Biology subjects

Horvatovich, P.

Publications and source records attributed to Horvatovich, P..

4 recordsLinked to original sources

bioRxiv2022

Age-associated Differences in the Human Lung Extracellular Matrix

IntroductionExtracellular matrix (ECM) remodelling has been associated with chronic lung diseases. However, information about specific age-associated differences in lung ECM is currently limited. In this study we aimed to identify and localize age-associated ECM differences in human lung using comprehensive transcriptomic, proteomic and immunohistochemical analyses. MethodsOur previously identified age-associated gene expression signature of the lung was re-analysed limiting it to an aging signature based on 270 control patients (37-80 years) and focused on the Matrisome core geneset using geneset enrichment analysis. To validate the age-associated transcriptomic differences on protein level, we compared the age-associated ECM genes (F <0.05) with a profile of age-associated proteins identified from a lung tissue proteomics dataset from 9 control patients (49-76 years) (FDR<0.05). Extensive immunohistochemical analysis was used to localize the age-associated ECM differences in lung tissues from control patients (9-82 years). ResultsComparative analysis of transcriptomic and proteomic data identified 7 ECM proteins with higher expression with age at both gene and protein level: COL1A1, COL6A1, COL6A2, COL14A1, FBLN2, LTBP4 and LUM. With immunohistochemistry we demonstrated higher protein expression with age for COL6A2 in whole tissue, parenchyma, airway wall and blood vessel, for COL14A1 in bronchial epithelium and blood vessel, and for FBLN2 and COL1A1 in lung parenchyma. ConclusionOur study revealed that higher age is associated with lung ECM remodelling, with specific differences occurring in defined regions within the lung. These differences may affect lung structure and physiology with aging and as such may increase susceptibility for developing chronic lung diseases. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSsummarise the state of scientific knowledge on this subject before you did your study and why this study needed to be done. In animal models, it has been demonstrated that aging alters the composition of the lung ECM, with more deposition of collagen and degradation of elastin. Similar ECM differences have been observed in age-associated chronic lung diseases, including COPD; moreover, we observed in lung tissue that several ECM genes associate differently with age in COPD patients compared to non-COPD controls(1). Detailed knowledge on age-associated changes in specific ECM proteins as well as regional differences within the lung is lacking. What this study addssummarise what we now know as a result of this study that we did not know before. We identified 7 age-associated ECM proteins i.e. COL1A1, COL6A1, COL6A2 COL14A1, FBLN2, LTBP4 and LUM with higher transcript and protein levels in human lung tissue with age. Extensive immunohistochemical analysis revealed significant age-associated differences for 3 of these ECM proteins in specific compartments of the lung, with the most notable differences in the blood vessels and parenchyma. How this study might affect research, practice, or policysummarise the implications of this study. The identification of age-associated differences in specific human lung ECM proteins lays a new foundation for the investigation of ECM differences in age-associated chronic lung diseases. Additionally, examining the function of these age-associated ECM proteins and their cellular interactions in lung injury and repair responses may provide novel insight in mechanisms underlying chronic lung diseases.

molecular biology↗

bioRxiv2022

Comparative assessment of quantification methods for tumor tissue phosphoproteomics

With increasing sensitivity and accuracy in mass spectrometry, the tumor phosphoproteome is getting into reach. However, the selection of quantitation techniques best-suited to the biomedical question and diagnostic requirements remains a trial and error decision as no study has directly compared their performance for tumor tissue phosphoproteomics. We compared label-free quantification (LFQ), spike-in-SILAC (stable isotope labeling by amino acids in cell culture) and TMT isobaric tandem mass tags technology for quantitative phosphosite profiling in tumor tissue. TMT offered the lowest accuracy and the highest precision and robustness towards different phosphosite abundances and matrices. Spike-in-SILAC offered the best compromise between these features but suffered from a low phosphosite coverage. LFQ offered the lowest precision but the highest number of identifications. Both spike-in-SILAC and LFQ presented susceptibility to matrix effects. Match between run (MBR)-based analysis enhanced the phosphosite coverage across technical replicates in LFQ and spike-in-SILAC but further reduced the precision and robustness of quantification. The choice of quantitative methodology is critical for both study design such as sample size in sample groups and quantified phosphosites, and comparison of published cancer phosphoproteomes. Using ovarian cancer tissue as an example, our study builds a resource for the design and analysis of quantitative phosphoproteomic studies in cancer research and diagnostics.

biochemistry↗

bioRxiv2021

Proteogenomics Reveals how Metastatic Melanoma Modulates the Immune System to Allow Immune Evasion

Malignant melanoma (MM) develops from the melanocytes and in its advanced stage is the most aggressive type of skin cancer. Here we report a comprehensive analysis on a prospective cohort study, including non-tumor, primary and metastasis tissues (n=77) with the corresponding plasma samples (n=56) from patients with malignant melanoma. The tumors and surrounding tissues were characterized with a combination of high-throughput analyses including quantitative proteomics, phosphoproteomics, acetylomics, and whole exome sequencing (WES) combined with in-depth histopathology analysis. Melanoma cell proliferation highly correlates with dysregulation at the proteome, at the posttranslational- and at the transcriptome level. Some of the changes were also verified in the plasma proteome. The metabolic reprogramming in melanoma includes upregulation of the glycolysis and the oxidative phosphorylation, and an increase in glutamine consumption, while downregulated proteins involved in the degradation of amino acids, fatty acids, and the extracellular matrix (ECM) receptor interaction. The pathways most dysregulated in MM including the MAP kinases-, the PI3K-AKT signaling, and the calcium homeostasis, are among the most affected by mutations, thus, dysregulation in these pathways can be manifested as drivers in melanoma development and progression. The phosphoproteome analysis combined with target-based prediction mapped 75% of the human kinome. Melanoma cell proliferation was driven by two key factors: i) metabolic reprogramming leading to upregulation of the glycolysis and oxidative phosphorylation, supported by HIF-1 signaling pathway and mitochondrial translation; and ii) a dysregulation of the immune system response, which was mirrored by immune system processes in the plasma proteome. Regulation of the melanoma acetylome and expression of deacetylase enzymes discriminated between groups based on tissue origin and proliferation, indicating a way to guide the successful use of HDAC inhibitors in melanoma. The disease progression toward metastasis is driven by the downregulation of the immune system response, including MHC class I and II, which allows tumors to evade immune surveillance. Altogether, new evidence is provided at different molecular levels to allow improved understanding of the melanoma progression, ultimately contributing to better treatment strategies. TOC figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/439245v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@937db9org.highwire.dtl.DTLVardef@1230b57org.highwire.dtl.DTLVardef@1bd889eorg.highwire.dtl.DTLVardef@547d90_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2020

Dynamic binning peak detection and assessment of various lipidomics liquid chromatography-mass spectrometry pre-processing platforms

Liquid chromatography-mass spectrometry (LC-MS) based lipidomics generate a large dataset, which requires high-performance data pre-processing tools for their interpretation such as XCMS, mzMine and Progenesis. These pre-processing tools rely heavily on accurate peak detection, which depends on setting the peak detection mass tolerance (PDMT) properly. The PDMT is usually set with a fixed value in either ppm or Da units. However, this fixed value may result in duplicates or missed peak detection. Therefore, we developed the dynamic binning method for accurate peak detection, which takes into account the peak broadening described by well-known physics laws of ion separation and set dynamically the value of PDMT as a function of m/z. Namely, in our method, the PDMT is proportional to [Formula] for FTICR, to [Formula] for Orbitrap, to m/z for Q-TOF and is a constant for Quadrupole mass analyzer, respectively. The dynamic binning method was implemented in XCMS [1,2] and the adopted source code is available in the Appendix. Our further goal was to compare the performance of different lipidomics pre-processing tools to find differential compounds. We have generated set samples with 43 lipids internal standards differentially spiked to aliquots of one human plasma lipid sample using Orbitrap LC-MS/MS. The performance of the various pipelines using aligned parameter sets was quantified by a quality score system which reflects the ability of a pre-processing pipeline to detect differential peaks spiked at various concentration levels. The quality score indicates that the dynamic binning method improves the performance of XCMS (maximum p-value 9.8{middle dot}10-3 of two-sample Wilcoxon test). The modified XCMS software was further compared with mzMine and Progenesis. The results showed that modified XCMS and Progenesis had a similarly good performance in the aspect of finding differential compounds. In addition, Progenesis shows lower variability as indicated by lower CVs, followed by XCMS and mzMine. The lower variability of Progenesis improve the quantification, however, provide an incorrect quantification abundance order of spiked-in internal standards.

biochemistry↗