Ren, S. | Search bioRxiv

bioRxiv2018

A particle-filter framework for robust cryoEM 3D reconstruction

Electron cryo-microscopy (cryoEM) is now a powerful tool in determining atomic structures of biological macromolecules under nearly natural conditions. The major task of single-particle cryoEM is to estimate a set of parameters for each input particle image to reconstruct the three-dimensional structure of the macromolecules. As future large-scale applications require increasingly higher resolution and automation, robust high-dimensional parameter estimation algorithms need to be developed in the presence of various image qualities. In this paper, we introduced a particle-filter algorithm for cryoEM, which was a sequential Monte Carlo method for robust and fast high-dimensional parameter estimation. The cryoEM parameter estimation problem was described by a probability density function of the estimated parameters. The particle filter uses a set of random and weighted support points to represent such a probability density function. The statistical properties of the support points not only enhance the parameter estimation with self-adaptive accuracy but also provide the belief of estimated parameters, which is essential for the reconstruction phase. The implementation of these features showed strong tolerance to bad particles and enabled robust defocus refinement, demonstrated by the remarkable resolution improvement at the atomic level.

biophysics↗

bioRxiv2017

A personalized, multi-omics approach identifies genes involved in cardiac hypertrophy and heart failure

Identifying genes underlying complex diseases remains a major challenge. Biomarkers are typically identified by comparing average levels of gene expression in populations of healthy and diseased individuals. However, genetic diversities may undermine the effort to uncover genes with significant but individual contribution to the spectrum of disease phenotypes within a population. Here we leverage the Hybrid Mouse Diversity Panel (HMDP), a model system of 100+ genetically diverse strains of mice exhibiting different complex disease traits, to develop a personalized differential gene expression analysis that is able to identify disease-associated genes missed by traditional population-wide methods. The population-level and personalized approaches are compared for isoproterenol(ISO)-induced cardiac hypertrophy and heart failure using pre- and post-ISO gene expression and phenotypic data. The personalized approach identifies 36 Fold-Change (FC) genes predictive of the severity of cardiac hypertrophy, and enriched in genes previously associated with cardiac diseases in human. Strikingly, these genes are either up- or down-regulated at the individual strain level, and are therefore missed when averaging at the population level. Using insights from the gene regulatory network and protein-protein interactome, we identify Hes1 as a strong candidate FC gene. We validate its role by showing that even a mild knockdown of 20-40% of Hes1 can induce a dramatic reduction of hypertrophy by 80-90% in rat neonatal cardiac cells. These findings emphasize the importance of a personalized approach to identify causal genes underlying complex diseases as well as to develop personalized therapies.\n\nSignificanceA traditional approach to investigate the genetic basis of complex diseases is to look for genes with a global change in expression between diseased and healthy individuals. Here, we investigate individual changes of gene expression by inducing heart failure in 100 strains of genetically distinct mice. We find that genes associated to the severity of the disease are either up- or down-regulated across individuals and are therefore missed by a traditional population level approach. However, they are enriched in human cardiac disease genes and form a coregulated module strongly interacting with a cardiac hypertrophic signaling network in the human interactome. Our analysis demonstrates that individualized approaches are crucial to reveal all genes involved in the development of complex diseases.

systems biology↗