Charged and Hydrophobic Surfaces on the A Chain of Shiga-Like Toxin 1 Recognize the C-Terminal Domain of Ribosomal Stalk Proteins

Shiga-like toxins are ribosome-inactivating proteins (RIP) produced by pathogenic E. coli strains that are responsible for hemorrhagic colitis and hemolytic uremic syndrome. The catalytic A1 chain of Shiga-like toxin 1 (SLT-1), a representative RIP, first docks onto a conserved peptide SD[D/E]DMGFGLFD located at the C-terminus of all three eukaryotic ribosomal stalk proteins and halts protein synthesis through the depurination of an adenine base in the sarcin-ricin loop of 28S rRNA. Here, we report that the A1 chain of SLT-1 rapidly binds to and dissociates from the C-terminal peptide with a monomeric dissociation constant of 13 µM. An alanine scan performed on the conserved peptide revealed that the SLT-1 A1 chain interacts with the anionic tripeptide DDD and the hydrophobic tetrapeptide motif FGLF within its sequence. Based on these 2 peptide motifs, SLT-1 A1 variants were generated that displayed decreased affinities for the stalk protein C-terminus and also correlated with reduced ribosome-inactivating activities in relation to the wild-type A1 chain. The toxin-peptide interaction and subsequent toxicity were shown to be mediated by cationic and hydrophobic docking surfaces on the SLT-1 catalytic domain. These docking surfaces are located on the opposite face of the catalytic cleft and suggest that the docking of the A1 chain to SDDDMGFGLFD may reorient its catalytic domain to face its RNA substrate. More importantly, both the delineated A1 chain ribosomal docking surfaces and the ribosomal peptide itself represent a target and a scaffold, respectively, for the design of generic inhibitors to block the action of RIPs

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

A novel Alzheimer disease locus located near the gene encoding tau protein

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordAPOE ε4, the most significant genetic risk factor for Alzheimer disease (AD), may mask effects of other loci. We re-analyzed genome-wide association study (GWAS) data from the International Genomics of Alzheimer's Project (IGAP) Consortium in APOE ε4+ (10 352 cases and 9207 controls) and APOE ε4- (7184 cases and 26 968 controls) subgroups as well as in the total sample testing for interaction between a single-nucleotide polymorphism (SNP) and APOE ε4 status. Suggestive associations (P<1 × 10-4) in stage 1 were evaluated in an independent sample (stage 2) containing 4203 subjects (APOE ε4+: 1250 cases and 536 controls; APOE ε4-: 718 cases and 1699 controls). Among APOE ε4- subjects, novel genome-wide significant (GWS) association was observed with 17 SNPs (all between KANSL1 and LRRC37A on chromosome 17 near MAPT) in a meta-analysis of the stage 1 and stage 2 data sets (best SNP, rs2732703, P=5·8 × 10-9). Conditional analysis revealed that rs2732703 accounted for association signals in the entire 100-kilobase region that includes MAPT. Except for previously identified AD loci showing stronger association in APOE ε4+ subjects (CR1 and CLU) or APOE ε4- subjects (MS4A6A/MS4A4A/MS4A6E), no other SNPs were significantly associated with AD in a specific APOE genotype subgroup. In addition, the finding in the stage 1 sample that AD risk is significantly influenced by the interaction of APOE with rs1595014 in TMEM106B (P=1·6 × 10-7) is noteworthy, because TMEM106B variants have previously been associated with risk of frontotemporal dementia. Expression quantitative trait locus analysis revealed that rs113986870, one of the GWS SNPs near rs2732703, is significantly associated with four KANSL1 probes that target transcription of the first translated exon and an untranslated exon in hippocampus (P≤1.3 × 10-8), frontal cortex (P≤1.3 × 10-9) and temporal cortex (P≤1.2 × 10-11). Rs113986870 is also strongly associated with a MAPT probe that targets transcription of alternatively spliced exon 3 in frontal cortex (P=9.2 × 10-6) and temporal cortex (P=2.6 × 10-6). Our APOE-stratified GWAS is the first to show GWS association for AD with SNPs in the chromosome 17q21.31 region. Replication of this finding in independent samples is needed to verify that SNPs in this region have significantly stronger effects on AD risk in persons lacking APOE ε4 compared with persons carrying this allele, and if this is found to hold, further examination of this region and studies aimed at deciphering the mechanism(s) are warranted

Prediction and Experimental Characterization of nsSNPs Altering Human PDZ-Binding Motifs

<div><p>Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations.</p></div

PDZ-binding linear motifs are affected by nsSNPs.

<p>A) Number of nsSNPs that are found in PDZ-binding motif containing C-termini for a wide range of thresholds on PWM scores to define PDZ-binding motifs. Higher threshold values (corresponding to more stringent definitions of PDZ-binding motifs) result in few C-termini being considered as containing a PDZ-binding motif, hence few nsSNPs falling in these motifs. B) Distribution of nsSNPs in PDZ-binding motifs, computed as the ration between the number nsSNPs shown in panel A and the total number of amino acids in PDZ-binding motif containing C-termini. For the highest thresholds (>−6), the number of nsSNPs falling in PDZ-binding motifs is not lower than expected. Then for thresholds between −6 and −9, it becomes slightly lower. For even lower thresholds, we tend to the expected distribution observed for all C-termini (9.9% of positions affected by nsSNPs, dashed line). C) The corresponding P-value assuming a uniform distribution of nsSNPs in all C-terminal segments (binomial test).</p

PDZ binding is affected by nsSNPs.

<p>(A) Sequence logo representing the peptide binding profile of the first and second PDZ domain of Multiple PDZ Domain Protein (MPDZ) and the PDZ domain of cytosine interacting protein (CYTIP) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094507#pone.0094507-Tonikian1" target="_blank">[19]</a>. The interactions predicted to be disrupted by an nsSNPs are shown above. (B) Saturation binding of MPDZ#1, MPDZ#2 and CYTIP to peptides derived from sequence logos shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094507#pone-0094507-g004" target="_blank">Figure 4A</a> (filled circle) and related peptides containing nsSNPs (open circles). The ELISA signal at 450 nm is plotted vs. the PDZ concentration in mg/ml. Note the C-terminus of PCDHA1 corresponds to the third isoform in UniProt (Q9Y5I3-3).</p

nsSNPs disrupting PDZ-binding motifs are only slightly under-represented in human proteins.

<p>The red curve shows the fraction of known nsSNPs predicted to disrupt PDZ-binding motifs for different values of the threshold <i>S_max</i>. The black curve shows the same data for random amino acid substitutions on residues affected by nsSNPs in human C-termini (error bars show standard deviation for 1000 randomization of nsSNPs).</p

nsSNPs can disrupt a PDZ-binding motif, create a new one, or not have an effect.

<p>The two arrows illustrate our algorithm to predict the first two cases. A nsSNP is considered to disrupt a PDZ-binding motif if the wild-type sequence (<i>j</i>) has a score <i>S_ij</i>><i>S_max</i> (green box), while the modified sequence (<i>j′</i>) has a score <i>S_ij′</i><<i>S_min</i> (red box) with at least one PDZ domain (<i>i</i>) (upper arrow). Reversely, nsSNPs can create new PDZ binding motifs if <i>S_ij</i><<i>S_min</i> and <i>S_ij′</i> ><i>S_max</i> (lower arrow). The confidence interval (yellow box) is used to ensure that the scores are different enough between the wild type and the mutant to predict nsSNPs effect on PDZ mediated interactions.</p