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Langelier, C.

Publications and source records attributed to Langelier, C..

17 recordsLinked to original sources
bioRxiv2025

Transcriptomic analysis reveals immune signatures associated with specific cutaneous manifestations of lupus in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) presents with diverse heterogenous cutaneous manifestations. However, the molecular and immunologic pathways driving specific cutaneous manifestations of SLE are poorly understood. Here, we leverage transcriptomics from a large well-phenotyped longitudinal cohort of SLE patients to map molecular pathways linked to ten distinct SLE-related rashes. Through whole blood and immune cell-sorted bulk RNA sequencing, we identified immune signatures specific to cutaneous subtypes of SLE. Subacute cutaneous lupus (SCLE) exhibited broad upregulation of interferon, TNF-, and IL6-JAK-STAT3 pathways suggesting potential unique therapeutic responses to JAK and type I interferon inhibition. While interferon signaling is prominent in SCLE, discoid lupus, and acute lupus, it is unexpectedly attenuated in patients with skin and mucosal ulcers. Pathway and cell-type enrichment analysis revealed unexpected roles for CD14+ monocytes in photosensitivity of SLE and NK cells in alopecia, mucosal ulceration, and livedo reticularis. These findings illuminate the immune heterogeneity of rashes in SLE, highlighting subtype-specific mechanistic targets, and presenting opportunities for precision therapies for SLE-associated skin phenotypes.

bioinformatics↗
bioRxiv2023

Cell-Specific Transposable Element Gene Expression Analysis Identifies Associations with Systemic Lupus Erythematosus Phenotypes

There is an established yet unexplained link between interferon (IFN) and systemic lupus erythematosus (SLE). The expression of sequences derived from transposable elements (TEs) may contribute to production of type I IFNs and generation of autoantibodies. We profiled cell-sorted RNA-seq data (CD4+ T cells, CD14+ monocytes, CD19+ B cells, and NK cells) from PBMCs of 120 SLE patients and quantified TE expression identifying 27,135 TEs. We tested for differential TE expression across 10 SLE phenotypes including autoantibody production and disease activity and discovered 731 differentially expressed (DE) TEs whose effects were mostly cell-specific and phenotype-specific. DE TEs were enriched for specific families and viral genes encoded in TE sequences. Increased expression of DE TEs was associated with genes involved in antiviral activity such as LY6E, ISG15, TRIM22 and pathways such as interferon signaling. These findings suggest that expression of TEs contributes to activation of SLE-related mechanisms in a cell-specific manner, which can impact disease diagnostics and therapeutics.

molecular biology↗
bioRxiv2023

BiP/GRP78 is a pro-viral factor for diverse dsDNA viruses that promotes the survival and proliferation of cells upon KSHV infection

The Endoplasmic Reticulum (ER)-resident HSP70 chaperone BiP (HSPA5) plays a crucial role in maintaining and restoring protein folding homeostasis in the ER. BiPs function is often dysregulated in cancer and virus-infected cells, conferring pro-oncogenic and pro-viral advantages. We explored BiPs functions during infection by the Kaposis sarcoma-associated herpesvirus (KSHV), an oncogenic gamma-herpesvirus associated with cancers of immunocompromised patients. Our findings reveal that BiP protein levels are upregulated in infected epithelial cells during the lytic phase of KSHV infection. This upregulation occurs independently of the unfolded protein response (UPR), a major signaling pathway that regulates BiP availability. Genetic and pharmacological inhibition of BiP halts KSHV viral replication and reduces the proliferation and survival of KSHV-infected cells. Notably, inhibition of BiP limits the spread of other alpha- and beta-herpesviruses and poxviruses with minimal toxicity for normal cells. Our work suggests that BiP is a potential target for developing broad-spectrum antiviral therapies against double-stranded DNA viruses and a promising candidate for therapeutic intervention in KSHV-related malignancies.

microbiology↗
bioRxiv2020

Global Absence and Targeting of Protective Immune States in Severe COVID-19.

While SARS-CoV-2 infection has pleiotropic and systemic effects in some patients, many others experience milder symptoms. We sought a holistic understanding of the severe/mild distinction in COVID-19 pathology, and its origins. We performed a whole-blood preserving single-cell analysis protocol to integrate contributions from all major cell types including neutrophils, monocytes, platelets, lymphocytes and the contents of serum. Patients with mild COVID-19 disease display a coordinated pattern of interferon-stimulated gene (ISG) expression across every cell population and these cells are systemically absent in patients with severe disease. Severe COVID-19 patients also paradoxically produce very high anti-SARS-CoV-2 antibody titers and have lower viral load as compared to mild disease. Examination of the serum from severe patients demonstrates that they uniquely produce antibodies with multiple patterns of specificity against interferon-stimulated cells and that those antibodies functionally block the production of the mild disease-associated ISG-expressing cells. Overzealous and auto-directed antibody responses pit the immune system against itself in many COVID-19 patients and this defines targets for immunotherapies to allow immune systems to provide viral defense. One Sentence SummaryIn severe COVID-19 patients, the immune system fails to generate cells that define mild disease; antibodies in their serum actively prevents the successful production of those cells.

immunology↗
bioRxiv2020

Designing and implementing programmable depletion in sequencing libraries with DASHit

Since Next-Generation Sequencing produces reads uniformly subsampled from an input library, highly abundant sequences may mask interesting low abundance sequences. The DASH (Depleting Abundant Sequences by Hybridization) technique takes advantage of the programmability of CRISPR/Cas9 to deplete unwanted high-abundance sequences. Because desired depletion targets vary by sample type, here we describe DASHit, software that outputs an optimal DASH target set given a sequencing dataset, an updated DASH protocol, and show depletion results with DASHit-designed targets for three different species.

microbiology↗
bioRxiv2019

Investigating Transfusion-Related Sepsis using Culture-Independent Metagenomic Sequencing

BackgroundTransfusion-related sepsis remains an important hospital infection control challenge. Investigating septic transfusion events is often restricted by the limitations of bacterial culture in terms of time requirements and low yield in the setting of prior antibiotic administration.\n\nMethodsIn three Gram-negative septic transfusion cases, we performed mNGS of direct clinical blood specimens in addition to standard culture-based approaches utilized for infection control investigations. Pathogen detection leveraged IDSeq, a new open-access microbial bioinformatics portal. Phylogenetic analysis was performed to assess microbial genetic relatedness and understand transmission events.\n\nResultsmNGS of direct clinical blood specimens afforded precision detection of pathogens responsible for each case of transfusion-related sepsis, and enabled discovery of a novel Acinetobacter species in a platelet product that had become contaminated despite photochemical pathogen reduction. In each case, longitudinal assessment of pathogen burden elucidated the temporal sequence of events associated with each transfusion-transmitted infection. We found that informative data could be obtained from culture-independent mNGS of residual platelet products and leftover blood specimens that were either unsuitable or unavailable for culture, or that failed to grow due to prior antibiotic administration. We additionally developed methods to enhance accuracy for detecting transfusion-associated pathogens sharing taxonomic similarity to contaminants commonly found in mNGS library preparations.\n\nConclusionsCulture-independent mNGS of blood products afforded rapid and precise assessment of pathogen identity, abundance and genetic relatedness. Together, these challenging cases demonstrated the potential for metagenomics to advance existing methods for investigating transfusion-transmitted infections.

microbiology↗
bioRxiv2019

Unbiased metagenomic sequencing for pediatric meningitis in Bangladesh reveals neuroinvasive Chikungunya virus outbreak and other unrealized pathogens

The disease burden due to meningitis in low and middle-income countries remains significant and failure to determine an etiology impedes appropriate treatment for patients and evidence-based policy decisions for populations. Broad-range pathogen surveillance using metagenomic next-generation sequencing (mNGS) of RNA isolated from cerebral spinal fluid (CSF) provides an unbiased assessment for possible infectious etiologies. In this study, our objective was to use mNGS to identify etiologies of pediatric meningitis in Bangladesh.\n\nWe conducted a retrospective case-control mNGS study on CSF from patients with known neurologic infections (n=36), idiopathic meningitis (n=25), without infection (n=30) and six environmental samples collected between 2012-2018. Using an open-access, cloud-based bioinformatics pipeline (IDseq) and machine learning, we identified potential pathogens which were confirmed through qPCR and Sanger sequencing. These cases were followed-up through phone/home-visits. The CSF samples were collected from children with WHO-defined meningeal signs during prospective meningitis surveillance at the largest pediatric referral hospital in Bangladesh.\n\nThe 91 participants (42% female) ranged in age from 0-160 months (median: 9 months). In samples with known infectious causes of meningitis and without infections (n=66), there was 83% concordance between mNGS and conventional testing. In idiopathic cases (n=25), mNGS identified a potential etiology in 40% (n=10), including bacterial and viral pathogens. There were three instances of neuroinvasive Chikungunya virus (CHIKV). The CHIKV genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause systemic illness in 2017. CHIKV qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital (n=472) revealed 17 additional CHIKV meningitis cases. Orthogonal molecular confirmation of each mNGS-identified infection, case-based clinical data, and follow-up of patients substantiated the key findings.\n\nUsing mNGS, we obtained a microbiological diagnosis for 40% of idiopathic meningitis cases and identified a previous unappreciated pediatric CHIKV meningitis outbreak. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis and facilitate informed policy decisions.

genomics↗
bioRxiv2018

Microbiome and Antimicrobial Resistance Gene Dynamics in International Travelers

We engaged metagenomic next generation sequencing to longitudinally assess the gut microbiota and antimicrobial resistomes of international travelers to understand global exchange of resistant organisms. Travel resulted in an increase in antimicrobial resistance genes and a greater proportion of Escherichia species within gut microbial communities without impacting diversity.

microbiology↗
bioRxiv2018

FLASH: A next-generation CRISPR diagnostic for multiplexed detection of antimicrobial resistance sequences

The growing prevalence of deadly microbes with resistance to previously life-saving drug therapies is a dire threat to human health. Detection of low abundance pathogen sequences remains a challenge for metagenomic Next Generation Sequencing (NGS). We introduce FLASH (Finding Low Abundance Sequences by Hybridization), a next-generation CRISPR/Cas9 diagnostic method that takes advantage of the efficiency, specificity and flexibility of Cas9 to enrich for a programmed set of sequences. FLASH-NGS achieves up to 5 orders of magnitude of enrichment and sub-attomolar gene detection with minimal background. We provide an open-source software tool (FLASHit) for guide RNA design. Here we applied it to detection of antimicrobial resistance genes in respiratory fluid and dried blood spots, but FLASH-NGS is applicable to all areas that rely on multiplex PCR.

microbiology↗
bioRxiv2018

Pediatric Brainstem Encephalitis Outbreak Investigation with Metagenomic Next-Generation Sequencing

In 2016, Catalonia experienced a pediatric brainstem encephalitis outbreak caused by enterovirus A71 (EV-A71). Conventional testing identified EV in peripheral body sites, but EV was rarely identified in cerebrospinal fluid (CSF). RNA was extracted from CSF (n=20), plasma (n=9), stool (n=15) and nasopharyngeal samples (n=16) from 10 children with brainstem encephalitis or encephalomyelitis and 10 contemporaneous pediatric controls with presumed viral meningitis or encephalitis. Unbiased complementary DNA libraries were sequenced, and microbial pathogens were identified using a custom bioinformatics pipeline. Full-length virus genomes were assembled for phylogenetic analyses. Metagenomic next-generation sequencing (mNGS) was concordant with qRT-PCR for all samples positive by PCR (n=25). In virus-negative samples (n=35), mNGS detected virus in 28.6% (n=10), including 5 CSF samples. mNGS co-detected EV-A71 and another EV in 5 patients. Overall, mNGS increased the proportion of EV-positive samples from 42% (25/60) to 57% (34/60) (McNemars test; p-value = 0.0077). For CSF, mNGS doubled the number of pathogen-positive samples (McNemars test; p-value = 0.074). Using phylogenetic analysis, the outbreak EV-A71 clustered with a neuroinvasive German EV-A71 isolate. Brainstem encephalitis specific, non-synonymous EV-A71 single nucleotide variants were not identified. mNGS demonstrated 100% concordance with clinical qRT-PCR of EV-related brainstem encephalitis and significantly increased the detection of enteroviruses. Our findings increase the probability that neurologic complications observed were virus-induced rather than para-infectious. A comprehensive genomic analysis confirmed that the EV-A71 outbreak strain was closely related to a neuroinvasive German EV-A71 isolate. There were no clear-cut viral genomic differences that discriminated between patients with differing neurologic phenotypes.

microbiology↗
bioRxiv2018

Etiology of fever in Ugandan children: identification of microbial pathogens using metagenomic next-generation sequencing and IDseq, a platform for unbiased metagenomic analysis

BackgroundFebrile illness is a major burden in African children, and non-malarial causes of fever are uncertain. We built and employed IDseq, a cloud-based, open access, bioinformatics platform and service to identify microbes from metagenomic next-generation sequencing of tissue samples. In this pilot study, we evaluated blood, nasopharyngeal, and stool specimens from 94 children (aged 2-54 months) with febrile illness admitted to Tororo District Hospital, Uganda.\n\nResultsThe most common pathogens identified were Plasmodium falciparum (51.1% of samples) and parvovirus B19 (4.4%) from blood; 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. Among other potential pathogens, we identified one novel orthobunyavirus, tentatively named Nyangole virus, from the blood of a child diagnosed with malaria and pneumonia, and Bwamba orthobunyavirus in the nasopharynx of a child with rash and sepsis. We also identified two novel human rhinovirus C species.\n\nConclusionsThis exploratory pilot study demonstrates the utility of mNGS and the IDseq platform for defining the molecular landscape of febrile infectious diseases in resource limited areas. These methods, supported by a robust data analysis and sharing platform, offer a new tool for the surveillance, diagnosis, and ultimately treatment and prevention of infectious diseases.

genomics↗
bioRxiv2018

A Metagenomic Comparison of Tracheal Aspirate and Mini-Bronchial Alveolar Lavage for Assessment of Respiratory Microbiota

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.\n\nSIGNIFICANCE 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.

microbiology↗
bioRxiv2018

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

Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). 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 lung 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 0.91 (95% CI = 0.80 - 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 - 1.00) and 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.\n\nSIGNIFICANCE 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, lung 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.\n\nFundingNHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).

genomics↗
bioRxiv2018

Pulmonary Metagenomic Sequencing Suggests Missed Infections in Immunocompromised Children

RATIONALEDespite improved diagnostics, pulmonary pathogens in immunocompromised children frequently evade detection, leading to significant morbidity and mortality.\n\nOBJECTIVESTo develop a highly sensitive metagenomic next generation sequencing (mNGS) assay capable of evaluating the pulmonary microbiome and identifying diverse pathogens in the lungs of immunocompromised children.\n\nMETHODSWe collected 41 lower respiratory specimens from 34 immunocompromised children undergoing evaluation for pulmonary disease at 3 childrens hospitals from 2014-2016. Samples underwent mechanical homogenization, paired RNA/DNA extraction, and metagenomic sequencing. Sequencing reads were aligned to the NCBI nucleotide reference database to determine taxonomic identities. Statistical outliers were determined based on abundance within each sample and relative to other samples in the cohort.\n\nMEASUREMENTS & MAIN RESULTSWe identified a rich cross-domain pulmonary microbiome containing bacteria, fungi, RNA viruses, and DNA viruses in each patient. Potentially pathogenic bacteria were ubiquitous among samples but could be distinguished as possible causes of disease by parsing for outlier organisms. Samples with bacterial outliers had significantly depressed alpha-diversity (median 0.58, IQR 0.33-0.62 vs. median 0.94, IQR 0.93-0.95, p<0.001). Potential pathogens were detected in half of samples previously negative by clinical diagnostics, demonstrating increased sensitivity for missed pulmonary pathogens (p<0.001).\n\nCONCLUSIONSAn optimized mNGS assay for pulmonary microbes demonstrates significant inoculation of the lower airways of immunocompromised children with diverse bacteria, fungi, and viruses. Potential pathogens can be identified based on absolute and relative abundance. Ongoing investigation is needed to determine the pathogenic significance of outlier microbes in the lungs of immunocompromised children with pulmonary disease.

microbiology↗
bioRxiv2017

Metagenomics for chronic meningitis: clarifying interpretation and diagnosis

ImportanceIdentifying infectious causes of subacute and chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed.\n\nObjectiveTo present a case series of patients with diagnostically challenging subacute and chronic meningitis in whom metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF), supported by a statistical framework generated from mNGS sequencing of non-infectious patients and environmental controls, identified a pathogen.\n\nDesignCase series. Using mNGS data from the CSF of 94 non-infectious neuroinflammatory cases and 24 water and reagent controls, we developed and implemented a weighted scoring metric based on z-scores at the species and genus level for both nucleotide and protein databases to prioritize and rank mNGS results. We performed mNGS on total RNA extracted from CSF of patients with subacute or chronic meningitis and highlight seven cases representing a diverse array of pathogens.\n\nSettingA multi-center study of mNGS pathogen discovery in patients with suspected neuroinflammatory conditions.\n\nParticipantsPatients with diagnostically challenging subacute or chronic meningitis enrolled in a research study of mNGS performed on CSF.\n\nInterventionmNGS was performed on total RNA extracted from CSF (0.25-0.5 mL). A weighted z-score was used to filter out environmental contaminants and facilitate efficient data triage and analysis.\n\nMain Outcomes1) Pathogens identified by mNGS and 2) ability of a statistical model to prioritize, rank, and simplify mNGS results.\n\nResultsmNGS identified parasitic worms, fungi and viruses in seven subjects: Taenia solium (n=2), Cryptococcus neoformans, human immunodeficiency virus-1, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis. Evaluating mNGS data with a weighted z-score based scoring algorithm effectively separated bona fide pathogen sequences from spurious environmental sequences.\n\nConclusions and RelevancemNGS of CSF identified a diversity of microbial pathogens in patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for one year, the first case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of Candida dubliniensis meningitis. Filtering metagenomic data with a scoring algorithm greatly clarified data interpretation and highlights the difficulties attributing biological significance to organisms that may be present in control samples used for metagenomic sequencing studies.\n\nKey PointsQuestion: How can metagenomic next-generation sequencing of cerebrospinal fluid be leveraged to aid in the diagnosis of patients with subacute or chronic meningitis?\n\nFindings: Metagenomic next-generation sequencing identified parasitic worms, fungi and viruses in a case series of seven subjects. A database of water-only and healthy patient controls enabled application of a z-score based scoring algorithm to effectively separate bona fide pathogen sequences from spurious environmental sequences.\n\nMeaning: Our scoring algorithm greatly simplified data interpretation in a series of patients with a wide range of challenging infectious causes of subacute or chronic meningitis identified by metagenomic next-generation sequencing.

microbiology↗
bioRxiv2017

Metagenomic Next-Generation Sequencing Detects Pulmonary Pathogens in Hematopoietic Cellular Transplant Patients with Acute Respiratory Illnesses

RATIONALECurrent microbiologic diagnostics often fail to identify the etiology of lower respiratory tract infections (LRTI) in hematopoietic cellular transplant recipients (HCT), which precludes the implementation of targeted therapies.\n\nOBJECTIVESTo address the need for improved LRTI diagnostics, we evaluated the utility of metagenomic next generation sequencing (mNGS) of bronchoalveolar lavage (BAL) to detect microbial pathogens in HCT patients with acute respiratory illnesses.\n\nMETHODSWe enrolled 22 post-HCT adults ages 19-69 years with acute respiratory illnesses who underwent BAL at the University of Michigan between January 2012 and May 2013. mNGS was performed on BAL fluid to detect microbes and simultaneously assess the host transcriptional response. Results were compared against conventional microbiologic assays.\n\nMEASUREMENTS & MAIN RESULTSmNGS demonstrated 100% sensitivity for detecting respiratory microbes (human metapneumovirus, respiratory syncytial virus, Stenotrophomonas maltophilia, human herpesvirus 6 and cytomegalovirus) when compared to standard testing. Previously unrecognized LRTI pathogens were identified in six patients for whom standard testing was negative (human coronavirus 229E, human rhinovirus A, Corynebacterium propinquum and Streptococcus mitis); findings were confirmed by independent PCR and 16S rRNA sequencing. Relative to patients without infection, patients with infection had increased expression of immunity related genes (p=0.022) and significantly lower diversity of their respiratory microbiome (p=0.017).\n\nCONCLUSIONSCompared to conventional diagnostics, mNGS enhanced detection of pathogens in BAL fluid from HCT patients. Furthermore, our results suggest that combining unbiased microbial pathogen detection with assessment of host gene biomarkers of immune response may hold promise for enhancing the diagnosis of post-HCT respiratory infections.

genomics↗