Biology subjects

Truong, T.

Publications and source records attributed to Truong, T..

31 records · Page 2Linked to original sources

bioRxiv2022

Potentiation of drug toxicity through virus latency reversal promotes preferential elimination of HIV infected cells

Eliminating latently infected cells is a highly challenging, indispensable step towards the overall cure for HIV/AIDS. We recognized that the unique HIV protease cut site (Phe-Pro) can be reconstructed using a potent toxin, monomethyl auristatin F (MMAF), which features Phe at its C-terminus. We hypothesized that this presents opportunities to design prodrugs that are specifically activated by the HIV protease. To investigate this, a series of MMAF derivatives was synthesized and evaluated in cell culture using latently HIV-infected cells. Cytotoxicity of compounds was enhanced upon latency reversal by up to 11-fold. In a mixed cell population, nanomolar concentrations of the lead compound depleted predominantly the HIV-infected cells and in doing so markedly enriched the pool with the uninfected cells. Despite expectation, mechanism of action of the synthesized toxins was not as HIV protease-specific prodrugs, but likely through the synergy of toxicities between the toxin and the reactivated virus.

pharmacology and toxicology↗

bioRxiv2021

Development and Characterization of Recombinant Vesicular Stomatitis Virus (rVSV)-based Bivalent Vaccine Against COVID-19 Delta Variant and Influenza Virus

COVID-19 and influenza are both highly contagious respiratory diseases with a wide range of severe symptoms and cause great disease burdens globally. It has become very urgent and important to develop a bivalent vaccine that is able to target these two infectious diseases simultaneously. In this study, we generated three attenuated replicating recombinant VSV (rVSV) vaccine candidates. These rVSV-based vaccines co-express SARS-CoV-2 Delta variant spike protein (SP) or the receptor binding domain (RBD) and four copies of the highly conserved M2 ectodomain (M2e) of influenza A fused with the Ebola glycoprotein DC-targeting/activation domain. Animal studies have shown that immunization with these bivalent rVSV vaccines induced efficient but variable levels of humoral and cell-mediated immune responses against both SARS-CoV-2 and influenza M2e protein. Significantly, our vaccine candidates induced production of high levels of neutralizing antibodies that protected cells against SARS-CoV-2 Delta and other SP-pseudovirus infections in culture. Furthermore, vaccination with the bivalent VSV vaccine via either intramuscular or intranasal route efficiently protected mice from the lethal challenge of H1N1 and H3N2 influenza viruses and significantly reduced viral load in the lungs. These studies provide convincing evidence for the high efficacy of this bivalent vaccine to prevent influenza replication and initiate robust immune responses against SARS-CoV-2 Delta variants. Further investigation of its efficacy to protect against SARS-CoV-2 Delta variants will provide substantial evidence for new avenues to control two contagious respiratory infections, COVID-19 and influenza.

microbiology↗

bioRxiv2021

Ultra-deep sequencing reveals no evidence of oncogenic mutations or enrichment by ex vivo CRISPR/Cas9 genome editing in human hematopoietic stem and progenitor cells

As CRISPR-based therapies enter the clinic, evaluation of the safety remains a critical and still active area of study. While whole genome sequencing is an unbiased method for identifying somatic mutations introduced by ex vivo culture and genome editing, this methodology is unable to attain sufficient read depth to detect extremely low frequency events that could result in clonal expansion. As a solution, we utilized an exon capture panel to facilitate ultra-deep sequencing of >500 tumor suppressors and oncogenes most frequently altered in human cancer. We used this panel to investigate whether transient delivery of high-fidelity Cas9 protein targeted to three different loci (using guide RNAs (gRNAs) corresponding to sites at AAVS1, HBB, and ZFPM2) at day 4 and day 10 timepoints post-editing resulted in the introduction or enrichment of oncogenic mutations. In three separate primary human HSPC donors, we identified a mean of 1,488 variants per Cas9 treatment (at <0.07% limit of detection). After filtering to remove germline and/or synonymous changes, a mean of 3.3 variants remained per condition, which were further reduced to six total mutations after removing variants in unedited treatments. Of these, four variants resided at the predicted off-target site in the myelodysplasia-associated EZH2 gene that were subject to ZFPM2 gRNA targeting in Donors 2 and 3 at day 4 and day 10 timepoints. While Donor 1 displayed on-target cleavage at ZFPM2, we found no off-target activity at EZH2. Sanger sequencing revealed a homozygous single nucleotide polymorphism (SNP) at position 14bp distal from the Cas9 protospacer adjacent motif in EZH2 that eliminated any detectable off-target activity. We found no evidence of exonic off-target INDELs with either of the AAVS1 or HBB gRNAs. These findings indicate that clinically relevant delivery of high-fidelity Cas9 to primary HSPCs and ex vivo culture up to 10 days does not introduce or enrich for tumorigenic variants and that even a single SNP outside the seed region of the gRNA protospacer is sufficient to eliminate Cas9 off-target activity with this method of delivery into primary, repair competent human HSPCs.

genetics↗

bioRxiv2021

Features of peptide fragmentation spectra in single cell proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single cell proteomics specializes in identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single cell spectra, including loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity and distinct fragmentation pattern compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

bioinformatics↗

bioRxiv2021

Benchmarking PSM identification tools for single cell proteomics

Single cell proteomics is an emerging sub-field within proteomics with the potential to revolutionize our understanding of cellular heterogeneity and interactions. Recent efforts have largely focused on technological advancements in sample preparation, chromatography and instrumentation to enable measuring proteins present in these ultra-limited samples. Although advancements in data acquisition have rapidly improved our ability to analyze single cells, the software pipelines used in data analysis were originally written for traditional bulk samples and their performance on single cell data has not been investigated. We benchmarked five popular peptide identification tools on single cell proteomics data. We found that MetaMorpheus achieved the greatest number of peptide spectrum matches at a 1% false discovery rate. Depending on the tool, we also find that post processing machine learning can improve spectrum identification results by up to [~]40%. Although rescoring leads to a greater number of peptide spectrum matches, these new results typically are generated by 3rd party tools and have no way of being utilized by the primary pipeline for quantification. Exploration of novel metrics for machine learning algorithms will continue to improve performance.

bioinformatics↗

bioRxiv2021

Notch Intracellular Domain Plasmid Delivery via Poly(lactic-co-glycolic acid)Nanoparticles to Upregulate Notch Signaling

Notch signaling is a highly conserved signaling system that is required for embryonic development and regeneration of organs. When the signal is lost, maldevelopment occurs and leads to a lethal state. Liposomes and retroviruses are most commonly used to deliver genetic material to cells. However, there are many drawbacks to these systems such as increased toxicity, nonspecific delivery, short half-life, and stability after formulation. We utilized the negatively charged and FDA approved polymer poly(lactic-co-glycolic acid) to encapsulate Notch Intracellular Domain-containing plasmid in nanoparticles. In this study, we show that primary human umbilical vein endothelial cells readily uptake the nanoparticles with and without specific antibody targets. We demonstrated that our nanoparticles also are nontoxic, stable over time, and compatible with blood. We also determined that we can successfully transfect primary human umbilical vein endothelial cells (HUVECs) with our nanoparticles in static and dynamic environments. Lastly, we elucidated that our transfection upregulates the downstream genes of Notch signaling, indicating that the payload was viable and successfully altered the genetic downstream effects.

bioengineering↗

bioRxiv2020

BBrowser: Making single-cell data easily accessible

BioTurings BBrowser is a software solution that helps scientists effectively analyze single-cell omics data. It combines big data with big computation and modern data visualization to create a unique platform where scientists can interact and obtain important biological insights from the massive amounts of single-cell data. BBrowser has three main components: a curated single-cell database, a big-data analytics layer, and a data visualization module. BBrowser is available for download at: https://bioturing.com/bbrowser/download.

bioinformatics↗

bioRxiv2020

The role of advective transport mechanism in continental-scale transport of fungal plant epidemics

In examining the continental-scale plant pathogen spread, we focus on the competition between the short-range stochastic hopping within the atmospheric boundary layer, and the laminar advection by the currents in the free troposphere. The latter is typically ignored, since it is assumed that the population of spores which have reached the troposphere is small, and the fraction of the remaining spores that survived the subsequent journey is negligible due to ultraviolet light and frigid temperatures. However, we claim that it is in fact a crucial mechanism for continental-scale spread. We argue that free tropospheric currents can not be ignored, even as the probability for spores to reach them and to survive within them approaches zero. In other words, models that neglect tropospheric advection are fragile - their predictions change qualitatively if this alternative transport channel becomes accessible - even when the rate at which spores actually make use of this transport channel approaches zero.

ecology↗

bioRxiv2020

Venice: A new algorithm for finding marker genes in single-cell transcriptomic data

AO_SCPLOWBSTRACTC_SCPLOWMost widely used tools for finding marker genes in single cell data (SeuratT/NegBinom/Poisson, CellRanger, EdgeR, limmatrend) use a conventional definition of differentially expressed genes: genes with different mean expression values. However, in single-cell data, a cell population can be a mixture of many cell types/cell states, hence the mean expression of genes cannot represent the whole population. In addition, these tools assume that gene expression of a population belongs to a specific family of distribution. This assumption is often violated in single-cell data. In this work, we define marker genes of a cell population as genes that can be used to distinguish cells in the population from cells in other populations. Besides log-fold change, we devise a new metric to classify genes into up-regulated, down-regulated, and transitional states. In a benchmark for finding up-regulated and down-regulated genes, our tool outperforms all compared methods, including Seurat, ROTS, scDD, edgeR, MAST, limma, normal t-test, Wilcoxon and Kolmogorov-Smirnov test. Our method is much faster than all compared methods, therefore, enables interactive analysis for large single-cell data sets in BioTuring Browser. Venice algorithm is available within Signac package: https://github.com/bioturing/signac 1).

bioinformatics↗

bioRxiv2020

Ultrasensitive single-cell proteomics workflow identifies >1000 protein groups per mammalian cell

We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification.

systems biology↗

bioRxiv2019

Appraising causal relationships of dietary, nutritional and physical-activity exposures with overall and aggressive prostate cancer: two-sample Mendelian randomization study based on 79,148 prostate cancer cases and 61,106 controls

BackgroundProstate cancer is the second most common male cancer worldwide, but there is substantial geographical variation suggesting a potential role for modifiable risk factors in prostate carcinogenesis.\n\nMethodsWe identified previously reported prostate cancer risk factors from the World Cancer Research Funds (WCRF) systematic appraisal of the global evidence (2018). We assessed whether each identified risk factor was causally associated with risk of overall (79,148 cases and 61,106 controls) or aggressive (15,167 cases and 58,308 controls) prostate cancer using Mendelian randomization (MR) based on genome wide association study (GWAS) summary statistics from the PRACTICAL and GAME-ON/ELLIPSE consortia. We assessed evidence for replication in UK Biobank (7,844 prostate cancer cases and 204,001 controls).\n\nFindingsWCRF identified 57 potential risk factors, of which 22 could be instrumented for MR analyses using single nucleotide polymorphisms (SNPs). In MR analyses for overall prostate cancer, we identified evidence compatible with causality for the following risk factors (odds ratio [OR] per standard deviation increase; 95% confidence interval): accelerometer-measured physical-activity, OR=0.49 (0.33-0.72; p=0.0003); serum iron, OR=0.92 (0.86-0.98; p=0.007); body mass index (BMI), OR=0.90 (0.84-0.97; p=0.003); and mono-unsaturated fat, OR=1.11 (1.02-1.20; p=0.02). Findings in our replication analyses in UK Biobank were compatible with our main analyses (albeit with wide confidence intervals). In MR analysis, height was positively associated with aggressive prostate cancer risk: OR=1.07 (1.01-1.15; p=0.03).\n\nInterpretationThe results for physical-activity, serum iron, BMI, mono-unsaturated fat and height are compatible with causality for prostate cancer but more research is needed to rule out violations of MR assumptions for some risk factors. The results suggest that interventions aimed at increasing physical activity may reduce prostate cancer risk, but the direction of effects of BMI, and iron are at odds with their effects on other diseases, so the overall public health impact of intervening on these need to be considered.\n\nFundingWorld Cancer Research Fund International (2015/1421), Cancer Research UK program grant (C18281/A19169), National Institute for Health Research, Bristol Biomedical Research Centre, and Victorian Cancer Agency (MCRF18005).

epidemiology↗

bioRxiv2019

Hera-T: an efficient and accurate approach for quantifying gene abundances from 10X-Chromium data with high rates of non-exonic reads.

An important but rarely discussed phenomenon in single cell data generated by the 10X-Chromium protocol is that the fraction of non-exonic reads is very high. This number usually exceeds 30% of the total reads. Without aligning them to a complete genome reference, non-exonic reads can be erroneously aligned to the transcriptome reference with higher error rates. To tackle this problem, Cell Ranger chooses to firstly align reads against the whole genome, and at a later step, uses a genome annotation to select reads that align to the transcriptome. Despite its high running time and large memory consumption, Cell Ranger remains the most widely used tool to quantify 10XGenomics single cell RNA-Seq data for its accuracy. In this work, we introduce Hera-T, a fast and accurate tool for estimating gene abundances in single cell data generated by the 10X-Chromium protocol. By devising a new strategy for aligning reads to both transcriptome and genome references, Hera-T reduces both running time and memory consumption from 10 to 100 folds while giving similar results compared to Cell Rangers. Hera-T also addresses some difficult splicing alignment scenarios that Cell Ranger fails to address, and therefore, obtains better accuracy compared to Cell Ranger. Excluding the reads in those scenarios, Hera-T and Cell Ranger results have correlation scores > 0.99. For a single-cell data set with 49 million of reads, Cell Ranger took 3 hours (179 minutes) while Hera-T took 1.75 minutes; for another single-cell data set with 784 millions of reads, Cell Ranger took about 25 hours while Hera-T took 32 minutes. For those data sets, Cell Ranger completely used all 32 GB of memory while Hera-T consumed at most 8 GB. Hera-T package is available for download at: https://bioturing.com/product/hera-t

bioinformatics↗

bioRxiv2018

A revisit of RSEM generative model and its EM algorithm for quantifying transcript abundances.

RSEM has been mainly known for its accuracy in transcript abundance quantification. However, its quantification time is extremely high compared to that of recent quantification tools. In this paper, we revised the RSEMs EM algorithm. In particular, we derived accurate M-step updates to eliminate incorrect heuristic updates in RSEM. We also implement some optimizations that reduce the quantification time about a hundred times while still have better accuracy compared to RSEM. In particular, we noticed that different parameters have different convergence rates, therefore we identified and removed early converged parameters to significantly reduce the model complexity in further iterations, and we also use SQUAREM method to further speed up the convergence rate. We implemented these revisions in a packaged named Hera-EM, with source code available at: https://github.com/bioturing/hera/tree/master/hera-EM

bioinformatics↗