Functional Classification of Immune Regulatory Proteins

SummaryThe members of the immunoglobulin superfamily (IgSF) control innate and adaptive immunity and are prime targets for the treatment of autoimmune diseases, infectious diseases, and malignancies. We describe a computational method, termed the Brotherhood algorithm, which utilizes intermediate sequence information to classify proteins into functionally related families. This approach identifies functional relationships within the IgSF and predicts additional receptor-ligand interactions. As a specific example, we examine the nectin/nectin-like family of cell adhesion and signaling proteins and propose receptor-ligand interactions within this family. Guided by the Brotherhood approach, we present the high-resolution structural characterization of a homophilic interaction involving the class-I MHC-restricted T-cell-associated molecule, which we now classify as a nectin-like family member. The Brotherhood algorithm is likely to have a significant impact on structural immunology by identifying those proteins and complexes for which structural characterization will be particularly informative

VISTA, a novel mouse Ig superfamily ligand that negatively regulates T cell responses

VISTA suppresses T cell proliferation and cytokine production and can influence autoimmunity and antitumor responses in mice

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

<p>Abstract</p> <p>Background</p> <p>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.</p> <p>Methods</p> <p>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray.</p> <p>Results</p> <p>We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p < 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used to give a more accurate estimation of the time till the next relapse (in resolution of 50 days). The error rate of the second stage predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p < 0.001). The predictors were further evaluated and found effective both for untreated MS patients and for MS patients that subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p < 0.001 for all the patient groups).</p> <p>Conclusion</p> <p>We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature.</p

Single-Cell Identity Generated by Combinatorial Homophilic Interactions between α, β, and γ Protocadherins

Individual mammalian neurons express distinct repertoires of protocadherin (Pcdh) -α, -β and -γ proteins that function in neural circuit assembly. Here we show that all three types of Pcdhs can engage in specific homophilic interactions, that cell surface delivery of alternate Pcdhα isoforms requires cis interactions with other Pcdh isoforms, and that the extracellular cadherin domain EC6 plays a critical role in this process. Analysis of specific combinations of up to five Pcdh isoforms showed that Pcdh homophilic recognition specificities strictly depend on the identity of all of the expressed isoforms, such that mismatched isoforms interfere with cell-cell interactions. We present a theoretical analysis showing that the assembly of Pcdh-α, −β and −γ isoforms into multimeric recognition units, and the observed tolerance for mismatched isoforms can generate the cell surface diversity necessary for single-cell identity. However, competing demands of non-self discrimination and self-recognition place limitations on the mechanisms by which recognition units can function

Single-Nucleotide Polymorphism Markers from De-Novo Assembly of the Pomegranate Transcriptome Reveal Germplasm Genetic Diversity

<div><p>Pomegranate is a valuable crop that is grown commercially in many parts of the world. Wild species have been reported from India, Turkmenistan and Socotra. Pomegranate fruit has a variety of health-beneficial qualities. However, despite this crop's importance, only moderate effort has been invested in studying its biochemical or physiological properties or in establishing genomic and genetic infrastructures. In this study, we reconstructed a transcriptome from two phenotypically different accessions using 454-GS-FLX Titanium technology. These data were used to explore the functional annotation of 45,187 fully annotated contigs. We further compiled a genetic-variation resource of 7,155 simple-sequence repeats (SSRs) and 6,500 single-nucleotide polymorphisms (SNPs). A subset of 480 SNPs was sampled to investigate the genetic structure of the broad pomegranate germplasm collection at the Agricultural Research Organization (ARO), which includes accessions from different geographical areas worldwide. This subset of SNPs was found to be polymorphic, with 10.7% loci with minor allele frequencies of (MAF<0.05). These SNPs were successfully used to classify the ARO pomegranate collection into two major groups of accessions: one from India, China and Iran, composed of mainly unknown country origin and which was more of an admixture than the other major group, composed of accessions mainly from the Mediterranean basin, Central Asia and California. This study establishes a high-throughput transcriptome and genetic-marker infrastructure. Moreover, it sheds new light on the genetic interrelations between pomegranate species worldwide and more accurately defines their genetic nature.</p></div

Molecular Logic of Neuronal Self-Recognition through Protocadherin Domain Interactions

SummarySelf-avoidance, a process preventing interactions of axons and dendrites from the same neuron during development, is mediated in vertebrates through the stochastic single-neuron expression of clustered protocadherin protein isoforms. Extracellular cadherin (EC) domains mediate isoform-specific homophilic binding between cells, conferring cell recognition through a poorly understood mechanism. Here, we report crystal structures for the EC1–EC3 domain regions from four protocadherin isoforms representing the α, β, and γ subfamilies. All are rod shaped and monomeric in solution. Biophysical measurements, cell aggregation assays, and computational docking reveal that trans binding between cells depends on the EC1–EC4 domains, which interact in an antiparallel orientation. We also show that the EC6 domains are required for the formation of cis-dimers. Overall, our results are consistent with a model in which protocadherin cis-dimers engage in a head-to-tail interaction between EC1–EC4 domains from apposed cell surfaces, possibly forming a zipper-like protein assembly, and thus providing a size-dependent self-recognition mechanism

Pomegranate gene ontology categories.

<p>Contigs were annotated by blast hits against the nr database and mapped to GO categories by Blast2GO. The distribution of contigs into the GO categories biological processes, molecular functions, and cellular components is plotted for a pool of tissues (root, leaf, flower and fruit developmental stage 3; red bars) and from peel (turquoise bars).</p