抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

CA : A Cancer Journal for Clinicians

This work was financially supported by the Australian Research Council (FL170100154, DP160104866, and DP190103472). Y.H.Z. acknowledges financial support from Zhejiang Provincial Natural Science Foundation of China (LR18B030003), National Natural Science Foundation of China (51701181, 21771161), and the Thousand Talents Program for Distinguished Young Scholars. J.S. was supported by the Chinese CSC Scholarship Program. DFT computations were performed by using services offered from the National Computational Infrastructure (NCI) and Phoenix High Performance Computing, which are supported by the Australian Government and the University of Adelaide. XAS measurements were conducted at the Beijing Synchrotron Radiation Facility (1W1B, BSRF)

Short-Range Ordered Iridium Single Atoms Integrated into Cobalt Oxide Spinel Structure for Highly Efficient Electrocatalytic Water Oxidation.

Pressure overload induces a transition from cardiac hypertrophy to heart failure, but its underlying mechanisms remain elusive. Here we reconstruct a trajectory of cardiomyocyte remodeling and clarify distinct cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure, by integrating single-cardiomyocyte transcriptome with cell morphology, epigenomic state and heart function. During early hypertrophy, cardiomyocytes activate mitochondrial translation/metabolism genes, whose expression is correlated with cell size and linked to ERK1/2 and NRF1/2 transcriptional networks. Persistent overload leads to a bifurcation into adaptive and failing cardiomyocytes, and p53 signaling is specifically activated in late hypertrophy. Cardiomyocyte-specific p53 deletion shows that cardiomyocyte remodeling is initiated by p53-independent mitochondrial activation and morphological hypertrophy, followed by p53-dependent mitochondrial inhibition, morphological elongation, and heart failure gene program activation. Human single-cardiomyocyte analysis validates the conservation of the pathogenic transcriptional signatures. Collectively, cardiomyocyte identity is encoded in transcriptional programs that orchestrate morphological and functional phenotypes.

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Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure

Single-cell RNA sequencing (scRNA-seq) is able to give an insight into the gene-gene associations or transcriptional networks among cell populations based on the sequencing of a large number of cells. However, traditional network methods are limited to the grouped cells instead of each single cell, and thus the heterogeneity of single cells will be erased. We present a new method to construct a cell-specific network (CSN) for each single cell from scRNA-seq data (i.e. one network for one cell), which transforms the data from 'unstable' gene expression form to 'stable' gene association form on a single-cell basis. In particular, it is for the first time that we can identify the gene associations/network at a single-cell resolution level. By CSN method, scRNA-seq data can be analyzed for clustering and pseudo-trajectory from network perspective by any existing method, which opens a new way to scRNA-seq data analyses. In addition, CSN is able to find differential gene associations for each single cell, and even 'dark' genes that play important roles at the network level but are generally ignored by traditional differential gene expression analyses. In addition, CSN can be applied to construct individual network of each sample bulk RNA-seq data. Experiments on various scRNA-seq datasets validated the effectiveness of CSN in terms of accuracy and robustness.

Cell-specific network constructed by single-cell RNA sequencing data

A complex disease generally results not from malfunction of individual molecules but from dysfunction of the relevant system or network, which dynamically changes with time and conditions. Thus, estimating a condition-specific network from a single sample is crucial to elucidating the molecular mechanisms of complex diseases at the system level. However, there is currently no effective way to construct such an individual-specific network by expression profiling of a single sample because of the requirement of multiple samples for computing correlations. We developed here with a statistical method, i.e. a sample-specific network (SSN) method, which allows us to construct individual-specific networks based on molecular expressions of a single sample. Using this method, we can characterize various human diseases at a network level. In particular, such SSNs can lead to the identification of individual-specific disease modules as well as driver genes, even without gene sequencing information. Extensive analysis by using the Cancer Genome Atlas data not only demonstrated the effectiveness of the method, but also found new individual-specific driver genes and network patterns for various types of cancer. Biological experiments on drug resistance further validated one important advantage of our method over the traditional methods, i.e. we can even identify such drug resistance genes that actually have no clear differential expression between samples with and without the resistance, due to the additional network information.

Y.M. Wang

Papers

Personalized characterization of diseases using sample-specific networks.

CalAtom: A software for quantitatively analysing atomic columns in a transmission electron microscope image.

Maximum entropy image deconvolution applied to structure determination for crystal Nd1.85Ce0.15CuO4−δ

Determining polarity and dislocation core structures at atomic level for epitaxial AlN/(0001)6H-SiC from a single image in HRTEM.

A further discussion on the peculiarity of maximum entropy image deconvolution in HREM.