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  1. Molineros JE, Yang W, Zhou XJ, Sun C, Okada Y, Zhang H, et al.
    Hum Mol Genet, 2017 03 15;26(6):1205-1216.
    PMID: 28108556 DOI: 10.1093/hmg/ddx026
    We recently identified ten novel SLE susceptibility loci in Asians and uncovered several additional suggestive loci requiring further validation. This study aimed to replicate five of these suggestive loci in a Han Chinese cohort from Hong Kong, followed by meta-analysis (11,656 cases and 23,968 controls) on previously reported Asian and European populations, and to perform bioinformatic analyses on all 82 reported SLE loci to identify shared regulatory signatures. We performed a battery of analyses for these five loci, as well as joint analyses on all 82 SLE loci. All five loci passed genome-wide significance: MYNN (rs10936599, Pmeta = 1.92 × 10-13, OR = 1.14), ATG16L2 (rs11235604, Pmeta = 8.87 × 10 -12, OR = 0.78), CCL22 (rs223881, Pmeta = 5.87 × 10-16, OR = 0.87), ANKS1A (rs2762340, Pmeta = 4.93 × 10-15, OR = 0.87) and RNASEH2C (rs1308020, Pmeta = 2.96 × 10-19, OR = 0.84) and co-located with annotated gene regulatory elements. The novel loci share genetic signatures with other reported SLE loci, including effects on gene expression, transcription factor binding, and epigenetic characteristics. Most (56%) of the correlated (r2 > 0.8) SNPs from the 82 SLE loci were implicated in differential expression (9.81 × 10-198 
  2. Sun C, Molineros JE, Looger LL, Zhou XJ, Kim K, Okada Y, et al.
    Nat Genet, 2016 Mar;48(3):323-30.
    PMID: 26808113 DOI: 10.1038/ng.3496
    Systemic lupus erythematosus (SLE) has a strong but incompletely understood genetic architecture. We conducted an association study with replication in 4,478 SLE cases and 12,656 controls from six East Asian cohorts to identify new SLE susceptibility loci and better localize known loci. We identified ten new loci and confirmed 20 known loci with genome-wide significance. Among the new loci, the most significant locus was GTF2IRD1-GTF2I at 7q11.23 (rs73366469, Pmeta = 3.75 × 10(-117), odds ratio (OR) = 2.38), followed by DEF6, IL12B, TCF7, TERT, CD226, PCNXL3, RASGRP1, SYNGR1 and SIGLEC6. We identified the most likely functional variants at each locus by analyzing epigenetic marks and gene expression data. Ten candidate variants are known to alter gene expression in cis or in trans. Enrichment analysis highlights the importance of these loci in B cell and T cell biology. The new loci, together with previously known loci, increase the explained heritability of SLE to 24%. The new loci share functional and ontological characteristics with previously reported loci and are possible drug targets for SLE therapeutics.
  3. Molineros JE, Looger LL, Kim K, Okada Y, Terao C, Sun C, et al.
    PLoS Genet, 2019 04;15(4):e1008092.
    PMID: 31022184 DOI: 10.1371/journal.pgen.1008092
    Human leukocyte antigen (HLA) is a key genetic factor conferring risk of systemic lupus erythematosus (SLE), but precise independent localization of HLA effects is extremely challenging. As a result, the contribution of specific HLA alleles and amino-acid residues to the overall risk of SLE and to risk of specific autoantibodies are far from completely understood. Here, we dissected (a) overall SLE association signals across HLA, (b) HLA-peptide interaction, and (c) residue-autoantibody association. Classical alleles, SNPs, and amino-acid residues of eight HLA genes were imputed across 4,915 SLE cases and 13,513 controls from Eastern Asia. We performed association followed by conditional analysis across HLA, assessing both overall SLE risk and risk of autoantibody production. DR15 alleles HLA-DRB1*15:01 (P = 1.4x10-27, odds ratio (OR) = 1.57) and HLA-DQB1*06:02 (P = 7.4x10-23, OR = 1.55) formed the most significant haplotype (OR = 2.33). Conditioned protein-residue signals were stronger than allele signals and mapped predominantly to HLA-DRB1 residue 13 (P = 2.2x10-75) and its proxy position 11 (P = 1.1x10-67), followed by HLA-DRB1-37 (P = 4.5x10-24). After conditioning on HLA-DRB1, novel associations at HLA-A-70 (P = 1.4x10-8), HLA-DPB1-35 (P = 9.0x10-16), HLA-DQB1-37 (P = 2.7x10-14), and HLA-B-9 (P = 6.5x10-15) emerged. Together, these seven residues increased the proportion of explained heritability due to HLA to 2.6%. Risk residues for both overall disease and hallmark autoantibodies (i.e., nRNP: DRB1-11, P = 2.0x10-14; DRB1-13, P = 2.9x10-13; DRB1-30, P = 3.9x10-14) localized to the peptide-binding groove of HLA-DRB1. Enrichment for specific amino-acid characteristics in the peptide-binding groove correlated with overall SLE risk and with autoantibody presence. Risk residues were in primarily negatively charged side-chains, in contrast with rheumatoid arthritis. We identified novel SLE signals in HLA Class I loci (HLA-A, HLA-B), and localized primary Class II signals to five residues in HLA-DRB1, HLA-DPB1, and HLA-DQB1. These findings provide insights about the mechanisms by which the risk residues interact with each other to produce autoantibodies and are involved in SLE pathophysiology.
  4. Klionsky DJ, Abdel-Aziz AK, Abdelfatah S, Abdellatif M, Abdoli A, Abel S, et al.
    Autophagy, 2021 Jan;17(1):1-382.
    PMID: 33634751 DOI: 10.1080/15548627.2020.1797280
    In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
  5. Klionsky DJ, Abdelmohsen K, Abe A, Abedin MJ, Abeliovich H, Acevedo Arozena A, et al.
    Autophagy, 2016;12(1):1-222.
    PMID: 26799652 DOI: 10.1080/15548627.2015.1100356
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