Biology subjects

Chen, S.-H.

Publications and source records attributed to Chen, S.-H..

24 records · Page 2Linked to original sources

bioRxiv2021

Synergy of chemotherapy and macrophage depletion leads to T cell memory activation and durable triple negative breast cancer regression

Immunosuppressive elements within the tumor microenvironment such as Tumor Associated Macrophages (TAMs) can present a barrier to successful anti-tumor responses by cytolytic T cells. We employed preclinical syngeneic p53 null mouse models of triple negative breast cancer (TNBC) to develop a treatment regimen that harnessed the immunostimulatory effects of low-dose cyclophosphamide coupled with the pharmacologic inhibition of TAMs using either a small molecule CSF1R inhibitor or an anti-CSF1R antibody. This therapeutic combination was used to successfully treat several highly aggressive TNBC murine mammary tumors and lung metastasis. Using this regimen and single cell RNA sequencing we characterized tumor infiltrating lymphocytes (TILs) including helper T cells and antigen-presenting B cells that were highly enriched in good responders to combination therapy. Using high dimensional imaging techniques, we identified the close spatial localization of B220+ CD86+ activated B cells and CD4+ T cells in tertiary lymphoid structures that were present up to 6 weeks post-treatment in one model that also exhibited long-term tumor regression post-treatment. We also characterized the transcriptional and metabolic heterogeneity of TAMS in these two closely related claudin-low/mesenchymal subtype tumor models with differential treatment responses. A murine TAM signature derived from the T12 model is highly expressed and conserved in human claudin-low breast cancers, and high expression of the T12 signature correlated with reduced overall survival. This T12 tumor TAM signature may help identify human claudin-low breast cancer patients that will benefit from the combination of cyclophosphamide and anti-CSF1R therapy. These studies illustrate the complexity of the tumor immune microenvironment and highlight different immune responses that result from rationale combinations of immunotherapy. SignificanceA treatment regimen that harnessed the immunostimulatory effects of cyclophosphamide coupled with the inhibition of CSF1R was used to successfully treat several highly aggressive claudin-low TNBC murine mammary tumors and lung metastasis.

cancer biology↗

bioRxiv2020

AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning

MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. ResultsOur results show that our prediction model is superior to the existing AMP predictors. AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contactcylin@iis.sinica.edu.tw

microbiology↗

bioRxiv2019

Two-Step Small Scale Purification of Recombinant Adeno-Associated Viruses

Recombinant adeno-associated viruses (AAVs) are robust and versatile tools for in vivo gene delivery. Natural and designer capsid variations in AAVs allow for targeted gene delivery to specific cell types. Low immunogenicity and lack of pathogenesis also add to the popularity of this virus as an innocuous gene delivery vector for gene therapy. AAVs are routinely used to express recombinases, sensors, detectors, CRISPR-Cas9 components, or to simply overexpress a gene of interest for functional studies. High production demand has given rise to multiple platforms for production and purification of AAVs. However, most platforms rely heavily on large amounts of starting material and multiple purification steps to produce highly purified viral particles. Often, researchers require several small-scale purified AAVs. Here, we describe a simple and efficient technique for purification of recombinant AAVs from small amounts of starting material in a two-step purification method. In this method, AAVs are released into the packaging cell medium using high salt concentration and pelleted by ultracentrifugation to remove soluble impurities. Then, the resuspended pellet is purified using a protein spin-concentrator. The two-step purification consisting of ultracentrifugation and spin-concentration eliminates the need for fraction collection and the time-consuming evaluation of individual fractionated aliquots for titer and purity. In this method, the resulting AAV preparations are comparable in titer and purity to commercially available samples. This simplified process can be used to rapidly generate highly purified AAV particles in small scale, thereby saving resources.

neuroscience↗

bioRxiv2018

Visualisation and analysis of RNA-Seq assembly graphs

RNA-sequencing (RNA-Seq) is a powerful transcriptome profiling technology enabling transcript discovery and quantification. RNA-Seq data are large, and most commonly used as a source of genelevel quantification measurements, whilst the underlying assemblies of reads, if inspected, are usually viewed as sequence reads mapped on to a reference genome. Whilst sufficient for many needs, when the underlying transcript assemblies are complex, this visualisation approach can be limiting; errors in assembly can be difficult to spot and interpretation of splicing events is challenging.\n\nHere we report on the development of a graph-based visualisation method as a complementary approach to understanding transcript diversity and read assembly from short-read RNA-Seq data. Following the mapping of reads to the reference genome, read-to-read comparison is performed on all reads mapping to a given gene, producing a matrix of weighted similarity scores between reads. This is used to produce an RNA assembly graph where nodes represent reads derived from a cDNA and edges similarity scores between reads, above a defined threshold. Visualisation of resulting graphs is performed using Graphia Professional. This tool can render the often large and complex graph topologies that result from DNA/RNA sequence assembly in 3D space and supports info rmatio no verlay on to nodes, e.g. transcript models. We have also implemented an analysis pipeline for the creation of RNA assembly graphs with both a command-line and web-based interface that allows users to create and visualise these data. Here we demonstrate the utility of this approach on RNA-Seq data, including the unusual structure of these graphs and how they can be used to identify issues in assembly, repetitive sequences within transcripts and splice variants. We believe this approach has the potential to significantly improve our understanding of transcript complexity.

bioinformatics↗

bioRxiv2018

Progression of chronic kidney disease in African American with type 2 diabetes mellitus using topology learning in electronic medical records

BackgroundChronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a \"real-world\" context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions. Genetics also plays an important role. In previous work, we and others have demonstrated that African Americans with high-risk APOL1 genotypes are more likely to develop CKD, tend to develop CKD earlier, and the disease progresses faster. Diabetes, which is more common in African Americans, also significantly increases risk for CKD.\n\nData and MethodElectronic medical records (EMRs) were used to outline the first CKD progression trajectory roadmap for an African American population with type 2 diabetes. By linking participants in 5 genome-wide association study (GWAS) to their clinical records at Wake Forest Baptist Medical Center (WFBMC), an EMR-GWAS cohort was established (n = 1,581). Patients health status was described by 18 Essential Clinical Indices across 84,009 clinical encounters. A novel graph learning algorithm, Discriminative Dimensionality Reduction Tree (DDRTree) was implemented, to establish the trajectories of declines in health. Moreover, a prediction model for new patients was proposed along the learned graph structure. We annotated these trajectories with clinical and genomic features including kidney function, other major risk indices of CKD, APOL1 genotypes, and age. The prediction power of the learned disease progression trajectories was further examined using the k-nearest neighbor model.\n\nResultsThe CKD progression trajectory roadmap revealed diverse kidney failure pathways associated with different clinical conditions. Specifically, we identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to the high-risk one was associated with accelerated decline in kidney function. On this roadmap, patients with APOL1 high-risk genotypes were enriched in the high-risk trajectory, suggesting fundamentally different disease progression mechanisms from those without APOL1 risk genotypes. The k-nearest neighbor-based prediction showed effective prediction rate of 87%.\n\nConclusionThe CKD progression trajectory roadmap revealed novel diverse renal failure pathways in African Americans with type 2 diabetes mellitus and highlights disease progression patterns that associate with APOL1 renal-risk genotypes.

bioinformatics↗

bioRxiv2018

Assembly of a Parts List of the Human Mitotic Cell Cycle Machinery

The set of proteins required for mitotic division remains poorly characterised. Here, an extensive series of correlation analyses of human and mouse transcriptomics data was performed to identify genes strongly and reproducibly associated with cells undergoing S/G2-M phases of the cell cycle. In so doing, a list of 701 cell cycle-associated genes was defined and shown that whilst many are only expressed during these phases, the expression of others is also driven by alternative promoters. Of this list, 496 genes have known cell cycle functions, whereas 205 were assigned as putative cell cycle genes, 53 of which are functionally uncharacterised. Among these, 27 were screened for subcellular localisation revealing many to be nuclear localised and at least four to be novel centrosomal proteins. Furthermore, 10 others inhibited cell proliferation upon siRNA knockdown. This study presents the first comprehensive list of human cell cycle proteins, identifying many new candidate proteins.

genomics↗