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Genome-wide association studies have identified 2p13.1 as a prominent susceptibility locus for systemic lupus erythematosus (SLE)—a complex, multisystem autoimmune disease. However, the identity of underlying causal variant (s) and molecular mechanisms for increasing disease susceptibility are poorly understood. Using meta-analysis (cases = 10,252, controls = 21,604) followed by conditional analysis, bioinformatic annotation, and eQTL and 3D-chromatin interaction analyses, we computationally prioritized potential functional variants and subsequently experimentally validated their effects. Ethnicity-specific meta-analysis revealed striking allele frequency differences between Asian and European ancestries, but with similar odds ratios. We identified 20 genome-wide significant (p < 5 × 10−8) variants, and conditional analysis pinpointed two potential functional variants, rs6705628 and rs2272165, likely to explain the association. The two SNPs are near DGUOK, mitochondrial deoxyguanosine kinase, and its associated antisense RNA DGUOK-AS1. Using luciferase reporter gene assays, we found significant cell type- and allele-specific promoter activity at rs6705628 and enhancer activity at rs2272165. This is supported by ChIP-qPCR showing allele-specific binding with three histone marks (H3K27ac, H3K4me3, and H3K4me1), RNA polymerase II (Pol II), transcriptional coactivator p300, CCCTC-binding factor (CTCF), and transcription factor ARID3A. Transcriptome data across 28 immune cell types from Asians showed both SNPs are cell-type-specific but only in B-cells. Splicing QTLs showed strong regulation of DGUOK-AS1. Genotype-specific DGOUK protein levels are supported by Western blots. Promoter capture Hi-C data revealed long-range chromatin interactions between rs2272165 and several nearby promoters, including DGUOK. Taken together, we provide mechanistic insights into how two noncoding variants underlie SLE risk at the 2p13.1 locus.

Objective: Systemic lupus erythematosus (SLE), an autoimmune disease with incompletely understood etiology, has a strong genetic component. Although genome‐wide association studies (GWASs) have revealed multiple SLE susceptibility loci and associated single‐nucleotide polymorphisms (SNPs), the precise causal variants, target genes, cell types, tissues, and mechanisms of action remain largely unknown. Methods: Here, we report a comprehensive post‐GWAS analysis using extensive bioinformatics, molecular modeling, and integrative functional genomic and epigenomic analyses to optimize fine‐mapping. We compile and cross‐reference immune cell–specific expression quantitative trait loci (cis– and trans–expression quantitative trait loci) with promoter capture high‐throughput capture chromatin conformation (PCHi‐C), allele‐specific chromatin accessibility, and massively parallel reporter assay data to define predisposing variants and target genes. We experimentally validate a predicted locus using CRISPR/Cas9 genome editing, quantitative polymerase chain reaction, and Western blot. Results: Anchoring on 452 index SNPs, we selected 9,931 high linkage disequilibrium (r2 > 0.8) SNPs and defined 182 independent non‐human leukocyte antigen (HLA) SLE loci. The 3,746 SNPs from 143 loci were identified as regulating 564 unique genes. Target genes are enriched in lupus‐related tissues and associated with other autoimmune diseases. Of these, 329 SNPs (106 loci) showed significant allele‐specific chromatin accessibility and/or enhancer activity, indicating regulatory potential. Using CRISPR/Cas9, we validated reference SNP identifier 57668933 (rs57668933) as a functional variant regulating multiple targets, including SLE‐risk gene ELF1 in B cells. Conclusion: We demonstrate and validate post‐GWAS strategies for using multidimensional data to prioritize likely causal variants with cognate gene targets underlying SLE pathogenesis. Our results provide a catalog of significantly SLE‐associated SNPs and loci, target genes, and likely biochemical mechanisms to guide experimental characterization. [ABSTRACT FROM AUTHOR]

Fascaplysin has been reported to exert anti-cancer effects by inhibiting cyclin-dependent kinase 4 (CDK4); however, the precise mode of action by which fascaplysin suppresses tumor growth is not clear. Here, we found that fascaplysin has stronger anti-cancer effects than other CDK4 inhibitors, including PD0332991 and LY2835219, on lung cancer cells that are wild-type or null for retinoblastoma (RB), indicating that unknown target molecules might be involved in the inhibition of tumor growth by fascaplysin. Fascaplysin treatment significantly decreased tumor angiogenesis and increased cleaved-caspase-3 in xenografted tumor tissues. In addition, survivin and HIF-1α were downregulated in vitro and in vivo by suppressing 4EBP1-p70S6K1 axis-mediated de novo protein synthesis. Kinase screening assays and drug-protein docking simulation studies demonstrated that fascaplysin strongly inhibited vascular endothelial growth factor receptor 2 (VEGFR2) and tropomyosin-related kinase A (TRKA) via DFG-out non-competitive inhibition. Overall, these results suggest that fascaplysin inhibits TRKA and VEGFR2 and downregulates survivin and HIF-1α, resulting in suppression of tumor growth. Fascaplysin, therefore, represents a potential therapeutic approach for the treatment of multiple types of solid cancer.

There is currently a transformed interest toward understanding the impact of fermentation on functional food development due to growing consumer interest on modified health benefits of sustainable foods. In this review, we attempt to summarize recent findings regarding the impact of Next-generation sequencing and other bioinformatics methods in the food microbiome and use prediction software to understand the critical role of microbes in producing fermented foods. Traditionally, fermentation methods and starter culture development were considered conventional methods needing optimization to eliminate errors in technique and were influenced by technical knowledge of fermentation. Recent advances in high-output omics innovations permit the implementation of additional logical tactics for developing fermentation methods. Further, the review describes the multiple functions of the predictions based on docking studies and the correlation of genomic and metabolomic analysis to develop trends to understand the potential food microbiome interactions and associated products to become a part of a healthy diet. [ABSTRACT FROM AUTHOR]