Plasmid-free CRISPR/Cas9 genome editing in <i>Plasmodium falciparum</i> confirms mutations conferring resistance to the dihydroisoquinolone clinical candidate SJ733

<div><p>Genetic manipulation of the deadly malaria parasite <i>Plasmodium falciparum</i> remains challenging, but the rise of CRISPR/Cas9-based genome editing tools is increasing the feasibility of altering this parasite’s genome in order to study its biology. Of particular interest is the investigation of drug targets and drug resistance mechanisms, which have major implications for fighting malaria. We present a new method for introducing drug resistance mutations in <i>P</i>. <i>falciparum</i> without the use of plasmids or the need for cloning homologous recombination templates. We demonstrate this method by introducing edits into the sodium efflux channel PfATP4 by transfection of a purified CRISPR/Cas9-guide RNA ribonucleoprotein complex and a 200-nucleotide single-stranded oligodeoxynucleotide (ssODN) repair template. Analysis of whole genome sequencing data with the variant-finding program MinorityReport confirmed that only the intended edits were made, and growth inhibition assays confirmed that these mutations confer resistance to the antimalarial SJ733. The method described here is ideally suited for the introduction of mutations that confer a fitness advantage under selection conditions, and the novel finding that an ssODN can function as a repair template in <i>P</i>. <i>falciparum</i> could greatly simplify future editing attempts regardless of the nuclease used or the delivery method.</p></div

Strategy for introducing plasmid-free CRISPR/Cas9 edits to the <i>Plasmodium falciparum</i> gene <i>pfatp4</i>.

<p>Synchronized ring-stage parasites at 17% parasitemia in fresh donor RBCs were nucleofected with Cas9 protein, guide RNA, and template ssODN. Cultures were kept under drug pressure with 500 nM SJ733 starting on day two post transfection. After drug-resistant parasites emerged from culture, genomic DNA was isolated with standard phenol-chloroform extraction methods for library preparation. The presence and penetrance of the targeted CRISPR edits were confirmed using Sanger sequencing and whole genome NGS.</p

Characterization of drug resistance.

<p>Dose-response curves and EC₅₀ values for the antimalarial SJ733 on the parent strain ACP-B6 and the mutants ACP-B6-L350H and ACP-B6-P412T. The growth inhibition assay was conducted by seeding synchronized ring-stage parasites from each strain at 0.8% parasitemia in media supplemented with SJ733 at concentrations ranging from 3.16 nM to 100 μM and allowing for growth over 72 hours. Parasites were fixed with 1% paraformaldehyde and stained with 50 nM YOYO-1. Final parasitemia was assessed by flow cytometry and values were normalized to DMSO-only controls. Values reported are mean ± standard error (n = 3). The inset shows parasitemia of each culture after 72 hours of growth in the presence of DMSO only.</p

Sequencing results.

<p>Sanger and NGS sequencing coverage of targeted CRISPR mutations at the <i>pfatp4</i> locus for ACP-B6-L350H and ACP-B6-P412T with clonal wild type parent strain ACP-B6. Red bars delineate the respective 20 nt guide RNA target sites and PAM sites required for each edit. NGS coverage at each location is indicated by blue columns. (<b>a</b>) Sequencing data of targeted locus 1002–1072 in <i>pfatp4</i> from strain ACP-B6-L350H showing SJ733 resistance-conferring SNPs in L350 and four other synonymous mutations introduced by CRISPR. Sequences of wild type <i>pfatp4</i> and repair template ssODN L350H are shown in alignment. The two silent mutations in ssODN L350H located 39 and 42 nt away were not incorporated into ACP-B6-L350H. (<b>b</b>) Sequencing data of targeted locus 1206–1276 in <i>pfatp4</i> from strain ACP-B6-P412T showing the SJ733 resistance-conferring SNP and silent mutations introduced by CRISPR. Sequences of wild type <i>pfatp4</i> and repair template ssODN P412T are shown in alignment.</p

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

ABSTRACT 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 STATEMENT Lower 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

Investigating Transfusion-related Sepsis Using Culture-Independent Metagenomic Sequencing.

BackgroundTransfusion-related sepsis remains an important hospital infection control challenge. Investigation of 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.MethodsIn 3 gram-negative septic transfusion cases, we performed metagenomic next-generation sequencing (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.ResultsmNGS 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 that share taxonomic similarity to contaminants commonly found in mNGS library preparations.ConclusionsCulture-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

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

Lower respiratory tract infections (LRTIs) 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, noninfectious inflammatory syndromes resembling LRTIs 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 a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious 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 noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (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 tool for LRTI diagnosis

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

Lower respiratory tract infections (LRTIs) 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, noninfectious inflammatory syndromes resembling LRTIs 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 a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious 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 noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (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 tool for LRTI diagnosis