Context-aware genomic surveillance
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB45529 | ENA |
REPOSITORIES: ENA
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Project description:Klebsiella pneumoniae is an arising threat to human health. However, host immune responses in response to this bacterium remain to be elucidated. The goal of this study was to identify the dominant host immune responses associated with Klebsiella pneumoniae pulmonary infection. Pulmonary mRNA profiles of 6-8-weeks-old BALB/c mice infected with/without Klebsiella pneumoniae were generated by deep sequencing using Illumina Novaseq 6000. qRT–PCR validation was performed using SYBR Green assays. Using KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, we identified several immune associated pathways, including complement and coagulation cascades, Toll-like receptor signaling pathway, Rap1 signaling pathway, chemokine signaling pathway, TNF signaling pathway, phagosome and NOD-like receptor signaling pathway, were involved in Klebsiella pneumoniae pulmonary infection. Using ICEPOP (Immune CEll POPulation) analysis, we found that several cell types were involved in the host immune response to Klebsiella pneumoniae pulmonary infection, including dendritic cells, macrophages, monocytes, NK (natural killer) cells, stromal cells. Further, IL-17 chemokines were significantly increased during Klebsiella pneumoniae infection. This study provided evidence for further studying the pathogenic mechanism of Klebsiella pneumoniae pneumonia infection.
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Project description:Genomic surveillance can inform effective public health responses to pathogen outbreaks. However, integration of non-local data is rarely done. We investigate two large hospital outbreaks of a carbapenemase-carrying Klebsiella pneumoniae strain in Germany and show the value of contextual data. By screening about 10 000 genomes, over 400 000 metagenomes and two culture collections using in silico and in vitro methods, we identify a total of 415 closely related genomes reported in 28 studies. We identify the relationship between the two outbreaks through time-dated phylogeny, including their respective origin. One of the outbreaks presents extensive hidden transmission, with descendant isolates only identified in other studies. We then leverage the genome collection from this meta-analysis to identify genes under positive selection. We thereby identify an inner membrane transporter (ynjC) with a putative role in colistin resistance. Contextual data from other sources can thus enhance local genomic surveillance at multiple levels and should be integrated by default when available.
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Project description:Liao2011 - Genome-scale metabolic
reconstruction of Klebsiella pneumoniae (iYL1228)
This model is described in the article:
An experimentally validated
genome-scale metabolic reconstruction of Klebsiella pneumoniae
MGH 78578, iYL1228.
Liao YC, Huang TW, Chen FC,
Charusanti P, Hong JS, Chang HY, Tsai SF, Palsson BO, Hsiung
CA.
J. Bacteriol. 2011 Apr; 193(7):
1710-1717
Abstract:
Klebsiella pneumoniae is a Gram-negative bacterium of the
family Enterobacteriaceae that possesses diverse metabolic
capabilities: many strains are leading causes of
hospital-acquired infections that are often refractory to
multiple antibiotics, yet other strains are metabolically
engineered and used for production of commercially valuable
chemicals. To study its metabolism, we constructed a
genome-scale metabolic model (iYL1228) for strain MGH 78578,
experimentally determined its biomass composition,
experimentally determined its ability to grow on a broad range
of carbon, nitrogen, phosphorus and sulfur sources, and
assessed the ability of the model to accurately simulate growth
versus no growth on these substrates. The model contains 1,228
genes encoding 1,188 enzymes that catalyze 1,970 reactions and
accurately simulates growth on 84% of the substrates tested.
Furthermore, quantitative comparison of growth rates between
the model and experimental data for nine of the substrates also
showed good agreement. The genome-scale metabolic
reconstruction for K. pneumoniae presented here thus provides
an experimentally validated in silico platform for further
studies of this important industrial and biomedical
organism.
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2024-10-15 | MODEL1507180054 | BioModels
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