Mature white adipocytes contain a characteristic unilocular lipid droplet. However, the molecular mechanisms underlying unilocular lipid droplet formation are poorly understood. We previously showed that Fsp27, an adipocyte-specific lipid droplet-associated protein, promotes lipid droplet growth by initiating lipid exchange and transfer. Here, we identify Perilipin1 (Plin1), another adipocyte-specific lipid droplet-associated protein, as an Fsp27 activator. Plin1 interacts with the CIDE-N domain of Fsp27 and markedly increases Fsp27-mediated lipid exchange, lipid transfer and lipid droplet growth. Functional cooperation between Plin1 and Fsp27 is required for efficient lipid droplet growth in adipocytes, as depletion of either protein impairs lipid droplet growth. The CIDE-N domain of Fsp27 forms homodimers and disruption of CIDE-N homodimerization abolishes Fsp27-mediated lipid exchange and transfer. Interestingly, Plin1 can restore the activity of CIDE-N homodimerization-defective mutants of Fsp27. We thus uncover a novel mechanism underlying lipid droplet growth and unilocular lipid droplet formation that involves the cooperative action of Fsp27 and Plin1 in adipocytes.

Background The overall prognosis of oral cancer remains poor because over half of patients are diagnosed at advanced-stages. Previously reported screening and earlier detection methods for oral cancer still largely rely on health workers’ clinical experience and as yet there is no established method. We aimed to develop a rapid, non-invasive, cost-effective, and easy-to-use deep learning approach for identifying oral cavity squamous cell carcinoma (OCSCC) patients using photographic images. Methods We developed an automated deep learning algorithm using cascaded convolutional neural networks to detect OCSCC from photographic images. We included all biopsy-proven OCSCC photographs and normal controls of 44,409 clinical images collected from 11 hospitals around China between April 12, 2006, and Nov 25, 2019. We trained the algorithm on a randomly selected part of this dataset (development dataset) and used the rest for testing (internal validation dataset). Additionally, we curated an external validation dataset comprising clinical photographs from six representative journals in the field of dentistry and oral surgery. We also compared the performance of the algorithm with that of seven oral cancer specialists on a clinical validation dataset. We used the pathological reports as gold standard for OCSCC identification. We evaluated the algorithm performance on the internal, external, and clinical validation datasets by calculating the area under the receiver operating characteristic curves (AUCs), accuracy, sensitivity, and specificity with two-sided 95% CIs. Findings 1469 intraoral photographic images were used to validate our approach. The deep learning algorithm achieved an AUC of 0·983 (95% CI 0·973–0·991), sensitivity of 94·9% (0·915–0·978), and specificity of 88·7% (0·845–0·926) on the internal validation dataset ( n = 401), and an AUC of 0·935 (0·910–0·957), sensitivity of 89·6% (0·847–0·942) and specificity of 80·6% (0·757–0·853) on the external validation dataset ( n = 402). For a secondary analysis on the internal validation dataset, the algorithm presented an AUC of 0·995 (0·988–0·999), sensitivity of 97·4% (0·932–1·000) and specificity of 93·5% (0·882–0·979) in detecting early-stage OCSCC. On the clinical validation dataset ( n = 666), our algorithm achieved comparable performance to that of the average oral cancer expert in terms of accuracy (92·3% [0·902–0·943] vs 92.4% [0·912–0·936]), sensitivity (91·0% [0·879–0·941] vs 91·7% [0·898–0·934]), and specificity (93·5% [0·909–0·960] vs 93·1% [0·914–0·948]). The algorithm also achieved significantly better performance than that of the average medical student (accuracy of 87·0% [0·855–0·885], sensitivity of 83·1% [0·807–0·854], and specificity of 90·7% [0·889–0·924]) and the average non-medical student (accuracy of 77·2% [0...

Recent studies have revealed that long non-coding RNAs participate in all steps of cancer initiation and progression by regulating protein-coding genes at the epigenetic, transcriptional, and post-transcriptional levels. Long non-coding RNAs are in turn regulated by other genes, forming a complex regulatory network. The regulation networks between the p53 tumor suppressor and these RNAs in nasopharyngeal carcinoma remains unclear. The aims of this study were to investigate the regulatory roles of the TP53 gene in regulating long non-coding RNA expression profiles and to study the function of a TP53-regulated long non-coding RNA (LOC401317) in the nasopharyngeal carcinoma cell line HNE2. Long non-coding RNA expression profiling indicated that 133 long non-coding RNAs were upregulated in the human NPC cell line HNE2 cells following TP53 overexpression, while 1057 were downregulated. Among these aberrantly expressed long non-coding RNAs, LOC401317 was the most significantly upregulated one. Further studies indicated that LOC401317 is directly regulated by p53 and that ectopic expression of LOC401317 inhibits HNE2 cell proliferation in vitro and in vivo by inducing cell cycle arrest and apoptosis. LOC401317 inhibited cell cycle progression by increasing p21 expression and decreasing cyclin D1 and cyclin E1 expression and promoted apoptosis through the induction of poly(ADP-ribose) polymerase and caspase-3 cleavage. Collectively, these results suggest that LOC401317 is directly regulated by p53 and exerts antitumor effects in HNE2 nasopharyngeal carcinoma cells.