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.

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.

Characterization of Reproductive Microbiota of Primiparous Cows During Early Postpartum Periods in the Presence and Absence of Endometritis

Endometritis has a major impact on fertility in postpartum dairy cows. Since previous studies showed an association between reproductive microbiota and perinatal disease, we monitored both bovine uterine and vaginal microbiota in primiparous cows to elucidate the effect of early postpartum microbiota on endometritis. Uterine and vaginal samples were collected at time points from pre-calving to 35 days postpartum (DPP), and analyzed by 16S rRNA sequencing, combined with ancillary bacterial culture. A total of seven healthy cows and seven cows diagnosed with endometritis on 35 DPP were used in the current study. The uterine and vaginal microbiota showed a maximum of 20.1% shared amplicon sequence variants (ASVs) at linked time points. 16S rRNA based analysis and traditional culture methods revealed that Trueperella showed a higher abundance in both uterus and vagina of the endometritis group compared to the healthy group on 21 DPP (U-test p &lt; 0.05). Differential abundance analysis of the uterine microbiota showed that Enterococcus and six bacterial genera including Bifidobacterium were unique to the healthy group on the day of calving (0 DPP) and 28 DPP, respectively. In contrast, Histophilus and Mogibacteriaceae were characteristic bacteria in the vagina pre-calving in cows that later developed endometritis, suggesting that these bacteria could be valuable to predict clinical outcomes. Comparing the abundances of bacterial genera in the uterine microbiota, a negative correlation was observed between Trueperella and several bacteria including Lactobacillus. These results suggest that building an environment where there is an increase in bacteria that are generally recognized as beneficial, such as Lactobacillus, may be one possible solution to reduce the abundance of Trueperella and control endometritis.

Analysis of the upper respiratory tract microbiota in mild and severe COVID-19 patients

The microbiota of the respiratory tract remains a relatively poorly studied subject. At the same time, like the intestinal microbiota, it is involved in modulating the immune response to infectious agents in the host organism. A causal relationship between the composition of the respiratory microbiota and the likelihood of development and the severity of COVID-19 may be hypothesized. We analyze biomaterial from nasopharyngeal smears from 336 patients with a confirmed diagnosis of COVID-19, selected during the first and second waves of the epidemic in Russia. Sequences from a similar study conducted in Spain were also included in the analysis. We investigated associations between disease severity and microbiota at the level of microbial community (community types) and individual microbes (differentially represented species). To search for associations, we performed multivariate analysis, taking into account comorbidities, type of community and lineage of the virus. We found that two out of six community types are associated with a more severe course of the disease, and one of the community types is characterized by high stability (very similar microbiota profiles in different patients) and low level of lung damage. Differential abundance analysis with respect to comorbidities and community type suggested association of Rothia and Streptococcus genera representatives with more severe lung damage, and Leptotrichia, unclassified Lachnospiraceae and Prevotella with milder forms of the disease.

Longitudinal variations in the gastrointestinal microbiome of the white shrimp, Litopenaeus vannamei

The shrimp gut is a long digestive structure that includes the Foregut (stomach), Midgut (hepatopancreas) and Hindgut (intestine). Each component has different structural, immunity and digestion roles. Given these three gut digestive tract components’ significance, we examined the bacterial compositions of the Foregut, Hindgut, and Midgut digestive fractions. Those bacterial communities’ structures were evaluated by sequencing the V3 hypervariable region of the 16S rRNA gene, while the functions were predicted by PICRUSt2 bioinformatics workflow. Also, to avoid contamination with environmental bacteria, shrimp were maintained under strictly controlled conditions. The pairwise differential abundance analysis revealed differences among digestive tract fractions. The families Rhodobacteraceae and Rubritalaceae registered higher abundances in the Foregut fraction, while in the Midgut, the families with a higher proportion were Aeromonadaceae, Beijerinckiaceae and Propionibacteriaceae. Finally, the Cellulomonadaceae family resulted in a higher proportion in the Hindgut. Regarding the predicted functions, amino acid and carbohydrate metabolism pathways were the primary functions registered for Foregut microbiota; conversely, pathways associated with the metabolism of lipids, terpenoids and polyketides, were detected in the Midgut fraction. In the Hindgut, pathways like the metabolism of cofactors and vitamins along with energy metabolism were enriched. Structural changes were followed by significant alterations in functional capabilities, suggesting that each fraction’s bacteria communities may carry out specific metabolic functions. Results indicate that white shrimp’s gut microbiota is widely related to the fraction analyzed across the digestive tract. Overall, our results suggest a role for the dominant bacteria in each digestive tract fraction, contributing with a novel insight into the bacterial community.

iSeq 100 for metagenomic pathogen screening in ticks

Background Ticks are blood-sucking ectoparasites that play a pivotal role in the transmission of various pathogens to humans and animals. In Korea, Haemaphysalis longicornis is the predominant tick species and is recognized as the vector of pathogens causing various diseases such as babesiosis, borreliosis, rickettsiosis, and severe fever with thrombocytopenia syndrome. Methods In this study, the targeted high-throughput sequencing of the 16S rRNA V4 region was performed using the state-of-the-art sequencing instrument, iSeq 100, to screen bacterial pathogens in H. longicornis, and the findings were compared with those using conventional PCR with specific primers. Microbiome analyses were performed with EzBioCloud, a commercially available ChunLab bioinformatics cloud platform. ANOVA-Like Differential Expression tool (ALDEx2) was used for differential abundance analysis. Results Rickettsia spp. were detected in 16 out of 37 samples using iSeq 100, and this was confirmed using a PCR assay. In the phylogenetic analysis using gltA and ompA sequences of the detected Rickettsia, the highest sequence similarity was found with ‘Candidatus Rickettsia jingxinensis’ isolate Xian-Hl-79, ‘Ca. R. jingxinensis’ isolate F18, and ‘Ca. R. longicornii‘ isolate ROK-HL727. In the microbiome study, Coxiella AB001519, a known tick symbiont, was detected in all 37 tick samples. Actinomycetospora chiangmaiensis was more abundant in Rickettsia-positive samples than in Rickettsia-negative samples. Conclusions In this study, iSeq 100 was used to investigate the microbiome of H. longicornis, and the potentially pathogenic Rickettsia strain was detected in 16 out of 37 ticks. We believe that this approach will aid in large-scale pathogen screening of arthropods to be used in vector-borne disease control programs. Graphical Abstract

The quality of dissolved organic matter shapes the biogeography of the active bathypelagic microbiome

The bathypelagic ocean (1000-4000 m depth) is the largest aquatic biome on Earth but it is still largely unexplored. Due to its prevalent low dissolved organic carbon concentrations, most of the prokaryotic metabolic activity is assumed to be associated to particles. The role of free-living prokaryotes has thus been mostly ignored, except that of some chemolithoautotrophic lineages. Here we used a global bathypelagic survey of size-fractionated metagenomic and 16S (genes and transcripts) data and performed a differential abundance analysis to explore the functional traits of the different prokaryotic life-strategies, their contribution to the active microbiome, and the role that the quality of the dissolved organic matter (DOM) plays in driving this contribution. We found that free-living prokaryotes have limited capacity to uplift their metabolism in response to environmental changes and display comparatively lower growth rates than particle associated prokaryotes, but are responsible for the synthesis of vitamins in the bathypelagic. Furthermore, their contribution to the active prokaryotic microbiome increased towards waters depleted of labile DOM, which represented a large fraction of the tropical and subtropical ocean sampled stations. This points to a relevant yet overlooked role of free-living prokaryotes in DOM cycling in the vast bathypelagic desert.

Censcyt: censored covariates in differential abundance analysis in cytometry

Background Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. Results To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show that multiple imputation based methods offer on-par performance with the Cox proportional hazards model in terms of sensitivity and error control, while offering flexibility to account for covariates. The tested methods are implemented in the package censcyt as an extension of diffcyt and are available at https://bioconductor.org/packages/censcyt. Conclusion Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.