IU Indianapolis ScholarWorks :: Browsing by Author "Guo, Yan"

Browsing by Author "Guo, Yan"

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Correction to: The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomic
(BMJ Publishing Group, 2020-05-06) Mathé, Ewy; Zhang, Chi; Wang, Kai; Ning, Xia; Guo, Yan; Zhao, Zhongming; Medical and Molecular Genetics, School of Medicine
After publication of this supplement article [1], it is requested the grant ID in the Funding section should be corrected from NSF grant IIS-7811367 to NSF grant IIS-1902617. Therefore, the correct ‘Funding’ section in this article should read: This article has not received sponsorship for publication. We thank the National Science Foundation (NSF grant IIS-1902617) and the Data Science and Informatics Core for Cancer Research (CPRIT grant RP170668) for the financial support of ICIBM 2019, as well as the support from Cancer Prevention and Research Institute of Texas (RP180734).
Genomic Profiling of T-Cell Neoplasms Reveals Frequent JAK1 and JAK3 Mutations With Clonal Evasion From Targeted Therapies
(American Society of Clinical Oncology, 2018) Greenplate, Allison; Wang, Kai; Tripathi, Rati M.; Palma, Norma; Ali, Siraj M.; Stephens, Phil J.; Miller, Vincent A.; Shyr, Yu; Guo, Yan; Reddy, Nishitha M.; Kozhaya, Lina; Unutmaz, Derya; Chen, Xueyan; Irish, Jonathan M.; Davé, Utpal P.; Medicine, School of Medicine
Purpose: The promise of precision oncology is that identification of genomic alterations will direct the rational use of molecularly targeted therapy. This approach is particularly applicable to neoplasms that are resistant to standard cytotoxic chemotherapy, like T-cell leukemias and lymphomas. In this study, we tested the feasibility of targeted next-generation sequencing in profiles of diverse T-cell neoplasms and focused on the therapeutic utility of targeting activated JAK1 and JAK3 in an index case. Patients and Methods: Using Foundation One and Foundation One Heme assays, we performed genomic profiling on 91 consecutive T-cell neoplasms for alterations in 405 genes. The samples were sequenced to high uniform coverage with an Illumina HiSeq and averaged a coverage depth of greater than 500× for DNA and more than 8M total pairs for RNA. An index case of T-cell prolymphocytic leukemia (T-PLL), which was analyzed by targeted next-generation sequencing, is presented. T-PLL cells were analyzed by RNA-seq, in vitro drug testing, mass cytometry, and phospho-flow. Results: One third of the samples had genomic aberrations in the JAK-STAT pathway, most often composed of JAK1 and JAK3 gain-of-function mutations. We present an index case of a patient with T-PLL with a clonal JAK1 V658F mutation that responded to ruxolitinib therapy. After relapse developed, an expanded clone that harbored mutant JAK3 M511I and downregulation of the phosphatase, CD45, was identified. We demonstrate that the JAK missense mutations were activating, caused pathway hyperactivation, and conferred cytokine hypersensitivity. Conclusion: These results underscore the utility of profiling occurrences of resistance to standard regimens and support JAK enzymes as rational therapeutic targets for T-cell leukemias and lymphomas.
High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes
(Elsevier, 2023) Duan, Yuan-Yuan; Chen, Xiao-Feng; Zhu, Ren-Jie; Jia, Ying-Ying; Huang, Xiao-Ting; Zhang, Meng; Yang, Ning; Dong, Shan-Shan; Zeng, Mengqi; Feng, Zhihui; Zhu, Dong-Li; Wu, Hao; Jiang, Feng; Shi, Wei; Hu, Wei-Xin; Ke, Xin; Chen, Hao; Liu, Yunlong; Jing, Rui-Hua; Guo, Yan; Li, Meng; Yang, Tie-Lin; Medical and Molecular Genetics, School of Medicine
Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.
Hypoplastic Left Heart Syndrome Sequencing Reveals a Novel NOTCH1 Mutation in a Family with Single Ventricle Defects
(Springer Nature, 2017-08) Durbin, Matthew D.; Cadar, Adrian G.; Williams, Charles H.; Guo, Yan; Bichell, David P.; Su, Yan Ru; Hong, Charles C.; Pediatrics, School of Medicine
Hypoplastic left heart syndrome (HLHS) has been associated with germline mutations in 12 candidate genes and a recurrent somatic mutation in HAND1 gene. Using targeted and whole exome sequencing (WES) of heart tissue samples from HLHS patients, we sought to estimate the prevalence of somatic and germline mutations associated with HLHS. We performed Sanger sequencing of the HAND1 gene on 14 ventricular (9 LV and 5 RV) samples obtained from HLHS patients, and WES of 4 LV, 2 aortic, and 4 matched PBMC samples, analyzing for sequence discrepancy. We also screened for mutations in the 12 candidate genes implicated in HLHS. We found no somatic mutations in our HLHS cohort. However, we detected a novel germline frameshift/stop-gain mutation in NOTCH1 in a HLHS patient with a family history of both HLHS and hypoplastic right heart syndrome (HRHS). Our study, involving one of the first familial cases of single ventricle defects linked to a specific mutation, strengthens the association of NOTCH1 mutations with HLHS and suggests that the two morphologically distinct single ventricle conditions, HLHS and HRHS, may share a common molecular and cellular etiology. Finally, somatic mutations in the LV are an unlikely contributor to HLHS.
Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue
(MDPI, 2020-04-17) Guo, Yan; Ning, Xia; Mathé, Ewy; Wang, Kai; Li, Lang; Zhang, Chi; Zhao, Zhongming; Medical and Molecular Genetics, School of Medicine
The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.
The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: genomics with bigger data and wider applications
(Biomed Central, 2019-02-04) Wu, Zhijin; Yan, Jingwen; Wang, Kai; Liu, Xiaoming; Guo, Yan; Zhi, Degui; Ruan, Jianhua; Zhao, Zhongming; BioHealth Informatics, School of Informatics and Computing
The sixth International Conference on Intelligent Biology and Medicine (ICIBM) took place in Los Angeles, California, USA on June 10-12, 2018. This conference featured eleven regular scientific sessions, four tutorials, one poster session, four keynote talks, and four eminent scholar talks. The scientific program covered a wide range of topics from bench to bedside, including 3D Genome Organization, reconstruction of large scale evolution of genomes and gene functions, artificial intelligence in biological and biomedical fields, and precision medicine. Both method development and application in genomic research continued to be a main component in the conference, including studies on genetic variants, regulation of transcription, genetic-epigenetic interaction at both single cell and tissue level and artificial intelligence. Here, we write a summary of the conference and also briefly introduce the four high quality papers selected to be published in BMC Genomics that cover novel methodology development or innovative data analysis.
The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): computational methods and applications in medical genomics
(BMC, 2020) Zhang, Chi; Mathé, Ewy; Ning, Xia; Zhao, Zhongming; Wang, Kai; Li, Lang; Guo, Yan; Medical and Molecular Genetics, School of Medicine
In this editorial, we briefly summarized the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9-11, 2019 at Columbus, Ohio, USA. We further introduced the 19 research articles included in this supplement issue, covering four major areas, namely computational method development, genomics analysis, network-based analysis and biomarker prediction. The selected papers perform cutting edge computational research applied to a broad range of human diseases such as cancer, neural degenerative and chronic inflammatory disease. They also proposed solutions for fundamental medical genomics problems range from basic data processing and quality control to functional interpretation, biomarker and drug prediction, and database releasing.
The International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019): conference summary and innovations in genomics
(Springer BioMed Central, 2019-12-30) Mathé, Ewy; Zhang, Chi; Wang, Kai; Ning, Xia; Guo, Yan; Zhao, Zhongming; Medical and Molecular Genetics, School of Medicine
The goal of this editorial is to summarize the 2019 International Conference on Intelligent Biology and Medicine (ICIBM 2019) conference that took place on June 9–11, 2019 in The Ohio State University, Columbus, OH, and to provide an introductory summary of the seven articles presented in this supplement issue. ICIBM 2019 hosted four keynote speakers, four eminent scholar speakers, five tutorials and workshops, twelve concurrent sessions and a poster session, totaling 23 posters, spanning state-of-the-art developments in bioinformatics, genomics, next-generation sequencing (NGS) analysis, scientific databases, cancer and medical genomics, and computational drug discovery. A total of 105 original manuscripts were submitted to ICIBM 2019, and after careful review, seven were selected for this supplement issue. These articles cover methods and applications for functional annotations of miRNA targeting, clonal evolution of bacterial cells, gene co-expression networks that describe a given phenotype, functional binding site analysis of RNA-binding proteins, normalization of genome architecture mapping data, sample predictions based on multiple NGS data types, and prediction of an individual’s genetic admixture given exonic single nucleotide polymorphisms data.
LDB1 Enforces Stability on Direct and Indirect Oncoprotein Partners in Leukemia
(American Society for Microbiology., 2020-05) Layer, Justin H.; Christy, Michael; Placek, Lindsey; Unutmaz, Derya; Guo, Yan; Davé, Utpal P.; Medicine, School of Medicine
The LMO2/LDB1 macromolecular complex is critical in hematopoietic stem and progenitor cell specification and in the development of acute leukemia. This complex is comprised of core subunits of LMO2 and LDB1 as well as single-stranded DNA-binding protein (SSBP) cofactors and DNA-binding basic helix-loop-helix (bHLH) and GATA transcription factors. We analyzed the steady-state abundance and kinetic stability of LMO2 and its partners via Halo protein tagging in conjunction with variant proteins deficient in binding their respective direct protein partners. We discovered a hierarchy of protein stabilities (with half-lives in descending order) as follows: LDB1 > SSBP > LMO2 > TAL1. Importantly, LDB1 is a remarkably stable protein that confers enhanced stability upon direct and indirect partners, thereby nucleating the formation of the multisubunit protein complex. The data imply that free subunits are more rapidly degraded than those incorporated within the LMO2/LDB1 complex. Our studies provided significant insights into LMO2/LDB1 macromolecular protein complex assembly and stability, which has implications for understanding its role in blood cell formation and for therapeutically targeting this complex in human leukemias.

Browsing by Author "Guo, Yan"

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