Biology subjects

You, L.

Publications and source records attributed to You, L..

At least 55 records · Page 3Linked to original sources

bioRxiv2022

Mapping single-cell responses to population-level dynamics during antibiotic treatment

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the probability of cell lysis increases with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single-and population-level responses with universal parameters.

microbiology↗

bioRxiv2022

DNA looping mediates cooperative transcription activation

Transcription factors respond to multi-level stimuli and co-occupy promoter regions of target genes to activate RNA polymerase (RNAP) in a cooperative manner. To decipher the molecular mechanism, here we report two cryo-electron microscopy structures of Anabaena transcription activation complexes (TACs): NtcA-TAC composed of RNAP holoenzyme, promoter and a global activator NtcA, and NtcA-NtcB-TAC comprising an extra context-specific regulator NtcB. Structural analysis showed that NtcA binding makes the promoter DNA bend by [~]50{o}, which facilitates RNAP to contact NtcB at the distal upstream NtcB box. The sequential binding of NtcA and NtcB induces looping back of promoter DNA towards RNAP, enabling the assembly of a fully activated TAC bound with two activators. Together with biochemical assays, we propose a DNA looping mechanism of cooperative transcription activation in bacteria.

molecular biology↗

bioRxiv2022

A SI3-sigma; arch stabilizes cyanobacteria transcription initiation complex

Multi-subunit RNA polymerases (RNAPs) associate with initiation factors ({sigma} in bacteria) to start transcription. The {sigma} factors are responsible for recognizing and unwinding promoter DNA in all bacterial RNAPs. Here, we report two cryo-EM structures of cyanobacterial transcription initiation complexes at near-atomic resolutions. The structures show that cyanobacterial RNAP forms an SI3-{sigma} arch interaction between domain 2 of {sigma}A ({sigma}2) and the sequence insertion 3 (SI3) in the mobile catalytic domain Trigger Loop (TL). The SI3-{sigma} arch facilitates transcription initiation from promoters of different classes through sealing the main cleft and thereby stabilizing RNAP-promoter DNA open complex. Disruption of the SI3-{sigma} arch disturbs cyanobacteria growth and stress response. Our study reports the structure of cyanobacterial RNAP and unique mechanism for its transcription initiation. Our data suggest functional plasticity of SI3 and provide foundation for further research into cyanobacteria and chloroplasts transcription.

biochemistry↗

bioRxiv2022

Structural basis for intrinsic transcription termination

Efficient and accurate termination is required for gene transcription in all living organisms. Cellular RNA polymerases (RNAP) in both bacteria and eukaryotes can terminate their transcription through a factor-independent termination pathway (called intrinsic termination transcription in bacteria), in which RNAP recognizes terminator sequences, stops nucleotide addition, and releases nascent RNA spontaneously. Here we report a set of single-particle cryo-EM structures of E. coli transcription intrinsic termination complexes representing key intermediate states of the event. The structures show how RNAP pauses at terminator sequences, how the terminator RNA hairpin folds inside RNAP, and how RNAP rewinds the transcription bubble to release RNA and then DNA. These macromolecular snapshots define a structural mechanism for bacterial intrinsic termination and a pathway for RNA release and DNA collapse relevant for factor-independent termination by all RNA polymerases.

molecular biology↗

bioRxiv2022

A non-lemniscal thalamic interface connecting alerting sensory cues to internal states in mice

Alterations in internal states, such as elevated arousal level and increased anxiety or fear, triggered by alerting environmental cues are required for behavioral state transitions promoting survival. However, the specific brain region that plays an interfacing role between alerting stimuli and internal states remains to be identified. Here, we report that the medial sector of the auditory thalamus (ATm), which consists of a group of non-lemniscal thalamic nuclei, can fulfill this function. VGluT2-expressing ATm (ATmVGluT2+) neurons receive direct and strong inputs from both visual and auditory midbrain regions, and project to multiple downstream structures critically involved in brain state regulation. Their activity was correlated with, and indispensable for, both blue light- and sound-induced NREM sleep-to-Wake transition, and their arousing effects were mainly mediated by, but not limited to, the temporal association cortices. ATmVGluT2+ neuron activation in awake behaving mice induced pupil dilation and behavioral responses suggestive of anxiety. Blocking the neurotransmitter release of ATmVGluT2+ neurons receiving auditory inputs selectively abolished loud noise-triggered escape behavior but not locomotion. Thus, the ATm is an interface in mouse brain that can transform alerting environmental cues into internal arousal and emotional state alterations that promote survival.

neuroscience↗

bioRxiv2022

Distributed information encoding and decoding using self-organized spatial patterns

Dynamical systems often generate distinct outputs according to different initial conditions, and one can infer the corresponding input configuration given an output. This property captures the essence of information encoding and decoding. Here, we demonstrate the use of self-organized patterns, combined with machine learning, to achieve distributed information encoding and decoding. Our approach exploits a critical property of many natural pattern-formation systems: in repeated realizations, each initial configuration generates similar but not identical output patterns due to randomness in the patterning process. However, for sufficiently small randomness, different groups of patterns that arise from different initial configurations can be distinguished from one another. Modulating the pattern generation and machine learning model training can tune the tradeoff between encoding capacity and security. We further show that this strategy is applicable to non-biological dynamical systems and scalable by implementing the encoding and decoding of all characters of the standard English keyboard. Significance StatementSelf-organized patterns are ubiquitous in biology. They arise from interactions in and between cells, and with the environment. These patterns are often used as a composite phenotype to distinguish cell states and environment conditions. Conceptually, pattern generation under an initial condition is encoding; discerning the initial condition from the pattern represents decoding. Inspired by these examples, we develop a scheme, integrating mathematical modeling and machine learning, to use self-organization for secure and accurate information encoding and decoding. We show that this strategy is applicable to non-biological dynamical systems. We further demonstrate the scalability of the scheme by generating a complete mapping of the standard English keyboard, allowing encoding of English text. Our work serves as an example of nature-inspired computation.

systems biology↗

bioRxiv2022

STOmicsDB: a database of Spatial Transcriptomic data

Recent technological development in spatial transcriptomics allows researchers to measure gene expression of cells and their spatial locations at the almost single-cell level, which generates detailed biological insight into biological processes. However, specialized spatial transcriptomics databases are rare. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a user-friendly database with multifunctions including search of relevant publications and tools, public dataset visualization, customized specialized databases, new data archive, and online analysis. The current version of STOmicsDB consists of 141 curated spatial transcript datasets covering 12 species, and includes 5,618 spatial multi-omics publications and 674 tools. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

genomics↗

bioRxiv2021

Functional single cell proteomic profiling of cells with abnormal DNA damage response dynamics

Tumor heterogeneity is an important source of cancer therapy resistance. Single cell proteomics has the potential to decipher protein content leading to heterogeneous cellular phenotypes. Single-Cell ProtEomics by Mass Spectrometry (SCoPE-MS) is a recently developed, promising, unbiased proteomic profiling techniques, which allows profiling several tens of single cells for >1000 proteins per cell. However, a method to link single cell proteomes with cellular behaviors is needed to advance this type of profiling technique. Here, we developed a microscopy-based functional single cell proteomic profiling technology, called FUNpro, to link the proteome of individual cells with phenotypes of interest, even if the phenotypes are dynamic or the cells of interest are sparse. FUNpro enables one i) to screen thousands of cells with subcellular resolution and monitor (intra)cellular dynamics using a custom-built microscope, ii) to real-time analyze (intra)cellular dynamics of individual cells using an integrated cell tracking algorithm, iii) to promptly isolate the cells displaying phenotypes of interest, and iv) to single cell proteomically profile the isolated cells. We applied FUNpro to proteomically profile a newly identified small subpopulation of U2OS osteosarcoma cells displaying an abnormal, prolonged DNA damage response (DDR) after ionizing radiation (IR). With this, we identified PDS5A and PGAM5 proteins contributing to the abnormal DDR dynamics and helping the cells survive after IR.

bioengineering↗

bioRxiv2021

Functional single cell selection and annotated profiling of dynamically changing cancer cells

A method connecting single cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, would be transformative for single cell and cancer biology. Here, we present fSCS: functional single cell selection. This technology combines a custom-built ultrawide field-of-view optical screening microscope, fast automated image analysis and a new photolabeling method, phototagging, using a newly synthesized visible-light-photoactivatable dye. Using fSCS, we screen, selectively photolabel and isolate cells of interest from large heterogeneous populations based on functional dynamics like fast migration, morphological variation, small molecule uptake or cell division. We combined fSCS with single cell RNA sequencing for functionally annotated transcriptomic profiling of fast migrating and spindle-shaped MCF10A cells with or without TGF{beta} induction. We identified critical genes and pathways driving aggressive migration as well as mesenchymal-like morphology that could not be detected with state-of-the-art single cell transcriptomic analysis. fSCS provides a crucial upstream selection paradigm for single cell sequencing independent of biomarkers, allows enrichment of rare cells and can facilitate the identification and understanding of molecular mechanisms underlying functional phenotypes.

bioengineering↗

bioRxiv2021

CDCP: a visualization and analyzing platform for single-cell datasets

Advances in single-cell sequencing technology provide a unique approach to characterize the heterogeneity and distinctive functional states at single-cell resolution, leading to rapid accumulation of large-scale single-cell datasets. A big challenge undertaken by research community especially bench scientists is how to simplify the way of retrieving, processing and analyzing the huge number of datasets. Towards this end, we developed Cell-omics Data Coordinate Platform (CDCP), a platform that aims to share and integrate comprehensive single-cell datasets, and to provide a network analysis toolkit for personalized analysis. CDCP contains single-cell RNA-seq and ATAC-seq datasets of 474 572 cells from 6 459 samples in species covering humans, non-human primate models and other animals. It allows querying and visualization of interested datasets and the expression profile of distinct genes in different cell clusters and cell types. Besides, this platform provides an analysis pipeline for non-bioinformatician experimental scientists to address questions not focused by the submitters of the datasets. In summary, CDCP provides a user-friendly interface for researchers to explore, visualize, analyze, download and submit published single-cell datasets and it will be a valuable resource for investigators to explore the global transcriptome profiling at single-cell level.

bioinformatics↗

bioRxiv2021

A model-based approach to characterize enzyme-mediated response to antibiotic treatments: going beyond the SIR classification

Commensal and pathogenic E. coli strains are increasingly found to be resistant to {beta}-lactams, one of the most widely prescribed classes of antibiotics. Understanding escape to such treatments is complex since {beta}-lactams have several cellular targets and since several mechanisms might be involved in treatment escape in a combined manner. Surprisingly, the accumulated knowledge has not yet proven effective enough to predict the bacterial response to antibiotic treatments at both cellular and population levels with quantitative accuracy for -producing bacteria. Here, we propose a mathematical model that captures in a comprehensive way key phenomena happening at the molecular, cellular, and population levels, as well as their interactions. Our growth-fragmentation model gives a central role to cellular heterogeneity and filamentation as a way for cells to gain time until the degradation of the antibiotic by the {beta}-lactamases released by the dead cell population. Importantly, the model can account for the observed temporal evolution of the total (live and dead) biomass and of the live cell numbers for various antibiotic concentrations. To our knowledge, this is the first model able to quantitatively reconciliate these two classical views on cell death (OD and CFUs) for clinical isolates expressing extended-spectrum beta-lactamases (ESBL). Moreover, our model has a strong predictive power. When calibrated using a slight extension of OD-based data that we propose here, it can predict the CFU profiles in initial and delayed treatments despite inoculum effects, and suggest non-trivial optimal treatments. Generating quality data in quantity has been essential for model development and validation on non-model E. coli strains. We developed protocols to increase the reproducibility of growth kinetics assays and to increase the throughput of CFU assays.

biophysics↗

bioRxiv2021

Double-stranded RNA drives SARS-CoV-2 nucleocapsid protein to undergo phase separation at specific temperatures

Betacoronavirus SARS-CoV-2 infections caused the global Covid-19 pandemic. The nucleocapsid protein (N-protein) is required for multiple steps in the betacoronavirus replication cycle. SARS-CoV-2-N-protein is known to undergo liquid-liquid phase separation (LLPS) with specific RNAs at particular temperatures to form condensates. We show that N-protein recognizes at least two separate and distinct RNA motifs, both of which require double-stranded RNA (dsRNA) for LLPS. These motifs are separately recognized by N-proteins two RNA binding domains (RBDs). Addition of dsRNA accelerates and modifies N-protein LLPS in vitro and in cells and controls the temperature condensates form. The abundance of dsRNA tunes N-protein-mediated translational repression and may confer a switch from translation to genome packaging. Thus, N-proteins two RBDs interact with separate dsRNA motifs, and these interactions impart distinct droplet properties that can support multiple viral functions. These experiments demonstrate a paradigm of how RNA structure can control the properties of biomolecular condensates.

molecular biology↗

bioRxiv2021

Deficiency of intellectual disability-related gene Brpf1 reduced inhibitory neurotransmission and Map2k7 expression in GABAergic interneurons

Intellectual disability is closely related to impaired GABA neurotransmission. Brpf1 was specifically expressed in medial ganglionic eminence (MGE), a developmental niche of GABAergic interneurons, and patients with BRPF1 mutations were mentally retarded. To test its role in development and function of MGE-derived GABAergic interneurons, we performed immunofluorescence staining, whole-cell patch-clamp, MGE transplantation and mRNA-Seq to understand its effect on neuronal differentiation, dendritic morphology, electrophysiology, migration and gene regulation, using mouse MGE-derived GABAergic interneurons infected with AAV-shBrpf1. We found a decreasing trend on parvalbumin+ interneuron differentiation. Moreover, increased firing threshold, decreased number of evoked APs, and a reduced amplitude of mIPSCs were observed before any significant change of MAP2+ dendritic morphology and in vivo migration appeared. Finally, mRNA-Seq analysis revealed that genes related to neurodevelopment and synaptic transmission such as Map2k7 were dysregulated. Our results demonstrated a key role of Brpf1 in inhibitory neurotransmission and related gene expression of GABAergic interneurons.

neuroscience↗

bioRxiv2020

Repulsive expansion dynamics in colony growth and patterning

Spatial expansion of a population of cells can arise from growth of microorganisms, plant cells, and mammalian cells. It underlies normal or dysfunctional tissue development and it can be exploited as the foundation for programming spatial patterns. This expansion is often driven by continuous growth and division of cells within a colony, which in turn pushes the peripheral cells outward. This process generates a repulsion velocity field at each location within the colony. Here we show that this process can be approximated as coarse-grained repulsive-expansion kinetics. This framework enables accurate and efficient simulation of growth and gene expression dynamics in radially symmetric colonies with homogenous z-directional distribution. It is robust even if cells are not spherical and vary in size. The simplicity of the resulting mathematical framework also greatly facilitates generation of mechanistic insights.

systems biology↗

bioRxiv2020

VirusDIP: Virus Data Integration Platform

MotivationThe Coronavirus Disease 2019 (COVID-19) pandemic poses a huge threat to human public health. Viral sequence data plays an important role in the scientific prevention and control of epidemics. A comprehensive virus database will be vital useful for virus data retrieval and deep analysis. To promote sharing of virus data, several virus databases and related analyzing tools have been created. ResultsTo facilitate virus research and promote the global sharing of virus data, we present here VirusDIP, a one-stop service platform for archive, integration, access, analysis of virus data. It accepts the submission of viral sequence data from all over the world and currently integrates data resources from the National GeneBank Database (CNGBdb), Global initiative on sharing all influenza data (GISAID), and National Center for Biotechnology Information (NCBI). Moreover, based on the comprehensive data resources, BLAST sequence alignment tool and multi-party security computing tools are deployed for multi-sequence alignment, phylogenetic tree building and global trusted sharing. VirusDIP is gradually establishing cooperation with more databases, and paving the way for the analysis of virus origin and evolution. All public data in VirusDIP are freely available for all researchers worldwide. Availabilityhttps://db.cngb.org/virus/ Contactweixiaofeng@cngb.org

bioinformatics↗

bioRxiv2020

CNSA: a data repository for archiving omics data

With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-dimensional biological data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on China National GeneBank (CNGB), we present CNGB Sequence Archive (CNSA) for archiving omics data, including raw sequencing data and its analytical data and related metadata which are organized into six objects, namely Project, Sample, Experiment, Run, Assembly, and Variation at present. Moreover, CNSA has created the correlation model of living samples, sample information, and analytical data on some projects, so that all data can be traced throughout the life cycle from the living sample to the sample information to the analytical data. Complying with the data standards commonly used in the life sciences, CNSA is committed to building a comprehensive and curated data repository for the storage, management and sharing of omics data, improving the data standards, and providing free access to open data resources for worldwide scientific communities to support academic research and the bio-industry. Database URL: https://db.cngb.org/cnsa/

genomics↗

bioRxiv2020

The Persistence Potential of Mobile Genetic Elements

Mobile genetic elements (MGEs), such as plasmids, phages, and transposons, play a critical role in mediating the transfer and maintenance of diverse traits and functions in microbial communities. This role depends on the ability of MGEs to persist. For a community consisting of multiple populations transferring multiple MGEs, however, the conditions underlying the persistence of these MGEs are poorly understood. Computationally, this difficulty arises from the combinatorial explosion associated with describing the gene flow in a complex community using the conventional modeling framework. Here, we describe an MGE-centric framework that makes it computationally feasible to analyze such transfer dynamics. Using this framework, we derive the persistence potential: a general, heuristic metric that predicts the persistence and abundance of any MGEs. We validate the metric with engineered microbial consortia transferring mobilizable plasmids and quantitative data available in the literature. Our modeling framework and the resulting metric have implications for developing a quantitative understanding of natural microbial communities and guiding the engineering of microbial consortia.

microbiology↗

bioRxiv2020

Single-cell RNA-seq Imputation using Generative Adversarial Networks

Single-cell RNA-sequencing (scRNA-seq) enables the characterization of transcriptomic profiles at the single-cell resolution with increasingly high throughput. However, it suffers from many sources of technical noises, including insufficient mRNA molecules that lead to excess false zero values, termed dropouts. Computational approaches have been proposed to recover the biologically meaningful expression by borrowing information from similar cells in the observed dataset. However, these methods suffer from oversmoothing and removal of natural cell-to-cell stochasticity in gene expression. Here, we propose the generative adversarial networks (GANs) for scRNA-seq imputation (scIGANs), which uses generated cells rather than observed cells to avoid these limitations and balances the performance between major and rare cell populations. Evaluations based on a variety of simulated and real scRNA-seq datasets show that scIGANs is effective for dropout imputation and enhances various downstream analysis. ScIGANs is robust to small datasets that have very few genes with low expression and/or cell-to-cell variance. ScIGANs works equally well on datasets from different scRNA-seq protocols and is scalable to datasets with over 100,000 cells. We demonstrated in many ways with compelling evidence that scIGANs is not only an application of GANs in omics data but also represents a competing imputation method for the scRNA-seq data.

bioinformatics↗