Biology subjects

Shao, M.

Publications and source records attributed to Shao, M..

6 recordsLinked to original sources

bioRxiv2017

Harnessing the Cross-talk between Tumor Cells and Tumor-associated Macrophages with a Nano-drug for modulation of Glioblastoma Immune Microenvironment

Glioblastoma (GBM) is the most frequent and malignant brain tumor with a high mortality rate. The presence of a large population of macrophages (M{varphi}) in the tumor microenvironment is a prominent feature of GBM and these so-called tumor-associated M{varphi} (TAM) closely interact with the GBM cells to promote the survival, progression and therapy resistance of the GBM. Various therapeutic strategies have been devised either targeting the GBM cells or the TAM but few have addressed the cross-talks between the two cell populations. The present study was carried out to explore the possibility of exploiting the cross-talks between the GBM cells (GC) and TAM for modulation of the GBM microenvironment through using Nano-DOX, a drug composite based on nanodiamonds bearing doxorubicin. In the in vitro work on human cell models, Nano-DOX-loaded TAM were first shown to be viable and able to infiltrate three-dimensional GC spheroids and release cargo drug therein. GC were then demonstrated to encourage Nano-DOX-loaded TAM to unload Nano-DOX back into GC which consequently emitted damage-associated molecular patterns (DAMPs) that are powerful immunostimulatory agents as well as indicators of cell damage. Nano-DOX was next proven to be a more potent inducer of GC DAMPs emission than doxorubicin. As a result, Nano-DOX-damaged GC exhibited an enhanced ability to attract both TAM and Nano-DOX-loaded TAM. Most remarkably, Nano-DOX-damaged GC reprogrammed the TAM from a pro-GBM phenotype to an anti-GBM phenotype that suppressed GC growth. Finally, the in vivo relevance of the in vitro findings was tested in animal study. Mice bearing orthotopic human GBM xenografts were intravenously injected with Nano-DOX-loaded mouse TAM which were found releasing drug in the GBM xenografts 24 h after injection. GC damage was evidenced by the induction of DAMPs emission within the xenografts and a shift of TAM phenotype was detected as well. Taken together, our results demonstrate a novel way with therapeutic potential to harness the cross-talk between GBM cells and TAM for modulation of the tumor immune microenvironment.\n\nAbbreviationsATP, adenosine triphosphate; BBB, blood-brain barrier; BCA, bicinchoninic acid; BMDM, bone marrow derived macrophages; CD, cluster of differentiation; CFSE, 5(6)-carboxyfluorescein diacetate, succinimidyl ester; CM, conditioned culture medium; CNS, central nervous system; CRT, calreticulin; DAMPs, damage-associated molecular patterns; DAB, diaminobenzidine; DOX, doxorubicin; ECL, enhanced chemiluminescence; ELISA, enzyme-linked immunosorbent assay; HMGB1, high mobility group protein B1; HSP90, heat shock protein 90; FACS, flow cytometry; GBM, glioblastoma; Guanylate Binding Protein 5 (GBP5); GC, glioblastoma cells; IHC, immunohistochemical; IL, interleukin; M{varphi}, macrophages; mBMDM, mouse BMDM; mBMDM2, Type-2 mBMDM; M1, Type-1 Mo; M2, Type-2 Mo; Nano-DOX, ND-PG-RGD-DOX; ND, nanodiamonds; Nano-DOX-mBMDM, Nano-DOX-loaded mouse BMDM; NGCM, Nano-DOX-treated-GC-conditioned medium; PBS, phosphate buffered saline; PG, polyglycerol; PMA, phorbol 12-myristate 13-acetate; PVDF, polyvinylidene fluoride; RGD, tripeptide of L-arginine, glycine and L-aspartic acid; RM, regular culture medium; SD, standard deviation; TAM, tumor-associated M{varphi}; TBST, Tris Buffered Saline with Tween(R) 20.\n\nGraphic abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC=\"FIGDIR/small/170282_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (111K):\norg.highwire.dtl.DTLVardef@180aea2org.highwire.dtl.DTLVardef@14922f7org.highwire.dtl.DTLVardef@96a696org.highwire.dtl.DTLVardef@92f050_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2017

SQUID: Transcriptomic Structural Variation Detection from RNA-seq

Transcripts are frequently modified by structural variations, which leads to a fused transcript of either multiple genes (known as a fusion gene) or a gene and a previously non-transcribing sequence. Detecting these modifications (called transcriptomic structural variations, or TSVs), especially in cancer tumor sequencing, is an important and challenging computational problem. We introduce SQUID, a novel algorithm to accurately predict both fusion-gene and non-fusion-gene TSVs from RNA-seq alignments. SQUID unifies both concordant and discordant read alignments into one model, and doubles the accuracy on simulation data compared to other approaches. With SQUID, we identified novel non-fusion-gene TSVs on TCGA samples.

bioinformatics↗

bioRxiv2017

DeepBound: Accurate Identification of Transcript Boundaries via Deep Convolutional Neural Fields

MotivationReconstructing the full-length expressed transcripts (a.k.a. the transcript assembly problem) from the short sequencing reads produced by RNA-seq protocol plays a central role in identifying novel genes and transcripts as well as in studying gene expressions and gene functions. A crucial step in transcript assembly is to accurately determine the splicing junctions and boundaries of the expressed transcripts from the reads alignment. In contrast to the splicing junctions that can be efficiently detected from spliced reads, the problem of identifying boundaries remains open and challenging, due to the fact that the signal related to boundaries is noisy and weak.\n\nResultsWe present DeepBound, an effective approach to identify boundaries of expressed transcripts from RNA-seq reads alignment. In its core DeepBound employs deep convolutional neural fields to learn the hidden distributions and patterns of boundaries. To accurately model the transition probabilities and to solve the label-imbalance problem, we novelly incorporate the AUC (area under the curve) score into the optimizing objective function. To address the issue that deep probabilistic graphical models requires large number of labeled training samples, we propose to use simulated RNA-seq datasets to train our model. Through extensive experimental studies on both simulation datasets of two species and biological datasets, we show that DeepBound consistently and significantly outperforms the two existing methods.\n\nAvailabilityDeepBound is freely available at https://github.com/realbigws/DeepBound.\n\nContactmingfu.shao@cs.cmu.edu, realbigws@gmail.com

bioinformatics↗

bioRxiv2017

Scallop Enables Accurate Assembly Of Transcripts Through Phasing-Preserving Graph Decomposition

We introduce Scallop, an accurate, reference-based transcript assembler for RNA-seq data. Scallop significantly improves reconstruction of multi-exon and lowly expressed transcripts. On 10 human samples aligned with STAR, Scallop produces (on average) 35.7% and 37.5% more correct multi-exon transcripts than two leading transcript assemblers, StringTie [1] and TransComb [2], respectively. For transcripts expressed at low levels in the same samples, Scallop assembles 65.2% and 50.2% more correct multi-exon transcripts than StringTie and TransComb, respectively. Scallop obtains this improvement through a novel algorithm that we prove preserves all phasing paths from reads (including paired-end reads), while also producing a parsimonious set of transcripts and minimizing coverage deviation.

bioinformatics↗

bioRxiv2017

Vegetally Localized Vrtn Functions As A Novel Repressor To Modulate bmp2b Transcription During Dorsoventral Patterning In Zebrafish

The vegetal pole cytoplasm represents a critical source of maternal signals for patterning the primary dorsoventral axis of the early embryo. Vegetally localized dorsal determinants are essential for the formation of the Spemann organizer, which expresses both bone morphogenetic proteins and their antagonists. The extracellular regulation of BMP signalling activity has been well characterized, however, transcriptional regulation of bmp genes along the dorsoventral axis remains largely unknown. Here, we report a novel mode of maternal regulation of BMP signalling in the lateral and ventral regions by analyzing the unexpected dorsalizing effect following vegetal yolk ablation experiment. We identified Vrtn as a novel vegetally localized maternal factor displaying dorsalizing activity. It functions as a novel transcriptional repressor to regulate bmp2b expression in the marginal region. Mechanistically, Vrtn binds bmp2b upstream sequence and inhibits its transcription independently of maternal Wnt/{beta}-catenin signalling. By creating vrtn loss-of-function mutation and analyzing maternal-zygotic mutant embryos, we further showed that Vrtn is required for the formation of dorsoventral axis. Our work thus unveils a novel maternal mechanism regulating BMP gradient during dorsoventral specification.

developmental biology↗

bioRxiv2016

Efficient Heuristic for Decomposing a Flow with Minimum Number of Paths

Motivated by transcript assembly and multiple genome assembly problems, in this paper, we study the following minimum path flow decomposition problem: given a directed acyclic graph G = (V,E) with source s and sink t and a flow f, compute a set of s-t paths P and assign weight w(p) for p [isin] P such that [Formula], and |P| is minimized. We propose an efficient pseudo-polynomialtime heuristic for this problem based on novel insights. Our heuristic gives a framework that consists of several components, providing a roadmap for continuing development of better heuristics. Through experimental studies on both simulated and transcript assembly instances, we show that our algorithm significantly improves the previous state-of-the-art algorithm. Implementation of our algorithm is available at https://github.com/Kingsford-Group/catfish.

bioinformatics↗