Extracellular matrix (ECM) stiffening with downstream activation of mechanosensitive pathways is strongly implicated in fibrosis. We previously reported that altered collagen nanoarchitecture is a ...key determinant of pathogenetic ECM structure-function in human fibrosis (Jones et al., 2018). Here, through human tissue, bioinformatic and ex vivo studies we provide evidence that hypoxia-inducible factor (HIF) pathway activation is a critical pathway for this process regardless of the oxygen status (pseudohypoxia). Whilst TGFβ increased the rate of fibrillar collagen synthesis, HIF pathway activation was required to dysregulate post-translational modification of fibrillar collagen, promoting pyridinoline cross-linking, altering collagen nanostructure, and increasing tissue stiffness. In vitro, knockdown of Factor Inhibiting HIF (FIH), which modulates HIF activity, or oxidative stress caused pseudohypoxic HIF activation in the normal fibroblasts. By contrast, endogenous FIH activity was reduced in fibroblasts from patients with lung fibrosis in association with significantly increased normoxic HIF pathway activation. In human lung fibrosis tissue, HIF-mediated signalling was increased at sites of active fibrogenesis whilst subpopulations of human lung fibrosis mesenchymal cells had increases in both HIF and oxidative stress scores. Our data demonstrate that oxidative stress can drive pseudohypoxic HIF pathway activation which is a critical regulator of pathogenetic collagen structure-function in fibrosis.
Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS ...experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.
To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.
We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.
Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called "ROCS", freely available from the CRAN repository http://cran.r-project.org/.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Alveolar type II (ATII) epithelial cells function as stem cells, contributing to alveolar renewal, repair and cancer. Therefore, they are a highly relevant model for studying a number of lung ...diseases, including acute injury, fibrosis and cancer, in which signals transduced by RAS and transforming growth factor (TGF)-β play critical roles. To identify downstream molecular events following RAS and/or TGF-β activation, we performed proteomic analysis using a quantitative label-free approach (LC-HDMS
) to provide in-depth proteome coverage and estimates of protein concentration in absolute amounts. Data are available via ProteomeXchange with identifier PXD023720. We chose ATII
as an experimental cell line in which RAS is activated by adding 4-hydroxytamoxifen (4-OHT). Proteomic analysis of ATII cells treated with 4-OHT or TGF-β demonstrated that RAS activation induces an epithelial-mesenchymal transition (EMT) signature. In contrast, under the same conditions, activation of TGF-β signaling alone only induces a partial EMT. EMT is a dynamic and reversible biological process by which epithelial cells lose their cell polarity and down-regulate cadherin-mediated cell-cell adhesion to gain migratory properties, and is involved in embryonic development, wound healing, fibrosis and cancer metastasis. Thus, these results could help to focus research on the identification of processes that are potentially driving EMT-related human disease.
Mycobacterium tuberculosis (Mtb) cell wall glycolipid mannose‐capped lipoarabinomannan (ManLAM) inhibits CD4+ T‐cell activation by inhibiting proximal T‐cell receptor (TCR) signaling when activated ...by anti‐CD3. To understand the impact of ManLAM on CD4+ T‐cell function when both the TCR–CD3 complex and major costimulator CD28 are engaged, we performed label‐free quantitative MS and network analysis. Mixed‐effect model analysis of peptide intensity identified 149 unique peptides representing 131 proteins that were differentially regulated by ManLAM in anti‐CD3‐ and anti‐CD28‐activated CD4+ T cells. Crosstalker, a novel network analysis tool identified dysregulated translation, TCA cycle, and RNA metabolism network modules. PCNA, Akt, mTOR, and UBC were found to be bridge node proteins connecting these modules of dysregulated proteins. Altered PCNA expression and cell cycle analysis showed arrest at the G2M phase. Western blot confirmed that ManLAM inhibited Akt and mTOR phosphorylation, and decreased expression of deubiquitinating enzymes Usp9x and Otub1. Decreased NF‐κB phosphorylation suggested interference with CD28 signaling through inhibition of the Usp9x‐Akt‐mTOR pathway. Thus, ManLAM induced global changes in the CD4+ T‐cell proteome by affecting Akt‐mTOR signaling, resulting in broad functional impairment of CD4+ T‐cell activation beyond inhibition of proximal TCR–CD3 signaling.
Pluripotent stem cells are capable of differentiating into all cell types of the body and therefore hold tremendous promise for regenerative medicine. Despite their widespread use in laboratories ...across the world, a detailed understanding of the molecular mechanisms that regulate the pluripotent state is currently lacking. Mouse embryonic (mESC) and epiblast (mEpiSC) stem cells are two closely related classes of pluripotent stem cells, derived from distinct embryonic tissues. Although both mESC and mEpiSC are pluripotent, these cell types show important differences in their properties suggesting distinct pluripotent ground states. To understand the molecular basis of pluripotency, we analyzed the nuclear proteomes of mESCs and mEpiSCs to identify protein networks that regulate their respective pluripotent states. Our study used label-free LC-MS/MS to identify and quantify 1597 proteins in embryonic and epiblast stem cell nuclei. Immunoblotting of a selected protein subset was used to confirm that key components of chromatin regulatory networks are differentially expressed in mESCs and mEpiSCs. Specifically, we identify differential expression of DNA methylation, ATP-dependent chromatin remodeling and nucleosome remodeling networks in mESC and mEpiSC nuclei. This study is the first comparative study of protein networks in cells representing the two distinct, pluripotent states, and points to the importance of DNA and chromatin modification processes in regulating pluripotency. In addition, by integrating our data with existing pluripotency networks, we provide detailed maps of protein networks that regulate pluripotency that will further both the fundamental understanding of pluripotency as well as efforts to reliably control the differentiation of these cells into functional cell fates.
Little is known about proteomic differences between pluripotent human peripheral blood monocytes (MN) and their terminally‐differentiated pulmonary counterparts, alveolar macrophages (AM). To better ...characterize these cell populations, we performed a label‐free shotgun proteomics assessment of matched AM and MN preparations from eight healthy volunteers. With an FDR of less than 0.45%, we identified 1754 proteins within AM and 1445 from MN. Comparison of the two proteomes revealed that 1239 of the proteins found in AM were shared with MN, whereas 206 proteins were uniquely identified in MN and 515 were unique to AM. Molecular and cellular functions, protein classes, development associations, and membership in physiological systems and canonical pathways were identified among the detected proteins. Analysis of biologic processes represented by these proteomes indicated that MN were most prominently enriched for proteins involved in cellular movement and immune cell trafficking. In contrast, AM were enriched for proteins involved in protein trafficking, molecular transport, and cellular assembly and organization. These findings provide a baseline proteomic resource for further studies aimed at better understanding of the functional differences between MN and AM in both health and disease.
Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These ...methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the “bait compatibility index”, a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.
Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal ...complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy.
Diabetes mellitus is estimated to affect ∼24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which ...can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring.
Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular ...molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks.
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