Genetic Drivers of Kidney Defects in the DiGeorge Syndrome

Background The DiGeorge syndrome, the most common of the microdeletion syndromes, affects multiple organs, including the heart, the nervous system, and the kidney. It is caused by deletions on chromosome 22q11.2; the genetic driver of the kidney defects is unknown. Methods We conducted a genomewide search for structural variants in two cohorts: 2080 patients with congenital kidney and urinary tract anomalies and 22,094 controls. We performed exome and targeted resequencing in samples obtained from 586 additional patients with congenital kidney anomalies. We also carried out functional studies using zebrafish and mice. Results We identified heterozygous deletions of 22q11.2 in 1.1% of the patients with congenital kidney anomalies and in 0.01% of population controls (odds ratio, 81.5; P=4.5×10(-14)). We localized the main drivers of renal disease in the DiGeorge syndrome to a 370-kb region containing nine genes. In zebrafish embryos, an induced loss of function in snap29, aifm3, and crkl resulted in renal defects; the loss of crkl alone was sufficient to induce defects. Five of 586 patients with congenital urinary anomalies had newly identified, heterozygous protein-altering variants, including a premature termination codon, in CRKL. The inactivation of Crkl in the mouse model induced developmental defects similar to those observed in patients with congenital urinary anomalies. Conclusions We identified a recurrent 370-kb deletion at the 22q11.2 locus as a driver of kidney defects in the DiGeorge syndrome and in sporadic congenital kidney and urinary tract anomalies. Of the nine genes at this locus, SNAP29, AIFM3, and CRKL appear to be critical to the phenotype, with haploinsufficiency of CRKL emerging as the main genetic driver. (Funded by the National Institutes of Health and others.)

2017 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1004/thumbnail.jp

Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen.

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design

CD11c identifies microbiota and EGR2-dependent MHCII+ serous cavity macrophages with sexually dimorphic fate in mice

The murine serous cavities contain a rare and enigmatic population of short-lived F4/80loMHCII+ macrophages but what regulates their development, survival, and fate is unclear. Here, we show that mature F4/80loMHCII+ peritoneal macrophages arise after birth, but that this occurs largely independently of colonization by microbiota. Rather, microbiota specifically regulate development of a subpopulation of CD11c+ cells that express the immunoregulatory cytokine RELM-α, are reliant on the transcription factor EGR2, and develop independently of the growth factor CSF1. Furthermore, we demonstrate that intrinsic expression of RELM-α, a signature marker shared by CD11c+ and CD11c– F4/80loMHCII+ cavity macrophages, regulates survival and differentiation of these cells in the peritoneal cavity in a sex-specific manner. Thus, we identify a previously unappreciated diversity in serous cavity F4/80loMHCII+ macrophages that is regulated by microbiota, and describe a novel sex and site-specific function for RELM-α in regulating macrophage endurance that reveals the unique survival challenge presented to monocyte-derived macrophages by the female peritoneal environment

Transcriptomic signatures of NK cells suggest impaired responsiveness in HIV-1 infection and increased activity post-vaccination

Natural killer (NK) cells limit viral replication by direct recognition of infected cells, antibody-dependent cellular cytotoxicity (ADCC), and releasing cytokines. Although growing evidence supports NK cell antiviral immunity in HIV-1 infection, further knowledge of their response is necessary. Here we show that NK cells responding to models of direct cell recognition, ADCC, and cytokine activation have unique transcriptional fingerprints. Compared with healthy volunteers, individuals with chronic HIV-1 infection have higher expression of genes commonly associated with activation, and lower expression of genes associated with direct cell recognition and cytokine stimulation in their NK cells. By contrast, NK cell transcriptional profiles of individuals receiving a modified vaccinia Ankara (MVA) vectored HIV-1 vaccine show upregulation of genes associated with direct cell recognition. These findings demonstrate that targeted transcriptional profiling provides a sensitive assessment of NK cell activity, which helps understand how NK cells respond to viral infections and vaccination

B-cell epitope prediction using second generation methods.

a<p>Accuracy measures the percentage of correct epitope classification across all residues.</p>b<p>A_roc is the area under the curve constructed by the Receiver Operational Characteristics (ROC), which is the function of the sensitivity and specificity of the epitope mapping score. A_roc = 1 indicates perfect prediction of epitopes, A_roc = 0.5 indicates completely random predictions.</p>c<p>p-value is the probability that the observed A_roc value was obtained by chance. In the null positive and null negative methods, all and no residues, respectively, were classified as epitopes.</p><p>A_roc standard errors (SE) were estimated using bootstrapping. <i>p</i>-values were calculated using permutation tests <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071610#pone.0071610-Larsen1" target="_blank">[6]</a>.</p