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1.
Cell Stem Cell ; 31(1): 52-70.e8, 2024 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-38181751

RESUMO

Human pluripotent stem cell-derived kidney organoids offer unprecedented opportunities for studying polycystic kidney disease (PKD), which still has no effective cure. Here, we developed both in vitro and in vivo organoid models of PKD that manifested tubular injury and aberrant upregulation of renin-angiotensin aldosterone system. Single-cell analysis revealed that a myriad of metabolic changes occurred during cystogenesis, including defective autophagy. Experimental activation of autophagy via ATG5 overexpression or primary cilia ablation significantly inhibited cystogenesis in PKD kidney organoids. Employing the organoid xenograft model of PKD, which spontaneously developed tubular cysts, we demonstrate that minoxidil, a potent autophagy activator and an FDA-approved drug, effectively attenuated cyst formation in vivo. This in vivo organoid model of PKD will enhance our capability to discover novel disease mechanisms and validate candidate drugs for clinical translation.


Assuntos
Cílios , Doenças Renais Policísticas , Humanos , Rim , Doenças Renais Policísticas/tratamento farmacológico , Autofagia , Organoides
2.
Nat Methods ; 19(7): 833-844, 2022 07.
Artigo em Inglês | MEDLINE | ID: mdl-35697834

RESUMO

Inosine is a prevalent RNA modification in animals and is formed when an adenosine is deaminated by the ADAR family of enzymes. Traditionally, inosines are identified indirectly as variants from Illumina RNA-sequencing data because they are interpreted as guanosines by cellular machineries. However, this indirect method performs poorly in protein-coding regions where exons are typically short, in non-model organisms with sparsely annotated single-nucleotide polymorphisms, or in disease contexts where unknown DNA mutations are pervasive. Here, we show that Oxford Nanopore direct RNA sequencing can be used to identify inosine-containing sites in native transcriptomes with high accuracy. We trained convolutional neural network models to distinguish inosine from adenosine and guanosine, and to estimate the modification rate at each editing site. Furthermore, we demonstrated their utility on the transcriptomes of human, mouse and Xenopus. Our approach expands the toolkit for studying adenosine-to-inosine editing and can be further extended to investigate other RNA modifications.


Assuntos
Nanoporos , RNA , Adenosina/genética , Animais , Inosina/genética , Camundongos , RNA/genética , RNA/metabolismo , Edição de RNA , Análise de Sequência de RNA
3.
Front Cell Dev Biol ; 10: 845401, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35309912

RESUMO

The prevalence of chronic kidney disease (CKD) is rapidly increasing over the last few decades, owing to the global increase in diabetes, and cardiovascular diseases. Dialysis greatly compromises the life quality of patients, while demand for transplantable kidney cannot be met, underscoring the need to develop novel therapeutic approaches to stop or reverse CKD progression. Our understanding of kidney disease is primarily derived from studies using animal models and cell culture. While cross-species differences made it challenging to fully translate findings from animal models into clinical practice, primary patient cells quickly lose the original phenotypes during in vitro culture. Over the last decade, remarkable achievements have been made for generating 3-dimensional (3D) miniature organs (organoids) by exposing stem cells to culture conditions that mimic the signaling cues required for the development of a particular organ or tissue. 3D kidney organoids have been successfully generated from different types of source cells, including human pluripotent stem cells (hPSCs), adult/fetal renal tissues, and kidney cancer biopsy. Alongside gene editing tools, hPSC-derived kidney organoids are being harnessed to model genetic kidney diseases. In comparison, adult kidney-derived tubuloids and kidney cancer-derived tumoroids are still in their infancy. Herein, we first summarize the currently available kidney organoid models. Next, we discuss recent advances in kidney disease modelling using organoid models. Finally, we consider the major challenges that have hindered the application of kidney organoids in disease modelling and drug evaluation and propose prospective solutions.

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