A Comparison Of Fecal Glucocorticoid Metabolite Concentration And Gut Microbiota Diversity In Bonobos (Pan paniscus)

Host sex, age, diet, stress, and social environment have all been found to influence the gut microbiota. In non-human primates (NHP), recent evidence from gorillas found fecal glucocorticoid metabolite concentration (FGMC) had no significant role in structuring their gut microbiota, but there was a significant differential abundance between family Anaerolineaceae and gorilla FGMC. This pattern has yet to be examined in other NHP, like bonobos (Pan paniscus). We compared FGMC to 16S rRNA amplicons for 201 bonobo fecal samples collected in the wild across five months to evaluate the impact of stress, measured with FGMC, on the gut microbiota. Simpsons index was the only alpha diversity index to have a significant linear relationship with FGMC [R2 = 0.9643, F(4, 210) = 28.56, p = 0.0023]. FGMC level explained 1.63% of the variation in beta diversity for Jensen-Shannon Distance, 2.49% for Weighted UniFrac, and 3.53% for Unweighted UniFrac using PERMANOVAs. Differential abundance models showed seventeen taxa that were significantly correlated with FGMC. We found that genus SHD-231 in the family Anaerolinaceae was significant in our differential abundance model results, similar to western lowland gorilla abundance model results. These results suggest bonobos exhibit different patterns than gorillas in alpha and beta diversity measures and that members of the family Anaerolinaceae may be differentially affected by host stress across great apes. Incorporating FGMC into gut microbiota research can provide a more robust understanding of how stress impacts the gut microbiota of primates and humans and has important ties to overall host health.

Metagenomic monitoring of soil bacterial community after the construction of a crude oil flowline

This study aimed to assess the metagenomic changes of soil bacterial community after constructing a crude oil flowline in Basilicata region, Italy. Soils identified a total of 56 taxa at the phylum level and 485 at the family level, with a different taxa distribution, especially in samples collected on 2014. Since microbiological diversity occurred in the soils collected after 2013 (the reference year), we performed a differential abundance analysis using DESeq2 by GAIA pipeline. In the forest area, 14 phyla and 126 families were differentially abundant (− 6.06 < logFC > 7.88) in 2014 compared to 2013. Nine families were differentially abundant in 2015, with logFC between − 3.16 and 4.66, while 20 families were significantly more abundant and 16 less abundant in 2016, with logFC between − 6.48 and 6.45. In the cultivated area, 33 phyla and 260 families showed differential abundance in 2014. In the next year (2015), 14 phyla were significantly more abundant and 19 less abundant, while 29 families were substantially more abundant and 139 less abundant, with fold changes ranging between − 5.67 and 4.01. In 2016, 33 phyla showed a significantly different abundance, as 14 were more abundant and 19 decreased, and 81 families showed a significantly increased amount with logFC between − 5.31 and 5.38. These results hypothesise that the analysed site is an altered soil where the development of particular bacterial groups attends to bioremediation processes, naturally occurring to restore optimal conditions.

High-resolution screening for marine prokaryotic and eukaryotic taxa with selective preference for PE and PET surfaces

Marine plastic debris serve as substrates for the colonization of a variety of prokaryote and eukaryote organisms. Of particular interest are the microorganisms that have adapted to thrive on plastic as they may contain genes, enzymes or pathways involved in the colonization or metabolism of plastics. We implemented DNA metabarcoding with nanopore MinION sequencing to compare the one-month-old biomes of hydrolysable (polyethylene terephthalate) and non-hydrolysable (polyethylene) plastics surfaces vs. those of glass and the surrounding water in a Mediterranean Sea marina. We sequenced longer 16S rRNA, 18S rRNA and ITS barcode loci for a more comprehensive taxonomic profiling of the bacterial, protist and fungal communities respectively. Long read sequencing enabled high-resolution mapping to genera and species. Using differential abundance screening we identified 32 bacteria and five eukaryotes, that were significantly differentially abundant on PE or PET compared to glass. This approach may be used in the future to characterize the plastisphere communities and to screen for microorganisms with a plastic-metabolism potential.

Correlation between faecal microbial taxa and ulcerative colitis in different phases of disease activity in a north Indian cohort

Objective: A link between gut microbiota and Ulcerative Colitis (UC) has been established in several studies. However, a few studies have examined specific changes in microbiota associated with different phases of disease activity in UC. In this study, we investigated phenotypic variability underlying genetically distinct north Indian (NI) UC patients by identifying differentially abundant taxa between (i) UC patients and healthy controls and (ii) different disease phases of disease activity. Design: 16S rRNA (V3,V4) sequencing of 105 patients with UC [newly diagnosed (n=14); patients in remission (n=36) and active disease (relapse, n=55)]; and 36 healthy controls was performed. The faecal microbiota composition in different phases of UC disease activity and healthy controls was analysed. Results: Lower gut microbial diversity; enrichment of lactate-producing bacteria namely Streptococcus, Bifidobacterium and Lactobacillus; and depletion of butyrate-producing bacteria (e.g., Lachnospiraceae and Ruminococcaceae), was observed among UC patients. Subgroup analysis revealed differential abundance of Escherichia-Shigella, Streptococcus, Enterococcus and Faecalibacterium in newly diagnosed UC patients. No discrete microbial features were observed between patients in remission and those with active disease. Co-occurrence network analysis revealed a mutualistic association between opportunistic pathogens and Bifidobacterium and Lactobacillus and antagonistic relationship with butyrate-producers. Conclusion: This first faecal microbiome study elucidated dysanaerobiosis; loss of short chain fatty acid producers and enrichment of inflammation associated microbes; population specific differential microbial genera; and microbial signature for early dysbiosis, among NI UC cohort.

A proteomics study of the subacute toxicity of rat brain after long-term exposure of Gelsemium elegans

Background: Gelsemium elegans (G. elegans) has been shown to have strong pharmacological and pharmacodynamic effects in relevant studies both in China and USA. G. elegans has been used as a traditional medicine to treat a variety of diseases and even has the potential to be an alternative to laboratory synthesized drugs. However, its toxicity severely limited its application and development. At present, there is little attention paid to protein changes in toxicity. Aim: This study investigated the toxicity effects after long-term exposure of G. elegans of the rat brain through proteomic. Method: 11 differential abundance proteins were detected, among which 8 proteins were higher in the G. elegans- exposure group than in the control group, including Ig-like domain-containing protein (N/A), receptor-type tyrosine-protein phosphatase C (Ptprc), disheveled segment polarity protein 3 (Dvl3), trafficking protein particle complex 12 (Trappc12), seizure-related 6 homolog-like (Sez6l), transmembrane 9 superfamily member 4 (Tm9sf4), DENN domain-containing protein 5A (Dennd5a) and Tle4, whereas the other 3 proteins do the opposite including Golgi to ER traffic protein 4 (Get4), vacuolar protein sorting 4 homolog B (Vps4b) and cadherin-related 23 (CDH23). Furthermore, we performed validation of WB analysis on the key protein CDH23. Result: Finally, only fewer proteins and related metabolic pathways were affected, indicating that there was no accumulative toxicity of G. elegans. G. elegans has the potential to develop and utilize of its pharmacological activity. CHD23, however, is a protein associated with hearing. Conclusion: Whether the hearing impairment is a sequela after G. elegans exposure remains to be further studied.

The accuracy of absolute differential abundance analysis from relative count data

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. In the differential abundance setting compositional effects are hypothesized to contribute to increased rates of spurious differences (false positives). However in practice, partial reconstruction of total abundance can be imputed through renormalization of observed per-sample abundance. Given the renormalized data differential abundance need not be called on relative counts themselves but on estimates of absolute counts. We use simulated data to explore the consistency of differential abundance calls made on these adjusted relative abundances and find that while overall rates of false positive calls are low substantial error is possible. Conditions consistent with microbial community profiling are the most at risk of error induced by compositional effects. Increasing complexity of composition (i.e. increasing feature number) is generally protective against this effect. In real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, results are similar: though median accuracy is high, microbial community profiling and single-cell transcriptomic data sets can have poor outcomes. However, we show that problematic data sets can often be identified by summary characteristics of their relative abundances alone, giving researchers a means of anticipating problems and adjusting analysis strategies where appropriate.

Proteomic Analysis of a Syntrophic Coculture of Syntrophobacter fumaroxidans MPOBT and Geobacter sulfurreducens PCAT

We established a syntrophic coculture of Syntrophobacter fumaroxidans MPOBT (SF) and Geobacter sulfurreducens PCAT (GS) growing on propionate and Fe(III). Neither of the bacteria was capable of growth on propionate and Fe(III) in pure culture. Propionate degradation by SF provides acetate, hydrogen, and/or formate that can be used as electron donors by GS with Fe(III) citrate as electron acceptor. Proteomic analyses of the SF-GS coculture revealed propionate conversion via the methylmalonyl-CoA (MMC) pathway by SF. The possibility of interspecies electron transfer (IET) via direct (DIET) and/or hydrogen/formate transfer (HFIT) was investigated by comparing the differential abundance of associated proteins in SF-GS coculture against (i) SF coculture with Methanospirillum hungatei (SF-MH), which relies on HFIT, (ii) GS pure culture growing on acetate, formate, hydrogen as propionate products, and Fe(III). We noted some evidence for DIET in the SF-GS coculture, i.e., GS in the coculture showed significantly lower abundance of uptake hydrogenase (43-fold) and formate dehydrogenase (45-fold) and significantly higher abundance of proteins related to acetate metabolism (i.e., GltA; 62-fold) compared to GS pure culture. Moreover, SF in the SF-GS coculture showed significantly lower abundance of IET-related formate dehydrogenases, Fdh3 (51-fold) and Fdh5 (29-fold), and the rate of propionate conversion in SF-GS was 8-fold lower than in the SF-MH coculture. In contrast, compared to GS pure culture, we found lower abundance of pilus-associated cytochrome OmcS (2-fold) and piliA (5-fold) in the SF-GS coculture that is suggested to be necessary for DIET. Furthermore, neither visible aggregates formed in the SF-GS coculture, nor the pili-E of SF (suggested as e-pili) were detected. These findings suggest that the IET mechanism is complex in the SF-GS coculture and can be mediated by several mechanisms rather than one discrete pathway. Our study can be further useful in understanding syntrophic propionate degradation in bioelectrochemical and anaerobic digestion systems.

Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data

Background For differential abundance analysis, zero-inflated generalized linear models, typically zero-inflated NB models, have been increasingly used to model microbiome and other sequencing count data. A common assumption in estimating the false discovery rate is that the p values are uniformly distributed under the null hypothesis, which demands that the postulated model fit the count data adequately. Mis-specification of the distribution of the count data may lead to excess false discoveries. Therefore, model checking is critical to control the FDR at a nominal level in differential abundance analysis. Increasing studies show that the method of randomized quantile residual (RQR) performs well in diagnosing count regression models. However, the performance of RQR in diagnosing zero-inflated GLMMs for sequencing count data has not been extensively investigated in the literature. Results We conduct large-scale simulation studies to investigate the performance of the RQRs for zero-inflated GLMMs. The simulation studies show that the type I error rates of the GOF tests with RQRs are very close to the nominal level; in addition, the scatter-plots and Q–Q plots of RQRs are useful in discerning the good and bad models. We also apply the RQRs to diagnose six GLMMs to a real microbiome dataset. The results show that the OTU counts at the genus level of this dataset (after a truncation treatment) can be modelled well by zero-inflated and zero-modified NB models. Conclusion RQR is an excellent tool for diagnosing GLMMs for zero-inflated count data, particularly the sequencing count data arising in microbiome studies. In the supplementary materials, we provided two generic R functions, called and , for calculating the RQRs given fitting outputs of the R package .

The Gut Microbiota in Patients with Polycythemia Vera: Novel Vs. Conventional Treatments

Background: The human gut microbiota (the population of microorganisms present) is important for digestion of food but also for development of the host immune system and protection against pathogens. Changes in the gut microbiota are linked to several inflammatory diseases such as diabetes, atopic diseases and Alzheimer's disease. Polycythemia vera (PV) is one of the Philadelphia chromosome negative classical myeloproliferative neoplasms (MPNs), which also include essential thrombocythemia (ET) and primary myelofibrosis (PMF). MPNs are increasingly recognized as inflammatory driven diseases. The role of the gut microbiota in patients with MPNs is largely unknown. In a small study (n=25) the microbiota of MPN patients had higher levels of Prevotellaceae compared to healthy controls, and differed significantly in composition between patients treated with hydroxyurea and ruxolitinib. Since MPNs are likely to be driven by chronic inflammation and the gut microbiota influences the immune system, investigations of the PV-microbiota are highly relevant. We compared the microbiota in a cross-sectional study of patients with PV stratified into five different treatment groups. Method and Material: Patients above 18 years diagnosed with PV, according to the 2016 World Health Organization (WHO) classification, were invited to participate in the study. The exclusion criteria were: pregnancy, use of antibiotics within the last 2 months, change in treatment within the last 3 months or inability to understand the oral and written information. Clinical and biochemical data for each patient were collected retrospectively and included co-morbidities, smoking status, anti-inflammatory treatment, hypertension, haematological parameters, haematological treatment, body mass index (BMI), among others. Stool samples, no more than 6 hours old were stored at -80°C. DNA was extracted by using the EMAG® Nucleic acid extraction system, [bioMérieux] according to the manufacturer's instructions. The bacterial microbiota was characterized by amplicon-based next generation sequencing of the V3-V4 region of the 16S ribosomal unit using a MiSeq instrument, Illumina. BION was used to assign taxonomic classification. The patients were divided into 5 groups according to treatment: no treatment (n=18), hydroxyurea (n=33), PEG-interferon-α2 (IFN) (n=23), IFN combined with ruxolitinib (COMBI) (n= 21) and patients treated with other combinations e.g. ruxolitinib, anagrelide, hydroxyurea combined with IFN hydroxyurea combined anagrelide, or hydroxyurea combined with ruxolitinib, (n=11). The alpha diversity was measured using the Shannon diversity index, and compared with a pairwise Wilcoxon test adjusted for multiple testing. Beta diversity (difference between the samples) was visualised by a PCoA plot, and compared using an ANOSIM test. Differential abundance analysis was performed by Linear discriminant analysis Effect Size (LEfSe). Results: In total, 116 patients with PV were included. Of these, 106 fulfilled the inclusion criteria (49 women and 57 men) and had a median age of 68 years (range: 31 - 85). The five treatment groups did not differ in alpha diversity. The bacterial composition differed slightly between IFN group and no treatment group (p=0.032, R=0.075), and between IFN and COMBI group (p= 0.031, R=0.054). Patients from the no treatment group had a higher relative abundance of the Bacteroides genus (39%) compared to the IFN group (14.7%) (p=0.003) and hydroxyurea group (23.1%) (p=0.047), but not the COMBI group (30.1%). A lower abundance of the Bacteroides genus was found in the IFN group compared to the COMBI group (p=0.004) and compared to the hydroxyurea group (p=0.04). Due to the small number of patients treated with other combinations, these were not included in the differential abundance analysis. Conclusions: Among the five treatment groups in patients with PV, the alpha diversity of the microbiota were similar, but the relative abundance of the Bacteroides genus in patients not receiving any treatment compared to patients treated with IFN and hydroxyurea was higher. A lower abundance of Bacteroides genus was seen in the IFN group compared to the COMBI group and compared to the hydroxyurea group. Whether these differences are explained as a treatment response to IFN or clinical parameters, such as comorbidities, age JAK2 allele burden or BMI need further investigations. Disclosures Hasselbalch: Novartis, AOP Orphan: Consultancy, Other: Advisory Board.

Multi-scale Adaptive Differential Abundance Analysis in Microbial Compositional Data

Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data is inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding another practical complexity to this already complicated problem. In this work, we introduce a new differential abundance test called the MsRDB test, which embeds the sequences into a metric space and integrates a multi-scale adaptive strategy for utilizing spatial structure to identify differentially abundant microbes. Compared with existing methods, the MsRDB test can detect differentially abundant microbes at the finest resolution offered by data and provide adequate detection power while being robust to zero counts, compositional effect, and experimental bias in the microbial compositional data set. Applications to both simulated and real microbial compositional data sets demonstrate the usefulness of the MsRDB test.