Browsing by Author "Zhang, Fan"
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Item BREAST CANCER-ASSOCIATED MISSENSE MUTANTS OF THE PALB2 WD40 DOMAIN, WHICH DIRECTLY BINDS RAD51C, RAD51 AND BRCA2, DISRUPT DNA REPAIR(Nature Publishing Group, 2014-10-02) Park, Jung-Young; Singh, Thiyam R.; Nassar, Nicolas; Zhang, Fan; Freund, Marcel; Hanenberg, Helmut; Meetei, Amom Ruhikanta; Andreassen, Paul R.; Department of Pediatrics, IU School of MedicineHeterozygous carriers of germ-line mutations in the BRCA2/FANCD1, PALB2/FANCN, and RAD51C/FANCO DNA repair genes have an increased life-time risk to develop breast, ovarian and other cancers; bi-allelic mutations in these genes clinically manifest as Fanconi anemia (FA). Here, we demonstrate that RAD51C is part of a novel protein complex that contains PALB2 and BRCA2. Further, the PALB2 WD40 domain can directly and independently bind RAD51C and BRCA2. To understand the role of these homologous recombination (HR) proteins in DNA repair, we functionally characterize effects of missense mutations of the PALB2 WD40 domain that have been reported in breast cancer patients. In contrast to large truncations of PALB2, which display a complete loss of interaction, the L939W, T1030I, and L1143P missense mutants/variants of PALB2 WD40 domain are associated with altered direct binding patterns to the RAD51C, RAD51 and BRCA2 HR proteins in biochemical assays. Further, the T1030I missense mutant is unstable, while the L939W and L1143P proteins are stable but partially disrupt the PALB2-RAD51C-BRCA2 complex in cells. Functionally, the L939W and L1143P mutants display a decreased capacity for DNA double-strand break-induced HR and an increased cellular sensitivity to ionizing radiation. As further evidence for the functional importance of the HR complex, RAD51C mutants that are associated with cancer susceptibility and FA also display decreased complex formation with PALB2. Together, our results suggest that three different cancer susceptibility and FA proteins function in a DNA repair pathway based upon the PALB2 WD40 domain binding to RAD51C and BRCA2.Item Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications(Office of the Vice Chancellor for Research, 2010-04-09) Chen, Jake Yue; Wu, Xiaogang; Zhang, Fan; Pandey, Ragini; Huang, Hui; Huan, TianxiaoWith the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications.Item Discovery of pathway biomarkers from coupled proteomics and systems biology methods(BMC, 2010-11-02) Zhang, Fan; Chen, Jake Yue; BioHealth Informatics, School of Informatics and ComputingBackground: Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker. Results: In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins. Conclusions: We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.Item Distinct Roles of Brd2 and Brd4 in Potentiating the Transcriptional Program for Th17 Cell Differentiation(Cell Press, 2017-03-16) Cheung, Ka Lung; Zhang, Fan; Jaganathan, Anbalagan; Sharma, Rajal; Zhang, Qiang; Konuma, Tsuyoshi; Shen, Tong; Lee, June-Yong; Ren, Chunyan; Chen, Chih-Hung; Lu, Geming; Olson, Matthew R.; Zhang, Weijia; Kaplan, Mark H.; Littman, Dan R.; Walsh, Martin J.; Xiong, Huabao; Zeng, Lei; Zhou, Ming-Ming; Pediatrics, School of MedicineThe BET proteins are major transcriptional regulators and have emerged as new drug targets, but their functional distinction has remained elusive. In this study, we report that the BET family members Brd2 and Brd4 exert distinct genomic functions at genes whose transcription they co-regulate during mouse T-helper 17 (Th17) cell differentiation. Brd2 is associated with the chromatin insulator CTCF and the cohesin complex to support cis-regulatory enhancer assembly for gene transcriptional activation. In this context, Brd2 binds the transcription factor Stat3 in an acetylation-sensitive manner and facilitates Stat3 recruitment to active enhancers occupied with transcription factors Irf4 and Batf. In parallel, Brd4 temporally controls RNA polymerase II (Pol II) processivity during transcription elongation through cyclinT1/Cdk9 recruitment and Pol II Ser2 phosphorylation. Collectively, our study uncovers both separate and interdependent Brd2 and Brd4 functions in potentiating the genetic program required for Th17 cell development and adaptive immunity., , Cheung et al. uncover both separate and interdependent Brd2 and Brd4 genomic functions in potentiating the genetic program required for Th17 cell development and adaptive immunity. Brd2 interacts with transcription factor Stat3 and chromatin insulator CTCF/cohesin complex to support enhancer assembly, whereas Brd4 temporally controls RNA PolII for transcription elongation.Item HIPK2 directs cell type-specific regulation of STAT3 transcriptional activity in Th17 cell differentiation(National Academy of Science, 2022) Cheung, Ka Lung; Jaganathan, Anbalagan; Hu, Yuan; Xu, Feihong; Lejeune, Alannah; Sharma, Rajal; Caescu, Cristina I.; Meslamani, Jamel; Vincek, Adam; Zhang, Fan; Lee, Kyung; Zaware, Nilesh; Qayum, Amina Abdul; Ren, Chunyan; Kaplan, Mark H.; He, John Cijiang; Xiong, Huabao; Zhou, Ming-Ming; Microbiology and Immunology, School of MedicineSignificanceSTAT3 (signal transducer and activator of transcription 3) is a master transcription factor that organizes cellular responses to cytokines and growth factors and is implicated in inflammatory disorders. STAT3 is a well-recognized therapeutic target for human cancer and inflammatory disorders, but how its function is regulated in a cell type-specific manner has been a major outstanding question. We discovered that Stat3 imposes self-directed regulation through controlling transcription of its own regulator homeodomain-interacting protein kinase 2 (Hipk2) in a T helper 17 (Th17) cell-specific manner. Our validation of the functional importance of the Stat3-Hipk2 axis in Th17 cell development in the pathogenesis of T cell-induced colitis in mice suggests an approach to therapeutically treat inflammatory bowel diseases that currently lack a safe and effective therapy.Item Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq(BMC, 2020-12-03) Zhang, Fan; Deng, Chris K.; Wang, Mu; Deng, Bin; Barber, Robert; Huang, Gang; Biochemistry and Molecular Biology, School of MedicineBackground: Alternative splicing isoforms have been reported as a new and robust class of diagnostic biomarkers. Over 95% of human genes are estimated to be alternatively spliced as a powerful means of producing functionally diverse proteins from a single gene. The emergence of next-generation sequencing technologies, especially RNA-seq, provides novel insights into large-scale detection and analysis of alternative splicing at the transcriptional level. Advances in Proteomic Technologies such as liquid chromatography coupled tandem mass spectrometry (LC-MS/MS), have shown tremendous power for the parallel characterization of large amount of proteins in biological samples. Although poor correspondence has been generally found from previous qualitative comparative analysis between proteomics and microarray data, significantly higher degrees of correlation have been observed at the level of exon. Combining protein and RNA data by searching LC-MS/MS data against a customized protein database from RNA-Seq may produce a subset of alternatively spliced protein isoform candidates that have higher confidence. Results: We developed a bioinformatics workflow to discover alternative splicing biomarkers from LC-MS/MS using RNA-Seq. First, we retrieved high confident, novel alternative splicing biomarkers from the breast cancer RNA-Seq database. Then, we translated these sequences into in silico Isoform Junction Peptides, and created a customized alternative splicing database for MS searching. Lastly, we ran the Open Mass spectrometry Search Algorithm against the customized alternative splicing database with breast cancer plasma proteome. Twenty six alternative splicing biomarker peptides with one single intron event and one exon skipping event were identified. Further interpretation of biological pathways with our Integrated Pathway Analysis Database showed that these 26 peptides are associated with Cancer, Signaling, Metabolism, Regulation, Immune System and Hemostasis pathways, which are consistent with the 256 alternative splicing biomarkers from the RNA-Seq. Conclusions: This paper presents a bioinformatics workflow for using RNA-seq data to discover novel alternative splicing biomarkers from the breast cancer proteome. As a complement to synthetic alternative splicing database technique for alternative splicing identification, this method combines the advantages of two platforms: mass spectrometry and next generation sequencing and can help identify potentially highly sample-specific alternative splicing isoform biomarkers at early-stage of cancer.Item An integrated proteomics analysis of bone tissues in response to mechanical stimulation(2010-07) Li, Jillian; Zhang, Fan; Chen, Jake YueBone cells can sense physical forces and convert mechanical stimulation conditions into biochemical signals that lead to expression of mechanically sensitive genes and proteins. However, it is still poorly understood how genes and proteins in bone cells are orchestrated to respond to mechanical stimulations. In this research, we applied integrated proteomics, statistical, and network biology techniques to study proteome-level changes to bone tissue cells in response to two different conditions, normal loading and fatigue loading. We harvested ulna midshafts and isolated proteins from the control, loaded, and fatigue loaded Rats. Using a label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) experimental proteomics technique, we derived a comprehensive list of 1,058 proteins that are differentially expressed among normal loading, fatigue loading, and controls. By carefully developing protein selection filters and statistical models, we were able to identify 42 proteins representing 21 Rat genes that were significantly associated with bone cells' response to quantitative changes between normal loading and fatigue loading conditions. We further applied network biology techniques by building a fatigue loading activated protein-protein interaction subnetwork involving 9 of the human-homolog counterpart of the 21 rat genes in a large connected network component. Our study shows that the combination of decreased anti-apoptotic factor, Raf1, and increased pro-apoptotic factor, PDCD8, results in significant increase in the number of apoptotic osteocytes following fatigue loading. We believe controlling osteoblast differentiation/proliferation and osteocyte apoptosis could be promising directions for developing future therapeutic solutions for related bone diseases.Item A method for identifying discriminative isoform-specific peptides for clinical proteomics application(BioMed Central, 2016-08-22) Zhang, Fan; Chen, Jake Yue; Department of Biohealth Informatics, IU School of Informatics and ComputingBACKGROUND: Clinical proteomics application aims at solving a specific clinical problem within the context of a clinical study. It has been growing rapidly in the field of biomarker discovery, especially in the area of cancer diagnostics. Until recently, protein isoform has not been viewed as a new class of early diagnostic biomarkers for clinical proteomics. A protein isoform is one of different forms of the same protein. Different forms of a protein may be produced from single-nucleotide polymorphisms (SNPs), alternative splicing, or post-translational modifications (PTMs). Previous studies have shown that protein isoforms play critical roles in tumorigenesis, disease diagnosis, and prognosis. Identifying and characterizing protein isoforms are essential to the study of molecular mechanisms and early detection of complex diseases such as breast cancer. However, there are limitations with traditional methods such as EST sequencing, Microarray profiling (exon array, Exon-exon junction array), mRNA next-generation sequencing used for protein isoform determination: 1) not in the protein level, 2) no connectivity about connection of nonadjacent exons, 3) no SNPs and PTMs, and 4) low reproducibility. Moreover, there exist the computational challenges of clinical proteomics studies: 1) low sensitivity of instruments, 2) high data noise, and 3) high variability and low repeatability, although recent advances in clinical proteomics technology, LC-MS/MS proteomics, have been used to identify candidate molecular biomarkers in diverse range of samples, including cells, tissues, serum/plasma, and other types of body fluids. RESULTS: Therefore, in the paper, we presented a peptidomics method for identifying cancer-related and isoform-specific peptide for clinical proteomics application from LC-MS/MS. First, we built a Peptidomic Database of Human Protein Isoforms, then created a peptidomics approach to perform large-scale screen of breast cancer-associated alternative splicing isoform markers in clinical proteomics, and lastly performed four kinds of validations: biological validation (explainable index), exon array, statistical validation of independent samples, and extensive pathway analysis. CONCLUSIONS: Our results showed that alternative splicing isoform makers can act as independent markers of breast cancer and that the method for identifying cancer-specific protein isoform biomarkers from clinical proteomics application is an effective one for increasing the number of identified alternative splicing isoform markers in clinical proteomics.Item PEPPI: a peptidomic database of human protein isoforms for proteomics experiments(BMC, 2010-10-07) Zhou, Ao; Zhang, Fan; Chen, Jake Yue; BioHealth Informatics, School of Informatics and ComputingBackground Protein isoform generation, which may derive from alternative splicing, genetic polymorphism, and posttranslational modification, is an essential source of achieving molecular diversity by eukaryotic cells. Previous studies have shown that protein isoforms play critical roles in disease diagnosis, risk assessment, sub-typing, prognosis, and treatment outcome predictions. Understanding the types, presence, and abundance of different protein isoforms in different cellular and physiological conditions is a major task in functional proteomics, and may pave ways to molecular biomarker discovery of human diseases. In tandem mass spectrometry (MS/MS) based proteomics analysis, peptide peaks with exact matches to protein sequence records in the proteomics database may be identified with mass spectrometry (MS) search software. However, due to limited annotation and poor coverage of protein isoforms in proteomics databases, high throughput protein isoform identifications, particularly those arising from alternative splicing and genetic polymorphism, have not been possible. Results Therefore, we present the PEPtidomics Protein Isoform Database (PEPPI, http://bio.informatics.iupui.edu/peppi), a comprehensive database of computationally-synthesized human peptides that can identify protein isoforms derived from either alternatively spliced mRNA transcripts or SNP variations. We collected genome, pre-mRNA alternative splicing and SNP information from Ensembl. We synthesized in silico isoform transcripts that cover all exons and theoretically possible junctions of exons and introns, as well as all their variations derived from known SNPs. With three case studies, we further demonstrated that the database can help researchers discover and characterize new protein isoform biomarkers from experimental proteomics data. Conclusions We developed a new tool for the proteomics community to characterize protein isoforms from MS-based proteomics experiments. By cataloguing each peptide configurations in the PEPPI database, users can study genetic variations and alternative splicing events at the proteome level. They can also batch-download peptide sequences in FASTA format to search for MS/MS spectra derived from human samples. The database can help generate novel hypotheses on molecular risk factors and molecular mechanisms of complex diseases, leading to identification of potentially highly specific protein isoform biomarkers.Item Plasma Total-Tau and Neurofilament Light Chain as Diagnostic Biomarkers of Alzheimer's Disease Dementia and Mild Cognitive Impairment in Adults with Down Syndrome(IOS Press, 2021) Petersen, Melissa E.; Rafii, Michael S.; Zhang, Fan; Hall, James; Julovich, David; Ances, Beau M.; Schupf, Nicole; Krinsky-McHale, Sharon J.; Mapstone, Mark; Silverman, Wayne; Lott, Ira; Klunk, William; Head, Elizabeth; Christian, Brad; Foroud, Tatiana; Lai, Florence; Rosas, H. Diana; Zaman, Shahid; Wang, Mei-Cheng; Tycko, Benjamin; Lee, Joseph H.; Handen, Benjamin; Hartley, Sigan; Fortea, Juan; O’Bryant, Sid; Alzheimer’s Biomarker Consortium – Down Syndrome (ABC-DS); Medical and Molecular Genetics, School of MedicineBackground: The need for diagnostic biomarkers of cognitive decline is particularly important among aging adults with Down syndrome (DS). Growing empirical support has identified the utility of plasma derived biomarkers among neurotypical adults with mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, the application of such biomarkers has been limited among the DS population. Objective: This study aimed to investigate the cross-sectional diagnostic performance of plasma neurofilament light chain (Nf-L) and total-tau, individually and in combination among a cohort of DS adults. Methods: Plasma samples were analyzed from n = 305 (n = 225 cognitively stable (CS); n = 44 MCI-DS; n = 36 DS-AD) participants enrolled in the Alzheimer's Biomarker Consortium -Down Syndrome. Results: In distinguishing DS-AD participants from CS, Nf-L alone produced an AUC of 90%, total-tau alone reached 74%, and combined reached an AUC of 86%. When age and gender were included, AUC increased to 93%. Higher values of Nf-L, total-tau, and age were all shown to be associated with increased risk for DS-AD. When distinguishing MCI-DS participants from CS, Nf-L alone produced an AUC of 65%, while total-tau alone reached 56%. A combined model with Nf-L, total-tau, age, and gender produced an AUC of 87%. Both higher values in age and total-tau were found to increase risk for MCI-DS; Nf-L levels were not associated with increased risk for MCI-DS. Conclusion: Advanced assay techniques make total-tau and particularly Nf-L useful biomarkers of both AD pathology and clinical status in DS and have the potential to serve as outcome measures in clinical trials for future disease-modifying drugs.