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| 1 | Interleukin-6 mediates neutrophil mobilization from bone marrow in pulmonary hypertension显示文摘Myeloid cells,such as neutrophils,are produced in the bone marrow in high quantities and are important in the pathogenesis of vascular diseases such as pulmonary hypertension(PH).Although neutrophil recruitment into sites of inflammation has been well studied,the mechanisms of neutrophil egress from the bone marrow are not well understood.Using computational flow cytometry,we observed increased neutrophils in the lungs of patients and mice with PH.Moreover,we found elevated levels of IL-6 in the blood and lungs of patients and mice with PH.We observed that transgenic mice overexpressing Il-6 in the lungs displayed elevated neutrophil egress from the bone marrow and exaggerated neutrophil recruitment to the lungs,resulting in exacerbated pulmonary vascular remodeling,and dysfunctional hemodynamics.Mechanistically,we found that IL-6-induced neutrophil egress from the bone marrow was dependent on interferon regulatory factor 4(IRF-4)-mediated CX3CR1 expression in neutrophils.Consequently,Cx3cr1 genetic deficiency in hematopoietic cells in Il-6-transgenic mice significantly reduced neutrophil egress from bone marrow and decreased neutrophil counts in the lungs,thus ameliorating pulmonary remodeling and hemodynamics.In summary,these findings define a novel mechanism of IL-6-induced neutrophil egress from the bone marrow and reveal a new therapeutic target to curtail neutrophil-mediated inflammation in pulmonary vascular disease. | Jonathan Florentin Jingsi Zhao Yi-Yin Tai Sathish Babu Vasamsetti Scott P.O’Neil Rahul Kumar Anagha Arunkumar Annie Watson John Sembrat Grant C.Bullock Linda Sanders Biruk Kassa Mauricio Rojas Brian B.Graham Stephen Y.Chan Partha Dutta | 2021 | Cellular & Molecular Immunology2021，18，2： | 3 |
| 2 | Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions显示文摘Background: Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci(QTL) with large effects, while other traits have one or several easily detectable QTL with large effects.Methods: Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect.A commonly used method for genome wide association studies is to fit a mixture model such as Bayes B that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a42 K SNP panel.Results: Using data from the first 7 generations, both Bayes B and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with Bayes B and less than 1 % with GBLUP. When predicting phenotypes in generation 8 Bayes B accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with Bayes B in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with Bayes B but only6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using Bayes B but had no predictive power with GBLUP.Conclusions: In conclusion, In the presence of large effect QTL, Bayes B did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP. | Anna Wolc Jesus Arango Petek Settar Janet E.Fulton Neil P.O'Sullivan Jack C.M.Dekkers Rohan Fernando Dorian J.Garrick | 2016 | Journal of Animal Science and Biotechnology2016，7，4： | 1 |
| 3 | Identification of recombination hotspots and quantitative trait loci for recombination rate in layer chickens显示文摘Background: The frequency of recombination events varies across the genome and between individuals, which may be related to some genomic features. The objective of this study was to assess the frequency of recombination events and to identify QTL(quantitative trait loci) for recombination rate in two purebred layer chicken lines.Methods: A total of 1200 white-egg layers(WL) were genotyped with 580 K SNPs and 5108 brown-egg layers(BL)were genotyped with 42 K SNPs(single nucleotide polymorphisms). Recombination events were identified within half-sib families and both the number of recombination events and the recombination rate was calculated within each0.5 Mb window of the genome. The 10% of windows with the highest recombination rate on each chromosome were considered to be recombination hotspots. A BayesB model was used separately for each line to identify genomic regions associated with the genome-wide number of recombination event per meiosis. Regions that explained more than 0.8% of genetic variance of recombination rate were considered to harbor QTL.Results: Heritability of recombination rate was estimated at 0.17 in WL and 0.16 in BL. On average, 11.3 and 23.2 recombination events were detected per individual across the genome in 1301 and 9292 meioses in the WL and BL,respectively. The estimated recombination rates differed significantly between the lines, which could be due to differences in inbreeding levels, and haplotype structures. Dams had about 5% to 20% higher recombination rates per meiosis than sires in both lines. Recombination rate per 0.5 Mb window had a strong negative correlation with chromosome size and a strong positive correlation with GC content and with CpG island density across the genome in both lines. Different QTL for recombination rate were identified in the two lines. There were 190 and 199 non-overlapping recombination hotspots detected in WL and BL respectively, 28 of which were common to both lines.Conclusions: Differences in the recombination rates, hotspot locations, and QTL regions associated with genomewide recombination were observed between lines, indicating the breed-specific feature of detected recombination events and the control of recombination events is a complex polygenic trait. | Ziqing Weng Anna Wolc Hailin Su Rohan L.FernANDo Jack C.M.Dekkers Jesus Arango Petek Settar Janet E.Fulton Neil P.O’Sullivan Dorian J.Garrick | 2019 | Journal of Animal Science and Biotechnology2019，10，2： | 0 |
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