and R. Alpha diversity (Shannon index) was compared by probiotic and length delta z-score using regression. Beta diversity (Bray-Curtis distance) was compared between probiotics and above/below median length delta z-score by PERMANOVA. Songbird was used for differential abundance testing. Results There was no association between length and the probiotic infants received, P = 0.34. There was no association between alpha diversity and probiotic, P = 0.58, or length, P = 0.43. There was an association between beta diversity and probiotic (P = 0.001), but not between beta diversity and length, P = 0.43. Songbird top and bottom ranked amplicon sequence variants (ASVs) are those most likely to be differentially abundant. For probiotic, top ranked ASVs (associated with L. reuteri) were Lactobacillus and L. reuteri. Bottom ranked ASVs (B. infantis) were Bifidobacterium and an Escherichia-Shigella. For length, top ranked ASVs (higher delta z-score) were Staphylococcus and probiotic associated Lactobacillus and L. reuteri. Bottom ranked ASVs (lower delta z-score) were Enterococcus, probiotic associated Escherchia-Shigella, Enterobacteriaceae, and Clostridium perfinigens. Conclusions Type of probiotic given was not associated with infant length growth, however, songbird identified ASVs that are likely to be differentially abundant by both probiotic and length. Additional work is needed to confirm the songbird results, and investigation into differences in probiotic administration methods and timing are on-going. Funding Sources F32HD093185 (DHT) and Peter J. Shields Endowed Chair in Dairy Food Science (DAM).

A novel normalization and differential abundance test framework for microbiome data.

Microbial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. The abundances or counts of microbiome species are usually on different scales and exhibit zero-inflation and over-dispersion. Normalization is a crucial step before the differential abundance test. However, existing normalization methods typically try to adjust counts on different scales to a common scale by constructing size factors with the assumption that count distributions across samples are equivalent up to a certain percentile. These methods often yield undesirable results when differentially abundant species are of low to medium abundance level. For differential abundance analysis, existing methods often use a single distribution to model the dispersion of species which lacks flexibility to catch a single species' distinctiveness. These methods tend to detect a lot of false positives and often lack of power when the effect size is small. We develop a novel framework for differential abundance analysis on sparse high-dimensional marker gene microbiome data. Our methodology relies on a novel network-based normalization technique and a two-stage zero-inflated mixture count regression model (RioNorm2). Our normalization method aims to find a group of relatively invariant microbiome species across samples and conditions in order to construct the size factor. Another contribution of the paper is that our testing approach can take under-sampling and over-dispersion into consideration by separating microbiome species into two groups and model them separately. Through comprehensive simulation studies, the performance of our method is consistently powerful and robust across different settings with different sample size, library size and effect size. We also demonstrate the effectiveness of our novel framework using a published dataset of metastatic melanoma and find biological insights from the results. The R package 'RioNorm2' can be installed from Github athttps://github.com/yuanjing-ma/RioNorm2. Supplementary data are available at Bioinformatics online.

Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences

BackgroundShotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters.ResultsTo evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2.ConclusionsOur results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.)

Nasopharyngeal Microbiota Profiles in Rural Venezuelan Children Are Associated With Respiratory and Gastrointestinal Infections.

BackgroundRecent research suggests that the microbiota affects susceptibility to both respiratory tract infections (RTIs) and gastrointestinal infections (GIIs). In order to optimize global treatment options, it is important to characterize microbiota profiles across different niches and geographic/socioeconomic areas where RTI and GII prevalences are high.MethodsWe performed 16S sequencing of nasopharyngeal swabs from 209 Venezuelan Amerindian children aged 6 weeks–59 months who were participating in a 13-valent pneumococcal conjugate vaccine (PCV13) study. Using random forest models, differential abundance testing, and regression analysis, we determined whether specific bacteria were associated with RTIs or GIIs and variation in PCV13 response.ResultsMicrobiota compositions differed between children with or without RTIs (P = .018) or GIIs (P = .001). Several species were associated with the absence of infections. Some of these health-associated bacteria are also observed in developed regions, such as Corynebacterium (log2(fold change [FC]) = 3.30 for RTIs and log2(FC) = 1.71 for GIIs), while others are not commonly observed in developed regions, such as Acinetobacter (log2(FC) = 2.82 and log2(FC) = 5.06, respectively). Klebsiella spp. presence was associated with both RTIs (log2(FC) = 5.48) and GIIs (log2(FC) = 7.20).ConclusionsThe nasopharyngeal microbiota of rural Venezuelan children included several bacteria that thrive in tropical humid climates. Interestingly, nasopharyngeal microbiota composition not only differed in children with an RTI but also in those with a GII, which suggests a reciprocal interplay between the 2 environments. Knowledge of region-specific microbiota patterns enables tailoring of preventive and therapeutic approaches.

Ear mite infection is associated with altered microbial communities in genetically depauperate Santa Catalina Island foxes (Urocyon littoralis catalinae).

The host-associated microbiome is increasingly recognized as a critical player in health and immunity. Recent studies have shown that disruption of commensal microbial communities can contribute to disease pathogenesis and severity. Santa Catalina Island foxes (Urocyon littoralis catalinae) present a compelling system in which to examine microbial dynamics in wildlife due to their depauperate genomic structure and extremely high prevalence of ceruminous gland tumors. Although the precise cause is yet unknown, infection with ear mites (Otodectes cynotis) has been linked to chronic inflammation, which is associated with abnormal cell growth and tumor development. Given the paucity of genomic variation in these foxes, other dimensions of molecular diversity, such as commensal microbes, may be critical to host response and disease pathology. We characterized the host-associated microbiome across six body sites of Santa Catalina Island foxes, and performed differential abundance testing between healthy and mite-infected ear canals. We found that mite infection was significantly associated with reduced microbial diversity and evenness, with the opportunistic pathogen Staphylococcus pseudintermedius dominating the ear canal community. These results suggest that secondary bacterial infection may contribute to the sustained inflammation associated with tumor development. As the emergence of antibiotic resistant strains remains a concern of the medical, veterinary, and conservation communities, uncovering high relative abundance of S.pseudintermedius provides critical insight into the pathogenesis of this complex system. Through use of culture-independent sequencing techniques, this study contributes to the broader effort of applying a more inclusive understanding of molecular diversity to questions within wildlife disease ecology.

Of microbes and mange: consistent changes in the skin microbiome of three canid species infected with Sarcoptes scabiei mites

BackgroundSarcoptic mange is a highly contagious skin disease caused by the ectoparasitic mite Sarcoptes scabiei. Although it afflicts over 100 mammal species worldwide, sarcoptic mange remains a disease obscured by variability at the individual, population and species levels. Amid this variability, it is critical to identify consistent drivers of morbidity, particularly at the skin barrier.MethodsUsing culture-independent next generation sequencing, we characterized the skin microbiome of three species of North American canids: coyotes (Canis latrans), red foxes (Vulpes vulpes) and gray foxes (Urocyon cinereoargenteus). We compared alpha and beta diversity between mange-infected and uninfected canids using the Kruskal–Wallis test and multivariate analysis of variance with permutation. We used analysis of composition of microbes and gneiss balances to perform differential abundance testing between infection groups.ResultsWe found remarkably consistent signatures of microbial dysbiosis associated with mange infection. Across genera, mange-infected canids exhibited reduced microbial diversity, altered community composition and increased abundance of opportunistic pathogens. The primary bacteria comprising secondary infections were Staphylococcus pseudintermedius, previously associated with canid ear and skin infections, and Corynebacterium spp., previously found among the gut flora of S. scabiei mites and hematophagous arthropods.ConclusionsThis evidence suggests that sarcoptic mange infection consistently alters the canid skin microbiome and facilitates secondary bacterial infection, as seen in humans and other mammals infected with S. scabiei mites. These results provide valuable insights into the pathogenesis of mange at the skin barrier of North American canids and can inspire novel treatment strategies. By adopting a “One Health” framework that considers mites, microbes and the potential for interspecies transmission, we can better elucidate the patterns and processes underlying this ubiquitous and enigmatic disease.