Zhide Fang

Bio: Zhide Fang is an academic researcher from LSU Health Sciences Center New Orleans. The author has contributed to research in topics: Optimal design & Polynomial regression. The author has an hindex of 18, co-authored 64 publications receiving 1453 citations. Previous affiliations of Zhide Fang include Louisiana State University & University of New Orleans.

Neoplastic reprogramming of patient-derived adipose stem cells by prostate cancer cell-associated exosomes.

Abstract: Emerging evidence suggests that mesenchymal stem cells (MSCs) are often recruited to tumor sites but their functional significance in tumor growth and disease progression remains elusive. Herein we report that prostate cancer (PC) cell microenvironment subverts PC patient adipose-derived stem cells (pASCs) to undergo neoplastic transformation. Unlike normal ASCs, the pASCs primed with PC cell conditioned media (CM) formed prostate-like neoplastic lesions in vivo and reproduced aggressive tumors in secondary recipients. The pASC tumors acquired cytogenetic aberrations and mesenchymal-to-epithelial transition and expressed epithelial, neoplastic, and vasculogenic markers reminiscent of molecular features of PC tumor xenografts. Our mechanistic studies revealed that PC cell-derived exosomes are sufficient to recapitulate formation of prostate tumorigenic mimicry generated by CM-primed pASCs in vivo. In addition to downregulation of the large tumor suppressor homolog2 and the programmed cell death protein 4, a neoplastic transformation inhibitor, the tumorigenic reprogramming of pASCs was associated with trafficking by PC cell-derived exosomes of oncogenic factors, including H-ras and K-ras transcripts, oncomiRNAs miR-125b, miR-130b, and miR-155 as well as the Ras superfamily of GTPases Rab1a, Rab1b, and Rab11a. Our findings implicate a new role for PC cell-derived exosomes in clonal expansion of tumors through neoplastic reprogramming of tumor tropic ASCs in cancer patients.

Design and validation issues in RNA-seq experiments

Abstract: The next-generation sequencing technologies are being rapidly applied in biological research. Tens of millions of short sequences generated in a single experiment provide us enormous information on genome composition, genetic variants, gene expression levels and protein binding sites depending on the applications. Various methods are being developed for analyzing the data generated by these technologies. However, the relevant experimental design issues have rarely been discussed. In this review, we use RNA-seq as an example to bring this topic into focus and to discuss experimental design and validation issues pertaining to next-generation sequencing in the quantification of transcripts.

Rnnotator: an automated de novo transcriptome assembly pipeline from stranded RNA-Seq reads.

Abstract: Comprehensive annotation and quantification of transcriptomes are outstanding problems in functional genomics. While high throughput mRNA sequencing (RNA-Seq) has emerged as a powerful tool for addressing these problems, its success is dependent upon the availability and quality of reference genome sequences, thus limiting the organisms to which it can be applied. Here, we describe Rnnotator, an automated software pipeline that generates transcript models by de novo assembly of RNA-Seq data without the need for a reference genome. We have applied the Rnnotator assembly pipeline to two yeast transcriptomes and compared the results to the reference gene catalogs of these organisms. The contigs produced by Rnnotator are highly accurate (95%) and reconstruct full-length genes for the majority of the existing gene models (54.3%). Furthermore, our analyses revealed many novel transcribed regions that are absent from well annotated genomes, suggesting Rnnotator serves as a complementary approach to analysis based on a reference genome for comprehensive transcriptomics. These results demonstrate that the Rnnotator pipeline is able to reconstruct full-length transcripts in the absence of a complete reference genome.

Sodium butyrate epigenetically modulates high-fat diet-induced skeletal muscle mitochondrial adaptation, obesity and insulin resistance through nucleosome positioning.

Abstract: Background and Purpose Sodium butyrate (NaB), an epigenetic modifier, is effective in promoting insulin sensitivity. The specific genomic loci and mechanisms underlying epigenetically induced obesity and insulin resistance and the targets of NaB are not fully understood.

Multi-State Consecutive- k -out-of- n Systems

Abstract: In the binary context, a consecutive- k -out-of- n system is failed if and only if at least k consecutive components are failed. In this paper we propose definitions of the multi-state consecutive- k -out-of- n :F and G systems. In the proposed definition, both the system and its components may be in one of M + 1 possible states: 0, 1,..., and M . The dual relationship between the proposed systems is identified. The concept of dominance is used to characterize the properties of multi-state systems. The concepts of duality, equivalence, and dominance are used in evaluation of system state distribution of multi-state consecutive- k -out-of- n systems. An algorithm is provided for evaluating system state distribution of decreasing multi-state consecutive- k -out-of- n :F systems. Another algorithm is provided to bound system state distribution of multi-state consecutive- k -out-of- n :F and G systems. Several examples are included to illustrate the proposed definitions, concepts, and algorithms.

De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis

Abstract: De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.

“Bioinformatics” 특집을 내면서

Abstract: BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

The biology, function, and biomedical applications of exosomes

Abstract: The study of extracellular vesicles (EVs) has the potential to identify unknown cellular and molecular mechanisms in intercellular communication and in organ homeostasis and disease. Exosomes, with an average diameter of ~100 nanometers, are a subset of EVs. The biogenesis of exosomes involves their origin in endosomes, and subsequent interactions with other intracellular vesicles and organelles generate the final content of the exosomes. Their diverse constituents include nucleic acids, proteins, lipids, amino acids, and metabolites, which can reflect their cell of origin. In various diseases, exosomes offer a window into altered cellular or tissue states, and their detection in biological fluids potentially offers a multicomponent diagnostic readout. The efficient exchange of cellular components through exosomes can inform their applied use in designing exosome-based therapeutics.

rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data

Abstract: Ultra-deep RNA sequencing (RNA-Seq) has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed multivariate analysis of transcript splicing (MATS), a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model and computer program, replicate MATS (rMATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to the analysis of unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The hypothesis-testing framework of rMATS is flexible and can assess the statistical significance over any user-defined magnitude of splicing change. The performance of rMATS is evaluated by the analysis of simulated and real RNA-Seq data. rMATS outperformed two existing methods for replicate RNA-Seq data in all simulation settings, and RT-PCR yielded a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. We demonstrate that it is essential to incorporate biological replicates in the study design. Of note, pooling RNAs or merging RNA-Seq data from multiple replicates is not an effective approach to account for variability, and the result is particularly sensitive to outliers. The rMATS source code is freely available at rnaseq-mats.sourceforge.net/. As the popularity of RNA-Seq continues to grow, we expect rMATS will be useful for studies of alternative splicing in diverse RNA-Seq projects.