Mutational Analysis of the Active Site and Antibody Epitopes of the Complement-inhibitory Glycoprotein, CD59

The Ly-6 superfamily of cell surface molecules includes CD59, a potent regulator of the complement system that protects host cells from the cytolytic action of the membrane attack complex (MAC). Although its mechanism of action is not well understood, CD59 is thought to prevent assembly of the MAC by binding to the C8 and/or C9 proteins of the nascent complex. Here a systematic, structure-based mutational approach has been used to determine the region(s) of CD59 required for its protective activity. Analysis of 16 CD59 mutants with single, highly nonconservative substitutions suggests that CD59 has a single active site that includes Trp-40, Arg-53, and Glu-56 of the glycosylated, membrane-distal face of the disk-like extracellular domain and, possibly, Asp-24 positioned at the edge of the domain. The putative active site includes residues conserved across species, consistent with the lack of strict homologous restriction previously observed in studies of CD59 function. Competition and mutational analyses of the epitopes of eight CD59-blocking and non-blocking monoclonal antibodies confirmed the location of the active site. Additional experiments showed that the expression and function of CD59 are both glycosylation independent

CD80 (B7-1) Binds Both CD28 and CTLA-4 with a Low Affinity and Very Fast Kinetics

The structurally related T cell surface molecules CD28 and CTLA-4 interact with cell surface ligands CD80 (B7-1) and CD86 (B7-2) on antigen-presenting cells (APC) and modulate T cell antigen recognition. Preliminary reports have suggested that CD80 binds CTLA-4 and CD28 with affinities (Kd values ∼12 and ∼200 nM, respectively) that are high when compared with other molecular interactions that contribute to T cell–APC recognition. In the present study, we use surface plasmon resonance to measure the affinity and kinetics of CD80 binding to CD28 and CTLA-4. At 37°C, soluble recombinant CD80 bound to CTLA-4 and CD28 with Kd values of 0.42 and 4 μM, respectively. Kinetic analysis indicated that these low affinities were the result of very fast dissociation rate constants (koff); sCD80 dissociated from CD28 and CTLA-4 with koff values of ⩾1.6 and ⩾0.43 s−1, respectively. Such rapid binding kinetics have also been reported for the T cell adhesion molecule CD2 and may be necessary to accommodate dynamic T cell–APC contacts and to facilitate scanning of APC for antigen

Publisher Correction:Germline de novo mutation clusters arise during oocyte aging in genomic regions with high double-strand-break incidence (Nature Genetics, (2018), 50, 4, (487-492), 10.1038/s41588-018-0071-6)

In the HTML version of the article originally published, the figures for Supplementary Figures 1–15 were incorrect and did not match the correct figures in the PDF of Supplementary Text and Figures. The error has been corrected in the HTML version of the article

Mutation and polymorphism spectrum in osteogenesis imperfecta type II: implications for genotype–phenotype relationships

Osteogenesis imperfecta (OI), also known as brittle bone disease, is a clinically and genetically heterogeneous disorder primarily characterized by susceptibility to fracture. Although OI generally results from mutations in the type I collagen genes, COL1A1 and COL1A2, the relationship between genotype and phenotype is not yet well understood. To provide additional data for genotype–phenotype analyses and to determine the proportion of mutations in the type I collagen genes among subjects with lethal forms of OI, we sequenced the coding and exon-flanking regions of COL1A1 and COL1A2 in a cohort of 63 subjects with OI type II, the perinatal lethal form of the disease. We identified 61 distinct heterozygous mutations in type I collagen, including five non-synonymous rare variants of unknown significance, of which 43 had not been seen previously. In addition, we found 60 SNPs in COL1A1, of which 17 were not reported previously, and 82 in COL1A2, of which 18 are novel. In three samples without collagen mutations, we found inactivating mutations in CRTAP and LEPRE1, suggesting a frequency of these recessive mutations of ∼5% in OI type II. A computational model that predicts the outcome of substitutions for glycine within the triple helical domain of collagen α1(I) chains predicted lethality with ∼90% accuracy. The results contribute to the understanding of the etiology of OI by providing data to evaluate and refine current models relating genotype to phenotype and by providing an unbiased indication of the relative frequency of mutations in OI-associated genes

Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology

Tools for annotation and comparison of structural variation [version 1; referees: 1 approved, 2 approved with reservations]

The impact of structural variants (SVs) on a variety of organisms and diseases like cancer has become increasingly evident. Methods for SV detection when studying genomic differences across cells, individuals or populations are being actively developed. Currently, just a few methods are available to compare different SVs callsets, and no specialized methods are available to annotate SVs that account for the unique characteristics of these variant types. Here, we introduce SURVIVOR_ant, a tool that compares types and breakpoints for candidate SVs from different callsets and enables fast comparison of SVs to genomic features such as genes and repetitive regions, as well as to previously established SV datasets such as from the 1000 Genomes Project. As proof of concept we compared 16 SV callsets generated by different SV calling methods on a single genome, the Genome in a Bottle sample HG002 (Ashkenazi son), and annotated the SVs with gene annotations, 1000 Genomes Project SV calls, and four different types of repetitive regions. Computation time to annotate 134,528 SVs with 33,954 of annotations was 22 seconds on a laptop