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1.
J Nanobiotechnology ; 21(1): 41, 2023 Feb 05.
Artículo en Inglés | MEDLINE | ID: mdl-36740689

RESUMEN

Clinically, activated EGFR mutation associated chemo-drugs resistance has severely threaten NSCLC patients. Nanoparticle based small interfering RNA (siRNA) therapy representing another promising alternative by silencing specific gene while still suffered from charge associated toxicity, strong immunogenicity and poor targetability. Herein, we reported a novel EGFR-mutant NSCLC therapy relying on edible and cation-free kiwi-derived extracellular vesicles (KEVs), which showed sevenfold enhancement of safe dosage compared with widely used cationic liposomes and could be further loaded with Signal Transducer and Activator of Transcription 3 interfering RNA (siSTAT3). siSTAT3 loaded KEVs (STAT3/KEVs) could be easily endowed with EGFR targeting ability (STAT3/EKEVs) and fluorescence by surface modification with tailor-making aptamer through hydrophobic interaction. STAT3/EKEVs with a controlled size of 186 nm displayed excellent stability, high specificity and good cytotoxicity towards EGFR over-expressing and mutant PC9-GR4-AZD1 cells. Intriguingly, the systemic administration of STAT3/EKEVs significantly suppressed subcutaneous PC9-GR4-AZD1 tumor xenografts in nude mice by STAT3 mediated apoptosis. This safe and robust KEVs has emerged as the next generation of gene delivery platform for NSCLC therapy after multiple drug-resistance.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Animales , Ratones , Humanos , ARN Interferente Pequeño/química , Ratones Desnudos , Frutas/metabolismo , Línea Celular Tumoral , Neoplasias Pulmonares/tratamiento farmacológico , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patología , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Carcinoma de Pulmón de Células no Pequeñas/genética , Receptores ErbB/genética , Receptores ErbB/metabolismo , Resistencia a Antineoplásicos/genética , Factor de Transcripción STAT3/genética , Factor de Transcripción STAT3/metabolismo
2.
Exp Neurol ; 354: 114101, 2022 08.
Artículo en Inglés | MEDLINE | ID: mdl-35504346

RESUMEN

Itch is an unpleasant sensation that induces the desire to scratch. Except for a sketchy map focusing on neural mechanisms underlying itch processing being drawn at the peripheral and spinal level over the past decades, the brain mechanisms remain poorly understood. Several previous studies indicated that anterior cingulate cortex (ACC) and prelimbic cortex (PrL), two subregions of the medial prefrontal cortex (mPFC) play an important role in regulating itch processing. However, the knowledge about whether infralimbic cortex (IL), another subregion of mPFC, is involved in modulating itch processing remains unclear. Here, we showed that the activity of IL excitatory pyramidal neurons was significantly elevated during itch-related scratching, and pharmacogenetic inhibition of IL pyramidal neurons significantly impaired itch-related scratching. Moreover, IL-medial striatum (MS) projections were verified as a critical neural pathway for modulating itch processing. Therefore, the present study firstly presents the regulatory function of IL pyramidal neurons during itch processing and also reveals that IL-MS projections are involved in modulating the itch processing.


Asunto(s)
Giro del Cíngulo , Corteza Prefrontal , Corteza Cerebral/metabolismo , Cuerpo Estriado/metabolismo , Humanos , Vías Nerviosas/fisiología , Corteza Prefrontal/metabolismo , Prurito/metabolismo
3.
J Integr Plant Biol ; 63(10): 1740-1752, 2021 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-34002536

RESUMEN

Photosystem I (PSI) is a large protein supercomplex that catalyzes the light-dependent oxidation of plastocyanin (or cytochrome c6 ) and the reduction of ferredoxin. This catalytic reaction is realized by a transmembrane electron transfer chain consisting of primary electron donor (a special chlorophyll (Chl) pair) and electron acceptors A0 , A1 , and three Fe4 S4 clusters, FX , FA , and FB . Here we report the PSI structure from a Chl d-dominated cyanobacterium Acaryochloris marina at 3.3 Å resolution obtained by single-particle cryo-electron microscopy. The A. marina PSI exists as a trimer with three identical monomers. Surprisingly, the structure reveals a unique composition of electron transfer chain in which the primary electron acceptor A0 is composed of two pheophytin a rather than Chl a found in any other well-known PSI structures. A novel subunit Psa27 is observed in the A. marina PSI structure. In addition, 77 Chls, 13 α-carotenes, two phylloquinones, three Fe-S clusters, two phosphatidyl glycerols, and one monogalactosyl-diglyceride were identified in each PSI monomer. Our results provide a structural basis for deciphering the mechanism of photosynthesis in a PSI complex with Chl d as the dominating pigments and absorbing far-red light.


Asunto(s)
Clorofila/metabolismo , Cianobacterias/química , Feofitinas/metabolismo , Complejo de Proteína del Fotosistema I/química , Microscopía por Crioelectrón , Cianobacterias/metabolismo , Cianobacterias/ultraestructura , Transporte de Electrón , Complejo de Proteína del Fotosistema I/metabolismo , Complejo de Proteína del Fotosistema I/ultraestructura , Estructura Cuaternaria de Proteína
4.
Nat Commun ; 12(1): 1100, 2021 02 17.
Artículo en Inglés | MEDLINE | ID: mdl-33597543

RESUMEN

Photosystem I (PSI) and II (PSII) balance their light energy distribution absorbed by their light-harvesting complexes (LHCs) through state transition to maintain the maximum photosynthetic performance and to avoid photodamage. In state 2, a part of LHCII moves to PSI, forming a PSI-LHCI-LHCII supercomplex. The green alga Chlamydomonas reinhardtii exhibits state transition to a far larger extent than higher plants. Here we report the cryo-electron microscopy structure of a PSI-LHCI-LHCII supercomplex in state 2 from C. reinhardtii at 3.42 Å resolution. The result reveals that the PSI-LHCI-LHCII of C. reinhardtii binds two LHCII trimers in addition to ten LHCI subunits. The PSI core subunits PsaO and PsaH, which were missed or not well-resolved in previous Cr-PSI-LHCI structures, are observed. The present results reveal the organization and assembly of PSI core subunits, LHCI and LHCII, pigment arrangement, and possible pathways of energy transfer from peripheral antennae to the PSI core.


Asunto(s)
Proteínas Algáceas/metabolismo , Chlamydomonas reinhardtii/metabolismo , Complejos de Proteína Captadores de Luz/metabolismo , Complejo de Proteína del Fotosistema I/metabolismo , Proteínas Algáceas/química , Proteínas Algáceas/ultraestructura , Clorofila/metabolismo , Microscopía por Crioelectrón , Transferencia de Energía , Complejos de Proteína Captadores de Luz/química , Complejos de Proteína Captadores de Luz/ultraestructura , Modelos Moleculares , Fotosíntesis , Complejo de Proteína del Fotosistema I/química , Complejo de Proteína del Fotosistema I/ultraestructura , Complejo de Proteína del Fotosistema II/química , Complejo de Proteína del Fotosistema II/metabolismo , Complejo de Proteína del Fotosistema II/ultraestructura , Unión Proteica , Conformación Proteica , Multimerización de Proteína , Tilacoides/metabolismo , Tilacoides/ultraestructura
8.
Nat Methods ; 16(12): 1254-1261, 2019 12.
Artículo en Inglés | MEDLINE | ID: mdl-31780840

RESUMEN

Pinpointing subcellular protein localizations from microscopy images is easy to the trained eye, but challenging to automate. Based on the Human Protein Atlas image collection, we held a competition to identify deep learning solutions to solve this task. Challenges included training on highly imbalanced classes and predicting multiple labels per image. Over 3 months, 2,172 teams participated. Despite convergence on popular networks and training techniques, there was considerable variety among the solutions. Participants applied strategies for modifying neural networks and loss functions, augmenting data and using pretrained networks. The winning models far outperformed our previous effort at multi-label classification of protein localization patterns by ~20%. These models can be used as classifiers to annotate new images, feature extractors to measure pattern similarity or pretrained networks for a wide range of biological applications.


Asunto(s)
Aprendizaje Profundo , Procesamiento de Imagen Asistido por Computador/métodos , Microscopía Fluorescente/métodos , Proteínas/análisis , Humanos
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