Biology subjects

Qiu, Z.

Publications and source records attributed to Qiu, Z..

5 recordsLinked to original sources

bioRxiv2018

Structure of the Human Volume Regulated Anion Channel

SWELL1 (LRRC8A) is the only essential subunit of the Volume Regulated Anion Channel (VRAC), which regulates cellular volume homeostasis and is activated by hypotonic solutions. SWELL1, together with four other LRRC8 family members, forms a vastly heterogeneous cohort of VRAC channels with different properties; however, SWELL1 alone is also functional. Here, we report a high-resolution cryo-electron microscopy structure of full-length human homo-hexameric SWELL1. The structure reveals a trimer of dimers assembly with symmetry mismatch between the pore-forming domain and the cytosolic leucine-rich repeat (LRR) domains. Importantly, mutational analysis demonstrates that a charged residue at the narrowest constriction of the homomeric channel is an important pore determinant of heteromeric VRAC. This structure provides a scaffold for further dissecting the heterogeneity and mechanism of activation of VRAC.

biochemistry↗

bioRxiv2018

MECP2 duplication and mutations impair NSCs differentiation via miR-197 regulated ADAM10

How MECP2 (Methyl-CpG-binding protein 2) duplication affects cortex development remains elusive. We found that elevated MeCP2 expression promotes neurogenesis during cortex development in Tg(MECP2) mouse brain. Ectopic expression of MeCP2 in NPCs inhibits ADAM10 and hence compromises the NOTCH pathway during NPC differentiation. MeCP2 up-regulates miR-197 to down-regulate ADAM10. The enhanced NPC differentiation/migration in Tg(MECP2) embryonic brain can be repressed by overexpression of ADAM10 or a miR-197 inhibitor.\n\nConsistently, the reduced neurogenesis induced by three rare MeCP2 missense mutations (H371R, E394K, G428S) identified in a Han Chinese autism spectrum disorders (ASD) cohort, can be reversed by miR-197 both in vitro and in vivo. Our results revealed that a regulatory axis involving MeCP2, miR-197, ADAM10, and NOTCH signaling is critical for neurogenesis, which is affected by both MeCP2 duplication and mutation.

developmental biology↗

bioRxiv2018

Loss of CREST leads to neuroinflammatory responses and ALS-like motor defects in mice

Amyotrophic lateral sclerosis (ALS) is a late onset neurodegenerative disease with fast progression. Mutations of the CREST gene (also known as SS18L1) are identified in sporadic ALS patients. Whether CREST mutations may lead to ALS remained largely unclear. In this study, we showed that the ALS-related CREST-Q388X mutation exhibited loss-of-function effects. Importantly, we found that microglial activation were prevalent in CREST haploinsufficieny mice and the Q394X mice mimicking the human CREST Q388X mutation. Furthermore, we showed that both CREST haploinsufficieny and the Q394X mice displayed deficits in motor coordination. Finally, we identified the critical role of CREST-BRG1 complex in repressing the expression of immune-related cytokines including Ccl2 and Cxcl10 in neurons, via histone deacetylation, providing the molecular mechanisms underlying inflammatory responses lack of CREST. These findings indicate that elevated inflammatory responses in a subset of ALS may be caused by neuron-derived factors, suggesting potential therapeutic methods through inflammation pathways.\n\nIn BriefCheng et al. discovered that neuronal loss of CREST reduces the protein level of FUS, de-represses the transcriptional inhibition of chemokine genes which in turn causes microglial activation and proinflammation, and ultimately leads to axonal degeneration of motor neurons and impairment of locomotion.

neuroscience↗

bioRxiv2018

The critical role of ASD-related gene CNTNAP3 in regulating synaptic development and social behavior in mice

Accumulated genetic evidences indicate that the contactin associated protein-like (CNTNAP) family is implicated in autism spectrum disorders (ASD). In this study, we identified genetic mutations in the CNTNAP3 gene from Chinese Han ASD cohorts and Simons Simplex Collections. We found that CNTNAP3 interacted with synaptic adhesion proteins Neuroligin1 and Neuroligin2, as well as scaffolding proteins PSD95 and Gephyrin. Significantly, we found that CNTNAP3 played an opposite role in controlling the development of excitatory and inhibitory synapses in vitro and in vivo, in which ASD mutants exhibited loss-of-function effects. In this study, we showed that Cntnap3-null mice exhibited deficits in social interaction, spatial learning and prominent repetitive behaviors. These evidences elucidate the pivotal role of CNTNAP3 in synapse development and social behaviors, providing the mechanistic insights for ASD.

neuroscience↗

bioRxiv2017

DeepGS: Predicting phenotypes from genotypes using Deep Learning

MotivationGenomic selection (GS) is a new breeding strategy by which the phenotypes of quantitative traits are usually predicted based on genome-wide markers of genotypes using conventional statistical models. However, the GS prediction models typically make strong assumptions and perform linear regression analysis, limiting their accuracies since they do not capture the complex, non-linear relationships within genotypes, and between genotypes and phenotypes.\n\nResultsWe present a deep learning method, named DeepGS, to predict phenotypes from genotypes. Using a deep convolutional neural network, DeepGS uses hidden variables that jointly represent features in genotypic markers when making predictions; it also employs convolution, sampling and dropout strategies to reduce the complexity of high-dimensional marker data. We used a large GS dataset to train DeepGS and compare its performance with other methods. In terms of mean normalized discounted cumulative gain value, DeepGS achieves an increase of 27.70%~246.34% over a conventional neural network in selecting top-ranked 1% individuals with high phenotypic values for the eight tested traits. Additionally, compared with the widely used method RR-BLUP, DeepGS still yields a relative improvement ranging from 1.44% to 65.24%. Through extensive simulation experiments, we also demonstrated the effectiveness and robustness of DeepGS for the absent of outlier individuals and subsets of genotypic markers. Finally, we illustrated the complementarity of DeepGS and RR-BLUP with an ensemble learning approach for further improving prediction performance.\n\nAvailabilityDeepGS is provided as an open source R package available at https://github.com/cma2015/DeepGS.

bioinformatics↗