Biology subjects

Guo, T.

Publications and source records attributed to Guo, T..

7 recordsLinked to original sources

bioRxiv2018

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

In this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high -fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV+ HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe.

systems biology↗

bioRxiv2018

Myricetin Attenuates LPS-induced Inflammation in RAW 264.7 Macrophages and Mouse Models

BackgroundMyricetin has been demonstrated to inhibit inflammation in a variety of diseases, but little is known about its characters in acute lung injury (ALI). In this study, we aimed to investigate the protective effects of myricetin on inflammation in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells and a LPS-induced lung injury model.\n\nMethodsSpecifically, we investigated its effects on lung edema and histological damage by lung W/D weight ratio, HE staining and Evans Blue dye. Then macrophage activation was detected by evaluating the TNF-, IL-6 and IL-1{beta} mRNA and protein iNOS and COX-2. Myricetin was used to detect the impact on the inflammatory responses in LPS-induced RAW264.7 cells with the same manners in mouse model. Finally, NF-{kappa}B and MAPK signaling pathways were investigated with Western blot assay in LPS-induced RAW264.7 cells.\n\nResultsMyricetin significantly inhibited the production of the pro-inflammatory cytokines in vitro and in vivo. The in vivo experiments showed that pretreatment with Myricetin markedly attenuated the development of pulmonary edema, histological severities and macrophage activation in mice with ALI. The underlying mechanisms were further demonstrated in vitro that myricetin exerted an anti-inflammatory effect through suppressing the NF-{kappa}B p65 and AKT activation in NF-{kappa}B pathway and JNK, p-ERK and p38 in mitogen-activated protein kinases signaling pathway.\n\nConclusionMyricetin alleviated ALI by inhibiting macrophage activation, and inhibited inflammation in vitro and in vivo. It may be a potential therapeutic candidate for the prevention of inflammatory diseases.

cell biology↗

bioRxiv2018

Colonization of phosphate-solubilizing Pseudomonas sp. strain P34-L in the wheat rhizosphere and its effects on wheat growth and the expression of phosphate transporter gene TaPT4 in wheat

The ability to colonize the rhizosphere is an important basics requirement for field application of plant growth-promoting rhizobacteria (PGPR) strains. There are complex signal exchanges and mutual recognition between microbes and plants. In this study, phosphate-solubilizing Pseudomonas sp. P34, a PGPR strain with affinity to wheat, was isolated from the wheat rhizosphere by wheat germ agglutinin (WGA). The plasmid pTR102 harboring the luciferase luxAB gene was transferred into P34 to create P34-L. The labeled strain was used to track the temporal and spatial characteristics of colonization in wheat rhizosphere and its effects on wheat development. The transcript level of phosphate transporter gene TaPT4, a phosphorus deficiency indicator gene, in wheat roots was monitored by quantitative reverse-transcription PCR. The experimental results indicated that there was a high density of stain P34-L within the top 8-cm depth of the wheat rhizosphere on day 36 of wheat growth. The strain could survive in the wheat rhizosphere for a long time, and colonize new spaces in wheat rhizosphere following the extension of wheat roots. Compared with uninoculated wheat plants, those inoculated with P34-L showed significantly increased phosphorus accumulation in leaves, seedling fresh and dry weight, root fresh and dry weight, total root length, and number of root tips, forks, crossings, which showed a great value of application of the strain on wheat production by promoting the root growth and dry matter accumulation. Strain P34-L down-regulated the transcript level of TaPT4 in wheat roots, which means a well phosphorus supplementation environment was established by P34-L.\n\nImportanceMany PGPR strains often failed to achieve the desired effects when applied in the field. One major reason for the failure is lack of a special affinity between a certain strain and the target host plant, so those strains have low competitive ability with the indigenous microorganism, and unable to survive constantly in rhizosphere. In this work, a new technique to isolate wheat-specific phosphate-solubilizing PGPR strain by WGA was established. The isolate P34 was confirmed can colonize the wheat rhizosphere, and have significantly ability in promoting phosphorus absorption and wheat growth by luminescence labeling techniques. Furthermore, the phosphate-solubilizing ability of this affinity PGPR strain was verified in gene level by quantitative reverse-transcription PCR. These results lay a firm foundation for further research on the relationships between PGPR and their host plants. Meanwhile, this work supplied a potential ideal biofertilizer producing strain for sustainable agriculture.

microbiology↗

bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗

bioRxiv2018

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Many tumors are characterized by large genomic heterogeneity and it remains unclear to what extent this impacts on protein biomarker discovery. Here, we quantified proteome intra-tissue heterogeneity (ITH) based on a multi-region analysis of 30 biopsy-scale prostate tissues using pressure cycling technology and SWATH mass spectrometry. We quantified 8,248 proteins and analyzed the ITH of 3,700 proteins. The level of ITH varied significantly depending on proteins and tissue types. Benign tissues exhibited generally more complex ITH patterns than malignant tissues. Spatial variability of ten prostate biomarkers was further validated by immunohistochemistry in an independent cohort (n=83) using tissue microarrays. PSA was preferentially variable in benign prostatic hyperplasia, while GDF15 substantially varied in prostate adenocarcinomas. Further, we found that DNA repair pathways exhibited a high degree of variability in tumorous tissues, which may contribute to the genetic heterogeneity of tumors. This study conceptually adds a new perspective to protein biomarker discovery by quantifying spatial proteome variation and it demonstrates the feasibility by exploiting recent technological progress.

systems biology↗

bioRxiv2016

Splice Expression Variation Analysis (SEVA) for Differential Gene Isoform Usage in Cancer

MotivationCurrent bioinformatics methods to detect changes in gene isoform usage in distinct phenotypes compare the relative expected isoform usage in phenotypes. These statistics model differences in isoform usage in normal tissues, which have stable regulation of gene splicing. Pathological conditions, such as cancer, can have broken regulation of splicing that increases the heterogeneity of the expression of splice variants. Inferring events with such differential heterogeneity in gene isoform usage requires new statistical approaches.\n\nResultsWe introduce Splice Expression Variability Analysis (SEVA) to model increased heterogeneity of splice variant usage between conditions (e.g., tumor and normal samples). SEVA uses a rank-based multivariate statistic that compares the variability of junction expression profiles within one condition to the variability within another. Simulated data show that SEVA is unique in modeling heterogeneity of gene isoform usage, and benchmark SEVAs performance against EBSeq, DiffSplice, and rMATS that model differential isoform usage instead of heterogeneity. We confirm the accuracy of SEVAin identifying known splice variants in head and neck cancer and perform cross-study validation of novel splice variants. A novel comparison of splice variant heterogeneity between subtypes of head and neck cancer demonstrated unanticipated similarity between the heterogeneity of gene isoform usage in HPV-positive and HPV-negative subtypes and anticipated increased heterogeneity among HPV-negative samples with mutations in genes that regulate the splice variant machinery.\n\nConclusionThese results show that SEVA accurately models differential heterogeneity of gene isoform usage from RNA-seq data.\n\nAvailabilitySEVA is implemented in the R/Bioconductor package GSReg.\n\nContactbahman@jhu.edu, favorov@sensi.org, ejfertig@jhmi.edu

genomics↗

bioRxiv2016

PatternMarkers and Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS) for data-driven detection of novel biomarkers via whole transcriptome Non-negative matrix factorization (NMF)

SummaryNon-negative Matrix Factorization (NMF) algorithms associate gene expression with biological processes (e.g., time-course dynamics or disease subtypes). Compared with univariate associations, the relative weights of NMF solutions can obscure biomarkers. Therefore, we developed a novel PatternMarkers statistic to extract genes for biological validation and enhanced visualization of NMF results. Finding novel and unbiased gene markers with PatternMarkers requires whole-genome data. However, NMF algorithms typically do not converge for the tens of thousands of genes in genome-wide profiling. Therefore, we also developed Genome-Wide CoGAPS Analysis in Parallel Sets (GWCoGAPS), the first robust whole genome Bayesian NMF using the sparse, MCMC algorithm, CoGAPS. This software contains analytic and visualization tools including a Shiny web application, patternMatcher, which are generalized for any NMF. Using these tools, we find granular brain-region and cell-type specific signatures with corresponding biomarkers in GTex data, illustrating GWCoGAPS and patternMarkers ascertainment of data-driven biomarkers from whole-genome data.\n\nAvailabilityPatternMarkers & GWCoGAPS are in the CoGAPS Bioconductor package (3.5) under the GPL license.\n\nContactgsteinobrien@jhmi.edu; ccolantu@jhmi.edu; ejfertig@jhmi.edu

bioinformatics↗