Biology subjects

Jia, C.

Publications and source records attributed to Jia, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

The subcommissural organ regulates brain development via secreted peptides

The subcommissural organ (SCO) is a gland located at the entrance of the aqueduct of Sylvius in the brain. It exists in species as distantly related as amphioxus and humans, but its function is largely unknown. To explore its function, we compared transcriptomes of SCO and non-SCO brain regions and found three genes, Sspo, Car3, and Spdef, that are highly expressed in the SCO. Mouse strains expressing Cre recombinase from endogenous promoter/enhancer elements of these genes were used to genetically ablate SCO cells during embryonic development, resulting in severe hydrocephalus and defects in neuronal migration and development of neuronal axons and dendrites. Unbiased peptidomic analysis revealed enrichment of three SCO-derived peptides, namely thymosin beta 4, thymosin beta 10, and NP24, and their reintroduction into SCO-ablated brain ventricles substantially rescued developmental defects. Together, these data identify a critical role for the SCO in brain development.

neuroscience↗

bioRxiv2024

Holimap: an accurate and efficient method for solving stochastic gene network dynamics

Gene-gene interactions are crucial to the control of sub-cellular processes but our understanding of their stochastic dynamics is hindered by the lack of simulation methods that can accurately and efficiently predict how the distributions of gene product numbers for each gene vary across parameter space. To overcome these difficulties, here we present Holimap (high-order linear-mapping approximation), an approach that approximates the protein or mRNA number distributions of a complex gene regulatory network by the distributions of a much simpler reaction system. We demonstrate Holimaps computational advantages over conventional methods by applying it to predict the stochastic time-dependent dynamics of various gene networks, including transcriptional networks ranging from simple autoregulatory loops to complex randomly connected networks, post-transcriptional networks, and post-translational networks. Holimap is ideally suited to study how the intricate network of gene-gene interactions results in precise coordination and control of gene expression.

systems biology↗

bioRxiv2024

The discovery of antimicrobial peptides from the gut microbiome of cockroach Blattella germanica using deep learning pipeline

Antimicrobial peptides (AMPs) are candidates for use against antibiotic-resistant microorganisms. However, due to high cytotoxicity and poor performance in biological contexts, most AMPs are unable to satisfy the requirements. The gut microbiomes of pathogenic species like Blattella germanica represent unexploited reservoirs of naturally evolved biocompatible AMPs. Here we developed a lightweight AI pipeline called AMPidentifer with two nine-layers Dense-Net blocks and one embedded new self-attention module to enable the discovery of biocompatible AMPs from microbiome. The core structure of AMPidentifer is simple, not requiring complexing code basis. On the independent test dataset, it showed robust performance and avoided high false-positive results. From the gut microbiome of B. germanica, new AMP candidates with potential low toxicities and antimicrobial activities were identified by AMPidentifer. The selected two AMPs demonstrated good antimicrobial effects in vitro and in vivo. The Cys residue was demonstrated to perform different action mechanisms in the antimicrobial activity of two AMPs, providing insights for future rational design. New efficient AMPs with low cytotoxicity identified by the new high-throughput AI pipeline from the gut microbiome of B. germanica successfully showed an important interdisciplinary strategy for discovering bio-safe AMPs from nature. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/580024v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@143e033org.highwire.dtl.DTLVardef@134620dorg.highwire.dtl.DTLVardef@11809forg.highwire.dtl.DTLVardef@1134ef6_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOThe Graphic AbstractC_FLOATNO C_FIG

microbiology↗

bioRxiv2023

Solving the time-dependent protein distributions for autoregulated bursty gene expression using spectral decomposition. Part I: Conventional models

In this study, we obtain an exact time-dependent solution of the chemical master equation (CME) of an extension of the two-state telegraph model describing bursty or non-bursty protein expression in the presence of positive or negative autoregulation. Using the method of spectral decomposition, we show that the eigenfunctions of the generating function solution of the CME are Heun functions, while the eigenvalues can be determined by solving a continued fraction equation. Our solution generalizes and corrects a previous time-dependent solution for the CME of a gene circuit describing non-bursty protein expression in the presence of negative autoregulation ["Exact time-dependent solutions for a self-regulating gene." Phys. Rev. E 83: 062902 (2011)]. In particular, we clarify that the eigenvalues are generally not real as previously claimed. We also investigate the relationship between different types of dynamic behavior and the type of feedback, the protein burst size, and the gene switching rate.

systems biology↗

bioRxiv2023

Social isolation recruits amygdala-cortical circuitry to escalate alcohol drinking

Social isolation profoundly alters motivation and increases vulnerability to alcohol misuse in humans, yet the underlying neural mechanisms remain unclear. Here we show that isolation escalates alcohol drinking in male mice but suppresses it in females. Whole-cell recordings revealed that neurons in the basolateral amygdala projecting to the medial prefrontal cortex (BLA-mPFC) track alcohol intake in both sexes. Isolation increased BLA-mPFC excitability in males but decreased it in females, mirroring their opposite behavioral adaptations. Given this divergence, we focused subsequent mechanistic studies on males to isolate neural pathway-level drivers of escalated alcohol intake. Cellular-resolution calcium imaging showed that activity in BLA-mPFC neurons encodes and predicts alcohol drinking, and optogenetic activation of this pathway increased alcohol intake. Simultaneous optogenetics and calcium imaging revealed that BLA-mPFC stimulation enhanced mPFC neuronal responses to alcohol, mimicking isolation-induced activity patterns, while photoinhibition reduced drinking in isolated mice. Together, these findings identify a BLA-mPFC pathway mechanism through which social isolation reconfigures prefrontal processing to promote alcohol intake.

neuroscience↗

bioRxiv2023

A reference genome and its epigenetic landscape of potential Orychophragmus violaceus, an industrial crop species

Orychophragmus violaceus, also called er-yue-lan in China, is an annual plant of the family of Brassicaceae. The seed oil of O. violaceus contained two specific di-hydroxy fatty acids which were produced by functional divergence between two FAD2 WGD copies determine its industrial property. Here, we assembled a high-quality chromosome-level genome of O. violaceus via based on PacBio CLR technology and its cis-regulatory landscape of five mature tissues, including root, leaf, flower, seed and stem, based on ATAC-seq technology. 1.2 Gb draft genomic sequences were anchored to 12 pseudo-chromosomes and 49904 protein-coding genes were annotated on these chromosomes. To fully understand the epigenetic landscape of O. violaceus, we further performed WGBS-seq for leaf, flower and silique tissues. In total, our multi-omics data provide opportunity to find out the differences between two WGD copies and also a valuable resource for downstream breeding effort of this potential industrial species.

genomics↗

bioRxiv2023

The super-pangenome of Populus unveil genomic facets for adaptation and diversification in widespread forest trees

Understanding the underlying mechanisms between genome evolution and phenotypic and adaptive innovations is a key goal of evolutionary studies. Poplars are the worlds most widely distributed and cultivated trees, with extensive phenotypic diversity and environmental adaptability. Here we report a genus-level super-pangenome of 19 Populus genomes. After integrating pan-genomes with transcriptomes, methylomes and chromatin accessibility mapping, we reveal that the evolutionary fate of pan-genes and duplicated genes are largely associated with local genomic landscapes of regulatory and epigenetic architectures. Further comparative genomic analyses enabled to identify 142,202 structural variations (SVs) across species, which overlap with substantial genes and play key roles in both phenotypic and adaptive divergence. We experimentally validated a [~]180 bp presence/absence variant located in the promoter of the CUC2 gene, which contributed critically to leaf serration divergence between species. Together, this first super-pangenome resource in forest trees will not only accelerate molecular functional studies and genetic breeding of this globally important tree genus, but also lays a foundation for our understanding of tree biology.

evolutionary biology↗

bioRxiv2023

Poisson representation: a bridge between discrete and continuous models of stochastic gene regulatory networks

Stochastic gene expression dynamics can be modeled either discretely or continuously. Previous studies have shown that the mRNA or protein number distributions of some simple discrete and continuous gene expression models are related by Gardiners Poisson representation. Here we systematically investigate the Poisson representation in complex stochastic gene regulatory networks. We show that when the gene of interest is unregulated, the discrete and continuous descriptions of stochastic gene expression are always related by the Poisson representation, no matter how complex the model is. In addition, using a simple counterexample, we find that the Poisson representation in general fails to link the two descriptions when the gene is regulated. However, for a general stochastic gene regulatory network, we demonstrate that the discrete and continuous models are approximately related by the Poisson representation in the limit of large protein numbers. These theoretical results are further applied to analytically solve many complex gene expression models whose exact distributions are previously unknown.

systems biology↗

bioRxiv2023

Chromosomal-level genome assembly of Populus davidiana

Despite the population structure and genetic differentiation of P. davidiana has been reported, little is known about the P. davidiana genome characterization with the genomes assembled at the chromosome level. As one of the most widely distributed and ecologically important tree species in China, P. davidiana is an excellent resource to understand population adaption to climate and environment change. Here we present a high-quality assembly based on long-read sequences and Hi-C data. This assembly is assembled into 19 contiguous chromosomes which provides a powerful tool for future association studies.

genomics↗

bioRxiv2023

What can we learn when fitting a complex gene expressionmodel to a simple telegraph model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

biophysics↗

bioRxiv2022

Brassinosteroids signaling component SlBZR1 promotes fruit ripening in tomato

Fruit ripening evolved to be attractive to frugivores that derive energy and nutrition from the fruits in exchange for assisting seed dispersal, which is accompanied by the dramatically change of fruit characteristics, including color, aroma, and texture. The plant hormone ethylene plays a key role in climacteric fruit ripening, while the role of other phytohormones as well as their cross talk with ethylene in modulating fruit ripening remains elusive. Here, we report growth-promoting phytohormone brassinosteroids promote fruit ripening in tomato through regulation of ethylene biosynthesis. Exogenous BR treatment and the increase of endogenous BR content in SlCYP90B3-OE promoted ethylene production and fruit ripening. SlBZR1, a central component and positive regulator of BR signaling pathway, promotes ethylene production and carotenoid accumulation through direct transcriptional regulation of SlACO1, SlACO3 and SlPSY1. Furthermore, SlBIN2, a negative regulator of BR signaling upstream of SlBZR1, decreases ethylene production and carotenoid accumulation. Together, our results demonstrate that BR signaling integrates ethylene and carotenoid biosynthetic pathway to regulate fruit ripening.

plant biology↗

bioRxiv2022

Accuracy and limitations of extrinsic noise models to describe gene expression in growing cells

The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.

systems biology↗

bioRxiv2022

Genomic insights into present local adaptation and future climate change vulnerability of a keystone forest tree species in East Asian

Rapid global climate change is posing a huge threat to biodiversity. Assessments of the adaptive capacity for most taxa is usually performed on the species as a whole, but fails to incorporate intraspecific adaptive variation that may play a fundamental role in buffering future shifting climates. Here we generate a chromosome-scale genome assembly for Populus koreana, a pioneer and keystone tree species in East Asia temperate forests. We also obtain whole-genome sequences of 230 individuals collected from 24 natural populations. An integration of population genomics and environmental variables was performed to reveal the genomic basis of local adaptation to diverse climate variable. We identify a set of climate-associated single nucleotide polymorphisms (SNPs), insertions-deletions (Indels) and structural variations (SVs), in particular numerous adaptive non-coding variants distributed across the genome of P. koreana. We incorporate these variants into an environmental modelling scheme to predict spatiotemporal responses of P. koreana to future climate change. Our results highlight the insights that the integration of genomic and climate data can shed on the future evolutionary adaptive capacities of a species to changing environmental conditions.

evolutionary biology↗

bioRxiv2022

Analytical time-dependent distributions for gene expression models with complex promoter switching mechanisms

Classical gene expression models assume exponential switching time distributions between the active and inactive promoter states. However, recent experiments have shown that many genes in mammalian cells may produce non-exponential switching time distributions, implying the existence of multiple promoter states and molecular memory in the promoter switching dynamics. Here we analytically solve a gene expression model with random bursting and complex promoter switching, and derive the time-dependent distributions of the mRNA and protein copy numbers, generalizing the steady-state solution obtained in [SIAM J. Appl. Math. 72, 789-818 (2012)] and [SIAM J. Appl. Math. 79, 1007-1029 (2019)]. Using multiscale simplification techniques, we find that molecular memory has no influence on the time-dependent distribution when promoter switching is very fast or very slow, while it significantly affects the distribution when promoter switching is neither too fast nor too slow. By analyzing the dynamical phase diagram of the system, we also find that molecular memory in the inactive gene state weakens transient and stationary bimodality of the copy number distribution, while molecular memory in the active gene state enhances such bimodality.

systems biology↗

bioRxiv2021

Concentration fluctuations due to size-dependent gene expression and cell-size control mechanisms

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. Furthermore, we find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) certain size control strategies are ideal because they maintain constant mean concentrations whilst minimising concentration noise. Predictions are confirmed using lineage data for E. coli, fission yeast and budding yeast.

systems biology↗

bioRxiv2021

iHypoxia: an integrative database of the expression dynamics of proteins in response to hypoxia in animals

Decreased oxygen concentrations (hypoxia) outside of the physiological range may severely subvert cell, tissue, and organism survival. Mammals have evolved mechanisms to sense hypoxia and induce a series of hypoxic responses. In recent years, high-throughput techniques have greatly promoted global perturbation studies of protein expression during hypoxia, and these studies have contributed to the understanding of the complex regulatory networks of hypoxia. In this study, we developed an integrated resource for the expression dynamics of proteins in response to hypoxia (iHypoxia), which contains 1,629 expression events of 1,215 proteins identified by low-throughput experiments (LTEs) and 154,953 quantitative expression events of 36,194 proteins identified by high-throughput experiments (HTEs) from five mammals that exhibit a response to hypoxia. Various experimental details such as the hypoxic experimental conditions, expression patterns, and samples were carefully collected and integrated. In addition, we conducted an orthologous search and identified 581,763 proteins that may respond to hypoxia among 50 animals. An enrichment analysis of human proteins identified from LTEs showed that these proteins were enriched in certain drug targets and cancer genes. The annotation of known posttranslational modification (PTM) sites to proteins identified by LTEs revealed that these proteins underwent extensive PTMs, particularly phosphorylation, ubiquitination and acetylation. Based on the results, iHypoxia provides a convenient and user-friendly method for users to obtain hypoxia-related information of interest. We anticipate that iHypoxia, which is freely accessible at http://ihypoxia.omicsbio.info, will advance the understanding of hypoxia and serve as a valuable data resource.

bioinformatics↗

bioRxiv2021

Characterizing non-exponential growth and bimodal cell size distributions in Schizosaccharomyces pombe: an analytical approach

Unlike many single-celled organisms, the growth of fission yeast cells within a cell cycle is not exponential. It is rather characterized by three distinct phases (elongation, septation and fission), each with a different growth rate. Experiments also show that the distribution of cell size in a lineage is often bimodal, unlike the unimodal distributions measured for the bacterium Escherichia coli. Here we construct a detailed stochastic model of cell size dynamics in fission yeast. The theory leads to analytic expressions for the cell size and the birth size distributions, and explains the origin of bimodality seen in experiments. In particular our theory shows that the left peak in the bimodal distribution is associated with cells in the elongation phase while the right peak is due to cells in the septation and fission phases. We show that the size control strategy, the variability in the added size during a cell cycle and the fraction of time spent in each of the three cell growth phases have a strong bearing on the shape of the cell size distribution. Furthermore we infer all the parameters of our model by matching the theoretical cell size and birth size distributions to those from experimental single cell time-course data for seven different growth conditions. Our method provides a much more accurate means of determining the cell size control strategy (timer, adder or sizer) than the standard method based on the slope of the best linear fit between the birth and division sizes. We also show that the variability in added size and the strength of cell size control of fission yeast depend weakly on the temperature but strongly on the culture medium. Author summaryAdvances in microscopy enable us to follow single cells over long timescales from which we can understand how their size varies with time and the nature of innate strategies developed to control cell size. This data shows that in many cell types growth is exponential and the distribution of cell sizes has one peak, namely there is a single characteristic cell size. However data for fission yeast shows remarkable differences: growth is non-exponential and the distribution of cell sizes has two peaks, meaning two characteristic cell sizes exist. Here we construct the first mathematical model of this organism; by solving the model analytically we show that it is able to predict the two peaked distributions of cell size seen in data and provides an explanation for each peak in terms of the various growth phases of the single-celled organism. Furthermore by fitting the model to the data, we infer values for the rates of all microscopic processes in our model. This method is shown to provide a much more reliable inference than current methods and sheds light on how the strategy used by fission yeast cells to control their size varies with external conditions.

systems biology↗

bioRxiv2021

Aplp1 and the Aplp1-Lag3 Complex facilitates transmission of pathologic alpha-synuclein

Pathologic -synuclein (-syn) spreads from cell-to-cell, in part, through binding to the lymphocyte-activation gene 3 (Lag3). Here we report that amyloid {beta} precursor-like protein 1 (Aplp1) forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -syn. Deletion of both Aplp1 and Lag3 eliminates the loss of dopaminergic neurons and the accompanying behavioral deficits induced by -syn preformed fibrils (PFF). Anti-Lag3 prevents the internalization of -syn PFF by disrupting the interaction of Aplp1 and Lag3, and blocks the neurodegeneration induced by -syn PFF in vivo. The identification of Aplp1 and the interplay with Lag3 for -syn PFF induced pathology advances our understanding of the molecular mechanism of cell-to-cell transmission of pathologic -syn and provides additional targets for therapeutic strategies aimed at preventing neurodegeneration in Parkinsons disease and related -synucleinopathies. One Sentence SummaryAplp1 forms a complex with Lag3 that facilitates the binding, internalization, transmission, and toxicity of pathologic -synuclein. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/442157v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1d746bforg.highwire.dtl.DTLVardef@65acb2org.highwire.dtl.DTLVardef@12cf2f0org.highwire.dtl.DTLVardef@19d6e_HPS_FORMAT_FIGEXP M_FIG Aplp1 and the Aplp1-Lag3 complex facilitates transmission of pathologic -synuclein. Aplp1 is a receptor that drives pathologic -syn transmission, and genetic depletion of Aplp1 can significantly reduce the -synuclein pathogenesis. Aplp1 and Lag3 forms an Aplp1-Lag3 complex that accounts for substantial binding of pathologic -syn to cortical neurons. Together Aplp1 and Lag3 play a major role in pathologic -syn internalization, transmission and toxicity. Double knockout of Aplp1 and Lag3 and or a Lag3 antibody that disrupts the Aplp1 and Lag3 complex almost completely blocks -syn PFF-induced neurodegeneration. C_FIG

neuroscience↗