Biology subjects

Tang, C.

Publications and source records attributed to Tang, C..

At least 145 records · Page 8Linked to original sources

bioRxiv2019

System-Level Quantification and Phenotyping of Early Embryonic Morphogenesis of Caenorhabditis elegans

Cell lineage consists of cell division timing, cell migration and cell fate, and is highly conserved during development of nematode species. An outstanding question is how differentiated cells are genetically and physically regulated in order to migrate to their precise destination among individuals. Here, we first generated a reference embryo using time-lapse 3 dimensional images of 222 wild-type C. elegans embryos at about 1.5-minute interval. This was achieved by automatic tracing and quantitative analysis of cellular phenotypes from 4- to 24-cell stage, including cell cycle duration, division orientation and migration trajectory. We next characterized cell division timing and cell kinematic state, which suggests that eight groups of cells can be clustered based on invariant and distinct division sequence. Cells may still be moving while others start to divide, indicating strong robustness against motional noise in developing embryo. We then devised a system-level phenotyping method for detecting mutant defect in global growth rate, cell cycle duration, division orientation and cell arrangement. A total of 758 genes were selected for perturbation by RNA interference followed by automatic phenotyping, which suggests a cryptic genetic architecture coordinating early morphogenesis spatially and temporally. The high-quality wild-type reference supports a conceptual close-packing model for cell arrangement during 4- to 8-cell stage, implying fundamental mechanical laws regulating the topological structure of early C. elegans embryo. Also, we observed a series of remarkable morphogenesis phenomena such as induced defect or recovery from defect in mutant embryo. To facilitate use of this quantification system, we built a software named STAR 1.0 for visualizing the wild-type reference and mutant phenotype. It also allows automatic phenotyping of new mutant embryo. Taken together, we not only provide a statistical wild-type reference with defined variability, but also shed light on both genetic and physical mechanisms coordinating early embryonic morphogenesis of C. elegans. The statistical reference permits a sensitive approach for mutant phenotype analysis, with which we phenotype a total of 1818 mutant embryos by depletion of 758 genes.\n\nHighlights & Graphical Abstract[bullet] Spatial-Temporal Wild-Type Reference for Early Embryonic Morphogenesis of C. elegans\n[bullet]Variability (Noise) of Division Timing, Division Orientation and Cell Arrangement\n[bullet]A Conceptual Close-Packing Model for Cell Arrangement Up to 8-Cell Stage\n[bullet]Quantitative Phenotyping Methods at Embryo and Cellular Level\n[bullet]Cellular Phenotypes of 1818 Mutant Embryos (758 Genes) Before Gastrulation\n[bullet]Categorized Phenotypes upon Gene Perturbation\n\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC=\"FIGDIR/small/776062v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (37K):\norg.highwire.dtl.DTLVardef@2ec730org.highwire.dtl.DTLVardef@11732deorg.highwire.dtl.DTLVardef@ccbf4org.highwire.dtl.DTLVardef@2218d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

Independent Activity Subspaces for Working Memory and Motor Preparation in the Lateral Prefrontal Cortex

The lateral prefrontal cortex is involved in the integration of multiple types of information, including working memory and motor preparation. However, it is not known how downstream regions can extract one type of information without interference from the others present in the network. Here we show that the lateral prefrontal cortex contains two independent low-dimensional subspaces: one that encodes working memory information, and another that encodes motor preparation information. These subspaces capture all the information about the target in the delay periods, and the information in both subspaces is reduced in error trials. A single population of neurons with mixed selectivity forms both subspaces, but the information is kept largely independent from each other. A bump attractor model with divisive normalization replicates the properties of the neural data. These results have implications for the neural mechanisms of cognitive flexibility and capacity limitations.

neuroscience↗

bioRxiv2019

HeveaDB: a genetic resource database for rubber tree genomic study

With the proceedings in the genome study in rubber tree, more and more data were produced. However, these data are still deposited in public databases as raw format, and the utilization of these NGS data is hindered for the researchers in rubber tree community. HeveaDB was therefore constructed and maintained under the organization of International Rubber Research and Development Board. The HeveaDB 1.0 (http://hevea.catas.cn) mainly stores 4 versions of Hevea draft genome, 99 NGS transcriptomes, 18451annotated EST sequences, 12 curated gene families, 30200 gene annotations, 5049 wild germplasm phenotype data, and 18328 Wickham rubber clonal information. Several bioinformatic tools are integrated in the database to facilitate the utilization of the data for the users who are not skilled in bioinformatics. With the periodical update and functional improvement, the HeveaDB will serve as a potential platform for genomic studies and breeding technology in rubber tree.

bioinformatics↗

bioRxiv2019

Rapid pacing by circulating traveling waves improves maturation of hiPSC-derived cardiomyocytes in self-organized tissue ring

Directed differentiation methods allow acquisition of high-purity cardiomyocytes (CMs) differentiated from human induced pluripotent stem cells (hiPSCs); however, their immaturity characteristic limits their application for drug screening and regenerative therapy. The rapid electrical pacing of cardiomyocytes have been used for efficiently promoting the maturation of cardiomyocytes, here we describe a simple device in modified culture plate on which hiPSC-derived CMs (hiPSC-CMs) can form three-dimensional self-organized tissue rings (SOTRs). Using calcium imaging, we show that within the ring, traveling waves (TWs) of action potential spontaneously originated and ran robustly at a frequency up to 4 Hz. After 2 weeks, SOTRs with TW training showed matured features including structural organization, increased cardiac-specific gene expression, enhanced Ca2+-handling properties, an increased oxygen-consumption rate, and enhanced contractile force. We subsequently used a mathematical model to interpret the origination, propagation, and long-term behavior of the TWs within the SOTRs. This new idea for spontaneous hiPSC-CM maturation also has potential for pacing the electrical excitable cells such as neuron and retina cells for various applications.

bioengineering↗

bioRxiv2019

A key interfacial residue identified with in-cell structure characterization of a Class-A GPCR dimer

G protein coupled receptors (GPCRs) have been shown homo-dimeric. Despite extensive studies, no single residue has been found essential for dimerization. Lacking an efficient method to shift the monomer-dimer equilibrium also makes functional relevance of GPCR dimer elusive. Here, using fluorescence lifetime-based imaging for distance measurements, we characterize the dimeric structure of GPR17, a class A GPCR, in cells. The structure reveals transmembrane helices 5 and 6 the dimer interface, and pinpoints F229 a key residue, mutations of which can render GPR17 monomeric or dimeric. Using the resulting mutants, we show that GPR17 dimer is coupled to both Gi and Gq signaling and is internalized, whereas GPR17 monomer is coupled to Gi signaling only and is not internalized. We further show that residues equivalent to F229 of GPR17 in several other class A GPCRs are also important for dimerization. Our findings thus provide fresh insights into GPCR structure and function.

biochemistry↗

bioRxiv2019

Cell cycle inhibitor Whi5 records environmental information to coordinate growth and division in yeast

Proliferating cells need to evaluate the environment to determine the optimal timing for cell cycle entry, which is essential for coordinating cell division and growth. In the budding yeast Saccharomyces cerevisiae, the commitment to the next round of division is made in G1 at the Start, triggered by the inactivation of the inhibitor Whi5 through multiple mechanisms. However, how a cell reads environmental condition and uses this information to regulate Start is poorly understood. Here, we show that Whi5 is a key environmental indicator and plays a crucial role in coordinating cell growth and division. We found that under a variety of nutrient and stress conditions, the concentration of Whi5 in G1 is proportional to the doubling time in the environment. Thus, under a poorer condition a longer doubling time results in a higher Whi5 concentration, which in turn delays the next cell cycle entry to ensure sufficient cell growth. In addition, the coordination between division and the environment is further fine-tuned in G1 by environmentally dependent G1 cyclin-Cdk1 contribution and Whi5 threshold at Start. Our results show that Whi5 serves as an environmental memory and that the cell adopts a simple and elegant mechanism to achieve an adaptive cellular decision making.

systems biology↗

bioRxiv2019

Time-Invariant Working Memory Representations in the Presence of Code-Morphing in the Lateral Prefrontal Cortex

Endogenous processes allow the maintenance of working memories. These processes presumably involve prefrontal networks with strong recurrent connections. Distractors evoke a morphing of the population code, even when memories are stable. But it is unclear whether these dynamic population responses contain stable memory information. Here we show that dynamic prefrontal activity contains stable memory information, and the stability depends on parallel movement of trajectories associated with different memories in state space. We used an optimization algorithm to find a subspace with stable memory information. In correct trials the stability extended to periods that were not used to find the subspace, but in error trials the information and the stability were reduced. A bump attractor model was able to replicate these behaviors. The model provided predictions that could be confirmed with the neural data. We conclude that downstream regions could read memory information from a stable subspace.

neuroscience↗

bioRxiv2019

Structural basis for the recognition of K48-linked Ub chain by proteasomal receptor Rpn13

The interaction between K48-linked ubiquitin (Ub) chain and Rpn13 is important for proteasomal degradation of ubiquitinated substrate proteins. Only the complex structure between the N-terminal domain of Rpn13 (Rpn13NTD) and Ub monomer has been characterized, and it remains unclear how Rpn13 specifically recognizes K48-linked Ub chain. Using single-molecule FRET, here we show that K48-linked diubiquitin (K48-diUb) fluctuates among three distinct conformational states, and a preexisting compact state is selectively enriched by Rpn13NTD. The same binding mode is observed for full-length Rpn13 and longer K48-linked Ub chain. Using solution NMR spectroscopy, we have solved the complex structure between Rpn13NTD and K48-diUb. In the structure, Rpn13NTD simultaneously interacts with proximal and distal Ub subunits of K48-diUb that remain associated in the complex, thus corroborating smFRET findings. The proximal Ub interacts with Rpn13NTD similarly as the Ub monomer in the known Rpn13NTD:Ub structure, while the distal Ub binds to a largely electrostatic surface of Rpn13NTD. Thus, a charge reversal mutation in Rpn13NTD can weaken the interaction between Rpn13 and K48-linked Ub chain, causing accumulation of ubiquitinated proteins. Moreover, blockage of the access of the distal Ub to Rpn13NTD with a proximity attached Ub monomer can also disrupt the interaction between Rpn13 and K48-diUb. Together, the bivalent interaction of K48-linked Ub chain with Rpn13 provides the structural basis for Rpn13 linkage selectivity, which opens a new window for modulating proteasomal function.

biophysics↗

bioRxiv2018

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology↗

bioRxiv2018

Statistical properties of the optimal sensitivity matrix for compressed sensing with nonlinear sensors

There are numerous different odorant molecules in nature. Typical odors are sparse mixtures of a few types of odorant molecules each with a wide range of concentrations. However, there are only a relatively small number of olfactory receptor neurons (ORNs), which respond to odorant concentration nonlinearly (sigmoidal) with a finite sensitivity range. Thus, how to encode a large number of sparse odor mixtures with a relatively small number of nonlinear ORNs - the nonlinear compressed sensing problem - remains a puzzle. Here, by using an information theory approach, we study the optimal coding strategies that enable nonlinear ORNs to best represent olfactory information (both the odorants identities and their concentrations) in sparse odor mixtures. Our extensive numerical simulations and analytical analysis show that the optimal odor-receptor sensitivity matrix is sparse and the nonzero sensitivities follow roughly a log-normal distribution(matching the statistics of the odorants), both of which are consistent with existing experiments. For ORNs with a finite basal activity, our study shows that co-existence of both odor-evoked excitation and inhibition increases coding capacity, which provides a plausible explanation for such co-existence observed in the fly olfactory system. Furthermore, we show that coding the inputs with the optimal sensitivity matrix can enhance accuracy of the downstream decoding and learning tasks. These general statistical properties of the optimal sensitivity matrix for nonlinear compressed sensing may shed light on understanding the peripheral olfactory sensory system and improving performance of artificial neural networks.

neuroscience↗

bioRxiv2018

A new massively parallel nanoball sequencing platform for whole exome research

BackgroundWhole exome sequencing (WES) has been widely used in human genetics research. BGISEQ-500 is a recently established next-generation sequencing platform. However, the performance of BGISEQ-500 on WES is not well studied. In this study, we evaluated the performance of BGISEQ-500 on WES by side-to-side comparison with Hiseq4000, on wellcharacterized human sample NA12878.\n\nResultsBGISEQ demonstrated similarly high reproducibility as Hiseq for variation detection. Also, the SNPs from BGISEQ data is highly consistent with Hiseq results (concordance 96.5%[~]97%). Variation detection accuracy was subsequently evaluated with data from the genome in a bottle project as the benchmark. Both platforms showed similar sensitivity and precision in SNP detection. While in indel detection, BGISEQ showed slightly higher sensitivity and lower precision. The impact of sequence depth and read length on variation detection accuracy was further analyzed, and showed that variation detection sensitivity still increasing when the sequence depth is larger than 100x, and the impact of read length is minor when using 100x data.\n\nConclusionsThis study suggested that BGISEQ-500 is a qualified sequencing platform for WES.

genomics↗

bioRxiv2018

Genetic control of the HDL proteome

High-density lipoproteins (HDL) are nanoparticles with >80 associated proteins, phospholipids, cholesterol and cholesteryl esters. A comprehensive genetic analysis of the regulation of proteome of HDL isolated from a panel of 100 diverse inbred strains of mice, Hybrid Mouse Diversity Panel (HMDP), revealed widely varied HDL protein levels across the strains. Some of this variation was explained by local, cis-acting regulation, termed cis-protein quantitative trait loci. Variations in apolipoprotein A-II and apolipoprotein C-3 affected the abundance of multiple HDL proteins indicating a coordinated regulation. We identified modules of co-varying proteins and define a protein-protein interaction network describing the protein composition of the naturally occurring subspecies of HDL in mice. Sterol efflux capacity varied up to 3-fold across the strains and HDL proteins displayed distinct correlation patterns with macrophage and ABCA1 specific cholesterol efflux capacity and cholesterol exchange, suggesting that subspecies of HDL participate in discrete functions. The baseline and stimulated sterol efflux capacity phenotypes associated with distinct QTLs with smaller effect size suggesting a multi genetic regulation. Our results highlight the complexity of HDL particles by revealing high degree of heterogeneity and intercorrelation, some of which is associated with functional variation, supporting the concept that HDL-cholesterol alone is not an accurate measure of HDLs properties such as protection against CAD.

systems biology↗

bioRxiv2018

Toward deciphering developmental patterning with deep neural network

Complex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation - the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN "black box". Our interpretable DNN approach should have broad applications in genotype-phenotype mapping. SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

developmental biology↗

bioRxiv2018

KS-Burden: Assessing distributional differences of rare variants in dichotomous traits

A number of rare variant tests have been developed to explore the effect of low frequency genetic variations on complex phenotypes. However, an often neglected aspect in these tests is the position of genetic variations. Here we are proposing a way to assess the differences in spatial organization of rare variants by assessing their distributional differences between affected and unaffected subjects. To do so, we have formulated an adaptation of the well know Kolmogorov-Smirnov (KS) test, combining both KS and a simple gene burden approach, called KS-Burden.\n\nThe performance of our test was evaluated under a comprehensive simulations framework using real data and various scenarios. Our results show that the KS-Burden test is able to outperform the commonly used SKAT-O test, as well as others, in the presents of clusters of causal variants within a genomic region. Furthermore, our test is able to maintain competitive statistical power in scenarios unfavorable to its original assumptions. Hence, the KS-Burden test is a valuable alternative to existing tests and provides better statistical power in the presents of causal clusters within a gene.

bioinformatics↗

bioRxiv2018

Template switching causes artificial junction formation and false identification of circular RNAs

Hundreds of thousands of putative circular RNAs have been identified through deep sequencing and bioinformatic analyses. However, the circularity of these putative RNA circles has not been experimentally validated due to limited methodologies currently available. We reported here that the template-switching capability of commonly used reverse transcriptases (e.g., SuperScript II) leads to the formation of artificial junction sequences, and consequently misclassification of large linear RNAs as RNA circles. Use of reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis is critical for the identification of physiological circular RNAs. We also report two methods, MonsterScript junction PCR and high-resolution melting curve analyses, which can reliably distinguish circular RNAs from their linear forms and thus, can be used to discover and validate true circular RNAs.\n\nSignificance StatementThe vast majority of circular RNAs were identified through computational detection of junction sequences in the deep sequencing reads because these unique fusion sequences represent back-splicing events. We found that artificial junction sequences could be formed through template switching (TS) when MMLV-derived reverse transcriptases, e.g., SuperScript II, are used to synthesize cDNAs. Thus, many of the reported circular RNAs may not be RNA circles, but rather experimental artifacts. Fake circular RNAs can be avoided by using reverse transcriptases without terminal transferase activity (e.g., MonsterScript) for cDNA synthesis. We developed two novel methods, MonsterScript junction PCR and high-resolution melting curve analyses, for distinguishing circular RNAs from their linear form.

molecular biology↗

bioRxiv2017

Time-resolved analyses of elemental distribution and concentration in living plants: An example using manganese toxicity in cowpea leaves

O_LIKnowledge of elemental distribution and concentration within plant tissues is crucial in the understanding of almost every process that occurs within plants. However, analytical limitations have hindered the microscopic determination of changes over time in the location and concentration of nutrients and contaminants in living plant tissues.\nC_LIO_LIWe developed a novel method using synchrotron-based micro X-ray fluorescence (-XRF) that allows for laterally-resolved, multi-element, kinetic analyses of plant leaf tissues in vivo. To test the utility of this approach, we examined changes in the accumulation of Mn in unifoliate leaves of 7-d-old cowpea (Vigna unguiculata) plants grown for 48 h at 0.2 and 30 M Mn in solution.\nC_LIO_LIRepeated -XRF scanning did not damage leaf tissues demonstrating the validity of the method. Exposure to 30 M Mn for 48 h increased the initial number of small spots of localized high Mn and their concentration rose from 40 to 670 mg Mn kg-1 fresh mass. Extension of the two-dimensional -XRF scans to a three-dimensional geometry provided further assessment of Mn localization and concentration.\nC_LIO_LIThis method shows the value of synchrotron-based -XRF analyses for time-resolved in vivo analysis of elemental dynamics in plant sciences.\nC_LI

plant biology↗

bioRxiv2017

The SAM domain of mouse SAMHD1 is critical for its activation and regulation

Human SAMHD1 (hSAMHD1) is a retroviral restriction factor that blocks HIV-1 infection by depleting the cellular nucleotides required for viral reverse transcription. SAMHD1 is allosterically activated by nucleotides that induce assembly of the active tetramer. Although the catalytic core of hSAMHD1 has been studied extensively, previous structures have not captured the regulatory SAM domain. In this study, we determined the first crystal structure of full-length SAMHD1 by capturing mouse SAMHD1 (mSAMHD1) structures in three different nucleotide bound states. Although mSAMHD1 and hSAMHD1 are highly similar in sequence and function, we found that mSAMHD1 possesses a more complex nucleotide-induced activation process, highlighting the regulatory role of the SAM domain. Our results provide new insights into the regulation of SAMHD1 activity, thereby will facilitate the improvement of HIV mouse models and the development of new therapies for certain cancers and autoimmune diseases.

biophysics↗

bioRxiv2017

AASRA: An Anchor Alignment-Based Small RNA Annotation Pipeline

SncRNA-Seq has become a routine for sncRNA profiling; however, software packages currently available are either exclusively for miRNA or piRNA annotation (e.g., miRDeep, miRanalyzer, Shortstack, PIANO), or for direct mapping of the sequence reads to the genome (e.g., Bowtie 2, SOAP and BWA), which tend to generate inaccurate counting due to repetitive matches to the genome or sncRNA homologs. Moreover, novel sncRNA variants in the sequencing reads, including those bearing small overhangs or internal insertions, deletions or mutations, are totally excluded from counting by these algorithms, leading to potential quantification bias. To overcome these problems, a comprehensive software package that can annotate all known small RNA species with adjustable tolerance towards small mismatches is needed. AASRA is based on our unique anchor alignment algorithm, which not only avoids repetitive or ambiguous counting, but also distinguishes mature miRNA from precursor miRNA reads. Compared to all existing pipelines for small RNA annotation, AASRA is superior in the following aspects: 1) AASRA can annotate all known sncRNA species simultaneously with the capability of distinguishing mature and precursor miRNAs; 2) AASRA can identify and allow for inclusion of sncRNA variants with small overhangs and/or internal insertions/deletions into the final counts; 3) AASRA is the fastest among all small RNA annotation pipelines tested. AASRA represents an all-in-one sncRNA annotation pipeline, which allows for high-speed, simultaneous annotation of all known sncRNA species with the capability to distinguish mature from precursor miRNAs, and to identify novel sncRNA variants in the sncRNA-Seq sequencing reads.\n\nAvailability and ImplementationThe AASRA software is freely available at https://github.com/biogramming/AASRA.

bioinformatics↗