Biology subjects

Cheng, H.

Publications and source records attributed to Cheng, H..

At least 145 records · Page 8Linked to original sources

bioRxiv2021

BTBD9 is a novel component of IGF signaling and regulates manganese-induced dopaminergic dysfunction

Restless legs syndrome (RLS) is a common neurological disorder associated with iron deficiency and dopaminergic (DAergic) neuronal dysfunction. BTBD9 is a genetic risk factor for RLS. However, its molecular function remains largely unknown. Here, we report the interaction between BTBD9, manganese (Mn) and insulin/insulin-like growth factor (IGF) signaling in Caenorhabditis elegans, mouse Neuro2a cells and humans. We found that elevated Mn downregulated BTBD9 mRNA levels; in turn, BTBD9 expression attenuated Mn-induced cellular stress and dopaminergic neurodegeneration. As Mn is a known co-factor for insulin receptor and IGF-1 receptor, which activates IGF signaling, we posited that BTBD9 negatively regulates IGF signaling. Our results showed that the protective effects of BTBD9 against Mn toxicity were dependent on the forkhead box O (FOXO) protein. Furthermore, BTBD9 overexpression significantly elevated FOXO level and decreased PKB level, while phosphoinositide-dependent kinase-1 (PDK1) level remained unchanged. We conclude that BTBD9 acts as a key component in the IGF signaling pathway. Meanwhile, the roles of Mn in DAergic neurotoxicity and regulating BTBD9 shed new light on the etiology of RLS.

neuroscience↗

bioRxiv2021

dSCOPE: a software to detect sequences critical for liquid-liquid phase separation

Membrane based cells are the fundamental structure and function units of organisms, while evidences were increasing that liquid-liquid phase separation (LLPS) is associated with the formation of membraneless organelles, such as P-bodies, nucleoli and stress granules. Many studies have been undertaken to explore the functions of protein phase separation, but these studies lacked an effective tool to identify the sequence segments that critical for LLPS (SCOPEs). In this study, we presented a novel software called dSCOPE (http://dscope.omicsbio.info) to predict the SCOPEs. To develop the predictor, we curated experimentally identified sequence segments that can drive LLPS from published literature. Then sliding sequence window based physiological, biochemical, structural and coding features were integrated by random forest algorithm to perform prediction. Through rigorous evaluation, dSCOPE was demonstrated to achieve satisfactory performance. Furthermore, large-scale analysis of human proteome based on dSCOPE showed that the predicted SCOPEs enriched various protein post-translational modifications and cancer mutations, and the proteins which contain predicted SCOPEs enriched critical cellular signaling pathways. Taken together, dSCOPE precisely predicted the protein sequence segments critical for LLPS, with various helpful information visualized in the webserver to facilitate LLPS related research.

bioinformatics↗

bioRxiv2021

Spatio-temporal evolution of urban thermal environment and its driving factors: Case study of Nanjing, China

In recent years, with the rapid urbanization, the urban underlying surface has changed dramatically. Various urban eco-environmental problems have emerged in the world, among which the urban heat island effect has become one of the most obvious urban eco-environmental problems. In this study, Nanjing, China is chosen as the study area. Based on the Landsat8 remote sensing image data in Nanjing from 2014 to 2018, the land surface temperature is retrieved, the spatiotemporal variation track and characteristics of the thermal environment pattern are systematically depicted, and its driving factors are revealed. The results show that: in the past five years, the spatial pattern of heat field in Nanjing has changed from scattered distribution in the periphery of the city to centralized in the center of the city, and the heat island intensity has increased year by year. The changes of administrative divisions, the layout of transportation trunk lines, the transfer of industrial centers, and ecological construction projects are important driving factors for the evolution of land surface thermal environment pattern of these regions. The research results will provide scientific and technological support for similar cities with typical heat island effect in the world to make urban planning and development decisions, and to govern and improve urban ecological environment.

ecology↗

bioRxiv2020

Collegiate athlete brain data for white matter mapping and network neuroscience

We describe a dataset of processed data with associated reproducible preprocessing pipeline collected from two collegiate athlete groups and one non-athlete group. The dataset shares minimally processed diffusion-weighted magnetic resonance imaging (dMRI) data, three models of the diffusion signal in the voxel, full-brain tractograms, segmentation of the major white matter tracts as well as structural connectivity matrices. There is currently a paucity of similar datasets openly shared. Furthermore, major challenges are associated with collecting this type of data. The data and derivatives shared here can be used as a reference to study the effects of long-term exposure to collegiate athletics, such as the effects of repetitive head impacts. We use advanced anatomical and dMRI data processing methods publicly available as reproducible web services at brainlife.io.

neuroscience↗

bioRxiv2020

Change in social rank and brain dopamine levels: findings from a novel pig model

Dopamine (DA) signaling is central in hypothesized causal paths linking the influence of social and environmental variables with cognition, behavior and affective states, including vulnerability to drug dependence. Here, we study whether change in one's social rank induces DA and norepinephrine (NE) changes using a novel pig model with a social-ranking-and-re-ranking protocol to investigate social context influences on catecholamine concentrations in cerebrospinal fluid (CSF) and blood. For two weeks, 16 recently weaned male piglets were socially housed in four groups, with video-recordings for social rank assessments (, {beta}, {gamma}, and {delta}); CSF and blood were obtained from these stable social groups. Next, all four were housed together, as were all four {beta}, etc., again with video recording for blinded social ranking. CSF and blood samples were collected at three time points: prior to initial social housing, following social housing and following re-organization. Regression analyses disclosed a positive relationship between changes in social rank and post-rank change in CSF levels of DA; one unit increase of social rank predicted a 17.4 pg/ml increase in CSF dopamine concentrations (95% CI= 1.2, 33.7). Compared to piglets with downward shifts in ranks (i.e., high-to-low), piglets with upward shifts (i.e., low-to-high) had a statistically significant greater increase in CSF DA levels. No relationship was observed for CSF NE or blood concentrations of DA or NE at any phase of this experiment. This work, using a novel pig model, adds new evidence on alteration of the brain dopaminergic system induced by social rank change.

neuroscience↗

bioRxiv2020

UV and bacteriophages as a chemical-free approach for cleaning membranes from anaerobic bioreactors

Anaerobic membrane bioreactor (AnMBR) for wastewater treatment has attracted much interest due to its efficacy in providing high quality effluent with minimal energy costs. However, membrane biofouling represents the main bottleneck for AnMBR because it diminishes flux and necessitates frequent replacement of membranes. In this study, we assessed the feasibility of combining bacteriophages and UV-C irradiation to provide a chemical-free approach to remove biofoulants on the membrane. The combination of bacteriophage and UV-C resulted in better log cells removal and twice higher extracellular polymeric substance (EPS) concentration reduction in mature biofoulants compared to UV-C. A reduction in the relative abundance of Acinetobacter spp. and selected gram-positive bacteria associated with the membrane biofilm was also achieved by the new cleaning approach. Microscopic analysis further revealed the formation of cavities in the biofilm due to bacteriophages and UV-C irradiation, which would be beneficial to maintain water flux through the membrane. When the combined treatment was further compared with the common chemical cleaning procedure, a similar reduction on the cell numbers was observed (1.4 log). However, combined treatment was less effective in removing EPS compared with chemical cleaning. These results suggest that the combination of UV-C and bacteriophage have an additive effect in biofouling reduction, representing a potential chemical-free method to remove reversible biofoulants on membrane fitted in an anaerobic membrane bioreactor. SIGNIFICANCEAnaerobic membrane bioreactors can achieve high quality effluent with a reduced energy consumption. However, biofouling represents the main bottleneck for membrane filtration efficiency. Biofouling is commonly reduced through chemical treatment. These agents are often detrimental for the environment and health safety due to the formation of toxic byproducts. Therefore, we present a new approach, based on the additive antifouling action of bacteriophages infection and UV-C irradiation, to reduce anaerobic membrane biofouling. This new strategy could potentially delay the occurrence of membrane fouling by removing the reversible fouling layers on membranes, in turn reducing the frequencies and amount of chemicals needed throughout the course of wastewater treatment.

microbiology↗

bioRxiv2020

Loss of Nupr1 promotes engraftment by tuning the dormancy threshold of hematopoietic stem cell repository via regulating p53-checkpoint pathway

Hematopoietic stem cells (HSCs) are dominantly quiescent under homeostasis, which is a key mechanism of maintaining the HSC pool for life-long hematopoiesis. Dormant HSCs poise to be immediately activated on urgent conditions and can return to quiescence after regaining homeostasis. To date, the molecular networks of regulating the threshold of HSC dormancy, if exist, remain largely unknown. Here, we unveiled that deletion of Nupr1, a gene preferentially expressed in HSCs, activated the quiescence HSCs under homeostatic status, which conferred engraftment competitive advantage on HSCs without compromising their stemness and multi-lineage differentiation abilities in serial transplantation settings. Following an expansion protocol, the Nupr1-/- HSCs proliferate more robustly than their wild type counterparts in vitro. Nupr1 inhibits the expression of p53 and the rescue of which offsets the engraftment advantage. Our data unveil the de novo role of Nupr1 as an HSC quiescence-regulator, which provides insights into accelerating the engraftment efficacy of HSC transplantation by targeting the HSC quiescence-controlling network.

cell biology↗

bioRxiv2020

Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming

BackgroundLow-cost genome-wide single-nucleotide polymorphisms (SNPs) are routinely used in animal breeding programs. Compared to SNP arrays, the use of whole-genome sequence data generated by the next-generation sequencing technologies (NGS) has great potential in livestock populations. However, a large number of animals are required to be sequenced to exploit the full potential of whole-genome sequence data. Thus, novel strategies are desired to allocate sequencing resources in genotyped livestock populations such that the entire population can be sequenced or imputed efficiently. MethodsWe present two applications of linear programming models called LPChoose for sequencing resources allocation. The first application is to identify the minimum number of animals for sequencing while meeting the criteria that each haplotype in the population is contained in at least one of the animals selected for sequencing. The second is to sequence a fixed number of animals whose haplotypes include as large a proportion as possible of the haplotypes present in the population given a limited sequencing budget. ResultsIn both applications LPChoose has similar or better performance than some other methods. The linear programming models we proposed are based on rigorous and well defined optimization techniques and easy and straightforward to implement. LPChoose is available as an open-source package.

genomics↗

bioRxiv2020

The Identification of A ROS Responsive Motif that is regulated by snoRNP in Arabidopsis

A GGGCC motif (site II like motif) was identified from the upstream sequences of reactive oxygen species (ROS) and light induced genes in Arabidopsis. This motif is highly enriched within the -50 to -250 bp region of the induced genes, and it is also specifically distributed in the same region in mouse and human genome. EMSA experiments revealed that several nuclear factors (NFs) bind to this motif, and the binding activities altered under H2O2 treatment. Two C/D family snoRNP proteins, namely fibrillarin 2 and NOP56, were identified from the site II like motif binding NFs. Several C/D family snoRNA, including R63, U24a and Z15, were also cloned from the motif binding NFs. These data suggest new regulatory roles of snoRNP in Arabidopsis.

plant biology↗

bioRxiv2020

Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

genetics↗

bioRxiv2020

Light-Sheet Fluorescence Imaging Charts the Gastrula Origin of Vascular Endothelial Cells in Early Zebrafish Embryos

It remains challenging to construct a complete cell lineage map of the origin of vascular endothelial cells in any vertebrate embryo. Here, we report the application of in toto light-sheet fluorescence imaging of embryos to tracing the origin of vascular endothelial cells (ECs) at single-cell resolution in zebrafish. We first adapted a previously-reported method to mount embryos and light-sheet imaging, created an alignment, fusion, and extraction all-in-one software (AFEIO) for processing big data, and performed quantitative analysis of cell lineage relationships using commercially-available Imaris software. Our data revealed that vascular ECs originated from broad regions of the gastrula along the dorsal-ventral and anterior-posterior axes, of which the dorsal-anterior cells contributed to cerebral ECs, the dorsal-lateral cells to anterior trunk ECs, and the ventral-lateral cells to posterior trunk and tail ECs. Therefore, this work, to our knowledge, charts the first comprehensive map of the gastrula origin of vascular ECs in zebrafish, and has potential applications for studying the origin of any embryonic organs in zebrafish and other model organisms.

developmental biology↗

bioRxiv2020

ZmCLA4 regulates leaf angle through multiple plant hormone-mediated signal pathways in maize

Leaf angle in cereals is an important agronomic trait contributing to plant architecture and grain yield by determining the plant compactness. Although ZmCLA4 was identified to shape plant architecture by affecting leaf angle, the detailed regulatory mechanism of ZmCLA4 in maize remains unclear. ZmCLA4 was identified as a transcriptional repressor using the Gal4-LexA/UAS system and transactivation analysis in yeast. The DNA affinity purification (DAP)-seq assay showed that ZmCLA4 not only acts as a repressor containing the EAR motif (CACCGGAC), but was also found to have two new motifs, CCGARGS and CDTCNTC. On analyzing the ZmCLA4-bound targeted genes, we found that ZmCLA4, as a cross node of multiple plant hormone-mediated pathways, directly bound to ARF22 and IAA26 to regulate auxin transport and mediated brassinosteroid signaling by directly binding to BZR3 and 14-3-3. ZmCLA4 bound two WRKY genes involved with abscisic acid, two genes (CYP75B1, CYP93D1) involved with jasmonic acid, B3 involved in the response to ethylene, and thereby negatively regulated leaf angle formation. We built a new regulatory network for the ZmCLA4 gene controlling leaf angle in maize, which contributed to the understanding of ZmCLA4s regulatory mechanism and will improve grain yields by facilitating optimization of plant architecture.

molecular biology↗

bioRxiv2020

Population sequencing enhances understanding of tea plant evolution

Tea is an economically important plant characterized by a large genome size and high heterozygosity and species diversity. In this study, we assembled a 3.26 Gb high-quality chromosome-scale genome for tea using the Longjing 43 cultivar of Camellia sinensis var. sinensis. Population resequencing of 139 tea accessions from around the world was used to investigate the evolution of tea and to reveal the phylogenetic relationships among tea accessions. With the spread of tea cultivation, hybridization has increased the heterozygosity and wide-ranging gene flow among tea populations. Population genetics and transcriptomics analyses revealed that during domestication, the selection for disease resistance and flavor in C. sinensis var. sinensis populations has been stronger than that in C. sinensis var. assamica populations. The data compiled in this study provide new resources for the marker assisted breeding of tea and are a basis for further research on the genetics and evolution of tea.

genomics↗

bioRxiv2020

A highly scalable method for joint whole genome sequencing and gene expression profiling of single cells

Understanding how genetic variation alters gene expression - how genotype affects phenotype - is a central challenge in biology. To address this question in complex cell mixtures, we developed Direct Nuclear Tagmentation and RNA-sequencing (DNTR-seq), which enables whole genome and mRNA sequencing jointly in single cells. When applied to biobanked leukemia samples, DNTR-seq readily identified minor subclones within patients, as well as cell-type specific gene editing such as T-cell receptor rearrangements. mRNA-seq quality is equal to RNA-only methods, and the high yield combined with low positional bias of the genomic library preparation allows detection of sub-megabase aberrations at ultra low coverage of 0.5-3M read pairs per genome. Since each cell library is individually addressable, rare subpopulations can be re-sequenced at increased depth, allowing multi-tiered study designs where depth of sequencing is informed by previous results. In addition, the direct tagmentation protocol enables coverage-independent estimation of ploidy, which can be used to unambiguously identify cell singlets. Thus, DNTR-seq directly links each cells state to its corresponding genome at a scale enabling routine analysis of heterogeneous tumors and other complex tissues.

genomics↗

bioRxiv2020

Association between proteomic blood biomarkers and DTI/NODDI metrics in adolescent football players

The objective of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [Tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football and cross-country runners at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players and 8 cross-country runners underwent MRI and blood biomarker measures during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Diffusion metrics and blood biomarker levels were compared between groups and associated within each group. The football group showed lower AD and MD than the cross-country group in various axonal tracts of the right hemisphere. Elevated ODI was observed in the football group in the right hemisphere of the corticospinal tract. Blood biomarker levels were consistent between groups except for elevated Tau levels in the cross-country group. Tau level was positively associated with MD and negatively associated with NDI in the corpus callosum of football players, but not in cross-country runners. Our data suggest that football players may develop axonal microstructural abnormality. Levels of MD and NDI in the corpus callosum were associated with serum Tau levels, highlighting the vulnerability of the corpus callosum against cumulative head impacts. Despite observing multimodal associations in some brain areas, neuroimaging and blood biomarkers may not strongly correlate to reflect the severity of brain damage.

neuroscience↗

bioRxiv2019

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) Model for Protein Kinase Inhibitor Response Prediction

Predicting drug sensitivity profiles from genotypes is a major challenge in personalized medicine. Machine learning and deep neural network methods have shown promise in addressing this challenge, but the "black-box" nature of these methods precludes a mechanistic understanding of how and which genomic and proteomic features contribute to the observed drug sensitivity profiles. Here we provide a combination of statistical and neural network framework that not only estimates drug IC50 in cancer cell lines with high accuracy (R2 = 0.861 and RMSE = 0.818) but also identifies features contributing to the accuracy, thereby enhancing explainability. Our framework, termed QSMART, uses a multi-component approach that includes (1) collecting drug fingerprints, cancer cell lines multi-omics features, and drug responses, (2) testing the statistical significance of interaction terms, (3) selecting features by Lasso with Bayesian information criterion, and (4) using neural networks to predict drug response. We evaluate the contribution of each of these components and use a case study to explain the biological relevance of several selected features to protein kinase inhibitor response in non-small cell lung cancer cells. Specifically, we illustrate how interaction terms that capture associations between drugs and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib) in non-small cell lung cancer cells. Although we have tested QSMART on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.

bioinformatics↗

bioRxiv2019

An optimized acetylcholine sensor for monitoring in vivo cholinergic activity

The ability to directly measure acetylcholine (ACh) release is an essential first step towards understanding its physiological function. Here we optimized the GRABACh (GPCR-Activation-Based-ACh) sensor with significantly improved sensitivity and minimal downstream coupling. Using this sensor, we measured in-vivo cholinergic activity in both Drosophila and mice, revealing compartmental ACh signals in fly olfactory center and single-trial ACh dynamics in multiple regions of the mice brain under a variety of different behaviors

neuroscience↗

bioRxiv2019

A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

Bayesian regression methods that incorporate different mixture priors for marker effects are used in multi-trait genomic prediction. These methods can also be extended to genome-wide association studies (GWAS). In multiple-trait GWAS, incorporating the underlying causal structures among traits is essential for comprehensively understanding the relationship between genotypes and traits of interest. Therefore, we develop a GWAS methodology, SEM-BayesC{Pi}, which, by applying the structural equation model (SEM), can be used to incorporate causal structures into a multi-trait Bayesian regression method using mixture priors. The performance of SEM-BayesC{Pi} was demonstrated by comparing its GWAS results with those from multi-trait BayesC{Pi}. Through the inductive causation (IC) algorithm, three potential causal structures were inferred of 0.9 highest posterior density (HPD) interval. SEM-BayesC{Pi} provides a more comprehensive understanding of the genotype-phenotype mapping than multi-trait BayesC{Pi} by performing GWAS based on indirect, direct and overall marker effects. The software tool JWAS offers open-source routines to perform these analyses.

genomics↗