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1.
Cell ; 182(1): 59-72.e15, 2020 07 09.
Artículo en Inglés | MEDLINE | ID: mdl-32492406

RESUMEN

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.


Asunto(s)
Infecciones por Coronavirus/sangre , Metabolómica , Neumonía Viral/sangre , Proteómica , Adulto , Aminoácidos/metabolismo , Biomarcadores/sangre , COVID-19 , Análisis por Conglomerados , Infecciones por Coronavirus/fisiopatología , Femenino , Humanos , Metabolismo de los Lípidos , Aprendizaje Automático , Macrófagos/patología , Masculino , Persona de Mediana Edad , Pandemias , Neumonía Viral/fisiopatología , Índice de Severidad de la Enfermedad
2.
Protein Expr Purif ; 85(1): 125-32, 2012 Sep.
Artículo en Inglés | MEDLINE | ID: mdl-22800658

RESUMEN

An important bottleneck in the use of infrared spectroscopy as a powerful tool for obtaining detailed information on protein structure is the assignment of vibrational modes to specific amino acid residues. Side-chain specific isotopic labeling is a general approach towards obtaining such assignments. We report a method for high yield isotope editing of the bacterial blue light sensor photoactive yellow protein (PYP) containing ring-D(4)-Tyr. PYP was heterologously overproduced in Escherichia coli in minimal media containing ring-D(4)-Tyr in the presence of glyphosate, which inhibits endogenous biosynthesis of aromatic amino acids (Phe, Trp, and Tyr). Mass spectrometry of the intact protein and of tryptic peptides unambiguously demonstrated highly specific labeling of all five Tyr residues in PYP with 98% incorporation and undetectable isotopic scrambling. FTIR spectroscopy of the protein reveals a characteristic Tyr ring vibrational mode at 1515 cm(-1) that is shifted to 1436 cm(-1), consistent with that from ab initio calculations. PYP is a model system for protein structural dynamics and for receptor activation in biological signaling. The results described here open the way to the analysis of PYP using isotope-edited FTIR spectroscopy with side-chain specific labeling.


Asunto(s)
Proteínas Bacterianas/química , Proteínas Bacterianas/genética , Halorhodospira halophila/química , Halorhodospira halophila/genética , Fotorreceptores Microbianos/química , Fotorreceptores Microbianos/genética , Tirosina/química , Clonación Molecular , Escherichia coli/genética , Marcaje Isotópico , Espectrometría de Masas , Espectroscopía Infrarroja por Transformada de Fourier , Regulación hacia Arriba
3.
Cell Discov ; 8(1): 70, 2022 Jul 25.
Artículo en Inglés | MEDLINE | ID: mdl-35879274

RESUMEN

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

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