Genes and Podocytes – New Insights into Mechanisms of Podocytopathy

After decades of primarily morphological study, positional cloning of the NPHS1 gene was the landmark event that established aberrant podocyte genetics as a pivotal cause of malfunction of the glomerular filter. This ended any uncertainty whether genetic mutation plays a significant role in hereditary nephrotic syndromes and confirmed podocytes as critical players in regulating glomerular protein filtration. Although subsequent sequencing of candidate genes chosen on the basis of podocyte biology had less success, unbiased analysis of genetically informative kindreds and syndromic disease has led to further gene discovery. However, the 45 genes currently associated with human nephrotic syndrome explain no more than 20-30% of hereditary and only 10-20% of sporadic cases. It is becoming increasingly clear both from genetic analysis and phenotypic data - including occasional response to immunosuppressive agents and post-transplant disease recurrence in Mendelian disease - that monogenic inheritance of abnormalities in podocyte-specific genes disrupting filter function are only part of the story. Recent advances in genetic screening technology combined with increasingly robust bioinformatics are set to allow identification and characterisation of novel disease causing variants and more importantly, disease modifying genes. Emerging data also supports a significant but incompletely characterised immunoregulatory component

Cutting Edge: CD8+ T Cell Priming in the Absence of NK Cells Leads to Enhanced Memory Responses

It is uncertain whether NK cells modulate T cell memory differentiation. By using a genetic model that allows the selective depletion of NK cells, we show in this study that NK cells shape CD8+ T cell fate by killing recently activated CD8+ T cells in an NKG2D- and perforin-dependent manner. In the absence of NK cells, the differentiation of CD8+ T cells is strongly biased toward a central memory T cell phenotype. Although, on a per-cell basis, memory CD8+ T cells generated in the presence or the absence of NK cells have similar functional features and recall capabilities, NK cell deletion resulted in a significantly higher number of memory Ag-specific CD8+ T cells, leading to more effective control of tumors carrying model Ags. The enhanced memory responses induced by the transient deletion of NK cells may provide a rational basis for the design of new vaccination strategies

Genetic variants alter T-bet binding <i>in vivo</i>.

<p><b>A.</b> Genomic context of rs1465321 and rs2058622, which is in high LD (r² = 1.0) with rs1465321. <b>B.</b> T-bet ChIP and input sequencing reads that cross rs2058622 (chr2: 102985274–102985565; left) or rs1465321 (chr2: 102986477–102986768; right) in two donors heterozygous for rs1465321. In each case, the number of reads that match the reference allele are shown in black and the alternative allele in green. <b>C.</b> T-bet ChIP and input (Inp) sequencing reads at the set of 19 additional heterozygous SNPs that exhibited allelic imbalanced T-bet binding. For each SNP, the color shows fold-enrichment in the number of sequencing reads matching the Ref or Alt allele, relative to the average number of reads across all samples, as indicated by the scale on the right hand side. SNPs are divided into those exhibiting greater T-bet binding to the reference (Ref) allele (Ref > Alt, top) or the alternative (Alt) allele (Alt > Ref, bottom).</p

T-bet binding at polymorphic sites.

A. Heat map showing T-bet occupancy around SNPs located within T-bet binding sites (T-bet hit-SNPs). Each row is centred on a single SNP, with T-bet binding shown across the genomic region stretching 2 kb up and downstream. Sequence reads (per million total reads) at each position are represented by colour, according to the scale on the left. Negative IgG ChIP-seq data are shown on the right at the same loci. B. T-bet binding at two example T-bet hit-SNPs. The number of sequencing reads from T-bet, IgG control and H3K27ac ChIP-enriched DNA are plotted per million input-subtracted total reads and aligned with the human genome. DNaseI hypersensitivity data (2 replicates) are from ENCODE. C. Left: Average number of ChIP-seq reads for H3K27ac and control total H3 in human Th1 cells plotted against the genomic distance from T-bet hit-SNPs or the complete set of GWAS SNPs plus those in high LD. Right: Average number of sequencing reads measuring DNaseI hypersensitivity plotted against genomic distance.</p

rs1465321 is an eQTL for <i>IL18RAP</i> and celiac disease.

<p><b>A</b>. RNA abundance (size factor-normalised counts) for selected genes in wild type (WT) and T-bet deficient (T-bet KO) naive lymphocytes cultured under Th1 polarising conditions. * significant change in expression (p<0.05, Wald test after Benjamini-Hochberg correction). <b>B.</b> “Locus-zoom” plot showing the distribution of association p-values for celiac disease in the <i>IL18RAP/IL18R1</i> chromosome region (genes shown below panel D). The x-axis shows the chromosome position. Colors show the level of linkage disequilibrium with rs1465321, which is indicated with a purple spot. <b>C.</b> “Locus-zoom” plot showing the distribution of association p-values for <i>IL18RAP</i> eQTL in 1,214 whole blood RNA samples. The Bayesian statistic for colocalisation with the celiac disease signal shows a posterior probability <i>against</i> colocalisation (PP3) greater than 99%, indicating that this primary whole blood <i>IL18RAP</i> eQTL signal is not compatible with a shared causal variant with celiac disease. <b>D.</b> “Locus-zoom” plot showing the secondary <i>IL18RAP</i> eQTL (conditional on rs1985329) signal in the same 1,214 whole blood RNA samples. For this secondary signal, the Bayesian statistic for colocalisation with the celiac disease signal shows a posterior probability <i>in favour of</i> colocalisation (PP4) greater than 99%, indicating that this secondary whole blood <i>IL18RAP</i> eQTL signal is compatible with a shared causal variant with celiac disease.</p