Browsing by Author "He, Bin"
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- A large-scale RNA interference screen identifies genes that regulate autophagy at different stagesGuo, Sujuan; Pridham, Kevin J.; Virbasius, Ching-Man; He, Bin; Zhang, Liqing; Varmark, Hanne; Green, Michael R.; Sheng, Zhi (Nature Publishing Group, 2018-02-12)Dysregulated autophagy is central to the pathogenesis and therapeutic development of cancer. However, how autophagy is regulated in cancer is not well understood and genes that modulate cancer autophagy are not fully defined. To gain more insights into autophagy regulation in cancer, we performed a large-scale RNA interference screen in K562 human chronic myeloid leukemia cells using monodansylcadaverine staining, an autophagy-detecting approach equivalent to immunoblotting of the autophagy marker LC3B or fluorescence microscopy of GFP-LC3B. By coupling monodansylcadaverine staining with fluorescence-activated cell sorting, we successfully isolated autophagic K562 cells where we identified 336 short hairpin RNAs. After candidate validation using Cyto-ID fluorescence spectrophotometry, LC3B immunoblotting, and quantitative RT-PCR, 82 genes were identified as autophagy-regulating genes. 20 genes have been reported previously and the remaining 62 candidates are novel autophagy mediators. Bioinformatic analyses revealed that most candidate genes were involved in molecular pathways regulating autophagy, rather than directly participating in the autophagy process. Further autophagy flux assays revealed that 57 autophagy-regulating genes suppressed autophagy initiation, whereas 21 candidates promoted autophagy maturation. Our RNA interference screen identifies identified genes that regulate autophagy at different stages, which helps decode autophagy regulation in cancer and offers novel avenues to develop autophagy-related therapies for cancer.
- A Machine Learning Approach for Data Unification and Its Application in Asset Performance ManagementHe, Bin (Virginia Tech, 2016-03-28)The amount of data is growing fast with the advance of data capturing and management technologies. However, data from different source are often isolated and not ready to be analyzed together as one data set. The effort of connecting pieces of isolated data into a unified data set is time consuming and often costly in terms of cognitive load and programming time. To address this problem, here we proposed an approach using machine learning to augment human intelligence in the data unification process, especially complex categorical data value unification. Many aspects of useful information are extracted from supervised machine learning models, then used to amplify intelligence of human experts in various aspects of the data unification process. An empirical study is performed applying the proposed methodology to the field of Asset Performance Management, specifically focus only on the performance of equipment asset. The experiments show that machine learning helps experts in the unification standard generation, unified value suggestion, batch data unification. We conclude that machine learning models contain valuable information that can facilitate the data unification process.
- Mitotic Dynamics of Normally and Mis-attached Chromosomes and Post-mitotic Behavior of Missegregated ChromosomesHe, Bin (Virginia Tech, 2015-06-01)Equal segregation of the replicated genomic content to the two daughter cells is the major task of mitotic cells. The segregation is controlled by a complex system in the cell and relies mainly on the interaction between microtubules (MTs) of the mitotic spindle and kinetochores (KTs), specialized protein structures that assemble on each chromatid of each mitotic chromosome. By combining computational modeling and quantitative light microscopy, we established a quantitative model of the forces and regulators controlling metaphase chromosome movement in the mammalian cell line derived from Potorous tridactylis kidney epithelial cells (PtK1) (Chapter 2). This model can explain key features of metaphase chromosome dynamics and related chromosome structural changes experimentally observed. Moreover, the model made predictions, which we tested experimentally, on how changes in spindle dynamics affect certain aspects of chromosome structure. This quantitative model was next used to study the metaphase dynamics of chromosomes with erroneous KT-MT attachments (Chapter 3). Once again, the model predictions were tested experimentally and showed that erroneous KT-MT attachment alters the dynamics not only of the mis-attached KT, but also of its sister KT. Even more strikingly, experimental data showed that the presence of a single mis-attached KT could perturb the dynamics of all other, normally attached, KTs in anaphase. Chapter 3 also describe how MT poleward flux ensures correct KT-MT attachment and correct chromosome segregation. Indeed, reduced flux is associated with an increase in merotelically attached anaphase lagging chromosomes (LCs). These LCs form micronuclei (MNi) upon mitotic exit. The final effort of this work focused on the fate of MNi and micronuclated (MNed) cells (Chapter 4). Experimental observations showed that most of the chromosomes in MNi missegregated at the cell division following MN formation and that frequently the chromatin in the MN displayed delayed condensation. This work, thus, established a direct link between LCs and aneuploidy through the MN cell cycle.
- Vindel: a simple pipeline for checking indel redundancyLi, Zhiyi; Wu, Xiaowei; He, Bin; Zhang, Liqing (Biomed Central, 2014-11-19)Background With the advance of next generation sequencing (NGS) technologies, a large number of insertion and deletion (indel) variants have been identified in human populations. Despite much research into variant calling, it has been found that a non-negligible proportion of the identified indel variants might be false positives due to sequencing errors, artifacts caused by ambiguous alignments, and annotation errors. Results In this paper, we examine indel redundancy in dbSNP, one of the central databases for indel variants, and develop a standalone computational pipeline, dubbed Vindel, to detect redundant indels. The pipeline first applies indel position information to form candidate redundant groups, then performs indel mutations to the reference genome to generate corresponding indel variant substrings. Finally the indel variant substrings in the same candidate redundant groups are compared in a pairwise fashion to identify redundant indels. We applied our pipeline to check for redundancy in the human indels in dbSNP. Our pipeline identified approximately 8% redundancy in insertion type indels, 12% in deletion type indels, and overall 10% for insertions and deletions combined. These numbers are largely consistent across all human autosomes. We also investigated indel size distribution and adjacent indel distance distribution for a better understanding of the mechanisms generating indel variants. Conclusions Vindel, a simple yet effective computational pipeline, can be used to check whether a set of indels are redundant with respect to those already in the database of interest such as NCBI’s dbSNP. Of the approximately 5.9 million indels we examined, nearly 0.6 million are redundant, revealing a serious limitation in the current indel annotation. Statistics results prove the consistency of the pipeline on indel redundancy detection for all 22 chromosomes. Apart from the standalone Vindel pipeline, the indel redundancy check algorithm is also implemented in the web server http://bioinformatics.cs.vt.edu/zhanglab/indelRedundant.php.