Glioblastoma multiforme (GBM), the most malignant of all gliomas is characterized by a high degree of heterogeneity and poor response to treatment. The sub-ventricular zone (SVZ) is the major site of neurogenesis in the brain and is rich in neural stem cells. Based on the proximity of the GBM tumors to the SVZ, the tumors can be further classified into SVZ+ and SVZ−. The tumors located in close contact with the SVZ are classified as SVZ+, while the tumors located distantly from the SVZ are classified as SVZ−. To gain an insight into the increased aggressiveness of SVZ+ over SVZ− tumors, we have used proteomics techniques like 2D-DIGE and LC-MS/MS to investigate any possible proteomic differences between the two subtypes. Serum proteomic analysis revealed significant alterations of various acute phase proteins and lipid carrying proteins, while tissue proteomic analysis revealed significant alterations in cytoskeletal, lipid binding, chaperone and cell cycle regulating proteins, which are already known to be associated with disease pathobiology. These findings provide cues to molecular basis behind increased aggressiveness of SVZ+ GBM tumors over SVZ− GBM tumors and plausible therapeutic targets to improve treatment modalities for these highly invasive tumors. It is also the most aggressive of all known gliomas with patients having a survival period lower than one year from the time of diagnosis in most cases2. WHO categorizes GBMs as grade IV gliomas3. Progenitor astrocytes that go on to form glial cells colonize the subventricular zone (SVZ), the largest germinal zone in the brain found along the lateral walls of the lateral ventricles4. This zone of the brain has been extensively studied as a site of neurogenesis5 and is responsible for the development of neurons in an adult. The aggressive nature of GBM tumors makes their treatment almost impossible thereby making them the most dangerous of all known gliomas. Surgical resection of the tumor tissues followed by radiotherapy (RT) and biopsy followed by radiotherapy (RT) have been reported to prolong the survival period in the patients with GBMs6.Glioblastoma Multiforme (GBM) is a malignant brain tumor, characterized by heterogeneity within the tumor and poor response to treatment7. Haskin et al. compared the tissue proteome of GBM tumors (tumors in close proximity to the SVZ region) with the control tissues and identified various protein signatures responsible for migratory potential and tumor growth in GBM tumors8. Till date there has been no report of any study involving two different sub-groups of GBMs based on SVZ involvement. Tumor proximity to the subventricular zone has been recently reported to play a significant role in the survival of GBM patients, where patients with tumors in SVZ contact had an overall survival period less than those with tumors distantly located to SVZ9. However, there has been no proteomic study to support the findings at a molecular level. In the current study, we have performed a comprehensive serum and tissue proteome analysis of two sub-populations of GBMs to explore if SVZ involvement had any direct influence on tumor aggressiveness.Despite all the advances in proteomics there still exists a huge void in our understanding of GBMs and the proteins with possible role in survival of the patients. A proteome based clustering of gliomas was correlated to survival in patientsp-value ≤ 0.05. Out of the 10 protein spots, six protein spots were found to be up-regulated and four were down-regulated in the serum of SVZ+ GBM patients. Protein identity was revealed for nine out of the 10 significantly altered protein spots using MALDI-TOF/TOF. It was found that 10 protein spots corresponded to four different proteins because of the presence of isoforms and multiple sub-units in the proteins, thereby resulting in same protein hits for multiple spots. Out of the four proteins identified, two were up-regulated and two were down-regulated in the SVZ+ GBM serum samples . Analysis of the 2D-DIGE gels of the GBM tumor tissue proteome was performed using DeCyder software. Approximately, 1650 protein spots were identified on each 2D-DIGE gel. After matching the gels, only five protein spots were found to be statistically significant having a ty Table .Figure 3Tissue proteome of normal brain tissue was compared with the tissue proteome of SVZ+ and SVZ− GBMs using iTRAQ method. In the six comparisons possible, proteins with two peptide signatures following similar trends in more than three comparisons and with an average fold change of 1.3 were considered significant. In the first comparison involving SVZ− GBM tissue proteome and normal brain tissue proteome, 165 proteins were found to be differentially regulated with 112 proteins being up-regulated and 53 proteins down-regulated in the SVZ− GBM tumors. The comparison of SVZ+ GBM tissue proteome with the normal brain tissue proteome, yielded 164 proteins with altered levels of expression (107 proteins being up-regulated while 57 proteins being down-regulated in SVZ+ tumors). The comparison between proteomes of SVZ+ GBM tumors and SVZ− GBM tumors indicated 96 significantly differentially regulated proteins. Of these, 43 proteins were found to be up-regulated while 53 proteins were found to be down-regulated in SVZ+ tumors in comparison with SVZ− tumors. Representative mass spectra for thymosin beta-4-like protein 3 and annexin A2 have been provided in Fig. Tissue proteomic alterations in SVZ+ and SVZ- GBM tumors were further compared with the mRNA expression data of long-term survivors and short-term survivors of GBM patients from TCGA. We identified 42 common proteins/genes from our proteomics data and TCGA mRNA data. Of the 42 common proteins/genes only 18 proteins/genes showed similar expression trends some of which included adenosylhomocysteinase, protein disulfide-isomerase, endoplasmin and alpha-2-macroglobulin  using DAVID version 6.7. Most of the pathways affected in both SVZ+ and SVZ- GBMs were found to be common and associated with sugar metabolism and blood coagulation cascades.The data was further subjected to partial least square discriminant analysis and resulted in separation of SVZ+ GBMs from SVZ- GBMs on the 3D-score plot  and SVZ− (n = 10) GBM patients were determined using competitive sandwich ELISA method. The serum levels of hemopexin in SVZ+ GBM patients (1.72 mg/ml) were found to be marginally higher than the SVZ− GBM patients (n = 1.25 mg/ml) and have been represented in graphical form in Fig. Serum western blotting analysis for hemopexin indicated a marginal increase in the levels of the protein in SVZ+ individuals in comparison to SVZ− individuals Fig. .Tissue western blotting analysis for annexin A2 revealed two fold up-regulation of the protein in SVZ− GBM patients as compared to SVZ+ GBM patients Fig. .9. The proteomics study performed by Haskins et al. involved comparison of SVZ+ GBM tumor proteome with control samples8. However, protein expression analysis of both SVZ+ and SVZ− GBM tumors is likely to reveal protein signatures specific to SVZ+ GBMs. Hence, we have performed proteomic analysis of serum and tissues from SVZ+ and SVZ- GBMs in an attempt to identify protein markers responsible for aggressive nature of these tumors.The current study aimed at offering an insight into the proteomic differences in serum and tissues from the two subgroups of GBMs and was based on available literature suggesting a possible role of SVZ in survival of GBM patients10 and plays an important role in differentiation of oligodendrocytes and myelin sheath formation11. SERPINA3, a serine protease inhibitor, is known to protect tissues from action of proteases12. Two major events in the process of metastasis include release of the tumor cells from the site of tumor by the action of proteases on the extracellular matrix and adhesion of the migrated tumor cells to the tissues in a new location with the help of cell surface molecules. Mass spectrometric analysis revealed a decrease in serum SERPINA3 levels in SVZ+ patients with respect to SVZ− patients, though the difference was negligible. The reduced levels of SERPINA3 in serum of SVZ+ GBM patients could be one of the plausible explanations for the increased aggressiveness of SVZ+ tumors over SVZ− tumors.Proteomics analysis using 2D-DIGE on serum from the GBM patients revealed differential expression of four proteins-hemopexin (HPX), apolipoprotein A1 (APOA1), alpha-1-antichymotrypsin (SERPINA3)and serum albumin (ALB). A significant change in levels of acute phase proteins, HPX and SERPINA3 was observed. Besides maintaining homeostasis, hemopexin protects cells from heme mediated oxidation13, have been reported to increase in serum of patients with pancreatic cancer, gastric cancer, ovarian cancer and glioblastomas16. Survival studies in mouse models with ovarian cancer revealed higher levels of APOA1 to play a role in increased survival13. Serum APOA1 levels in our study were found to be down-regulated in SVZ+ GBMs. Apolipoprotein E (APOE) forms a major component of LDL carrying cholesterol from liver to extra hepatic tissues and activates cell proliferation and anti-apoptotic cascades upon binding to LDL receptors thereby helping the cells in proliferation and evading apoptosis. It has been reported to be up-regulated in breast, colon, stomach and prostate cancers17. Increased levels of APOE in serum of SVZ+ GBMs possibly indicates its role in membrane synthesis and evading apoptosis.Levels of APOA1, a major component of HDL involved in cholesterol transport19. Free vitamin-D is known to cross the blood brain barrier freely20. Vitamin-D binding protein binds to free vitamin-D in the serum and transports it to the target sites. We observed decreased levels of vitamin-D binding protein in SVZ− GBM serum which supports our hypothesis that the decreased protein levels possibly inhibits tumor growth by increasing availability of free vitamin-D.Vitamin-D has been reported to inhibit tumor angiogenesis, increase cell adhesion and induce cellular apoptosisiTRAQ based quantitative mass spectrometric analysis of GBM tissue (SVZ+/-) proteome revealed significant changes in expression of proteins in many important metabolic pathways. Pathways involved in carbohydrate metabolism (glycolysis/gluconeogenesis and pentose phosphate pathway), amino acid metabolism (cys and met metabolism), complement and coagulation cascades, ECM-receptor interaction and neurodegenerative diseases  were found to be affected in SVZ− and SVZ+ GBMs  intermediates for the synthesis of nucleic acid precursors23 and astrocytomas24. Activation of the blood coagulation system was associated with poor prognosis25, while increased tissue factor (activator of blood coagulation system) correlated with the decreased survival of breast cancer patients26. Many proteins associated with blood coagulation system were found to be up-regulated in SVZ+ GBMs with respect to SVZ− GBMs in our study. Protease inhibitors like alpha-2-macroglobulin (A2M), alpha-1-antitrypsin (SERPINA1) and antithrombin-III (SERPINC1) associated with blood coagulation system, were found to be up-regulated in SVZ+ GBM tumors as compared to SVZ− GBM tumors. A2M binds to a number of proteases at their active sites using its bait region. On proteolysis, A2M changes its conformation rendering the protease inactive27. A2M was found to be expressed in many different cancers like human gliomas, melanomas, colon cancer etc.30 indicating its role in tumorigenicity30. SERPINA1, another important protease inhibitor with affinity for inhibition of neutrophil elastase31, is known to play an important role in modulating immunity, inflammation, apoptosis etc.32 and has been reported to protect brain tumor cells from the action of other proteases33. Fibrin/fibrinogen, a blood coagulation factor was found to be deposited in various cancers like brain tumors, prostate cancers, mesothelioma, colon cancer and lymphoma38. Fibrinogen plays critical role in angiogenesis, tumor cell growth, proliferation and development by sequestering the growth factors like fibroblast growth factor-2 and vascular endothelial growth factor40. Tumor cells utilize sequestered growth factors by degrading the fibrinogen41.Proteins involved in complement pathway and blood coagulation cascades were found to be altered in both SVZ+ and SVZ- GBMs. Coagulation cascade related genes were found to be over expressed in epithelial cancer, ovarian cancer42. Fibrin also protects the tumor cells from host mediated inflammatory response against the tumor cells44.Fibrin increases the metastatic capacity of tumor by virtue of its ability to bind to various cell surface molecules like integrins and non integrin receptors, thereby helping the circulating tumor cells to adhere to the cells at new location. Increased levels of fibrinogen induce plasmin, a protease and increase the invasive and metastatic nature of tumors by degrading the extracellular matrix45. Cattaruzza et al. studied the role of collagen VI and NG2/chondroitin sulphate proteoglycan 4 (CSPG4) molecular interactions in progression of soft tissue sarcomas. Patients suffering from soft tissue sarcomas with over-expressed Collagen VI and CSPG4 together were found to have the worst disease free survival rates46. Interaction of Collagen VI with NG2/CSPG4 resulted in increased cell motility in human glioma cell lines47. Col VI was found to protect the cells from apoptosis by activating Akt/PI3K pathway48 and has been reported to be over expressed in breast cancer, ovarian cancer and gliomas50. Fibronectin, a glycoprotein with cell adhesive regions on it, plays a major role in the tumor cell migration and invasion through its interaction with partner proteins called integrins51. Increased levels of fibronectin have been reported to cause an increase in migration rate of glial tumors52, in addition to increasing the invasive nature of the tumors by up-regulating a protein tyrosine kinase tie2, which activates PI3-kinase and PAK53. We found increased levels of Collagen alpha-3 (VI) chain and fibronectin in SVZ+ GBM tumors (with respect to SVZ− GBM tumors), providing a plausible explanation for their increased invasiveness over SVZ− GBM tumors. Tenascin-C, an extracellular protein majorly expressed during embryonic development is known to disappear in adults but reappears at the site of wound healing in cases of brain cancer and colorectal carcinoma56. It interferes with syndecan-4 binding to fibronectin and enhances proliferation of glioma and breast cancer cells57. Increase in levels of tenascin were correlated with increase in grade of tumors and invading tumor cells besides tumor cell proliferation and angiogenesis in gliomas58. In the current study, levels of tenascin-C were found to be increased in both SVZ+ and SVZ− tumors as compared to peritumoral tissue, with the levels of tenascin-C in SVZ+ tumors being higher than SVZ− tumors.Extracellular matrix (ECM)-receptor interaction pathways which play a role in tumor cell migration and adhesion were found to be altered in GBMs in the current study. Extracellular matrix proteins like collagen and fibronectin (FN1) were found to be up-regulated in SVZ+ GBM tumors. Collagen VI is an important extracellular matrix protein and plays a role in providing structural support to the cells, cell signaling and in increasing the availability of various growth factors60. Expression of thymosin beta-4 was found to have a negative correlation with survival period of patients with non-small cell lung cancer (NSCLC), where the protein expression was associated with increased metastasis and poor prognosis61. We observed an up-regulation of thymosin beta-4 like protein 3 in SVZ+ GBM patients as compared to SVZ− GBM in the current study. BASP1 is involved in organization of cytoskeletal elements and determines the morphology of plasma membrane62. Hartl et al. showed a decrease in expression levels of BASP1 in cells transformed with the Myc, whereas the ectopic expression of BASP1 in the cells transfected with v-myc oncogene showed resistance to v-myc induced transformation63. BASP1 over expression was shown to result in death of cells by apoptosis, whereas decreased expression of BASP1 was found to increase cell survival64. In the current study, both SVZ+ and SVZ− GBM tumors showed reduced levels of BASP1, which possibly helps the tumor cells in evading apoptosis.Some cytoskeleton associated proteins like thymosin beta 4 (TMSB4X) and brain acid soluble protein 1 (BASP1) were found to be significantly altered in SVZ+ GBMs, and showed good correlation with the aggressive nature of SVZ+ tumors. Thymosin beta-4, an actin binding protein plays a major role in actin polymerization and maintains dynamic equilibrium between G-actin and F-actin. It is also involved in the proliferation, migration, survival and aggressiveness of colorectal cancer and pancreatic cancer via activation of Akt and JNK signaling pathways, respectivelyThe proteins identified in the current study provide a molecular evidence for the aggressive nature of SVZ+ GBM tumors over SVZ− GBM tumors. However, these proteins need to be further explored in biological models i.e., glioma cell lines and animal models to authenticate their role in increased aggressiveness of SVZ+ tumors.GBM serum and tissue samples were collected from Tata Memorial Centre’s, Advanced Centre for Treatment, Research and Education (TMC-ACTREC), Mumbai, India. The samples were categorized into two groups based on sub-ventricular zone involvement and median age of the patients. A total of 21 tissue samples  and 33 serum samples (16 SVZ− and 17 SVZ+) were used in the current proteomics study. The study was carried out after approval of the institutional ethics committee of ACTREC (IEC-80) and IIT Bombay. Pre-informed consent of all the subjects enrolled for this study were taken before sample collection. All experiments were performed in accordance with guidelines and regulations laid down by institutional ethics committees of TMH and IIT Bombay. Wherever possible, peritumoral tissue samples from presumed uninvolved brain included in the resection specimen (with confirmed histology) were used as normal control samples for the proteomics study.65, whereas the brain tissue proteins were extracted using trizol method66. The pre-processing steps for the serum and tissue protein extraction are described in detail in the supplementary materials and methods section. The extracted protein samples were quantified using 2D-Quant kit  and the protein samples were stored at −20 °C until further use .Protein extraction from serum was performed using TCA–Acetone precipitation methodThe protein samples in rehydration buffer were minimal labeled with CyDyes as per the manufacturer’s instructions , pooled and loaded onto a 18 cm IPG strip of pH 4–7 (linear) followed by isoelectric focusing (IEF) for 64 kVh (for tissue proteins) and 78 kVh (for serum proteins). After IEF, the proteins were further separated on a 12.5% SDS-Polyacrylamide gels followed by scanning the gels using Typhoon FLA 9500 scanner  at different excitation and emission wavelengths. All the images were scanned at 100 µm resolution and the images obtained were further processed using DeCyder 2D software version 7.0  .67 followed by mass spectrometric analysis using 4800 MALDI TOF/TOF MS (AB Sciex) in reflectron mode. The mass spectrometry data was analysed using MASCOT version 2.1 search engine for identification of the protein against Swiss-Prot database .The significantly altered protein spots were subjected to in-gel digestionThe brain tissue and serum proteins were made LC-MS compatible by exchanging the buffer with 0.5 M TEAB using 3 kDa molecular weight cutoff filters. The protein samples were subjected to denaturation, reduction and alkylation followed by overnight trypsin digestion for 16 hr. The in-solution digested normal brain tissue proteins, SVZ− and SVZ+ GBM tumor tissue protein samples were then labeled with iTRAQ reagents 114, 115 and 116, respectively. In case of first two iTRAQ sets, iTRAQ reagent 117 was used for labeling of in-solution digested SVZ− GBM tissue proteins, whereas in the iTRAQ sets three and four, iTRAQ reagent 117 was used for labeling of in-solution digested SVZ+ GBM tissue proteins. For serum proteomic analysis, trypsin digested SVZ− serum proteins were labeled with iTRAQ reagent 114 and 116, where as the SVZ+ GBM patient’s serum proteins were labeled with iTRAQ reagent 115 and 117. The labeled samples were finally pooled and subjected to off-gel fractionation .200 µg of the labeled and pooled peptide sample was subjected to Offgel fractionation on 3–10 non-linear, 24 cm high resolution IPG strips using Agilent 3100 Offgel fractionator. Minor modifications were made to the manufacturer’s protocol (Agilent Technologies). Isoelectrically focused and concentrated peptide fractions were later reconstituted in 0.1% formic acid and subjected to LC-MS/MS .https://gdc-portal.nci.nih.gov/) and the patients were classified into short-term survivors (STS), median-term survivors (MTS) and long-term survivors (LTS) based on their overall survival. GBM patients with an overall survival of <1 year, 1–3 years and >3 years were classified into STS, MTS and LTS, respectively. In the current study, we compared the mRNA expression data of 217 STS with 38 LTS GBMs using BRB-Array tools68. Using class comparison module of BRB-Array tools, the expression levels of different genes in short-term survivors of GBMs was compared with long-term survivors of GBM patients. Further, the mRNA expression data  was correlated with protein expression data obtained from our quantitative mass spectrometric studies.The mRNA expression data for 558 GBM samples was downloaded from TCGA data portal  version 6.7. Significantly altered proteins obtained from the iTRAQ analysis of SVZ+ GBM vs Normal, SVZ− GBM vs Normal and SVZ+ vs SVZ− GBM were subjected to DAVID analysis71. Both SVZ− and SVZ+ GBMs were segregated from each other on a 3D-score plot obtained from this multivariate analysis.iTRAQ data for the tissue proteomic analysis of SVZ−, SVZ+ GBM tumors and normal/control tissues was subjected to PLSDA using METAGENassist, an online toolHemopexin levels in GBM patient (both SVZ+ and SVZ−) serum samples were determined using competitive sandwich ELISA method  following manufacturer’s instructions. The absorbance of the chromogenic substrate was read at two different wavelengths, i.e., 450 nm and 570 nm using SpectraMax microplate reader (Molecular devices). The absorbance values at 570 nm were substracted from the values at 450 nm and the resulting absorbance values were used for determining the serum concentrations of hemopexin from the standard curve.2O2, GeNei, lot no. 033091) was added, followed by scanning the membrane using LabScan . Densitometric analysis of the hemopexin bands was performed using ImageQuant TL software .Western blotting was performed on the serum samples of SVZ+ (n = 8) and SVZ− (n = 8) GBM patients to validate the serum hemopexin levels. The serum proteins were separated using SDS-PAGE followed by their transfer onto PVDF membrane. Blocking was done in 5% skimmed milk followed by incubation with primary antibody  against hemopexin  followed by incubation with HRP conjugated secondary antibody  against the primary antibody, followed by washes with TBST buffer. A chromogenic substrate (TMB/HWestern blotting was carried out on SVZ+ and SVZ− GBM tissue lysates (n = 3 for each subtype) and compared with normal brain tissue lysates (n = 2) in order to validate the levels of Annexin A2. 10 µg of the protein lysates were resolved on 8% SDS-PAGE gels following which the proteins were transferred onto a PVDF membrane. Post transfer, the membrane was blocked using 5% non-fat dried milk in 0.1% TBST (pH- 7.4) for one hour. The blots were then probed with primary antibodies for Actin, which was used as loading control  and Annexin A2  overnight at 4 °C. Following overnight incubation, the blots were washed three times in 0.1% TBST and incubated for 1 hour with secondary antibodies anti- mouse (1:8000) and anti-rabbit (1:5000) for Actin and Annexin A2, respectively. The blots were finally washed three times in 0.1% TBST and developed using chemiluminescent reagent (GE Amersham) followed by autoradiography. Densitometric analysis of the blots was performed using ImageQuant TL software .Supplementary information


We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only < 1% false discoveries, the reactionet lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso.Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the  reactionet lasso, a structure learning approach that takes advantage of novel, information-rich single cell data and a tractable problem formulation to achieve structure learning for problem instances hundreds of orders of magnitude larger than previously reported. This approach opens the prospect of obtaining quantitative and predictive reaction models in many areas of biology and medicine, and in particular areas such as cancer biology, which are characterized by significant system alterations and many unknown reactions.Virtually all biological processes are driven by biochemical reactions. However, their quantitative description in terms of stochastic chemical reaction networks is often precluded by the computational difficulty of structure learning, i.e. the identification of biologically active reaction networks among the combinatorially many possible topologies. This work describes the  PLOS Computational Biology Methods paper.This is a Cellular processes are essentially implemented by networks of biochemical reactions. The topology of such networks is typically only partially known, rendering the identification of the correct network from experimental data a key challenge. Despite the importance of this task, only little progress has been made in devising methods to systematically and comprehensively infer topologies of non-trivial chemical reaction networks. In this work, we propose a sparse regression approach tailored to the task of large-scale model selection for chemical reaction networks.Different model classes have been developed to describe biochemical reaction systems. In order of increasing level of detail these comprise statistical time series models, such as autoregressive models and dynamic Bayesian networks, deterministic ordinary differential equation or stochastic differential equation based kinetic models . The choTwo main computational tasks arise when learning any of these models from data: parameter inference, and structure learning. Parameter inference aims at finding model parameters (e.g. kinetic rate constants). Parameter inference has been performed by sampling from posterior parameter distributions, or global non-convex or convex optimization methods . StructuParameter inference becomes increasingly computationally intensive for larger systems with numerous parameters . Structureactionet lasso, a convex relaxation of the structure learning task. This approach yields a single best sparse reaction set from all possible reactions by translating a recent sparse identification approach for nonlinear dynamic systems [We propose the  systems  to operaMr of the abundance distributions  are polynomials over these moments such as abundance means and variances of individual species. Frl will be referred to as stoichiometric moment functions herein :Parameter estimation for a mass action kinetics network typically requires the costly integration of the moment equations for every considered parameter configuration. Imputation of the moment gradients by gradient matching procedures see  circumvedure see  and the This linear regression formulation has been applied for parameter inference of deterministic chemical reaction models , 11.1) regularization on the parameters k to promote the identification of sparse solutions, i.e. solutions with many zero-valued parameter estimates.Model selection across small sets of model variants has previously been performed with information criteria  or modelVarious extensions of the Lasso method were introduced in literature to improve its shrinkage properties in the presence or absence of heteroscedasticity .While the Lasso has been used in recent reports to identify general nonlinear dynamical systems  or to seThis section introduces the reactionet lasso , a compuki (rate constants) resulting from the occurrence of large a spectrum of fast and slow reactions. The reactionet lasso addresses each of these challenges in as delineated in the following.The following properties of such structure learning instances preclude the application of conventional least squares based approaches for parameter estimation and selection: (1) noise and heteroscedasticity of the observed response ϵA and ϵb to be their respective intrinsic variability induced deviations from the estimated/observed quantities. If we knew the true values of the latent variables, finding the rate constants k would translate to solving the following equation:The intrinsic variability of stochastic chemical kinetics result induces variability of the empirical estimates of moments and their gradients. Therefore the observed response vector as well as the stoichiometric moment functions in the design matrix are expected to deviate from the true latent correspondents. We capture this by defining ϵ: = ϵAk − ϵb.By substituting the variables in ϵ). However, it becomes apparent that this strategy is not valid due to the residual ϵ being a function of the parameters k (by virtue of the noise in the observed design matrix).ϵb and ϵA via bootstrapping of the gradient estimates and stoichiometric moment functions on the basis the single-cell data. A preliminary least squares fit is then performed to achieve an estimate kLS for ϵb and ϵA, we use kLS to achieve an estimate of the component-wise variance ϵ. To achieve this estimate we use only first order moments (means), as they are less subjected to noise in the design matrix and provide a more robust estimate of the covariance matrix Σϵ. This estimate will allow us to operate with the rescaled observed response vector The reactionet lasso implements a stepwise strategy to address this dependency. The first step (Step 1) is a Feasible Generalized Least Squares (FG) estimate. It comprises the estimation of the variances of the residuals gression .k^FG=arki, which render conventional sparse regression approaches (such as the Lasso) suboptimal due to the uniform penalization strength of the L1 norm ‖.‖1 across all components ki of the parameter vector k. The adaptive Lasso [i.The subsequent steps aim at addressing the second challenge introduced above, i.e. the different scales of individual parameters ve Lasso  constituIn Step 2 of the reactionet lasso, we apply a combination of the adaptive and relaxed Lasso, stability selection based prioritization of reactions and an additional stepwise backward regression to achieve the final set of reported reactions. We use the parameter estimates from Step 1 (obtained with Moore–Penrose pseudoinverse matrix), i.e. Φ.To improve shrinkage, the adaptive Lasso is followed by a relaxed Lasso  that recThe adaptive relaxed Lasso solution has been computed by optimizing the respective Alternating Direction Method of Multipliers (ADMM) formulations . The adahttp://www.imsb.ethz.ch/research/claassen/Software/reactionet_lasso.html.In summary, the reactionet lasso procedure constitutes a stepwise sparse regression approach that addresses the parameter-dependent noise and heteroscedasticity in the response and design matrix for structure learning of stochastic chemical reaction systems. See also We first consider an extreme and yet conceptually simple scenario where we aim at learning the structure of a reaction network without any prior knowledge about the underlying reactions. While this scenario rarely occurs in a real world application because typically some prior knowledge of relevant reactions is available, we first investigate this scenario to demonstrate the structure learning capabilities of the reactionet lasso.600 possible reaction network candidates. For these systems we simulated 5 replicates each with either 103, 104 or 105 single cell trajectories with the stochastic simulation algorithm [We study two systems varying in number of components and reactions: (1) the enzymatic reaction system with four components and three reactions, (2) the receptor subunit of a recently reported kinetic model of TRAIL induced apoptosis with fourteen components and thirteen reactions, which can be combined in a total of 2275 possible unary or binary reactions, giving a total of more than 10lgorithm . We thenThe reactionet lasso achieves structure learning of chemical reaction networks via a two step sparse regression formulation that (1) specifically accounts for heteroscedasticity in the response vector and the design matrix of the regression instances and (2) assumes a regularizer that encourages sparse reaction sets by suppressing compensatory reaction sets with small rate constants . The firWe further evaluated the impact of different gradient estimation approaches on structure learning performance –S4 Figs.We evaluated how measurement noise affects the ability of reactionet lasso to learn the reaction network structure. We assume a binomial measurement noise model that reflects the incomplete capture efficiency inherent to all single cell technologies see , S3 TextTo assess the relative importance of the amount of available data, we varied the amount of time points and single cell recordings used at each time point. Interestingly, we found that the inclusion of additional measurement time points did not improve structure learning performance. However, the tradeoff between true and false positive reaction discoveries worsened considerably with fewer time points . While wp = 0.1), , , the inc4 or more single cell measurements, for the accurate estimation of higher order moments. In these situations the reactionet lasso is capable of ab initio recovery of almost the complete reaction network structure with more than a dozen components.In summary, the benchmarks above strongly advocate for the use of an experimental setup that allows for sufficiently dense sampling across time to ensure accurate empirical moment gradient estimates, as well as single cell technology, such as flow/mass cytometry, which provide 10We now consider a scenario where we aim at learning the structure of a large reaction network with partial knowledge about the underlying reactions. For this situation we demonstrate how reactionet lasso is capable of recovering a sizable amount of the unknown reactions, for a reaction network as large as the 70 reaction TRAIL induced apoptosis cascade .Structure learning tasks for chemical reaction networks typically aim at complementing already available partial knowledge on reaction sets. We assessed the ability of the reactionet lasso to complement a set of known reactions for the 70 reaction TRAIL induced apoptosis cascade. Specifically, we defined six modules for this cascade following , and ass5 single cell trajectories, 33 time points, capture efficiency 0.05, In the absence of ground truth it is difficult to identify a regularization strength that achieves a desirable tradeoff between true and false positive reaction discoveries. We evaluated the BIC and report solutions that map to large initial improvements of BIC . StructuWhile published structure learning approaches are only available for problem instances of sizes hundreds of orders of magnitude smaller, we compared these results to more simple variants of the reactionet lasso procedure, either exhibiting inferior accuracy or exceedingly high computational complexity .The above results demonstrate how single cell snapshot time series data and the reactionet lasso can be used to complement prior mechanistic knowledge by a sizable set of candidate reactions that is highly enriched for true positive discoveries, and do so for systems and structure learning tasks of unprecedented size , 24.In this work we introduce the reactionet lasso for comprehensive structure learning of stochastic chemical reaction networks.Chemical reaction networks constitute a highly detailed and mechanistic description for biological processes and are qualitatively different from other popular network models in biology. These comprise probabilistic graphical models seeking to discover statistical dependencies between measured system components. These approaches range from simple correlation  or regreBy virtue of formulating the task of structure learning of chemical reaction networks as a sequence of convex optimization problems, this procedure is able to assess an unprecedented number of potential network topologies without need for explicit enumeration , 24. We 5. For single cell transcriptomics experiments it seems advisable to resort to novel droplet based techniques achieving > 104 single cell snapshots per experiment [The challenging structure learning task crucially depends on sensible experimental design yielding informative data. A central design choice concerns the selection of time points recording the relevant dynamical changes of the process of interest. These are typically chosen from prior knowledge or preliminary dense snapshot time series experiments with a cheap readout, such as population based instead of single cell measurements. Another important experimental parameter concerns the number of single cell snapshots. Our benchmarks advocate for having at least thousands of snapshots per time point. Flow cytometry experiments easily achieve snapshot counts in the order of 10periment , 33.Structure learning performance of the reactionet lasso depends on the accuracy of the gradient estimates from the time series snapshots. We find that estimates based on gradients obtained by rather simple approaches such as finite difference approximations or spline curve fitting achieve competitive performance. Improvements are conceivable by resorting to other techniques specifically designed for gradient estimation in differential equation systems , 35. Thebk and design matrices Ak for each condition k are utilized to construct a problem instance by concatenation. For this problem instance reactionet lasso can be applied as described  measured in each cell. For each measurement time point t = 1, …, T for a cell c = 1, …, C we denote a vector of measured N protein abundances t vectors We assume time series data with single cell resolved population snapshots obtained at discrete time points. We denote by N different chemical species with abundances X1, …, XN involved in L reactions. Each reaction l is characterized by stoichiometry vector sl and propensity function al with x representing the collection of species abundances (system state) and kl the reaction rate. In our work we consider systems described by mass action kinetics, resulting propensities al = kl*gl(x), where gl(x) is a known function of the system’s state. The state of the system evolves probabilistically according to the possible reactions, with probability P of occupying state x at time t. The probabilistic evolution of the system’s state is described by the Chemical Master Equation:We assume a biochemical reaction network of r = . The moment generating function of the probability distribution P can be used for the derivation of moment equations [We denote by quations . AssuminMr(t), rendering the initial ODE system an algebraic equation system with the parameters as unknowns. This formulation further eliminates the need for moment closure, in contrast to integration based techniques. Previously, gradients have been estimated with spline interpolators [Gradient matching approaches avoid costly integration by instead interpolating the discrete snapshot time series data and estimating the empirical moment gradients polators , 39, 40,polators , 41 or fpolators . Parametpolators , 39, 40 polators , 35, 41.polators , 11.Mr(t) of the moment equations. As a ground truth estimate for simulated data, we use a smoothed finite difference approximation of the single cell trajectories at the evaluation point of interest (smooth). Gradient estimates are obtained via a smoothing procedure that relies on a sliding window estimate of finite differences on the simulated trajectories using the smoothing function “smooth” in Matlab.We used and compared cubic spline interpolators (spline) and finite difference approximations (FDS) to estimate empirical moment gradients for the Bi whose success probability p corresponds to the capture efficiency for the analyte present at amount X [Single cell data such as obtained from flow/mass cytometry and single cell sequencing exhibit measurement noise. These technologies each detect a random fraction of the total molecular content of every individual cell. This relationship between the measurement signal and cellular analyte abundance has been frequently modeled by a binomial distribution amount X , 43. We X represent the true abundance of one species at a given time point. Let Xobs be the corresponding measured signal, such that Xobs ∼ Bi, where p is the capture efficiency. The binomial noise model allows for specifying the following analytical relationships between the first and second order moments of X and Xobs:We assume that measurement noise can be represented by the following binomial model. Let p of the single cell instrument is known [X on the basis of the empirical moments of Xobs by solving the above equations for the respective moment of X. The resulting moment estimates are then used in the regression procedure described above to perform structure learning.For a derivation see is known , 43 and S1 Fig5 single cell trajectories evaluated at 13 time points for (A) apoptotic receptor subunit (no measurement noise); (B) the enzymatic system. Empirical moment gradients estimated with cubic splines. Solution selected with Bayesian Information Criteria (BIC).10(TIF)Click here for additional data file.S2 Figreactionet lasso application to apoptotic receptor subunit (no measurement noise). (A-B) Empirical moment gradients estimated with “smooth” procedure: (A) 105 single cell trajectories evaluated at different amount of time points (tp) as indicated in the legend. (B) Different number of single cell trajectories: 103, 104, 105 evaluated at thirteen time points. (C-E) Results for different empirical moment gradient estimates: smooth (red), splines (blue), FDS (green) for different amount of time points: 28 (C), 13 (D), 7 (E).Results for (TIF)Click here for additional data file.S3 Figreactionet lasso application to enzymatic system (no measurement noise). (A-B) Empirical moment gradients estimated with “smooth” procedure: (A) 105 single cell trajectories evaluated at different amount of time points (tp) as indicated in the legend. (B) Different number of single cell trajectories: 103, 104, 105 evaluated at thirteen time points. (C-E) Results for different empirical moment gradient estimates: smooth (red), splines (blue), FDS (green) for different amount of time points: 28 (C), 13 (D), 7 (E).Results for (TIF)Click here for additional data file.S4 Figreactionet lasso application to apoptotic receptor subunit (p = 0.05) with 105 trajectories. Results for different empirical moment gradient estimates: splines (red), FDS (blue) for different amount of time points: 28 (A), 13 (B), 7 (C).Results for (TIF)Click here for additional data file.S5 Fig5 single cell trajectories evaluated at 13 time points for apoptotic receptor subunit (p = 0.05). Empirical moment gradients estimated with cubic splines. Solution selected with Bayesian Information Criteria (BIC).10(TIF)Click here for additional data file.S6 Fig5 (red), 104 (blue), 103 (green) trajectories, 13 time points. (B) Relative change of standard deviation of the moment estimates with decreasing number of trajectories compared to 105. (C) Corresponding absolute and relative change of standard deviation of design matrix estimate (with stoichiometric moment functions as entries) with decreasing number of samples compared to 105.Results for application to apoptotic receptor subunit (p = 0.05). (A) Absolute values of standard deviation of moment estimate estimated from bootstrap for the apoptotic receptor subunit with no noise with 10(TIF)Click here for additional data file.S7 Figreactionet lasso application to apoptotic receptor subunit for uniform selection of time points. Results for different empirical moment gradient estimates: splines (red), FDS (blue) for different amount of time points and different levels of noise: 28 , 13 , 7 .Results for (TIF)Click here for additional data file.S8 FigDifferent modules colored in different colors. Reactions connecting the models depicted in gray.(TIF)Click here for additional data file.S9 Figreactionet lasso; STlsq = sequential thresholded regression, TF = Topological filtering. All methods applied to Moment Equations of 1st and 2nd order correspondingly. Results for: (A) the apoptotic receptor subunit with noise (p = 0.05) with 105 trajectories, 13 time points; (B) TRAIL-induced apoptosis with noise (p = 0.05) with 105 trajectories, 33 time points. TF2 was interrupted after 2h hours and didn’t produce any solution in the range of cardinality represented on the plot.RL = (TIF)Click here for additional data file.S10 Figp = 0.05) were generated with 105 single cell trajectories each evaluated at 13 time points. Red dots correspond to different replicates. Size of the dot proportional to the frequency of the solution between the replicates. Blue line corresponds to the strategy of concatenating design and response matrices.5 replicates of the apoptotic receptor subunit ((TIF)Click here for additional data file.S11 Fig5 single cell trajectories evaluated at 13 time points for apoptotic receptor subunit without measurement noise. Solution selected with AIC for two distinct scenarios: ab initio learning (blue), a priori specified reaction identified false negative in ab initio learning setting (green).Red: observed data for 10(TIF)Click here for additional data file.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.S4 Text(PDF)Click here for additional data file.S1 Dataset(PDF)Click here for additional data file.


EPSPS glyphosate tolerant mutant gene (OsmEPSPS) alone and in combination with the glyphosate detoxifying encoding igrA gene, recently characterized from Pseudomonas. The molecular analysis of all transgenic plant lines showed a stable integration of transgenes and their active expression in foliar tissues. The physiological analysis of glyphosate treated transgenic lines at seed germination and vegetative stages showed a significant difference in glyphosate tolerance between the two transgenic groups. The transgenic plants with OsmEPSPS and igrA genes, representing dual glyphosate tolerance mechanisms, showed an improved root-shoot growth, physiology, overall phenotype and higher level of glyphosate tolerance compared to the OsmEPSPS transgenic plants. This study highlights the advantage of igrA led detoxification mechanism as a crucial component of glyphosate tolerance strategy in combination with glyphosate tolerant OsmEPSPS gene, which offered a better option to tackle in vivo glyphosate accumulation and imparted more robust glyphosate tolerance in rice transgenic plants.Weeds and their devastating effects have been a great threat since the start of agriculture. They compete with crop plants in the field and negatively influence the crop yield quality and quantity along with survival of the plants. Glyphosate is an important broad-spectrum systemic herbicide which has been widely used to combat various weed problems since last two decades. It is very effective even at low concentrations, and possesses low environmental toxicity and soil residual activity. However, the residual concentration of glyphosate inside the plant has been of major concern as it severely affects the important metabolic pathways, and results in poor plant growth and grain yield. In this study, we compared the glyphosate tolerance efficiency of two different transgenic groups over expressing proline/173/serine (P173S) rice  Weeds are the most serious biological constraints in agriculture, which have enormous impact on crop plants in terms of growth, development, and grain yield. They instantly highjack the whole agricultural field and aggressively compete with crop plants for nutrients, water, sunlight, and space. Weeds also act as an alternate host for major insects, pests, and diseases . In addiLolium rigidum Gaudin) was reported in Australia in 1996 , an important plant enzyme responsible for biosynthesis of aromatic amino acids and other crucial plant metabolites , 2009. T in 1996 .EPSPS, reduced translocation of glyphosate to meristematic region and increased vacuolar sequestration, collectively known as non-target site modifications , glyphosate oxidoreductase (GOX), glycine oxidase (GO) and D-amino acid oxidase (DAAO) which have been identified and successfully used in various crops for development of glyphosate tolerant transgenic plants. The increased glyphosate resistance (igrA) is another bacterial enzyme, reported in 1990 and still less explored, that metabolizes glyphosate into sarcosine and inorganic phosphate, and subsequently into formaldehyde and glycine  gene, to impart glyphosate tolerance. We developed two groups of transgenic rice plants expressing either OsmEPSPS alone, referred as ‘S’ transgenic lines, or in combination with igrA gene, referred as ‘D’ transgenic lines. After comparing the glyphosate tolerance efficiencies of both groups by various expression and physiological experiments, it was concluded that the co-expression of both OsmEPSPS and igrA genes could be an effective strategy for developing more robust glyphosate tolerant transgenic rice plants since the strategy is a combination of two different glyphosate tolerance mechanisms.In this study, we analyzed the effective contribution of glyphosate degrading encoding Oryza sativa, Arabidopsis thaliana, Eleusine indica, Gossypium hirsutum, Nicotiana tabacum, Solanum lycopersicum, Sorghum halepense, Triticum aestivum, Lolium multiflorum, and Zea mays, were obtained from NCBI database. The multiple sequences were analyzed using MEGA 6 to identify the conserved proline amino acid position in rice EPSPS which was mutated to serine to make the enzyme glyphosate tolerant.The EPSPS protein sequences from 10 different plant species, i.e., in silico analysis of various plant EPSPS polypeptide sequences revealed a conserved position of proline amino acid at position 173 (173 in rice and 182 in Arabidopsis), which is replaced with serine in several glyphosate resistant weeds  with AGC codon (Ser) by designing specific primers EPSPS-AR and EPSPS-BF overlapping primers and introduced a new HindIII restriction site which was absent in native EPSPS sequence (Supplementary Table EPSPS gene sequence (OsmEPSPS), encoding P173S substitution, was generated by digesting the two fragments and ligating them back with the help of HindIII and T4 DNA ligase enzymes, respectively. The rice EPSPS promoter  and rice EPSPS 3′ terminator (236 bp) regions were separately PCR amplified from rice genomic DNA using sequence specific primer pairs EPSPS-PF – EPSPS-PR and EPSPS-TF – EPSPS-TR, respectively  in Gateway® compatible entry vector 1 (pL12R34-Amp). For making expression cassette with igrA, the rice codon optimized synthetic 1,058 bp igrA coding DNA sequence was cloned under rice actin 2 promoter  and its 3′ terminator  in Gateway® compatible entry vector 2 (pL34R12-Cm-ccdB) (The Cm-ccdB) .OsmEPSPS expression cassette (OsEPSPS-P:OsmEPSPS:OsEPSPS-T) in entry vector 1 was finally stacked into plant transformation vector, pMDC99  and igrA expression cassette (OsAct2-P:igrA:OsAct2-T) from entry vector 2 (pL34R12-Cm-ccdB) into plant transformation vector pMDC99. The Agrobacterium EHA 105 colonies, harboring the recombinant pMDC99-OmEPSPS-igrA vector, were used for plant transformation to develop ‘D’ transgenic rice lines co-expressing both genes.The , pMDC99  using GaAgrobacterium mediated transformation. The embryogenic calli, developed from mature rice seeds, were infected and co-cultivated with Agrobacterium culture containing either recombinant pMDC99-OsmEPSPS or recombinant pMDC99-OsmEPSPS-igrA vector separately. The transformed calli were selected on 50 mg/l hygromycin with three rounds of selections and putative transgenic rice plants were generated from secondary calli after regeneration and rooting according to The indica rice cultivar Swarna was used for OsmEPSPS with the help of PCR using sequence specific primers EPSPS-F and EPSPS-R . The igrA specific primers, igrA-F and igrA-R, were expected to amplify DNA fragment of 1,056 bp length from transgenic plants, and no amplification from wild type (wt) plants. The PCR was performed using 200 ng template genomic DNA from putative transgenic lines and wt plant separately. All the PCR conditions and components are same as mentioned above. The amplified DNA PCR products were resolved on 1% agarose gel followed by visualization on UV gel documentation system.The putative transgenic plants were initially screened for the presence of wt plant lines, was separately digested with NcoI restriction enzyme. The restricted DNA fragments were separated on 0.8% agarose gel and subsequently transferred to positively charged nylon membrane using capillary movement method. The membrane was then sequentially hybridized with EPSPS DIG probe, amplified with EPSPS-F and EPSPS-R primers (to identify both ‘S’ and ‘D’ transgenic lines) and proceeded for final unique DNA signal detection. The hybridization, post hybridization washing conditions and detection of signals were performed according to manufacturer’s instruction . Following, probing with EPSPS gene, the same nylon membrane was reprobed with DIG labeled igrA probe, amplified with igrA-F and igrA-R primers, and further steps were followed as described earlier.The genomic DNA was isolated following the protocol described by BglII restriction enzyme and ligated with partially double stranded genome walker adapters. The adapter ligated restricted genomic DNA of 100 ng was used as template to PCR amplify the T-DNA integration site in the rice genome using genome walking specific primers listed in Supplementary Table The genome walk technique was used to identify the integration site of transgene expression cassette in the genome of best transgenic event showing higher glyphosate tolerance following the method described by wt plants, and rest of the procedure was completed according to EPSPS probe (for both ‘S’ and ‘D’ transgenic lines) and igrA probe (for ‘D’ transgenic lines) consecutively in separate experiments. All further steps, including membrane washing, detection of northern signals, were completed according to the supplier’s instructions .For northern analysis, the total RNA was isolated from 30 days old transgenic and wt plants were prepared using 2 μg total RNA with the help of SuperScript III (Invitrogen). For the expression analysis of EPSPS and IgrA, the PCR conditions were 94°C for 1 min, 55°C for 1 min, and 72°C for 1 min for 30 cycles with initial denaturation at 94°C for 4 min using gene specific primers. The expression of house-keeping rice actin 1 (Act1) gene was used as the internal standard in PCR.The semi-quantitative real time PCR (semi quantitative RT-PCR) was performed using primers EPSPS-F and EPSPS-R  were grown on the half strength MS media supplemented with 100 μM of glyphosate. Seeds of wt were grown on same conditions with and without glyphosate served as positive and negative controls, respectively. The root and shoot lengths were measured on 20 days of seed germination and photographed.For determining the level of tolerance of Swarna rice to glyphosate concentration at seed germination stage, the surface sterilized wt plants under greenhouse conditions. Further to examine difference in the level of glyphosate tolerance by ‘S’ and ‘D’ plant groups, 1-month-old plants (8–12 leaf stage) from both ‘S’ and ‘D’ lines were uniformly foliar sprayed with glyphosate (2 mL/L) in green house condition at 26°C in day-light (12–12 h) conditions. Similar age group plants from wt were simultaneously foliar sprayed with water and with glyphosate (2 mL/L) which served as negative and positive controls, respectively, and handled alike. The glyphosate effect was regularly monitored carefully for the appearance of any visual symptoms and subsequently photographed.The natural tolerance of Swarna rice against commercially available glyphosate marketed as Roundup  was determined by foliar spray applications of different doses of glyphosate  on 30 days old p ≤ 0.05 or 0.01 levels of significance.The experimental data were reproduced and indicated in their respective figures, and pooled data were analyzed for one-way analysis of variance (ANOVA) followed by least significance difference (LSD) statistic at Oryza sativa, Arabidopsis thaliana, Eleusine indica, Gossypium hirsutum, Nicotiana tabacum, Solanum lycopersicum, Sorghum halepense, Triticum aestivum, Lolium multiflorum, and Zea mays, were sequence aligned. The analysis resulted in a consecutive 19 amino acid long conserved sequence group from 170 and 189 aa position . The position of amino acid proline was found conserved in all the plant EPSPS protein and found at position 182 corresponding to A. thaliana, 180 to L. multiflorum and 173 aa in rice EPSPS.Development of resistant weeds against herbicide is a global concern and has a great threat to modern agriculture. A total of 32 glyphosate-resistant weed biotypes has been identified in various parts of the world due to continuous and overuse of different herbicides. Weeds develop resistance against herbicide by changing amino acid sequence composition in the active site of targeted enzyme due to high herbicide selection pressure. To identify the common conserve proline position in the plant EPSPS polypeptides of different plant species, i.e., Figure 1A. This single amino acid substitution endows moderate level of glyphosate resistance without any fitness cost on its enzyme kinetics. In a previous report, Similarly, the sequence analysis of EPSPS from various glyphosate resistant and sensitive weed biotypes have shown single amino acid substitution at highly conserved P106  as shown in Supplementary Table Agrobacterium EHA105 strain carrying either single or double gene expression cassette was transferred separately into indica rice cultivar Swarna . Following the rice tissue culture method, total 10 putative rice transgenic plants from ‘S’ and 6 from ‘D’ transgenic lines were generated . All putative transgenic plants were initially screened for the integration of OsmEPSPS gene expression cassette using rice EPSPS gene specific internal screening primers EPSPS-F and EPSPS-R . On the contrary, the EPSPS gene expression cassettes were made from the full length EPSPS coding sequence without introns, and hence the transgenic plants amplified a smaller DNA fragment of size 615 bp from the OsmEPSPS expression cassette along with 1,677 bp DNA fragment from the native EPSPS in PCR using above internal primers. The above PCR based strategy act as a marker to differentiate transgenic lines from the wt one. A total of 11 independent transgenic plants were found PCR positive for the integration of OsmEPSPS transgene in both ‘S’ and ‘D’ transgenic plants . Out of 11 transgenic lines, seven transgenic plants were from ‘S’ group alone and remaining four plants were from ‘D’ group. The ‘D’ transgenic plants were further screened for the integration of igrA gene and a total three plants were seen PCR positive. We selected three best independent transgenic lines from each ‘S’ and ‘D’ transgenic group based on seed germination assay in the presence of 40 μM glyphosate (data is not shown) and these lines were used for further analysis.The a Figure A. Followwt rice plants was digested with NcoI restriction enzyme  and initially probed with EPSPS gene . All six transgenic lines showed two distinct southern positive signals, one signal around 1.8 kb region which was present in all transgenic lines including wt control, representing the native EPSPS copy. However, another signal represented the transgenic OsmEPSPS integration which appeared with distinct hybridization band pattern for each transgenic line. From the southern blotting, it was confirmed that all the transgenic lines from ‘S’ and ‘D’ groups showed single OsmEPSPS transgene integration. Furthermore, the transgenic lines D1 and D3 possessed same southern positive pattern which were considered as transgenic lines with same event. The same blot was reprobed with igrA gene. The transgenic plants from ‘D’ group showed clear single positive signals with different band pattern except for D1 and D3 lines  which showed same hybridization pattern for both EPSPS and igrA probe, so considered as same transgenic events.The purified DNA of six PCR confirmed single and double gene transgenic plants along with wt plants . However, the intensity of DIG-EPSPS signals were found high in all the transgenic lines from ‘S’ and ‘D’ groups compared to wt plant since both native and transgenic OsmEPSPS genes were expected to express simultaneously. Similarly, the blot was re-probed with DIG-labeled igrA probe, and a single distinct band signal appeared in D1 and D3 transgenic lines. Further, the wt plant and ‘S’ transgenic group did not show any signals with igrA probe . The results confirm the active transcription of both the transgenes in transgenic rice plants. To ascertain the concentration of total RNA equally used in the present study, the blot was further reprobed with Act1 gene which appeared with clear distinct positive signals having equal level of expression in all the plant samples .The RNA blot analysis showed a single hybridization signal with EPSPS probe which was observed in all transgenic and OsmEPSPS and igrA genes was further validated with the help of semi quantitative RT-PCR which showed a decreased expression of EPSPS gene in wt compared to transgenic lines from ‘S’ and ‘D’ groups . Similarly, the transgenic lines from ‘D’ group showed an active expression of igrA gene. The expression of native Act1 was used as internal reference gene showing equal expression in all plant samples .The expression of OsEPSPS and transgenic OsmEPSPS in the ‘S’ and ‘D’ groups, we performed restriction–digestion analysis of amplified DNA products obtained from the respective cDNAs by reverse transcription of all transgenic and wt plants. The HindIII restriction enzyme could not digest 615 bp PCR product from wt sample while it was resulted in 253 bp and 362 bp DNA fragments in all transgenic plants along with the native 615 bp PCR band . These results confirm the simultaneous expression of both native and trans EPSPS genes in all ‘S’ and ‘D’ transgenic rice lines. The introduction of unique HindIII restriction site, while replacing the existing proline with serine (P173S) in transgenic OsmEPSPS expression cassette, facilitated easy distinction between native and transgenic EPSPS gene transcripts.To distinguish the expression level of native wt rice seeds in different concentrations of glyphosate in MS medium, was started from 20 μM glyphosate concentration and there was a complete inhibition at and from 40 μM concentration onward. So, 40 μM concentration of glyphosate was regarded as the natural tolerance of Swarna rice at seed germination stage. To establish the performance of ‘S’ and ‘D’ transgenic plant lines in glyphosate, the sterilized rice seeds from ‘S,’ ‘D,’ and wt rice plants were grown on a half strength MS media supplemented with 100 μM of glyphosate [since the P173S mutation gives at least twofold to threefold resistance against glyphosate . The ‘S’ group transgenic lines, expressing OsmEPSPS, exhibited a comparable shoot growth, however, the root growth was significantly inhibited . These results clearly suggest that co-expression of both OsmEPSPS and igrA genes confers better glyphosate tolerance and phenology compared to ‘S’ transgenic plant lines expressing only OsmEPSPS gene. Further, we noticed that the transgenic lines did not show any phenological abnormalities or differences from wt plants.The root growth inhibition, while germinating the wt plants started showing typical yellow fleshing symptoms from the third day after glyphosate spray. Similarly, the ‘S’ group transgenic lines started showing visual symptoms from seventh day, however, we did not notice such symptoms or other necrotic fleshing in ‘D’ transgenic lines upon foliar spraying with glyphosate. Further, the negative wt plants completely died after 20 days of glyphosate spraying, while the ‘S’ transgenic plants showed high yellow fleshing and injuries in whole plant which were started with leaf tips, although these plants survived. However, the ‘D’ transgenic plants appeared healthy and there was no sign of any visual injury , and the phenology was comparable to wt control plants without glyphosate treatment. We further identified the best transgenic event among ‘D’ transgenic lines (D1 and D3) by foliar treatment with next higher dose of 3 mL/L of glyphosate. The results showed that there were no symptoms of visible injury up to 7 days, and after that D1 transgenic lines started responding to the concentration earlier than D3 line. So, the D3 line was found best among all the glyphosate tolerant transgenic lines. Furthermore, the high glyphosate tolerant D3 transgenic line was subjected to genome walk to identify the transgene integration in the rice genome. The BLAST results confirmed that the T-DNA region was integrated in chromosome 6 at the nucleotide position 28454001 of the D3 transgenic line .The ‘S’ and ‘D’ transgenic lines were screened by foliar spraying with 2 mL/L concentration of commercially used isopropyl amine salt of glyphosate herbicide (Roundup) and regularly monitored for any visual appearance of injury symptoms. The Glyphosate is one of the leading post-emergent, systemic, non-selective, and broad-spectrum herbicides with high unit activity and favorable toxicology. It indiscriminately kills all the plants by completely inhibiting EPSP synthase, a crucial enzyme of shikimate pathway in plants which is required for the biosynthesis of aromatic amino acids  and many other important plant metabolites. The plant EPSPS enzymes are highly sensitive to glyphosate and rapidly inhibited by its application. Enormous agricultural success has been achieved to develop commercially important glyphosate-tolerant transgenic crops using insensitive/tolerant EPSPS enzymes, e.g., corn, cotton, and soybean . AlthougigrA gene to impart higher glyphosate tolerance was analyzed in the presence of glyphosate tolerant OsmEPSPS gene in rice transgenic plants. Previously, researchers have confirmed the glyphosate degrading efficiency of igrA gene alone in transgenic tobacco and rice plants  in germination study. Similar results have previously reported in many transgenic plants expressing proline to serine EPSPS mutation  without any effect on Vmax of the reaction, with a significant decrease in glyphosate binding affinity  from various sources have been used for the development of glyphosate tolerant transgenic plants . We have also reported the similar results when glyphosate (2 mL/L) was sprayed on both the transgenic lines . The co-expression of igrA and P173S mutant OsmEPSPS resulted in higher tolerance compared to P173S mutant OsmEPSPS alone. Glyphosate is a potent inhibitor of EPSPS enzyme even in micromolar concentration and the enzyme kinetics of igrA is slow to degrade the glyphosate which subsequently results in low glyphosate tolerance when expressed alone (Pyramiding of both glyphosate tolerance and degradation strategies, and simultaneous overexpression of both physically linked genes provided the synergistic effects for higher glyphosate tolerance. As expected, the transgenic rice seedlings expressing both ed alone . HoweverOsmEPSPS and igrA, having both glyphosate tolerance as well as degradation mechanisms, showed enhanced glyphosate tolerance compared to transgenic plants expressing only OsmEPSPS gene. The study pointed out the importance of igrA as a supportive mechanism in imparting glyphosate tolerance along with the use of glyphosate tolerant EPSPS enzyme for the development of glyphosate tolerant crops for effective weed management. The study also suggests a significant role of igrA gene in transgenic plants to degrade the accumulated glyphosate in actively growing meristematic tissues.The transgenic plants co-expressing DF and MR conceived the idea and designed all the experiments. DF, AA, DJ, and BB were involved in generation of transgenics and completed molecular analysis. DF, BR, VS, and MM helped in physiological analysis of transgenic plants. DF, RY, and PV assisted in molecular analysis. DF, VA, and MR critically analyzed the data and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D  Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by diverse clinical manifestations which affect multiple end organs including joints, skin, heart, lungs, blood vessels and kidneys145561Metabolomics, the analysis of concentration profiles of low molecular weight metabolites present in biological fluids, has immense potential in identifying new biomarkers that are highly discriminatory for biological perturbations or diseased states10111212141516171819202123241H Carr Purcell Meiboom Gill (CPMG) NMR spectra of sera obtained from a HC, SLE and LN patient, respectively. The respective diffusion edited NMR spectra are shown in the inset of 1H CPMG spectra of serum samples showed signals mainly from lipids/lipoproteins (e.g. low-density lipoprotein (LDL), very low density lipoprotein (VLDL), unsaturated fatty acids (UFAs) etc.) and amino acids . Other metabolites such as glucose, lactate, acetate, creatinine, citrate, formate, 1,2-propanediol, choline and N-acetyl glycoproteins (NAG) were also assigned well. Visual inspection of the CPMG spectra revealed higher levels of glucose and lower levels of lactate in LN/SLE sera compared to HC. Comparison of CPMG and diffusion edited spectra revealed clear differences in their lipid and lipoprotein profiles too. While SLE serum samples have decreased levels of lipids and lipoproteins, LN serum samples show increased levels of lipids and lipoproteins compared to HC , indicating that the models possessed a satisfactory fit with good predictive power  was performed on the NMR spectra and the obtained score plots showed a clear trend of group clustering and discrimination between the three cohorts see  and S2. ve power . To evalp values . Receivep values . Each ofp values  clearly Metabolites responsible for discrimination in the PLS-DA score plots could be visualized by loading plots color-coded according to the absolute value of correlation coefficients (|r|), where a hot-colored signal (red) indicated more significant contribution to class separation than a cold-colored one (blue). 1H NMR spectra. PLS-DA was again repeated using the quantified metabolite data and comparable results to those as mentioned above were obtained  is the commonest manifestation of LN26262831Serum samples of SLE and LN patients contained significantly increased levels of N-acetyl glycoproteins (NAGs) and decreased levels of choline. NAGs are mainly acute phase proteins with anti-inflammatory properties and are expressed more during inflammation and immune responsesCompared to HC, SLE/LN patients showed a significant increase in glucose accompanied by decrease in lactate levels, suggesting disturbed glucose metabolism with dampened aerobic glycolytic activity. Further, we have also observed decreased levels of citrate in the sera of SLE/LN patients, indicating impaired aerobic glycolysis and thus dampened oxidative phosphorylation and ATP production in these patients. The similar higher glucose levels and decreased lactate and citrate levels have also been found in the sera of SLEOur data also revealed decreased serum levels of several amino acids  in both SLE and LN patients. Similar alterations have also been reported in autoimmune disorders associated with systemic inflammation and oxidative stress like SLE2335The metabolic signatures in conjunction with biomarkers derived from proteomics and genomics studies have immense potential to predict disease course and follow treatment response which is a crucial pre-requisite to improve efficacy and decrease toxicity of the current treatment protocols. The present study provided proof of concept that NMR-based serum metabolomics approach has high sensitivity and specificity to discriminate LN from SLE cohorts. A potential metabolic hallmark discovered in this study apart from creatinine, is the elevated serum levels of LDL/VLDL lipoproteins (triglyceride and fatty acid) and decreased serum levels of acetate in LN compared to SLE patients. The obtained metabolic disturbances delineate some of the underlying biochemical processes and aid the understanding of the pathogenesis of this life-threatening complication of SLE. It is important to mark here that the current study was performed with a limited number of samples and the metabolic changes present in the sera of LN patients should be further validated on larger prospective patient cohorts. After validation, these differential metabolic signatures would be used in clinical settings to improve the diagnosis and clinical management of LN.All experimental protocols of this study were approved by the Institutional Research Ethics Committee, SGPGIMS, Lucknow and informed consents were obtained from all patients and healthy volunteers before enrolment in the study. The methods used in this study were carried out in accordance with the approved protocols and guidelines.The serum samples used in this study were collected from SLE and LN patients attending the Department of Clinical Immunology at SGPGIMS, Lucknow, India. Serum samples were collected from twenty two patients with SLE , forty patients with LN , and thirty healthy control subjects . Serum creatinine was measured using automated analyzer . The normal range of serum creatinine is 0.5–1.6 mg/dl and of urea is 17–52 mg/dl. The demographics and clinical characteristics of SLE and LN patients are tabulated in 2O, NaCl 0.9%, 50 mM sodium phosphate buffer and pH 7.4) to minimize the variation in pH. The samples were centrifuged for 5 min at 6164 rcf and 450 μL of each sample supernatant was subsequently moved into a 5 mm NMR tube .Venous blood samples were obtained from all subjects at early morning, after overnight fast to minimize the effect of dietary factors and inter-individual variations in metabolomics data372O) was inserted separately both for the purpose of locking and chemical shift referencing. Deuterium oxide (D2O) and sodium salt of trimethylsilylpropionic acid-d4 (TSP) used for NMR spectroscopy were purchased from Sigma-Aldrich . One‐dimensional CPMG and diffusion edited 1H-NMR spectra were recorded on all the serum samples using the Carr–Purcell–Meiboom–Gill pulse sequence 2 filtering was obtained with an echo time of 200 μs repeated 300 times, resulting in a total duration of effective echo time of 60 ms; number of scans: 128; window function: exponential and line broadening: 0.3 Hz. For diffusion edited 1H NMR pulse sequence, the square gradients of 70% of the maximum gradient strength (56 G/cm) and 2 ms duration  were used. Diffusion time of 120 ms was used to attenuate the signals from low molecular weight compounds without affecting the lipid signals. All the spectra or FIDs (free induction decays) were processed using Topspin-2.1 (Bruker NMR data Processing Software) using standard Fourier Transformation (FT) procedure following manual phase and baseline-correction. Prior to FT, each FID was zero-filled to 4096 data points and a sine–bell apodisation function was applied. 2D homonuclear 1H-1H total correlation spectroscopy (TOCSY) and 1H-13C heteronuclear single-quantum correlation spectroscopy (HSQC) spectra were acquired for selected samples to aid the spectral assignment. The details of various NMR parameters of these 2D homonuclear and heteronuclear experiments are given in 424344The NMR experiments were performed at 298 K on Bruker Avance III 800 MHz NMR spectrometer (equipped with Cryoprobe) with a 5 mm broad-band inverse probe-head and Z-shielded gradient. The 450 μL of serum sample was filled in 5 mm NMR tube  and a sealed capillary tube containing the known concentration of 0.1 mM TSP -propionic acid) dissolved in deuterium oxide  in the chemical shift region δ0.5–8.5 ppm for CPMG and δ0.5–5.5 ppm for diffusion edited spcetra. The spectra were then binned into 0.01 ppm integrated spectral buckets. The chemical shift region δ4.7–5.2 ppm was excluded from the analysis to elminite the residual signal of water and distorted region due to water suppression. The lipid regions  were also removed from the CPMG spectra to reveal the contribution of low molecular weight metabolites. The binned spectral data were obtained from AMIX after mean centering and normalization which was performed by dividing each data point by the sum of all data points present in the sample to compensate for the differences in concentration of metabolites among individual serum samples. The data were scaled using unit variance in which identical weight was given to all variables. The resulting data matrices were then exported into Microsoft Office Excel 2010 and used for multivariate analysis using Unscrambler X Software  and SIMCA . To be noted here is that the diffusion edited spectra contain signals mainly from lipid metabolites and these are not attenuated by overlapping signals of low molecular weight (MW) metabolites. Thus, DE spectra provide exquisite and reliable profiling of lipid metabolites. On the other hand, in CPMG spectra, the lipid signals may understate the statistical importance of low MW metabolites; therefore by excluding them from the analysis allows better comparison of low MW metabolites and surmounts their statistical importance as well. Therefore, lipid excluded CPMG spectral data has been complemented with diffusion edited spectra.The multivariate data analysis was performed on the 1D For determining the differences among HC and disease groups (SLE and LN), pair-wise multivariate analysis was performed for HC vs. LN, HC vs. SLE and LN vs. SLE groups. The obtained binned CPMG and BPP-LED NMR data matrices were first subjected to unsupervised principal component analysis (PCA) to examine inherent clustering and to identify outliers. To further demonstrate the differences between the different groups, supervised partial least square-discriminant analysis (PLS-DA) was employed to help identifying potential discriminatory metabolites. Model validation was done using repeated 7-fold internal cross validation. The reliability of the models were further rigorously validated by the permutation tests (n = 100) and CV-ANOVA  tests. The receiver operating characteristic (ROC) analysis was obtained from Y predicted values to verify the robustness of PLS-DA models in discriminating the different cohorts. Area under the ROC curve (AUROC) was computed using the SPSS software . The marker metabolites were identified from the loading plots (for PLS-DA) and the scores of variable importance on projection (VIPs).Further, univariate analysis was performed by applying the independent samples T-test to several metabolites of interest  using SPSS software . The correlations between the discriminatory serum metabolites and clinical disease activity index (SLEDAI score) were determined using the Pearson correlation coefficient. A 0.05 level of probability was used as the criterion for statistical significance.How to cite this article: Guleria, A. et al. NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis. Sci. Rep. 6, 35309; doi: 10.1038/srep35309 (2016).


Dictyostelium cells switch from using predominately pseudopods to blebs. Bleb formation has been shown to be chemotactic and can be influenced by the direction of the chemotactic gradient. In this study, we determine the blebbing responses of developed cells of Dictyostelium discoideum to cAMP gradients of varying steepness produced in microfluidic channels with different confining heights, ranging between 1.7 μm and 3.8 μm. We show that microfluidic confinement height, gradient steepness, buffer osmolarity and Myosin II activity are important factors in determining whether cells migrate with blebs or with pseudopods. Dictyostelium cells were observed migrating within the confines of microfluidic gradient channels. When the cAMP gradient steepness is increased from 0.7 nM/μm to 20 nM/μm, cells switch from moving with a mixture of blebs and pseudopods to moving only using blebs when chemotaxing in channels with confinement heights less than 2.4 μm. Furthermore, the size of the blebs increases with gradient steepness and correlates with increases in myosin-II localization at the cell cortex. Reduction of intracellular pressure by high osmolarity buffer or inhibition of myosin-II by blebbistatin leads to a decrease in bleb formation and bleb size. Together, our data reveal that the protrusion type formed by migrating cells can be influenced by the channel height and the steepness of the cAMP gradient, and suggests that a combination of confinement-induced myosin-II localization and cAMP-regulated cortical contraction leads to increased intracellular fluid pressure and bleb formation.Migrating cells can extend their leading edge by forming myosin-driven blebs and F-actin-driven pseudopods. When coerced to migrate in resistive environments,  During migration, motile cells must restrict protrusive activity to their periphery if they are to migrate efficiently, and during chemotaxis, these projections must be controlled by the chemotactic gradient. Migrating cells move by extending their leading edge using two main types of protrusions: pseudopods (or lamellipods) driven by actin polymerization, and from pressure-driven membrane blebs ,2. BlebsDictyostelium amoebae can also move using blebs [Dictyostelium discoideum is a fast-moving genetically accessible single cell organism, and has become an ideal model for studying basic aspects of cell motility [Dictyostelium cells undergo a developmental process where signaling proteins are upregulated, and after a few hours, they develop a polarized morphology as well as the ability to sense and chemotax towards sources of cyclic adenosine 3’,5’-monophosphate (cAMP). Oscillatory pulses of cAMP coordinate and recruit chemotaxing cells to form multicellular structures and these cells make a natural transition from moving individually on a planar surface to moving within confined three-dimensional aggregates [ng blebs –19. Dictmotility ,21. Whengregates .Dictyostelium move mainly using F-actin-driven pseudopods, but switch to using blebs when migrating through mechanically resistant environments [During chemotaxis under buffer, ronments . This beronments .Dictyostelium cells [Dictyostelium cell blebbing is also strongly polarized up-gradient and is regulated through PI3-kinase [PI3-kinase null cells, where all five “type-1” PI3-kinases in the genome have been knocked out, cells migrate using significantly less blebs than parental cells [Dictyostelium cells is dependent on gradient steepness [Chemotactic gradients can also control the position where blebs preferentially form . During um cells ,24. Dict3-kinase . In PI3-al cells . Previouteepness .Dictyostelium is stimulated by cAMP and regulated, in part, through phosphorylation of its regulatory light chain, which is simulated by cAMP signaling through downstream guanylyl-cyclases and cyclic-GMP-binding proteins. Chemotactic stimulation of Dictyostelium cells therefore results in a transient increase in cGMP and phosphorylation of myosin II heavy and regulatory light chains [Dictyostelium chemotactic motility, we sought to investigate the influence of cAMP steepness and myosin II activity on cell blebbing during cell migration in confined environments.Blebbing requires sufficient intracellular fluid pressure to drive membrane expansion –4. This t chains . BecauseDictyostelium cells use blebs to migrate in resistive confined environments. However, the influence of gradient steepness on blebbing has not been investigated. Microfluidics has been used to produce stable gradients for in vitro cell migration studies. Moreover, many different types of gradient geometries have been used to study migrating cell populations both on 2D and 3D substrates [Dictyostelium [A major disadvantage of utilizing agarose overlays for observing migrating cell populations is that they do not incorporate well-controlled chemical and mechanical constraints. During migration, cells must squeeze and move underneath the agarose. During this process they locally detach the overlay and move it upward. Cells therefore face spatial variations in height and mechanical resistance since the cell must deform and lift the agarose off the substrate surface to move forward. During this process, the cell’s leading edge is more confined. The chemical gradient is also difficult to control since is not stable and continues to change over time . Furtherbstrates –39. Micrbstrates ,40–42, nbstrates , and theostelium . Howeverostelium  and trigDictyostelium cells are loaded into non-confined microchannel chambers and coerced to migrate through confined channels with a fixed width (50 μm) and varying height . For each confinement experiment, we quantified the rate to which cells formed blebs or pseudopods at different cAMP gradient steepness, ranging between 0.7–20 nM/μm. We observed that cells migrated through all channels, but the degree of confinement influenced the degree to which cells moved with blebs as is consistent with other reports conducted using agarose overlays. Blebbing was observed to increase with increasing steepness of the cAMP gradient, and cells produced larger blebs when the gradient was increased. Moreover, when the internal cell pressure was reduced using buffer containing sorbitol, cells formed smaller blebs with reduced frequency. Myosin-II concentration at the cortex was seen to be independent of gradient steepness, but increased by as much as 250% when cells were coerced to migrate in channels 1.7 μm in height. Finally, inhibition of myosin-II by blebbistatin or through the use of Myosin II-null cells led to a reduction in both the percentage and size of blebs used by migrating confined cells.In this work, we offer insight into the role that confined environments can play in promoting the formation of blebs during chemotaxis. In particular, we make use of microfluidics to create confining microchannels with stable linear cAMP gradients. D. discoideum. Cells were grown in 1.5X HL-5 medium with glucose (FORMEDIUM Ltd.), and all experiments were performed at 22°C. Ax2 was transformed with markers for F-actin (GFP-LimEΔcoil), the plasma membrane  or myosin II (GFP-myosin II). Transformed cell lines were selected with G418 and Hygromyocin B. When exponentially growing cells reached a concentration of 2–4 x 106 cells/mL, they were washed free of growth medium in development buffer  and resuspended in DB buffer at a concentration of 2 x 107 cells/mL. Cells were then starved by shaking at 180 rpm for 4 h with pulses of 100 nM cAMP added every 6 min after the first hour.Cells were derived from the axenic strain Ax2 of All microfluidic devices were made of PDMS  using standard soft lithographic methods. Briefly, a 1:10 mixture of PDMS elastomer and curing agent was poured atop a lithographically fabricated polymer mold, cured, and gently peeled off. Fluid ports were punched into the PDMS using a 0.75 mm biopsy punch , the microfluidic device and coverslip were exposed to oxygen plasma  and immediately aligned and sealed under an inverted microscope, and the assembled device was baked at 90°C for 2 hours to ensure strong bonding. Buffer solutions (DB and DB + cAMP) were delivered to each microfluidic inlet at a flow rate of 1.5 μL/min using a low-cost external pressure driven flow controller . A solutDictyostelium cells can form actin foci on both surfaces, glass and PDMS [To create regions of controlled microfluidic cell confinement, we fabricated “thin” gradient channels orthogonal to “thick” buffer channels using a two-step soft-lithography process inspired from previous chemotaxis work conducted by . First, and PDMS  and theiand PDMS .Two inlets upstream of the gradient chambers served as loading ports for buffer and chemoattractant, while the two downstream inlets were used for cell seeding into the device. The gradient generator operated using constant pressure-driven flow. The upstream inlets were pressurized at equal pressures using an external constant pressure source to deliver cell buffer (0 μM cAMP) to one side of the device and chemoattractant to the other side. The concentration of cAMP solutions used to generate the gradients were 0.1 μM cAMP, 0.5 μM cAMP, 2 μM cAMP, and 3 μM cAMP. Because the microfluidic design is symmetric and the fluid pressures are equal, no fluid flow is produced across the two main flow channels and stable gradients of chemoattractant are generated within the confinement chambers through molecular diffusion between these two flow channels . To visu5 cells/mL. Cell activity was captured in 4 minute increments. Cell speed was determined by dividing total distance of the cell trajectory by 4 min and the chemotactic velocity was calculated by dividing the net distance cells travel towards the cAMP source by 4 min. The chemotaxis index was obtained by dividing the net distance towards the source by the total migration distance. These calculations were performed using ImageJ (Fiji) with the MtrackJ plugin. Cell trajectories were calculated using the ImageJ chemotaxis plugin. Lastly, the bleb surface area was calculated based on measurments obtained with two dimensional confocal micrographs; we measured the perimeter of each cell bleb and converted this data into a 2D surface area. Results of blebbing for different microchannel heights were compared using one way ANOVA and unpaired Student’s t-tests. Differences were considered significant at P < 0.05. Each experiment was performed with between 20–50 cells from several microfluidics gradient channels.Measurements were performed with a Nikon Eclipse Ti swept field confocal microscope  equipped with an Andor iXon 897 camera, four 50 mW solid-state lasers for excitation, and a 60x oil objective (NA 1.49), which produced a 1.1 μm-thick optical slice. Protrusion activity was verified using brightfield microscopy to ensure that the thickness of the confocal optical slice was adequate to capture all blebs across the cell cross section. Cell imaging was performed in DB buffer and cells were seeded into the microfluidic chamber at a concentration of 2.5 x 10Dictyostelium cells as they migrated under agarose towards a nearby well containing cAMP [Blebbing was previously observed in ing cAMP . The proDictyostelium cells migrating through channels of varying height. For each case, wild-type Ax2 cells were coerced to migrate on glass through a confinement channel using a stable linear gradient of cAMP. The channel height was controlled using standard soft lithography techniques, and the cAMP gradients were imaged using confocal microscopy  and the compared this with the height of confined migrating cells expressing GFP-LimEΔcoil using confocal microscopy , the chemotactic motion of Dictyostelium is governed by the steepness of the applied gradient and is independent of the local cAMP concentration [Dictyostelium in linear gradients was shown previously to occur when gradient steepness was greater than 10−3 nM/μm [In ntration ,39,47. S−3 nM/μm , we used−3 nM/μm . To deteDictyostelium cells move by using blebs. Previous work attributes increased blebbing activity to the mechanical resistance of the environment, which has been reported as “the force cells exert to deform the matrix” [Our experiments illustrate that microfluidic confinement significantly influences the degree to which  matrix” . In our  matrix” ,48.Dictyostelium. Heavy chain null-mutants and cells treated with blebbistatin, for example, are unable to bleb [Myosin II activity is also associated with bleb formation in  to bleb –19. A ke to bleb . Hydrost to bleb . The myo to bleb . Once thThe tendency for cells to form blebs through cortical contractility suggests that cells might adapt to confinement conditions through increases in myosin activity since confined cells experience external stress from the environment and require sufficient cortical tension to overcome these forces and migrate. Interestingly, previous studies show that myosin II localizes at the cell membrane in cells undergoing chemotaxis under agarose overlay . BecauseDictyostelium was confined in a 1.7 μm channel  gradient in DB buffer containing 100 mM sorbitol, which reduces the osmotic pressure drop across the membrane. We found that cells confined in 3.8 μm channels were not influenced by sorbitol. However, cells confined in 1.7 μm channels formed smaller blebs  Constant pressure system for delivering development buffer and cAMP solutions to the microfluidic device. (B) Depiction of microfluidic gradient generator device used to study chemotactic Dictyostelium cells migrating under confinement. A linear gradient produces constant relative cAMP concentration over the entire channel length while the local cAMP concentration is low at the inlet of the gradient channel and approaches the concentration of the cAMP solution used to form the gradient at the outlet.((TIF)Click here for additional data file.S2 FigA) The percentage of blebs utilized Dictyostelium cells in the vicinity of the inlet and outlet of the gradient channels remained constant. The first bar in each group represents the percentage of blebs at low cAMP concentration and the second bar corresponds to the high concentration end of the gradient. Cell numbers are shown on bars. Error bars represent SEM.((TIF)Click here for additional data file.