Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.

Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available athttps://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.

The development of hepatocellular carcinomas (HCC) depends on their local microenvironment and the induction of neovascularization is a decisive step in tumor progression, since the growth of solid tumors is limited by nutrient and oxygen supply. Hypoxia is the critical factor that induces transcription of the hypoxia inducible factor-1α (HIF-1α) encoding gene HIF1A and HIF-1α protein accumulation to promote angiogenesis. However, the basis for the transcriptional regulation of HIF1A expression in HCC is still unclear. Here, we show that Bclaf1 levels are highly correlated with HIF-1α levels in HCC tissues, and that knockdown of Bclaf1 in HCC cell lines significantly reduces hypoxia-induced HIF1A expression. Furthermore, we found that Bclaf1 promotes HIF1A transcription via its bZIP domain, leading subsequently to increased transcription of the HIF-1α downstream targets VEGFA, TGFB, and EPO that in turn promote HCC-associated angiogenesis and thus survival and thriving of HCC cells. Moreover, we demonstrate that HIF-1α levels and microvessel density decrease after the shRNA-mediated Bclaf1 knockdown in xenograft tumors. Finally, we found that Bclaf1 levels increase in hypoxia in a HIF-1α dependent manner. Therefore, our study identifies Bclaf1 as a novel positive regulator of HIF-1α in the hypoxic microenvironment, providing new incentives for promoting Bcalf1 as a potential therapeutic target for an anti-HCC strategy.

Background: Parkinson’s disease (PD) is characterized by degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNpc), accompanied by accumulation of α-synuclein, chronic neuroinflammation and autophagy dysfunction. Previous studies suggested that misfolded α-synuclein induces the inflammatory response and autophagy dysfunction in microglial cells. The NLRP3 inflammasome signaling pathway plays a crucial role in the neuroinflammatory process in the central nervous system. However, the relationship between autophagy deficiency and NLRP3 activation induced by α-synuclein accumulation is not well understood. Methods: Through immunoblotting, immunocytochemistry, immunofluorescence, flow cytometry, ELISA and behavioral tests, we investigated the role of p38-TFEB-NLRP3 signaling pathways on neuroinflammation in the α-synuclein A53T PD models. Results: Our results showed that increased protein levels of NLRP3, ASC, and caspase-1 in the α-synuclein A53T PD models. P38 is activated by overexpression of α-synuclein A53T mutant, which inhibited the master transcriptional activator of autophagy TFEB. And we found that NLRP3 was degraded by chaperone-mediated autophagy (CMA) in microglial cells. Furthermore, p38-TFEB pathways inhibited CMA-mediated NLRP3 degradation in Parkinson's disease. Inhibition of p38 had a protective effect on Parkinson's disease model via suppressing the activation of NLRP3 inflammasome pathway. Moreover, both p38 inhibitor SB203580 and NLRP3 inhibitor MCC950 not only prevented neurodegeneration in vivo, but also alleviated movement impairment in α-synuclein A53T-tg mice model of Parkinson’s disease. Conclusion: Our research reveals p38-TFEB pathways promote microglia activation through inhibiting CMA-mediated NLRP3 degradation in Parkinson's disease, which could be a potential therapeutic strategy for PD. Graphical p38-TFEB pathways promote microglia activation through inhibiting CMA-mediated NLRP3 degradation in Parkinson's disease. In this model, p38 activates NLRP3 inflammasome via inhibiting TFEB in microglia. TFEB signaling negatively regulates NLRP3 inflammasome through increasing LAMP2A expression, which binds to NLRP3 and promotes its degradation via chaperone-mediated autophagy (CMA). NLRP3-mediated microglial activation promotes the death of dopaminergic neurons.

A number of settings arise in which it is of interest to predict Principal Component (PC) scores for new observations using data from an initial sample. In this paper, we demonstrate that naive approaches to PC score prediction can be substantially biased toward 0 in the analysis of large matrices. This phenomenon is largely related to known inconsistency results for sample eigenvalues and eigenvectors as both dimensions of the matrix increase. For the spiked eigenvalue model for random matrices, we expand the generality of these results, and propose bias-adjusted PC score prediction. In addition, we compute the asymptotic correlation coefficient between PC scores from sample and population eigenvectors. Simulation and real data examples from the genetics literature show the improved bias and numerical properties of our estimators.

Poly (ADP‐ribose) polymerase 1 (PARP1) is a master regulator of diverse biological processes such as DNA repair, oxidative stress, and apoptosis. PARP1 can be activated by aggregated α‐synuclein, and this process in turn exacerbates toxicity of α‐synuclein. This circle is closely linked to the evolution of Parkinson's disease (PD) that characterized by progressive neurodegeneration and motor deficits. Here, we reported the PARP1, as a novel upstream molecular of transcription factor EB (TFEB), participates in regulation of autophagy in α‐synuclein aggregated cells and mice. PARP1 inhibition not only enhances the nuclear transcription of TFEB via SIRT1 mediated down‐regulation of mTOR signaling but also reduces nuclear export of TFEB by attenuating the TFEB‐CRM1 interaction. Our results revealed that PARP1 inhibition lessened the accumulation of α‐synuclein in PD models. Also, oral administration of PARP1 inhibitor Veliparib prevented neurodegeneration and improved motor ability in α‐synucleinA53T transgenic mice. These findings identify that PARP1 signaling pathway regulates TFEB‐mediated autophagy, pointing to potential therapeutic strategy of PD via enhancing protein degradation systems.