Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.
Project description:Protein expression is regulated by production and degradation of mRNAs and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics to build a quantitative genomic model of the differential regulation of gene expression in LPS stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle. Mouse primary dendritic cells were treated with LPS or mock stimulus and profiled over a 12-hour time course. Cells were grown in M-labeled SILAC media, which was replaced with H-labeled SILAC media at time 0. Aliquots were taken at 0, 0.5, 1, 2, 3, 4, 5, 6, 9, and 12 hours post-stimulation and added to equal volumes of a master mix of unlabeled (L) cells for the purpose of normalization. RNA-Seq was performed at 0, 1, 2, 4, 6, 9, and 12 hours post-stimulation.
Project description:Fatal COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood because there are no biomarker-targeted therapeutics for patients with ARDS. We hypothesized that plasma proteomics may uncover unique biomarkers that correlate with disease severity in COVID-19 ARDS. We analyzed the circulating plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform, which measures 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at 2 time points (Days 1 and 7 following ICU admission). We compared differential protein abundance and SOFA scores at each individual time point and identified 119 proteins at Day 1 and 46 proteins at Day 7 that correlated with patient SOFA scores. We modeled the relationship between dynamic protein abundance and changes in SOFA score between Days 1 and 7 and identified 39 proteins that significantly correlated with changes in SOFA score. Using Ingenuity Pathway Analysis, we identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores over time, while pathways related to pulmonary fibrosis signaling and wound healing had an inverse relationship with SOFA scores between Days 1 and 7. These findings suggest that persistent inflammation may drive worsened disease severity, while repair processes correlate with improvements in organ dysfunction over time. This approach is generalizable to more diverse ARDS cohorts for identification of protein biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics.
Project description:Goals: 1) To analyse amyloid-b aggregation and proteomic and transcriptomic changes in 2, 5 and 8 months old 5xFAD mice 2) To determine differentially expressed proteins correlating and anti-correlating with aggregate formation 3) To analyse whether correlating or anti-correlating proteins change at transcriptional or posttranscriptional level 4) To determine differentially expressed or correlating and anti-correlating proteins which overlap with proteomic changes found in human AD brains 5) To analyse, whether Arl8b, which is an Ab aggregate correlating protein, show significant changes in CSF of AD patients Summary: Our study revealed that Ab42 driven aggregate formation leads to distinct brain region-specific proteome changes in 5xFAD mouse brains. We detected 195 dysregulated proteins correlating or anti-correlating with Ab-aggregation in hippocampus and cortex of 5xFAD mice. Most of these protein changes were caused by posttranscriptional mechanisms, only a minor part was associated with transcriptional dysregulation. A fraction of the Ab-correlated and anti-correlated DEPs was conserved in post-mortem brains of AD patients revealing that proteome changes in 5xFAD mice recapitulate disease-relevant changes in AD patient brains. Among the group of Ab42-correlating proteins, we have found the lysosome associated protein Arl8b, which is present in increased levels in CSF samples of AD patients and might have potential as an AD biomarker.
Project description:Saccharomyces cerevisiae is unique among yeasts for its ability to grow rapidly in the complete absence of oxygen. S. cerevisiae is therefore an ideal eukaryotic model to study physiological adaptation to anaerobiosis. Recent transcriptome analyses have identified hundreds of genes that are transcriptionally regulated by oxygen availability but the relevance of this cellular response has not been systematically investigated at the key control level of the proteome. Therefore, the proteomic response of the S. cerevisiae to anaerobiosis was investigated using metabolic stable isotope labeling in aerobic and anaerobic glucose-limited chemostat cultures, followed by proteome analysis to relatively quantify protein expression. Using independent replicate cultures and stringent statistical filtering, a robust dataset of 474 quantified proteins was generated, of which 249 showed differential expression levels. While some of these changes were consistent with previous transcriptome studies, many responses of S. cerevisiae to oxygen availability were hitherto unreported. Comparison of transcriptome and proteome from identical cultivations yielded strong evidence for post-transcriptional regulation of key cellular processes, including glycolysis, amino-acyl tRNA synthesis, purine-nucleotide synthesis and amino-acid biosynthesis. The use of chemostat cultures provided well-controlled and reproducible culture conditions, which are essential for generating robust datasets at different cellular information levels. Integration of transcriptome and proteome data led to new insights in the physiology of anaerobically growing yeast that would not have been apparent from differential analyses at either the messenger RNA or protein level alone, thus illustrating the power of multi-level studies in yeast systems biology. Protein levels versus transcript level: Systematic analysis of the control levels at which the yeast response to anaerobiosis takes place was performed using previously published transcript data obtained from yeast cultures grown under strictly identical conditions as described for the current proteome analysis. Affymetrix microarrays from five aerobic and four anaerobic independent culture replicates were used for this analysis. These comparison data are summarized in the table below. These array data are publicly available at the gene expression repository Gene Expression Omnibus under accession number GSE4804. Keywords: proteomic, nanoflow-LC-MS/MS
Project description:Cellular communication is a fundamental process in biology. We developed an in vitro procedure for quantitatively analyzing proteome-wide changes triggered by interaction of different cell types. Using an adipocyte-macrophage bilayer co-culture model we detected thousands of proteins and deciphered regulatory pathways involved in low-grade inflammation leading to insulin resistance. The method can be applied for multiple cell-cell combinations to gain understanding in cellular interaction.
Project description:Protein expression is regulated by production and degradation of mRNAs and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics to build a quantitative genomic model of the differential regulation of gene expression in LPS stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle.