Biology subjects

Lin, Y.-Y.

Publications and source records attributed to Lin, Y.-Y..

5 recordsLinked to original sources

bioRxiv2018

Whole-brain imaging and characterization of Drosophila brains based on one-, two-, and three-photon excitations

To study functional connectome, optical microscopy provides the advantages of in vivo observation, molecular specificity, high-speed acquisition, and sub-micrometer spatial resolution. Now, the most complete single-neuron-based anatomical connectome is built upon Drosophila; thus it will be a milestone to achieve whole-brain observation with sub-cellular resolution in living Drosophila. Surprisingly, two-photon microscopy cannot penetrate through the 200-m-thick brain, due to the extraordinarily strong aberration/scattering from tracheae. Here we achieve whole-Drosophila-brain observation by degassing the brain or by using three-photon microscopy at 1300-nm, while only the latter provides in vivo feasibility, reduced aberration/scattering and exceptional optical sectioning capability. Furthermore, by comparing one-photon (488-nm), two-photon (920-nm), and three-photon (1300-nm) excitations in the brain, we not only demonstrate first quantitative reduction of both scattering and aberration in trachea-filled tissues, but unravel that the contribution of aberration exceeds scattering at long wavelengths. Our work paves the way toward constructing functional connectome in a living Drosophila.

neuroscience↗

bioRxiv2018

MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

MotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.\n\nApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.\n\nResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.\n\nAvailabilityMechRNA is available for download at https://bitbucket.org/compbio/mechrna\n\nContactbackofen@informatik.uni-freiburg.de, cenksahi@indiana.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

BAP1 Loss Predicts Therapeutic Vulnerability in Malignant Peritoneal Mesothelioma

BackgroundMalignant Peritoneal Mesothelioma (PeM) is a rare but frequently fatal cancer that originates from the peritoneal lining of the abdomen. Standard treatment of PeM is limited to cytoreductive surgery and/or chemotherapy, and no effective targeted therapies for PeM yet exist. In the search for novel therapeutic target candidates in PeM, we performed a comprehensive integrative multi-omics analysis of 19 treatment-naive PeM tumors.\n\nResultsThe analysis identified PeM tumors with BAP1 loss to form a distinct molecular subtype characterized by distinct expression patterns of genes involved in chromatin remodeling, DNA repair pathway, and immune checkpoint receptor activation. This PeM subtype could potentially benefit from immune checkpoint, PARP, or HDAC inhibition therapies.\n\nConclusionsOur findings uncover BAP1 as a trackable prognostic and predictive biomarker, and refine PeM disease classification. This integrated molecular characterization provides a comprehensive foundation for developing PeM precision medicine.

cancer biology↗

bioRxiv2017

Computational proteogenomic identification and functional interpretation of translated fusions and micro structural variations in cancer

MotivationRapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. In this paper we introduce a novel computational framework which can integratively analyze all three types of omics data to obtain a complete molecular profile of a tissue sample, in normal and disease conditions. Our framework includes MiStrVar, an algorithmic method we developed to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can provide an accurate profile of structurally aberrant transcripts in cancer samples. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures in the respective proteomics data sets. Our framework's ability to observe structural aberrations at three levels of omics data provides means of validating their presence.\n\nResultsWe have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq data sets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides [1]. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations (in particular, fusions) were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Moreover, the most significantly enriched genes involved in translated fusions are cancer-related. Furthermore a number of the somatic, translated microSVs are observed in tumor suppressor genes.\n\nContactcenksahi@indiana.edu

bioinformatics↗

bioRxiv2017

Targeted gene correction of FKRP by CRISPR/Cas9 restores functional glycosylation of α-dystroglycan in cortical neurons derived from human induced pluripotent stem cells

Mutations in genes required for functional glycosylation of -dystroglycan cause a group of congenital muscular dystrophies associated with brain malformations, referred to as dystroglycanopathies. The lack of isogenic, physiology-relevant human cellular models has limited our understanding of the cortical abnormalities in dystroglycanopathies. Here we generate induced pluripotent stem cells (iPSCs) from a severe dystroglycanopathy patient with homozygous mutations in the ribitol-5-phosphate transferase gene, FKRP. We carry out targeted gene correction in FKRP-iPSCs using CRISPR/Cas9-mediated genome editing. We characterise the directed differentiation of FKRP- and corrected-iPSCs to neural stem cells, cortical progenitors and cortical neurons. Importantly, we show that targeted gene correction of FKRP restores functional glycosylation of -dystroglycan in iPSC-derived cortical neurons. We independently validate this result by showing targeted gene mutation of FKRP disrupts functional glycosylation of -dystroglycan. This work demonstrates the feasibility of using CRISPR/Cas9-engineered human iPSCs for modelling dystroglycanopathies and provides a foundation for therapeutic development.\n\nHighlightsO_LIGeneration of FKRP-iPSCs for modelling cortical abnormalities in dystroglycanopathies\nC_LIO_LIPrecise gene correction by CRISPR/Cas9-mediated genome editing\nC_LIO_LIDirected differentiation of isogenic control and FKRP-iPSC to cortical neurons\nC_LIO_LIFunctional glycosylation of -dystroglycan is restored in cortical neurons derived from CRISPR/Cas9-corrected iPSCs\nC_LIO_LITargeted gene mutation of FKRP disrupts functional glycosylation of -dystroglycan in cortical neurons\nC_LI

molecular biology↗