Integrating metabolomics and transcriptomics reveals convergent pathways driving radiation-induced salivary gland dysfunction
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PROVIDER: PRJNA656037 | ENA |
REPOSITORIES: ENA
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Project description:Purpose: The goal of this study is to integrate NGS-derived parotid salivary gland transcriptome profiling (RNA-seq) to metabolomics (UPLC-MS/MS) methods and identify mechanisms driving radiation-induced xerostomia, with potential clinical application to head and neck cancer patients. Methods: Parotid salvary gland mRNA profiles of 28-day-old wild-type (WT) and 5 Gy radiation treated mice were generated by deep sequencing, in triplicate, using Illumina TruSeq. The sequenced reads that passed quality filters were trimmed with Trimmomatic, mapped to mm10 genome with HISAT2, and summarized at the gene level with HTSeq-count and analyzed with DESeq2. Results: Using an optimized data analysis workflow, we mapped sequenced reads per sample to the mouse genome (build mm10) and identified 25422 genes with at least 1 read in the parotid glands of WT and radiation treated mice with HISTA2 workflow. RNA-seq data QA/QC by Principal Component Analysis showed a clear separation between conditions. 155 genes showed significant differential expression between the WT and radiation treated salivary gland, with an adjsuted p-value < 0.05. Pre ranked Gene Set Enrichment Analysis against the CPDB database led to 859 altered pathways with a p-value < 0.05. Integrative analysis pointed to 103 RNA-Seq/Metabolite joint enriched pathways with a p-value < 0.05.
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2019-11-25 | PXD014186 | Pride
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Project description:Human-induced pluripotent stem cells (iPSCs) are derived from differentiated somatic cells using defined factors and provide a renewable source of autologous cells for cell therapy. Many reprogramming methods have been employed to generate human iPSCs, including the use of integrating vectors and non-integrating vectors. Maintenance of the genomic integrity of iPSCs is highly desirable if the cells are to be used in clinical applications. Here, using the Affymetrix Cytoscan HD array, we investigated the genomic aberration profiles of 19 human cell lines: 5 embryonic stem cell (ESC) lines, 6 iPSC lines derived using integrating vectors (“integrating iPSC lines”), 6 iPSC lines derived using non-integrating vectors (“non-integrating iPSC lines”), and the 2 parental cell lines from which the iPSCs were derived. The genome-wide copy number variation (CNV), loss of heterozygosity (LOH) and mosaicism patterns of integrating and non-integrating iPSC lines were investigated. The maximum sizes of CNVs in the genomes of the integrating iPSC lines were 20 times higher than those of the non-integrating iPSC lines. Moreover, the total number of CNVs was much higher in integrating iPSC lines than in other cell lines. The average numbers of novel CNVs with a low degree of overlap with the DGV and of likely pathogenic CNVs with a high degree of overlap with the ISCA database were highest in integrating iPSC lines. Different SNP calls revealed that, using the parental cell genotype as a reference, integrating iPSC lines displayed more single nucleotide variations and mosaicism than did non-integrating iPSC lines. This study describes the genome stability of human iPSCs generated using either a DNA-integrating or non-integrating reprogramming method, of the corresponding somatic cells, and of hESCs. Our results highlight the importance of using a high-resolution method to monitor genomic aberrations in iPSCs intended for clinical applications to avoid any negative effects of reprogramming or cell culture. 19 human cell lines: 5 embryonic stem cell (ESC) lines, 6 iPSC lines derived using integrating vectors (“integrating iPSC lines”), 6 iPSC lines derived using non-integrating vectors (“non-integrating iPSC lines”), and the 2 parental cell lines from which the iPSCs were derived.
2015-06-08 | E-GEOD-69632 | biostudies-arrayexpress
Project description:The interplay between pathogens and hosts has been studied for decades using targeted approaches such as the analysis of mutants and host immunological responses. Although much has been learned from such studies, they focus on individual pathways and fail to reveal the global effects of infection on the host. To alleviate this issue, high-throughput methods such as transcriptomics and proteomics have been used to study host-pathogen interactions. Recently, metabolomics was established as a new method to study changes in the biochemical composition of host tissues. We report a metabolomics study of Salmonella enterica serovar Typhimurium infection. We used Fourier Transform Ion Cyclotron Resonance Mass Spectrometry with Direct Infusion to reveal that dozens of host metabolic pathways are affected by Salmonella in a murine infection model. In particular, multiple host hormone pathways are disrupted. Our results identify unappreciated effects of infection on host metabolism and shed light on mechanisms used by Salmonella to cause disease, and by the host to counter infection. Female C57BL/6 mice were infected with Salmonella enterica serovar Typhimurium SL1344 cells by oral gavage. Feces and livers were collected and metabolites extracted using acetonitrile. For experiments with feces, samples were collected from 4 mice before and after infection. For liver experiments, 11 uninfected and 11 infected mice were used. Samples were combined into 3 groups of 3-4 mice each, resulting in the analysis of 3 group samples of uninfected and 3 of infected mice. Extracts were infused into a 12-T Apex-Qe hybrid quadrupole-FT-ICR mass spectrometer equipped with an Apollo II electrospray ionization source, a quadrupole mass filter and a hexapole collision cell. Raw mass spectrometry data were processed as described elsewhere (Han et al. 2008. Metabolomics. 4:128-140 [PMID 19081807]). To identify differences in metabolite composition between uninfected and infected samples, we filtered the list of masses for metabolites which were present on one set of samples but not the other. Additionally, we calculated the ratios between averaged intensities of metabolites from uninfected and infected mice. To assign possible metabolite identities, monoisotopic neutral masses of interest were queried against MassTrix (http://masstrix.org). Masses were searched against the Mus musculus database within a mass error of 3 ppm. Data were analyzed by unpaired t tests with 95% confidence intervals.
2011-02-08 | E-GEOD-21720 | biostudies-arrayexpress
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Project description:Salivary glands develop as highly branched structures designed to produce and secrete saliva. Advances in mouse genetics, stem cell biology, and regenerative medicine are having a tremendous impact on our understanding of salivary gland organogenesis. Understanding how submandibular gland (SMG) initiation, branching morphogenesis, and cell differentiation occur, as well as defining the progenitor/stem cells and cell and tissue interactions that drive SMG development will help guide regenerative approaches for patients suffering from loss of salivary gland function. This review focuses on recent literature from the past 5 years investigating the regulatory mechanisms driving SMG organogenesis.
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Project description:PurposeTo identify metabolomic biomarkers of acute radiation exposure in saliva that show time-dependent changes.Methods and materialsNonhuman primates were exposed to 4 Gy of total body irradiation with γ-rays. Saliva was collected from 7 animals twice before and at days 1, 3, 5, 7, 15, 21, 28, and 60 after irradiation. Profiling was conducted with liquid chromatography time-of-flight mass spectrometry. Multivariate data analysis and potential biomarker identification was conducted through random Forests and the software MetaboAnalyst. Candidate biomarkers were validated through tandem mass spectrometry, and receiver operating characteristic curves were constructed to show the diagnostic ability of the signature over time.ResultsUntargeted metabolomic analysis revealed significant and persistent effects up to the 60 days evaluated in this study. Biomarkers spanning primarily amino acids and nucleotides were identified, with a significant number showing long-term responses. Fifteen biomarkers showed high statistical significance in the first week after irradiation and 16 at >7 days after irradiation (false discovery rate-adjusted P < .05). The combination of the biomarkers in a single biosignature was able to accurately show the diagnostic ability of the signature in a binary classifier system with receiver operating characteristic curves.ConclusionsRadiation can alter the metabolome in saliva, and metabolomics could effectively be used to monitor radiation responses, as a biodosimetry method, in the event of a radiological incident. Saliva metabolomics also has potential relevance in a clinical setting.
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