The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions. Pareto optimality analysis between energy efficiency and protein cost reveals that the naturally evolved ED and EMP pathways are indeed among the most protein cost-efficient pathways in their respective ATP yield categories and remain thermodynamically feasible across a wide range of ATP/ADP ratios and pathway intermediate metabolite concentration ranges. In contrast, pathways with higher ATP yield (>2) while feasible, are bound within stringent and often extreme operability ranges of cofactor and intermediate metabolite concentrations. The preponderance of EMP and ED is thus consistent with not only optimally balancing energy yield vs. enzyme cost but also with ensuring operability for wide metabolite concentration ranges and ATP/ADP ratios. Uniquely among them, the canonical Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) pathways are by far the most prevalent in nature1. These two pathways differ in the first few but share six of the remaining enzymatic steps. They both generate two moles of reduced redox cofactor NAD(P)H, which can be used for generating additional ATP through oxidative phosphorylation, but differ in the overall ATP yield. The ED pathway, often found in organisms living in carbon/energy/oxygen-rich environments , sacrifices energy yield for a pathway with a larger driving force1. In addition, the canonical ED pathway confers higher tolerance to oxidative stress as it generates NADPH as opposed to NADH in the EMP pathway3. In contrast, the EMP pathway is common in prokaryotes and eukaryotes with higher energy demands or those living in anoxic or low-energy environments1. The presence of the key EMP enzyme 6-phosphofructokinase (PFK) or key ED enzymes 2-keto-3-deoxygluconate-6-phosphate (KDPG) aldolase and 6-phosphogluconate dehydratase is often used to identify whether a strain is capable of using either pathways4. The two pathways, however, are not mutually exclusive and often co-exist in many organisms5. In particular, enteric bacteria such as Escherichia coli can switch between them in response to the availability of different substrates6.Billions of years of evolution led to highly genetically and phenotypically diverse organisms, yet most of them retain largely identical routes for sugar catabolism despite the presence of a myriad of ways in nature’s enzymatic repertoire for converting glucose to pyruvate7. Semi-phosphorylative and non-phosphorylative ED pathways were reported in anaerobic Clostridia and archaea, wherein the first ATP phosphorylation step is catalyzed by 2-dehydro-3-deoxy-D-gluconate (KDG) kinase or glycerate kinase, yielding one or zero ATP per glucose, respectively9. Modified EMP pathways are found in (hyper)thermophilic archaea employing variants of glycolytic enzymes utilizing alternative cofactors such as ADP-dependent glucokinase and PFK (in euryarchaeota), pyrophosphate (PPi)-dependent PFK (in Thermoproteus tenax)10, non-phosphorylating glyceraldehyde-3-phosphate (GAP) dehydrogenase (in crenarchaeon Aeropyrum pernix and T. tenax) and GAP ferredoxin oxidoreductase (in microaerobe Pyrobaculum aerophilum)12. A recent study confirmed that Clostridium thermocellum operates GTP and PPi-dependent glycolysis and predominantly employs a malate shunt to convert phosphoenolpyruvate (PEP) to pyruvate in the absence of pyruvate kinase13. An additional ATP per glucose by using a PPi-dependent PFK9 could potentially be gained, however, the source of PPi in C. thermocellum remains elusive13. Another recently found variant of EMP relies on an NADP-dependent GAP dehydrogenase (in Kluyveromyces lactis)14. Notably, the NADP-dependent C. acetobutylicum gapC was expressed heterologously in E. coli replacing the native NAD-dependent GAP dehydrogenase to improve NADPH availability15. Alternate glucose processing pathways not conforming to the ED or EMP structure include coupling phosphoketolase (PK) pathway with the EMP pathway as a shunt for glycolysis by the heterofermentative lactic acid bacterium Lactobacillus reuteri ATCC 5573016. Bifidobacteria also utilize the bifid shunt for conversion of glucose to acetate: lactate: ATP in a 1.5:1:2.5 ratio17. In a groundbreaking work18, a non-oxidative glycolysis (NOG) was designed and constructed which operates cyclically to convert glucose to acetyl-coA in a redox-independent and carbon-neutral manner. The NOG pathway generates two ATPs and three acetate moieties per glucose molecule.Variants of the canonical ED and EMP pathways have also been discovered especially in extremophileset al. suggested that the canonical EMP pathway is an optimal series of chemical reactions that maximize ATP yield at high kinetic efficiency19. By designing a series of shortest pathways connecting different pairs of central carbon metabolites using a set of 30 reaction rules (that acts on carbohydrates), Noor et al. proposed that the canonical glycolytic pathway is the shortest pathway (in E. coli) that ensures the production of essential precursors of cellular biomass20. A recent biochemical analysis suggested that the glycolytic pathway uniquely manages to avoid toxic intermediates  and phosphorylates intermediates thus reducing metabolite leakage21. Furthermore, a recent analysis put forth the hypothesis that the lower glycolytic pathway shared by both ED and EMP pathways is able to sustain the highest flux when compared to other alternatives22. Absolute quantitative measurement of intracellular metabolite concentrations and fluxes by Park et al. revealed that the lower glycolysis has a higher overall driving force in terms of change in free energy (roughly six-fold higher) than currently stated in biochemistry textbooks23.Previous studies have already tried to shed light on why the canonical glycolytic pathways are so uniquely prevalent despite the presence of many alternative routes with higher carbon and energy yields. A theoretical study by Melendez-Hevia 25. For example, fast-growing cells have to invest more resources for the synthesis of growth-related proteins 25. Instead of simply making proportionally more protein to accommodate higher growth requirements, they often shift metabolism towards pathways with more modest catalytic resources per unit of growth at the expense of energy efficiency26. Basan et al. further verified that E. coli switches from respiration to the more proteome-efficient fermentation under high growth rates27. Cellular metabolism has been shaped by evolution to ensure that carbon catabolic pathways are carefully selected to be in tune with both growth rate requirements and resource availability. Optimal glycolytic pathways must thus be able to balance high ATP production capacity while generating important intermediates and redox molecules at minimal proteome cost. These requirements are in direct conflict with one another requiring the establishment of Pareto optimal curves to decipher the relative “weights” between objectives that nature responds to when selecting different pathway designs.Concomitant to energy production and precursor synthesis hypotheses, recent studies have reaffirmed minimization of enzymes production as a key driver of optimizing resource allocation28 database using a modified implementation of the optStoic protocol29 . Driving thermodynamics closer to the limit  lowers the overall thermodynamic driving force thereby demanding a higher protein cost to drive the same amount of flux through the pathway. High (>2) ATP yielding pathways are also less tolerant to the changes in metabolite concentration ranges and ATP/ADP ratios, whereas the two canonical glycolytic pathways remain feasible under highly varying redox and metabolite states.In this study, we aim to systematically assess the relative importance between various objectives driving pathway selection by exhaustively generating over 11,916 routes from glucose to pyruvate with varying energy production efficiency per mole of converted glucose and quantifying the corresponding total protein investment. The pathways were designed by combining annotated reactions from the entire chemical repertoire of organisms accessed from the KEGGWe first exhaustively traced pathways from glucose to pyruvate that conform to a general glycolysis stoichiometry while generating 1 to 5 ATP per glucose. We then filtered the pathways based on their thermodynamic feasibility and subsequently predicted the minimal protein cost of each pathway. We identified the Pareto optimal for the tradeoff between protein cost and ATP yield of glycolytic pathways and further determined the main factor(s) that affect the protein cost for a pathway. Several novel pathway designs with higher ATP yields are discussed and their lack of robustness to wide ATP/ADP fluctuations is demonstrated.(i)+ = 2 Pyruvate + 2 NAD(P)H + 4 H+ (Glucose + 2 NAD(P)(ii)+ = ATP + H2O (ADP + Phosphate + HA glycolytic pathway is defined here as the conversion of glucose into pyruvate accompanied by the generation of energy cofactor ATP and redox cofactor NAD(P)H. This conversion can be described by splitting the overall reaction into two balanced sub-reactions:n) of ATP produced is given by:The overall reaction allowing for a varying amount . Alternate pathways were identified by iteratively appending integer cuts (see SI for details).The maximum number of ATP that can be generated while maintaining 19. Using directly optStoic29 led to a significant number of pathways containing disjoint subnetworks such as ATP generating cycles  matrix of the reaction database resulting in the Sint matrix which contains only the internal reactions. The rows involving cofactors  of the Sred matrix is generated with each row indicating a closed loop of reactions that result in no net non-cofactor metabolite production. A disjoint subnetwork  that exchanges only cofactors with the main glycolytic chain is now a closed loop on its own. The constraints  derived from the loop law33 prevent net flux traversing any such a loop in a cyclic manner and essentially prohibit the selection of the disjoint subnetwork. Therefore, ATP production can occur only on the main carbon transfer pathway  ). When the concentration range of CO2 was also restricted based on experimental measurements35, twenty-four more 1 ATP-yielding pathways and one more 2 ATP-yielding pathways became infeasible (condition (iii)). In addition, when the cofactor ratios  were allowed to vary only within experimentally observed ranges (condition (iv)), 77% of the pathways designed were rejected including all of the 5 ATP yielding pathways. Finally, when the metabolite bounds were further restricted to between 1 µM to 10 mM (condition (v)), all of the 3 to 5 ATP-yielding pathways were found to be thermodynamically infeasible, whereas both canonical glycolytic pathways  remained thermodynamically feasible. This strongly suggests that EMP and ED not only lie on the optimal tradeoff curve for yield vs. protein cost but do so while maintaining robustness to cofactor and metabolite concentration changes.As expected the fraction of pathways deemed thermodynamically feasible strongly depends on the imposed metabolite concentration bounds. Tighter concentration bounds reduced the number of feasible pathways 37 to ensure the production of many intermediate metabolites and redox equivalents. We computationally explored whether it is possible to identify a glycolytic pathway variant with a lower cost than the canonical glycolysis (ED and EMP). The absence of a lower cost pathway would bolster the cost-benefit hypothesis that natural evolution converges toward parsimonious enzyme expression38. Often the sum of absolute fluxes through a pathway is used as a proxy to total enzyme requirement39, however, actual enzyme demand depends both on the enzyme catalytic efficiency and the metabolite concentrations. The lack of experimentally measured kinetic parameters, intracellular metabolite concentrations, and enzyme mechanisms hampers the development of a detailed mechanistic model for each pathway across different organisms.A significant fraction (from 10% to 20%) of the total proteome is allocated to glycolytic pathways 30. We employed this computationally tractable approach to evaluate the minimal protein cost for operating any glycolytic pathway variants in a host cell-agnostic manner by assuming that all enzymes are equally efficient (see Methods) and metabolite concentrations are allowed to vary between 1 µM and 100 mM. Note that this analysis provides a lower bound to the actual enzyme cost40. Due to a large number of glycolytic variants, an automated pipeline was developed for the generation of the kinetic models of each thermodynamically feasible pathway (under the same condition) and the subsequent analysis of the minimal enzyme cost.To this end, we used the scalable convex optimization-based enzyme cost minimization (ECM) algorithmThe minimal protein cost of glycolytic pathway variants for each ATP yield spans a wide range regardless of the redox cofactor(s) generated  see Fig. . By plotNotably, the canonical ED and EMP pathways lie close to the Pareto front suggesting that they are among the most protein cost-efficient pathways in their respective ATP yield category. The distance between the canonical ED pathway and the Pareto front is 0.129 mg Protein/mmol Glc/h, whereas the distance between the EMP pathway and the Pareto front is slightly larger at 0.429 mg Protein/mmol Glc/h. Out of the many possible ways  that nature can construct a glycolytic pathway, EMP and ED are consistent with a near-optimal protein resource allocation.M) for their respective substrates. Therefore, it is expected that the thermodynamic driving force for the thermodynamic bottleneck  of a pathway is the main factor that affects protein cost 46. ATP is first invested in the phosphorylation of glyceraldehyde that is generated from the cleavage of KDG by KDG aldolase. Three ATPs are then produced downstream through the conversions of 1,3-BPG to 3-PG, 2,3-BPG to 2-PG and PEP to pyruvate. This is made possible by the investment of inorganic phosphate to phosphorylate 3-PG to form the higher energy intermediate 2,3-BPG. The lack of phosphorylation of glucose to minimize the escape  of neutral charged glucose from the cell in the first step possibly made this pathway less favorable for most organisms. Nevertheless, the second step converts glucono-1,5-lactone into gluconic acid which is a polar compound with reduced membrane permeability19.The EMP glycolysis generates two ATP per glucose. It incurs 55% more protein cost than the ED pathway under condition i Fig. . The mos+-dependent phosphorylating GAP dehydrogenase and ATP-generating phosphoglycerate kinase, the other bypasses the ATP-forming phosphoglycerate kinase step by using the non-phosphorylating GAP dehydrogenase (GAPN). Both NAD+ and NADP+-dependent GAPN have been previously identified in hyperthermophilic archaea T. tenax as well as in photosynthetic higher eukaryotes47. The resulting 3-PG is routed through the modified Rapoport-Luebering shunt (described in the previous section) to form 2-PG, thereby generating two extra ATPs. Five ATPs are produced in lower glycolysis which compensates for the two ATPs invested in the upper glycolysis for a net of three ATPs.In this section, we will discuss a few identified pathways generating more than two ATPs per glucose molecule. To derive an extra ATP from a pathway resembling EMP, the carbon flux from GAP is first split into two branches Fig. . While oC. cellulolyticum48 glycolysis by utilizing a PPi-dependent PFK thereby bypassing an ATP investment upstream requirement  per glucose molecule through its glycolytic pathway. This is achieved by (i) using a reversible PPi-dependent PFK; (ii) generating 2 GTPs from the reactions catalyzed by phosphoglycerate kinase  and a GTP-dependent PEP carboxykinase ; and (iii) generating 2 ATPs from an ATP-dependent pyruvate carboxylase . The PPi consumed by PFK is suggested by Rabinowitz et al. to be regenerated through the conversion of sedoheptulose-1,7-bisphosphate (SBP) to sedoheptulose 7-phosphate (S7P)47. Accounting for PPi usage, this C. cellulolyticum pathway generates an equivalent of 4 ATPs. In the pathway designed using the modified optStoic procedure  and E4P, which is catalyzed by fructose-bisphosphate (FBP) aldolase. The latter returns to the previous steps, while DHAP is channeled into the lower glycolysis. The lower glycolysis is similar to the 4 ATP pathway in Fig. Finally, a 5 ATP yielding pathway bypasses an ATP investment in the upper glycolysis by combining transaldolase from the non-oxidative pentose phosphate pathway with the reverse of the Calvin cycle Fig. . It firsM). A reaction close to equilibrium is often followed by a reaction that is substrate significantly sub-saturated. Even though the overall pathway C. cellulolyticum, may become infeasible in a different organism with a higher growth rate and/or different intracellular metabolite pool. Overall, a combination of higher protein cost, lower pathway driving force and sensitivity to metabolite pools may explain why these higher ATP yielding pathways have not become dominant in glycolytic organisms. In addition, instead of optimizing ATP yield, organisms may choose to maximize the rate of ATP generation by increasing substrate uptake rate through lower yield pathways50.Comparison of the protein cost distribution between the low ATP Fig.  and highays Fig. , the oveΔrG′Fig. . Near-eqE. coli are all above unity52. We repeated the MDF test when the ATP/ADP ratio is constrained to be above unity  growth phase, where cofactors and intracellular metabolites in organisms undergo high fluctuations 55. We find that the robustness of a pathway to different intracellular concentrations may ultimately determine whether a pathway is universally adopted. We have demonstrated that at least for glycolytic pathways, the Pareto optimal surface for protein cost vs. energetic yield must be extended to include the dimension of robustness. It is plausible that robustness imperatives that apply to naturally selected pathways may also be important for synthetic ones. This may explain why it is important to a posteriori subject engineered cells to directed evolution56, conditioning57 or adaptive evolution58 to identify variants with robust phenotypes. Interestingly, evolved strains often alleviate these bottlenecks with mutations that confer robustness to the engineered pathway  and subsequently growth rate58. Alternatively, one may impose feasibility of operation of a pathway over a wide range of concentrations and relevant energetic and redox cofactor ratios H/NAD(P)) as a design imperative. Our procedure can be used in this case to facilitate the design, analysis, and down-selection of (synthetic) bioconversion pathways.While optimizing protein resource is an important factor in pathway selection, robustness to extracellular changes  also plays a major role in shaping metabolic pathwaysin silico experiments using the widest possible range of metabolite concentrations.In this study, we assume that the metabolite concentrations are homogeneous within a cell. Compartmentation of metabolites and proteins may affect the optimal metabolite concentration for MDF  as well as the protein cost required to operate the pathway. For example, cells may need specific proteins/energetic demand to transport the metabolites between compartments. However, it is possible that with compartmentations, multiple local concentrations of a metabolite are allowed in different compartments and a pathway that was infeasible may become feasible. In addition, the concentration of metabolites varies significantly across species. This would ultimately affect the feasibility of various glycolytic alternatives. Due to the difficulty of obtaining high quality absolute quantitative metabolomics data for a large number of species, we have decided to perform these C. cellulolyticum and C. thermocellum utilize59. Recent efforts have started to leverage these organisms for the biosynthesis of various biofuels and bioproducts61.Beyond the current focus of this study, this work provides templates for designing alternate glycolytic pathways for potential metabolic engineering applications , especially for metabolites near central carbon metabolism that require additional ATP for biosynthesis. While most of the designed glycolytic pathways generating >2 ATP become thermodynamically infeasible, we did identify several alternatives that can be coupled with other engineered pathways for improving overall yield, albeit with higher protein cost and lower flux rates. This introduces a separate Pareto optimality problem between yield and productivity and designing pathways to systematically optimize between them. For example, we identified multiple such high ATP yielding glycolytic pathways see Fig.  that res29 was curated to ensure that all reactions are elementally  balanced and updated with new reactions from the KEGG database28. The updated optStoic reaction database contains a total of 7,164 reactions and 5,969 metabolites  was retrieved from KEGG and converted into InChI using Open Babel62 or ChemAxon ). The Gibbs free energy of formation of these compounds was then calculated using the same method and added to the database. Note that the same standard Gibbs free energy change of a reaction ) were removed. The standard transformed Gibbs free energy of all reactions (Step 1:Step 2:Step 3: A reaction is deemed (i) irreversible in the forward direction if both 29. Consequently, the updated database contains a total of 5,014 reversible reactions  per glucose mole is given by:29 to identify the set of reactions that conform to the above glycolytic stoichiometry. The minFlux formulation is given byI and J represent metabolites and reactions, respectively. i and each column representing reaction j, vj is the flux of reaction j, ite i. The set of all exchange reactions vj and qi is the stoichiometric coefficient of metabolite i in the design reaction. j. The objective function  formulation from the optStoic procedure2O) exchanges. This often results in pathway designs where the entire driving force of the conversion is accomplished by futile cycles disconnected from the main metabolism  with only energy , redox H) or other cofactors  was constructed by first removing all exchange reactions (columns). Subsequently, rows containing the selected cofactors  ATP hydrolysis, (ii) redox generation or (iii) water-splitting, can be represented by linear combination of the null basis Nred.Step 3: The following constraints are then appended to the optStoic formulation:L is the set of all loops, l and reaction j, M is a large positive number , and the variable Gj is a pseudo free energy parameter33, which does not reflect the actual thermodynamic driving force equivalent of reaction j  modeling system and Gurobi Optimizer v6.5.1 using Python 2.7. The codes for the optStoic procedure are provided as a part of the 34. The MDF formulation in essence attempts to identify a set of metabolite concentrations that ensure the lowest free energy changes for all the reactions in a pathway. If the objective value of MDF is positive, then the pathway is thermodynamically infeasible. See details in SI supplementary text.The thermodynamic feasibility of each pathway under physiological concentration ranges are assessed using the max-min driving force (MDF) formulation40. See details in SI supplementary text.The minimal enzyme demand in units of mg protein/mmol glucose/h for each one of the thermodynamically feasible pathways is then estimated based on the enzyme cost minimization (ECM) methodoptstoic-python package described in the previous section.To assist in the analysis of a large number of pathways designed using the modified optStoic approach, each pathway and reaction are represented as a Pathway Class object and Reaction Class object, respectively. A directed bipartite graph for each pathway is generated and rendered as SVG, PNG or JPEG format using the Graphviz software accessed through the Graphviz Python Package. The Python codes for pathway visualization are included in the Supplementary InformationSupplementary Dataset


Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels.https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license.The TriPOINT open source software is freely available at Bioinformatics online. Methods for pathway analysis over the years have fallen into three categories  and  gene inconsistent with their upstream targets and (ii) genes that are highly supported by their upstream associated genes, which can be useful for identifying enriched pathways. To assess the significance of each score, TriPOINT calculates permuted P-values. Permuted P-values are obtained by randomly reassigning gene expression values between genes from the expression values provided and recalculating scores based on the number of permutations to generate a null distribution.TriPOINT utilizes graph representations of pathways obtained from GRAPHITE  profiled by The Cancer Genome Atlas . Among these genes/pathways with negative triangulation scores, we identified ACVR1 and BMPR1A in the ‘Signaling pathways regulating pluripotency of stem cells’ KEGG pathway as among the top genes/pathways using these scoring criteria (triangulation scores. ACVR1 and BMPR1A have each been previously associated with breast cancer (We obtained 864 gene/pathway combinations with criteria . We alsoConflict of Interest: none declared.bty998_Supplementary_InformationClick here for additional data file.


Arabidopsis thaliana (Ath) is considered drought‐sensitive, whereas, as we demonstrate, its close relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa) are drought‐resistant.Droughts cause severe crop losses worldwide and climate change is projected to increase their prevalence in the future. Similar to the situation for many crops, the reference plant To understand the molecular basis for this plasticity we conducted a deep phenotypic, biochemical and transcriptomic comparison using developmentally matched plants.Aly responds most sensitively to decreasing water availability with early growth reduction, metabolic adaptations and signaling network rewiring. By contrast, Esa is in a constantly prepared mode as evidenced by high basal proline levels, ABA signaling transcripts and late growth responses. The stress‐sensitive Ath responds later than Aly and earlier than Esa, although its responses tend to be more extreme. All species detect water scarcity with similar sensitivity; response differences are encoded in downstream signaling and response networks. Moreover, several signaling genes expressed at higher basal levels in both Aly and Esa have been shown to increase water‐use efficiency and drought resistance when overexpressed in Ath.We demonstrate that Our data demonstrate contrasting strategies of closely related Brassicaceae to achieve drought resistance. Here the concept of ‘phenes’ will be useful  is considered sensitive to drought and salt stress. The closely related Eutrema salsugineum (Esa) exhibits a much higher tolerance to salt and water deprivation, and has been proposed as an extremophile model to investigate mechanisms underlying resistance to drought, salinity and freezing  is a closer Ath relative and has been described as resistant to freezing and drought  was obtained from Nottingham Arabidopsis Stock Center (http://arabidopsis.info). Aly strain MN47   was used for high‐throughput phenotypic characterization. Pots were prepared as described  at VIB Ghent , 20 (Esa) and 22 (Aly) d after sowing (DAS). After 15 d without watering, plants were rewatered and survival was scored 3 d later. Two independent replicates were performed in trays. Plants were grown under constant environmental conditions: 16 h day, 21°C, 55% relative humidity and 110–120 μmol m−2 s−1 light intensity.Water deficit (WD) treatment started when leaf 6 (L6) was initiated on the apex  as judged by manual inspection (Boyes https://pippa.psb.ugent.be) was used. Segmented images were used to measure projected rosette area, perimeter and convex hull area to calculate relative growth rate (RGR), stockiness and compactness.For analysis, visualization and management of phenotypic datasets, the PSB Interface for Plant Phenotype Analysis (Ath), 33 (Aly) and 31 (Esa) DAS and photographed. Individual leaf area was calculated using imagej (https://imagej.nih.gov/ij/). For cellular analysis, Chl of leaf 6 was removed and five to eight leaves were used for cellular drawings and analysis as described  with or without 10 μM ABA (Sigma Aldrich). The ratio between the width and length of ostiols (Rwl) was measured.Rosette leaves of 4‐wk‐old plants were incubated under light conditions for 3 h with buffer . Fv/Fm measurements were obtained by application of a single saturating pulse to dark‐adapted plants. Average Fv/Fm of the entire rosette was calculated using the imagej macro ‘PHENOPSIS‐Fluo’ ; acetic acid 60% (v/v); ethanol 20% (v/v)) for 20 min at 95°C. Absorbance at 520 nm was measured for 100 μl of the reaction in a microplate reader. Anthocyanins were extracted with five volumes of extraction buffer  for 5 min  per g FW. Three independent experiments were performed with similar results (not shown).Total rosettes were collected, and fresh weight was measured before freezing in liquid nitrogen. Proline content was determined spectrophotometrically using ninhydrin  was used for read quality control before and after trimming. High‐quality reads were mapped to the Ath (TAIR10), Aly (v.2.1) and Esa (v.1.0) reference genomes and quantified using kallisto  according to the manufacturer's protocol. RNA was subjected to DNA digestion with RQ1‐RNase‐Free‐DNase (Promega). Impurities were removed with an RNeasy clean‐up‐kit (Qiagen). Libraries were prepared using the TruSeq RNA Sample Preparation Kit v. 2 . Sequencing was done using a HiSeq2500 with the HiSeq SBS Kit_v4 (Illumina) in paired‐end mode with a read length of 100 bp. Each experiment was performed with three biological replicates. RNA‐sequencing (RNA‐seq) data were deposited at NCBI (SRP155798). Adapter removal and quality‐based sequence trimming data was done with Ath, Aly and Esa were identified using blastp with a 10−3E‐value cutoff. Coexpressed gene modules were identified using a weighted gene co‐expression network analysis (WGCNA) . Biological function analysis was performed using a WGCNA in combination with Fisher's exact test included in the topgo package, from which GO enrichment was determined using revigo  between Ath and its reportedly more resistant relatives Aly and Esa. Critical for comparative drought studies is the dependence of water requirement on developmental parameters   was considerably larger than those of Esa (94 mm2) and Ath (100 mm2) , 1.66 (Ath) and 1.50 (Esa) g water g−1 dry soil  Table . Howeveroil Fig. . Thus, wAth and Aly entered a short adaptation period, which was not observed in Esa , whereas in Ath and Esa the respective reductions of 42% and 44% were comparable  was most drastically reduced whereas in Esa, cell size (i.e. growth) was most prominently affected , except cotyledons and late emerging leaves, showed a dramatic decrease Fig. a–c. Thisble Fig. . Of noteted Fig. a. Stomatnts Fig. b,c. ThesAly and Esa in several response phenes. While Aly responds most sensitively to drought stress, the similarly resistant Esa responds much later with growth reduction and even later than the sensitive Ath. We aimed to understand the underlying molecular changes using transcriptional profiling.These phenotypic analyses revealed substantial differences in the specific drought responses of closely related Brassicaceae. Most remarkable is the contrasting behavior of the drought‐resistant 2TPM > 1 was 20 586 (Ath), 21 092 (Aly) and 19 708 (Esa), representing 74.4%, 67.9% and 74.8% genome coverage, respectively. Of note is the different pattern of transcriptional changes between the three species. Whereas for Aly dramatic changes are evident at T11, Ath responses peak only at T14, but more genes are induced. By contrast, the transcriptional changes in Esa are rather moderate, indicating that this species may require fewer transcriptional adjustments , 149 (Aly) and 134 (Esa) genes are expressed specifically in response to drought stress  of WW and WD plants for RNA‐seq‐based transcriptome analysis. The total number of expressed genes with a lognts Fig. , S3, posess Fig.  but thesess Fig. .Aly was the drought stress marker RD29B was upregulated at T5, whereas in Ath and Esa its levels did not rise until T11  was commonly induced at T5 in all three species Fig. . This geP < 0.001, emp. P‐value, Figs For subsequent analyses we conducted one‐to‐one orthology assignments and focused on 15 883 EMOs Table , which rAly and Ath. Functionally these genes include transcriptional regulators, and vesicle trafficking‐related processes , which are functionally enriched in RNA processing categories such as ‘RNA modification’ (FDR 10−24), and embryogenesis‐related terms . Aly‐specific EMOs  are highly enriched in protein phosphorylation and signaling proteins (FDR 10−05), whereas Esa‐specific EMOs  are moderately enriched in cell wall‐related proteins and transcriptional regulators (FDR 0.08)  Table . Thus, oAth response is apparent . However, more prominent features are metabolic reprogramming and tissue remodeling  contrasting with T14 in Ath. Lastly, rewiring of the hormone signaling network is common to all species, but here also important differences can be detected. Common to all three species is a strong induction of salicylic acid  signaling proteins  pathway were upregulated in Ath and Esa. No term related to ethylene was found in any of the species. However, in Aly at T11 the l‐methionine salvage pathway was strongly upregulated , and this recycles 5′‐methylthioadenosine, a by‐product of ethylene biosynthesis  and numerous terms indicating phosphorylation‐ and ubiquitination‐mediated signal transduction were found specifically among the T11 Aly regulated genes. The significant enrichment of terms in different signaling systems  indicates that a major element of the Aly response is a substantial rewiring of the intracellular signal processing network. Importantly, the observed early growth reduction and reduced cell division phenes of Aly were mirrored by six terms related to cell cycle, cell division and growth that were enriched among the Aly EMOs at T11 but in none of the other species  and ‘lipid metabolism’, were upregulated  and the KEGG pathway ‘cutin, suberin and wax biosynthesis’ were most significant  among the Esa‐regulated EMOs  and 0.42 (Esa) vs c. 0.6 for Ath). In response to ABA, stomata aperture in Ath was reduced by 54% in comparison with mock treatment while in Aly and Esa the average aperture was reduced by 14% and 12%, respectively  of the ABRF/ABFs, WRKY and the nuclear factor Y (NF‐Y) families  or increasing intracellular osmolarity, resistant Aly and Esa have higher basal anthocyanin levels and the transcriptional upregulation of biosynthesis genes does not translate into elevated anthocyanin levels.Anthocyanin biosynthesis provided a similar picture. Anthocyanin‐metabolism‐related transcripts were upregulated during stress in els Fig. c. Duringies Fig. c,d. ThusAly responds more sensitively to lack of water, whereas Esa is in a prepared state that requires fewer adjustments in response to drought.Together the molecular data reveal a picture that is more complex than the phenotypic data suggested. While all three species share a common early transcriptional response, subsequent signal processing and response dynamics appear to have diverged, thus giving rise to the contrasting phenotypic manifestations. The data support the conclusion that Aly and positively with Esa.After the targeted analyses we aimed for an unbiased systems approach to analyse the molecular drought responses using a WGCNA  and pathway (KEGG) enrichment Figs . The modays Figs e, S13e.Aly (lightcyan) contained mostly poorly or unannotated EMOs such that no meaningful analysis was possible  and vacuole organization indicating that Esa also had to cope with energy deprivation. Striking in both modules was the enrichment of mRNA processing functions, for example spliceosome  and mRNA surveillance , suggesting that alternative splicing may play an important role in Esa drought response. Fascinatingly, while the Eigengenes for the EMOs in these modules indicate their expression in Esa throughout development and only a moderate upregulation in response to drought, genes with similar functions are strongly upregulated in Ath at T14. Also remarkable was the enrichment of all major DNA repair pathways, that is ‘nonhomologous end‐joining’, ‘nucleotide excision repair’ and ‘homologous recombination’  . We wondered whether this was a consequence of an increased production of ROS. However, at T14 we saw a dramatic decline of H2O2 levels in WD Esa plants compared to WW controls (not shown), making stress‐induced ROS‐mediated DNA damage less likely. In animals the DNA damage response is closely linked to chromatin remodeling . Moreover, at T5 and T11 histone‐modifying genes were expressed at substantially higher levels in Esa than in Aly and in Ath, providing additional support for an important role of epigenetic programming in Esa drought stress resistance  should be directed to the New Phytologist Central Office.Fig. S1 Effects of severe water deficit on photosynthetic efficiency and survival.Fig. S2 Growth rate dynamics.Fig. S3 Rosette morphology parameters.Fig. S4 Reduction in area of detached leaves at T11.Fig. S5 Measurements of stomatal parameters.Fig. S6 Dynamics of transcriptional changes.Fig. S7 Dynamics of transcriptional changes and their GO‐based functional classification.Fig. S8 Drought stress response gene expression.Fig. S9 Significant overlap of differentially regulated genes.Fig. S10 GO terms enriched at T11.Fig. S11 Stomatal aperture in response to ABA.Fig. S12 GO‐based functional classification of treatment‐associated modules.Fig. S13 KEGG‐based functional classification of treatment‐associated modules.Click here for additional data file.Table S1 Overview of phenotypic characteristics.Click here for additional data file.Table S2 Differential regulated genes and GO enrichment.Click here for additional data file.Table S3 Ortholog relationships and complete expression matrix.Click here for additional data file.Table S4 Dynamics of transcriptional changes of drought‐induced genes.Click here for additional data file.Table S5 Significant GO terms of dynamic transcriptional changes.Click here for additional data file.Table S6 Not shared GO terms and KEGG pathways at T11.Click here for additional data file.Table S7 Significant GO terms and KEGG pathways of modules.Click here for additional data file.Table S8 Gene abundance of histone modification genes.Click here for additional data file.


The visualization of molecular interactions annotated in web resources is useful to offer to users such information in a clear intuitive layout. These interactions are frequently represented as binary interactions that are laid out in free space where, different entities, cellular compartments and interaction types are hardly distinguishable. Signaling Pathway Visualizer is a free open source JavaScript library, which offers a series of pre-defined elements, compartments and interaction types meant to facilitate the representation of signaling pathways consisting of causal interactions without neglecting simple protein–protein interaction networks.https://github.com/Sinnefa/SPV_Signaling_Pathway_Visualizer_v1.0. Language: JavaScript; Web technology: Scalable Vector Graphics; Libraries: D3.js.Freely available under Apache version 2 license; Source code:  Signaling pathways are the mental abstraction used by biologists to represent a coordinated cascade of events leading to the expression of one or more genes ultimately resulting in observable phenotypes. These series of events are usually described as a collection of entities, proteins, small molecules, protein complexes, chemicals and so on connected by oriented or non-oriented links, representing interactions or signal propagations.One way of representing biological pathways is the reaction-based model. In this representation, a reaction is defined by the stoichiometry of the participating elements and by differential equations specifying their dynamics. This representation is adopted, for instance, by Reactome  on its target. Some online resources have started collecting signaling interaction information from the literature and to represent it as a set of causal relationships  whose development started with A clear, intuitive representation of signaling pathways can be hard because of two major factors: (i) the large variety of entity types and signaling effects and (ii) the need to visualize pathways in the way biologists are used to, i.e. in a top-down cascade where the signal is propagated from the membrane to the nucleus. To simplify these two tasks, we have developed a JavaScript library based on D3.js, which simplifies signaling pathways visualization. The advantages are particularly evident when representing the highly detailed annotation of SIGNOR , nucleus (transcription factors) and a bottom layer to place phenotypes. Unconstrained nodes are confined in a bounding box that represents the cytosol of the cell. This bounding box prevents nodes from going outside of the membrane and inside the nucleus but, at the same time, it lets unfixed nodes free to move according to the default layout based on spring layout  with somThe top menu bar offers some filtering options to visualize only specific interaction types or only connections above a certain edge score threshold so that the user can easily see if and how two nodes are connected, for instance, only by up-regulations. In addition, it offers the possibility of exporting the currently visualized image as a standard SVG to be used in publications, presentations and posters. Finally, it allows the user to export the network as plain-text comma-separated file.Our group is an active member of the IMEx Consortium , which will soon be formalized and integrated in SPV. In the meanwhile, we made SPV compatible with PSI-MI tab separated files downloadable via any PSICQUIC ;http://signor.uniroma2.it/pathway_browser.php? organism=&pathway_list=SIGNOR-MS&x=20&y=14).The complexity of interactions in a pathway requires a rich variety of shapes and lines. The mTOR signaling pathway, as annotated in SIGNOR, is a good example of such complexity which, SPV makes it easier to understand and explore (mentha to visualize protein-protein interactions (SPV is implemented in Causal interactions are a relatively new focus of data curation and, as such, are still missing a clear web visualization tool. SPV is a JavaScript library for visualizing molecular interaction data with a particular focus on causal interactions.SPV has been developed, tested and embedded in our major databases and we are planning to extend it to other online resources and possibly add other features to make data visualization even more effective without overlooking simplicity. In particular, we are focused on implementing other layouts and extend the SPV file format compatibility as new standards will become available.We are releasing our code not only to let others benefit from our work but also to benefit from and promote open source collaborations in biological data visualization.


Impulse control and related disorders (ICDs-RD) encompasses a heterogeneous group of disorders that involve pleasurable behaviors performed repetitively, excessively, and compulsively. The key common symptom in all these disorders is the failure to resist an impulse or temptation to control an act or specific behavior, which is ultimately harmful to oneself or others and interferes in major areas of life. The major symptoms of ICDs include pathological gambling (PG), hypersexualtiy (HS), compulsive buying/shopping (CB) and binge eating (BE) functioning. ICDs and ICDs-RD have been included in the behavioral spectrum of non-motor symptoms in Parkinson's disease (PD) leading, in some cases, to serious financial, legal and psychosocial devastating consequences. Herein we present the prevalence of ICDs, the risk factors, its pathophysiological mechanisms, the link with agonist dopaminergic therapies and therapeutic managements. Impulse control and related disorders (ICDs-RD) encompass a heterogeneous group of disorders that involve pleasurable behaviors performed repetitively, excessively, and compulsively –8.The common key symptom in all of these disorders is the failure to resist an impulse or temptation to control an act or specific behavior , 3, 9, wThe American Psychiatric Association's Diagnostic and Statistical Manual (DSM-5) included impulse control disorders (ICDs) in the chapter of “Disruptive, Impulse-Control, and Conduct Disorders” as a dysregulation of self-emotional and behavioral control .ICDs have recently been sub-classified as ICD groups and ICD-related disorder (ICDs-RD) groups , 3, 6, 7The major symptoms of ICDs include pathological gambling (PG), hypersexuality (HS), compulsive buying/shopping (CB) and binge eating (BE) , 12–21.However, PG was moved from the category of ICDs to a new category of “Substance-Related and Addictive Disorders” in the DSM-5 –3, 6, 7,The spectrum of ICDs-RD also includes punding, hobbyism, walkabout, hoarding, and compulsive medication use.ICDs and ICDs-RD have been included in the behavioral spectrum of non-motor symptoms in Parkinson's disease (PD), leading in some cases to serious financial, legal and psychosocially devastating consequences with a greater impact on the quality of life. Moreover, in recent years we have noticed that PD patients are at increased risk of developing more than one of the major ICDs.Along these lines, although it is not the focus of the present paper, some authors have suggested that the increased drive or motivation to certain behaviors cannot be harmful but rather beneficial . TherefoThree main aspects that characterize ICDs groups and ICD-related disorders in relation to reward-driven activities are:The presence of impulsive aspects (lack of forethought or consideration of consequences) , 3, 9.The presence of compulsive aspects (repetitive behaviors with a lack of self-control) , 3, 9.A negative or harmful behavior to oneself or to others , 3, 6.The four major ICDs include:Pathological Gambling (PG) characterized by an excessive and uncontrollable “preoccupation with gambling and the excitement that gambling with increasing risk provides” despite financial loss and social problems , 25–27. Hypersexual disorder (HS) included in “The Sexual and Gender Identity Disorders Workgroup” of DSM-5 . It coulBinge eating has been included in “Feeding and Eating Disorders” in DSM-5 , 6, 7. IEpisodes of recurrent binge eating in the absence of any maladaptive compensatory behaviors.Sense of lack of control over eating during the episodes.Intake, in a discrete period of time (within any 2 h period), of an amount of food that is much larger than most people would eat in a similar period of time under normal circumstances.The difference between binge eating and bulimia is that the former tends to be fluctuating while the latter is permanent , 7.Compulsive buying (CB) is characterized by a constant urge to buy that leads to senseless contraction of debts with continuous delay of payment until a catastrophe clears the situation. As other ICDs, the repetitive loss of control over spending and the negative emotional state that emerges when not buying resemble substance use disorders , 7.A prevalence of 5.8% in the general population at risk of CB is described , 3.per se.ICDs-RD are classified as related behaviors that have a contrast clinical presentation with respect to the four major ICDs. However, the biological link between both conditions may be identified in the dysregulation or inappropriate regulation of the reward pathways in the mesocorticolimbic network , 30. ICDICDs-RD include the following:Dopamine dysregulation syndrome (DDS) is a drug addiction-like state characterized by a compulsive and excessive desire for use of high potency and short-acting dopaminergic medication  , 30, 31.Punding is characterized by repetitive, purposeless behaviors and excessive preoccupation with specific items or activities, collecting, arranging or taking objects apart , 26, 32.Hobbyism pertains to higher-level repetitive behaviors  , 17–21.Walkabout is excessive aimless wandering –21, 26.Hoarding is the acquisition of and failure to discard a large number of items with no objective value , 7, 12, The prevalence of ICDs in the general population, which has been underestimated, shows a wide range with variability according to different populations: from 0.2 to 5.3% . This enAlthough the ICDs were initially reported in PD patients on DA therapies, some studies report the occurrence of ICDs in the general population and in novo PD patients , 11, 34.As mentioned above, it remains under discussion whether or not PD itself confers an increased risk for developing ICDs . Identifde novo patients, and in PD patients with and without DA agonist therapies in different populations represents a significant challenge since a number of variables must be analyzed, including assessment tools, DA dose, DA formulations, years of disease, as well as cultural and other factors. Moreover, in many cases more than one ICD has been identified  and between extended releases or immediate formulations , 29, 60.It remains under discussion whether there is an association between ICDs and DA dose. The same controversial results were reported regarding DA treatment duration, higher daily dose and DA higher peak dose , 29, 60.+ Personal or family history: history of cigarette smoking, drug abuse, depression, apathy, REM behavior disorders (RBD), tea, coffee and mate consumption, positive personal or family history of alcoholism or gambling, and impulsive or novelty-seeking traits increase the risk for ICDs and their predictors , 59, 60.+ PD onset and related ICDs: prevalence increases over time, while ICDs tend to occur in the first years of the disease. Early PD onset and the presence of motor complications of PD may predict a higher risk for ICDs , 29, 60.+ Cultural factors: it remains to be determined if cultural factors may increase the risk for ICDs and ICRDs. Some authors suggest that cultural factors probably contribute not only to the prevalence of ICD but also the type of ICD , 17. One+ Deep Brain Stimulation (DBS): the relationship between ICDs and DBS remain under discussion. Initial studies reported improvement in ICDs after DBS, while subsequent studies showed ICD exacerbation , 60, 62.DBS of the subthalamic nucleus (STN) is an effective, widely used treatment for motor fluctuations or disabling dyskinesias in PD .STN-DBS has been identified as an independent risk factor for ICRDs; however, the reduction of dopamine agonist dosage after STN-DBS could improve or decrease ICD occurrence , 60, 62.On the other hand, several studies suggest that DBS may contribute to impulsivity, excessive reward seeking and ICDs. Consistent with this hypothesis, PD patients without ICDs showed impulsive decision making when DBS is turned on , 62, 64.To explain these controversial findings, it has been hypothesized that STN stimulation plays a role in dynamic aspects of impulse and inhibitory control , 60.+ Personality, Neuropsychiatric symptoms and Cognition in ICDs: a higher level of neuroticism, ineffective coping skills, and lower levels of agreeableness and conscientiousness in PD patients with ICDs has been reported . Early oA large constellation of comorbid affective symptoms and behavioral traits have been reported in PD with/or at risk for ICDs including depression, anxiety, novelty seeking, impulsivity symptoms and anhedonia , 65, 66.+ Cognition: controversial data have been identified in cognitive battery tests between PD patients with and without ICD , 36; theVisuo-spatial working memory and reward-punishment learning impairments have been reported in different studies; however, many results could not be replicated , 17.Interestingly, patients with ICDs showed a more immediate reward response and greater choice impulsivity leading to increased risk behavior .When the cognitive performance was compared according to the type of ICD it was found that patients with HS showed greater general cognitive impairment, including lower performances on learning tests and were more impaired on the Stroop test and memory tasks than were patients with PG , 68. How+ Genetics: genetic factors have been involved in ICDs in PD. Although heritability was estimated to be 57%, consensus remains a challenge and data need to be replicated in large cohorts from different populations . A largeIn Interestingly, the ICARUS study, the largest prospective observational study in an Italian population, contributes to the identification of additional risk factors that include non-motor symptoms , mood symptoms (depression), sleep disorders and a low level of quality of life .Recently, the overexpression of ΔFosB, a transcriptional regulator involved in addiction induced by drugs of abuse and in many types of compulsive behaviors has been reported to be associated with L-dopa induced dyskinesia and to be triggered by pramipexole .The ΔFosB overexpression was identified in the nucleus accumbens (NA) and the striatum (brain regions important for addiction) of healthy and DA-lesioned rats exposed to pramipexole and found to be NMDA receptor dependent. These findings suggest that enhanced ΔFosB expression may represent the strongest predictor of PD patients at risk of ICDs , 60.Although an extensive number of studies have focused on the pathophysiologic mechanisms of ICDs in PD, these remain to be clarified , 9. ClasNevertheless, in recent years, evidence has suggested a complex multifactorial mechanism beyond the dopaminergic corticostriatal networks, including a complex serotoninergic and noradrenergic interaction. Further investigation is required .Dopaminergic receptors, Dopamine 1 receptor 1 (D1R) (D1 and D5) and Dopamine 2 receptor (D2R)  types possess contrasting roles with inhibitory and excitatory signaling, respectively. These contrasting roles are present not only in the nigro-striatal pathway but also in the mesolimbic and mesocortical circuits. The pathways link cortical and subcortical regions ; both circuits are implicated in reward learning and executive decision making or reinforcement behaviors, respectively , 22, 74.Anatomical regions involved in ICDs:Planning and judgment areas: caudal orbitofrontal cortex, ventromedial prefrontal cortex (PFC).Reward system: ventral striatum (VS-nucleus accumbens [NA]).Conditioned responses and emotional processing: amygdala.Medial dorsal and anterior nucleus of the thalamus , 75.In PD with ICDs a marked decrease ventrostriatal D3R-binding has been reported, while experimental PD models have shown an increase in DA levels in the NA associated to bilateral nigrostriatal DA denervation , 76. TheIn this scenario, the tonic “overdosed” by D2/D3 receptor agonists in the mesocorticolimbic circuit could contribute to suppress, through the impairment of top-down inhibitory control from prefrontal cortical area (PFC) inputs to the ventral striatum, reward-related learning and induce compulsive, perseverative behavior through the direct D1 receptor pathway , 9, 22.Dopaminergic agonists (DA) show a high D3R affinity in the mesolimbic system , 9, 60. The D1 receptor family localize in the direct pathway of reward-based behaviors. Stimulation increases the activity of striatal projections to the nucleus accumbens/ventral striatum, while D2 receptors elicit suppression of the cortico-accumbens network , 22, 80.In recent years neuroimaging, particularly that which is focused on the dopaminergic system, has significantly contributed to the knowledge of neurobiological factors for ICDs , 81, 82 Structural MRI changes have been reported in PD patients with ICDs with a selective atrophy in the orbitofrontal and anterior cingulate cortices . Atrophy in the orbitofrontal cortex has been reported in PD patients with ICDs , 91.Functional brain resonance (fMRI) studies have reported an abnormal metabolism on the frontostriatal and cingulate cortices, the nucleus accumbens and the amygdala , 120.A connectivity dysfunction between the striatal and limbic areas has been proposed. Brain connectivity was impaired in PD patients with ICDs with respect to the PD individuals without ICDs involving the neurocognitive network. A decreased connectivity has been identified in the central executive networks , while an increased connectivity has been identified in the salience network  and in the default mode network  , 97.Single photon emission computed tomography (SPECT) of the dopamine transporter (DAT).DAT regulates dopamine turnover. A reduced DAT binding in PD patients with PG and ICDs has been identified in PD patients with ICD compared to PD patients without ICD or healthy controls. This reduced binding of DAT has been suggested as a potential biomarker for risk of developing ICD symptoms , 36, 60.Positron emission tomography (PET) neuroimaging with 11C-raclopride explores the DA fluxes within the basal ganglia. The 11C-raclopride is a reversible binding to the post-synaptic D2/3 receptor that competes with endogenous DA , 107. DeA significant reduction of 11C-raclopride binding has been reported in ventral striatum, but not in dorsal striatum, in PD with ICDs (single or multiple) as compared to PD individuals without ICDs, following generic reward-related vs. neutral visual stimuli.A more selective radioligand [18F]fallypride, with high affinity D2-like receptors (D2/D3 receptors) confirmed a reduced binding within the VS and putamen .All of these findings contribute to support a mesocorticolimbic imbalance in PD with ICDs .The first approach for ICD is prevention, and a key element is patient and family education concerning potential risks of different dopaminergic therapies. Physicians should be aware of predisposing risk factors and balance cost/benefit before DA prescriptions, excluding genetic factors and taking into consideration clinical findings, such as young age, early PD onset, lengthy disease duration, personal history of addictive behaviors, male gender, short-acting DA drugs, behavior and mood disorders , DBS and certain cultural factors that require attention before prescription.When ICDs appear, treatment continues to be a challenge. Individualized treatment must be conducted, identifying potential variables, such as motor status, comorbidities, other non-motor symptoms and quality of life , 123.The relevance of prevention is supported by NICE guidance that includes written information, or verbal information recorded in writing, at DA initiation of treatment. The authors emphasize the relevance of communicating to patients, relatives and carers the risk of ICDs due to the potential impact on their lives and for early detection .The first approach for the treatment of ICD symptoms is the reduction or discontinuation of DAs. However, it should be considered that neuropsychiatric traits may persist for at least 12 weeks after drug withdrawal , 61, 123Nonetheless, in certain cases this strategy is not feasible, and some patients are at risk of developing DA withdrawal syndrome and worsening motor symptoms , 61, 123Although animal PD models have identified serotonin (5HT) depletion as a higher risk for impulsivity and risk behaviors, the serotonin reuptake inhibitors (SSRIs) used to treat ICDs had controversial results , 123.Atypical antipsychotics, such as clozapine and quetiapine have been used to treat ICDs in PD, but no randomized trials have been conducted and evidence is limited , 7.Taking into consideration that specific SNP opioid receptors have been identified as stronger risk factors for ICDs, opioid antagonists employed in the treatment of PG have produced controversial results  , 60, 123A number of drugs administered to increase Gabaergic inhibition , as well as new drugs to preserve ventral striatal DA system  have been essayed .As previously mentioned, controversial data are available concerning DBS and ICD treatment. A favorable response through reduction in dopaminergic requirements has been noted. It has been suggested that STN stimulation could reduce the risk for ICDs by increased reward-driven behaviors by inhibitor effect in the indirect dopaminergic pathway. However, some patients may develop transient de novo ICDs after STN DBS, and selective patients may develop ICDs a long time after DBS , 125.A non-pharmacologic approach includes cognitive behavioral therapy and patient and caregiver education , 60.The treatment used for PD, particularly DA, is associated with the development of ICDs and related behaviors. Susceptibility to these disorders depends on the associated risk factors.ICDs can have serious personal, family, psychosocial, financial, and medical consequences. However, in contrast, artistic activities have been described in patients with PD while undergoing treatment with DA. These patients are compulsive but report a positive influence on quality of life.These findings highlight the need for a very critical approach at the moment of Dopaminergic Replacement therapy choice.EG: study concept, design, and editing. VA: study concept and editing of 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.


Imaging techniques can provide information about the tumor non-invasively and have been shown to provide information about the underlying genetic makeup. Correlating image-based phenotypes (radiomics) with genomic analyses is an emerging area of research commonly referred to as “radiogenomics” or “imaging-genomics”. The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.We performed quantitative radiomic analysis on 47 invasive breast cancers based on dynamic contrast enhanced 3 Tesla MR images acquired before surgery and obtained gene expression data by performing total RNA sequencing on corresponding fresh frozen tissue samples. We used gene set enrichment analysis to identify significant associations between the 186 gene pathways and the 38 image-based features that have previously been validated.All radiomic size features were positively associated with multiple replication and proliferation pathways and were negatively associated with the apoptosis pathway. Gene pathways related to immune system regulation and extracellular signaling had the highest number of significant radiomic feature associations, with an average of 18.9 and 16 features per pathway, respectively. Tumors with upregulation of immune signaling pathways such as T-cell receptor signaling and chemokine signaling as well as extracellular signaling pathways such as cell adhesion molecule and cytokine-cytokine interactions were smaller, more spherical, and had a more heterogeneous texture upon contrast enhancement. Tumors with higher expression levels of JAK/STAT and VEGF pathways had more intratumor heterogeneity in image enhancement texture. Other pathways with robust associations to image-based features include metabolic and catabolic pathways.We provide further evidence that MR imaging of breast tumors can infer underlying gene expression by using RNA sequencing. Size and shape features were appropriately correlated with proliferative and apoptotic pathways. Given the high number of radiomic feature associations with immune pathways, our results raise the possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.The online version of this article (10.1186/s40644-019-0233-5) contains supplementary material, which is available to authorized users. Recent advances in image acquisition, computational power, and algorithmic development have increased the awareness of computational image analysis methods, allowing for computer-extracted features, i.e. phenotypes, from computer-aided diagnosis and quantitative imaging algorithms to yield “radiomics”, i.e., the high throughput conversion of complex imaging data sets into a multi-dimensional feature space with extractable characteristics –3. ImagiCorrelating specific image-based phenotypes (radiomics) with large-scale genomic analyses (genomics) is an emerging area of research commonly referred to as “radiogenomics” or more specifically “imaging-genomics”. This emerging field addresses novel high-throughput methods of associating information-rich radiographic images with genomic data as well as other clinically relevant information –25, 27. BRCA1/2 mutation [UGT2B gene variation [The application of radiogenomics to breast cancer has recently been gaining more attention. For example, studies using mammographic images showed that radiographic texture analysis could identify patients who are more likely to carry mutation , and parariation . Higher-ariation , 30–34. ariation , 13. Mazariation . Agrawalariation . Gene exariation .Recent studies conducted by the TCGA Breast Phenotype Group examined breast MRI radiomics with clinical stage, molecular classification, risk of recurrence, gene expression profiling, and other genomic analyses –25. In tHere, we sought to further elucidate and validate relationships between imaging phenotypes and gene expression pathways by analyzing clinical MRI data of breast cancers taken from higher resolution 3 T scanners at the University of Chicago Medical Center using RNA sequencing transcriptomic data extracted from corresponding frozen tissue samples.BRCA1/2 mutations, and those who had received neoadjuvant chemotherapy, as we were interested in pre-treatment gene expression and imaging profiles. Out of the remaining 139 cases, we selected 50 for this study, which included only images taken from our institution’s Phillips 3 T MRI scanner in order to reduce image acquisition variability. Forty-seven of these cases had complete dynamic contrast enhanced time-lapsed images, while three cases only had a single pre and post-contrast image. These 47 cases used for further analyses included 12 triple negative breast cancer cases as well as 26 randomly selected ER positive cases, 4 ER/HER2 positive cases, and 5 ER negative, HER2 positive cases. Three pairs of cases were derived from bilateral breast cancers, and two cases represented separate biopsy areas from the same tumor.  were acquired using a dedicated 16-channel Philips SENSE-16 M breast coil on a 3 T Philips Healthcare Achieva system . The DCE-MRI protocol acquires one pre- and 5 post-contrast-enhanced axial T1-weighted images with fat suppression using 3D gradient-echo sequences . Gadodiamide contrast material of 0.1 mmol/kg  was injected intravenously followed by a 20-mL saline flush at 2 mL/s.  database  and the p-value for evaluating the statistical significance of an association was calculated based on 10,000 random gene sets. The False Discovery Rate (FDR) was controlled for each radiomic phenotype over its association tests with all KEGG pathways using the Benjamini-Hochberg procedure [p-value was ≤0.05. The associations between gene sets and radiomic phenotypes were tested separately for two different directions, i.e. positive association and negative association.For each of the 38 radiomic phenotypes, we performed the GSEA for identifying the KEGG pathways whose transcriptional changes associated with the change of radiomic phenotype. The gene-level statistic used to characterize the relationship between a gene’s expression and a radiomic phenotype was the correlation coefficient resulted from the Spearman rank correlation test. Nominal rocedure . An assoWe successfully obtained radiomic and gene expression data from 47 cases Fig. . AnalysiWe first examined the relationship of radiomic features to gene expression pathways involved in replication, proliferation, and apoptosis Fig. . Based oOut of all pathway categories, those related to immune regulation had the highest number of significant correlations with radiomic features, with an average of 18.9 associations per pathway Fig. . A diverPathways related to extracellular signaling molecules had the second highest number of significant correlations, with an average of 16 associations per pathway Fig. . We compWe next examined the relationship between the signaling pathways and radiomic features. Six pathways had at least one significant association with a radiomic feature Fig. . Two of Many of our findings suggest biological plausibility and highlight interesting associations between the gene expression pathways and radiomic phenotypes that may be clinically relevant. For example, all size phenotypes were positively correlated with cell proliferation pathways and negatively correlated with the apoptosis pathway. The relationship between cell death and shape phenotypes suggests that increased expression of the apoptosis pathway was associated with more spherical and less irregular tumors. While this particular relationship is biologically predictable, it provides reassurance about the validity of using these imaging features to gain insight about others, perhaps less obvious gene expression pathways.Perhaps most interestingly, we found that a diverse array of immune related pathways had the most robust relationship with imaging features. As a group, these pathways tended to associate with similar features in the same directionality: tumors with increased degree of overall immune activation appeared to be more confined on imaging as shown by negative correlation with size features and positive correlation with sphericity. They also tended to be more heterogeneous in texture upon contrast enhancement as seen by positive correlation with entropy and negative correlation with energy. Many of these imaging features were also similarly associated with cytokine-cytokine and CAM pathways as well as the JAK/STAT signaling cascades, which play an important role in immune regulation and in particular, lymphocyte signaling and activation. For example, Additional file The association between the VEGF pathway and several radiomic phenotypes was also demonstrated in our study and has a plausible mechanistic link. Dynamic contrast enhanced MRI can quantify changes in the microvascular physiology of tumors , and stuThis is the first report to our knowledge that integrates whole transcriptome analysis using RNASeq with 3 T DCE MRI data of patients with breast cancer. However, we acknowledge limitations of the study including the relatively small sample size leading to an inability to determine the causal relationship between the gene pathway and radiomic features. Nevertheless, the associations that we do see are biologically plausible and could lead to further advances in the analysis presented here. Several other challenges remain that must be overcome in order to better utilize the potential offered by this approach. For example, studies to date have only centered on data gathered from a single tissue sample of a given primary tumor. It is well known that the genetic makeup of tumors is spatially heterogeneous, and thus, a more accurate approach would be to correlate multiple tumor biopsies with their respective anatomic location on MRI. With techniques such as whole mount pathology being developed, it may become more feasible to pursue this idea. Also, standardization of imaging features poses a challenge. While our focus on obtaining images from one type of scanner and protocol improved the reliability of our radiomics data set, we acknowledge that image acquisition protocols also vary across institutions, and generalization of our findings will need further validation with larger sample sizes and examination of robustness studies as previously conducted for computer aided detection . Lastly,The recently published TCGA/TCIA Breast Phenotype Group study also combined DCE-MRI with RNA sequencing data in 91 patients, using the same quantitative radiomic approach developed by the Giger lab, although the MRIs performed in that study were acquired across several different institutions and used 1.5 T scanners. Our work further supports the feasibility of incorporating radiomics from clinical MRIs with gene expression data in support of the TCGA/TCIA Breast Phenotype Group approach using higher resolution 3 T scanners .The incorporation of automated feature extraction algorithms (i.e. quantitative radiomics) into routinely performed, noninvasive imaging modalities, such as DCE-MRI has the ability to stimulate the development and use of imaging biomarkers that may provide clinicians inferred biological information otherwise attainable only through direct tissue biopsy. Potential applications include not only improvement of diagnostic ability, but also adaptation of neoadjuvant treatment plans of primary tumors in real time, such as that seen in the recent I-SPY trials .We provide further evidence that MR imaging of breast tumors can infer underlying gene expression using RNA sequencing data. As expected, size and shape features were appropriately correlated with proliferative and apoptotic pathways. We also discovered a large number of radiomic feature associations with immune pathways, which raises the interesting possibility of using MR imaging to distinguish tumors that are more immunologically active, although further studies are necessary to confirm this observation.Additional file 1:Table S1. Gene Set Enrichment Analysis and Significant Pathways. The first tab includes all statistically significant associations between radiomic features and genetic pathways included in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All these associations are identified through Gene Set Enrichment Analysis (GSEA). In the second tab, pathways are grouped by major categories and listed with the total number of radiomic associations. (XLS 129 kb)Additional file 2:Figure S1. Clinicopathologic Characteristics of Patient Cases. Clinicopathologic parameters including age, pathologic stage, grade, histologic subtype, receptor status, as well as additional parameters involving type of surgery, chemoradiation, recurrence, and death are shown. (PPTX 61 kb)Additional file 3:Figure S2. Sample 3T MR Images. Four representative cases are shown (A-D) with transverse cross-sections of breast tumors depicted in each image. Figs. C and D represents the case with the highest and lowest score with respect to T-cell receptor signaling pathway, respectively. Pathway score was obtained as follows: for each gene in a pathway, gene’s expressions were ranked over samples from high to low, and the average rank of all genes in the pathway for each sample was obtained. (PPTX 225 kb)Additional file 4:Figure S3. Radiomic Feature Extraction Pipeline. Time-lapsed dynamic contrast enhanced MR images of breast cancer tissue are collected, and a staff radiologist manually indicates the tumor center. This serves as the input for computerized tumor segmentation, which delineates the boundaries for the final step, extraction of image phenotypes. Image phenotypes fall under six characteristic categories. (PPTX 79 kb)Additional file 5:Figure S4. Radiomic Features by Category. All thirty-eight radiomic features extracted are listed according to six characteristic category including size, shape, morphology, enhancement texture, kinetic curve assessment, and enhancement-variance kinetics. (PPTX 205 kb)


Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework . ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals . By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction. The general gene-set or gene module can be used to find combinatory biomarkers or signatures to indicate particular phenotypic change of a biological system, e.g. the disease development or worsening –3. HowevBiological pathway consists of a set of interactive genes or other biomolecules, which is well-known to execute a series of functional cascades for particular cellular response/outcome . NowadayIndeed, pathway centered models and methods have been widely applied in diverse biological and clinical studies. For instance, the well-known gene set enrichment analysis (GSEA)  can recoActually, there exists some pathway-related studies based on the time-course experiment and data, e.g. a pathway-based phylogenetic approach , a web-bHere, as shown in Fig. Normalization of gene expression data produced by high-throughput technologies, e.g. RNA sequencing or microarray;Quantification of pathway activity according to pathway measuring approaches, i.e. GSVA using the expression data of each sample and gene-sets from KEGG database;P < 0.05), where the number of finally selected pathways is dependent on the maximal number of significantly dys-regulated pathways at different time points;Selection of key phenotype-associated pathways through differential significance test , thus, the organization of pathway activity profile is a cubic dataset, including key pathways, individuals, and time points; of note, the number of key pathways should be less than the number of time points, which is required by the follow-up BN construction;Reconstruction of individual specific Boolean network based on corresponding time-course pathway activity profile, which is calculated by BoolNet method, using binarizeTimeSeries function with “scanStatistic” method, windowSize with intervals from 0.1 to 0.2, sign.level with intervals from 0.05 to 0.15; and applying reconstructNetwork function with “bestfit” method and default arguments.Extraction of individual specific attractors corresponding to each Boolean network, which represent the individual specific steady pathway state so as to distinguish dissimilar phenotypes by unsupervised (i.e. HCL) or supervised  methods.Our proposed ABP framework consists of a few main steps and parameters as illustrated in Fig. These key steps applied in above framework will be introduced in details in below.Due to the reduced costs of high-throughput technologies, omics data is being produced under more complex experimental designs with multiple phenotypic and/or clinical samples. The Gene Set Variation Analysis (GSVA)  allows tNotably, several approaches have proposed some pathway-level aggregation methods, but, it still remains unclear how they compare with one another due to limited evaluations. One recent benchmarking investigation has pointed that there is actually necessity for further improving pathway-level aggregation . As knowThus, any alternative kinds of activity score can also be used in this step if necessary in particular analysis routines, which would supply tunable analysis strategies dependent on researcher’s objective and experience.With an assumption of that the subjects / individuals with the same clinical symptoms should have the similar molecular / pathway reaction mechanism, the pathway activity may be various in individuals, but the final steady state of a network among pathways should be similar among the same symptom group or distinguishing between different symptom groups. Thus, we rank the differentially activated pathways between different phenotypic individual groups at each time point, which are dependent on the difference test of the activity scores by Wilcoxon rank sum test.Then, according to the number of high-ranked dys-regulated pathways at each time point, we determine one critical time point, and the pathways significantly enriched (e.g. observed) at this time point are key pathways. These selected key pathways and their activities across consecutive time points can be regarded as the state indices which could trace the active pathway during a dynamical process, even in each subject. Noted, the final state or converged state (rather than currently observed state) of one pathway is characterized and quantified by the steady sate of this pathway in a subject, and this steady sate is modeled by network attractor and deduced from following Boolean network analysis.N kinds of interacting nodes (i.e. pathways or meta-genes) are collected, the states of which are modeled as either “on”  or “off” . Then, at any given time, the system of such N key pathways is just in a system- or network-state. Along with a dynamical process, the system will change from one state to another depending on the interactions between these pathways. Thus, from any start state, the system is assumed to happen a confirmed sequence of state changes and ends up in a stable state (known as an attractor), and this sequence or trajectory of such system state change is defined as a Boolean process corresponding to discretized time-course data.To characterize the dynamical features of a biological system among above filtered key pathways, a network of pathways is constructed based on Boolean network model. At the starting point for a Boolean Network Model, i(t) represents the state of some variable (e.g. a node in network) i at time point t; aji points the influence weight of another variable j for this variable i; thus, the state of variable i at time point t + 1 would be determined by all other variables’ states at the time point ahead, i.e. at time point t.In practice, the Boolean network model is applied to convert the gene or pathway expression snapshots over a time course into a Boolean form by noting which genes or pathways are active or not. The dynamics of a Boolean network (BN) model (simply using the current state to determine the next state) can be described as follows:In this study, the BoolNet package is used for the construction and evaluation of Boolean networks, which has been successfully applied in many biological and biomedical researches . In partAs assumed, the attractors represent a final steady state of a biological system, e.g. the success-infection or failure-infection states during virus infection. Obviously, these final states are expected to classify or even predict the particular phenotypes of individuals. Here, it is relevant to evaluate the indicative power of those state features rather than expression features, so that, the clustering of those states and expressions would be applicable and comparable, e.g. hierarchical clustering of pathway states and gene expressions respectively. However, in math terms, a state of an attractor is just a binary vector (attractor with simple structure) or a group of vectors (attractor with loop structure), so the conventional clustering method is not applied before we can supply the distance matrix among states. To generally measure the distance between two states (attractors), the canonical-correlation analysis (CCA) is used to infer the association information from cross-covariance matrices, which can indicate the association (distance) between two groups of vectors, i.e. two attractors.a and b to maximize the correlation between a ′ X and b ′ Y, i.e.XX = Cov, ΣYY = Cov, ΣXY = Cov, and Cov is covariance matrix.Given two matrix X and Y, canonical-correlation analysis is to seek two vectors Then, the distance matrix converted from CCA matrix between multiple attractors corresponding to multiple individual Boolean networks, is used to build the hierarchical tree. Two clusters are divided, expecting one corresponds to Sx group and the other one corresponds to Asx group. And the clustering accuracy, defined as the application efficiency, is evaluated by the index Acc . Given sThen, the application efficiency of network reconstruction is calculated as:Besides, the performance measurement of SVM adopts the AUC with random 5-fold cross-validation, which is used to evaluate the robustness of SVM features (i.e. pathway states as indicators).To assess ABP, we collected and analyzed serially sampled gene expression data from a challenge study , noted ath time point (i.e. 48 h after inoculation), and 13 pathways significantly changed at this time point suggest a critical point between two groups of subjects. These selected pathways in Table th time point, there were also nine dys-regulated pathways observed, only two of which were also observed at 12 th time point , and the remains had less significance relevant to the studied disease. Thus, the selected 13 pathways at 12th time point should be more pathogen informative in following analysis.Based on the pathway activities across multiple samples, we determined the critical time point and corresponding selected pathways. Seeing Fig. Rhinovirus (RV) leads the majority of common colds, and also causes exacerbations in patients with asthma and chronic obstructive pulmonary disease. In the detected key pathways by ABP, many signaling pathways were efficiently detected and could be productive entry pathways for HRV. By examining the effects of tiotropium on RV infection and RV infection-induced airway inflammation, RV14 titres, RNA and cytokine concentrations would be reduced, along with the reduction of the expression of intercellular adhesion molecule and the number of cellular acidic endosomes modulating airway inflammation in rhinovirus infection . InfluenIn addition, a few our detected biological processes and functions are also associated with virus infection –42, suchFor each subject / individual, the topological structure of his / her Boolean network is displayed in Fig. In addition to the aforementioned comparison and discussion about topological structures of individual specific BN, we further compared the individual specific final states of BN represented by the network attractors of networks. As illustrated in Fig. To further validate the robustness of ABP, we used another gene expression data also inoculated with live human rhinovirus (HRV) , and thiIn this study, we designed and implemented a computational framework ABP to investigate the dynamic of biological process for individual comparisons, especially on the network of pathways rather than genes. Based on the quantification of pathways activity (i.e. estimated activity score based on expression), the Boolean networks are reconstructed for each individual, so that the important network attractor can be obtained and applied to infer the final stable biological system state of individuals after particular phenotypic change (e.g. virus infection). Noted, some other pathway score like Pathifier has also been tested, however, the results show it is weak to obtain reasonable Boolean network, which would be caused by these scores being limited into a numeric region that would greatly affect the following network inference. Thus, the optimized combination of pathways activity estimation and Boolean network construction should be deserved to future study. Actually, both the structure and the attractor of the Boolean network can reveal indicative features (e.g. pathway associations or pathway activations) distinguishing Sx individuals or its sub-group from the Asx individuals. They would provide candidate disease identification or prediction approaches when there are enough individuals with temporal samples available .Indeed, our proposed ABP has shown its efficiency and accuracy on identifying phenotype-associated pathways. These identified pathways could explain the phenotypic change in a state-transition manner, which is strongly supported by wide analysis and evaluation on multiple temporal gene expression datasets of HRV infections. A future direction is to further investigate how to build attractor-focused disease prediction model , and esp