Biology subjects

Jing, Q.

Publications and source records attributed to Jing, Q..

14 recordsLinked to original sources

bioRxiv2024

Rfx3 controls outer hair cell differentiation, maintenance, and hair bundle formation by regulating the expression of Insm1, Ikzf2, and Triobp genes

The RFX family of transcription factors plays crucial roles in the regulation of ciliogenesis and organ development. Mutations of RFX transcription factors lead to various genetic diseases, including ciliopathies and hearing loss, although the underlying mechanisms remain unclear. This study comprehensively analyzed the expression patterns of RFX family members during different inner ear developmental stages. Rfx3 and Rfx7 were identified as the dominantly expressed members in cochlear hair cells, exhibiting alternative splicing variants and dynamic subcellular localization at different stages. Integration of single-cell transcriptomics, ChIP-seq, and ATAC-seq data indicates that Rfx3 functions as either a transcriptional activator or repressor, binding to numerous genes related to ciliogenesis, hair bundle structures, and planar cell polarity. Rfx3 regulates the spatiotemporal expression of hair bundle gene Triobp in hair cells by binding to and modulating its intronic enhancer. Additionally, Rfx3 binds to hair cell differentiation and fate determination genes Tbx2, Insm1, and Ikzf2. The results demonstrate that Rfx3 and Rfx7 are dominantly expressed in outer hair cells, with their subcellular localization shifting to cytoplasm at later developmental stages. This suggests a previously unknown function of Rfx3 and Rfx7 beyond transcriptional regulation, highlighting their complex roles in hair cell differentiation and maintenance.

developmental biology↗

bioRxiv2023

Uncertainty in crop phenology simulations is driven primarily by parameter variability

It has become common to compare crop model results in multi-model simulation experiments. In general, one observes a large variability in such studies, which reduces the confidence one can have in such models. It is important to understand the causes of this variability as a first step toward reducing it. For a given data set, the variability in a multi-model study can arise from uncertainty in structure or in parameter values for a given structure. Previous studies have made assumptions about the origin of parameter uncertainty, and then quantified its contribution, generally finding that parameter uncertainty is less important than structure uncertainty. However, those studies do not take account of the full parameter variability in multi-model studies. Here we propose estimating parameter uncertainty based on open-call multi-model ensembles where the same structure is used by more than one modeling group. The variability in such a case is due to the full variability of parameters among modeling groups. Then structure and parameter contributions can be estimated sing random effects analysis of variance. Based on three multi-model studies for simulating wheat phenology, it is found that the contribution of parameter uncertainty to total uncertainty is, on average, more than twice as large as the uncertainty from structure. A second estimate, based on a comparison of two different calibration approaches for multiple models leads to a very similar result. We conclude that improvement of crop models requires as much attention to parameters as to model structure.

plant biology↗

bioRxiv2022

Mouse lemur transcriptomic atlas elucidates primate genes, physiology, disease, and evolution

Mouse lemurs (Microcebus spp.) are an emerging primate model organism. However, little is known about their genetics or cellular and molecular biology. In the accompanying paper, we used large-scale single cell RNA-sequencing of 27 organs and tissues to identify over 750 molecular cell types, characterize their full transcriptomic profiles, and study evolution of primate cell types. Here we use the atlas to characterize mouse lemur genes, mutations, physiology, and disease. We uncover thousands of previously unidentified lemur genes and hundreds of thousands of new splice junctions that globally define lemur gene structures and reveal over 85,000 primate splice junctions missing in mice. We systematically explore the lemur immune system, comparing the global expression profiles of key immune genes in health and disease, and molecular mapping of immune cell development, trafficking, and their local and global activation to infection. We characterize primate/lemur-specific physiology and disease including molecular features of the immune program, of lemur adipocytes that exhibit dramatic seasonal rhythms, and of metastatic endometrial cancer that resembles the human malignancy. We identify and describe the expression patterns of over 400 primate genes missing in mice, many with similar expression patterns in human and lemur and some implicated in human disease. Finally, we provide an experimental framework for reverse genetic analysis by identifying naturally-occurring nonsense (null) mutations in three primate genes missing in mice and analyzing their transcriptional phenotypes. This work establishes mouse lemur as a tractable primate model organism for genetic and molecular analysis, and it prioritizes primate genes, splice junctions, physiology, and disease for future study.

genetics↗

bioRxiv2022

Calibration of crop phenology models: Going beyond recommendations

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. Here we propose an original, detailed approach, a protocol, for calibration of such models. The protocol covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are "obligatory" parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their "usual" calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and significantly reduced prediction error.

plant biology↗

bioRxiv2021

Transcriptomic Analysis of 3D Vasculature-On-A-Chip Reveals Paracrine Factors Affecting Vasculature Growth and Maturation

In vitro models of vasculature are of great importance for modelling vascular physiology and pathology. However, there is usually a lack of proper spatial patterning of interacting heterotypic cells in conventional vasculature dish models, which might confound results between contact and non-contact interactions. We use a microfluidic platform with structurally defined separation between human microvasculature and fibroblasts to probe their dynamic, paracrine interactions. We also develop a novel, versatile technique to retrieve cells embedded in extracellular matrix from the microfluidic device for downstream transcriptomic analysis, and uncover growth factor and cytokine expression profiles associated with improved vasculature growth. Paired receptor-ligand analysis further reveals paracrine signaling molecules that could be supplemented into the medium for vasculatures models where fibroblast co-culture is undesirable or infeasible. These findings also provide deeper insights into the molecular cues for more physiologically relevant vascular mimicry and vascularized organoid model for clinical applications such as drug screening and disease modeling.

bioengineering↗

bioRxiv2021

Conformable and robust force sensors to enable precision joint replacement surgery

Balancing forces within weight-bearing joints such as the hip during joint replacement surgeries is essential for implant longevity. Minimising implant failure is vital to improve patient wellbeing and alleviate pressure on healthcare systems. With improvements in surgery, hip replacement patients are now often younger and more active than in previous generations, and their implants correspondingly need to survive higher stresses. However, force balancing currently depends entirely on surgical skill: no sensors can provide quantitative force feedback within the hip joints small, complex geometry. Here, we solve this unmet clinical need by presenting a thin and conformable microfluidic force sensor, which is compatible with the standard surgical process. We optimised the design using finite element modelling, then incorporated and calibrated our sensor in a model hip implant. Using a bespoke testing rig, we demonstrated high sensitivity at typical forces experienced during hip replacements. We anticipate that these sensors will aid implant positioning, increasing the lifetime of hip replacements, and represent a powerful new surgical tool for a range of orthopaedic procedures where force balancing is crucial.

bioengineering↗

bioRxiv2021

m6A-mediated Cell-cell Communication Controls Planarian Regeneration

Regeneration is the regrowth of damaged tissues or organs, a vital mechanism responding to damages from primitive organisms to higher mammals. Planarian possesses active whole-body regenerative capability owning to its vast reservoir of adult stem cells, neoblasts, thus provides an ideal model to delineate the underlying mechanisms for regeneration. N6-methyladenosine (m6A) regulates stem cell renewal and differentiation. However, how m6A controls regeneration at whole-organism level remains largely unknown. Here, we demonstrate that the depletion of m6A methyltransferase regulatory subunit wtap abolishes planarian regeneration, through regulating cell-cell communication and cell cycle. scRNA-Seq analysis unveils that the wtap knockdown induces a unique type of neural progenitor-like cells (NP-like cells), characterized by specific expression of the cell-cell communication ligand grn. Intriguingly, the depletion of m6A-modified transcripts grn/cdk9 (or cdk7) axis rescues the defective regeneration of planarian without wtap. Overall, our study reveals an indispensable role of m6A-dependent cell-cell communication essential for whole-organism regeneration.

molecular biology↗

bioRxiv2020

Regulation of pancreatic fibrosis by acinar cell-derived exosomal miR-130a-3p via targeting of stellate cell PPAR-γ

As endogenous miRNA carriers,exosomes play a role in the pathophysiological process of various diseases. However, their functions and regulation mechanisms in pancreatic fibrosis remain unclear. In this study, an RNA microarray was used to detect differentially expressed exosomal miR-130a-3p in AR42J cells after taurolithocholate (TLC) treatment. mRNA-seq was used to screen the differentially expressed PPAR-{gamma} after pancreatic stellate cell (PSC) activation. Fluorescence labeling of exosomes and dynamic tracing showed that exosomes can fuse with the cell membrane of PSCs and transport miR-130a-3p into PSCs. A luciferase reporter gene assay was used to confirm that miR-130a can bind to PPAR-{gamma} to inhibit PPAR-{gamma} expression. In vitro and in vivo functional experiments were performed for gain-of-function studies and loss-of-function studies, respectively. These studies showed that acinar cell-derived exosomal miR-130a-3p promotes PSC activation and collagen formation through targeting of cellular PPAR-{gamma}. Knockdown of miR-130a-3p significantly improved pancreatic fibrosis. Notably, miR-130a-3p knockdown reduced serum levels of hyaluronic acid (HA) and {beta}-amylase and increased the C-peptide to protect endocrine and exocrine pancreatic functions and the function of endothelial cells. The exosomal miR-130a-3p/PPAR-{gamma} axis participates in the activation of PSCs and the mechanism of CP fibrosis, thus providing a potential new target for the treatment of chronic pancreatitis fibrosis.

cell biology↗

bioRxiv2020

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices. HighlightsO_LIWe propose a new approach to deriving calibration recommendations for system models C_LIO_LIApproach is based on analyzing calibration in multi-model simulation exercises C_LIO_LIResulting recommendations are holistic and anchored in actual practice C_LIO_LIWe apply the approach to calibration of crop models used to simulate phenology C_LIO_LIRecommendations concern: objective function, parameters to estimate, software used C_LI

plant biology↗

bioRxiv2020

Multi-model evaluation of phenology prediction for wheat in Australia

Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques.

plant biology↗

bioRxiv2020

MINERVA: A facile strategy for SARS-CoV-2 whole genome deep sequencing of clinical samples

The novel coronavirus disease 2019 (COVID-19) pandemic poses a serious public health risk. Analyzing the genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from clinical samples is crucial for the understanding of viral spread and viral evolution, as well as for vaccine development. Existing sample preparation methods for viral genome sequencing are demanding on user technique and time, and thus not ideal for time-sensitive clinical samples; these methods are also not optimized for high performance on viral genomes. We have developed MetagenomIc RNA EnRichment VirAl sequencing (MINERVA), a facile, practical, and robust approach for metagenomic and deep viral sequencing from clinical samples. This approach uses direct tagmentation of RNA/DNA hybrids using Tn5 transposase to greatly simplify the sequencing library construction process, while subsequent targeted enrichment can generate viral genomes with high sensitivity, coverage, and depth. We demonstrate the utility of MINERVA on pharyngeal, sputum and stool samples collected from COVID-19 patients, successfully obtaining both whole metatranscriptomes and complete high-depth high-coverage SARS-CoV-2 genomes from these clinical samples, with high yield and robustness. MINERVA is compatible with clinical nucleic extracts containing carrier RNA. With a shortened hands-on time from sample to virus-enriched sequencing-ready library, this rapid, versatile, and clinic-friendly approach will facilitate monitoring of viral genetic variations during outbreaks, both current and future.

molecular biology↗

bioRxiv2020

Lnc-ECAL-1 controls cerebrovascular homeostasis by targeting endothelium-specific tight junction protein Cldn5b

Cerebrovascular disorder-induced brain blood flow interruption or intracranial hemorrhage pose a great threaten to health. Emerging roles of long-noncoding RNAs (lncRNAs) in diagnosis and treatment of cardiovascular diseases have been recognized. However, whether and how lncRNAs modulate vascular homeostasis, especially network formation remain largely unknown. Here, we identified ECAL-1, a long non-coding RNA, as an important determinant for cerebrovascular homeostasis. Using the morpholino- and CRISPR /Cas9-based genetic modifications in combination with in vivo confocal imaging in zebrafish, we claimed that inactivation of ECAL-1 induced the apparent distortion of cerebral vascular pattern accompanied by intracranial hemorrhage. These cerebrovascular abnormalities were associated with decreased proliferation and anomalous interconnection of endothelial cells. Importantly, overexpression of Cldn5b, an endothelial cell-specific tight junction protein-encoding gene, could partially rescued the phenotype induced by ECAL-1 deficiency. Furthermore, bioinformatic analysis and experimental validation revealed that ECAL-1 sponged miR-23a, which targeted Cldn5b 3UTR and modulated Cldn5b expression, to maintain cerebrovascular pattern formation and integrity. Our results presented here revealed that ECAL-1 specifically controls cerebrovascular network formation and integrity through targeting miR-23a-Cldn5b axis. These findings provide a new regulation modality for cerebrovascular patterning and the potential neurovascular disorders, and ECAL-1-miR-23a axis represents as an attractive therapeutic target for cerebrovascular diseases.

developmental biology↗

bioRxiv2019

How well do crop models predict phenology, with emphasis on the effect of calibration?

Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population.

plant biology↗

bioRxiv2019

New genotype invasion of dengue virus serotype 1 drove massive outbreak in Guangzhou, China

BackgroundDengue fever is a mosquito-borne infectious disease that has caused major health problems. Variations in dengue virus (DENV) genes are important features of epidemic outbreaks. However, the associations of DENV genes with epidemic scale have not been extensively examined. Here, we assessed new genotype invasion of DENV-1 isolated from Guangzhou in China to evaluate associations with epidemic outbreaks.\n\nMethodology/Principal FindingsWe used DENV-1 strains isolated from sera of dengue cases from 2002 to 2016 in Guangzhou for complete genome sequencing. A neighbor-joining phylogenetic tree was constructed to elucidate the genotype characteristics and determine if new genotype invasion correlated with major outbreaks. In our study, a new genotype invasion event was observed during each significant outbreak period in 2002-2003, 2006-2007 and 2013-2014. Genotype II was the main epidemic genotype in 2003 and before. Invasion of genotype I in 2006 caused an unusual outbreak with 765 cases (relative risk (RR)=16.24, 95% confidence interval (CI) =12.41-21.25). At the middle and late stages of the 2013 outbreak, genotype III was introduced to Guangzhou as a new genotype invasion responsible for 37340 cases with RR 541.73 (95%CI=417.78-702.45), after which genotypes I and III began co-circulating. Base mutations occurred after new genotype invasion, and the gene sequence of NS3 protein had the lowest average similarity ratio (99.82%), followed by the gene sequence of E protein (99.86%), as compared to the 2013 strain.\n\nConclusions/SignificanceGenotype replacement and co-circulation of multiple DENV-1 genotypes were observed. New genotype invasion was highly correlated with local unusual outbreaks. In addition to DENV-1 genotype I in the unprecedented outbreak in 2014, new genotype invasion by DENV-1 genotype III occurred in Guangzhou.\n\nAuthor SummaryNew genotype invasion of dengue virus highly correlates with the massive outbreaks. In this study, we examined the association of the genotype of dengue virus serorype 1 (DENV-1) from human cases through complete genome sequencing with outbreak scale during 2002 and 2016 in Guangzhou, China. It was observed that genotype replacement and co-circulation of multiple genotypes occurred. Most importantly, it indicated that new genotype invasion was highly related with local unusual outbreaks in major outbreak periods in 2002-2003, 2006-2007 and 2013-2014. DENV-1 genotype II was the main epidemic genotype in 2003 and before. Invasion of genotype I in 2006 caused an unusual outbreak with 765 cases reported. In addition to genotype I circulation, new genotype invasion by genotype III was the key determinant for the 2014 massive outbreak reaching the highest number of cases with 37340. Furthermore, base mutations appeared after genotype III invasion, and the gene sequence of NS3 protein had the lowest average similarity ratio, followed by the gene sequence of E protein, as compared to the 2013 strain.

epidemiology↗