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1.
Nat Commun ; 12(1): 5399, 2021 09 13.
Artigo em Inglês | MEDLINE | ID: mdl-34518535

RESUMO

Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with data-independent acquisition (DIA)-MS and neural network-based data processing specifically optimized for ubiquitinomics. Compared to data-dependent acquisition (DDA), our method more than triples identification numbers to 70,000 ubiquitinated peptides in single MS runs, while significantly improving robustness and quantification precision. Upon inhibition of the oncology target USP7, we simultaneously record ubiquitination and consequent changes in abundance of more than 8,000 proteins at high temporal resolution. While ubiquitination of hundreds of proteins increases within minutes of USP7 inhibition, we find that only a small fraction of those are ever degraded, thereby dissecting the scope of USP7 action. Our method enables rapid mode-of-action profiling of candidate drugs targeting DUBs or ubiquitin ligases at high precision and throughput.

2.
Life Sci Alliance ; 4(9)2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34226277

RESUMO

Here, we recorded serum proteome profiles of 33 severe COVID-19 patients admitted to respiratory and intensive care units because of respiratory failure. We received, for most patients, blood samples just after admission and at two more later time points. With the aim to predict treatment outcome, we focused on serum proteins different in abundance between the group of survivors and non-survivors. We observed that a small panel of about a dozen proteins were significantly different in abundance between these two groups. The four structurally and functionally related type-3 cystatins AHSG, FETUB, histidine-rich glycoprotein, and KNG1 were all more abundant in the survivors. The family of inter-α-trypsin inhibitors, ITIH1, ITIH2, ITIH3, and ITIH4, were all found to be differentially abundant in between survivors and non-survivors, whereby ITIH1 and ITIH2 were more abundant in the survivor group and ITIH3 and ITIH4 more abundant in the non-survivors. ITIH1/ITIH2 and ITIH3/ITIH4 also showed opposite trends in protein abundance during disease progression. We defined an optimal panel of nine proteins for mortality risk assessment. The prediction power of this mortality risk panel was evaluated against two recent COVID-19 serum proteomics studies on independent cohorts measured in other laboratories in different countries and observed to perform very well in predicting mortality also in these cohorts. This panel may not be unique for COVID-19 as some of the proteins in the panel have previously been annotated as mortality markers in aging and in other diseases caused by different pathogens, including bacteria.


Assuntos
COVID-19/sangue , COVID-19/mortalidade , Proteoma/metabolismo , Índice de Gravidade de Doença , Idoso , COVID-19/virologia , Estudos de Coortes , Feminino , Hospitalização , Humanos , Imunoglobulinas/sangue , Masculino , SARS-CoV-2/fisiologia , Sobreviventes
3.
Cell Syst ; 12(8): 780-794.e7, 2021 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-34139154

RESUMO

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.


Assuntos
Biomarcadores/análise , COVID-19/patologia , Progressão da Doença , Proteoma/fisiologia , Fatores Etários , Contagem de Células Sanguíneas , Gasometria , Ativação Enzimática , Humanos , Inflamação/patologia , Aprendizado de Máquina , Prognóstico , Proteômica , SARS-CoV-2/imunologia
4.
Nat Biotechnol ; 39(7): 846-854, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33767396

RESUMO

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.


Assuntos
Proteômica/métodos , Espectrometria de Massas em Tandem , Arabidopsis/metabolismo , Biomarcadores/metabolismo , COVID-19/sangue , COVID-19/diagnóstico , Linhagem Celular , Humanos , Peptídeos/análise , Proteoma/análise , Saccharomyces cerevisiae/metabolismo , Proteínas de Saccharomyces cerevisiae/metabolismo , Índice de Gravidade de Doença
5.
Cell Syst ; 11(1): 11-24.e4, 2020 07 22.
Artigo em Inglês | MEDLINE | ID: mdl-32619549

RESUMO

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.


Assuntos
Proteínas Sanguíneas/metabolismo , Infecções por Coronavirus/sangue , Pneumonia Viral/sangue , Proteômica/métodos , Adulto , Idoso , Idoso de 80 Anos ou mais , Betacoronavirus/isolamento & purificação , Biomarcadores/sangue , Proteínas Sanguíneas/análise , COVID-19 , Infecções por Coronavirus/classificação , Infecções por Coronavirus/patologia , Infecções por Coronavirus/virologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Pandemias/classificação , Pneumonia Viral/classificação , Pneumonia Viral/patologia , Pneumonia Viral/virologia , SARS-CoV-2 , Adulto Jovem
6.
Nat Methods ; 17(1): 41-44, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31768060

RESUMO

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.


Assuntos
Ensaios de Triagem em Larga Escala/métodos , Espectrometria de Massas/métodos , Redes Neurais de Computação , Proteoma/análise , Proteômica/métodos , Software , Zea mays/metabolismo , Células HeLa , Humanos , Especificidade da Espécie
7.
Cell Syst ; 7(3): 269-283.e6, 2018 09 26.
Artigo em Inglês | MEDLINE | ID: mdl-30195436

RESUMO

A challenge in solving the genotype-to-phenotype relationship is to predict a cell's metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype.


Assuntos
Fosfotransferases/genética , Saccharomyces cerevisiae/fisiologia , Deleção de Sequência/genética , Regulação Fúngica da Expressão Gênica , Técnicas de Inativação de Genes , Estudos de Associação Genética , Aprendizado de Máquina , Metaboloma , Microrganismos Geneticamente Modificados , Proteoma
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