Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 26438525

Ptpn22 and Cd2 Variations Are Associated with Altered Protein Expression and Susceptibility to Type 1 Diabetes in Nonobese Diabetic Mice.

Heather I Fraser·Sarah Howlett·Jan Clark·Daniel B Rainbow·Stephanie M Stanford·Dennis J Wu·Yi-Wen Hsieh·Christian J Maine·Mikkel Christensen·Vijay Kuchroo·Linda A Sherman·Patricia L Podolin·John A Todd·Charles A Steward·Laurence B Peterson·Nunzio Bottini·Linda S Wicker
View original source ↗Export metadata ↓

Abstract

By congenic strain mapping using autoimmune NOD.C57BL/6J congenic mice, we demonstrated previously that the type 1 diabetes (T1D) protection associated with the insulin-dependent diabetes (Idd)10 locus on chromosome 3, originally identified by linkage analysis, was in fact due to three closely linked Idd loci: Idd10, Idd18.1, and Idd18.3. In this study, we define two additional Idd loci--Idd18.2 and Idd18.4--within the boundaries of this cluster of disease-associated genes. Idd18.2 is 1.31 Mb and contains 18 genes, including Ptpn22, which encodes a phosphatase that negatively regulates T and B cell signaling. The human ortholog of Ptpn22, PTPN22, is associated with numerous autoimmune diseases, including T1D. We, therefore, assessed Ptpn22 as a candidate for Idd18.2; resequencing of the NOD Ptpn22 allele revealed 183 single nucleotide polymorphisms with the C57BL/6J (B6) allele--6 exonic and 177 intronic. Functional studies showed higher expression of full-length Ptpn22 RNA and protein, and decreased TCR signaling in congenic strains with B6-derived Idd18.2 susceptibility alleles. The 953-kb Idd18.4 locus contains eight genes, including the candidate Cd2. The CD2 pathway is associated with the human autoimmune disease, multiple sclerosis, and mice with NOD-derived susceptibility alleles at Idd18.4 have lower CD2 expression on B cells. Furthermore, we observed that susceptibility alleles at Idd18.2 can mask the protection provided by Idd10/Cd101 or Idd18.1/Vav3 and Idd18.3. In summary, we describe two new T1D loci, Idd18.2 and Idd18.4, candidate genes within each region, and demonstrate the complex nature of genetic interactions underlying the development of T1D in the NOD mouse model.

Explore related subjects

AllelesAnimalsB-LymphocytesCD2 AntigensChromosomes, MammalianDiabetes Mellitus, Type 1Gene Expression RegulationGenetic LociGenetic Predisposition to DiseaseHumansMiceMice, Inbred NODMice, TransgenicMolecular Sequence DataPolymorphism, Single NucleotideProtein Tyrosine Phosphatase, Non-Receptor Type 22Signal TransductionT-LymphocytesJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Heather I Fraser, Sarah Howlett, Jan Clark, Daniel B Rainbow, Stephanie M Stanford, Dennis J Wu, Yi-Wen Hsieh, Christian J Maine, Mikkel Christensen, Vijay Kuchroo, Linda A Sherman, Patricia L Podolin, John A Todd, Charles A Steward, Laurence B Peterson, Nunzio Bottini, Linda S Wicker. 2015-10-05. Ptpn22 and Cd2 Variations Are Associated with Altered Protein Expression and Susceptibility to Type 1 Diabetes in Nonobese Diabetic Mice.. https://doi.org/10.4049/jimmunol.1402654

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A de novo algorithm for allele reconstruction from Oxford nanopore amplicon reads, with application to CYP2D6.

MOTIVATION: The Oxford Nanopore Technologies' sequencing platform offers a path towards bedside genomics, producing long reads that can completely cover a gene of interest, and detect any known or novel variant the gene contains. However, the analysis of these long reads to identify actionable genotypes remains challenging and typically requires customization depending on the target gene. RESULTS: Here, we describe a generic algorithm to accurately reconstruct allele sequences derived from long-reads of amplicon-based data. Rather than calling variants directly from these long-reads, our method takes a "sequence-first" approach, performing an unbiased reconstruction of the underlying amplicon sequences to generate high-confidence reconstructed allele sequences. This is done without user input of the target gene, allowing for any source amplicon to be reconstructed. These high-confidence reconstructed allele sequences are then compared to the genomic reference sequence of the gene to infer the specific diplotype present in the sample. This approach is agnostic towards the number of genes and alleles present and readily detects novel variants. We demonstrate our approach using three independent data sets for CYP2D6, a diverse and complex gene with over 175 known alleles of clinical significance. We show how our approach can accurately recover validated CYP2D6 diplotypes from 20 Coriell samples covering 14 distinct alleles, using different amplicons, flow cell versions, and depths. This includes inferring occurrences of allele duplication events from relative abundances of each allele, a critical factor for ascribing functional effects to a diplotype. Further, we demonstrate our approach's utility for other genomic regions, including HLA. AVAILABILITY: Custom code is available at the following GitHub repository, along with instructions for use and test data: https://github.com/scottdbrown/allele-reconstruction-long-read-amplicon-data. A snapshot of the code at the time of publication is available on Zenodo.org; doi 10.5281/zenodo.19716004. Raw .fastq sequence data for our three sequencing runs is available at the SRA under Bioproject PRJNA1357883 (https://www.ncbi.nlm.nih.gov/bioproject/1357883).

Alleles↗
PubMed2026

Supergene control of chiral development in mirror-image flowers.

How genes determine the development of chiral structures is a fascinating question. The reciprocal placement of female and male organs on opposite sides of mirror-image flowers promotes efficient cross-pollination. Here, we identified that in butterfly lilies, female and male organs deflect by a combination of genetically controlled chirality and gravitropism, orienting left and right with respect to an external rather than internal reference axis. We found coordinated organ placement to be controlled by a hemizygous supergene containing two candidate causal loci, MIR156-R and YUCCA-R, that are responsible for opposite female and male organ orientation, respectively. The resulting differential placement of pollen carrying the two supergene alleles on pollinators' bodies leads to their transfer to the stigmas of flowers with opposite handedness and maintenance of the reproductive polymorphism.

Alleles↗
PubMed2026

DirectASRM: uncovering allele-specific post-transcriptional RNA modifications through direct RNA sequencing.

SUMMARY: We developed DirectASRM, a comprehensive database for the systematic identification, integration, and annotation of allele-specific RNA modifications (ASRMs) from direct RNA sequencing data. DirectASRM enables single-base, transcript-level detection of ASRMs across multiple RNA modification types, diverse organisms and condition-specific contexts. The database further evaluates the confidence of each ASRM-SNP pair association within isoform context by jointly considering statistical evidence of allelic modification imbalance and independent support from external next-generation sequencing (NGS) - based RNA modification resources. DirectASRM also provides extensive functional annotations for ASRMs and their associated variants, including intra-sample transcript-level allele-specific expression (ASE) and allele-specific splicing, as well as additional post-transcriptional regulatory features such as miRNA binding, circRNA, RNA-protein interactions, and disease relevance. Overall, DirectASRM serves as a comprehensive resource that supports systematic investigation of the potential functional impact of genetic variants in epitranscriptomic regulation. AVAILABILITY AND IMPLEMENTATION: DirectASRM database is freely accessible at http://modinfor.com/DirectASRM/. DirectASRM pipeline is available at GitHub (https://github.com/jiayin1101/DirectASRM_pipeline) and Zenodo (DOI: https://doi.org/10.5281/zenodo.19876077).

Alleles↗