Biology subjects

Cheng, A.

Publications and source records attributed to Cheng, A..

10 recordsLinked to original sources

bioRxiv2018

ATM is activated by ATP depletion and modulates mitochondrial function through NRF1

We have uncovered new insights into the symptoms of ataxia-telangiectasia (A-T). Neurons with high physiological activity, particularly cerebellar Purkinje cells, have large and dynamic ATP demands. Depletion of ATP generates reactive oxygen species that activate ATM (the A-T Mutated gene product). Activated in this way, but not by DNA damage, ATM phosphorylates nuclear respiratory factor-1 (NRF1). This leads to NRF1 dimerization, nuclear translocation and the upregulation of nuclear-encoded mitochondrial genes, thus enhancing the capacity of the electron transport chain (ETC) and restoring mitochondrial function. In cells with ATM deficiency, resting ATP levels are normal, but cells replenish ATP poorly following surges in energy demand and chronic ATP insufficiency endangers cell survival. This is a particular problem for energy-intensive cells such as Purkinje cells, which degenerate in A-T. Our findings thus identify ATM as a guardian of mitochondrial output as well as genomic integrity, and suggest that alternate fuel sources may ameliorate A-T disease symptoms.\n\nSummaryOxidative stress, resulting from neuronal activity and depleted ATP levels, activates ATM, which phosphorylates NRF1, causing nuclear translocation and upregulation of mitochondrial gene expression. In ATM deficiency, ATP levels recover more slowly, particularly in active neurons with high energy demands.

neuroscience↗

bioRxiv2018

MoMo: Discovery of statistically significant post-translational modification motifs

MotivationPost-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called \"motifs\" that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation.\n\nResultsWe describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate p-values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo reimplements the two most widely used PTM motif discovery algorithms--motif-x and MoDL--while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing \"background\" peptides from an unshuffled proteome database. Our results thus suggest that many of the hundreds of papers that use motif-x to find motifs may be reporting results that lack statistical support.\n\nAvailabilityhttp://meme-suite.org\n\nContacttimothybailey@unr.edu

bioinformatics↗

bioRxiv2018

The Genetic Landscape of Diamond-Blackfan Anemia

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure disorder that affects 1 in 100,000 to 200,000 live births and has been associated with mutations in components of the ribosome. In order to characterize the genetic landscape of this genetically heterogeneous disorder, we recruited a cohort of 472 individuals with a clinical diagnosis of DBA and performed whole exome sequencing (WES). Overall, we identified rare and predicted damaging mutations in likely causal genes for 78% of individuals. The majority of mutations were singletons, absent from population databases, predicted to cause loss of function, and in one of 19 previously reported genes encoding for a diverse set of ribosomal proteins (RPs). Using WES exon coverage estimates, we were able to identify and validate 31 deletions in DBA associated genes. We also observed an enrichment for extended splice site mutations and validated the diverse effects of these mutations using RNA sequencing in patientderived cell lines. Leveraging the size of our cohort, we observed several robust genotype-phenotype associations with congenital abnormalities and treatment outcomes. In addition to comprehensively identifying mutations in known genes, we further identified rare mutations in 7 previously unreported RP genes that may cause DBA. We also identified several distinct disorders that appear to phenocopy DBA, including 9 individuals with biallelic CECR1 mutations that result in deficiency of ADA2. However, no new genes were identified at exome-wide significance, suggesting that there are no unidentified genes containing mutations readily identified by WES that explain > 5% of DBA cases. Overall, this comprehensive report should not only inform clinical practice for DBA patients, but also the design and analysis of future rare variant studies for heterogeneous Mendelian disorders.

genetics↗

bioRxiv2018

Benchmarking cryo-EM single particle analysis workflow

Cryo electron microscopy facilities running multiple instruments and serving users with varying skill levels need a robust and reliable method for benchmarking both the hardware and software components of their single particle analysis workflow. The workflow is complex, with many bottlenecks existing at the specimen preparation, data collection and image analysis steps; the samples and grid preparation can be of unpredictable quality, there are many different protocols for microscope and camera settings, and there is a myriad of software programs for analysis that can depend on dozens of settings chosen by the user. For this reason, we believe it is important to benchmark the entire workflow, using a standard sample and standard operating procedures, on a regular basis. This provides confidence that all aspects of the pipeline are capable of producing maps to high resolution. Here we describe benchmarking procedures using a test sample, rabbit muscle aldolase.

molecular biology↗

bioRxiv2018

High Resolution Single Particle Cryo-Electron Microscopy using Beam-Image Shift

Automated data acquisition is now used widely for the single-particle averaging approach to reconstruction of three-dimensional (3D) volumes of biological complexes preserved in vitreous ice and imaged in a transmission electron microscope (cryo-EM). Automation has become integral to this method because of the very large number of particle images required to be averaged in order to overcome the typically low signal-to-noise ratio of these images.\n\nFor optimal efficiency, all automated data acquisition software packages employ some degree of beam-image shift because this method is fast and accurate (+/- 0.1 m). Relocation to a targeted area under low-dose conditions can only be achieved using stage movements in combination with multiple iterations or long relaxation times, both reducing efficiency. It is, however, well known that applying beam-image shift induces beam-tilt and hence structure phase error. A {pi}/4 phase error is considered as the worst that can be accepted, and is used as an argument against the use of any beam-image shift for high resolution data collection.\n\nIn this study, we performed cryo-EM single-particle reconstructions on a T20S proteasome sample using applied beam-image shifts corresponding to beam tilts from 0 to 10 mrad. To evaluate the results we compared the FSC values, and examined the water density peaks in the 3D map. We conclude that the {pi}/4 phase error does not limit the validity of the 3D reconstruction from single-particle averaging beyond the {pi}/4 resolution limit.

biophysics↗

bioRxiv2018

Routine Determination of Ice Thickness for Cryo-EM Grids

Recent advances in instrumentation and automation have made cryo-EM a popular method for producing near-atomic resolution structures of a variety of proteins and complexes. Sample preparation is still a limiting factor in collecting high quality data. Thickness of the vitreous ice in which the particles are embedded is one of the many variables that need to be optimized for collection of the highest quality data. Here we present two methods, using either an energy filter or scattering outside the objective aperture, to measure ice thickness for potentially every image collected. Unlike geometrical or tomographic methods, these can be implemented directly in the single particle collection workflow without interrupting or significantly slowing down data collection. We describe the methods as implemented into the Leginon/Appion data collection workflow, along with some examples from test cases. Routine monitoring of ice thickness should prove helpful for optimizing sample preparation, data collection, and data processing.

biophysics↗

bioRxiv2018

Gut bacterial metabolite Urolithin A (UA) mitigates Ca2+ 1 entry inT cells by regulating miR-10a-5p

The gut microbiota influences several biological functions including immune response. Inflammatory bowel disease is favourably influenced by consumption of several dietary natural plant products such as pomegranate, walnuts and berries containing polyphenolic compounds such as ellagitannins and ellagic acid. The gut microbiota metabolises ellagic acid leading to formation of bioactive urolithins A, B, C and D. Urolithin A (UA) is the most active and effective gut metabolite and acts as a potent anti-inflammatory and anti-oxidant agent. However, how gut metabolite UA affects the function of immune cells remained incompletely understood. T cell proliferation is stimulated by store operated Ca2+ entry (SOCE) resulting from stimulation of Orai1 by STIM1/STIM2. We show here that treatment of murine CD4+ T cells with UA (10 {micro}M, 3 days) significantly blunted SOCE in CD4+ T cells, an effect paralleled by significant downregulation of Orai1 and STIM1/2 transcript levels and protein abundance. UA treatment further increased miR-10a-5p abundance in CD4+ T cells in a dose dependent fashion. Overexpression of miR-10a-5p significantly decreased STIM1/2 and Orai1 mRNA and protein levels as well as SOCE in CD4+ T cells. UA further decreased CD4+ T cell proliferation. Thus, bacterial metabolite UA up-regulates miR-10a-5p thus interfering with Orai1/STIM1/STIM2 expression, store operated Ca2+ entry and proliferation of murine CD4+ T cells.

immunology↗

bioRxiv2017

Routine Single Particle CryoEM Sample and Grid Characterization by Tomography

Single particle cryo-electron microscopy (cryoEM) is often performed under the assumption that particles are freely floating away from the air-water interfaces and in thin, vitreous ice. In this study, we performed fiducial-less tomography on over 50 different cryoEM grid/sample preparations to determine the particle distribution within the ice and the overall geometry of the ice in grid holes. Surprisingly, by studying particles in holes in 3D from over 1,000 tomograms, we have determined that the vast majority of particles (approximately 90%) are adsorbed to an air-water interface. The implications of this observation are wide-ranging, with potential ramifications regarding protein denaturation, conformational change, and preferred orientation. We also show that fiducial-less cryo-electron tomography on single particle grids may be used to determine ice thickness, optimal single particle collection areas and strategies, particle heterogeneity, and de novo models for template picking and single particle alignment.

biophysics↗

bioRxiv2017

MoMo: Discovery of post-translational modification motifs

bioinformatics↗

bioRxiv2017

A robust statistical framework to detect multiple sources of hidden variation in single-cell transcriptomes

Single cell RNA-sequencing (scRNA-seq) precisely characterize gene expression levels and dissect variation in expression associated with the state (technical or biological) and the type of the cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for bulk measurements (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden and correlated sources of variation by identifying a set of genes affected with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.

bioinformatics↗