Genetic Factors Regulating Lung Vasculature and Immune Cell Functions Associate with Resistance to Pneumococcal Infection

Jonczyk, Magda S.; Simon, Michelle; Kumar, Saumya; Fernandes, Vitor E.; Sylvius, Nicolas; Mallon, Ann‐Marie; Denny, Paul; Andrew, Peter W.

doi:10.1371/journal.pone.0089831

Cited by 15 publications

(24 citation statements)

References 92 publications

(125 reference statements)

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“…After adding 5× first strand buffer and 10 mM DTT (Thermo Fisher Scientific), 200 U M-MLV (Thermo Fisher Scientific) was added and the tube was incubated for 50 min at 37˚C and 15 min at 70˚C. Real-time PCR assay was carried out using 2 μl of cDNA as the template, 10 μl of SYBR Select Master Mix (Thermo Fisher Scientific) and primers listed in S1 Table [11,37] on the ABI Prism 7000 Sequence Detection System (Thermo Fisher Scientific). Data was analyzed by 2 −ΔΔCt methods and normalized by GAPDH.…”

Section: Quantitative Rt-pcrmentioning

confidence: 99%

Pathological roles of MRP14 in anemia and splenomegaly during experimental visceral leishmaniasis

et al. 2020

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PLOS Neglected Tropical Diseases | https://doi.funders had no role in study design, data collection and leishmaniasis caused by Leishmania major and visceral leishmaniasis caused by L. donovani, the molecule showed distinct roles in protection/pathology; although the molecule was protective against L. major infection, depletion of the molecule resulted in easing the pathology during L. donovani infection. These may prove the complexity of MRP14, but at the same time support understanding of the mechanisms behind the complexity.Myeloid-related protein (MRP) 14, also known as S100A9, belongs to the S100 calcium-binding protein family and can form the heterodimer with MRP8, which is known as S100A8 [1,2]. S100 protein family proteins contain two Ca 2+ -binding regions known as EF-hands and play a role in cell differentiation, cell cycle progression, regulation of kinase activity, and cytoskeletalmembrane interactions when Ca2 + bind [3]. The expression of MRP14 and MRP8 is specific for myeloid cells such as granulocytes, monocytes and macrophages in inflamed tissue [4]. They are abundant cytoplasmic proteins of neutrophils and monocytes [1,5], and also known as markers of inflammatory macrophages. MRP14 expression is abundant in immature monocytes and is lost as the cells terminally differentiate into tissue macrophages, so MRP14 can be associated with monocytic differentiation [6].MRP14 has been characterized as an inflammation-related protein [7][8][9]. Extracellular MRP14 and MRP8 are known to function as damage-associated molecular patterns (DAMP), which are endogenous molecules or alarmins released after cell activation or necrotic cells and are secreted by the inflammatory cells when activated [10]. Neither MRP14 nor MRP8 has a signal sequence for secretion via classical ER/Golgi route, but it is demonstrated that these proteins are secreted after activation of protein kinase C via tubulin-dependent pathway [10]. Extracellular MRP14 and MRP8 bind Toll-like receptor (TLR) 2, TLR4 and receptor for advanced glycation endproducts (RAGE) and induce cell recruitment and cell activation [11][12][13][14]. It is also reported that MRP14 promotes inflammatory process in infection and autoimmunity via TLR4 [9,12,15], and cell growth in cancer and cell migration via RAGE [16,17]. Actually, MRP8 and MRP14 in serum are elevated in various diseases [18][19][20]. In inflammatory diseases such as rheumatoid arthritis, psoriatic arthritis, and coronary syndromes, the accumulation of cells expressing MRP14 or MRP8 is observed at inflammatory sites [18,[21][22][23]. Therefore, it is considered that MRP14 plays a critical role in the pathogenesis in these diseases. In malaria, we reported that macrophages expressing MRP14 accumulated in the spleen and liver of BALB/c mice and MRP14 level in the plasma was also elevated during Plasmodium berghei ANKA infection [24]. In addition, the administration of recombinant MRP14 exacerbated hepatic injury and promoted the up-regulation of pro-inflammatory molecules in the liver [11]. These re...

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Section: Quantitative Rt-pcrmentioning

confidence: 99%

Pathological roles of MRP14 in anemia and splenomegaly during experimental visceral leishmaniasis

et al. 2020

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“…SKOR2 has been reported as a candidate gene for changes in weight and BMI in male and female adolescents [38]. STAMBPL1 was reported as a candidate gene for lung vasculature and immune cell functions in 26 inbred mouse strains [39]. We observed that one SNP (BovineHD1400017459) located in RIMS2 on BTA14 was associated with MP.…”

Section: Gwas With Additive and Dominance Effectsmentioning

confidence: 67%

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Liu

Wang

et al. 2019

Animals

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Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

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“…To define module driver genes we made use of the module eigengene concept, defined as the first principal component of the module expression matrix and the module membership measure, k (21,23). To provide robustness to our findings we performed weighted gene coexpression network analysis (WGCNA) on an independent publicly available lung transcriptome dataset generated from female mice (9-12 wks old) uninfected (n = 5) and 6 h after S. pneumoniae serotype 2 strain D39 intransal infection (n = 5) (GSE49533) (24).…”

Section: Differential Gene Expression Analysis and Modular Network Comentioning

confidence: 93%

“…To provide robustness to our findings we performed differential gene expression and unsupervised weighted gene coexpression network analysis using a publicly available gene expression dataset of pneumococcal lung infection in BALB/c mice (GSE49533) (24). Differential gene expression analysis between BALB/c mice discordant for pneumococ- Modular organization of the pulmonary transcriptome during pneumococcal pneumonia.…”

Section: Temporal Patterns and Regulatory Drivers Of The Host Modularmentioning

confidence: 93%

“…Thus 57% of the significantly altered network modules in C57BL/6 mice (at 6 h postinfection) was conserved in the BALB/c pneumococcal pneumonia model. The lack of complement system and interferon signaling pathway (Figures 3 and 4) enrichment in the BALB/c model motivated us to evaluate network genes that coincide within a chromosome 7 quantitative trait locus that is understood to modulate the susceptibility to severe pneumococcal pneumonia, namely Spir1 (24). Using the Biomart application (28) we identified one IL17A/IL17F signaling module gene, two NF-κB signaling module genes, two complement system module genes and eight interferon signaling module genes that coincided within the Spir1 QTL support interval (Table 1).…”

Section: Temporal Patterns and Regulatory Drivers Of The Host Modularmentioning

confidence: 99%

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Modular Transcriptional Networks of the Host Pulmonary Response during Early and Late Pneumococcal Pneumonia

Scicluna

Lieshout

Blok

et al. 2015

Mol Med

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Streptococcus pneumoniae (Spneu) remains the most lethal bacterial pathogen and the dominant agent of communityacquired pneumonia. Treatment has perennially focused on the use of antibiotics, albeit scrutinized due to the occurrence of antibiotic-resistant Spneu strains. Immunomodulatory strategies have emerged as potential treatment options. Although promising, immunomodulation can lead to improper tissue functions either at steady state or upon infectious challenge. This argues for the availability of tools to enable a detailed assessment of whole pulmonary functions during the course of infection, not only those functions biased to the defense response. Thus, through the use of an unbiased tissue microarray and bioinformatics approach, we aimed to construct a comprehensive map of whole-lung transcriptional activity and cellular pathways during the course of pneumococcal pneumonia. We performed genome-wide transcriptional analysis of whole lungs before and 6 and 48 h after Spneu infection in mice. The 4,000 most variable transcripts across all samples were used to assemble a gene coexpression network comprising 13 intercorrelating modules (clusters of genes). Fifty-four percent of this whole-lung transcriptional network was altered 6 and 48 h after Spneu infection. Canonical signaling pathway analysis uncovered known pathways imparting protection, including IL17A/IL17F signaling and previously undetected mechanisms that included lipid metabolism. Through in silico prediction of cell types, pathways were observed to enrich for distinct cell types such as a novel stromal cell lipid metabolism pathway. These cellular mechanisms were furthermore anchored at functional hub genes of cellular fate, differentiation, growth and transcription. Collectively, we provide a benchmark unsupervised map of whole-lung transcriptional relationships and cellular activity during early and late pneumococcal pneumonia.

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Genetic Factors Regulating Lung Vasculature and Immune Cell Functions Associate with Resistance to Pneumococcal Infection

Cited by 15 publications

References 92 publications

Pathological roles of MRP14 in anemia and splenomegaly during experimental visceral leishmaniasis

Pathological roles of MRP14 in anemia and splenomegaly during experimental visceral leishmaniasis

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Modular Transcriptional Networks of the Host Pulmonary Response during Early and Late Pneumococcal Pneumonia

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