Biology subjects

Shen, Y.

Publications and source records attributed to Shen, Y..

At least 433 records · Page 24Linked to original sources

bioRxiv2019

A comprehensive genome-scale model for Rhodosporidium toruloides IFO0880 accounting for functional genomics and phenotypic data

BackgroundRhodosporidium toruloides is a basidiomycetes yeast that can accumulate large amount of lipids and natively produce carotenoids. To better assess this non-model yeasts metabolic capabilities, we reconstruct a genome-scale model of R. toruloides IFO0880s metabolic network (iRhto1108) using recent functional genomics and phenotypic data in literature or generated herein.\n\nResultsThe model iRhto1108 accounts for 2,203 reactions, 1,985 metabolites and 1,108 genes. In this work, we integrate and supplement the current knowledge with in-house generated biomass composition and experimental measurements pertaining to the organisms metabolic capabilities. Phenotype-genotype relationship predictions were improved through manual curation of gene-protein-reaction rules for 543 reactions and validations with gene essentiality data leading to correct recapitulations of 84.5% of gene essentiality data (sensitivity of 94.3% and specificity of 53.8%). Organism-specific macromolecular composition and ATP maintenance requirements were experimentally measured for two separate growth conditions: (i) carbon and (ii) nitrogen limitations. Overall, iRhto1108 reproduced R. toruloidess utilization capabilities for 18 alternate substrates, matched measured wild-type growth yield, and recapitulated the viability of 772 out of 819 deletion mutants. As a demonstration to the models fidelity in guiding engineering interventions, the OptForce procedure was applied on iRhto1108 for the overproduction of triacylglycerol. Suggested interventions recapitulated many of the previously successfully implemented genetic modifications and put forth a few new ones.\n\nConclusioniRhto1108 offers a highly curated model for a non-model yeast supported by multiple layers of experimental data that can be used to inform genetic interventions.

systems biology↗

bioRxiv2019

Rapid, precise, and reliable phenotyping of delay discounting using Bayesian adaptive design optimization

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 minutes of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.

neuroscience↗

bioRxiv2019

Novel risk genes and mechanisms implicated by exome sequencing of 2,572 individuals with pulmonary arterial hypertension

Group 1 pulmonary arterial hypertension (PAH) is a rare disease with high mortality despite recent therapeutic advances. Pathogenic remodeling of pulmonary arterioles leads to increased pulmonary pressures, right ventricular hypertrophy and heart failure. Mutations in bone morphogenetic protein receptor type 2 and other risk genes predispose to disease, but the vast majority of non-familial cases remain genetically undefined. To identify new risk genes, we performed exome sequencing in a large cohort from the National Biological Sample and Data Repository for PAH. By statistical association of rare deleterious variants, we found tissue kallikrein 1 and gamma glutamyl carboxylase as new candidate risk genes for idiopathic PAH associated with a later age-of-onset and relatively moderate disease phenotype compared to bone morphogenetic receptor type 2. Both genes play important roles in vascular hemodynamics and inflammation but have not been implicated in PAH previously. These data suggest new genes, pathogenic mechanisms and therapeutic targets for this lethal vasculopathy.

genetics↗

bioRxiv2019

Predicting Pathogenicity of Missense Variants with Weakly Supervised Regression

Quickly growing genetic variation data of unknown clinical significance demand computational methods that can reliably predict clinical phenotypes and deeply unravel molecular mechanisms. On the platform enabled by CAGI (Critical Assessment of Genome Interpretation), we develop a novel "weakly supervised" regression (WSR) model that not only predicts precise clinical significance (probability of pathogenicity) from inexact training annotations (class of pathogenicity) but also infers underlying molecular mechanisms in a variant-specific fashion. Compared to multi-class logistic regression, a representative multi-class classifier, our kernelized WSR improves the performance for the ENIGMA Challenge set from 0.72 to 0.97 in binary AUC (Area Under the receiver operating characteristic Curve) and from 0.64 to 0.80 in ordinal multi-class AUC. WSR model interpretation and protein structural interpretation reach consensus in corroborating the most probable molecular mechanisms by which some pathogenic BRCA1 variants confer clinical significance, namely metal-binding disruption for C44F and C47Y, protein-binding disruption for M18T, and structure destabilization for S1715N.

bioinformatics↗

bioRxiv2019

Topological data analysis reveals principles of chromosome structure throughout cellular differentiation

Three-dimensional chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. Many methods for describing the chromosomal architecture focus on specific substructures such as topologically-associating domains (TADs) or compartments, but we are still missing a global view of all geometric features of chromosomes. Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data sets, making it well-suited to better understand the key features of chromosome structure. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding generally, and observe structural changes across lineages. We identify both global and local differences in chromosome topology through differentiation, identifying trends consistent across human cell lines. AvailabilityScripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/hictda Contactcarlk@cs.cmu.edu

genomics↗

bioRxiv2019

Blood-brain barrier dysfunction in aging induces hyper-activation of TGF-beta signaling and chronic yet reversible neural dysfunction

Aging involves a decline in neural function that contributes to cognitive impairment and disease. However, the mechanisms underlying the transition from a young-and-healthy to aged-and-dysfunctional brain are not well understood. Here, we report breakdown of the vascular blood-brain barrier (BBB) in aging humans and rodents, which begins as early as middle age and progresses to the end of the lifespan. Gain-of-function and loss-of-function manipulations show that this BBB dysfunction triggers hyperactivation of transforming growth factor {beta} (TGF{beta}) signaling in astrocytes, which is necessary and sufficient to cause neural dysfunction and age-related pathology. Specifically, infusion of the serum protein albumin into the young brain (mimicking BBB leakiness) induced astrocytic TGF{beta} signaling and an aged brain phenotype including aberrant electrocorticographic activity, vulnerability to seizures, and cognitive impairment. Furthermore, conditional genetic knockdown of astrocytic TGF{beta} receptors, or pharmacological inhibition of TGF{beta} signaling, reversed these symptomatic outcomes in aged mice. Finally, we found that this same signaling pathway is activated in aging human subjects with BBB dysfunction. Our study identifies dysfunction in the neurovascular unit as one of the earliest triggers of neurological aging, and demonstrates that the aging brain may retain considerable latent capacity which can be revitalized by therapeutic inhibition of TGF{beta} signaling.

neuroscience↗

bioRxiv2019

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

MotivationAb initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is no docking method available for rigorous uncertainty quantification (UQ) of its solution quality (e.g. interface RMSD or iRMSD). ResultsWe introduce a novel algorithm, Bayesian Active Learning (BAL), for optimization and UQof such black-box functions and flexible protein docking. BAL directly models the posterior distribution of the global optimum (or native structures for protein docking) with active sampling and posterior estimation iteratively feeding each other. Furthermore, we use complex normal modes to represent a homogeneous Euclidean conformation space suitable for high-dimension optimization and construct funnel-like energy models for encounter complexes. Over a protein docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improve against both starting points by rigid docking and refinements by particle swarm optimization, providing for one third targets a top-3 near-native prediction. BAL also generates tight confidence intervals with half range around 25% of iRMSD and confidence level at 85%. Its estimated probability of a prediction being native or not achieves binary classification AUROC at 0.93 and AUPRC over 0.60 (compared to 0.14 by chance); and also found to help ranking predictions. To the best of knowledge, this study represents the first uncertainty quantification solution for protein docking, with theoretical rigor and comprehensive assessment. AvailabilitySource codes are available at https://github.com/Shen-Lab/BAL. Contactyshen@tamu.edu Supplementary informationhttps://github.com/Shen-Lab/BAL/tree/master/Paper_SI/

bioinformatics↗

bioRxiv2019

Exome sequencing of 457 autism families recruited online provides evidence for novel ASD genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. Additionally, we identified variants that are possibly associated with autism in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.2 provides statistical support for 34 ASD risk genes with at least one damaging variant identified in SPARK. Nine of these genes (BRSK2, DPP6, EGR3, FEZF2, ITSN1, KDM1B, NR4A2, PAX5 and RALGAPB) are newly emerging genes in autism, of which BRSK2 has the strongest statistical support as a risk gene for autism (TADA q-value = 0.0015). Future studies leveraging the thousands of individuals with ASD that have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate autism research that incorporates genetic etiology.

genetics↗

bioRxiv2018

cis-Regulatory Chromatin Contacts in Neural Cells Reveal Contributions of Genetic Variants to Complex Neurological Disorders

Mutations in gene regulatory elements have been associated with a wide range of complex neurological disorders. However, due to their inherent cell type-specificity and difficulties in characterizing their regulatory targets, our ability to identify causal genetic variants has remained limited. To address these constraints, we perform integrative analysis of chromatin interactions using promoter capture Hi-C (pcHi-C), open chromatin regions using ATAC-seq, and transcriptomes using RNA-seq in four functionally distinct neural cell types: iPSC-induced excitatory neurons and lower motor neurons, iPSC-derived hippocampal dentate gyrus (DG)-like neurons, and primary astrocytes. We identify hundreds of thousands of long-range cis interactions between promoters and distal promoter-interacting regions (PIRs), enabling us to link regulatory elements to their target genes and reveal putative pathways that are dysregulated in disease. We validate several novel PIRs using CRISPR techniques in human excitatory neurons, demonstrating that CDK5RAP3, STRAP, and DRD2 are transcriptionally regulated by physically linked enhancers. Finally, we show that physical chromatin interactions mediate genetic interactions in autism spectrum disorder (ASD). Our study illustrates how characterizing the 3D epigenome elucidates novel regulatory relationships in the central nervous system (CNS), shedding light on previously unknown functions for noncoding variants in complex neurological disorders.

genomics↗

bioRxiv2018

Clonal seeds in hybrid rice using CRISPR/Cas9

Heterosis, the observation that first generation hybrids outcompete the parental lines, is widely used in increasing the productivity and yield of agricultural crops1,2. However, heterosis is lost in the following generations because of genetic segregation. In addition, the high cost of hybrid seed production hinders the application of heterosis in many crops. Clonal reproduction through seeds could be revolutionary for agriculture by allowing self-propagation of F1 hybrids3,4. Here we show that heterozygosity of F1 hybrid rice can be fixed and thus propagated without additional crossing. First, we showed that multiplex editing of three key meiotic genes5,6 in hybrid rice leads to the production of clonal diploid gametes and tetraploid seeds. Next, editing of the MATRILINEAL (MTL) gene that involved in fertilization7,8 results in the induction of haploid seeds in hybrid rice. By simultaneous editing of these four endogenous genes in hybrid rice using the CRISPR/Cas9 system, we obtained in one generation plants able to propagate clonally through seeds. This opens the possibility to fix heterozygosity of hybrid varieties in food crops.

plant biology↗

bioRxiv2018

Targeting RyR2 with a phosphorylation site-specific nanobody Reverses Dysfunction of Failing Cardiomyocytes in Rat

Chronic PKA phosphorylation of RyR2 has been shown to increased diastolic SR Ca2+ leak and lead to cardiac dysfunction. Since the change of phosphorylation level of RyR2 is a biomarker of failing heart, we attempted to verify the hypothesis that intracellular gene delivery of a RyR2 targeting phosphorylation site-specific nanobody could preserve contractility of failing myocardium. In present study, we acquired the RyR2-specific nanobodies from a phage display library which are variable domains of camellidae heavy chain-only antibodies (VHH). One of the monoclonal nanobodies, AR185, inhibiting RyR2 phosphorylation in an in vitro assay was then chosen for further investigation. We investigated the potential of adeno-associated virus (AAV)-9-mediated cardiac expression of AR185 against post-ischemic heart failure. Adeno-associated virus gene delivery elevated the intracellular expression AR185 protein in the ischemic heart failure model of rats, and this treatment normalized the systolic and diastolic dysfunction of the failing myocardium in vivo and in vitro by reversing myocardial Ca2+ handling. Furthermore, AR185 gene transfer to failing cardiomyocytes reduced the frequency of sarcoplasmic reticulum (SR) calcium leak, thereby restoring the attenuated intracellular calcium transients and SR calcium load. Moreover, AR185 gene transfer inhibited PKA phosphorylation of RyR2 in failing cardiomyocytes. Our results provided strong pre-clinical experimental evidence of the cardiac expression of RyR2 nanobody with AAV9 vectors as a promising therapeutic strategy for ischemic heart failure.

pathology↗

bioRxiv2018

Understanding the Ca2+-dependent Fluorescence Change in Red Genetically Encoded Ca2+ Indicators

Genetically encoded Ca2+ indicators (GECIs) are widely used to illuminate dynamic Ca2+ signaling activity in living cells and tissues. Various fluorescence colors of GECIs are available, including red. Red GECIs are promising because longer wavelengths of light scatter less in tissue, making it possible to image deeper. They are engineered from a circularly permuted red fluorescent protein fused to a Ca2+ sensing domain, calmodulin and a calmodulin-binding peptide. A conformational change in the sensing domain upon binding Ca2+ causes a change in the fluorescence intensity of the fluorescent protein. Three factors could contribute to this change in fluorescence: 1) a shift in the protonation state of the chromophore, 2) a change in fluorescence quantum yield, and 3) a change in the extinction coefficient for one-photon excitation or the two-photon cross section for two-photon excitation. We conducted a systematic study of the photophysical properties of a select cohort of red GECIs in their Ca2+-free and Ca2+-saturated states to determine which factors are most important for the Ca2+-dependent change in fluorescence. In total, we analyzed nine red GECIs, including jRGECO1a, K-GECO1, jRCaMP1a, R-GECO1, R-GECO1.2, CAR-GECO1, O-GECO1, REX-GECO1, and a new variant termed jREX-GECO1. We found that these red GECIs could be separated into three classes that each rely on a particular set of factors. Furthermore, in some cases the magnitude of the change in fluorescence was different depending on one-photon excitation or two-photon excitation by up to a factor of two.

biophysics↗

bioRxiv2018

Genome-wide selection footprints and deleterious variations in young Asian allotetraploid rapeseed

Brassica napus (AACC, 2n=38), is an important oilseed crop grown worldwide. However, little is known about the population evolution of this species, the genomic difference between its major genetic clusters, such as European and Asian rapeseed, and impacts of historical large-sale introgression events in this young tetraploid. In this study, we reported the de novo assembly of the genome sequences of an Asian rapeseed (B. napus), Ningyou 7 and its four progenitors and carried out de novo assembly-based comparison, pedigree and population analysis with other available genomic data from diverse European and Asian cultivars. Our results showed that Asian rapeseed originally derived from European rapeseed, but it had subsequently significantly diverged, with rapid genome differentiation after intensive local breeding selection. The first historical introgression of B. rapa dramatically broadened the allelic pool of Asian B. napus, but decreased their deleterious variations. The secondary historical introgression of European rapeseed (canola-quality) has reshaped Asian rapeseed into two groups, accompanied by an increase in genetic load. This study demonstrates distinctive genomic footprints by recent intra- and inter-species introgression events for local adaptation, and provide novel insights for understanding the rapid genome evolution of a young allopolyploid crop.

genomics↗

bioRxiv2018

Structure mapping of dengue and Zika viruses reveals new functional long-range interactions

Dengue and Zika are clinically important members of the Flaviviridae family that utilizes an 11kb positive strand RNA for genome regulation. While structures have been mapped primarily in the UTRs, much remains to be learnt about how the rest of the genome folds to enable function. Here, we performed secondary structure and pair-wise interaction mapping on four dengue serotypes and four Zika strains in their native virus particles and infected cells. Comparative analysis of SHAPE reactivities across serotypes nominated potentially functional regions that are highly structured, show structure conservation, and low synonymous mutation rates, including a structure associated with ribosome pausing. Pair-wise interaction mapping by SPLASH further reveals new pair-wise interactions, in addition to the known circularization sequence. 40% of pair-wise interactions form alternative structures, suggesting extensive structural heterogeneity. Analysis of shared pair-wise interactions between serotypes revealed macro-organization whereby interactions are preserved at their physical locations, beyond their sequence identities. In addition, structure mapping of virus genomes released in solution-as well as inside host cells-showed that other helicases, in addition to the ribosome, play a role in unwinding viral structures inside cells. Mutational experiments that disrupt in cell and in virion pair-wise interactions result in virus attenuation, demonstrating their importance during the virus life-cycle.

genomics↗

bioRxiv2018

Proximal recolonization by self-renewing microglia re-establishes microglial homeostasis in the adult mouse brain

Microglia are resident immune cells that play critical roles in maintaining normal physiology of the central nervous system. Remarkably, microglia have intrinsic capacity to replenish after being acutely ablated. However, the underlying mechanisms that drive such microglial restoration remain elusive. Here, we removed microglia via CSF1R inhibitor PLX5622 and characterized repopulation both spatially and temporally. We also investigated the cellular origin of repopulated microglia and report that microglia are replenished via self-renewal, with little contribution from non-microglial lineage progenitors, including nestin+ progenitors and the circulating myeloid population. Interestingly, spatial analyses with multi-color labeling reveal that newborn microglia recolonize the parenchyma by forming distinctive clusters that maintain stable territorial boundaries over time, indicating proximal expansion nature of adult microgliogenesis and stability of microglia tiling. Temporal transcriptome profiling from newborn microglia at different repopulation stages revealed that the adult newborn microglia gradually regain steady-state maturity from an immature state that is reminiscent of neonatal stage and follow a series of maturation programs that include NF-{kappa}B activation, interferon immune activation and apoptosis, etc. Importantly, we show that the restoration of microglial homeostatic density requires NF-{kappa}B signaling as well as apoptotic egress of excessive cells. In summary, our study reports key events that take place from microgliogenesis to homeostasis re-establishment.

neuroscience↗

bioRxiv2018

Seasonal changes in diet and toxicity in the Climbing Mantella frog (Mantella laevigata)

Poison frogs acquire chemical defenses from the environment for protection against potential predators. These defensive chemicals are lipophilic alkaloid toxins that are sequestered by poison frogs from dietary arthropods and stored in skin glands. Despite decades of research focusing on identifying poison frog toxins, we know relatively little about how environmental variation and subsequent arthropod availability impacts toxicity in poison frogs. We investigated how seasonal environmental variation influences poison frog toxin profiles through changes in the diet of the Climbing Mantella (Mantella laevigata). We collected M. laevigata females on the Nosy Mangabe island reserve in Madagascar during the wet and dry seasons and tested the hypothesis that seasonal differences in rainfall is associated with changes in the diet and skin toxin profiles of M. laevigata. The arthropod diet of each frog was characterized into five groups (i.e. ants, termites, mites, insect larvae, or other) using visual identification and cytochrome oxidase 1 DNA barcoding. We found that frog diet differed between the wet and dry seasons, where frogs had a more diverse diet in the wet season and consumed a higher percentage of ants in the dry season. To determine if seasonality was associated with variation in frog defensive chemical composition, we used gas chromatography / mass spectrometry to quantify toxins from individual skin samples. Although the assortment of identified toxins was similar across seasons, we detected significant differences in the abundance of certain alkaloids, which we hypothesize reflects seasonal variation in the diet of M. laevigata. We suggest that these variations could originate from seasonal changes in either arthropod leaf litter composition or changes in frog behavioral patterns. Although additional studies are needed to understand the consequences of long-term environmental shifts, this work suggests that toxin profiles are relatively robust against short-term environmental perturbations.

ecology↗

bioRxiv2018

CRISPR delivery particles for developing therapeutic strategies in metabolic disease

RNA-guided engineered nucleases derived from a prokaryotic adaptive immune system known as CRISPR-Cas represent a promising platform for gene deletion and editing. As a therapeutic approach, direct delivery of Cas9 protein and guide RNA could circumvent the safety problems associated with plasmid delivery and therefore represents an attractive tool for genome engineering. Gene deletion or editing in adipose tissue to enhance its energy expenditure, fat oxidation and secretion of bioactive factors through a \"browning\" process presents a potential therapeutic strategy to alleviate metabolic disease. Here, we developed novel CRISPR delivery particles, denoted CriPs, composed of nano-size complexes of Cas9 protein and single guide (sg)RNA, coated with an amphipathic peptide called Endo-Porter that mediates entry into cells. Efficient CRISPR-Cas9 mediated gene deletion of ectopically expressed Green fluorescent protein (GFP) by CriPs was achieved in multiple cell types including a macrophage cell line, primary macrophages and primary pre-adipocytes. Significant GFP loss was also observed in peritoneal exudate cells with minimum systemic toxicity in GFP expressing mice following intraperitoneal injection of CriPs containing sgRNA targeting Gfp. Furthermore, the disruption of the Nrip1 gene in white adipocytes by CriPs enhanced adipocyte \"browning\" with a marked increase of UCP1 expression. Deletion of Nrip1 by CriPs did not produce detectable off-target effects. Thus CriPs represent a novel CRISPR delivery system for Cas9 and sgRNA that is effective for ablating targeted gene products in cultured cells and in vivo, and provide a potential therapeutic strategy for metabolic disease.

bioengineering↗

bioRxiv2018

DeepAffinity: Interpretable Deep Learning of Compound Protein Affinity through Unified Recurrent and Convolutional Neural Networks

MotivationDrug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability.\n\nResultsWe present a seamless integration of domain knowledges and learning-based approaches. Under novel representations of structurally-annotatedprotein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both unlabeled and labeled data, for jointly encoding molecular representations and predicting affinities. Our representations and models outperform conventional options in achieving relative error in IC50 within 5-fold for test cases and 20-fold for protein classes not included for training. Performances for new protein classes with few labeled data are further improved by transfer learning. Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions. Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.\n\nAvailabilityData and source codes are available at https://github.com/Shen-Lab/DeepAffinity\n\nContactyshen@tamu.edu\n\nSupplementary informationSupplementary data are available at http://shen-lab.github.io/deep-affinity-bioinf18-supp-rev.pdf.

bioinformatics↗