Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME-model mostly agree with the standard biomass objective function used in models of metabolism alone (M-models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of peroxyl scavenging acids in the proteins used to sustain aerobic growth; (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated resource allocation models, such as genome-scale models of metabolism and gene expression (ME-models), have the ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we apply the ME-model for  Escherichia coli is capable of growing in many environments, each of which requires a different collection of enzymes to metabolize the nutrients within that environment. Each individual enzyme requires its own set of amino acids and oftentimes cofactors, which are accessory molecules essential for the enzyme to function. Thus, the composition of the micronutrients  within a cell will differ depending on its metabolic needs. The presented work is the first effort to employ metabolic models to probe the connection between E. coli’s diverse growth environments and its biomass composition. We first show how differences in model-predicted enzyme use for aerobic or anaerobic growth results in distinct amino acid and cofactor usage. Alternatively, we show that the metabolic models can predict how modifying the cell’s biomass composition will affect growth. For example, by modeling the exposure of E. coli to trimethoprim or sulfamethoxazole—two antibiotics that target folate (vitamin B9) synthesis—we predicted how E. coli could adapt to grow under folate-limited conditions. This work demonstrates how models can be used to study antibiotic resistance of drugs that target amino acid or cofactor synthesis. Genome-scale metabolic models (M-model) are established approach for studying an organism’s metabolic capabilities. M-models have shown significant success in predicting the metabolic capabilities of a cell by integrating all the experimentally determined enzymatic reactions taking place in an organism –4. TheseE. coli growing in iron-limited stress conditions [The use of this biomass objective function, however, implies that the abundance of all major components in a cell does not change based on growth rate or condition. In actuality, the macromolecular composition of a cell is highly dependent on its specific growth environment. This variability is due to the fact that the macromolecular composition of a cell is a function of the specific collection of proteins used to sustain growth in a particular environment. A key component of synthesizing a functional proteome—along with translating the amino acid chain and folding the peptides into their proper 3D structure—involves equipping enzymes with the necessary prosthetic groups and coenzymes. These accessory enzyme cofactors often drive the chemical conversions at the heart of an enzyme’s activity, making their presence essential for detectable catalytic activity ,7. The fnditions .Despite their importance in sustaining metabolism, cofactor biosynthesis is not modeled mechanistically in M-models. This is due to the fact that cofactors are either enzyme prosthetic groups and thus have no modeled metabolic function  or can be recycled , meaning there is no metabolic process driving their biosynthesis. Thus, cofactors have often been incorporated into the biomass objective function to force the essential biosynthetic activity of enzymes forming these cofactors . BiomassMetabolism and Expression (ME-models) [E. coli ME-model [E. coli.To that end, various methods have extended M-models to explicitly include the synthesis and use of the gene expression machinery, including coenzymes and prosthetic groups . These m-models) –20, and -models) , the eff-models) , and the-models) . FurtherME-model  to examiE. coli K-12 MG1655, along with many other microbes, are capable of de novo synthesizing all of the essential cofactors needed for growth listed in Table 1. Thus, the pathways that require these cofactors are included in both the E. coli K-12 MG1655 M-model . Thus, for a particular enzymatic reaction to carry flux in the model, not only must the amino acids be synthesized in the proper proportions, but enzyme prosthetic groups must also be available. The condition-specific synthesis demand of both prosthetic groups and amino acids can therefore be assessed through ME-model computation.(iJO1366 ) and ME-iJL1678b ). UnlikeiJL1678b was thus modified to account for the activity of these coenzymes and to couple coenzyme synthesis to its metabolic function . Coenzymes were included in the analysis if they met 2 criteria: 1) they function in the cell exclusively as coenzymes within the E. coli metabolic network  2) they (or a very close derivative) are included in the E. coli wild-type biomass objective function. The activity of each coenzyme that met these criteria was determined based on the flux through the reaction that synthesizes the coenzyme . Using this modified ME-model, growth simulations could effectively de novo predict the composition of E. coli K-12 MG1655’s appropriate biomass objective function in a condition dependent manner.Unlike prosthetic groups, coenzymes such as NAD and folates act as carriers that donate and accept energy ) or chemical moieties . These coenzymes are therefore regenerated throughout the network, meaning the synthesis of a coenzyme is not directly coupled to its activity. As is the case in M-models, coenzyme synthesis is not required for growth in the default ME-model and therefore is included as part of a “biomass constituent demand” reaction , analogoin silico conditions. The computed synthesis fluxes of amino acids, prosthetic groups, and coenzymes were then growth normalized to enable a comparison with the iJO1366 biomass objective function . The computed amino acid synthesis fluxes quantitatively agreed with the empirically derived numbers contained in the iJO1366 wild-type biomass objective function (BOF) and showed slight differences between aerobic and anaerobic simulations. This agreement suggests that amino acid composition of the proteome under multiple growth conditions is generally well represented by the BOF, though subtle changes in the amino acid composition could be observed depending on the growth condition.With the model extensions outlined above, the ME-model was simulated under aerobic and anaerobic glucose M9 minimal media iJO1366 BOF are not derived from empirical data. In many cases, they are simply included with small coefficient values to ensure that the essential cofactor biosynthetic pathways are active in M-model computations [quantitative comparison of the ME-model predicted cofactor usage to the M-model biomass objective function does not provide a high-confidence validation. This comparison does, however, confirm that the ME-model predicts the synthesis of most cofactors within a reasonable range, which is suitable for this study.Unlike amino acids, many of the cofactor requirements in the utations ,10. TherFig 2). Reactions that are less utilized in anaerobic conditions, such as oxidative phosphorylation reactions and pyruvate dehydrogenase, see a decrease in their accompanying cofactors, ubiquinone 8 (q8) and thiamine diphosphate , respectively.The predictions of cofactor composition provided by the ME-model are dependent on both the activity of specific reactions in a computed solution as well as the kinetic parameters used to couple reaction flux to enzyme abundance . Thus, aiJO1366 BOF and ME-model predicted biomass compositions. For example, bis-molybdopterin guanine dinucleotide (bmocogdp), adenosyl-cobalamin , 2-demethyl menaquinone 8 (2dmmq8), and menaquinone 8  are included in the wild-type objective function but have a predicted ME-model synthesis of zero for aerobic and anaerobic conditions. This discrepancy is partly because the wild-type objective function is formulated based on the wild-type biomass content of the cell, not the minimal set of biomass metabolites required for growth (represented by the “core” biomass objective function). The ME-model solutions, however, will predict only the minimal cellular content required for growth. Bis-molybdopterin guanine dinucleotide and adenosyl-cobalamin cofactors are not required for growth on glucose M9 minimal media and are thus not synthesized by the ME-model. 2-demethyl menaquinone 8 and menaquinone 8 are ancient naphthoquinone electron carriers that have been mostly associated with anaerobic growth, as opposed to ubiquinone 8 which is mostly used for aerobic growth [There are a few discrepancies between the c growth . The ME-c growth .iJL1678b model was used to simulate growth on 557 nitrogen, phosphorus, sulfur, and carbon sources under aerobic and anaerobic in silico conditions (S1 Table). For these simulations, glucose M9 minimal media was used as the base in silico condition. Then the base carbon, sulfur, phosphorus, or nitrogen source was supplemented with one of the 557 metabolites, and the model was optimized. To facilitate a comparison across a diverse set of in silico conditions, the computed biosynthesis demand of each cofactor and amino acid was normalized by the total protein biomass predicted for the condition . The computed micronutrient demands from the 592 growth-supporting simulations effectively provided condition-dependent biomass compositions predicted de novo from the ME-model.The modified  using BiGG IDs [Table B in S1 Text). The range of amino acid biosynthetic demands across conditions, however, were much narrower. The standard deviation of each micronutrient was further observed as a function of nutrient source and aerobicity . These computations showed that the variation in cofactor use was largely driven by the carbon and nitrogen sources used for the simulations. Carbon and nitrogen sources also displayed greater variation in the computed growth rates . Little variation in micronutrient demand and growth rate was observed among phosphorus and sulfur sources. Furthermore, Fig 3B suggests that aerobic and anaerobic conditions show a similar amount of biomass composition variability.Variability was observed for many of the micronutrients depending on nutrient source and aerobicity. This variability was particularly notable for the cofactors, which were found to vary by several orders of magnitude . This behavior is expected for these cofactors, as they are primarily required for aerobic respiration functions. The demand of other cofactors such as bis-molybdopterin guanine dinucleotide (bmocogdp), adenosylcobalamin (adocbl), and siroheme (sheme) are specific to individual growth conditions. For example, bis-molybdopterin guanine dinucleotide is a molybdopterin-containing prosthetic group required for numerous membrane-bound oxidoreductases used for respiration on non-oxygen electron acceptors and for detoxifying oxidation products of biotin and methionine [in silico cell when the primary carbon source is a derivative of formate, glycine, or a purine. FDH is also required for the catabolism of urate [Furthermore, a subset of the enzyme cofactors was computationally predicted to be required only in specific growth conditions. The use of some of these cofactors , and heme O (hemeO)) differed based on the aerobicity of the model simulations . Principal component 1 thus describes the biomass constituents with demands that differ based on the aerobicity of simulation. Further investigation of the vector weightings of principle component 1 suggested a general decrease in cofactor use for anaerobic conditions relative to aerobic growth. The cofactors most negatively weighted in component 1 (favored in aerobic conditions) include ubiquinone-8 (q8), protoheme (pheme), and hemo O (hemeO) which are cofactors highly involved in the electron transport chain in aerobic conditions. Alternatively, one of the few cofactors that was positively weighted in component 1 (favored under anaerobic conditions) was NAD. This observation is expected given the increase in the rate of glycolysis (and thus NAD turnover) observed in fermentative anaerobic metabolism. The distinct increase in the use of NAD in anaerobic solutions is shown as a histogram in The differences in biomass composition between aerobic and anaerobic growth conditions were also analyzed. PCA decomposition of the computed micronutrient demands showed that aerobic (filled points) and anaerobic (outlined points) simulations could be differentiated along principal component 1 . This suggests that this component could capture the components in the model that are still dependent on growth rate even following the normalization by computed protein biomass.Principal component 2, on the other hand, does not appear to be associated with the aerobicity of the simulation but it is well correlated with growth rate  biosynthesis demand than the remaining conditions. These conditions include glycine (gly) and prolylglycine (progly) as carbon or nitrogen sources, formate (for) and L-cysteine-L-glycine (cgly) as carbon sources, and L-threonine O-3-phosphate (thrp) and L-threonine (thr__L) as nitrogen sources. On average these growth conditions require 8-fold higher folate demand than the average of all remaining conditions. Wild-type ynthesis . The modAdditionally, computed pyridoxal phosphate  demand is significantly higher (6.6-fold higher than remaining nutrient sources) when 2,3-diaminopropionate (23dappa), D-serine (ser__D), putrescine (ptrc), agmatine (agm), L-arginine (arg__L), D-cysteine (cys__D) are carbon sources and when L-ornithine (orn) is a nitrogen or carbon source. Thus, growth on this set of nutrients uniquely requires high activity of enzymes with pyridoxal phosphate prosthetic groups. Four nutrient sources  are key precursors or intermediates in L-arginine or L-ornithine metabolism. Pyridoxal phosphate has an important role in amino acid metabolism as a common cofactor for transaminase reactions . TherefoE. coli to convert D- to L- serine directly, causing these two stereoisomers to be metabolized differently. L-serine, in fact, is an outlier for a different set of biomass components than D-serine including coenzyme A  and 4Fe-4S (4fe4s) iron sulfur clusters. This is because L-serine deaminase (SERD_L) requires a 4Fe-4S iron sulfur cluster cofactor instead of pyridoxal phosphate. Alternatively, D-cysteine has a high computational pyridoxal phosphate requirement due to D-cysteine desulfhydrase (CYSDDS) activity. L-cysteine desulfhydrase (CYSDS) also requires pyridoxal phosphate for activity [The remaining three growth conditions  are outliers due to activity of unique pyridoxal phosphate-containing enzymes such as 2,3-diaminopropionate ammonia lyase (DAPAL) , D-serinactivity  but is iE. coli K-12 MG1655 is capable of growing only on long chain fatty acids, though E. coli mutants exist that are capable of growing on short and medium chain fatty acid as well [Also of note are 10 fatty acid carbon sources , tetradecenoate (ttdcea), etc.) which are distinguished by the high demand for riboflavin  needed for growth on these substrates. Wild-type  as well . This isFig 5A were removed and the remaining aerobically computed biomass compositions were normalized by the maximum cofactor or amino acid demand . Using hierarchical clustering, these 274 growth supporting conditions were partitioned into 6 groups based on the similarity of their computed biomass composition (Table 2). The differences in the biomass composition of the growth conditions could be examined in greater detail if the number of clusters was further increased, but 6 clusters was found to optimize the gap statistic for the computed solutions [in silico biomass compositions. The biomass compositions for the conditions within each of these clusters could therefore be considered different enough to necessitate a unique biomass objective function (S2 Table). The clusters were characterized by performing a Wilcoxon rank-sum test on the micronutrient demands of conditions in the cluster compared to all conditions outside of the cluster. The biomass demands that were significantly different (p < 1x10-5 and absolute log2 fold change >0.15 relative to the non-cluster average) for each cluster are summarized in Fig 5B.The outlier conditions in olutions . The 6 cin silico media). This cluster represented the growth substrates that are predicted to have a minimal impact on the computed biomass composition relative to the default growth state. Cluster 3 included the vast majority of the phosphorus and sulfur sources, consistent with the observation in Fig 3B that these sources cause little variability in the biomass composition. These are the conditions in which a biomass objective function obtained from growth on glucose M9 minimal media would be most applicable.The largest cluster was cluster 3 which contained growth substrates computationally metabolized with proteome compositions similar to the default growth environment . Cluster 2 contained 41 growth conditions, most of which were conditions where the primary carbon source is a nucleotide or nucleotide derivative. Cluster 5, however, contained 12 growth conditions where the primary carbon source is the deoxynucleotide version of many of the nucleotides in Cluster 2. Despite the minor difference in chemical structure for the metabolites in the two clusters, they differed greatly in the biomass constituents used for their metabolism . For example, the deoxynucleotide required ~50–25% less thiamine diphosphate , riboflavin , and biotin  due to lessened computed pyruvate hydrogenase and lipid metabolism activity when metabolizing deoxynucleotides. Metabolizing the deoxynucleotides in Cluster 5 additionally required a proteome containing 18% more L-cysteine content than the proteome of metabolites outside of Cluster 5. This increase in L-cysteine content implies that metabolizing the metabolites in Cluster 5 requires the expression of more proteins with active sites involved in proton shuttling or covalent catalysis through nucleophilic interactions [Interestingly, nucleotide nutrient sources partition into three different clusters  can include growth in the human gut, while nutrient limited conditions can include growth under antibiotic stress  or auxotrophic growth in conditions of nutrient limitation. Given E. coli’s notable capabilities to evolve and adapt to growth under stress, we apply the ME-model to predict how E. coli could adapt to nutrient limitation by reallocating its existing proteome.There are multiple physiological scenarios where bacteria could face growth in excess or limitation of one of the essential biomass constituents studied here. Conditions of nutrient excess (Table C in S1 Text). To gauge how nutrient limitation of these biomass components impacts growth, in silico growth rates were observed as a function of the uptake rate of each of the 11 essential amino acids or cofactors. Optimal growth rates were computed as a function of essential metabolite availability. For these simulations, the uptake rate of each essential metabolite was determined at the maximum growth rate. The auxotrophic model was then optimized with the metabolite uptake rate constrained to values ranging from 100% to 5% the uptake rate at maximum growth. The simulations predicted that, while amino acid limitation elicited a consistent response in the computed growth rate, there was notable variability in the response of the in silico cells depending on the essential cofactor . Most notably, tetrahydrofolate auxotrophs were predicted to be particularly growth sensitive to drops in tetrahydrofolate availability below the optimal amount . This drop in growth occurred in three phases, one in which growth drops gradually to 55.5% of the maximum as folate availability decreased to 65.0% of the optimal. The second phase consists of the sharp decrease in growth from 55.5% to 28.9% of the maximum growth as folate availability decreased from 65.0% to 60.0%. The third phase displayed a gradual decrease in the computed growth rate to 0.Reactions were knocked out that rendered the ME-model auxotrophic for 11 essential biomass components  in purine and methionine metabolism, which is known to occur upon treatment with antifolate antibiotics [The change in protein allocation underlying folate limitation and the resulting drop in growth rate was also assessed. This analysis was performed using ME-model predictions of protein abundance and the metabolic subsystem annotations provided by ibiotics ,52.Fig 6). For example, when folate is optimally available, the phosphoribosylglycinamide formyltransferase  reaction is active which uses formyl-tetrahydrofolate (10fthf) to produce N2-Formyl-N1-(5-phospho-D-ribosyl)glycinamide (fgam), an important intermediate in purine biosynthesis. Folate limitation results in a transition to using GAR transformylase-T , an ATP driven reaction that can produce fgam from free formate. The shift from using GARFT to GART likely contributes to the increase in protein allocation needed for purine metabolism. Folate inhibitors have in fact been shown to significantly impact the activity of this metabolic node, leading to rapid accumulation of N1-(5-Phospho-D-ribosyl)glycinamide (gar) and depletion of fgam [purN activity was shown to be essential for E. coli survival during antifolate treatment, potentially to recycle tetrahydrofolate from the cellular reserves of formyl-tetrahydrofolate. Additionally, the large drop in growth observed in phase 2 of Fig 6 coincides with a decrease in 3-methyl-2-oxobutanoate hydroxymethyltransferase (MOHMT), an essential step in coenzyme A biosynthesis.Using the ME-model, metabolic changes can be observed at a higher resolution by characterizing the individual reactions driving the global changes described above . In a niacin limited environment, the cellular pools of reduced and oxidized NADP and NAD would be highly depleted. Therefore, an optimally growing cell in this state would likely have to redirect flux into pathways that maximize growth while requiring less activity of these two cofactors. iJL1678b predicts that the optimal approach to optimize NAD and NADP use is: 1) to upregulate the Entner-Doudoroff pathway (bypassing lower glycolysis) 2) to increase activity of the glyoxylate shunt to donate electrons to the quinate pool via malate and 3) to donate electrons to the quinate pool via formate (pyruvate formate lyase (PFL) and formate dehydrogenase (FDH4pp)) and lactate (D-lactate dehydrogenase (LDH_D2)). It is unclear the metabolic route to donate electrons from formate is feasible since PFL is typically only expressed in anaerobic conditions in E. coli K-12 MG1655 [Alternatively, a similar analysis was performed for niacin limited growth states , the relationship between metabolism and cofactor or amino acid availability was examined. These results provide insight into the metabolic deficiencies that could accompany a drop in the availability of essential nutrients.The presented work provides the first computational study applying resource allocation models to highlight the systems-level interplay between Fig 2), simulations were performed for growth on 557 nutrient conditions aerobically and anaerobically, thus providing 592 de novo predictions of condition-dependent biomass objective functions. Analysis of these biomass compositions suggested that unique biomass functions could be appropriate for anaerobic and aerobic simulations. For example, an aerobic objective function could include an increase in the abundance of peroxyl scavenging amino acids such as L-histidine, L-tryptophan, and L-methionine along with an increase in most enzyme cofactors, with NAD being the exception .After validating the ME-model predicted biomass objective functions , provide good targets for antibiotic treatments, the model predictions of growth condition-dependent cofactor demand could be useful for designing cellular microenvironments to increase or decrease the susceptibility of tibiotic . The anaThe presented work contains several notable limitations. First, the computed synthetic demand for each biomass component was normalized by the computed protein biomass. This normalization works well for amino acids and prosthetic groups, whose synthetic demands are a function of protein abundance. However, coenzyme  usage is not necessarily a function of protein abundance. Future work should be done to normalize and analyze these classes of biomass components separately to gain a more nuanced view of their condition-dependent activity. Second, a pseudo-kinetic term with a constant value was applied to couple the activity of coenzymes to their biosynthetic demand. This approach was sufficient for the current study as we sought to characterize only the differences in cofactor use across conditions. Future studies to characterize coenzyme demand at a quantitative level should estimate this term from experimental data and perform a sensitivity analysis around the estimated value. Last, this work provides a new look into the inherent coupling between cell synthesized cofactors and metabolism. However, a separate integral part of a functional proteome is the metal ion cofactors that form the enzymatic center of many enzymes. Due to the interchangeability of some ion cofactors and intricate mechanisms underlying enzyme mismetallation, fully examining metal ion cofactors was out of the scope of this study . Future E. coli K-12 MG1655, meaning these strains either already exist in single knockout libraries [E. coli adaptation to re-invest its protein toward pathways that maximize growth, while minimizing cofactor use. Second, this work provides predictions of ways to manipulate the growth environment of E. coli to potentiate the effect of antibiotics. Future testing of these predictions in vivo would further underscore the utility of this modeling method.Lastly, the predictions from this computational study are well suited for future experimental validation. First, many of the cofactor and amino acid auxotrophs are the product of only single gene knockouts in ibraries  or can biJO1366 model of E. coli K-12 MG1655 metabolism [iJL1678b-ME was reconstructed using this M-model of E. coli as a scaffold. All M-model optimizations were performed using the Gurobi  linear programming (LP) solver.All constraint-based modeling analyses were performed using Python 3.6 and the COBRApy software . ME-modetabolism , since iiJL1678b ME-model of E. coli K-12 MG1655 was used for all simulations in this study. The activity of enzyme prosthetic groups is inherent in the ME-model formulation [The mulation , which uiJL1678b ME-model was thus modified to couple the biosynthesis of coenzymes to their activity, similar to other enzymes in the model. This is accomplished using a pseudo-kinetic term to relate the concentration of the coenzyme pool to its activity in the metabolic network, which we will simply call kactivity. This term represents a very rough estimation of the first-order kinetics of the reactions involving the coenzyme in the network. This term was chosen as 1x104 hr -1 and applied to each reaction where the uncharged version of the coenzyme acts as a reactant:The With the addition of this new constraint the formal ME-model optimization problem becomes the following:For more details and discussion on the ME-model formulation and theory please refer to .Fig 2), but quantitative accuracy of the abundance predictions was not necessary. Therefore, accounting for the complex kinetics of the coenzymes throughout the reconstruction was outside the scope of this work. This simple approach effectively relates the rate of coenzyme biosynthesis with its metabolic activity and growth rate.For this study, we were interested in the relative activity of these coenzymes across varying growth conditions. It was thus important that the computed coenzyme abundances were within a reasonable range  and 2-oxoglutarate dehydrogenase  were updated to correctly require FAD and thiamine diphosphate as prosthetic groups.Changes and corrections were applied to k et al. . Correcteff coupling parameters [iJL1678b-ME were determined based on a machine learning approach that incorporated enzyme features, network properties, and proteomics data to predict keffs [in vivo derived enzyme turnover rates [eff of this reaction was set to 1500 s-1 [effs for expression machinery and transport reactions were set to a default value of 65 s-1 [-1 , well above the observed growth rate of E. coli. However, since this parameter is similarly applied to all simulations, the high computed growth rates will not impact the relative changes in computed proteome compositions seen in the various growth conditions. All remaining parameters were set to their default values [The krameters  for eachct keffs ,61 from er rates . In orde1500 s-1 . The remf 65 s-1 , as paraf 65 s-1  was set t values .-13).Due to non-linearities stemming from the enzyme coupling constraints, ME-models cannot be optimized directly as an LP and thus are solved using a binary search algorithm. To perform the binary search, the following procedure was implemented. First, each symbolic coefficient  or reaction bound was compiled into a function by sympy . Then, aTable A in S1 Text). This was sufficient given that each cofactor contains one direct biosynthetic pathway. For the comparison of the ME-model predictions to the iJO1366 biomass objective function, the computed biomass constituent demand values were normalized by the computed growth rate. For the analysis of conditions dependent biomass compositions, however, the computed biomass demands were normalized by the total protein biomass of the simulation, provided by the “protein_biomass_to_biomass” reaction in the ME-model solution. Global RNA and protein abundances in the ME-model are governed by an empirical RNA-to-protein ratio constraint that is a nonlinear function of growth rate [The amino acid biosynthetic demand was determined based on the translation flux and amino acid composition of each protein in the model. Prosthetic group demand was determined based on the sum of the complex formation fluxes of all enzyme complexes containing the appropriate prosthetic group. Due to the high participation of coenzymes throughout the metabolic network, the biosynthetic flux of each cofactor was determined based on the activity of a reaction in its biosynthetic pathway . Fig F in S1 Text. Model-predicted metabolic changes in response to niacin limitation. Top panel: Fraction of protein allocated to each metabolic subsystem by mass for varying niacin availability (columns) Bottom panel: Heatmap of reaction fluxes normalized by the maximum flux value for the reaction across all levels of niacin limitation. The 20 reactions with the highest standard deviation are shown and are highlighted in red if the reaction relies on NAD or NADP activity. If reaction fluxes were perfectly correlated throughout the niacin limitation simulations, then these reactions were grouped together. The number in parenthesis shows the number of other reactions represented by the row. The rightmost column depicts a simulation with the highest niacin availability and the leftmost column depicts a simulation with the lowest niacin availability. Fig G in S1 Text: Comparison of the computed metabolic flux state in optimal niacin availability and the flux state with 10% of the optimal niacin availability. Reactions are shown in green or red if they are upregulated or downregulated in limited niacin availability, respectively.(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Tableprotein).The growth conditions represented in each cluster along with the average biomass composition (in mmol / g(XLSX)Click here for additional data file.


Sus scrofa) aortic valve and endocardium for the profiling of proteins. Label-free proteomics was performed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Our proteomics analysis revealed that 29 proteins were highly expressed, and 25 proteins were less expressed in the valve than the endocardial endothelium. The cell surface markers, such as CD63, ICAM1, PECAM1, PROCR, and TFRC, were highly expressed in EE. In contrast, CD44 was highly expressed in AVE. The pathway analysis showed that metabolic process-related proteins and extracellular matrix-related proteins were enriched in valves. Differential enrichment of signaling pathways was observed in the endocardium. The hemostasis function-related proteins were increased in both endothelial cells. The proteins and pathways enriched in aortic-valvular and endocardial endothelial cells revealed the distinct phenotype of these two closely related cells.The variations in the protein profile of aortic-valvular (AVE) and endocardial endothelial (EE) cells are currently unknown. The current study’s objective is to identify differentially expressed proteins and associated pathways in both the endothelial cells. We used endothelial cells isolated from the porcine ( Staphylococcus aureus is the primary causative agent for infective endocarditis -Fibrinopeptide B human (Sigma) was used for the lock mass acquisition every 30 s at a flow rate of 500 nL/minutes. The proteomics data were uploaded in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD025922. The previously published endocardial endothelium proteomics data set was used to compare the aortic valvular endothelial cells in this study [The data were acquired in continuum format in elevated collision energy . Briefly, an automatic sensitivity method based on a noise estimation algorithm was used for the determination of noise in the data for the peak picking of the data. A maximum ion charge of 20 was used. A false discovery rate of 4% was used for the search. The detection of at least one unique peptide was used for the identification. The carbamidomethylation of cysteine and oxidation of methionine were selected as fixed and variable modifications, respectively. A specificity of one missed cleavage was used for the trypsin digestion parameter during database search. Normalized intensity values obtained from three biological replicates samples were used for the label-free quantification analysis. The Student’s t-test was used for the differential expression analysis using Progenesis QI for Proteomics. A protein with a raw p-value < 0.05 and fold change > 1.5 was considered significant. An adjusted p-value  < 0.25 was used to select significantly changed proteins since the number of differentially expressed proteins was markedly less in this study.Progenesis QI for Proteomics  was used for the analysis of proteomics data. The p-value < 0.05) enriched pathways associated with altered proteins [A WEB-based GEne SeT AnaLysis Toolkit  was used to identify significantly  and endocardial endothelial (EE) cells by proteomics analysis. The overall distribution showed that 911 proteins were commonly observed in both types of endothelial cells a. The diThe GSEA analysis showed that cell differentiation marker protein called CD44-antigen (CD44) is significantly overexpressed in AVE cells compared to the EE cells. However, marker proteins such as CD63-antigen (CD63), intercellular adhesion molecule-1 (ICAM1), platelet endothelial cell adhesion molecule (PECAM1), endothelial protein C receptor (PROCR), and transferrin receptor protein-1 (TFRC) were highly expressed in endocardial endothelial cells .2+ were enriched in both endothelial cells  [The gene ontology analysis showed that proteins involved in the metabolic process are enriched in AVE cells. The differential expression of proteins involved in the metabolic process in AVE cells can be linked to the constant movement of the valve and increased stress and strain on the valvular endothelial cells compared to the endocardium . We obses aries) . The ECMs aries) . The ions aries) . The regs aries) .Biological regulations related to proteins were upregulated in EE cells. The signaling pathways such as cytokine signaling in the immune system, signaling by PDGF, and interleukins were enriched in EE cells. A blood–heart barrier made by the endocardial endothelial cells can modulate the function of cardiomyocytes by sensory and endocrine roles. Endocardial endothelial can secrete signaling molecules such as angiopoietin, vascular endothelial growth factor (VEGF), endothelin, angiotensin-II, prostaglandins, nitric oxide, and fibroblast growth factor ,34.2+ were enriched in both endothelial cells. Endothelial cells prevent thrombosis by different antiplatelet and anticoagulant mechanisms in normal physiological circumstances. Endothelial dysfunction can lead to atherogenesis as well as arterial thrombosis [The endothelial cells maintain the hemostasis function by releasing different fibrinolytic proteins, anticoagulants, vasodilators, vasoconstrictors, and procoagulants . In the rombosis .This study is the first proteome level comparison of endothelial cells isolated from the aortic valve and endocardium. Our results showed that most of the proteins and pathways are shared in both types of endothelial cells. In addition, we have observed several marker proteins in the AVE and EE cells. Our future objective will be to validate the cell surface markers using Western blots and fluorescently labeled antibody-based methods. We also believe that those markers will be helpful to develop a methodology to sort the AVE and EE cells from a heterogeneous group of cells using the fluorescence-activated cell sorting (FACS) method. The segregated group of specific endothelial cells will help us to understand pathogenic and normal physiological functions. We have detected several differentially expressed proteins in AVE and EE cells. The study’s primary limitation is that the physiological impact of those changed proteins was not explored further. Different types of functional studies such as shear-stress measurements and wound-healing assays are needed to link the protein levels difference to physiological functions of the endothelium in the future.In conclusion, the proteome profile comparison of aortic valvular and endocardial endothelial cells suggests that these cells have a distinct phenotype. The biological functions, mechanical and shear stress, might determine the differences in the proteome of these two cells. The markers identified in both cells could be helpful for future studies targeted for a subpopulation of endothelial cells. The pathway analysis showed that metabolic process-related proteins and extracellular matrix-related proteins were enriched in valves. An enrichment of signaling pathways was observed in the endocardium. The hemostasis function-related proteins were increased in both endothelial cells. The present results provide a foundation for future studies to unravel normal physiology and different disease mechanisms that affect aortic valvular and endocardial endothelial cells.


Objective Polymeric framework represent an innovative approach for implant-supported dental prostheses. However, the mechanical response of ultra-high performance polymers as frameworks for full-arch prostheses under the “all-on-four concept” remains unclear. The present study applied finite element analysis to examine the behavior of polyetherketoneketone (PEKK) and polyetheretherketone (PEEK) prosthetic frameworks.Materials and Methods A three-dimensional maxillary model received four axially positioned morse-taper implants, over which a polymeric bar was simulated. The full-arch prosthesis was created from a previously reported database model, and the imported geometries were divided into a mesh composed of nodes and tetrahedral elements in the analysis software. The materials were assumed as isotropic, elastic, and homogeneous, and all contacts were considered bonded. A normal load (500 N magnitude) was applied at the occlusal surface of the first left molar after the model was fixed at the base of the cortical bone. The microstrain and von-Mises stress were selected as criteria for analysis.Results Similarities in the mechanical response were observed in both framework for the peri-implant tissue, as well as for stress generated in the implants (263–264 MPa) and abutments (274–273 MPa). The prosthetic screw and prosthetic base concentrated more stress with PEEK  than with PEKK (192 and 49 MPa), while the prosthetic framework showed the opposite behavior (59 MPa for PEEK and 67 MPa for PEKK).Conclusion The main differences related to the mechanical behavior of PEKK and PEEK frameworks for full-arch prostheses under the “all-on-four concept” were reflected in the prosthetic screw and the acrylic base. The superior shock absorbance of PEKK resulted in a lower stress concentration on the prosthetic screw and prosthetic base. This would clinically represent a lower fracture risk on the acrylic base and screw loosening. Conversely, lower stress concentration was observed on PEEK frameworks. Incorporating three-dimensional 3D) printing techniques in the clinical dental setting such as stereolithography, digital light processing, photopolymer jetting, material jetting, binder jetting, selective laser sintering, selective laser melting, and fused filament represents a new challenge in restorative dentistry.D printinIn this scenario, polymeric materials have gained attention, especially those classified as ultra-high performance. Polyetherketoneketone (PEKK) and polyetheretherketone (PEEK) are both part of an ultra-high performance thermoplastic polymer family called polyaryletherketones (PAEKs). These semi-crystalline polymers are characterized for their excellent mechanical performance, which have attracted researchers and clinicians to investigate their application in several designs of dental prostheses,PEKK and PEEK have similar chemical structures, except for two key differences: (1) PEKK replaces one of the flexible ether linkages with a more rigid ketone group. This increases the glass transition temperature (Tg)  by about 15°C over PEEK. (2) The second ketone group is selectively ortho (straight) or para (kinked) substituted. It is possible to control the melting point and crystallization rate by varying the number of straight and kinked sections. Nonetheless, these subtle differences imply easier additive manufacturing (AM) and affects the mechanical response, especially in shock absorbance capacity and shear compression.Nevertheless, PEEK presents a sensitive cooling process similar to yttria-stabilized tetragonal zirconia polycrystal (3-YTZP)The “all-on-four concept” represents a simplified option for rehabilitation. It was introduced in the early 2000s aiming to maximize the use of available remnant bone in atrophic jaws, enabling immediate function and avoiding regenerative procedures which increase the treatment costs and patient morbidity, as well as the complications inherent to these procedures.Biological requirements are not a concern since both PEKK and PEEK are inert and nonallergenic polymeric biomaterials indicated as a substitute for metal alloys in assorted types of prostheses and orthoses. Moreover, these polymers are biocompatible and have an elastic modulus close to native bone and dentin.Finite element analysis allows us to understand how strain distribution in bone tissue and stress in implants can be influenced by the restorative material, prosthesis and framework design, manufacturing technique, and number and distribution of implants.Thus, the objective of this study was to evaluate the mechanical response of two ultra-high performance polymers (PEKK and PEEK) as frameworks for full-arch prostheses under the “all-on-four concept.” The null hypothesis is that different polymers for the framework will not modify the mechanical response in the analyzed structures.A computer tomography image saved in digital imaging and communications in medicine (DICOM) format was retrieved from the São Paulo State University database and then converted to stereolithography (STL) file in a 3D slicer software program. Afterward, an edentulous maxilla model was constructed following the main anatomical features of the patient’s bone: size, shape, and absence of pathology using CAD software . The BioCAD methodNext, previously modeledThe bar  was modEach solid geometry was imported to the analysis software  in STEP format. A 3D mesh was generated, and tetrahedral elements were considered for the models . A convThe bottom surface of cancellous bone was restricted in all directions for the boundary conditions . The loThe calculated microstrain distribution in the maxilla as a function of the framework’s material were plotted in colorimetric graphs for cortical and cancellous bone, respectively . It wasIt was observed that a higher stress concentration in the PEKK framework (67 MPa) promoted a lower stress concentration on the full-arch prostheses supported by it. The inverse occurred with the PEEK bar, showing higher stress concentration in the acrylic resin base . The peA higher stress concentration in the PEKK framework (compared with the PEEK bar) promoted a lower stress concentration in the implant (263 MPa) and in the prosthetic screw (192 MPa). However, by observing the results displayed inThe results in terms of stress peak values (MPa) in the full-arch prosthesis, framework, prosthetic screw, abutment, and implant are summarized inPEKK and PEEK presented different mechanical response in simulating full-arch dental prostheses under the “all-on-four” concept in this study, despite their chemical similarities. Thus, the null hypothesis was rejected due to the stress concentration differences observed among the materials. These thermoplastic composites compete against each other for engineering applications, especially in development of aircraft structures.Regarding the cancellous and cortical peri-implant bone, insignificant, almost imperceptible differences on the microdeformation promoted by the microstrain generated over the polymeric bars were observed herein. In fact, it was difficult to qualitative compare the differences under colorimetric pattern analysis. These results are corroborated by a study in which a variation of infrastructure material rigidity did not demonstrate a significant effect on the stress values in the marginal bone around the implants.In the same view, Erkmen et al claimed that the use of less rigid material for the superstructure of the implant retained prostheses decreased the stresses within the framework and veneering parts of the superstructure due to the flexible nature of the material which absorbs stresses.The current study simulated implants in an upright position, which differed from the original “all-on-four” protocol that envisaged two posterior implants in a titled position. This decision was supported by a 5-year follow-up study, which compared axial and tilted implants and found 100% overall survival rates for axially positioned implants, and 98.44% for tilted implants.The current study analyzed the behavior of two polymeric bars, and the von Mises maps showed only a slight difference between stress concentration on implants which support different bars. This similarity was not observed in a previous study that compared PEEK with alloy barsOutcomes of clinical studies about “all-on-four” retained by metallic bars have suggested the predictability of this treatment concept.In previous studies, it was found that flexible prosthetic frameworks increase the stress generated in the prosthetic screw threadsIt is important to note some inherent limitations of FEA studies.The FEA showed that the main differences related to the mechanical behavior of PEKK and PEEK frameworks for full-arch prostheses under the “all-on-four concept” were reflected in the prosthetic screw and the acrylic base. The higher compression strength and the superior shock absorbance of PEKK resulted in a lower stress concentration on the prosthetic screw and prosthetic base. This would clinically represent a lower fracture risk on the acrylic base and screw loosening. Conversely, lower stress concentration was observed on PEEK frameworks.


RNA-based therapeutics have entered the mainstream with seemingly limitless possibilities to treat all categories of neurological disease. Here, common RNA-based drug modalities such as antisense oligonucleotides, small interfering RNAs, RNA aptamers, RNA-based vaccines and mRNA drugs are reviewed highlighting their current and potential applications. Rapid progress has been made across rare genetic diseases and neurodegenerative disorders, but safe and effective delivery to the brain remains a significant challenge for many applications. The advent of individualized RNA-based therapies for ultra-rare diseases is discussed against the backdrop of the emergence of this field into more common conditions such as Alzheimer’s disease and ischaemic stroke. There remains significant untapped potential in the use of RNA-based therapeutics for behavioural disorders and tumours of the central nervous system; coupled with the accelerated development expected over the next decade, the true potential of RNA-based therapeutics to transform the therapeutic landscape in neurology remains to be uncovered. The messenger RNA (mRNA)-based COVID-19 vaccines have thrust RNA-based therapeutics into the mainstream, but RNA drugs are far from new. Over 40 years of research is now culminating in the rapid expansion of these new classes of drugs, and approvals for diseases involving the nervous system are leading the way with ten approvals to date . RNA-bastrans splicing (SMaRT) . The. The8]. nscripts ,67, howen signal ,69. Morefor FSHD . An addiynamin 2 .The example of using ssAONs to remove a polyglutamine-expanded exon in SCA3 was discussed above. The trinucleotide repeat disorders are also good candidates for AON targeting designed to downregulate toxic protein expression and reduce aggregation. In the case of SCA1, SCA2, SCA3 and SCA7 this has been investigated in mouse models where AONs delivered via intracerebroventricular injection reduced disease protein levels and improved phenotypes highlighting significant promise for AON therapy in this group of diseases . For HunSNCA gene. It is suggested that decreasing SNCA expression could delay disease onset or modify progression; this hypothesis has been tested using AONs. An amido-bridged nucleic acid (AmNA)-modified AON efficiently reduced SNCA mRNA and protein levels in-vitro and was efficiently delivered to the mouse brain via intracerebroventricular injection resulting in an amelioration of neurological defects in a PD mouse model expressing human wild-type SNCA . ,93. traned decay . In the ystrophy . For SMA protein . In the survival . Exon 10ng SMaRT . SMaRT cFTDP-17) . SMaRT cneurones  and can auopathy . Thus, tDMD exon 2 leading to either mRNA containing a single copy of exon 2 or no copies at all, the latter resulting in a functional but N-terminally truncated protein isoform [Another more nuanced alterative to standard AONs takes advantage of the U-rich small nuclear ribonucleoproteins (snRNPs) which mediate pre-mRNA splicing. The U7 snRNP however does not play a role in splicing but rather processes the 3ʹend of replication-dependent histone mRNAs. The U7 snRNP histone binding domain can be custom-modified to target the splicing of a gene of interest and delivered using traditional gene therapy vectors such as adeno-associated viral (AAV) vectors  [3]. Thi isoform . Planned isoform . A potenIn principle many neurological and neurodegenerative disorders could be treated or greatly diminished via gene silencing, however delivery roadblocks have slowed progress in comparison to siRNA delivery elsewhere such as the liver . The oneet al. observed minimal evidence for immune stimulation demonstrating its high tolerability as well as minimal off-target effects. Alternative conjugated-based delivery systems are being pioneered by Alnylam Pharmaceuticals who have used the sugar molecule, N-acetylgalactosamine  to effectively target the liver, the company have presented on the development of a similarly novel conjugate system to target the central nervous system with data in non-human primates on the silencing of β-catenin, a component of the Wnt signalling pathway and have an siRNA targeting APP in the pipeline [Until recent advances, when directly injected into the brain, siRNA affects only nearby cells for a short duration, leading to the undesirable requirement for frequent administration. However, potent and sustained huntingtin gene silencing throughout the central nervous system in small and non-human primate animal models has now been achieved using a divalent chemical scaffold (di-siRNA) delivered via a single injection into the cerebrospinal fluid . Di-siRNpipeline .BACE1 in an Alzheimer’s disease mouse model showing improved cognitive capacity [3 platform developed by Bioasis works via peptide conjugation  and receptor-mediated transcytosis. Proof-of-concept has been obtained for the silencing of NOX4 which plays a role in ischaemic stroke [An siRNA that efficiently crosses the BBB, Gal-NP@siRNA, has shown to effectively target capacity . Gal-NP@capacity . Anotherc stroke . After ic stroke .Many studies have explored the potential for siRNA to treat glioblastoma, the most aggressive form of glioma. These include for example the use of synthetic protein nanoparticles to target signal transducer and activator of transcription 3 (STAT3)  and siRNsiRNA also holds promise for the many genetic diseases discussed above for AON therapy. For example, there are numerous studies on the development of siRNA for the SCAs using various experimental systems, of which siRNA for SCA3/Machado-Joseph disease (MJD) has seen the most extensive effort towards allele-specific gene silencing . SimilarRNA aptamers are RNA molecules with a stable three-dimensional structure that are selected to bind a target with high affinity and specificity; targets include proteins, ions, whole cells and viruses . SelectiThe specificity of RNA aptamers offers a functional advantage over small-molecule therapy owing to reduced toxicity and off-target effects and their immunogenicity is limited due to chemical modification . Currentet al. show that aptamer treatment in the context of intracranial haemorrhage can be reversed using a specific antidote, thus presenting a safer approach to the treatment of stroke. Similarly, an antidote-controlled RNA aptamer targeting Von Willebrand Factor has also been shown to significantly reduce stroke volumes in a dog model [MS is characterized by pro-inflammatory leukocyte infiltration. Two therapeutic RNA aptamers targeting interleukin-17 (IL-17) and midkine (a heparin-binding growth factor) have both shown reduced inflammation in the experimental autoimmune encephalitis mouse model which resembles MS . For iscog model . For epiog model . Targetsog model . For neuog model . For exaog model . Furtherog model ,131.ex-vivo and redelivered. A first in human phase I/II study (NCT04573140) of an RNA-loaded nanoparticle vaccine is also underway for glioblastoma [Administering mRNA as a therapeutic to treat neurological disorders has several advantages such as being able to produce proteins in their native form in mature non-dividing cells, without having to enter the nucleus. However, like all RNA-based therapeutics, delivery poses a challenge since mRNA is not particularly stable and is strongly immunogenic. There are currently no approved mRNA drugs to treat neurological diseases and clinical research is limited; but spurred by the success of COVID-19 vaccines, renewed efforts are underway for example to develop an mRNA vaccine for glioblastoma immunotherapy ,133. Sucblastoma .Figure et al. who, using a novel polyplex nanomicelle carrier, delivered brain-derived neurotrophic factor (BDNF) expressing mRNA via daily intranasal administration to a mouse model of olfactory dysfunction. They showed increased neurological recovery of olfactory function as well as the repair of olfactory epithelium to a nearly normal architecture [More in line with the other RNA-based therapeutics approaches discussed thus far, the feasibility of using mRNA to treat a sensory nerve disorder has been demonstrated by Baba itecture . An mRNAitecture .Circulating endogenous non-coding RNAs such as miRNAs, circular RNAs (circRNAs) and long non-coding RNAs are increasingly recognized for their potential use as accessible biomarkers for the diagnosis and prognosis of CNS diseases. Underlying disease mechanisms often begin pre-symptomatically and earlier diagnosis could be achieved (without direct access to brain tissue) through profiling the expression of miRNAs and other RNA species which are secreted into circulation. For example, many individual studies show that Alzheimer’s disease and/or mild cognitive impairment can be differentiated from cognitively normal controls through peripheral miRNA biomarkers , though circDLGAP4 (a known mir-143 sponge) is downregulated in the brains of a rodent stroke model as well as in the plasma of human acute ischaemic stroke patients [circDLGAP4 delivered using a lentivirus to the lateral brain ventricle ameliorated stroke outcomes and improved neurological function in a mouse model of stroke [Improvements in sequencing technologies and bioinformatic pipelines have also revealed the potential for circRNAs as biomarkers in, for example, Alzheimer’s and Parkinson’s diseases where their association with pathological processes is well established . Howeverpatients . The ovef stroke .There is now a long-standing recognition of the value of miRNAs as biomarkers, but as with circRNAs they are also viable drug targets and therapeutic agents themselves. The expression of target genes can be increased or decreased using miRNA inhibitors  and synthetic miRNA mimics respectively. miRNA mimics and inhibitors have been identified for application in CNS injuries such as stroke, traumatic brain injury and spinal cord injuries . SimilarThe rapid development of new chemistries and delivery systems for RNA-based therapeutics warrants careful screening for unwanted on- and off-target side effects. There is limited information on the toxicological properties of RNA aptamers but hybridization-dependent and hybridization-independent effects are well documented for AONs . HybridiHybridization-independent effects are more common than the above on- and off-target effects caused by Watson–Crick base pairing. AON toxicities induced by RNA-protein interactions are common and depend on the delivery system and chemistry used . For exaRegardless of the chemistry used, after intravenous administration a very high concentration of AON is found in the liver and kidneys (high exposure organs) where it accumulates as basophilic granules. This accumulation is generally considered non-adverse since the effect is reversed when treatment is terminated. However, some gapmers do induce a more severe and sequence-specific acute liver toxicity in mice after only a single treatment ,152. ThuThe possibilities to develop RNA-based drug modalities to treat neurological diseases appear limitless. Whilst progress is rapid across basic, translational and clinical research for most disease categories, there remains significant untapped potential in the use of RNA-based therapeutics for behavioural disorders and tumours of the central nervous system. Efficient, and safe, delivery remains an important hurdle but there is precedent to overcome such challenges from for example the discovery of GalNAc conjugation for liver targeting. With the advent of n-of-1 therapies, we are likely to witness changes in the regulatory processes for individualized medicine to best accommodate the requirement for the rapid treatment of rare progressive brain diseases, as well as the need to collectively measure the success of this approach across many different disease types. In parallel to the excitement surrounding n-of-1 therapies, the field is fast emerging out of the rare genetic disease research communities and witnessing the testing of RNA-based drugs in more common and chronic conditions. This is an essential development since AON drug approvals to date have been based on testing only small numbers of patients. Thus, their true potential to transform the neurological therapeutic landscape is yet to be uncovered.


The mosquito-borne viral disease dengue is a global public health problem causing a wide spectrum of clinical manifestations ranging from mild dengue fever to severe dengue with plasma leakage and bleeding which are often fatal. To date, there are no specific medications to treat dengue and prevent the risk of hemorrhage. Dengue is caused by one of four genetically related but antigenically distinct serotypes DENV-1–DENV-4. The growing burden of the four DENV serotypes has intensified both basic and applied research to better understand dengue physiopathology. Research has shown that the secreted soluble hexameric form of DENV nonstructural protein-1 (sNS1) plays a significant role in the pathogenesis of severe dengue. Here, we provide an overview of the current knowledge about the role of sNS1 in the immunopathogenesis of dengue disease. We discuss the potential use of sNS1 in future vaccine development and its potential to improve dengue vaccine efficiency, particularly against severe dengue illness. Aedes aegypti and, to a lesser extent, of Aedes albopictus as major vectors for dengue virus (DENV) transmission. The four serotypes of DENV, DENV-1–DENV-4, sharing 60–80% homology in their genomic sequences, can cause flu-like illness, but some individuals can experience severe plasma leakage associated with exacerbated inflammatory responses leading to potentially fatal shock. The mechanisms of severe dengue are poorly understood and presumably multifactorial with viral and host factors having significant roles. The immune status of patients might play a key role in the risk of severe dengue. Indeed, the antibody-dependent enhancement (ADE) or the original antigenic sin phenomenon have been associated with the development of severe dengue which relates to secondary infection with a DENV serotype different of that responsible for the primary infection. Thus, preexisting immunity against DENV could be associated with the development of severe forms of dengue disease during a secondary infection. To date, no specific treatments nor therapies are available for clinical management of severe dengue disease.Dengue disease has become one of the most significant mosquito-borne viral diseases worldwide. The global incidence of dengue has dramatically increased over the last decades causing a major public health problem in tropical and subtropical regions where endemicity relates to the profusion of Flavivirus genus of the Flaviviridae family sharing great similarity with other medically important arboviruses such as yellow fever virus, West Nile virus and Zika virus . Ex. Ex9]. EAt the onset of the DENV infection, sNS1 elicits a potent humoral and cell-mediated immune response A. The NS−/− mice exhibited higher survivability in the presence of sNS1 [On the other hand, the anti-NS1 cell-mediated response is based on DENV sNS1 antigen recognition by peripheral blood mononuclear cells via pattern recognition receptors (PRRs). Activated PRRs from the toll-like receptors family have the ability to trigger the release of major proinflammatory cytokines and chemokines involved in the DENV immunopathogenesis ,11. Prot of sNS1 . However of sNS1 .Development of a safe and effective vaccine against all four serotypes of DENV is challenging because of limited understanding of the mechanisms of severe dengue in relation to the immunopathogenesis of the disease. To date, the only licensed dengue vaccine currently available on the market is Dengvaxia developed by Sanofi Pasteur and registered by regulatory authorities in 20 dengue-endemic countries, the European Union and the United States. This is a live-attenuated tetravalent dengue vaccine which contains DENV E and prM genes from the four serotypes inserted in the yellow fever 17D backbone. The objective of this vaccine is to induce preventive humoral and cell-mediated immune responses ,33. HoweTargeting NS1 for dengue vaccine development may have many advantages. The benefits of an sNS1-based dengue vaccine relate to a high degree of NS1 conservation amongst DENV serotypes (about 70% of amino-acid identity), a strong immunogenic potential of sNS1 and the evidence of an efficient anti-DENV immune response based on the stimulation of B and T cell-dependent immunity. According to the immunopathogenesis of dengue, a major advantage of using NS1 is to bypass the risk of ADE, which mainly relates to the production of antibodies that promote viral growth and severe disease. Herein, several DENV NS1-based vaccine platforms are presented and illustrated with one vaccine candidate for each platform.The second generation of dengue vaccine candidates based on live-attenuated viruses (LAV) includes all proteins of DENV, whereas the licensed Dengvaxia elicits immunity only against the prM and E proteins as dengue antigens. The more advanced LAV dengue vaccine is tetravalent TAK-003 developed by Takeda . TAK-003Several technologies are currently in use to elicit an immune response against the DENV NS1 protein, as illustrated in A number of DENV NS1-based vaccines have been and still are in development .N,N,N-trimethyl chitosan nanoparticles (NS11–279TMC NPs) investigated in a murine model and in humans ex vivo [1–279 protein in monocyte-derived dendritic cells (MODCs) and also stimulate those cells, resulting in an increased expression of maturation marker CD83, costimulatory molecules CD80, CD86 and HLA-DR and secreting diverse immune cytokines/chemokines [1–279TMC NPs resulted in both B cell and T cell responses leading to IgG production and CD8+ T cells activation. One important finding is that DENV2 NS11–279-directed antibodies have the ability to kill DENV-infected cells through antibody-dependent complement-mediated cytotoxicity [The next generation of dengue vaccines in development, including DNA subunit, virus-like particles (VLP) and viral vector vaccines, were reviewed by Redoni et al. . VLPs ex ex vivo . In a huemokines . Immuniztoxicity . ConsequIt is well-known that interactions of the DENV NS1 protein with clotting factors and endothelial cells contribute to the immunopathogenesis of severe dengue. Several studies have identified NS1 peptide sequences that can generate anti-DENV antibodies exhibiting cross-reactivity to self-antigens . Such NSIt is widely admitted that the threat of dengue disease requires a successful vaccine against the risk of severe dengue. An efficient dengue vaccine must be able to elicit long-term immunity to DENV regardless of the serotype or genotype of the infecting viral strain. At our stage of knowledge of the dengue disease, the key elements of the most popular dengue vaccine strategies include the notion of designing viral antigens that are targets of effective antibody-mediated neutralization of the four DENV serotypes. The Dengvaxia efficacy trials conducted to date have demonstrated that a dengue vaccine is possible and have made important contributions to our understanding of the path towards the development of such a vaccine. Since the licensed dengue vaccine Dengvaxia has shown debated efficacy, the mechanisms by which a dengue vaccine might confer efficient protection against the DENV infection need to be better defined. Consequently, new insights that will help to guide rational vaccine design against DENV are necessary. The NS1 glycoprotein has recently emerged as a potential viral antigen target for the development of dengue candidate vaccines. As a viral toxin, released soluble NS1 has been shown to play a key role in the immunopathogenesis of severe dengue. One can estimate that LAV of DENV and NS1-based vaccines represent promising strategies that have the potential to significantly advance dengue vaccine development. Given that their efficacy could be greatly improved by reducing off-target antibody responses to the DENV NS1 protein, it will be of great interest to modify using the mutational approach the NS1-associated irrelevant epitopes presenting the risk of mimicry with host factors involved in coagulation and integrity of the vascular endothelium.


GW approach were initially developed in the solid-state physics community, the method and its combination with the Bethe-Salpeter Equation (BSE) has more recently taken a foothold among quantum chemists. Across disciplines, the field continues to grow and it currently carries great momentum: it is therefore timely to collect articles showcasing the latest triumphs and outstanding shortcomings of this family of methods.The utility of many-body Green’s function methods for computing excitation energies and spectra of correlated systems continues to impress. While applications and implementations of the GW to organic crystals and FeS2 are motivated by the importance of an atomistic understanding of excited states for technological applications such as photovoltaics. Contributions that highlight the predictive power of the GW approach and present low-scaling implementations showcase the potential of the theory for applications to complex heterogeneous materials. Additionally, method and theory development for GW, BSE, and non-GW calculations of the single-particle Green’s function form important parts of this collection of articles.The contributions to this Research Topic range from theory and method development to applications of Green’s function-based methods. Applications of The GW/BSE Method in Magnetic Fields, Holzer et al. benchmark their new GW/BSE implementation in external magnetic fields. External fields pose a challenge to electronic structure theory because they require complex orbitals to ensure gauge-invariant results, raising the computational expense. Furthermore, kernels of time-dependent density functional theory can be unstable in the presence of the perturbing field. GW/BSE presents a solution to the latter problem, and the authors implement the theory with complex London orbitals to circumvent gauge dependence.In 2 remain somewhat undetermined: theoretical calculations are scattered and experiments only reliably measure the optical band gap. In Accurate Prediction of Band Structure of FeS2: A Hard Quest of Advanced First-Principles Approaches, Zhang and Jiang set out to resolve the theoretical discrepancy by performing GW calculations on both phases with the most complete basis to date: a linearized augmented plane wave basis (LAPW) supplemented with high-energy local orbitals (HLOs). The authors find that very high momentum HLOs are necessary to converge the sum over unoccupied states in GW and attribute inconsistencies in previous theoretical work to underconvergence.The fundamental band gaps of two phases of FeSGW. Their high tunability through packing and choice of substrate render them an interesting playing field for computational exploration. The GW approach affords an excellent description of the important effect of substrate screening. In Exciton Modulation in Perylene-Based Molecular Crystals Upon Formation of a Metal-Organic Interface From Many-Body Perturbation Theory, Shunak et al. explore the change in quasiparticle and excitonic properties of octyl perylene diimide (C8-PDI) adsorbed on gold. They find that exciton binding energies are unchanged due to the local character of screening between electron and hole but that fundamental band gaps are dependent on layer number, stacking, and substrate.Molecular crystals are a modern success of GW algorithms are restricted to diagonal G0W0. In Low-Order Scaling Quasiparticle Self-Consistent GW for Molecules, Förster and Visscher present their low-scaling implementation of quasiparticle self-consistent GW (qsGW) based on the space-time method. They validate their implementation against other results and then include a polarizable continuum model to study DNA oligomers.Most low-scaling Equation-of-Motion Coupled-Cluster Cumulant Green’s Function for Excited States and X-Ray Spectra by Vila et al. presents a real-time formulation for computing X-ray spectra that draws on connections between coupled-cluster and cumulant approaches. Core ionization energies from the improved EOM-CC method are presented for CH4, NH3, H2O, HF, and Ne, and a full X-ray absorption spectrum is presented for NH3.The cumulant approximation makes an exponential ansatz to the Green’s function Scrutinizing GW-Based Methods Using the Hubbard Dimer by Di Sabatino et al. finds that different starting points for constructing G0 and W0 lead to the existence of multiple quasiparticle solutions, loss of particle number, or discontinuities in physical quantitites as a function of interaction strength. Fully self-consistent GW removes these artifacts but does not necessarily improve the accuracy of the results.The Hubbard model remains a valuable test bed for theoretical physics since it permits both analytic and numerically exact solutions in certain cases. The GW Miracle in Many-Body Perturbation Theory for the Ionization Potential of Molecules systematically tests ionization potentials for the GW100 test set computed with G0W0 against a series of alternative many-body theories. Bruneval et al. find that, while more accurate theories may exist, they come at much greater expense; the cost-accuracy balance of G0W0 is unbeatable. They also note that the most substantial improvement to G0W0 comes not by incorporating additional diagrams but from an improved mean-field starting point.k-point grid due to the extended character of excitons in real space. This remains a bottleneck in applying the method to solids. Double k-Grid Method for Solving the Bethe-Salpeter Equation via Lanczos Approaches by Alliati et al. presents an efficient interpolation scheme to ameliorate this problem. It computes the optical absorption on a fine k-grid, on which each diagonal block of the Hamiltonian is taken from the calculation of the kernel on a coarse k-grid. The scheme reproduces optical spectra of Si, GaAs, and MoS2 with good accuracy.Calculations of optical spectra in solids with the Bethe-Salpeter equation require an enormously dense Photoemission Spectra from the Extended Koopman’s Theorem, Revisited, Di Sabatino et al. show that their recent work developing many-body effective energy theory (MEET) is more closely related to the well-known Extended Koopman’s Theorem (EKT) than previously appreciated. The lowest level of approximation to MEET is equivalent to the diagonal approximation of the EKT. The authors explore the asymmetric Hubbard dimer and find that the MEET energies depend on the choice of basis, corroborating previous work.In The continued development of theory and algorithms, progression of computational power, and importance of excited state spectra ensure that many-body Green’s functions will play a role in fundamental science for the forseeable future.


Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure.Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes.We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.The online version contains supplementary material available at 10.1186/s12859-021-04375-2. Natural selection is the main driver of local adaptation. Instead of tracing the adaptive phenotypic trait, a “reverse ecology” approach is commonly applied , where tentclass1pt{minimaThe reduced expenses for whole genome DNA sequencing, thanks to advanced High-throughput DNA sequencing technologies, has facilitated larger sample sizes in population genetics studies in the recent years, including samples with similar genetic ancestry , 29, 30.K principal components (PCs) and variant weights through a truncated singular value decomposition (SVD)  and pcadapt [3] applied to the four East Asian populations obtained. Red horizontal line is the Bonferroni adjusted significance level. pcadapt has been corrected for genomic inflation. CG standard: Called genotypes from low-coverage data with a genotype quality threshold on 20. Figure S5: QQ-plots and Manhattan plots of the selection statistics from FastPCA and pcadapt applied to the four European populations obtained. Red horizontal line is the Bonferroni adjusted significance level. pcadapt has been corrected for genomic inflation. CG standard: Called genotypes from low-coverage data with a genotype quality threshold on 20. Figure S6: PCA plot, QQ-plots and Manhattan plots of the selection statistics obtained from PCAngsd, FastPCA and pcadapt applied to a European (CEU), Asian (CHB), and African (AFR) population. Red horizontal line is the Bonferroni adjusted significance level. Only one PCA plot is shown as they were all identical. pcadapt has been corrected for genomic inflation. HQG: High quality genotype data. Figure S7: Read length bias in the low-coverage sequencing data of the East Asian populations. (AB) PCA plots of the data only filtered using a callability filter, where in (A) individuals are colored by population, and (B) displays the individuals colored by sequencing read length. (C-D) PCA plots of the data filtered by a callability filter and corrected for read length bias. Figure S8: No read length bias in the low-coverage sequencing data of the European populations. (A-B) PCA plots of the data filtered using a callability filter, where in (A) individuals are colored by population, and (B) displays the individuals colored by sequencing read length. Figure S9: PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four East Asian populations for SNPs called from the low-coverage sequencing data using ANGSD [2]. The called SNPs have additionally been filtered using a callability filter and corrected for readlength bias. Red horizontal line is the Bonferroni adjusted significance level. Figure S10: PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four European populations for SNPs called from the low-coverage sequencing data using ANGSD [2]. The called SNPs have additionally been filtered using a callability filter. Red horizontal line is the Bonferroni adjusted significance level. Figure S11: Downsampling to 0.5 fraction of the reads of the low-coverage sequencing data. PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four East Asian populations for SNPs called from the downsampled low-coverage sequencing data using ANGSD [2]. Red horizontal line is the Bonferroni adjusted significance level. Figure S12: Downsampling to 0.5 fraction of the reads of the low-coverage sequencing data. PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four European populations for SNPs called from the downsampled low-coverage sequencing data using ANGSD [2]. Red horizontal line is the Bonferroni adjusted significance level. Figure S13: PCA plots from PCAngsd based on the low-coverage sequencing datasets with individuals colored by their estimated individual allele frequencies in the top hits for the East Asian and European populations, respectively. The individual allele frequencies reveal the direction of the PC-based selection signals in regards to the reference allele. (A) shows the top significant hit on PC1 for the East Asian populations for the LILRA3 region (rs434124), and (B) shows the top significant hit on PC1 for the European for the LCT/MCM6 region (rs6754311).


Retraction Note: BMC Neurol 8, 52 (2008)https://doi.org/10.1186/1471-2377-8-52The authors have retracted this article after concerns were raised with respect to the source of donor organs .This study retrospectively reviewed the data of patients with in-hospital cerebrovascular complications after receiving liver transplants at the Organ Transplantation Center, First Affiliated Hospital of Sun Yat-sen University, from January 1996 to June 2005. The authors have stated that during this period most of the transplanted organs were obtained from executed prisoners; therefore the study reported in this article does not meet the international standards of ethics in organ transplantation and informed donor consent. Li Ling, Jinsheng Zeng and Zhijian Liang agree with this retraction. Xiaoshun He has not responded to correspondence from the Publisher about this retraction.