Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast.

Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.

dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.

The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.

A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study.

Background: Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies. Methods: We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688). Findings: The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores. Interpretation: Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials. Funding: This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection.

The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.

Increased prevalence of microangiopathic brain lesions among siblings of patients with lacunar stroke. A prospective multicenter study.

BACKGROUND: Family and twin studies suggest predisposing genetic factors in stroke. Lacunar infarcts represent a homogeneous phenotype, which is a prerequisite for genetic analyses. Applying an affected sib -pair analysis, we prospectively assessed the prevalence of microangiopathic brain lesions (MBL) and associated risk factors among siblings of patients with lacunar stroke. METHODS: Index patients fulfilled clinical criteria of a lacunar stroke in combination with a corresponding MBL on CT or MRI. Siblings were characterized as affected if MBL demonstrated on MRI. The prevalence of MBL was compared with population prevalence data extracted from other studies. RESULTS: From 784 patients screened, 81 index patients with lacunar stroke and 97 siblings were recruited, of which 42% were identified as affected. Compared with data from unselected historical controls and stratified by age groups, prevalence was between 2 and 5 times higher. CONCLUSIONS: Our results indicate that genetic stroke studies are feasible even in subtypes of ischemic stroke. The high prevalence of MBL among siblings of patients with lacunar infarct might suggest a familial aggregation. However, due to the small sample size these results need to be interpreted with caution and require confirmation by planned genetic analyses.

Tumor cell lines expressing the proteasome subunit isoform LMP7E1 exhibit immunoproteasome deficiency.

The immune system can recognize antigenic peptides derived from tumors by their presentation on MHC class I complexes to CTLs. Immunoproteasomes (i20S) can substantially enhance the MHC class I peptide repertoire, making down-regulation of i20S an important strategy of tumor cells in manipulating immune surveillance. Here, we report that human cancer cells express the nonfunctional immunosubunit-variant LMP7E1, in addition to, or instead of LMP7E2, in response to IFN-gamma. This preferential expression of LMP7E1 and the consequent down-regulation of LMP7E2 results in i20S deficiency. The molecular explanation for this phenomenon is the incapacity of LMP7E1 to interact efficiently with the proteasome maturation protein, which regularly recruits LMP7E2 into nascent i20S precursor complexes. In contrast to previous reports, i20S formation in these cancer cells cannot be restored by IFN-gamma treatment. However, expression of LMP7E2 in these cells restores the i20S-deficient phenotype. Thus, our data describe a novel mechanism that contributes to the process of oncogenesis.

IFN-gamma-induced immune adaptation of the proteasome system is an accelerated and transient response.

Peptide generation by the proteasome is rate-limiting in MHC class I-restricted antigen presentation in response to IFN-gamma. IFN-gamma-induced de novo formation of immunoproteasomes, therefore, essentially supports the rapid adjustment of the mammalian immune system. Here, we report that the molecular interplay between the proteasome maturation protein (POMP) and the proteasomal beta5i subunit low molecular weight protein 7 (LMP7) has a key position in this immune adaptive program. IFN-gamma-induced coincident biosynthesis of POMP and LMP7 and their direct interaction essentially accelerate immunoproteasome biogenesis compared with constitutive 20S proteasome assembly. The dynamics of this process is determined by rapid LMP7 activation and the immediate LMP7-dependent degradation of POMP. Silencing of POMP expression impairs recruitment of both beta5 subunits into the proteasome complex, resulting in decreased proteasome activity, reduced MHC class I surface expression, and induction of apoptosis. Furthermore, our data reveal that immunoproteasomes exhibit a considerably shortened half-life, compared with constitutive proteasomes. In consequence, our studies demonstrate that the cytokine-induced rapid immune adaptation of the proteasome system is a tightly regulated and transient response allowing cells to return rapidly to a normal situation once immunoproteasome function is no longer required.

The bronchiolar epithelium as a prominent source of pro-inflammatory cytokines after lung irradiation.

PURPOSE: To study in detail the temporal and spatial release of the pro-inflammatory cytokines tumor necrosis factor alpha, interleukin (IL)-1alpha, and IL-6 in the lung tissue of C57BL/6 mice after thoracic irradiation with 12 Gy. METHODS AND MATERIALS: C57BL/6J mice were exposed to either sham irradiation or a single fraction of 12 Gy delivered to the thorax. Treated and sham-irradiated control mice were killed at 0.5 h, 1 h, 3 h, 6 h, 12 h, 24 h, 48 h, 72 h, 1 week, 2 weeks, 4 weeks, 8 weeks, 16 weeks, and 24 weeks post-irradiation (p.i.). Real-time multiplex reverse transcriptase polymerase chain reaction was established to evaluate the relative messenger RNA (mRNA) expression of TNF-alpha, IL-1alpha, and IL-6 in the lung tissue of the mice (compared with nonirradiated lung tissue). Immunohistochemical detection methods (alkaline phosphatase anti-alkaline phosphatase, avidin-biotin-complex [ABC]) and automated image analysis were used to quantify the protein expression of TNF-alpha, IL-1alpha, and IL-6 in the lung tissue (percentage of the positively stained area). RESULTS: Radiation-induced release of the pro-inflammatory cytokines TNF-alpha, IL-1alpha, and IL-6 in the lung tissue was detectable within the first hours after thoracic irradiation. We observed statistically significant up-regulations for TNF-alpha at 1 h p.i. on mRNA (4.99 +/- 1.60) and at 6 h p.i. on protein level (7.23% +/- 1.67%), for IL-1alpha at 6 h p.i. on mRNA (11.03 +/- 0.77) and at 12 h p.i. on protein level (27.58% +/- 11.06%), for IL-6 at 6 h p.i. on mRNA (6.0 +/- 3.76) and at 12 h p.i. on protein level (7.12% +/- 1.93%). With immunohistochemistry, we could clearly demonstrate that the bronchiolar epithelium is the most prominent source of these inflammatory cytokines in the first hours after lung irradiation. During the stage of acute pneumonitis, the bronchiolar epithelium, as well as inflammatory cells in the lung interstitium, produced high amounts of TNF-alpha (with the maximal value at 4 weeks p.i.: 9.47% +/- 1.78%), IL-1alpha (with the peak value at 8 weeks p.i.: 14.76% +/- 7.77%), and IL-6 (with the peak value at 8 weeks p.i.: 4.28% +/- 1.33%). CONCLUSIONS: In the present study we have clearly demonstrated the immediate expression of the pro-inflammatory cytokines TNF-alpha, IL-1alpha, and IL-6 in the bronchiolar epithelium in the first hours after lung irradiation. A second, long-lasting release of these cytokines by the bronchiolar and alveolar epithelium, as well as by inflammatory cells, was observed at the onset of acute pneumonitis. Therefore, we postulate that lung irradiation causes immediate epithelial reaction, with the bronchiolar epithelium becoming a significant source of pro-inflammatory cytokines capable of promoting inflammation through recruitment and activation of inflammatory cells.

Radiosensitivity of tumor cell lines after pretreatment with the EGFR tyrosine kinase inhibitor ZD1839 (Iressa).

BACKGROUND AND PURPOSE: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor ZD1839 (Iressa) reduces survival and augments radiation response of certain tumor cells. The aim of this study was to identify cellular events that are associated with the modulation of radiosensitivity by ZD1839. MATERIAL AND METHODS: Three tumor cell lines (A549, H596, FaDu) were exposed to ionizing radiation, treatment with ZD1839, and combined treatment. Clonogenic cell survival was determined by colony assays, EGFR and transforming growth factor-(TGF-)alpha expression by quantitative real-time reverse transcription-polymerase chain reaction (RT-PCR), cell cycle distribution and apoptosis by flow cytometry. RESULTS: In A549 and H596 cells ZD1839 had little effect on clonogenic growth, but survival curves revealed a radiosensitizing effect of 5 microM ZD1839 on A549 cells. Both cell lines expressed moderate amounts of EGFR mRNA and very low levels of TGF-alpha mRNA. FaDu cells expressed relatively high amounts of EGFR and TGF-alpha transcripts and showed marked inhibition of clonogenic growth, reduction of S-phase cells, and induction of apoptosis after treatment with 1 microM ZD1839 and combined treatment. Surprisingly, the subpopulation of FaDu cells surviving ZD1839 pretreatment was more radioresistant. Exposure to ZD1839 caused a decrease in EGFR mRNA expression in A549 cells, no change in H596, and even an increase in FaDu cells. CONCLUSION: The sensitivity to ZD1839 correlated with the EGFR expression level, an inhibition of cell proliferation, and induction of apoptosis in the cell lines analyzed. A radiosensitizing effect of ZD1839 was associated with downregulation of EGFR mRNA expression.