Biology subjects

Xiao, F.

Publications and source records attributed to Xiao, F..

At least 73 records · Page 4Linked to original sources

bioRxiv2020

Proofreading through spatial gradients

Key enzymatic processes in biology use the nonequilibrium error correction mechanism called kinetic proofreading to enhance their specificity. Kinetic proofreading typically requires several dedicated structural features in the enzyme, such as a nucleotide hydrolysis site and multiple enzyme-substrate conformations that delay product formation. Such requirements limit the applicability and the adaptability of traditional proofreading schemes. Here, we explore an alternative conceptual mechanism of error correction that achieves delays between substrate binding and subsequent product formation by having these events occur at distinct physical locations. The time taken by the enzyme-substrate complex to diffuse from one location to another is leveraged to discard wrong substrates. This mechanism does not require dedicated structural elements on the enzyme, making it easier to overlook in experiments but also making proofreading tunable on the fly. We discuss how tuning the length scales of enzyme or substrate concentration gradients changes the fidelity, speed and energy dissipation, and quantify the performance limitations imposed by realistic diffusion and reaction rates in the cell. Our work broadens the applicability of kinetic proofreading and sets the stage for the study of spatial gradients as a possible route to specificity.

biophysics↗

bioRxiv2020

The ORF8 Protein of SARS-CoV-2 Mediates Immune Evasion through Potently Downregulating MHC-I

SARS-CoV-2 infection have caused global pandemic and claimed over 5,000,000 tolls1-4. Although the genetic sequences of their etiologic viruses are of high homology, the clinical and pathological characteristics of COVID-19 significantly differ from SARS5,6. Especially, it seems that SARS-CoV-2 undergoes vast replication in vivo without being effectively monitored by anti-viral immunity7. Here, we show that the viral protein encoded from open reading frame 8 (ORF8) of SARS-CoV-2, which shares the least homology with SARS-CoV among all the viral proteins, can directly interact with MHC-I molecules and significantly down-regulates their surface expression on various cell types. In contrast, ORF8a and ORF8b of SARS-CoV do not exert this function. In the ORF8-expressing cells, MHC-I molecules are selectively target for lysosomal degradation by an autophagy-dependent mechanism. As a result, CTLs inefficiently eliminate the ORF8-expressing cells. Our results demonstrate that ORF8 protein disrupts antigen presentation and reduces the recognition and the elimination of virus-infected cells by CTLs8. Therefore, we suggest that the inhibition of ORF8 function could be a strategy to improve the special immune surveillance and accelerate the eradication of SARS-CoV-2 in vivo.

microbiology↗

bioRxiv2020

Intra-host Variation and Evolutionary Dynamics of SARS-CoV-2 Population in COVID-19 Patients

As of middle May 2020, the causative agent of COVID-19, SARS-CoV-2, has infected over 4 million people with more than 300 thousand death as official reports1,2. The key to understanding the biology and virus-host interactions of SARS-CoV-2 requires the knowledge of mutation and evolution of this virus at both inter- and intra-host levels. However, despite quite a few polymorphic sites identified among SARS-CoV-2 populations, intra-host variant spectra and their evolutionary dynamics remain mostly unknown. Here, using deep sequencing data, we achieved and characterized consensus genomes and intra-host genomic variants from 32 serial samples collected from eight patients with COVID-19. The 32 consensus genomes revealed the coexistence of different genotypes within the same patient. We further identified 40 intra-host single nucleotide variants (iSNVs). Most (30/40) iSNVs presented in single patient, while ten iSNVs were found in at least two patients or identical to consensus variants. Comparison of allele frequencies of the iSNVs revealed genetic divergence between intra-host populations of the respiratory tract (RT) and gastrointestinal tract (GIT), mostly driven by bottleneck events among intra-host transmissions. Nonetheless, we observed a maintained viral genetic diversity within GIT, showing an increased population with accumulated mutations developed in the tissue-specific environments. The iSNVs identified here not only show spatial divergence of intra-host viral populations, but also provide new insights into the complex virus-host interactions.

genomics↗

bioRxiv2020

Integrating Genomic Correlation Structure Improves Copy Number Variations Detection

Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e., boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we therefore proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic correlation (i.e., LD). To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presents high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. We also theoretically demonstrated the correlation structure of CNV data, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. This new segmentation algorithm has a wide scope of application with next-generation sequencing data analysis and single-cell sequencing analysis. Author SummaryCopy number variants (CNVs) refers to gains or losses of the DNA segments in comparison to a reference genome. CNVs have garnered extensive interests in recent years as they play an important role susceptibility to disorders and diseases such as autism, schizophrenia and cancer [1-7]. Although innovation in modern technology is promoting the discoveries related to CNVs, the methodology for CNV detection is still lagging, which limits the novel discoveries regarding the role of CNVs in complex diseases. In this study, we are proposing a novel segmentation algorithm, LDcnv, to accurately locate the breakpoints or boundaries of CNVs in the human genome. Instead of utilizing an independent assumption of the signal intensities as has been used in traditional segmentation algorithms, LDcnv models the correlation structure in the genome in a change-point CNV detection model, which allows for accurate and fast computation with a whole genome scan. Our study showed strong theoretical evidence of the existence of correlation structure in real CNV data, and we believe that taking this evidence into consideration will improve the power of CNV detection. Extensive simulation studies have demonstrated the advantage of the LDcnv algorithm in stability, robustness and accuracy over existing methods. We also used high-quality CNV profiles to further support the superior performance of the LDcnv algorithm over existing methods. The development of the LDcnv algorithm provides great insights for new directions in developing CNV detection tools.

genetics↗

bioRxiv2020

Paxillin Promotes ATP-induced Activation of P2X7 Receptor and NLRP3 Inflammasome

The stimulation of P2X7 receptor by extracellular ATP leads to activation of NLRP3 inflammasome and release of pro-inflammatory cytokines. Here, we reveal a distinct mechanism by which Paxillin promotes ATP-induced activation of P2X7 receptor and NLRP3 inflammasome. Extracellular ATP induces Paxillin phosphorylation and facilitates Paxillin-NLRP3 interaction. Interestingly, Paxillin enhances NLRP3 deubiquitination and activates NLRP3 inflammasome upon ATP treatment and K+ efflux. Moreover, we reveal that UPS13 is a key enzyme for Paxillin-mediated NLRP3 deubiquitination upon ATP treatment. Notably, extracellular ATP promotes Paxillin and NLRP3 migration from cytosol to plasma membrane and facilitates P2X7-Paxillin interaction and Paxillin-NLRP3 association, resulting in the formation of P2X7-Paxillin-NLRP3 complex. Functionally, Paxillin is essential for ATP-induced NLRP3 inflammasome activation in mouse BMDMs and BMDCs as we as in human PBMCs and THP-1-differentiated macrophages. Thus, Paxillin plays key roles in ATP-induced activation of P2X7 receptor and NLRP3 inflammasome by facilitating the formation of the P2X7-Paxillin-NLRP3 complex.

immunology↗

bioRxiv2020

A geometric and structural approach to the analysis and design of biological circuit dynamics: a theory tailored for synthetic biology

Much of the progress in developing our ability to successfully design genetic circuits with predictable dynamics has followed the strategy of molding biological systems to fit into conceptual frameworks used in other disciplines, most notably the engineering sciences. Because biological systems have fundamental differences from systems in these other disciplines, this approach is challenging and the insights obtained from such analyses are often not framed in a biologically-intuitive way. Here, we present a new theoretical framework for analyzing the dynamics of genetic circuits that is tailored towards the unique properties associated with biological systems and experiments. Our framework approximates a complex circuit as a set of simpler circuits, which the system can transition between by saturating its various internal components. These approximations are connected to the intrinsic structure of the system, so this representation allows the analysis of dynamics which emerge solely from the systems structure. Using our framework, we analyze the presence of structural bistability in a leaky autoactivation motif and the presence of structural oscillations in the Repressilator.

synthetic biology↗

bioRxiv2020

SCANNER: A Web Server for Annotation, Visualization and Sharing of Single Cell RNA-seq Data

MotivationIn recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality brought challenges in data modeling, analysis, visualization and interpretation. Available analysis tools require extensive knowledge and training of data properties, statistical modeling and computational skills. It is challenging for biologists to efficiently view, browse and interpret the data. ResultsHere we developed SCANNER, as a public webserver resource to equip the biologists and bioinformatician to share and analyze scRNA-seq data in a comprehensive and collaborative manner. It is effort-less and host-free without requirement on software setup or coding skills, and enables a user-friendly way to compare the activation status of gene sets on single cell basis. Also, it is equipped with multiple data interfaces for easy data sharing and currently provide a database for studying the smoking effect on single cell gene expression in lung. Using SCANNER, we have identified larger proportions of cancer-associated fibroblasts cells and activeness of fibroblast growth related genes in melanoma tissues in females compared to males. Moreover, we found ACE2 is mainly expressed in pneumocytes, secretory cells and ciliated cells with disparity in gene expression by smoking behavior. Availability and implementationSCANNER is available at https://www.thecailab.com/scanner/. Supplementary informationSupplementary data are available online. ContactGCAI@mailbox.sc.edu or XIAOF@mailbox.sc.ecu Key PointsO_LISCANNER provides a new web server resource for promoting scRNA-seq data analysis C_LIO_LISCANNER enables comprehensive and dynamic analysis and visualization, novel functional annotation and activeness inference, online databases and easy data sharing. C_LIO_LISCANNER bridges the data analysis and the biological experiment units. C_LI

bioinformatics↗

bioRxiv2019

AKT2 deficiency causes sarcopenia and metabolic disorder of skeletal muscle

Skeletal muscle is responsible for the majority of glucose disposal in the body. Insulin resistance in the skeletal muscle accounts for 85-90% of the impairment of total body glucose disposal in patients with tye 2 diabetes (T2D). However, the mechanism remains controversial. AKT2 is a protein kinase performing important functions in the regulation of glucose metabolism. We observed that mice deficient for AKT2 (AKT2 KO) exhibited decreased body weight and lean mass and showed impaired glucose tolerance, compared to their age- and gender-matched wild type mice (WT). Therefore, to test whether AKT2 deficiency causes deficits in skeletal muscle development and metabolism, we analyzed the expression of molecules related to skeletal muscle development, glucose uptake and metabolism in young (3 months) and old (8 months) mice. We found that AMPK phosphorylation and MEF2A expression were downregulated in young AKT2 KO mice, and this downregulation was inverted by AMPK activation. We also observed reduced mtDNA abundance and reduced expression of genes involved in mitochondrial biogenesis in the skeletal muscle of adult AKT2 KO mice, which was prevented by AMPK activation. However, GLUT4 expression was regulated by AKT2 in an AMPK-independent manner in skeletal muscle. During high-fat-diet (HFD)-induced obesity, AKT2 KO mice exhibited increased insulin resistance compared to WT mice. Our study establishes a new and important function of AKT2 in regulating glucose uptake and AMPK-dependent development and mitochondrial biogenesis in skeletal muscle.

molecular biology↗

bioRxiv2019

A statistical method for joint estimation of cis-eQTLs and parent-of-orign effects using an orthogonal framework with RNA-seq data

In the past few years extensive studies have been put on the analysis of genome function, especially on expression quantitative trait loci (eQTL) which offered promise for characterization of the functional sequencing variation and for the understanding of the basic processes of gene regulation. However, most studies of eQTL mapping have not implemented models that allow for the non-equivalence of parental alleles as so-called parent-of-origin effects (POEs); thus, the number and effects of imprinted genes remain important open questions. Imprinting is a type of POE that the expression of certain genes depends on their allelic parent-of-origin which are important contributors to phenotypic variations, such as diabetes and many cancer types. Besides, multi-collinearity is an important issue arising from modeling multiple genetic effects. To address these challenges, we proposed a statistical framework to test the main allelic effects of the candidate eQTLs along with the POE with an orthogonal model for RNA sequencing (RNA-seq) data. Using simulations, we demonstrated the desirable power and Type I error of the orthogonal model which also achieved accurate estimation of the genetic effects and over-dispersion of the RNA-seq data. These methods were applied to an existing HapMap project trio dataset to validate the reported imprinted genes and to discovery novel imprinted genes. Using the orthogonal method, we validated existing imprinting genes and discovered two novel imprinting genes with significant dominance effect.\n\nAuthor SummaryIn the past decades, an unprecedented wealth of knowledge has been accumulated for understanding variations in human DNA level. However, this DNA-level knowledge has not been sufficiently translated to understanding the mechanisms of human diseases. Gene expression quantitative trait locus (eQTL) mapping is one of the most promising approaches to fill this gap, which aims to explore the genetic basis of gene expression. Genomic imprinting is an important epigenetic phenomenon which is an important contributor to phenotypic variation in human complex diseases and may explain some of the \"hidden\" heritable variability. Many imprinting genes are known to play important roles in human complex diseases such as diabetes, breast cancer and obesity. However, traditional eQTL mapping approaches does not allow for the detection of imprinting which is usually involved in gene expression imbalance. In this study, we have for the first time demonstrated the orthogonal statistical model can be applied to eQTL mapping for RNA sequencing (RNA-seq) data. We showed by simulated and real data that the orthogonal model outperformed the usual functional model for detecting main effects in most cases, which addressed the issue of confounding between the dominance and additive effects. Application of the statistical model to the HapMap data resulted in discovery of some potential eQTLs with imprinting effects and dominance effects on expression of RB1 and IGF1R genes.\n\nIn summary, we developed a comprehensive framework for modeling imprinting effect for eQTL mapping, by decomposing the effects to multiple genetic components. This study is providing new insights into statistical modeling of eQTL mapping with RNA-seq data which allows for uncorrelated parameter estimation of genetic effects, covariates and over-dispersion parameter.

genetics↗

bioRxiv2019

STING recruits NLRP3 to the ER and deubiquitinates NLRP3 to activate the inflammasome

One of the fundamental reactions of the innate immune responses to pathogen infection is the release of pro-inflammatory cytokines, including IL-1{beta}, processed by the NLRP3 inflammasome. STING is essential for innate immune responses and inflammasome activation. Here we reveal a distinct mechanism by which STING regulates the NLRP3 inflammasome activation, IL-1{beta} secretion, and inflammatory responses in human cell lines, mice primary cells, and mice. Interestingly, upon HSV-1 infection and cytosolic DNA stimulation, STING binds to NLRP3 and promotes the inflammasome activation through two approaches. First, STING recruits NLRP3 and promotes NLRP3 translocation to the endoplasmic reticulum, thereby facilitating the inflammasome formation. Second, STING interacts with NLRP3 and removes K48- and K63-linked polyubiquitination of NLRP3, thereby promoting the inflammasome activation. Collectively, we demonstrate that the cGAS-STING-NLRP3 signaling is essential for host defense against DNA virus infection.

immunology↗

bioRxiv2019

Pan-genome analysis reveals the molecular basis of niche adaptation of Staphylococcus epidermidis strains

Staphylococcus epidermidis is the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with their diversity and adaptation as a \"double-side spy\", a skin dominant colonization, and a successful pathogen. The pan-genome of S. epidermidis is open with 435 core proteins and a pan-genome size of 8034 proteins. Genome-wide phylogenetic tree shows that whole genome sequence is a powerful tool to analyze the complex evolutionary process of S. epidermidis and investigate the source of infection. Comparative genome analyses demonstrate the high diversity of antimicrobial resistances, especially mobile genetic elements. The complicated relationships of host-bacterium and bacterium-bacterium help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may be the result of its success in adapting to broad habitats, which is necessary to deal with complex environments. This study gives the general landscape of S. epidermidis pan-genome and provides valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.

microbiology↗

bioRxiv2018

Quantum energy levels of glutamate modulate neural biophotonic signals

Glutamate is the most abundant excitatory neurotransmitter in the brain, and it plays an essential and important role in neural functions. Hypofunction of the glutamatergic pathway and the changes in the glutamate-glutamine cycle function are important neuropathological mechanisms of severe mental disorders including schizophrenia and depression. Current studies have shown that glutamate can induce neural biophotonic activity and transmission, which may involve the mechanism of photon quantum brain; however, it is unclear whether such a mechanism follows the principle of quantum mechanics. Here we show that the action of glutamate on its receptors leads to a decrease in its quantum energy levels, and glutamate then partially or completely loses its function to further induce the biophotonic activity in mouse brain slices. The reduced quantum energy levels of glutamate can be restored by direct-current electrical discharges and the use of energy transfer of chloroplast photosynthesis; hence, the quantum energy recovered glutamate can again induce significant biophotonic activity. Furthermore, the changes in quantum energy levels of glutamate are related to the exchange and transfer of electron energy on its active hydrogen atom. These findings suggest that the glutamate-induced neural biophotonic signals may be involved in the transfer of the quantum energy levels of glutamate, which implies a quantum mechanism of neurotransmitter action. The process of glutamate recycling that is related to the synergism of neurons and glial cells and certain key enzymes may be necessary for the recovery of quantum energy levels of glutamate after completion of the neural signal transmission. These findings may also provide a new idea to develop \"quantum drugs\".

neuroscience↗

bioRxiv2018

Coupled Reaction Networks for Noise Suppression

Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widelycited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology.

systems biology↗

bioRxiv2018

Architectural Principles for Characterizing the Performance of Sequestration Feedback Networks

As we begin to design increasingly complex synthetic biomolecular systems, it is essential to develop rational design methodologies that yield predictable circuit performance. Here we apply theoretical tools from the theory of control and dynamical systems to yield practical insights into the architecture and function of a particular class of biological feedback circuit. Specifically, we show that it is possible to analytically characterize both the operating regime and performance tradeoffs of a sequestration feedback circuit architecture. Further, we demonstrate how these principles can be applied to inform the design process of a particular synthetic feedback circuit.

synthetic biology↗

bioRxiv2018

Stable changing fluid shear stress promotes osteogenesis

ObjectiveBone marrow cells encounter various chemical and mechanical stimuli from the internal environment. In vivo, fluid shear stress (FSS) is one of the primary mechanical stimuli that affect bone marrow-derived mesenchymal stem cells (BMCs) activity. Since various cases of FSS influence BMCs activity differently, the purpose of this study is to determine how BMC activity in osteogenesis and osteoclastgenesis is affected by stable and unstable changing FSS.\n\nMethodBMCs samples from the femur of the mouse, divided in to three groups: stimulate by stable changing FSS, unstable changing FSS, and no FSS. RT-PCR would be applied to detect OPG, RANKL, ALP, OCN, RUNX2, and RANK of all the samples at the 3rd day. Alizarin red staining and TRAP staining would be applied to test all the samples at the 6th day.\n\nResultsThe S group samples showed the lowest level in RANKL mRNA and showed the highest level in OPG mRNA. RANKL/OPG mRNA in the S group was the smallest during three groups. Comparing with the C group samples, RUNX2 mRNA in the S group was increased significantly. RANK mRNA in the C group was three times more than S group. S group had the largest area of mineralized nodules, and largest number of area [≥]100{micro}m, [≥]500{micro}m2 or [≥]1000{micro}m2. The area of positive TRAP stain in S group was the smallest among three groups.\n\nConclusionStable changing FSS significantly increases osteogenesis relating to OPG-RANKL-RANK pathway. Compared with unstable changing FSS, stable changing FSS afford a more appropriate stimulation to osteogenesis.\n\nAbbreviations

cell biology↗

bioRxiv2018

Increased GITRL impairs the function of MDSCs and exacerbates primary Sjögren’s syndrome

It is largely unclear how MDSCs contribute to the development of primary Sjogrens syndrome (pSS). In experimental SS (ESS) mice, MDSCs were significantly increased but exhibited gradually diminished suppressive capacity during the disease progression. The ligand for glucocorticoid-induced TNFR family-related protein (GITRL) was increased with the development of pSS, and the increased GITRL was found to down-regulate the function of MDSCs while blocking GITR signal in MDSCs significantly restored their function and ameliorated ESS progression in mice. In pSS patients, expanded MDSCs expressed lower level of arginase were observed in patients with higher SSDAI. Moreover, the increased GITRL in serum was also found to closely correlate with the aberrant function of MDSCs. Together, our studies have demonstrated a critical role of GITRL in modulating the suppressive capacity of MDSCs in pSS, which may facilitate the validation of GITRL as a therapeutic target for the treatment of pSS.

immunology↗

bioRxiv2018

Robust Perfect Adaptation in Biomolecular Reaction Networks

For control in biomolecular systems, the most basic objective of maintaining a small error in a target variable, say the expression level of some protein, is often difficult due to the presence of both large uncertainty of every type and intrinsic limitations on the controllers implementation. This paper explores the limits of biochemically plausible controller design for the problem of robust perfect adaptation (RPA), biologists term for robust steady state tracking. It is well-known that for a large class of nonlinear systems, a system has RPA iff it has integral feedback control (IFC), which has been used extensively in real control systems to achieve RPA. However, we show that due to intrinsic physical limitations on the dynamics of chemical reaction networks (CRNs), cells cannot implement IFC directly in the concentration of a chemical species. This contrasts with electronic implementations, particularly digital, where it is trivial to implement IFC directly in a single state. Therefore, biomolecular systems have to achieve RPA by encoding the integral control variable into the network architecture of a CRN. We describe a general framework to implement RPA in CRNs and show that well-known network motifs that achieve RPA, such as (negative) integral feedback (IFB) and incoherent feedforward (IFF), are examples of such implementations. We also develop methods to solve the problem of designing integral feedback variables for unknown plants. This standard control notion is surprisingly nontrivial and relatively unstudied in biomolecular control. The methods developed here connect different existing fields and approaches on the problem of biomolecular control, and hold promise for systematic chemical reaction controller synthesis as well as analysis.

synthetic biology↗

bioRxiv2018

Assessment of an organ-specific de novo transcriptome of the nematode trap-crop, Solanum sisymbriifolium

Solanum sisymbriifolium, also known as \"Litchi Tomato\" or \"Sticky Nightshade,\" is an undomesticated and poorly researched plant related to potato and tomato. Unlike the latter species, S. sisymbriifolium induces eggs of the cyst nematode, Globodera pallida, to hatch and migrate into its roots, but then arrests further nematode maturation. In order to provide researchers with a partial blueprint of its genetic make-up so that the mechanism of this response might be identified, we used single molecule real time (SMRT) sequencing to compile a high quality de novo transcriptome of 41,189 unigenes drawn from individually sequenced bud, root, stem, and leaf RNA populations. Functional annotation and BUSCO analysis showed that this transcriptome was surprisingly complete, even though it represented genes expressed at a single time point. By sequencing the 4 organ libraries separately, we found we could get a reliable snapshot of transcript distributions in each organ. A divergent site analysis of the merged transcriptome indicated that this species might have undergone a recent genome duplication and re-diploidization. Further analysis indicated that the plant then retained a disproportionate number of genes associated with photosynthesis and amino acid metabolism in comparison to genes with characteristics of R-proteins or involved in secondary metabolism. The former processes may have given S. sisymbriifolium a bigger competitive advantage than the latter did.

bioinformatics↗