Metagenomic Sequencing Detects Respiratory Pathogens in Hematopoietic Cellular Transplant Patients

Langelier, Charles; Zinter, Matt S; Kalantar, Katrina; Yanik, Gregory A.; Christenson, S.; O’Donovan, Brian; White, Corin V.; Wilson, Michael R.; Sapru, Anil; Dvorak, Christopher C.; Miller, Steve; Chiu, Charles Y.; DeRisi, Joseph L.

doi:10.1164/rccm.201706-1097le

Cited by 196 publications

(195 citation statements)

References 11 publications

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“…The role of commensal lung microbiota in health and disease is an area of active investigation. We corroborated prior findings demonstrating microbiome differences between subjects with respiratory infections and those with non-infectious airway disease (20,38). More specifically, we found that LRTI was associated with reduced intra-patient alpha diversity of the airway microbiome and that collectively, patients with LRTI differed significantly from those without in terms of beta diversity and microbial burden.…”

Section: Discussionsupporting

confidence: 90%

“…Several studies have demonstrated reduced diversity of the airway microbiome in the setting of LRTI (20,(38)(39)(40). We measured intra-patient (alpha) diversity of airway genera using the Shannon Diversity Index (SDI) and found that LRTI +C+M subjects had significantly lower SDI compared to no-LRTI subjects when assessed by both RNA-Seq (Fig 4A, (Table S5).…”

Section: Lrti Prediction Based On Lung Microbiome Diversitymentioning

confidence: 93%

“…This approach allowed for simultaneous host transcriptional profiling, permitted detection of RNA viruses, and enriched for actively transcribing microbes (versus latent or nonviable taxa). In addition, requiring concordant detection of microbes across both nucleic acid types reduced spurious alignments derived from reagent contaminants intrinsic to the library preparations of each nucleic-acid type (20). From each TA sample, we generated a mean of 19.6 and 32.6 million paired-end sequencing reads, from DNA and RNA-Seq respectively, of which the median fraction of microbial reads was 0.04% (IQR 0.01% -0.16%).…”

Section: Pathogen Detectionmentioning

confidence: 99%

“…From each TA sample, we generated a mean of 19.6 and 32.6 million paired-end sequencing reads, from DNA and RNA-Seq respectively, of which the median fraction of microbial reads was 0.04% (IQR 0.01% -0.16%). Raw reads were analyzed using a rapid computational pipeline that aligns and classifies microbial taxa by nucleotide and peptide translation using the National Center for Biotechnology Information (NCBI) NT and NR databases, respectively (20,21). RNA-Seq yielded a greater abundance of sequences as compared to DNA-Seq for 78% of identified microbes, with a median of 2.2 times more reads per microbe.…”

Section: Pathogen Detectionmentioning

confidence: 99%

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Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Kalantar

Moazed

Christenson

et al. 2018

Preprint

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135

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Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 – 0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI = 0.75 – 1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.SIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, airway microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.

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Section: Discussionsupporting

confidence: 90%

Section: Lrti Prediction Based On Lung Microbiome Diversitymentioning

confidence: 93%

Section: Pathogen Detectionmentioning

confidence: 99%

Section: Pathogen Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Kalantar

Moazed

Christenson

et al. 2018

Preprint

Self Cite

135

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“…Bacteria identified in NP samples included four dominant genera, which together comprised 79% of all microbial reads-Moraxella (39.4%), Haemophilus (16.7%), Streptococcus (16.2%), and Corynebacterium (6.6%). Given that diversity loss in the microbial flora in lower respiratory tract samples correlates with pneumonia [23,24], we compared the Simpson Diversity Index (SDI) in patients with and without clinical diagnoses of respiratory tract infection. We found no significant difference in the SDI of upper airway samples between patients with (mean SDI = 0.51, IQR 0.37-0.65) or without (mean SDI = 0.51, IQR = 0.42-0.65; p = 0.86) diagnoses of respiratory infection (S1 Fig).…”

Section: Mngs Of Np Swabsmentioning

confidence: 99%

Metagenomic next-generation sequencing of samples from pediatric febrile illness in Tororo, Uganda

et al. 2019

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Febrile illness is a major burden in African children, and non-malarial causes of fever are uncertain. In this retrospective exploratory study, we used metagenomic next-generation sequencing (mNGS) to evaluate serum, nasopharyngeal, and stool specimens from 94 children (aged 2–54 months) with febrile illness admitted to Tororo District Hospital, Uganda. The most common microbes identified were Plasmodium falciparum (51.1% of samples) and parvovirus B19 (4.4%) from serum; human rhinoviruses A and C (40%), respiratory syncytial virus (10%), and human herpesvirus 5 (10%) from nasopharyngeal swabs; and rotavirus A (50% of those with diarrhea) from stool. We also report the near complete genome of a highly divergent orthobunyavirus, tentatively named Nyangole virus, identified from the serum of a child diagnosed with malaria and pneumonia, a Bwamba orthobunyavirus in the nasopharynx of a child with rash and sepsis, and the genomes of two novel human rhinovirus C species. In this retrospective exploratory study, mNGS identified multiple potential pathogens, including 3 new viral species, associated with fever in Ugandan children.

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The microbiome in pediatric oncology

Rotz

Dandoy

2020

Cancer

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The human microbiome comprises a diverse set of microorganisms, which play a mostly cooperative role in processes such as metabolism and host defense. Next-generation genomic sequencing of bacterial nucleic acids now can contribute a much broader understanding of the diverse organisms composing the microbiome. Emerging evidence has suggested several roles of the microbiome in pediatric hematology/oncology, including susceptibility to infectious diseases, immune response to neoplasia, and contributions to the tumor microenvironment as well as changes to the microbiome from chemotherapy and antibiotics with unclear consequences. In this review, the authors have examined the evidence of the role of the microbiome in pediatric hematology/oncology, discussed how the microbiome may be modulated, and suggested key questions in need of further exploration.

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Metagenomic Sequencing Detects Respiratory Pathogens in Hematopoietic Cellular Transplant Patients

Cited by 196 publications

References 11 publications

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Metagenomic next-generation sequencing of samples from pediatric febrile illness in Tororo, Uganda

The microbiome in pediatric oncology

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