Biology subjects

Khan, L. M.

Publications and source records attributed to Khan, L. M..

3 recordsLinked to original sources

bioRxiv2018

Miniaturization and optimization of 384-well compatible metagenomic sequencing library preparation

Preparation of high-quality sequencing libraries is a costly and time-consuming component of metagenomic next generation sequencing (mNGS). While the overall cost of sequencing has dropped significantly over recent years, the reagents needed to prepare sequencing samples are likely to become the dominant expense in the process. Furthermore, libraries prepared by hand are subject to human variability and needless waste due to limitations of manual pipetting volumes. Reduction of reaction volumes, combined with sub-microliter automated dispensing of reagents without consumable pipette tips, has the potential to provide significant advantages. Here, we describe the integration of several instruments, including the Labcyte Echo 525 acoustic liquid handler and the iSeq and NovaSeq Illumina sequencing platforms, to miniaturize and automate mNGS library preparation, significantly reducing the cost and the time required to prepare samples. Through the use of External RNA Controls Consortium (ERCC) spike-in RNAs, we demonstrated the fidelity of the miniaturized preparation to be equivalent to full volume reactions. Furthermore, detection of viral and microbial species from cell culture and patient samples was also maintained in the miniaturized libraries. For 384-well mNGS library preparations, we achieved a savings of over 80% in materials and reagents alone, and reduced preparation time by 90% compared to manual approaches, without compromising quality or representation within the library.

molecular biology↗

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↗

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↗