The human lens is comprised largely of crystallin proteins assembled into a highly ordered, interactive macro-structure essential for lens transparency and refractive index. Any disruption of intra- or inter-protein interactions will alter this delicate structure, exposing hydrophobic surfaces, with consequent protein aggregation and cataract formation. Cataracts are the most common cause of blindness worldwide, affecting tens of millions of people, and currently the only treatment is surgical removal of cataractous lenses. The precise mechanisms by which lens proteins both prevent aggregation and maintain lens transparency are largely unknown. Lanosterol is an amphipathic molecule enriched in the lens. It is synthesized by lanosterol synthase (LSS) in a key cyclization reaction of a cholesterol synthesis pathway. Here we identify two distinct homozygous LSS missense mutations (W581R and G588S) in two families with extensive congenital cataracts. Both of these mutations affect highly conserved amino acid residues and impair key catalytic functions of LSS. Engineered expression of wild-type, but not mutant, LSS prevents intracellular protein aggregation of various cataract-causing mutant crystallins. Treatment by lanosterol, but not cholesterol, significantly decreased preformed protein aggregates both in vitro and in cell-transfection experiments. We further show that lanosterol treatment could reduce cataract severity and increase transparency in dissected rabbit cataractous lenses in vitro and cataract severity in vivo in dogs. Our study identifies lanosterol as a key molecule in the prevention of lens protein aggregation and points to a novel strategy for cataract prevention and treatment.
The surface of the cornea consists of a unique type of non-keratinized epithelial cells arranged in an orderly fashion, and this is essential for vision by maintaining transparency for light transmission. Cornea epithelial cells (CECs) undergo continuous renewal from limbal stem or progenitor cells (LSCs)1,2, and deficiency in LSCs or corneal epithelium—which turns cornea into a non-transparent, keratinized skin-like epithelium—causes corneal surface disease that leads to blindness in millions of people worldwide3. How LSCs are maintained and differentiated into corneal epithelium in healthy individuals and which key molecular events are defective in patients have been largely unknown. Here we report establishment of an in vitro feeder-cell-free LSC expansion and three-dimensional corneal differentiation protocol in which we found that the transcription factors p63 (tumour protein 63) and PAX6 (paired box protein PAX6) act together to specify LSCs, and WNT7A controls corneal epithelium differentiation through PAX6. Loss of WNT7A or PAX6 induces LSCs into skin-like epithelium, a critical defect tightly linked to common human corneal diseases. Notably, transduction of PAX6 in skin epithelial stem cells is sufficient to convert them to LSC-like cells, and upon transplantation onto eyes in a rabbit corneal injury model, these reprogrammed cells are able to replenish CECs and repair damaged corneal surface. These findings suggest a central role of the WNT7A–PAX6 axis in corneal epithelial cell fate determination, and point to a new strategy for treating corneal surface diseases.
The repair and regeneration of tissues using endogenous stem cells represents an ultimate goal in regenerative medicine. To our knowledge, human lens regeneration has not yet been demonstrated. Currently, the only treatment for cataracts, the leading cause of blindness worldwide, is to extract the cataractous lens and implant an artificial intraocular lens. However, this procedure poses notable risks of complications. Here we isolate lens epithelial stem/progenitor cells (LECs) in mammals and show that Pax6 and Bmi1 are required for LEC renewal. We design a surgical method of cataract removal that preserves endogenous LECs and achieves functional lens regeneration in rabbits and macaques, as well as in human infants with cataracts. Our method differs conceptually from current practice, as it preserves endogenous LECs and their natural environment maximally, and regenerates lenses with visual function. Our approach demonstrates a novel treatment strategy for cataracts and provides a new paradigm for tissue regeneration using endogenous stem cells.
SUMMARY Glaucoma, a blinding neurodegenerative disease, whose risk factors include elevated intraocular pressure (IOP), age and genetics, is characterized by accelerated and progressive retinal ganglion cell (RGC) death. Despite decades of research, the mechanism of RGC death in glaucoma is still unknown. Here, we demonstrate that the genetic effect of the SIX6 risk-variant (rs33912345, His141Asn) is enhanced by another major POAG risk gene P16/INK4A (cyclin-dependent kinase inhibitor 2A). We further show that the upregulation of homozygous SIX6 risk alleles (CC) leads to an increase in P16/INK4A expression with a subsequent cellular senescence, as evidenced in a mouse model of elevated IOP and in human POAG eyes. Our data indicate that SIX6 and/or IOP promotes POAG by directly increasing P16/INK4A expression, leading to RGC senescence in adult human retinas. Our study provides important insights linking genetic susceptibility to the underlying mechanism of RGC death and provides a unified theory of glaucoma pathogenesis.
Common lung diseases are first diagnosed via chest X-rays. Here, we show that a fully automated deep-learning pipeline for chest-X-ray-image standardization, lesion visualization and disease diagnosis can identify viral pneumonia caused by Coronavirus disease 2019 (COVID-19), assess its severity, and discriminate it from other types of pneumonia. The deep-learning system was developed by using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.88–0.99, between severe and non-severe COVID-19 with an AUC of 0.87, and between severe or non-severe COVID-19 pneumonia and other viral and non-viral pneumonia with AUCs of 0.82–0.98. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists, and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide clinical-decision support.
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