Project description:To better understand proteostasis in health and disease, determination of protein half-lives is essential. We improved the precision and accuracy of peptide-ion intensity based quantification in order to enable accurate determination of protein turnover in non-dividing cells using dynamic-SILAC. This enabled precise and accurate protein half-life determination ranging from 10 to more than 1000 hours. We achieve good proteomic coverage ranging from four to six thousand proteins in several types of non-dividing cells, corresponding to a total of 9699 unique proteins over the entire dataset. Good agreement was observed in half-lives between B-cells, natural killer cells and monocytes, while hepatocytes and mouse embryonic neurons showed substantial differences. Our comprehensive dataset enabled extension and statistical validation of the previous observation that subunits of protein complexes tend to have coherent turnover. Furthermore, we observed complex architecture dependent turnover within complexes of the proteasome and the nuclear pore complex. Our method is broadly applicable and might be used to investigate protein turnover in various cell types.
Project description:As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodnessof- fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.
Project description:A defining characteristic of quiescent cells is their low level of gene activity compared to growing cells. Using a yeast model for cellular quiescence, we compared the genome-wide profiles of multiple histone modifications between growing and quiescent cells, and correlated these profiles with the presence of RNA polymerase II and its transcripts. Quiescent cells retained several forms of histone methylation normally associated with transcriptionally active chromatin and had many transcripts in common with growing cells. Quiescent cells also contained high levels of RNA polymerase II, but only low levels of the canonical initiating and elongating forms of the polymerase. The data suggest that the transcript and histone methylation marks in quiescent cells were either inherited from growing cells or established early during the development of quiescence and then retained in this non-growing cell population. This might ensure that quiescent cells can rapidly adapt to a changing environment to resume growth. RNA-seq analysis was performed in yeast Log-phase cells and purified Quiescent yeast cells and the transcriptomes in each were compared. The RNA data was correlated with genomic RNA polymerase II and histone H3 methylation occupancy profiles in the log and quiescent cells.
Project description:To better understand proteostasis in health and disease, determination of protein half-lives is essential. We improved the precision and accuracy of peptide-ion intensity based quantification in order to enable accurate determination of protein turnover in non-dividing cells using dynamic-SILAC. This enabled precise and accurate protein half-life determination ranging from 10 to more than 1000 hours. We achieve good proteomic coverage ranging from four to six thousand proteins in several types of non-dividing cells, corresponding to a total of 9699 unique proteins over the entire dataset. Good agreement was observed in half-lives between B-cells, natural killer cells and monocytes, while hepatocytes and mouse embryonic neurons showed substantial differences. Our comprehensive dataset enabled extension and statistical validation of the previous observation that subunits of protein complexes tend to have coherent turnover. Furthermore, we observed complex architecture dependent turnover within complexes of the proteasome and the nuclear pore complex. Our method is broadly applicable and might be used to investigate protein turnover in various cell types.
Project description:To better understand proteostasis in health and disease, determination of protein half-lives is essential. We improved the precision and accuracy of peptide-ion intensity based quantification in order to enable accurate determination of protein turnover in non-dividing cells using dynamic-SILAC. This enabled precise and accurate protein half-life determination ranging from 10 to more than 1000 hours. We achieve good proteomic coverage ranging from four to six thousand proteins in several types of non-dividing cells, corresponding to a total of 9699 unique proteins over the entire dataset. Good agreement was observed in half-lives between B-cells, natural killer cells and monocytes, while hepatocytes and mouse embryonic neurons showed substantial differences. Our comprehensive dataset enabled extension and statistical validation of the previous observation that subunits of protein complexes tend to have coherent turnover. Furthermore, we observed complex architecture dependent turnover within complexes of the proteasome and the nuclear pore complex. Our method is broadly applicable and might be used to investigate protein turnover in various cell types.
Project description:Genome-wide identification of transcription factor (TF) binding sites in the genome of the fission yeast Schizosaccharomyces pombe. The ChIP-nexus method was used. TFs included were: Cbf11-TAP and Cbf12-TAP (and their DBM mutants with impaired DNA binding), TAP-Mga2, and Fkh2-TAP (as an irrelevant control TF). IPs from an untagged WT strain were also analyzed. Cbf11-related IPs were performed from exponential cultures, while Cbf12-related IPs were performed from stationary cultures. YES complex medium was used for all cultivations.
Project description:The aim of this pilot study was to apply a novel combined metabolomic and proteomic approach in analysis of gestational diabetes mellitus. The investigation was performed with plasma samples derived from pregnant women with diagnosed gestational diabetes mellitus (n = 18) and a matched control group (n = 13). The mass spectrometry-based analyses allowed to determine 42 free amino acids and low molecular-weight peptide profiles. Different expressions of several peptides and altered amino acid profiles were observed in the analyzed groups. The combination of proteomic and metabolomic data allowed obtaining the model with a high discriminatory power, where amino acids ethanolamine, L-citrulline, L-asparagine, and peptide ions with m/z 1488.59; 4111.89 and 2913.15 had the highest contribution to the model. The sensitivity (94.44%) and specificity (84.62%), as well as the total group membership classification value (90.32%) calculated from the post hoc classification matrix of a joint model were the highest when compared with a single analysis of either amino acid levels or peptide ion intensities. The obtained results indicated a high potential of integration of proteomic and metabolomics analysis regardless the sample size. This promising approach together with clinical evaluation of the subjects can also be used in the study of other diseases.
Project description:Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations.
Project description:Background: Renal dysfunction is a common and serious complication in patients with end-stage liver diseases. While some patients recover renal function after liver transplantation (LT), others do not. Additionally, patients with normal kidney function (Normal-KF) before LT may develop post-transplant renal dysfunction. Early identification of patients at risk for impaired kidney function (Impaired-KF) post-LT is critical to improving outcomes. This study integrated metabolomic and proteomic analyses to investigate molecular profiles distinguishing Normal-KF from Impaired-KF post-LT. Methods: Nine LT recipients were classified into Normal-KF (n=5) and Impaired-KF (n=4) groups. One additional recipient with pre-transplant renal function impairment who recovered renal function after LT, was analyzed separately. Plasma samples were collected at 2- and 5-weeks post-LT. The metabolomic and proteomic profiles were assessed by untargeted liquid chromatography-tandem mass spectrometry. Results: Metabolomic analysis identified 29 significantly altered metabolites between Normal-KF and Impaired-KF (fold change>2, p<0.05). Proteomic analysis revealed 45 differentially expressed proteins (fold change>1.25, p<0.05). For the recovered patient, the metabolomic profile closely resembled Normal-KF, whereas the proteomic profile remained aligned with Impaired-KF at both 14- and 35-days post-LT. From week 2 to week 5, both the metabolomic and proteomic profiles of the recovered patient showed trends toward the Normal-KF. Conclusion: This study revealed distinct metabolomic and proteomic signatures associated with renal dysfunction post-LT. Proteomic profiles indicated a delayed recovery compared to metabolomic profiles, suggesting a dynamic and muti-layered renal recovery process. Further research is warranted to elucidate the functional implications of the differential proteins and metabolites for improved monitoring and therapeutic strategies.