Biology subjects

He, D.

Publications and source records attributed to He, D..

66 records · Page 4Linked to original sources

bioRxiv2020

Latent defense response to non-pathogenic microbial factors impairs plant-rhizobacteria mutualism

Unlike pathogens that trigger plant defense responses, commensal or beneficial microbes are compatible with plants and do not elicit a defense response. An assumption underlying the compatibility is that plants are inert in mounting a defense response to non-pathogenic microbial factors. However, the mechanisms underlying this inertness in defense are unknown. Here a forward genetic screen led to the isolation of an Arabidopsis mutant displaying a new type of immunity which we named as latent defense response (LDR) to a beneficial rhizobacterium. The mutant, known as gp1 for Growth-Promotion 1, is impaired in rhizobacteria-induced plant growth-promotion due to disrupted oleic acid homeostasis and consequent activation of defense responses. Several bacterial volatile compounds trigger LDR in gp1 but not wild type plants. GP1 dysfunction strongly represses colonization of the beneficial rhizobacterium and alters root-associated microbiota. Our findings reveal a hidden layer of plant defense, LDR, which is suppressed by GP1 to allow mutualistic association between plants and beneficial rhizobacteria. One Sentence SummaryA hidden layer of host immunity against non-pathogenic microbes leads to plant incompatibility with beneficial rhizobacteria.

plant biology↗

bioRxiv2020

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

BackgroundsAn ongoing outbreak of a novel coronavirus (2019-nCoV) pneumonia hit a major city of China, Wuhan, December 2019 and subsequently reached other provinces/regions of China and countries. We present estimates of the basic reproduction number, R0, of 2019-nCoV in the early phase of the outbreak. MethodsAccounting for the impact of the variations in disease reporting rate, we modelled the epidemic curve of 2019-nCoV cases time series, in mainland China from January 10 to January 24, 2020, through the exponential growth. With the estimated intrinsic growth rate ({gamma}), we estimated R0 by using the serial intervals (SI) of two other well-known coronavirus diseases, MERS and SARS, as approximations for the true unknown SI. FindingsThe early outbreak data largely follows the exponential growth. We estimated that the mean R0 ranges from 2.24 (95%CI: 1.96-2.55) to 3.58 (95%CI: 2.89-4.39) associated with 8-fold to 2-fold increase in the reporting rate. We demonstrated that changes in reporting rate substantially affect estimates of R0. ConclusionThe mean estimate of R0 for the 2019-nCoV ranges from 2.24 to 3.58, and significantly larger than 1. Our findings indicate the potential of 2019-nCoV to cause outbreaks.

ecology↗

bioRxiv2019

Estrogen Receptor α Regulates Ethanol Excitation of Ventral Tegmental Area Neurons and Binge Drinking in Female Mice

Elevations in estrogen (17{beta}-estradiol, E2) are associated with increased alcohol drinking by women and experimentally in rodents. E2 alters the activity of the dopamine system, including the ventral tegmental area (VTA) and its projection targets, which plays an important role in binge drinking. A previous study demonstrated that during high E2 states, VTA dopamine neurons in female mice are more sensitive to ethanol excitation. However, the mechanisms responsible for the ability of E2 to enhance ethanol sensitivity of VTA dopamine neurons have not been investigated. In this study, we used selective agonists and antagonists to examine the role of estrogen receptor subtypes (ER and ER{beta}) in regulating the ethanol sensitivity of VTA dopamine neurons and found that ER promotes the enhanced ethanol response of VTA dopamine neurons. We also demonstrated that the E2-induced increase in ethanol excitation requires the activity of the metabotropic glutamate receptor, mGluR1, which is known to couple with ER at the plasma membrane. To investigate the behavioral relevance of these findings, we administered lentivirus expressing short hairpin RNAs targeting either ER or ER{beta} into the VTA and found that knockdown of each receptor in the VTA reduced binge-like ethanol drinking in female, but not male, mice. Reducing ER in the VTA had a more dramatic effect on binge-like drinking than reducing ER{beta}, consistent with the ability of ER to alter ethanol sensitivity of dopamine neurons. These results provide important insight into sex-specific mechanisms that drive excessive alcohol drinking.\n\nSignificance StatementEstrogen has potent effects on the dopamine system and increases the vulnerability of females to develop addiction to substances such as cocaine and alcohol. We investigated the mechanisms by which estrogen increases the response of dopamine neurons in the ventral tegmental area to ethanol. We found that activation of the estrogen receptor, ER, increased the ethanol-induced excitation of dopamine neurons and that this required the metabotropic glutamate receptor mGluR1. We also demonstrated that estrogen receptors in the ventral tegmental area regulate binge-like alcohol drinking by female, but not male, mice. The influence of estrogen receptors on binge drinking in female mice suggests that treatments for alcohol use disorder in women may need to account for this sex difference.

neuroscience↗

bioRxiv2019

Dissecting the structural and functional roles of a vicinal iron-binding site in encapsulated ferritins

Encapsulated ferritins belong to the universally distributed ferritin superfamily, which function as iron detoxification and storage systems. Encapsulated ferritins have a distinct annular structure and must associate with an encapsulin nanocage to form a competent iron store that is capable of holding significantly more iron than classical ferritins. The catalytic mechanism of iron oxidation in the ferritin family is still an open question, due to differences in organization of the ferroxidase catalytic site and secondary metal binding sites vicinal to this. We have previously identified a metal binding site on the inner surface of the Rhodospirillum rubrum encapsulated ferritin at the interface between the two-helix subunits and proximal to the ferroxidase center. Here we present a comprehensive structural and functional study to investigate the functional relevance of this proposed iron entry site by means of enzymatic assays, mass-spectrometry, and X-ray crystallography. We show that catalysis occurs in the ferroxidase center and suggest a dual role for the secondary site, which both serves to attract metal ions to the ferroxidase center and acts as a flow-restricting valve to limit the activity of the ferroxidase center. Moreover, confinement of encapsulated ferritins within the encapsulin nanocage, while enhancing the ability of the encapsulated ferritin to undergo catalysis, does not influence the function of the secondary site.

biochemistry↗

bioRxiv2019

New estimates of the Zika virus epidemic attack rate in Northeastern Brazil from 2015 to 2016: A modelling analysisbased on Guillain-Barre Syndrome (GBS) surveillance data

BackgroundBetween January 2015 and August 2016, two epidemic waves of Zika virus (ZIKV) disease swept the Northeastern region of Brazil. As a result, two waves of Guillain-Barre Syndrome (GBS), were observed concurrently. The mandatory reporting of ZIKV disease began region-wide in February 2016, and it is believed that ZIKV cases were significantly under-reported before that. The changing reporting rate has made it difficult to estimate the ZIKV infection attack rate, and studies in the literature vary widely from 17% to > 50%. The same applies for other key epidemiological parameters. In contrast, the diagnosis and reporting of GBS cases were reasonably reliable given the severity and easy recognition of the diseases symptoms. In this paper, we aim to estimate the real number of ZIKV cases (i.e., the infection attack rate), and their dynamics in time, by scaling up from GBS surveillance data in NE Brazil.\n\nMethodologyA mathematical compartmental model is constructed that makes it possible to infer the true epidemic dynamics of ZIKV cases based on surveillance data of excess GBS cases. The model includes the possibility that asymptomatic ZIKV cases are infectious. The model is fitted to the GBS surveillance data and the key epidemiological parameters are inferred by using the plug-and-play likelihood-based estimation. We make use of regional weather data to determine possible climate-driven impacts on the reproductive number [R]0, and to infer the true ZIKV epidemic dynamics.\n\nFindings and ConclusionsThe GBS surveillance data can be used to study ZIKV epidemics and may be appropriate when ZIKV reporting rates are not well understood. The overall infection attack rate (IAR) of ZIKV is estimated to be 24.1% (95% CI: 17.1% - 29.3%) of the population. By examining various asymptomatic scenarios, the IAR is likely to be lower than 33% over the two ZIKV waves. The risk rate from symptomatic ZIKV infection to develop GBS was estimated as{rho} = 0.0061% (95% CI: 0.0050% - 0.0086%) which is significantly less than current estimates. We found a positive association between local temperature and the basic reproduction number, [R]0. Our analysis revealed that asymptomatic infections affect the estimation of ZIKV epidemics and need to also be carefully considered in related modelling studies. According to the estimated effective reproduction number and population wide susceptibility, we comment that a ZIKV outbreak would be unlikely in NE Brazil in the near future.\n\nAuthor SummaryThe mandatory reporting of Zika virus (ZIKV) disease began region-wide in February 2016, and it is believed that ZIKV cases could have been highly under-reported before that. Given the Guillain-Barre syndrome (GBS) is relatively well reported, the GBS surveillance data has the potential to act as a reasonably reliable proxy for inferring the true ZIKV epidemics. We developed a mathematical model incorporating the weather effects to study the ZIKV-GBS epidemics and estimated the key epidemiological parameters. We found the attack rate of ZIKV is likely lower than 33% over the two epidemic waves. The risk rate from symptomatic ZIKV case to develop GBS is likely 0.0061%. According to the analysis, we comment that there would be difficult for a ZIKV outbreak to appear in NE Brazil in the near future.

epidemiology↗

bioRxiv2019

Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall

BackgroundLassa fever (LF) is increasingly recognized as an important rodent-borne viral hemorrhagic fever presenting a severe public health threat to sub-Saharan West Africa. In 2018, LF caused an unprecedented outbreak in Nigeria, and the situation was worse in 2019. This work aims to study the epidemiological features of outbreaks in different Nigerian regions and quantify the association between reproduction number (R) and local rainfall by using modeling analysis.\n\nMethodsWe quantify the infectivity of LF by the reproduction numbers estimated from four different growth models: the Richards, three-parameter logistic, Gompertz, and Weibull growth models. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points ({tau}) in different regions at different time periods. Cochrans Q test is further applied to test the spatial heterogeneity of the LF epidemics. A linear random-effect regression model is adopted to quantify the association between R and local rainfall with various lag terms.\n\nFindingsOur estimated Rs for 2017-18 (1.33 with 95% CI: [1.29, 1.37]) and 2018-19 (1.29 with 95% CI: [1.27, 1.32]) are significantly higher than those for 2016-17 (1.23 with 95% CI: [1.22, 1.24]). We report spatial heterogeneity in the Rs for outbreaks in different Nigerian regions. For the association between rainfall and R, we find that a one unit (mm) increase in average rainfall over the past 7 months could cause a 0.62% (95% CI: [0.20%, 1.05%]) rise in R.\n\nConclusionThere is significant spatial heterogeneity in the LF epidemics in different Nigerian regions. We report clear evidence of rainfall impacts on LF outbreaks in Nigeria and quantify the impact.

epidemiology↗

bioRxiv2019

Mouse Model Systems of Autism Spectrum Disorder: Replicability and Informatics Signature

3.BackgroundPhenotyping mouse model systems of human disease has proven to be a difficult task, with frequent poor inter- and intra-laboratory replicability and translatability, particularly in behavioral domains such as social and verbal function. However, establishing robust animal model systems with strong construct validity is of fundamental importance as they are central tools for understanding disease pathophysiology and developing therapeutics. To complete our studies of mouse model systems relevant to autism spectrum disorder (ASD), we present a replication of the main findings from our two published studies comprising five genetic mouse model systems of ASD. MethodsTo assess the robustness of our previous results, we chose the two model systems that showed the greatest phenotypic differences, the Shank3/F and Cntnap2, and repeated assessments of general health, activity, and social behavior. We additionally explored all five model systems in the same framework, comparing all results obtained in this three-yearlong effort using informatics techniques to look for commonalities and differences. ResultsResults in the current study were very similar to our previously published results. The informatics signatures of the two model systems chosen for the replication showed that they were most distinguished by activity levels. Although the two model systems were opposite in this regard, those aspects of their social behavior not confounded by activity (vocalizations) were similar. ConclusionsOur results showed high intra-laboratory replicability of results, even for those with effect sizes that were not particularly large, suggesting that discrepancies in the literature may be dependent on subtle differences in testing conditions, housing enrichment, or background strains and not so much on the variability of the behavioral phenotypes. The overall informatics analysis suggests two main classes of model systems that in some aspects lie on opposite ends of the behavioral spectrum, supporting the view that autism is not a unitary concept.

animal behavior and cognition↗

bioRxiv2018

Identifying Context-specific Network Features for CRISPR-Cas9 Targeting Efficiency Using Accurate and Interpretable Deep Neural Network

CRISPR-Cas9 is a powerful genome editing tool, whose efficiency and safety depends on the selection of single-guide RNA (sgRNA). Machine learning has been applied to optimize sgRNA selection, but several challenges remain. The performance of predictive model is limited by the amount of available data in many cell lines, ignorance of gene network function and its variable effect on phenotype, and elusive biological interpretation of computational models. We develop an accurate and interpretable deep learning model SeqCrispr to address these problems. In benchmark studies, SeqCrispr outperforms state-of-the-art algorithms and improves the prediction accuracy when applied to small sample size cell lines. Furthermore, we find that gene context-specific network properties are critical for the prediction accuracy in addition to the last three nucleotides in sgRNA 3end. Our findings will bolster developing more accurate predictive models of CRISPR-Cas9 across wide spectrum of biological conditions as well as efficient and safe gene therapy.

bioinformatics↗

bioRxiv2018

Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology

Although remarkable progresses have been made in the cancer treatment, existing anti-cancer drugs are associated with increasing risk of heart failure, variable drug response, and acquired drug resistance. To address these challenges, for the first time, we develop a novel genome-scale multi-target screening platform 3D-REMAP that integrates data from structural genomics and chemical genomics as well as synthesize methods from structural bioinformatics, biophysics, and machine learning. 3D-REMAP enables us to discover marked drugs for dual-action agents that can both reduce the risk of heart failure and present anti-cancer activity. 3D-REMAP predicts that levosimendan, a drug for heart failure, inhibits serine/threonine-protein kinase RIOK1 and other kinases. Subsequent experiments confirm this prediction, and suggest that levosimendan is active against multiple cancers, notably lymphoma, through the direct inhibition of RIOK1 and RNA processing pathway. We further develop machine learning models to identify cancer cell-lines and patients that may respond to levosimendan. Our findings suggest that levosimendan can be a promising novel lead compound for the development of safe and effective multi-targeted cancer therapy, and demonstrate the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.\n\nAuthor SummaryMulti-target drug design (a.k.a targeted polypharmacology) has emerged as a new strategy for discovering novel therapeutics that can enhance therapeutic efficacy and overcome drug resistance in tackling multi-genic diseases such as cancer. However, it is extremely challenging for conventional computational tools that are either receptor-based or ligand-based to screen compounds for selectively targeting multiple receptors. Existing multi-target drug design mainly focuses on compound screening against receptors within the same gene family but not across different gene families. Here, we develop a new computational tool 3D-REMAP that enables us to identify chemical-protein interactions across fold space on a genome scale. The genome-scale chemical-protein interaction network allows us to discover dual-action drugs that can bind to two types of targets simultaneously, one for mitigating side effect and another for enhancing the therapeutic effect. Using 3D-REMAP, we predict and subsequently experiments validate that levosimendan, a drug for heart failure, is active against multiple cancers, notably, lymphoma. This study demonstrates the potential of genome-wide multi-target screening in designing polypharmacology and drug repurposing for precision medicine.

systems biology↗

bioRxiv2018

Conservation of the structural and functional architecture of encapsulated ferritins in bacteria and archaea

Iron is an essential element for many biological processes; however, due to its high reactivity iron can also be very toxic, producing reactive oxygen species through Fenton chemistry. Ferritins protect the cell from oxidative stress by catalytically converting Fe(II) into less toxic Fe(III) and storing the resulting iron minerals within their core. Encapsulated ferritins (EncFtn) are a sub-family of ferritin-like proteins, which are widely distributed in all bacterial and archaeal phyla. We recently characterised the Rhodospirillum rubrum EncFtn, showing that although enzymatically active, due to its open structure it requires the association with an encapsulin nanocage in order to act as an iron store. Given the wide distribution of the EncFtn family in organisms with diverse environmental niches, a question arises as to whether the structure and catalytic activity is conserved across the family. Here we structurally characterise two EncFtn members from the halophile Haliangium ochraceum and the thermophile Pyrococcus furiosus, which show the same distinct annular decamer topology observed in R. rubrum EncFtn, with the ferroxidase centre (FOC) formed between one of the dimer interfaces. Solution and native mass spectrometr analyses show that the stability of the protein quaternary structure differs between EncFtn proteins from different species. The catalytic role of the EncFtn proteins was confirmed by biochemical assays, and we show that Zn(II) ions inhibit the ferroxidase activity of the EncFtn proteins to varying degrees. Our results represent a further step in the characterisation of the recently discovered EncFtn ferritin-like sub-family, indicating a common structural organisation and catalytic activity, despite diverse host environments.

biochemistry↗

bioRxiv2018

IFN-γ induces epithelial-to-mesenchymal transition of cancer cells via an unique microRNA processing

Interferon-{gamma} (IFN{gamma}) is a potent cytokine in modulating tumor immunity and tumoricidal effects. We demonstrate a new function of IFN{gamma} in inducing epithelial-to-mesenchymal transition (EMT) in normal and cancer cells from different cell types. IFN{gamma} activates JAK-STAT signaling pathway leading to the transcription of IFN-stimulated genes (ISGs), such as interferon-induced tetratricopeptide repeat 5 (IFIT5). We unveil a new function of IFIT5 complex in degrading precursor microRNAs (pre-miRNA) that include pre-miR-363 from the miR-106a-363 cluster, as well as pre-miR-101 and pre-miR-128 with a similar 5-end structure with pre-miR-363. Noticeably, these suppressive miRNAs have similar functions by targeting EMT transcription factors in prostate cancer (PCa) cells. We further demonstrated that IFIT5 plays a critical role in IFN{gamma}-induced cell invasiveness in vitro and lung metastasis in vivo. Clinically, IFIT5 is highly elevated in high-grade PCa and its expression inversely correlates with these suppressive miRNAs. Altogether, this study unveils pro-tumorigenic role of the IFN pathway via a new mechanism of action, which certainly raises concern about its clinical application.

cancer biology↗

bioRxiv2017

JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data

MotivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.\n\nResultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data.\n\nAvailabilityR scripts available at https://github.com/jijiadong/JDINAC\n\nContactlxie@iscb.org\n\nSupplementary informationSupplementary data are available at bioRxiv online.

systems biology↗