Biology subjects

Wang, M.

Publications and source records attributed to Wang, M..

At least 811 records · Page 45Linked to original sources

bioRxiv2020

Origin time and epidemic dynamics of the 2019 novel coronavirus

The 2019 novel coronavirus (2019-nCoV) have emerged from Wuhan, China. Studying the epidemic dynamics is crucial for further surveillance and control of the outbreak. We employed a Bayesian framework to infer the time-calibrated phylogeny and the epidemic dynamics represented by the effective reproductive number (Re) changing over time from 33 genomic sequences available from GISAID. The time of the most recent common ancestor (MRCA) was December 17, 2019 (95% HPD: December 7, 2019 - December 23, 2019). The median estimate of Re shifted from 1.6 to 1.1 on around January 1, 2020. This study provides an early insight of the 2019-nCoV epidemic. However, due to limited amount of data, one should be cautious when interpreting the results at this stage.

microbiology↗

bioRxiv2020

Inhibition and Crystal Structure of the Human DHTKD1-Thiamin Diphosphate Complex

DHTKD1 is the E1 component of the 2-oxoadipic acid dehydrogenase complex (OADHc), which functions in the L-lysine degradation pathway. Mutations in DHTKD1 have been associated with 2-aminoadipic and 2-oxoadipic aciduria, Charcot-Marie-Tooth disease type 2Q (CMT2Q) and eosinophilic esophagitis (EoE). A crystal structure and inhibitors of DHTKD1 could improve the understanding of these clinically distinct disorders, but are currently not available. Here we report the identification of adipoylphosphonic acid and tenatoprazole as DHTKD1 inhibitors using targeted and high throughput screening, respectively. We furthermore elucidate the DHTKD1 crystal structure with thiamin diphosphate bound at 2.1 [A]. The protein assembles as a dimer with residues from both monomers contributing to cofactor binding. We also report the impact of ten DHTKD1 missense mutations on the encoded proteins by enzyme kinetics, thermal stability and structural modeling. Some DHTKD1 variants displayed impaired folding (S777P and S862I), whereas other substitutions rendered the enzyme inactive (L234G, R715C and R455Q) or affected the thermal stability and catalytic efficiency (V360A and P773L). Three variants (R163Q, Q305H and G729R) surprisingly showed wild type like properties. Our work provides a structural basis for further understanding of the function of DHTKD1 and a starting point for selective small molecule inhibitors of the enzyme, which could help tease apart the role of this enzyme in several human pathologies.

biochemistry↗

bioRxiv2020

Network Insights into Improving Drug Target Inference Algorithms

To improve the efficacy of drug research and development (R&D), a better understanding of drug mechanisms of action (MoA) is needed to improve drug discovery. Computational algorithms, such as ProTINA, that integrate protein-protein interactions (PPIs), protein-gene interactions (PGIs) and gene expression data have shown promising performance on drug target inference. In this work, we evaluated how network and gene expression data affect ProTINAs accuracy. Network data predominantly determines the accuracy of ProTINA instead of gene expression, while the size of an interaction network or selecting cell/tissue-specific networks have limited effects on the accuracy. However, we found that protein network betweenness values showed high accuracy in predicting drug targets. Therefore, we suggested a new algorithm, TREAP (https://github.com/ImmuSystems-Lab/TREAP), that combines betweenness values and adjusted p-values for target inference. This algorithm has resulted in higher accuracy than ProTINA using the same datasets.

systems biology↗

bioRxiv2020

Counteracting Genome Instability by p53-dependent Mintosis

Entosis was proposed to promote aneuploidy and genome instability by cell-in-cell mediated engulfment in tumor cells. We reported here, in epithelial cells, that entosis coupled with mitotic arrest functions to counteract genome instability by targeting aneuploid mitotic progenies for engulfment and elimination. We found that the formation of cell-in-cell structures associated with prolonged mitosis, which was sufficient to induce entosis. This process was controlled by the tumor suppressor p53 (wild type) that upregulates Rnd3 expression in response to DNA damages associated with prolonged metaphase. Rnd3 compartmentalized RhoA activities accumulated during prolonged metaphase to drive cell-in-cell formation. Remarkably, this prolonged mitosis-induced entosis (mintosis) selectively targets non-diploid progenies for internalization, blockade of which increased aneuploidy. Thus, our work uncovered a heretofore unrecognized mechanism of mitotic surveillance for entosis, which eliminates newly-born abnormal daughter cells in a p53-depedent way to maintain genome integrity.

cell biology↗

bioRxiv2020

Single cell atlas of trisomy 21 cerebral cortex

Down syndrome (DS) is one of the most common human birth defects caused by trisomy 21 (T21), leading to a variety of cognitive impairments. The cellular composition of human brain has been explored using single cell RNA sequencing in both physiological and pathological conditions. However, the cellular heterogeneity of human brain with chromosome aneuploidy is largely unknown. Here, we profiled the transcriptome of 36046 cells in cerebral cortex of T21 human fetus, covering frontal lobe, parietal lobe, occipital lobe and temporal lobe. Intriguingly, we detected several genes positively associated with neurons maturation was dysregulated in T21 frontal cortex (HIC2, POU2F2, ZGLP1 and FOXK1). To share, explore and utilized the data resources of T21 cerebral cortex, we developed a comprehensive platform named T21atlas, composing of two functional modules (T21cluster and T21talk). Overall, our study provides, as far as we know, the first single cell atlas for T21 cerebral cortex, which could promote our understanding of the molecular mechanism of DS at an unprecedented resolution and could potentially facilitate the development of novel clinical therapeutics against T21.

neuroscience↗

bioRxiv2019

Explaining the genetic causality for complex diseases via deep association kernel learning

The genetic effect explains the causality from genetic mutation to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a Deep Association Kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects that existing approaches fail. When applied to real-world GWAS data, our approach discovered potential casual pathways that could be explained by alternative biological studies.

bioinformatics↗

bioRxiv2019

Revisiting microbe-metabolite interactions: doing better than random

I.Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets.

bioinformatics↗

bioRxiv2019

AdaReg: Data Adaptive Robust Estimation in Linear Regression with Application in GTEx Gene Expressions

With the development of high-throughput RNA sequencing (RNA-seq) technology, the Genotype Tissue-Expression (GTEx) project (Consortium et al., 2015) generated a valuable resource of gene expression data from more than 11,000 samples. The large-scale data set is a powerful resource for understanding the human transcriptome. However, the technical variation, sequencing background noise and unknown factors make the statistical analysis challenging. To eliminate the possibility that outliers might affect the estimation of population distribution, we need a more robust estimation method, a method that will adapt to heterogeneous genes and further optimize the estimate for each gene. We followed the approach of the robust estimation based on{gamma} -density-power-weight (Fujisawa and Eguchi, 2008; Windham, 1995), where{gamma} is the exponent of density weight which controls the balance between bias and variance. As far as we know, our work is the first to propose a procedure to tune the parameter{gamma} to balance the bias-variance trade-off under the mixture distributions. We constructed a robust likelihood criterion based on weighted densities in the mixture model of Gaussian population distribution mixed with unknown outlier distribution, and developed a data-adaptive{gamma} -selection procedure embedded into the robust estimation. We provided a heuristic analysis on the selection criterion and found that our practical selection trend under various{gamma} s in average performance has similar capability to capture minimizer{gamma} as the inestimable Mean Squared Error (MSE) trend from our simulation studies under a series of settings. Our data-adaptive robustifying procedure in the linear regression problem (AdaReg) shows a significant advantage in both simulation studies and real data application of heart samples from the GTEx project compared to the fixed{gamma} procedure and other robust methods. This paper discusses some limitations of this method, and future work.

bioinformatics↗

bioRxiv2019

AdaTiSS: A Novel Data-Adaptive Robust Method forQuantifying Tissue Specificity Scores

MotivationAccurately detecting tissue specificity (TS) in genes helps researchers understand tissue functions at the molecular level, and further identify disease mechanisms and discover tissue-specific therapeutic targets. The Genotype-Tissue Expression (GTEx) project (Consortium, 2015), and the Human Protein Atlas (HPA) project (Uhlen, et al., 2015) are two publicly available data resources, providing large-scale gene expressions across multiple tissue types. Multiple tissue comparisons, technical background noise and unknown variation factors make it challenging to accurately identify tissue specific gene expressions. Several methods worked on measuring the overall TS in gene expressions and classifying genes into tissue-enrichment categories. There still lacks a robust method to provide quantitative TS scores for each tissue. MethodsWe recognized that the key to quantify tissue specific gene expressions is to properly define a concept of expression population. We considered that inside the population, the sample expressions from various tissues are more or less balanced, and the outlier expressions outside the population may indicate tissue specificity. We then formulated the question to robustly estimate the population distribution. In a linear regression problem, we developed a novel data-adaptive robust estimation based on density-power-weight under unknown outlier distribution and non-vanishing outlier proportion (Wang, et al., 2019). In the question of quantifying TS, we focused on the Gaussian-population mixture model. We took into account gene heterogeneities and applied the robust data-adaptive procedure to estimate the population. With the robustly estimated population parameters, we constructed the AdaTiSS algorithm to obtain data-adaptive quantitative TS scores. ResultsOur TS scores from the AdaTiSS algorithm achieve the goal that the TS scores are comparable across tissues and also across genes, which standardize gene expressions in terms of TS. Compared to the categorical TS method such as the HPA criterion, our method provides more information on the population fitting, and shows advantages in quantitatively analyzing tissue specific functions, making the biology functional analysis more precise. We also discuss some limitations and possible future work. Contactmpsnyder@stanford.edu

bioinformatics↗

bioRxiv2019

LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis

BackgroundClinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients. MethodsWe enrolled 1372 subjects, including clinically diagnosed PTB patients, non-TB disease controls and healthy controls, in three cohorts (Screening, Selection and Validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and qRT-PCR in the Screening Cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the Selection Cohort. These models were evaluated by AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. The biological function of lncRNAs was interrogated using ELISA, lactate dehydrogenase release analysis and flow cytometry. ResultsThree differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHR variables (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. ENST00000497872 may regulate inflammatory cytokine production, cell death and apoptosis during MTB infection. ConclusionsLncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.

molecular biology↗

bioRxiv2019

NIR-II fluorescence microscopic imaging of cortical vasculature in non-human primates

Vasculature architecture in the brain can provide revealing information about mental and neurological function and disease. Fluorescence imaging in the second near-infrared (NIR-II) regime with less light scattering is a more promising method for detecting cortical vessels than traditional visible and NIR-I modes. Here, for the first time, we developed, NIR-II fluorescence microscopy capabilities for imaging brain vasculature in macaque monkey. The first is a wide-field microscope with high temporal resolution (25 frames/second) for measuring blood flow velocity and cardiac impulse period, and the second is a high spatial resolution (<10 m) confocal microscope producing three-dimensional maps of the cortical microvascular network ([~]500 m deep). Both were designed with flexibility to image various cortical locations on the head. Use of a clinically approved dye provided high brightness in NIR-II region. This comprises an important advance towards studies of neurovascular coupling, stroke, and other diseases relevant to neurovascular health in humans.

neuroscience↗

bioRxiv2019

Enhancing Gesture Decoding Performance Using Signals from Posterior Parietal Cortex: A Stereo-Electroencephalograhy (SEEG) Study

ObjectiveHand movement is a crucial function for humans daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI. ApproachStereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding. Main ResultsWe find that a majority(L: >60%, R: >40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used. SignificanceThis is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

neuroscience↗

bioRxiv2019

SUMOylation targets shugoshin to stabilize sister kinetochore biorientation

The accurate segregation of chromosomes during mitosis relies on the attachment of sister chromatids to microtubules from opposite poles, called biorientation. Sister chromatid cohesion resists microtubule forces, generating tension which provides the signal that biorientation has occurred. How tension silences the surveillance pathways that prevent cell cycle progression and correct erroneous kinetochore-microtubule remains unclear. Here we identify SUMOylation as a mechanism that promotes anaphase onset upon biorientation. SUMO ligases modify the tension-sensing pericentromere-localized chromatin protein, shugoshin, to stabilize bioriented sister kinetochore-microtubule attachments. In the absence of SUMOylation, Aurora B kinase removal from kinetochores is delayed. Shugoshin SUMOylation prevents its binding to protein phosphatase 2A (PP2A) and release of this interaction is important for stabilizing sister kinetochore biorientation. We propose that SUMOylation modulates the kinase-phosphatase balance within pericentromeres to inactivate the error correction machinery, thereby allowing anaphase entry in response to biorientation.

cell biology↗

bioRxiv2019

Redefining Interleukin 11 as a regeneration-limiting hepatotoxin

Acetaminophen (APAP) overdose is a leading cause of untreatable liver failure. In the mouse model of APAP-induced liver injury (AILI), the administration of recombinant human interleukin 11 (rhIL11) is protective. Here we show that the beneficial effect of rhIL11 in the mouse is due to its unexpected and paradoxical inhibition of endogenous mouse IL11 activity. Contrary to the accepted paradigm IL11 is a potent hepatotoxin across species, which is secreted from damaged hepatocytes to drive an autocrine loop of NOX4 and JNK-dependent apoptosis. Mice with hepatocyte-specific Il11 expression spontaneously develop liver failure whereas those with Il11ra1 deletion are remarkably protected from AILI. Neutralizing anti-IL11R antibodies administered to moribund mice 10 hours following a lethal APAP overdose results in 90% survival that is associated with very large liver regeneration. Our data overturn a misconception, identify a new disease mechanism and suggest IL11 as a therapeutic target for liver regeneration.

physiology↗

bioRxiv2019

Stochastic simulation platform for visualization and estimation of transcriptional kinetics

We present an implementation of the Gillespie algorithm that simulates the stochastic kinetics of nascent and mature RNA. Our model includes two-state gene regulation, RNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise degradation, a granular description currently tractable only by simulation. To facilitate comparison with experimental data, the algorithm predicts fluorescent probe signals measurable by single-cell RNA imaging. We approach the inverse problem of estimating underlying parameters in a five-dimensional parameter space and suggest optimization heuristics that successfully recover known reaction rates from simulated gene expression turn-on data. The simulation framework includes a graphical user interface, available as a MATLAB app at https://data.caltech.edu/records/1287.

biophysics↗

bioRxiv2019

Bone morphogenetic protein 9 induces osteogenic differentiation of germ cell 1 spermatogonial cells

Germ cell 1 spermatogonial (GC-1spg) cells are multipotent progenitor cells. We previously confirmed that bone morphogenetic protein (BMP) 9 is among the most osteogenic BMPs. However, whether GC-1spg cells are driven toward osteogenic differentiation under proper stimuli is uncertain. Additionally, the molecular mechanism of BMP9 remains unclear. In the present study, we aimed to determine whether BMP9 can induce osteogenic differentiation of GC-1spg cells. Recombinant adenoviruses were generated by the AdEasy system to regulate the BMP9 expression in GC-1spg cells. We identified osteogenic markers by real-time PCR and staining techniques in vitro. Ectopic ossification assays and histological analysis were also performed to verify the in vivo activity of BMP9. Finally, potential signaling pathways of BMP9 were assessed by transcriptome sequencing and KEGG enrichment analysis. Using recombinant adenoviruses, we demonstrate that BMP9 upregulates osteogenic markers including Runx2, osteocalcin, osteopontin, and Sox9. BMP9 also activates alkaline phosphatase activity and calcium deposition in GC-1spg cells. In vivo results show that BMP9 overexpression in GC-1spg cells promotes ectopic bone formation and chondrogenesis. In addition, RNA-sequencing and KEGG pathway analysis demonstrate that several signaling pathways are involved in BMP9-mediated osteogenesis. GC-1spg cells not only maintain spermatogenesis but also retain the ability to form bone tissue. Therefore, BMP9 activity in GC-1spg cells may help identify signaling pathways implicated in bone formation and could be of use in regenerative medicine.

cell biology↗

bioRxiv2019

A portable and cost-effective microfluidic system for massively parallel single-cell transcriptome profiling

Single-cell technologies are becoming increasingly widespread and have been revolutionizing our understanding of cell identity, state, diversity and function. However, current platforms can be slow to apply to large-scale studies and resource-limited clinical arenas due to a variety of reasons including cost, infrastructure, sample quality and requirements. Here we report DNBelab C4 (C4), a negative pressure orchestrated, portable and cost-effective device that enables high-throughput single-cell transcriptional profiling. C4 system can efficiently allow discrimination of species-specific cells at high resolution and dissect tissue heterogeneity in different organs, such as murine lung and cerebral cortex. Finally, we show that the C4 system is comparable to existing platforms but has huge benefits in cost and portability and, as such, it will be of great interest for the wider scientific community.

genomics↗

bioRxiv2019

Reconstructing lost BOLD signal in individual participants using deep machine learning

The blood oxygen level-dependent (BOLD) signal in functional neuroimaging suffers from magnetic susceptibility artifacts and interference from metal implants. The resulting signal loss hampers functional neuroimaging studies and can lead to misinterpretation of findings. Here, we reconstructed compromised BOLD signal using deep machine learning. We trained a deep learning model to learn principles governing BOLD activity in one dataset and reconstructed artificially-compromised regions in another dataset, frame by frame. Strikingly, BOLD time series extracted from reconstructed frames were correlated with the original time series, even though the frames did not independently carry information about BOLD fluctuations through time. Moreover, reconstructed functional connectivity (FC) maps exhibited good correspondence with the original FC maps, indicating that the deep learning model recovered functional relationships among brain regions. We replicated this result in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions captured individual-specific information rather than group information learned during training. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individuals own functional brain organization.

neuroscience↗