Klebsiella pneumoniae is a common cause of sepsis in adults and children. Given the emergence of multidrug-resistant isolates, there is an urgent need to develop novel therapeutic strategies. Essential bacterial genes and their products, which are critical to survival and replication, represent intrinsic vulnerabilities that can be leveraged as novel drug targets. A comprehensive, empirically derived essential genome required for growth under conditions relevant to human infection biology and conserved across a majority of clinically relevant strains of K. pneumoniae is currently lacking.
To define the core essential genome of K. pneumoniae, libraries comprising hundreds of thousands of individual transposon-insertion mutants were generated across multiple strains using a transposon mutagenesis vector (pSC189). To determine the K. pneumoniae strain MGH_66 genes required for growth in laboratory media, approximately 150,000 unique mutants were grown on lysogeny broth agar (LBA), and their genomic DNA (gDNA) was extracted. The gDNA was fragmented, adapter-ligated, and PCR-amplified using custom primers targeting transposon–genome junctions. Libraries were sequenced on an Illumina MiSeq platform, and insertion sites were mapped to the K. pneumoniae MGH_66 genome using Bowtie2 software. Gene essentiality was analyzed using the statistical analysis software FiTnEss.
The library of 150,000 K. pneumoniae MGH_66 transposon-insertion mutants saturated approximately 50% of all permissive transposon-insertion sites. Based on individual p values, 544 genes were predicted to be essential for K. pneumoniae MGH_66 growth on LBA, including gyrA, fusA, and argS; which encode subunit A of DNA gyrase, elongation factor G, and arginyl-tRNA synthetase, respectively. The present dataset was not powered to detect statistically significant essential genes after correction for multiple hypothesis testing.
These data suggest 544 K. pneumoniae MGH_66 genes essential for growth on laboratory media. Future work will focus on increasing statistical power of the present dataset and using this workflow to identify genes essential for growth in host-specific environments (e.g., blood, urine) for MGH_66 and additional strains. The resulting core essential genome will comprise genes required for growth across all tested conditions and strains. These findings may inform the identification of potential therapeutic targets for multidrug-resistant K. pneumoniae.
C. Wijers, J. Bagnall, Deborah T. Hung· Journal of the Pediatric Inf...· 0 citations
The alpha-variant wave of the COVID-19 pandemic provided a unique opportunity to study, at single-cell resolution, how near-universal exposure to the same pathogen can lead to either effective or dysfunctional immune responses in humans.
We analyzed 2.5 million circulating immune cells from 428 patients across time points (840 PBMC samples), encompassing three contemporaneous SARS-CoV-2 cohorts: acutely infected patients at five WHO disease severity levels and three time points, patients from the first randomized control trial to study efficacy of tocilizumab in management of COVID-19, and convalescent patients three months after infection. We used linear modeling to integrate multiple data types – single-cell RNA-seq, CITE-seq, TCR and BCR sequencing, viral load measurements, viral neutralization assays, detection of 75 autoantibodies, HLA genotype data, and serum proteomics covering 1,463 targets – to derive the most comprehensive view to-date of the biological features of COVID-19 disease severity.
We show that myeloid-derived suppressor cells (MDSCs) act as a key immunologic pivot point in severe COVID-19. Myeloid dysfunction is marked by impaired antigen presentation and drives a non-productive adaptive immune response. Severe disease is also linked to autoantibodies targeting type I interferons, specific HLA-DQB1 allelic variants, and serum IL-6 levels. Tocilizumab treatment eliminates CLU-expressing MDSCs and ISG-positive myeloid subsets, restores antigen presentation, and reactivates productive adaptive immunity. In convalescence 3-months post-infection, we found persistently high ICOS expression in regulatory T cells.
Overall, we define distinct innate and adaptive host immune responses associated with acute, IL-6—responsive, and convalescent SARS-CoV-2 infection. Our multimodal and high-dimensional dataset with curated clinical metadata provides a foundational and clinically relevant resource for modeling host immune response biology in health and disease.
We acknowledge the following funding sources: this work was supported by several training grants, including a NIAID grant T32AR007258 (to KS), three NHLBI grants 5T32HL116275-13 (to CC), 5T32HL129970-09 (to APN), and the K08HL157725 (to PS), as well as the American Heart Association Career Development Award (to PS). PS was also supported by the Brigham and Women’s Hospital Innovation Evergreen Fund. EY was supported by funding from the Stanford Medical Scholars program. RJX acknowledges supports from NIH DK43351 and U19AI142784. RJX and AR were supported by the Manton Foundation and the Klarman Cell Observatory. PJU was supported by Third Rock Ventures; Henry Gustav Floren Trust; Stanford Department of Medicine Team Science Program; Stanford Medicine Office of the Dean; and National Institutes of Health R01 grants AI175771 and AI182319-02. RPB acknowledges funding support from the Massachusetts General Hospital Executive Committee on Research, the American Lung Association, and the Broad Institute’s Next Generation Scholar award. MBG, MRF, and NH were supported by an American Lung Association COVID-19 Action Initiative grant. MBG and MRF were supported by a grant from the Executive Committee on Research at MGH. NH acknowledges was supported by NIH/NIAID U19 AI082630, a Chair and gift from Sandra, Sarah, and Arthur Irving. ACV acknowledges funding support from the COVID-19 Clinical Trials Pilot grant from the Executive Committee on Research at MGH; a COVID-19 Chan Zuckerberg Initiative grant (2020-216954); the funds from the Manton Foundation and the Klarman Family Foundation; the Broad Institute’s Next Generation Scholar award; the MGH Howard M. Goodman Fellowship; the National Institutes of Health (DP2CA247831); work at the Broad Institute was supported by a gift from an anonymous donor.
Computational and Systems Immunology (COMP)
Kamil Slowikowski, Pritha Sen, C. Cosgriff et al.· Journal of Immunology· 0 citations
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