Dataset Information

Rigorous Plasma Microbiome Analysis Method Enables Disease Association Discovery in Clinic.

ABSTRACT: Blood microbiome is important to investigate microbial-host interactions and the effects on systemic immune perturbations. However, this effort has met with major challenges due to low microbial biomass and background artifacts. In the current study, microbial 16S DNA sequencing was applied to analyze plasma microbiome. We have developed a quality-filtering strategy to evaluate and exclude low levels of microbial sequences, potential contaminations, and artifacts from plasma microbial 16S DNA sequencing analyses. Furthermore, we have applied our technique in three cohorts, including tobacco-smokers, HIV-infected individuals, and individuals with systemic lupus erythematosus (SLE), as well as corresponding controls. More than 97% of total sequence data was removed using stringent quality-filtering strategy analyses; those removed amplicon sequence variants (ASVs) were low levels of microbial sequences, contaminations, and artifacts. The specifically enriched pathobiont bacterial ASVs have been identified in plasmas from tobacco-smokers, HIV-infected individuals, and individuals with SLE but not from control subjects. The associations between these ASVs and disease pathogenesis were demonstrated. The pathologic activities of some identified bacteria were further verified in vitro. We present a quality-filtering strategy to identify pathogenesis-associated plasma microbiome. Our approach provides a method for studying the diagnosis of subclinical microbial infection as well as for understanding the roles of microbiome-host interaction in disease pathogenesis.

SUBMITTER: Luo Z

PROVIDER: S-EPMC7820181 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Rigorous Plasma Microbiome Analysis Method Enables Disease Association Discovery in Clinic.

Luo Zhenwu Z Alekseyenko Alexander V AV Ogunrinde Elizabeth E Li Min M Li Quan-Zhen QZ Huang Lei L Tsao Betty P BP Kamen Diane L DL Oates Jim C JC Li Zihai Z Gilkeson Gary S GS Jiang Wei W

Frontiers in microbiology 20210108

Blood microbiome is important to investigate microbial-host interactions and the effects on systemic immune perturbations. However, this effort has met with major challenges due to low microbial biomass and background artifacts. In the current study, microbial 16S DNA sequencing was applied to analyze plasma microbiome. We have developed a quality-filtering strategy to evaluate and exclude low levels of microbial sequences, potential contaminations, and artifacts from plasma microbial 16S DNA se ...[more]

PMID: 33488555

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Project description:Many vector-borne pathogens rely on antigenic variation to prolong infections and increase their likelihood of onward transmission. This immune evasion strategy often involves mutually exclusive switching between members of gene families that encode functionally similar but antigenically different variants during the course of a single infection. Studies of different pathogens have suggested that switching between variant genes is non-random and that genes have intrinsic probabilities of being activated or silenced. These factors could create a hierarchy of gene expression with important implications for both infection dynamics and the acquisition of protective immunity. Inferring complete switching networks from gene transcription data is problematic, however, because of the high dimensionality of the system and uncertainty in the data. Here we present a statistically rigorous method for analysing temporal gene transcription data to reconstruct an underlying switching network. Using artificially generated transcription profiles together with in vitro var gene transcript data from two Plasmodium falciparum laboratory strains, we show that instead of relying on data from long-term parasite cultures, accuracy can be greatly improved by using transcription time courses of several parasite populations from the same isolate, each starting with different variant distributions. The method further provides explicit indications about the reliability of the resulting networks and can thus be used to test competing hypotheses with regards to the underlying switching pathways. Our results demonstrate that antigenic switch pathways can be determined reliably from short gene transcription profiles assessing multiple time points, even when subject to moderate levels of experimental error. This should yield important new information about switching patterns in antigenically variable organisms and might help to shed light on the molecular basis of antigenic variation.

Project description:Microbiome analyses of low-biomass samples are challenging because of contamination and inefficiencies, leading many investigators to employ low-throughput methods with minimal controls. We developed a new automated protocol, KatharoSeq (from the Greek katharos [clean]), that outperforms single-tube extractions while processing at least five times as fast. KatharoSeq incorporates positive and negative controls to reveal the whole bacterial community from inputs of as few as 50 cells and correctly identifies 90.6% (standard error, 0.013%) of the reads from 500 cells. To demonstrate the broad utility of KatharoSeq, we performed 16S rRNA amplicon and shotgun metagenome analyses of the Jet Propulsion Laboratory spacecraft assembly facility (SAF; n = 192, 96), 52 rooms of a neonatal intensive care unit (NICU; n = 388, 337), and an endangered-abalone-rearing facility (n = 192, 123), obtaining spatially resolved, unique microbiomes reproducible across hundreds of samples. The SAF, our primary focus, contains 32 sOTUs (sub-OTUs, defined as exact sequence matches) and their inferred variants identified by the deblur algorithm, with four (Acinetobacter lwoffii, Paracoccus marcusii, Mycobacterium sp., and Novosphingobium) being present in >75% of the samples. According to microbial spatial topography, the most abundant cleanroom contaminant, A. lwoffii, is related to human foot traffic exposure. In the NICU, we have been able to discriminate environmental exposure related to patient infectious disease, and in the abalone facility, we show that microbial communities reflect the marine environment rather than human input. Consequently, we demonstrate the feasibility and utility of large-scale, low-biomass metagenomic analyses using the KatharoSeq protocol. IMPORTANCE Various indoor, outdoor, and host-associated environments contain small quantities of microbial biomass and represent a niche that is often understudied because of technical constraints. Many studies that attempt to evaluate these low-biomass microbiome samples are riddled with erroneous results that are typically false positive signals obtained during the sampling process. We have investigated various low-biomass kits and methods to determine the limit of detection of these pipelines. Here we present KatharoSeq, a high-throughput protocol combining laboratory and bioinformatic methods that can differentiate a true positive signal in samples with as few as 50 to 500 cells. We demonstrate the application of this method in three unique low-biomass environments, including a SAF, a hospital NICU, and an abalone-rearing facility.

Dataset Information

Rigorous Plasma Microbiome Analysis Method Enables Disease Association Discovery in Clinic.

Publications

Rigorous Plasma Microbiome Analysis Method Enables Disease Association Discovery in Clinic.

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