Biology subjects

Gao, T.

Publications and source records attributed to Gao, T..

66 records · Page 4Linked to original sources

bioRxiv2019

Oncologic Therapy Shapes the Fitness Landscape of ClonalHematopoiesis

Clonal hematopoiesis (CH) is frequent in cancer patients and associated with increased risk of therapy related myeloid neoplasms (tMN). To define the relationship between CH, oncologic therapy, and tMN progression, we studied 24,439 cancer patients. We show that previously treated patients have increased rates of CH, with enrichment of mutations in DNA Damage Response (DDR) genes (TP53, PPM1D, CHEK2). Exposure to radiation, platinum and topoisomerase II inhibitors have the strongest association with CH with evidence of dose-dependence and gene-treatment interactions. We validate these associations in serial sampling from 525 patients and show that exposure to cytotoxic and radiation therapy imparts a selective advantage specifically in hematopoietic cells with DDR mutations. In patients who progressed to tMN, the clone at CH demarcated the dominant clone at tMN diagnosis. CH mutational features predict risk of therapy-related myeloid neoplasm in solid tumor patients with clinical implications for early detection and treatment decisions.

genetics↗

bioRxiv2019

An Inflammatory Clock Predicts Multi-morbidity, Immunosenescence and Cardiovascular Aging in Humans

While many diseases of aging have been linked to the immunological system, immune metrics with which to identify the most at-risk individuals are lacking. Here, we studied the blood immunome of 1001 individuals age 8-96 and derived an inflammatory clock of aging (iAge), which tracked with multi-morbidity and immunosenescence. In centenarians, iAge was on average, 40 years lower than their corresponding chronological age. The strongest contributor to this metric was the chemokine CXCL9, which was involved in cardiac aging, affected vascular function, and down-regulated Sirtuin-3, a longevity-associated molecule. Thus, our results identify an important link between inflammatory molecules and pathways known to govern lifespan.

immunology↗

bioRxiv2019

Microglial ERK activation is a critical regulator of pro-inflammatory immune responses in Alzheimer’s disease

BackgroundThe mitogen-activated protein kinase (MAPK) pathway is a central regulator of gene expression, pro-survival signaling, and inflammation. However, the importance of MAPK pathway signaling in regulating microglia-mediated neuroinflammation in Alzheimers Disease (AD) remains unclear. Here we examined the role of MAPK signaling in microglia using pre-clinical in-vitro and in-vivo models of AD pathology integrated with quantitative proteomics studies of post-mortem human brains.\n\nMethodsWe performed multiplexed immunoassay analyses of MAPK phosphoproteins, particularly ERK1/2, in acutely-isolated microglia and brain tissue from wild-type and 5xFAD mice. Neuropathological studies of mouse and human brain tissues were performed to quantify total and phosphorylated ERK protein in AD. The importance of ERK signaling in unstimulated and interferon {gamma} (IFN{gamma})-stimulated primary microglia cultures was investigated using NanoString transcriptomic profiling, coupled with functional assays of amyloid {beta} (A{beta}) and neuronal phagocytosis. Receptor tyrosine kinases (RTKs) likely responsible for ERK signaling in homeostatic microglia and disease-associated-microglia (DAM) states and ERK-regulated human AD risk genes were identified using gene expression data. Total and phosphorylated MAPKs in human post-mortem brain tissues were measured in quantitative proteomic datasets.\n\nResultsPhosphorylated ERK was the most strongly up-regulated signaling protein within the MAPK pathway in microglia acutely isolated from 5xFAD brains. Neuroinflammatory transcriptomic and phagocytic profiling of mouse microglia confirmed that ERK is a critical regulator of IFN{gamma}-mediated pro-inflammatory activation of microglia, although it was also important for constitutive microglial functions. Phospho-ERK was an upstream regulator of disease-associated microglia (DAM) gene expression (Trem2, Tyrobp), as well as of several human AD risk genes (Bin1, Cd33, Trem2, Cnn2). Among RTKs that signal via ERK, CSF1R and MERTK were primarily expressed by homeostatic microglia while AXL and FLT1 were likely regulators of ERK signaling in DAM. Within DAM, FLT4 and IGF1R were specifically expressed by pro- and anti-inflammatory DAM sub-profiles respectively. In quantitative proteomic analyses of post-mortem human brains from non-disease, asymptomatic and cognitively-impaired AD cases, ERK1 and ERK2 were the only MAPK pathway signaling proteins with increased protein expression and positive associations with neuropathological grade. Moreover, in a phospho-proteomic study of post-mortem human brains from controls, asymptomatic and symptomatic AD cases, we found evidence for a progressive increased flux through the ERK signaling pathway.\n\nConclusionsOur integrated analyses using pre-clinical models and human proteomic data strongly suggest that ERK phosphorylation in microglia is a critical regulator of pro-inflammatory immune response in AD pathogenesis and that modulation of ERK via upstream RTKs may reveal novel avenues for immunomodulation.

neuroscience↗

bioRxiv2019

EAGLE: an algorithm that utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interaction

Long-range regulation by distal enhancers is crucial for many biological processes. The existing methods for enhancer-target gene prediction often require many genomic features. This makes them difficult to be applied to many cell types, in which the relevant datasets are not always available. Here, we design a tool EAGLE, an enhancer and gene learning ensemble method for identification of Enhancer-Gene (EG) interactions. Unlike existing tools, EAGLE used only six features derived from the genomic features of enhancers and gene expression datasets. Cross-validation revealed that EAGLE outperformed other existing methods. Enrichment analyses on special transcriptional factors, epigenetic modifications, and eQTLs demonstrated that EAGLE could distinguish the interacting pairs from non- interacting ones. Finally, EAGLE was applied to mouse and human genomes and identified 7,680,203 and 7,437,255 EG interactions involving 31,375 and 43,724 genes, 138,547 and 177,062 enhancers across 89 and 110 tissue/cell types in mouse and human, respectively. The obtained interactions are accessible through an interactive database enhanceratlas.org. The EAGLE method is available at https://github.com/EvansGao/EAGLE and the predicted datasets are available in http://www.enhanceratlas.org/.\n\nAuthor summaryEnhancers are DNA sequences that interact with promoters and activate target genes. Since enhancers often located far from the target genes and the nearest genes are not always the targets of the enhancers, the prediction of enhancer-target gene relationships is a big challenge. Although a few computational tools are designed for the prediction of enhancer-target genes, its difficult to apply them in most tissue/cell types due to a lack of enough genomic datasets. Here we proposed a new method, EAGLE, which utilizes a small number of genomic features to predict tissue/cell type-specific enhancer-gene interactions. Comparing with other existing tools, EAGLE displayed a better performance in the 10-fold cross-validation and cross-sample test. Moreover, the predictions by EAGLE were validated by other independent evidence such as the enrichment of relevant transcriptional factors, epigenetic modifications, and eQTLs.\n\nFinally, we integrated the enhancer-target relationships obtained from human and mouse genomes into an interactive database EnhancerAtlas, http://www.enhanceratlas.org/.

bioinformatics↗

bioRxiv2019

Tissue of Origin Dictates GOT1 Dependence and Confers Synthetic Lethality to Radiotherapy

Metabolic programs in cancer cells are influenced by genotype and the tissue of origin. We have previously shown that central carbon metabolism is rewired in pancreatic ductal adenocarcinoma (PDA) to support proliferation through a glutamate oxaloacetate transaminase 1 (GOT1)-dependent pathway. Here we tested if tissue type impacted GOT1 dependence by comparing PDA and colorectal cancer (CRC) cell lines and tumor models of similar genotype. We found CRC to be insensitive to GOT1 inhibition, contrasting markedly with PDA, which exhibit profound growth inhibition upon GOT1 knockdown. Utilizing a combination of metabolomics strategies and computational modeling, we found that GOT1 inhibition disrupted glycolysis, nucleotide metabolism, and redox homeostasis in PDA but not CRC. These insights were leveraged in PDA, where we demonstrate that radiotherapy potently enhanced the effect of GOT1 inhibition on tumor growth. Taken together, these results illustrate the role of tissue type in dictating metabolic dependencies and provide new insights for targeting metabolism to treat PDA.

cancer biology↗

bioRxiv2019

SINATRA: A Sub-Image Analysis Pipeline for Selecting Features that Differentiate Classes of 3D Shapes

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). We present SINATRA: the first statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two classes of shapes and highlights the physical features that best describe the variation between them. We use a rigorous simulation framework to assess our approach. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.

bioinformatics↗

bioRxiv2019

CoGAPS 3: Bayesian non-negative matrix factorization for single-cell analysis with asynchronous updates and sparse data structures

MotivationBayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational costs than their gradient-based counterparts. These costs are often prohibitive for analysis of large single-cell datasets. Many such methods can be run in parallel which enables this limitation to be overcome by running on more powerful hardware. However, the constraints imposed by the prior distributions in CoGAPS limit the applicability of parallelization methods to enhance computational efficiency for single-cell analysis.\n\nResultsWe upgraded CoGAPS in Version 3 to overcome the computational limitations of Bayesian matrix factorization for single cell data analysis. This software includes a new parallelization framework that is designed around the sequential updating steps of the algorithm to enhance computational efficiency. These algorithmic advances were coupled with new software architecture and sparse data structures to reduce the memory overhead for single-cell data. Altogether, these updates to CoGAPS enhance the efficiency of the algorithm so that it can analyze 1000 times more cells, enabling factorization of large single-cell data sets.\n\nAvailabilityCoGAPS is available as a Bioconductor package and the source code is provided at github.com/FertigLab/CoGAPS. All efficiency updates to enable single-cell analysis available as of version 3.2.\n\nContactejfertig@jhmi.edu

bioinformatics↗

bioRxiv2018

Cytologic, Genetic, and Proteomic Analysis of a Yellow Leaf Mutant of Sesame (Sesamum indicum L.), Siyl-1

Leaf color mutation in sesame always affects the growth and development of plantlets, and their yield. To clarify the mechanisms underlying leaf color regulation in sesame, we analyzed a yellow-green leaf mutant. Genetic analysis of the mutant selfing revealed 3 phenotypes--YY, light-yellow (lethal); Yy, yellow-green; and yy, normal green--controlled by an incompletely dominant nuclear gene, Siyl-1. In YY and Yy, the number and morphological structure of the chloroplast changed evidently, with disordered inner matter, and significantly decreased chlorophyll content. To explore the regulation mechanism of leaf color mutation, the proteins expressed among YY, Yy, and yy were analyzed. All 98 differentially expressed proteins (DEPs) were classified into 5 functional groups, in which photosynthesis and energy metabolism (82.7%) occupied a dominant position. Our findings provide the basis for further molecular mechanism and biochemical effect analysis of yellow leaf mutants in plants.

genetics↗

bioRxiv2018

Gallic Acid Disrupts Aβ1-42 Aggregation and Rescues Cognitive Decline of APP/PS1 Transgenic Mouse

Alzheimers disease (AD) treatment represents one of the largest unmet medical needs. Developing drugs capable of preventing A{beta} aggregation is an excellent approach to prevent and treat AD. Here, we show that gallic acid (GA), a naturally occurring polyphenolic small molecule rich in grape seeds and fruits, has the capacity to alleviate cognitive decline of APP/PS1 transgenic mouse through reduction of A{beta}1-42 aggregation and neurotoxicity. Oral administration of GA not only improved the spatial reference memory and spatial working memory of early stage AD mice (4-month-old), but also significantly reduced the more severe deficits in spatial learning, reference memory, short-term recognition and spatial working memory of the late stage AD mice (9-month-old). The hippocampal long-term-potentiation (LTP) was also significantly elevated in the GA-treated late stage APP/PS1 AD mice. Atomic force microscopy (AFM), dynamic light scattering (DLS) and thioflavin T (ThT) fluorescence densitometry analyses showed that GA can reduce A{beta}1-42 aggregation from forming toxic oligomers and fibrils. Indeed, pre-incubating GA with oligomeric A{beta}1-42 reduced A{beta} 1-42-mediated intracellular calcium influx and neurotoxicity. Molecular docking studies identified that the 3,4,5-hydroxyle groups of GA were essential in noncovalently stabilizing GA binding to the Lys28-Ala42 salt bridge and the -COOH group is critical for disrupting the salt bridge of A{beta}1-42. The predicated covalent interaction through Schiff-base formation between the carbonyl group of the oxidized product and {varepsilon}-amino group of Lys16 is also critical for the disruption of A{beta}1-42 S-shaped triple-{beta}-motif and toxicity. Together, these studies demonstrated that GA can prevent and protect the AD brain through disrupting A{beta}1-42 aggregation.

neuroscience↗

bioRxiv2017

Efficient online-group-screening designs for agent identification

Identifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as high-throughput screening (HTS), the ratio of causal to non-causal agents is usually very low.\n\nIn this paper, we introduce a group-screening strategy to efficiently screen causal agents by grouping them into treatments. Our analysis shows that when a large number of candidates factors are screened and true agent percentage is very low (less than 1%), even in the worst case we could save up to 80% of the experiment runs. In the case where experiments span many rounds, we provide an online version of the group-screening that can determine the best strategy automatically based on the existing results. We applied this method to a real HTS experiment with 50,000 candidates that would require 9 rounds to finish in an exhaustive case. Our analysis showed that by applying the online-group-screening method, in the worst case, we can use 3 rounds and 19.7% (9828/50000) total tests to identify all the agents.\n\nFinally, we show that with minor modifications, this framework extends to more complex agent discovery problems.

bioinformatics↗

bioRxiv2017

Assessment of the World Largest Afforestation Program: Success, Failure, and Future Directions

The Three-North Afforestation Program (TNAP), initiated in 1978 and scheduled to be completed in 2050, is the worlds largest afforestation project and covers 4.07 x 106 km2 (42.4%) of China. We systematically assessed goals and outcomes of the first 30 years of the TNAP using high-resolution remote sensing and ground survey data. With almost 23 billion dollars invested between 1978 and 2008, the forested area within the TNAP region increased by 1.20 x 107 ha, but the proportion of high quality forests declined by 15.8%. The establishment of shelterbelts improved crop yield by 1.7%, much lower than the 5.9% expected once all crop fields are fully protected by shelterbelts. The area subjected to soil erosion by water decreased by 36.0% from 6.72 x 107 to 4.27 x 107 ha; the largest reductions occurred in areas where soil erosion had been most severe and forests contributed more than half of this improvement. Desertification area increased from 1978 to 2000 but decreased from 2000 to 2008; the 30-year net reduction was 13.0% (4.05x106 ha), with 8.0% being accounted for by afforestation in areas with only slight, prior desertification. In addition to its direct impacts, the TNAP has enhanced peoples awareness of environmental protection and attracted consistent attention and long-term commitment from the Chinese government to the restoration and protection of fragile ecosystems in the vast Three-North region. The significant decline in forest quality, limited success in reducing desertification, and low coverage of shelterbelts are aspects of the TNAP in need of re-assessment, and additional ca. 34 billion dollars will be needed to ensure the completion of the TNAP.

ecology↗

bioRxiv2016

Development and assessment of fully automated and globally transitive geometric morphometric methods, with application to a biological comparative dataset with high interspecific variation

Automated geometric morphometric methods are promising tools for shape analysis in comparative biology: they improve researchers abilities to quantify biological variation extensively (by permitting more specimens to be analyzed) and intensively (by characterizing shapes with greater fidelity). Although use of these methods has increased, automated methods have some notable limitations: pairwise correspondences are frequently inaccurate or lack transitivity (i.e., they are not defined with reference to the full sample). In this study, we reassess the accuracy of two previously published automated methods, cPDist [1] and auto3Dgm [2], and evaluate several modifications to these methods. We show that a substantial fraction of alignments and pairwise maps between specimens of highly dissimilar geometries were inaccurate in the study of Boyer et al. [1], despite a taxonomically sensitive variance structure of continuous Procrustes distances. We also show these inaccuracies can be remedied by utilizing a globally informed methodology within a collection of shapes, instead of only comparing shapes in a pairwise manner (c.f. [2]). Unfortunately, while global information generally enhances maps between dissimilar objects, it can degrade the quality of correspondences between similar objects due to the accumulation of numerical error. We explore a number of approaches to mitigate this degradation, quantify the performance of these approaches, and compare the generated pairwise maps (as well as the shape space characterized by these maps) to a \"ground truth\" obtained from landmarks manually collected by geometric morphometricians. Novel methods both improve the quality of the pairwise correspondences relative to cPDist, and achieve a taxonomic distinctiveness comparable to auto3Dgm.

bioinformatics↗