Biology subjects

Xie, L.

Publications and source records attributed to Xie, L..

At least 217 records · Page 12Linked to original sources

bioRxiv2019

Screening and functional analysis of differentially expressed genes in an animal model of EBV-associated lymphomas

Epstein-Barr virus (EBV) is an important human oncogenic virus. This paper is to explore how EBV induce malignant transformation of human lymphocytes and the related mechanism of lymphomagenesis. We have constructed hu-PBL/SCID chimeric mice and established a model of EBV-associated human-derived lymphomas. By using Agilent human whole genome microarray and a series of bioinformatic analyses, a total of 202 differentially expressed genes were screened from the EBV-induced lymphomas in hu-PBL/SCID mice, including 44 up-regulated and 158 down-regulated genes. Calculation of the rank score (RS) values of these genes in the HIPPIE protein interaction networks showed that topoisomerase II alpha (TOP2A), ubiquitin like with PHD and ring finger domains 1 (UHRF1), histone cluster 2 H2B family member E (HIST2H2BE), phosphoglycerate dehydrogenase (PHGDH), vinculin (VCL), insulin-like growth factor 1 receptor (IGF1R), Fos proto-oncogene (FOS), snail family transcriptional repressor 1 (SNAI1), PDZ binding kinase (PBK), and ring finger protein 144B (RNF144B) were the top 10 key node genes of EBV-induced lymphoma. In which, PBK, an up-regulated genes with the highest number of GO annotations, was verified by cellular function experiments and clinical lymphoma samples.\n\nAuthor summaryEB virus is closely associated with human lymphoma and nasopharyngeal carcinoma. Since the susceptible hosts of EBV limit to human and cottontop tammarins, there are no appropriate animal models so far to study the EBV-associated oncogenesis. In our previous experiments, the EBV-associated lymphomas were induced in hu-PBL/SCID chimera (a new humanized mouse model). However, the cellular and molecular mechanisms of malignant transformation of normal human cells and tumor formation induced by EBV remain unclear. In this study, we examined and compared the gene expression profiles of EBV-induced lymphomas and normal human lymphocytes of the same origin in SCID mice. By constructing the gene-function relationship network, we preliminarily found that TOP2A, UHRF1, HIST2H2BE, PHGDH, VCL, IGF1R, FOS, SNAI1, PBK, and RNF144B may be the key genes in EBV-induced lymphomas. These findings suggest that the induction of lymphoma by EBV is a complex process that involves multiple genes and pathways.

cancer biology↗

bioRxiv2019

Fecal microbiota as a non-invasive biomarker to predict the tissue iron accumulation in intestine epithelial cells and liver

Iron is an essential trace mineral for the growth, systemic metabolism, and immune response. Imbalance of tissue iron absorption and storage leads to various diseases. The excessive iron accumulation is associated with inflammation and cancer while iron deficiency leads to growth retardation. Studies investigated in Kenyan infants and school children suggests that both low and high iron intake result in dysbiosis of gut microbiota. This would lead to the disruption of microbial diversity, an increase of pathogen abundance and the induction of intestinal inflammation. Despite this progress, in-depth studies investigating the relationship between iron availability and gut microbiota is not completely explored. In the current study, we established a murine model to study the connection between iron and microbiota by feeding mice with either iron-deprived or -fortified diet. To identify key microbiota related to iron levels, we combined the 16S rRNA amplicon sequencing with the innovated bioinformatic algorithms, such as RDA, co-occurrence, and machine learning to identify key microbiota. Manipulation of iron levels in the diet leads to systemic iron dysregulation and dysbiosis of gut microbiota. The bioinformatic algorithms used here detect five key bacteria that correlate with systemic iron levels. Leveraging on these key microbiotas, we also established a prediction model which could precisely distinguish the individual under either iron-deprived or iron-fortified physiological condition to further prove the link between microbiota and systemic iron homeostasis. This innovated and non-invasive approach could be potentially used for the early diagnosis and therapy of iron-dysregulation related diseases, e.g. anemia, inflammatory disease, fibrosis, and cancers.

microbiology↗

bioRxiv2019

Human Chrysomya bezziana myiasis: A systematic review

BackgroundMyiasis due to Old World screw-worm fly, Chrysomya bezziana, is an important obligate zoonotic disease in the OIE-list of diseases and is found throughout much of Africa, the Indian subcontinent, southeast and east Asia. C. bezziana myiases cause not only morbidity and death to animals and humans, but also economic losses in the livestock industries. Because of the aggressive and destructive nature of this disease in hosts, we initiated this study to provide a comprehensive understanding of human myiasis caused by C. bezziana.\n\nMethodsWe conducted a systematic search of the databases in English (PubMed, Embase and African Index Medicus) and Chinese (CNKI, Wanfang, and Duxiu), and international government online reports to 6th February, 2019, to identify studies concerning Chrysomya bezziana. Another ten human cases in China and Papua New Guinea that our team had recorded were also included.\n\nResultsWe retrieved 1,048 reports from which 202 studies were ultimately eligible for inclusion in the present descriptive analyses. Since the first human case due to C. bezziana was reported in 1909, we have summarized 291 cases and found that these cases often occurred in poor hygiene, low socio-economic conditions, old age, and underlying diseases including infections, age-related diseases, and noninfectious chronic diseases. But C. bezziana myiasis appears largely neglected as a serious medical or veterinary condition, with human and animal cases only reported in 16 and 24 countries respectively, despite this fly species recorded as present in 44 countries worldwide.\n\nConclusionOur findings indicate that cryptic myiasis cases due to the obligate parasite, C. bezziana, are under-recognized. Through this in-depth study to clarify the knowledge of human C. bezziana myiasis including its etiology, clinical features, diagnosis, treatment, epidemiology, prevention and control, we call for more vigilance and awareness of the disease from governments, health authorities, clinicians, veterinary workers, nursing homes, and also the general public.\n\nAuthor summaryChrysomya bezziana larvae are characterized by feeding aggressively on the living tissues and body fluids of the host. The nightmare-like feelings of patients suffering from this myiasis, severe tissues and bones destruction, even death, and enormous economic loss in the livestock industries have been described in the previous reports. But our findings indicate that C. bezziana myiases still appear to be under-recognized as a serious medical or veterinary condition throughout the world. Both in China and the world at large, it is probable that C. bezziana distribution could be greater than currently reported. For the first time, we have therefore systematically investigated human myiasis caused by C. bezziana. Our study provides an opportunity for clinicians and health authorities to gain a better understanding of this disease from its etiology, pathology, clinical features, diagnosis, treatment, epidemiology, prevention and control. In addition, our findings will hopefully engage governments, health staff, veterinary workers, the elderly homes, and also the general public in efforts to recognize, prevent and control such infestations.

epidemiology↗

bioRxiv2019

A spatiotemporal ventricular myocyte model incorporating mitochondrial calcium cycling

Intracellular calcium (Ca2+) cycling dynamics in cardiac myocytes are spatiotemporally generated by stochastic events arising from a spatially distributed network of coupled Ca2+ release units (CRUs) that interact with an intertwined mitochondrial network. In this study, we developed a spatiotemporal ventricular myocyte model that integrates mitochondria-related Ca2+ cycling components into our previously developed ventricular myocyte model consisting of a 3-dimensional CRU network. Mathematical formulations of mitochondrial membrane potential, mitochondrial Ca2+ cycling, mitochondrial permeability transition pore (MPTP) stochastic opening and closing, intracellular reactive oxygen species (ROS) signaling, and oxidized Ca2+/calmodulin-dependent protein kinase II (CaMKII) signaling were incorporated into the model. We then used the model to simulate the effects of mitochondrial depolarization on mitochondrial Ca2+ cycling, Ca2+ spark frequency and amplitude, which agree well with experimental data. We also simulated the effects of the strength of mitochondrial Ca2+ uniporters and their spatial localization on intracellular Ca2+ cycling properties, which substantially affected diastolic and systolic Ca2+ levels in the mitochondria but exhibited only a small effect on sarcoplasmic reticulum and cytosolic Ca2+ levels under normal conditions. We show that mitochondrial depolarization can cause Ca2+ waves and Ca2+ alternans, which agrees with previous experimental observations. We propose that this new spatiotemporal ventricular myocyte model, incorporating properties of mitochondrial Ca2+ cycling and ROS-dependent signaling, will be useful for investigating the effects of mitochondria on intracellular Ca2+ cycling and action potential dynamics in ventricular myocytes.

biophysics↗

bioRxiv2019

Sickle cell disease complications: prevalence and resource utilization

This study evaluated the prevalence rate of vaso-occlusive crisis (VOC) episodes, rates of uncomplicated and complicated VOC episodes, and the primary reasons for emergency room (ER) visits and inpatient admissions for sickle cell disease (SCD) patients. The Medicaid Analytic extracts database was used to identify adult SCD patients using claims from 01JUL2009-31DEC2012. The date of the first observed SCD claim was designated as the index date. Patients were required to have continuous medical and pharmacy benefits for [≥]6 months baseline and [≥]12 months follow-up period. Patient demographics, baseline clinical characteristics, the rate of uncomplicated and complicated VOC (VOC with concomitant SCD complications) episodes, and reasons for ER visits and inpatient stays were analyzed descriptively. A total of 8,521 patients were included in the analysis, with a median age of 30 years. The average follow-up period was 2.7 years. The rate of VOC episodes anytime in the follow-up was 3.31 in person-years. During the first-year follow-up period, an average of 2.79 VOC episodes were identified per SCD patients, with 1.06 VOC episodes treated in inpatient setting and 0.90 VOC episodes in ER without admission. A total of 76,154 VOC episodes were identified during the entire follow-up period for the overall SCD patients. Most of the VOC episodes (70.3% [n=53,523]) were uncomplicated episodes, and 29.7% were complicated episodes. Using primary diagnosis claims only, the most frequent complications during the VOC episode were infectious diseases (25.9%), fever (21.8%), and pulmonary disorders (16.2%). Among ER and hospitalizations related to VOC or SCD complication, ~85.0% had VOCs as the primary reason for admission; 15.0% had SCD complications as the primary reason. In summary, SCD and its related comorbidities and complications result in high acute health care utilization. In addition, VOC remains the primary reason for SCD patients ER visits and inpatient admissions.

epidemiology↗

bioRxiv2019

Direct control of shell regeneration by the mantle tissue in the pearl oyster Pinctada fucata via accelerating CaCO3 nucleation

Molluscan bivalves rapidly repair the damaged shells to prevent further injury. However, it remains unclear how this process is precisely controlled. In this study, we applied scanning electronic microscopy, transmission electronic microscopy and histochemical analysis to examine the detailed shell regeneration process of the pearl oyster Pinctada fucata. It was found that the shell damage caused the mantle tissue to retract, which resulted in dislocation of the mantle zones to their correspondingly secreted shell layers. However, the secretory repertoires of the different mantle zones remained unchanged. As a result, the dislocation of the mantle tissue dramatically affected the shell morphology, and the unusual presence of the submarginal zone on the nacreous layers caused de novo precipitation of prismatic layers on the nacreous layers. Real-time PCR revealed that the expression of the shell matrix proteins (SMPs) were significantly upregulated, which was confirmed by the thermal gravimetric analysis (TGA) of the newly formed shell. The increased matrix secretion accelerated CaCO3 nucleation thus promoting shell deposition. Taken together, our study revealed the close relationship between the physiological activities of the mantle tissue and the morphological change of the regenerated shells.

zoology↗

bioRxiv2019

EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences

The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the effcacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign is able to extract recurrent chromatin state patterns along a single epigenome, and many of these patterns carry cell-type-specific characteristics. EpiAlign can also detect common chromatin state patterns across multiple epigenomes, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.

bioinformatics↗

bioRxiv2019

Unbinding Kinetics and Multiple Pathways of p38 MAPK Inhibitors

Rational Drug Discovery still faces a major hurdle for the prediction of drug efficacy in vivo solely based on its binding affinity for the target in vitro. The traditional perspective has clearly proven to be inadequate as it lacks the consideration of essential aspects such as pharmacokinetics and binding kinetics in determining drug efficacy as well as toxicity. Residence time, the average lifetime of drug-target complex, has undoubtedly gained broader recognition as a better predictor for lead optimization. Long residence time can lead to sustained pharmacological effect and may also mitigate off-target toxicity. To showcase the importance of drug-target binding kinetics and unravel the underlining mechanism for variation of residence time, unbinding kinetics of two distinct type II inhibitors of p38 MAP kinase were for the first time investigated and compared by molecular dynamics and metadynamics simulation approaches. Free energy landscape of key motions associated with unbinding process was constructed for inhibitors with different unbinding rates. Multiple unbinding pathways and rebinding were revealed during the drug-target dissociation process of faster unbinder Lig3 and slower unbinder Lig8 respectively, suggesting a novel mechanism of unbinding kinetics. The comparative studies imply the hydrophobic and Hydrogen-bonding interaction in the R1 group of ligands is crucial for the slow unbinding. Such kind of computational approaches could also be applied to predict unbinding pathways and kinetics of many other drugs, and facilitate the design of efficient kinase inhibitors.

pharmacology and toxicology↗

bioRxiv2018

Temporal modulation of host aerobic glycolysis determines the outcome of M. marinum infection

Macrophages are the first-line host defense where the invading Mycobacterium tuberculosis (Mtb) encounters. It has been recently reported that host aerobic glycolysis was elevated post the infection by a couple of virulent mycobacterial species. However, whether this metabolic transition is required for host defense against intracellular pathogens and the underlying mechanisms remain to be further investigated. By analyzing carbon metabolism, we found that macrophages infected by M. marinum, a surrogate mycobacterial specie to Mtb, showed a strong elevation of glycolysis. Next, three glycolysis inhibitors were examined for their ability to inhibit mycobacterial proliferation inside RAW264.7, a murine macrophage-like cell line. Among them, a glucose analog, 2-deoxyglucose (2-DG) displayed a protective effect on assisting host to resist mycobacterial infection, which was further validated in zebrafish-infection model. The phagocytosis of M. marinum was significantly decreased in macrophages pre-treated with 2-DG at concentrations of 0.5 and 1 mM, at which no inhibitory effect was posed on M. marinum growth in vitro. Moreover, 2-DG pre-treatment exerted a significant protective effect on zebrafish larvae to limit the proliferation of M. marinum, and such effect was correlated to tumor necrosis factor alpha (TNF-). On the contrary, the 2-DG treatment post infection did not restrain proliferation of M. marinum in WT zebrafish, and even accelerated bacterial replication in TNF--/- zebrafish. Together, modulation of glycolysis prior to infection boosts host immunity against M. marinum infection, indicating a potential intervention strategy to control mycobacterial infection.\n\nAuthor SummaryAs an intracellular pathogen, Mtb exploits multiple strategies to invade and hijack macrophages for its own advantages. Accordingly, recent investigations have shown that Mtb infection is accompanied with an alteration of host glucose metabolism. Macrophage and zebrafish infection models of M. marinum, facilitating our understanding towards mycobacterial pathogenesis, were applied in this study. We found that the pre-treatment of macrophages with a glucose analog, 2-DG, inhibited aerobic glycolysis and made host cells more inert to phagocytose the bud. In infected zebrafish larvae, bacterial load inside host pretreated with 2-DG remains at a significantly lower level compared to the untreated group. These findings imply that the modulation of host glycolysis regulates the fate of M. marinum infection, and indicate a promising metabolic target in TB intervention.

microbiology↗

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

The mitochondrial DNA content can not predict the embryo viability

ObjectiveTo investigate whether the mitochondrial DNA content could predict the embryo viability\n\nDesignRetrospective analysis.\n\nSettingReproductive genetics laboratory\n\nPatient(s)A total of 421 biopsied samples obtained from 129 patients\n\nIntervention(s)Embryo biopsies samples underwent whole genome amplification (WGA) and were tested by next generation sequencing (NGS) and array Comparative Genomic Hybridization (aCGH), 30 samples were selected randomly to undergo quantitative real-time polymerase chain reaction (qPCR).\n\nMain Outcome Measure(s)Those embryos which obtained the consistent chromosome status determined both aCGH and NGS platform were further classified. We investigated the relationship of mtDNA content with several factors including female patient age, embryo morphology, chromosome status, and live birth rate of both blastocysts and blastomeres.\n\nResult(s)A total of 386 (110 blastomeres and 276 blastocysts) out of 399 embryos showed consistent chromosome status outcome. We found no statistically difference was observed in aneuploid and euploid blastocysts (p=0.14), the same phenomenon was observed in aneuploid and euploid blastomeres (p=0.89). Similarly, the mtDNA content was independent of female patient age, embryo morphology and live birth rate.\n\nConclusion(s)The mtDNA content did not provide a reliable prediction of the viability of blastocysts to initiate a pregnancy.

ecology↗

bioRxiv2018

Dynamic Brain Connectivity Alternation Detection via Matrix-variate Differential Network Model

MotivationNowadays brain connectivity analysis has attracted tremendous attention and has been at the foreground of neuroscience research. Brain functional connectivity reveals the synchronization of brain systems through correlations in neurophysiological measures of brain activity. Growing evidence now suggests that the brain connectivity network experiences alternations with the presence of numerous neurological disorders, thus differential brain network analysis may provides new insights into disease pathologies. For the matrix-valued data in brain connectivity analysis, existing graphical model estimation methods assume a vector normal distribution that in essence requires the columns of the matrix data to be independent. It is obviously not true, they have limited applications. Among the few solutions on graphical model estimation under a matrix normal distribution, none of them tackle the estimation of differential graphs across different populations. This motivates us to consider the differential network for matrix-variate data to detect the brain connectivity alternation.\n\nResultsThe primary interest is to detect spatial locations where the connectivity, in terms of the spatial partial correlation, differ across the two groups. To detect the brain connectivity alternation, we innovatively propose a Matrix-Variate Differential Network (MVDN) model. MVDN assumes that the matrix-variate data follows a matrix-normal distribution. We exploit the D-trace loss function and a Lasso-type penalty to directly estimate the spatial differential partial correlation matrix where the temporal information is fully excavated. We propose an ADMM algorithm for the Lasso penalized D-trace loss optimization problem. We investigate theoretical properties of the estimator. We show that under mild and regular conditions, the proposed method can identify all differential edges accurately with probability tending to 1 in high-dimensional setting where dimensions of matrix-valued data p, q and sample size n are all allowed to go to infinity. Simulation studies demonstrate that MVDN provides more accurate differential network estimation than that achieved by other state-of-the-art methods. We apply MVDN to Electroencephalography (EEG) dataset, which consists of 77 alcoholic individuals and 45 controls. The hub genes and differential interaction patterns identified are consistent with existing experimental studies.\n\nContactheyong@sdufe.edu.cn\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2018

Gata4 drives Hh-signaling for second heart field migration and outflow tract development

Dominant mutations of Gata4, an essential cardiogenic transcription factor (TF), cause outflow tract (OFT) defects in both human and mouse. We investigated the molecular mechanism underlying this requirement. Gata4 happloinsufficiency in mice caused OFT defects including double outlet right ventricle (DORV) and conal ventricular septum defects (VSDs). We found that Gata4 is required within Hedgehog (Hh)-receiving second heart field (SHF) progenitors for normal OFT alignment. Increased Pten-mediated cell-cycle transition, rescued atrial septal defects but not OFT defects in Gata4 heterozygotes. SHF Hh-receiving cells failed to migrate properly into the proximal OFT cushion in Gata4 heterozygote embryos. We find that Hh signaling and Gata4 genetically interact for OFT development. Gata4 and Smo double heterozygotes displayed more severe OFT abnormalities including persistent truncus arteriosus (PTA) whereas restoration of Hedgehog signaling rescued OFT defects in Gata4-mutant mice. In addition, enhanced expression of the Gata6 was observed in the SHF of the Gata4 heterozygotes. These results suggested a SHF regulatory network comprising of Gata4, Gata6 and Hh-signaling for OFT development. This study indicates that Gata4 potentiation of Hh signaling is a general feature of Gata4-mediated cardiac morphogenesis and provides a model for the molecular basis of CHD caused by dominant transcription factor mutations.\n\nAuthor SummaryGata4 is an important protein that controls the development of the heart. Human who possess a single copy of Gata4 mutation display congenital heart defects (CHD), including the double outlet right ventricle (DORV). DORV is an alignment problem in which both the Aorta and Pulmonary Artery originate from the right ventricle, instead of originating from the left and the right ventricles, respectively. To study how Gata4 mutation causes DORV, we used a Gata4 mutant mouse model, which displays DORV. We showed that Gata4 is required in the cardiac precursor cells for the normal alignment of the great arteries. Although Gata4 mutation inhibits the rapid increase in number of the cardiac precursor cells, rescuing this defects does not recover the normal alignment of the great arteries. In addition, there is a movement problem of the cardiac precursor cells when migrating toward the great arteries during development. We further showed that a specific molecular signaling, Hh-signaling, is responsible to the Gata4 action in the cardiac precursor cells. Importantly, over-activating the Hh-signaling rescues the DORV in the Gata4 mutant embryos. This study provides an explanation for the ontogeny of CHD.

developmental biology↗

bioRxiv2018

A new insight into underlying disease mechanism through semi-parametric latent differential network model

BackgroundIn genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types.\n\nResultsWe propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness.\n\nConclusionsThe proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.

bioinformatics↗

bioRxiv2018

Resolving challenges in quantitative modeling of microbial community dynamics

Microbial communities can perform biochemical activities that monocultures cannot. Controlling communities requires an understanding of community dynamics. Here, we mathematically predict the growth rate of an engineered community consisting of two S. cerevisiae strains, each releasing a metabolite required and consumed by the partner. Initial model parameters were based on strain phenotypes measured in batch mono-cultures with zero or excess metabolite, and failed to quantitatively predict experimental results. To resolve model-experiment discrepancy, we chemically identified the correct exchanged metabolites, but this did not improve model performance. We then re-measured strain phenotypes in chemostats mimicking the metabolite-limited community environments, while mitigating or incorporating effects of rapid evolution. Almost all phenotypes we measured varied significantly with the metabolite environment. Once we used parameters measured in community-like chemostat environments, prediction agreed with experimental results. In summary, using a simplified community, we uncovered, and devised means to resolve, modeling challenges that are likely general.

microbiology↗

bioRxiv2018

Microscopy quantification of microbial birth and death dynamics

Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness (birth and death) in a wide range of environments is critical for understanding microbial evolution as well as ecological interactions where one species alters the fitness of another. Here, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing. For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates depend on the metabolite concentration and can vary with time. Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial dynamics and to quantify growth rates in many environments.

microbiology↗

bioRxiv2018

Reducing compositional fluctuations facilitates artificial selection of microbial community function

Multi-species microbial communities often display functions - biochemical activities unattainable by member species alone, such as fighting pathogens. To improve community function, we can artificially select communities by growing \"Newborn\" communities over \"maturation time\" into \"Adult\" communities, and selecting highest-functioning Adults to \"reproduce\" by diluting each into multiple Newborns of the next cycle. Community selection has been attempted a few times on complex communities, often generating mixed results that are difficult to interpret. Here, we ask how costly community function may be improved via mutations and community selection. We simulate selection of two-species communities where Helpers digest Waste and generate Byproduct essential to Manufacturers; Manufacturers divert a fraction of their growth to make Product. Community function, the total Product in an \"Adult\", is sub-optimal even when both species have been pre-optimized as monocultures. If we dilute an Adult into Newborns by pipetting (a common experimental procedure), stochastic fluctuations in Newborn composition prevents community function from improving. Reducing fluctuations via cell sorting allows selection to work. Our conclusions hold regardless of whether H and M are commensal or mutualistic, or variations in model assumptions.

evolutionary biology↗