Browsing by Author "Sangani, Neel"

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    FOXP3 exon 2 controls Treg stability and autoimmunity
    (American Association for the Advancement of Science, 2022) Du, Jianguang; Wang, Qun; Yang, Shuangshuang; Chen, Si; Fu, Yongyao; Spath, Sabine; Domeier, Phillip; Hagin, David; Anover-Sombke, Stephanie; Haouili, Maya; Liu, Sheng; Wan, Jun; Han, Lei; Liu, Juli; Yang, Lei; Sangani, Neel; Li, Yujing; Lu, Xiongbin; Janga, Sarath Chandra; Kaplan, Mark H.; Torgerson, Troy R.; Ziegler, Steven F.; Zhou, Baohua; Pediatrics, School of Medicine
    Differing from the mouse Foxp3 gene that encodes only one protein product, human FOXP3 encodes two major isoforms through alternative splicing-a longer isoform (FOXP3 FL) containing all the coding exons and a shorter isoform lacking the amino acids encoded by exon 2 (FOXP3 ΔE2). The two isoforms are naturally expressed in humans, yet their differences in controlling regulatory T cell phenotype and functionality remain unclear. In this study, we show that patients expressing only the shorter isoform fail to maintain self-tolerance and develop immunodeficiency, polyendocrinopathy, and enteropathy X-linked (IPEX) syndrome. Mice with Foxp3 exon 2 deletion have excessive follicular helper T (TFH) and germinal center B (GC B) cell responses, and develop systemic autoimmune disease with anti-dsDNA and antinuclear autoantibody production, as well as immune complex glomerulonephritis. Despite having normal suppressive function in in vitro assays, regulatory T cells expressing FOXP3 ΔE2 are unstable and sufficient to induce autoimmunity when transferred into Tcrb-deficient mice. Mechanistically, the FOXP3 ΔE2 isoform allows increased expression of selected cytokines, but decreased expression of a set of positive regulators of Foxp3 without altered binding to these gene loci. These findings uncover indispensable functions of the FOXP3 exon 2 region, highlighting a role in regulating a transcriptional program that maintains Treg stability and immune homeostasis.
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    Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease
    (Wiley, 2024) He, Bing; Wu, Ruiming; Sangani, Neel; Pugalenthi, Pradeep Varathan; Patania, Alice; Risacher, Shannon L.; Nho, Kwangsik; Apostolova, Liana G.; Shen, Li; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Introduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. Discussion: Our risk score holds great potential to improve the precision of early risk assessment. Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.
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    Trancriptome-Wide Applications of Protein Occupancy Profile Sequencing (POP-seq)
    (2023-06) Sangani, Neel; Janga, Sarath Chandra; Yan, Jingwen; Srivastava, Mansi
    Dynamic protein-RNA interactions regulate RNA metabolism and alter cellular physiology by altering key regulatory processes such as capping, splicing, polyadenylation, and localization. Several high throughput methods have been developed to detect protein-RNA interactions, but they often exhibit biases due to the inherent limitations of crosslinking-based approaches. We propose Protein Occupancy Profile-Sequencing (POP-seq), a phase separation-based method that does not require crosslinking to detect protein occupancy transcriptome wide. In this study, we employed POP-seq to examine the unbiased regulatory protein-RNA interactions in the following cancer cell lines: K562, HepG2, A549, MCF7, Jurkat, and HEK293. In our preliminary analysis, we performed a comparison of the POP-seq identified interactions using two protocols, one involving UV crosslinking (UPOP-seq) and the other with no-crosslinking (NPOP-seq), in K562 and HepG2 cells. This comparative analysis of two protocol showed >70% overlapping genes detected by both approaches in the two cell lines. Most of these peaks were mapped to intronic regions of the protein coding gene. Concurrently, we also implemented this crosslinking free approach on two leukemia cell lines: Jurkat and K562. Differential analysis shows higher binding activity in Jurkat compared to K562 with majority of the peaks spanned over intronic protein coding region followed by SINE and LINE. Differential proximal binding analysis shows that SE events followed by A3SS events plays a major role in alternative splicing suggesting enriched regions plays vital role in cellular functions including post-transcriptional regulation of gene expression. Motif analysis shows clinically relevant significant motif enrichment of POP-seq identified peaks. This study was further expanded by adding three human additional cell lines: MCF7, A459, and HEK293. Differential peak analysis across cell lines revealed a closer association between A549 and MCF7 cells based on the normalized POP-seq peaks per gene. We observed that genes associated with differential peaks between cell lines exhibited enrichment for crucial cellular functions, particularly in the post-transcriptional regulation of gene expression. Our analysis unveiled a notable enrichment of specific motifs within the identified peaks obtained from POP-seq. These overrepresented motifs were significantly linked to somatic variation, phenotypic variation (Phenvar), clinical variation (Clinvar), GWAS, and allele-specific expression (ASE), with a preferential abundance of the motifs on the C and G bases. Additionally, our alternative splicing analysis revealed that POP-seq detected protein-RNA interactions that substantially contributed to splicing events in certain cell line pairs, while their impact was less pronounced in others. Overall, our study offers the first extensive dataset of protein-RNA interaction maps across the transcriptome in multiple cell lines, utilizing a crosslinking-free approach. This valuable resource not only provides comprehensive insights into regulatory interactions but also opens new possibilities for applying this method in primary tissues to detect and study protein-RNA interactions in a broader biological context.