Autism Spectrum Disorder (ASD) is associated with many variations in metabolism, but the ex-act correlates of these metabolic disturbances with behavior and development and their links to other core metabolic disruptions are understudied. In this study, large-scale targeted LC-MS/MS metabolomic analysis was conducted on fasting morning plasma samples from 57 children with ASD  and 37 healthy controls of similar age and gender. Linear model determined the metabolic signatures of ASD with and without NDR, measures of behavior and neurodevelopment, as well as markers of oxidative stress, inflammation, redox, methylation, and mitochondrial metabolism. MetaboAnalyst ver 5.0  identified the pathways associated with altered metabolic signatures. Differences in histidine and glutathione metabolism as well as aromatic amino acid (AAA) biosynthesis differentiated ASD from controls. NDR was associated with disruption in nicotinamide and energy metabolism. Sleep and neurodevelopment were associated with energy metabolism while neurodevelopment was also associated with purine metabolism and aminoacyl-tRNA biosynthesis. While behavior was as-sociated with some of the same pathways as neurodevelopment, it was also associated with alternations in neurotransmitter metabolism. Alterations in methylation was associated with aminoacyl-tRNA biosynthesis and branched chain amino acid (BCAA) and nicotinamide metabolism. Alterations in glutathione metabolism was associated with changes in glycine, serine and threonine, BCAA and AAA metabolism. Markers of oxidative stress and inflammation were as-sociated with energy metabolism and aminoacyl-tRNA biosynthesis. Alterations in mitochondrial metabolism was associated with alterations in energy metabolism and L-glutamine. Using behavioral and biochemical markers, this study finds convergent disturbances in specific metabolic pathways with ASD, particularly changes in energy, nicotinamide, neurotransmitters, and BCAA, as well as aminoacyl-tRNA biosynthesis. Autism spectrum disorder (ASD) is one of the most concerning medical problems of our era. ASD continues to increase in prevalence with the most recent Autism and Developmental Disabilities Monitoring Network estimates suggesting that 1 in 44 children have the disorder . DespiteASD is associated with several systemic metabolic abnormalities. Although only 5% of children with ASD exhibit classically mitochondrial disease , up to 8Studies on blood amino acids in ASD tend to find variable results, but, overall, studies find lower overall amino acid concentrations in the blood  and urinOne interesting finding in previous studies that might link many of the aforementioned metabolic abnormalities is disruption of niacin metabolism. De novo synthesis of niacin in the human body utilizes the essential amino acid tryptophan , and niaOne complication of metabolic biomarker studies is the large number of metabolites considered in the analysis, making any one metabolite often non-specific. Thus, for such analysis, pathway analysis is often helpful to better understand the complex data . In thisclinicaltrials.gov (NCT02000284). Parents of participants provided written informed consent. Participants characteristics are given in The participants in this study represent a subset of previously published cohorts ,36. ThusExclusion criteria were (i) chronic treatment with medications that would detrimentally affect mitochondrial function such as antipsychotic medications; (ii) vitamin or mineral supplementation exceeding the recommended daily allowance, and (iii) prematurity. Inclusion criteria included the ability to tolerate phlebotomy and, for those with ASD, a diagnosis of ASD as documented using our previous published criteria ,36. TypiThe neurodevelopmental regression (NDR) history was obtained using the Developmental and Neurobehavioral Regression (DANR) questionnaire which is described in detail elsewhere . The DANAs standard practice for our laboratory ,38, reseg for 10 min at 4 °C to separate plasma within 30 min of collection. Plasma was removed and stored at −80 °C for later analysis and replaced with room temperature wash buffer containing Ca+2/Mg+2-free PBS with 0.1% BSA and 2 mM EDTA. Diluted blood was then layered on top of Histopaque-1077  and centrifuged at 400× g for 30 min at room temperature. Peripheral blood mononuclear cells (PBMCs) were washed twice with wash buffer and counted using a hemocytometer. Viability exceeded 95% in all cases and recovery was approximately 106/mL in most cases. Isolation procedure duration was 90–120 min. Fresh PBMCs were used for the mitochondrial respiration assay.Up to 20 mL of blood was collected into an ethylenediaminetetraacetic acid (EDTA)-Vacutainer tube, chilled on ice, and centrifuged at 1500× High Performance Liquid Chromatography with electrochemical detection was used to measure free and total reduced glutathione (GSH), as well as oxidized glutathione (GSSG) from which the free (fGSH/GSSG) and total (tGSH/GSSG) glutathione redox ratios were calculated. Intracellular redox ratio (iGSH/GSSG) from frozen PBMC was also measured. Methylation potential was measured as the S-adenosylmethionine (SAM) to S-adenosylhomocysteine (SAH) ratio. Finally, 3-nitrotyrosine (3-NT), a measure of oxidative damage to proteins, and 3-chlorotyrosine (3-CT), a measure of immune activity, were also measured. Measurements were performed within 2 weeks of collection ,41.Bioenergetic data was obtained from fresh PBMCs the same day as collection using a state-of-the-art Seahorse XFe96 Analyzer . The Seahorse analyzer measures oxygen consumption rate (OCR) in real-time in a 96-well plate in a wide range of intact living cell types ,43. The 5 viable PBMCs per well. After seeding, the plates were spun with slow acceleration  to a maximum of 100 g for 2 min and then allowed to stop with zero braking (Eppendorf Model 5810R Centrifuge). The plate orientation was reversed, and the plate was spun again to 100 g in the same fashion. Prior to Seahorse assay, XFe96 wells were visualized using an inverted microscope to ensure PBMCs were evenly distributed in a single layer and viability of the cells was confirmed by trypan blue exclusion. Four replicate samples were measured simultaneously to improve assay reliability. Runs with clear measurement probe failure, reagent injection failures, or non-physiology measurements (ALR or PLR < −1 pmol/min) were eliminated. In our previous study, measurement reliability was found to be excellent [PBMCs were placed in assay media  that was warmed to 37 °C and pH adjusted to 7.4 prior to cell suspension. XFe96 plates  were prepared by adding 25 μL of 50 μg/mL Poly-D-lysine  for two hours, washing with 250 μL sterile water and drying in a laminar flow hood overnight prior to seeding with 4 × 10xcellent . Procedures of metabolomic analysis have been outlined previously  but the 13C3-lactate and 142 μM 13C5-glutamic acid). The mixture was then vortexed for 10s and stored at −20 °C for 30 min, followed by centrifugation at 14,000 RPM for 10 min at 4 °C. The supernatant (450 μL) was collected into a new Eppendorf vial, and dried using a CentriVap Concentrator . The dried samples were reconstituted in 150 μL of 40% PBS/60% ACN.Frozen samples were first thawed overnight under 4 °C, and 50 μL of each sample was placed in a 2 mL Eppendorf vial. The initial step for protein precipitation and metabolite extraction was performed by adding 500 μL MeOH and 50 μL internal standard solution (containing 1810.5 μM Acetonitrile (ACN), methanol (MeOH), ammonium acetate, and acetic acid, all LC-MS grade, were purchased from Fisher Scientific . Ammonium hydroxide was bought from Sigma-Aldrich . DI water was provided in-house by a Water Purification System from EMD Millipore . PBS was bought from GE Healthcare Life Sciences . The standard compounds corresponding to the measured metabolites were purchased from Sigma-Aldrich  and Fisher Scientific .2O). After the initial 1 min isocratic elution of 90% B, the percentage of Solvent B decreased to 40% at t = 11 min. The composition of Solvent B maintained at 40% for 4 min (t = 15 min), and then the percentage of B gradually went back to 90%, to prepare for the next injection. The large-scale targeted LC-MS/MS method is widely used ,50,51,52The mass spectrometer was equipped with an electrospray ionization (ESI) source. Targeted data acquisition was performed in multiple-reaction-monitoring (MRM) mode. The whole LC-MS system was controlled by Agilent Masshunter Workstation software . The extracted MRM peaks were integrated using Agilent MassHunter Quantitative Data Analysis . A pooled sample, which was a mixture of all blood samples was used as the quality-control (QC) sample. We ran the QC once every 10 study samples to ensure the good data quality.10 transformation and auto-scaling (mean-centered and divided by the standard deviation of each variable)  was used for data analysis . Data weble) See .Linear Model Analysis was used to define the significantly different metabolites from one disease group (ASD) and two sub disease groups  as compared to control (CNT) group. Correlation Analysis was used to define significant metabolites that were correlated to participants from both ASD and CNT groups and their behavioral scores from the CL, VABS, SRS, CSHQ, and ABC. Correlation Analysis was also used to define significant metabolomic measurements with methylation potential, tGSH/GSSG, fGSH/GSSG, iGSH/GSSG, 3-NT and 3-CT. Alpha was set to ≤0.05 to identify potential important metabolites at the risk of incorrectly identifying metabolites which are not important. Since the metabolites identified were then entered into pathway analysis, metabolites which may be erroneously identified most likely will be eliminated as they will not be consistent with other metabolites identified in common pathways. Furthermore, by examining the relationships between behavior and metabolic changes, the common pathways which converge across multiple analyses can be identified. Regarding statistical power of the comparisons in this study, the average statistic across comparisons was used to calculate the effect size. For group comparisons an t-value of 2.5 resulted in a medium effect size of a Cohen d’ of 0.52 which provides a power of 68%. For correlation analysis an average r of 0.3 is a medium effect size and provides an 85% power. G * Power v 3.1.9.7  was used for the power calculations. Key metabolites identified by linear models and correlation were investigated using the pathway and metabolite-metabolite interaction tool of the network analysis package MetaboAnalyst 5.0. Metabolite networks were analyzed in detail. The major key metabolites were determined by decreasing node scope to minimum number of nodes to the top-level nodes.Comparison of ASD vs. CNT found 23 significant metabolites associated with three significant pathways . MetabolA comparison of ASD individuals with and without NDR identified three significant metabolites which mapped to one significant pathway . MetabolComparison of CNT to individuals with ASD but without NDR found 18 significant metabolites which mapped to the aromatic amino acid (AAAs) pathway . HoweverComparing CNT to individuals with ASD with NDR found 19 significant metabolites which mapped to four significant pathway . MetabolCSHQ correlated with 25 metabolites that mapped to three pathways . MetabolThe VABS was correlated with 28 metabolites that mapped to four pathways . MetabolThe SRS correlated with 30 metabolites that mapped to 7 significant pathways . MetabolThe CL correlated with 8 metabolites that mapped to two significant pathways . MetabolThe ABC was correlate with 14 metabolites that mapped to two significant pathways: phenylalanine metabolism and AAA biosynthesis . MetabolMethylation potential correlated with 28 metabolites with mapping to 6 pathways . MetaboltGSH/GSSG correlated with two significant metabolites and mapped to two significant pathways . The onlfGSH/GSSG correlated with two significant metabolites and two significant pathways, interesting the same pathways associated with aberrant behavior . The ketiGSH-GSSG was correlated with 7 metabolites and mapped to one pathway . Metabol3-NT correlated with 16 metabolites and four pathways . Metabol3-CT correlated with 31 metabolites and mapped to three pathways . MetabolALR correlated with 14 metabolites and two pathways . MetabolPLR correlated with 7 metabolites and mapped to three pathways . Three gThis study used large-scale targeted LC-MS/MS metabolomic analysis to examine the metabolic profiles in blood from 57 children with ASD (29 with NDR) and compared these metabolic profiles to TD controls of similar age and gender. Furthermore, we examined the associations between metabolic measurements and behavioral and neurodevelopmental measurements and targeted metabolic biomarkers known to be abnormal in children with ASD. As a group, those with ASD were compared to TD controls, and the NDR subgroups were compared to each other and to TD controls, separately. Overall, 23 metabolites were found to be significantly different between ASD participants and TD controls representing significant differences in three pathways, histidine and GSH metabolism, as well as AAA biosynthesis. Nicotinamide metabolism differentiated those with ASD with NDR from those without NDR. The AAAs differentiated TD controls from individuals with ASD without NDR. Interesting, a different metabolic signature was found for those with NDR when compared to TD controls. Those with a history of NDR demonstrate disruption of aminoacyl-tRNA biosynthesis and histidine metabolism, as well as metabolism of glyoxylate and dicarboxylate (energy metabolism) and glycine, serine and threonine (energy and purine metabolism). Thus, these results suggest that subsets of children with ASD have distinct metabolism profiles specifically dependent on their developmental profile (NDR vs. no NDR). To better understand the heterogeneity of children with ASD, variations in behavior and neurodevelopment were correlated with changes in metabolism. Abnormalities in sleep were directly associated with disruption is energy metabolism, specifically CAC metabolism as well as the pentose phosphate pathway, an important source of nicotinamide adenine dinucleotide phosphate (NADPH). Overall neurodevelopment, as measured by the VABS, found that an association with energy and purine metabolism as well as aminoacyl-tRNA biosynthesis. Language was associated not only with a primary disruption in energy pathways but also in glycine, an amino acid neurotransmitter. ASD symptoms, as measured by the SRS, was found to be associated with energy, BCAA and glutamate metabolism, as well as aminoacyl-tRNA biosynthesis. Interestingly, disruptive behavior was primarily associated with AAA metabolism, implicating monoamine neurotransmitters. Thus, from this analysis, neurodevelopment metrics seems to be associated with energy metabolism, but ASD and aberrant behavior appears to be more related to amino acid metabolism and monoamine neurotransmitters.Correlations between targeted measures of ASD metabolism and metabolomics were also considered. A measure of methylation potential, the SAM/SAH ratio, was found to be related to a wide range of metabolic processes, including very strongly related to aminoacyl-tRNA biosynthesis and related to BCAA, histidine and nicotinamide metabolism, as well as disruption in amino acids related to methylation such as methionine. Interestingly, the total GSH redox ratio was related to glycine, serine, threonine, arginine and proline metabolism through its relation to disruption of creatine while the free GSH redox ratio was associated with changed in AAA metabolism. The intracellular GSH redox ratio was related to BCAAs. Oxidative stress, as measured by 3-NT, was found to be related to glutamine, glycine, serine and threonine metabolism, as well as CAC and fatty acid energy metabolism. Metabolic-metabolic interaction analysis highlighted the importance of vitamin B6 and B12 in this network as both pyridoxal-5-phosphate and methylmalonic acid were highlighted nodes in the network. Interestingly, two D-amino acids, including D-glutamic acid and D-glutamate, which are related to bacterial metabolism were associated with 3-NT. 3-CT, a marker of immune activation, was found to be related to a wide range of metabolic pathways, including a very strong relationship to aminoacyl-tRNA biosynthesis and also histidine, glycine, serine and threonine metabolism, as well as CAC energy metabolism. Metabolic-metabolic interaction analysis highlighted the involvement of ATP and L-glutamine in these pathways. As expected, ALR, a measure of the mitochondrial ATP production, was associated with CAC metabolites. Interestingly, PLR, a measure of the mitochondria’s handling of oxidative stress at the inner mitochondrial membrane, was found to be related to glutamine metabolism. Disruption in energy metabolism was demonstrated in several different comparative analyses of ASD and TD individuals and in the correlations with behavior, neurodevelopment, and targeted metabolic pathways. Energy pathways were found to differentiate individuals with ASD and NDR and be related to sleep, overall neurodevelopment, language, and ASD symptoms (SRS). Interestingly, methylation and GSH metabolism did not seem to be connected to energy yet measures of oxidative stress and immune activation were related. Not surprisingly, ALR, a measure of mitochondrial production of energy, was strongly linked to the CAC while proton linked respiration was linked to glutamate metabolism. This latter finding is interesting as glutamate is an alternative fuel for the mitochondrial that is understudied in mitochondrial research of developmental disorders.The repeated association of energy metabolism with many aspects of ASD is not surprising given the high rate of mitochondrial dysfunction associated with ASD ,11. FurtInterestingly, pathways other than central CAC metabolites were also identified. For example, disruption in glyoxylate and dicarboxylate pathway was identified in several analyses. This is an interesting pathway as it provides a short cut between the proximal CAC (isocitrate) and distal CAC , thereby bypassing α-ketoglutarate and succinyl-CoA. These two intermediates that are bypassed are important in ASD physiology as propionic acid, an important short chain fatty acid associated with ASD symptoms ,57, enteDysregulation of glycine, serine and threonine pathways is connected to the CAC through serine that is directly metabolized into pyruvate. Disruption in serine metabolism has been linked to ASD in the past , and glySeveral pathways were associated with changes in the AAA  biosynthesis. Examining the metabolite changes demonstrates that this was driven by phenylpyruvate, a product which accumulates when phenylalanine breakdown is reduced, usually when hydroxylase activity is inhibited. This enzyme and other the hydroxylases required to metabolize AAA precursor into monoamine neurotransmitters are dependent on tetrahydrobiopterin as a cofactor, and low tetrahydrobiopterin levels are known to be associated with ASD . TreatmeL-Glutamine was found to be related to measure of neurodevelopment (VABS), ASD symptoms (SRS), as well as markers of methylation, oxidative, inflammation, and mitochondrial control of oxidative stress (PLR). Glutamine is a central amino acid which connects neurotransmission, redox metabolism, and mitochondrial function. Glutamine is the precursor/product of both glutamate, the main cortical excitatory neurotransmitter, and gamma-aminobutyric acid (GABA), the main cortical inhibitory neurotransmitter. As excitatory/inhibitory balance is known to be disrupted in ASD through these two neurotransmitters , glutamaGlutamate is an essential component of GSH which is the body’s major antioxidant, consistent with the findings in this study that it is associated with measures of oxidative damage and inflammation. One of the products of the methylation cycle is homocysteine which can be metabolized into cysteine. Cysteine is one of the major three components of GSH, along with glutamate and glycine. As such, glutamate is connected to methylation and control of oxidative stress through the production of GSH.Interestingly, in several of the analysis, glutamine was also related to markers of mitochondrial function. As mentioned above, glutamine is a mitochondrial substrate for producing energy which enters the CAC through α-ketoglutarate. The repeated findings of an activation of the glyoxylate and dicarboxylate pathway which bypasses this portion of the CAC suggests an increase in glutamine is entering the CAC. Thus, this suggests a close association of the neurotransmitter regulation and mitochondrial function in ASD.2 and ammonium. Thus, aside from being a neurotransmitter, glycine is involved in several other critical pathways that could influence brain function and development. Glycine is an inhibitory neurotransmitter with anti-inflammatory, cytoprotective, and immune modulating properties . In thisBCAA biosynthesis was found to correlate with ASD symptoms, methylation potential and intracellular GSH ratio, although with low pathway impact scores. It is estimated that approximately 17% of children with ASD have relative reduced concentrations of BCAAs . This fiNDR was associated with changes in nicotinamide metabolism. Previous studies have found that urine nicotinamide metabolites may be a biomarker for ASD  and may Variations in aminoacyl-tRNA biosynthesis were found in participants with ASD and a history of NDR and were related to measures of neurodevelopment, ASD symptoms, methylation potential, and immune activation. Aminoacyl-tRNAs, tRNAs bonded to their cognate amino acid, serve as key substrates responsible for translating the genetic code into proteins. Synthesis of these tRNAs directly relies on the aminoacyl-tRNA synthase . Thus, aDisruptions in histidine metabolism differentiated TD controls from those with ASD, particularly those with ASD and a history of NDR. Methylation potential and 3-CT, a marker of immune activation, were also strongly related to the histidine metabolism. The strong correlation between the 3-CT measurement and histidine metabolism may be explained by how histamine, an integral of the immune system, can be easily synthesized by decarboxylation of histidine . VariousInterestingly, the data from this study converged on several pathways. One interesting aspect of the many of the pathways and metabolites is that many of the pathways were all connected to various intermediates within the CAC. As energy metabolism, particularly with respect to the mitochondria and CAC, is repeatedly identified in many of the pathway analysis, it is very possible that the majority of the non-mitochondrial pathways found to be disrupted could be caused by or the cause of mitochondrial dysfunction through disruption of the flux of metabolites in the CAC. This could explain the high prevalence of mitochondrial dysfunction without the findings of mutation is mitochondrial genes. Specifically, disruption in function of these non-mitochondrial pathways could be the cause of secondary mitochondrial dysfunction in many individuals with ASD. A better understanding of these connections between mitochondrial and non-mitochondrial pathways could lead to improved treatments for individuals with ASD.This study has several limitations, particularly the limited sample size, providing only a limited ability to understand the heterogeneity of ASD. In order to account for this, the variation in behavior and other specific metabolic disruptions associated with ASD were correlated with metabolites measured. To investigate the relationship between metabolic signatures and mitochondrial function parameters derived from whole fresh intact cellular based respirometry were examined. However, in the future, additional markers could be examined. The study participants were derived from a natural history study of individuals with ASD with much of the data obtained retrospectively. Thus, future cohorts derived from prospective early life histories would increase the strength of the study.This study examined the metabolic profiles of ASD in relation to several behavior, neurodevelopmental and targeted metabolic biomarkers in an effort to uncover a metabolic signature related to ASD. We used both pathway analysis and metabolomic-metabolomic interactions to examine metabolites. Energy and amino acid pathways were commonly disrupted with many of the amino acids being closely linked to energy production. This study had several limitations including selection of samples from general samples of ASD which were not preselected to have a specific range of severity and a limited number of controls. Further studies should use larger sample sizes to better characterize the patients and a greater number of controls. Simultaneous collection of urine and stool samples may be very useful to better understand how systematic and microbiome changes can influence metabolism.


Twenty years have passed since the identification of the target of rapamycin (TOR) protein kinase in Arabidopsis. Research has expanded from functional characterization of the TOR complex (TORC1) and development of specific chemical inhibitors, to mapping the diverse biological relationships upstream and downstream of TORC1. New insights have been obtained on the mechanisms linking environmental perception to TORC1-mediated growth responses under optimal and stress conditions. Furthermore, molecular connections have been established between TORC1 and several phytohormone and nutrient signalling pathways , which together reinforce this pathway as a major hub controlling growth responses from unicellular algae to flowering plants.The TOR signalling pathway integrates nutrient, phytohormone, and environmental signals to adjust plant growth to the available resources. Its conservation among eukaryotes highlights its physiological relevance, which ranges from unicellular organisms to humans . In thisIn animals, TOR assembles into two complexes with independent functions. TORC1 includes the master kinase TOR and its associated partners Lst8 and Raptor, whereas in TORC2, Raptor is replaced by Rictor/Avo3, and Sin1/Avo1 is present. However, photosynthetic organisms only possess TORC1, and it is likely that although TORC2 could have been present in the Last Eukaryotic Common Ancestor, it was lost early on during the evolution of the green lineage. This hypothesis is strengthened by the observation that Rictor homologs are present in some ancient microalgae (Chlamydomonas reinhardtii (hereafter Chlamydomonas), assimilated carbon, nitrogen, phosphorus, and acetate regulate TORC1  and components of the CBB cycle . This process depends on Arabidopsis Trithorax 1 (ATX1), a H3K4 methyltransferase that is regulated by E2Fa, a TOR target  mRNAs in Arabidopsis . Althougbona fide TOR targets  are integrated to ensure temporal and spatial adjustment of plant growth and development by regulating a diverse range of specific effectors and targets.Although great progress has been made in uncovering the TORC1 interactome it will be important to functionally characterize many of the identified candidates that could function as alternative read-outs of the activity of this complex.Expanding our knowledge of TORC1 within the green lineage could uncover novel plant-specific functions commonly associated with TORC2 in mammals, and thus improve our understanding of growth regulation in model plants and crops.in vitro, it will be important to investigate the activity of this complex under standard plant-growth conditions (e.g. in the soil).Although the glucose–TORC1 module has been extensively characterized in seedlings grown Several findings point to a close connection between TORC1 and chloroplast function, development, and overall architecture; further dissection of this relationship could uncover how energy generation and consumption are balanced at the cellular level.


Gryllus firmus, selected either for long wings  or short wings  to test the hypothesis that selection on dispersal capability drives the evolution of metabolic capacities. While resting metabolic rates were similar, long‐winged crickets reached higher maximal metabolic rates than short‐winged crickets, resulting in improved running performance. We further provided insight into the mechanisms responsible for covariation between life history and metabolism by comparing mitochondrial content of tissues involved in powering locomotion and assessing the function of mitochondria isolated from long‐ and short‐winged crickets. Our results demonstrated that larger metabolic capacities in long‐winged crickets were underpinned by increases in mitochondrial content of dorsoventral flight muscle and enhanced bioenergetic capacities of mitochondria within the fat body, a tissue responsible for fuel storage and mobilization. Thus, selection on flight capability correlates with increases in maximal, but not resting metabolic rates, through modifications of tissues powering locomotion at the cellular and organelle levels. This allows organisms to meet high energetic demands of activity for life history. Dispersal capability should therefore explicitly be considered as a potential factor driving the evolution of metabolic capacities.Life history and metabolism covary, but the mechanisms and individual traits responsible for these linkages remain unresolved. Dispersal capability is a critical component of life history that is constrained by metabolic capacities for energy production. Conflicting relationships between metabolism and life histories may be explained by accounting for variation in dispersal and maximal metabolic rates. We used female wing‐polymorphic sand field crickets,  Gryllus firmus) selected for flight  over reproduction . Large metabolic capacities for energy production required for active locomotion, evolve in female sand field crickets ( Metabolic rates can be a pacemaker for biological processes, such as growth and development, thereby setting the pace of life along a fast‐to‐slow continuum . Adult crickets are either long‐winged (LW) and flight‐capable or short‐winged (SW) and flightless . LW criss Roff, . HoweverWe hypothesized that metabolic remodelling increases energy production capacities required to meet energetic demands of dispersal by active locomotion, resulting in higher maximal metabolic rates and correspondingly greater locomotor performance in flight‐capable compared to flightless crickets. To test this hypothesis, we first used a treadmill respirometer to simultaneously assess metabolic rates  and running performance of individual LW and SW crickets. While running is less energetically costly compared to flight Butler,  we focus22.1Gryllus firmus, that came from a genetic stock (Block II) selected for wing‐length (long‐winged (LW) or short‐winged (SW)) in the 1990s  used in these experiments was visually confirmed  by dissection, and the three long‐winged individuals found with histolysed muscles were excluded from analysis.All experiments were conducted using adult female sand field crickets, 2.2n= 25 SW and n= 30 LW) were measured as a proxy for organismal metabolic rates, using open‐flow respirometry with an electrochemical oxygen analyzer ; both at rest and during running on a treadmill. The respirometry chamber encapsulated a miniature treadmill controlled by a motor that set the speed and direction of belt movement. The flow rate of air through the respirometry chamber was 100 ml·min−1 and controlled by a flow meter .After calibration, a cricket was added to the respirometry chamber and allowed to acclimate for ten minutes or until a resting metabolic rate (RMR), defined as a four‐minute period of inactivity during which the rate of oxygen consumption was constant, was reached. Once the cricket reached RMR, the treadmill was turned on at a starting speed of 25 rpm. Every three minutes, the speed was increased by 2.5 rpm until the cricket became exhausted. Exhaustion was defined as the moment when the cricket began hitting the back of the chamber because it stopped running and could no longer keep up with the speed of the treadmill. The peak steady state, defined as a period in which variation in oxygen consumption rate was less than 5%, recorded just prior to exhaustion was considered an individual's maximal metabolic rate (MMR). At exhaustion, the treadmill was stopped, and the duration of running and maximal speed reached by the cricket were recorded as indices of locomotor performance. All crickets were frozen following running and stored at −20°C until dissection and tissue collection for measurements of citrate synthase activity and muscle status determination (see ‘2) × flow rate (100 ml·h−1) × 60 min·h−1 × Standard Temperature Pressure Dry)/100.Running trials were conducted across sixteen days and crickets tested on a given day always included an equal number of LW and SW individuals. Prior to each trial, room temperature was measured with a thermometer (ranged between 22°C and 25°C) and cricket mass, to the nearest 0.01 g, was recorded using an electronic balance . MATLAB was used to monitor, collect and process the respirometry data. Oxygen consumption during resting and maximal states were calculated as (the oxygen concentration difference (∆O2.3n = 12 LW and n = 12 SW). These three tissues were chosen because of their roles in running; the leg and dorsoventral muscles (DVM) directly power leg movement, while the fat body is involved in synthesizing, storing and mobilizing nutrients to support muscle activity. For dissections, frozen crickets were thawed, and fine point micro‐scissors were used to make a midline cut through the cuticle on the ventral side. The cricket was pinned open to expose the body cavity and fine‐tipped forceps were used to collect tissues. All tissues were immediately weighed to the nearest mg with an electronic balance , and homogenized in a 1: 25 (w: v) of homogenization buffer , using a PRO250 homogenizer . After homogenization, the samples were centrifuged , then supernatants were collected and stored at −80°C until enzyme activity measures were taken.Citrate synthase activity is used as an indicator of mitochondria content and aerobic capacity of tissues .Tissue‐specific citrate synthase activity was measured spectrophotometrically based on the production of citrate from the conversion of oxaloacetate and acetyl‐CoA, as described by Chung et al. . Immedia2.4n = 10 LW and n = 10 SW) following a modified protocol based on Slocinska et al. . The supernatant was collected and centrifuged at high speed (10 min at 10 000 g at 4°C) to obtain a mitochondrial pellet. The mitochondrial pellet was re‐suspended in ice‐cold wash buffer  and centrifuged again at high speed to wash the mitochondria and separate nuclei (10 min at 12 000 g at 4°C). The final mitochondrial pellet was re‐suspended in ice‐cold wash buffer and the protein concentration was determined using a Bicinchoninic Acid (BCA) assay. The BCA assay was conducted following manufacturer's instructions using bovine serum albumin (BSA) as the protein standard (0–15 mg·ml−1) .Mitochondria were isolated from fat body tissue . To stimulate respiration, we first injected saturating amounts of substrates  and ADP (0.5 mM) into the chamber. Under these conditions, the maximal steady state oxygen consumption rate reached prior to depletion of ADP was recorded as a measure of mitochondrial capacity for oxidative phosphorylation (OXPHOS). Depletion of ADP was followed by a decline in respiration only sustaining mitochondrial proton leak, proton slip and cation cycling (LEAK). We calculated the respiratory control ratio (OXPHOS/LEAK) to assess mitochondrial coupling efficiency and sample quality. After measurements of LEAK, a chemical uncoupler  was added until maximal rates of oxygen consumption were reached to estimate electron transport system capacity (ETS). Finally, during trials with pyruvate and malate as NADH generating substrates, we also measured cytochrome c oxidase capacity (COX) through the addition of N,N,N’,N'‐Tetramethyl‐p‐phenylenediamine dihydrochloride , which directly donates electrons to COX in presence of ascorbate (2 mM) . Three respiration trials were conducted on every mitochondrial preparation to compare mitochondrial performance using different substrate titration protocols in a randomized order. During respiration trials, mitochondria (0.2 mg) were added to the respirometer chambers maintained at 27°C and filled with air‐equilibrated respiration buffer  Figure .−1 during a trial, a gas phase was introduced to re‐oxygenate the chamber. Prior to data analysis, respiration rates were normalized to protein concentration and corrected for background levels of oxygen consumption by electrodes across a range of oxygen tensions (250–0 nmol·ml−1). A separate background calibration was used for measurements of COX to account for auto oxidation of ascorbate and TMPD. Oroboros DatLab version 7.4 was used to monitor, collect and process the respirometry data.In case of oxygen tension falling below 50 nmol·mlc oxidase subunit IV (COX‐IV) with a modified immunoblot protocol , was diluted into water and sample buffer (4X LDS and 10% β‐mercaptoethanol) and heated . Proteins were separated in a 12% Bis–Tris gel for 40 min at 200 V in 1X MES SDS buffer and transferred to a nitrocellulose membrane (0.45 μm pore size) for 90 min at 35 V in transfer buffer (1X NuPAGE transfer buffer and 20% methanol). Non‐specific protein interactions were inhibited by blocking the membrane using (Pierce Protein Free (PBS) blocking buffer, Thermo Fisher Scientific, MA USA) for 1 h at room temperature and membranes were subsequently incubated overnight at 4°C with primary antibody for COX‐IV  diluted 1: 1000 (v: v) in blocking buffer. The following day, membranes were washed (5 min at room temperature with gentle shaking) five times with 0.1% PBS‐tween to remove excess primary antibody, before incubation (1 h at room temperature with gentle shaking) with secondary antibody  prepared at a 1: 1000 (v: v) dilution in blocking buffer. Prior to visualization, excess secondary antibody was removed by washing (5 min at room temperature with gentle shaking) the membrane four times with 0.1% PBS‐Tween and twice with PBS. An Azure c500 imaging system  was used to visualize the blot. COX‐IV abundance was quantified based on the optical density (OD) of the band present at ~19 kDA using Image J (v. 1.53c). Prior to statistical analysis, OD was corrected for variation in total amounts of protein present in each lane, which was quantified using Revert 700 total protein stain  and normalized to average abundances in short‐winged crickets.Finally, we quantified relative protein abundance of cytochrome 2.5α = 0.05.All statistical analyses were conducted using R version 4.0.4. Prior to analysis, data were checked for normality and homogeneity of variances. All averages are reported as mean ± standard error of the mean, and post hoc pairwise comparisons were performed using Tukey's HSD tests with an experiment‐wise type I error of p > 0.05).To compare whole organismal metabolic rates and estimate metabolic rate‐mass scaling coefficients for long‐ and short‐winged crickets, we applied a logarithmic transformation to both body mass and oxygen consumption rates (RMR and MMR) and conducted an ANCOVA with wing‐length (LW or SW) as a fixed effect and body mass as covariate. As an index of organismal metabolic capacities, we calculated (1) Absolute Aerobic Scope (AAS), as the difference between an individual crickets’ resting (RMR) and the maximal metabolic rate (MMR) during running (AAS = MMR–RMR) and (2) Factorial Aerobic Scope (FAS), as the fold increase between an individual crickets’ resting (RMR) and the maximal metabolic rate (MMR) during running (FAS = MMR/RMR). An ANCOVA with wing‐length as a fixed effect and body mass as a covariate without transformation was subsequently used to evaluate differences in absolute and factorial aerobic scopes of crickets during running. All linear models testing for differences in organismal metabolic traits  between LW and SW crickets also initially included ambient room temperature as a covariate, which was removed from the final model if not significant  to compare locomotor performance of LW and SW crickets. A model with a log‐logistic error distribution was selected based on having the lowest AIC score and included wing‐length (LW or SW) as a fixed effect. Room temperature during running trials was not significantly associated with either running performance or endurance  and excluded from final models. In addition, we used a linear model with wing‐length as a fixed effect, to test for a significant association between time spent running prior to exhaustion and absolute aerobic scope.During exercise trials running performance was quantified as the maximum speed at which running could be sustained, and endurance was quantified as the time spent running before exhaustion for individual crickets. We then used an accelerated failure time model  we used a linear mixed model with wing‐length (LW or SW), tissue type , and their interaction as fixed effects and cricket identity as a random effect to account for individual variation.To compare citrate synthase activity rates across tissues , substrate type  and the interaction between wing‐length and substrate as fixed effects for each respiratory measure separately . All initial models also included random effects of respirometry chamber, sample ID and trial number. A stepwise approach was used to remove random effects that did not significantly improve the explanatory power of the model based on ∆AIC < 2. Final models for LEAK included both trial number and sample ID as random effects, whereas only sample ID was retained as a random effect in models testing for differences in OXPHOS and ETS between LW and SW crickets. The final linear mixed model for the respiratory control ratio included a random effect of trial number.c oxidase capacity (COX) and COX‐IV relative abundance levels from western blots differed in isolated mitochondria from LW and SW crickets. Finally, a linear model that included wing‐length as a fixed effect was conducted to test for an association between OXPHOS and COX, when fueled by pyruvate and malate.A two‐sample t‐test was conducted to determine if apparent cytochrome 33.1F1,49 = 7.25, p = 0.01; MMR: F1,48 = 45.85, p < 0.001; Figure G. firmus crickets. The estimated slope of the relationship between mass and metabolic rate was 0.8 ± 0.3 ml O2·h−1·g−1 for resting metabolic rates and for 1.32 ± 0.2 ml O2·h−1·g−1 maximal metabolic rate. Resting metabolic rates did not differ between LW and SW crickets  was greatest in dorsoventral muscle compared to the other tissues , and similar in leg compared to fat body tissue  .Citrate synthase activity was significantly greater in the dorsoventral muscle of LW compared to SW crickets, but similar between LW and SW crickets in leg muscle and fat body  and SW  crickets. In contrast, when glutamate was provided as a substrate, low oxygen consumption rates were observed, reflecting a low capacity for glutamate oxidation and maximal oxygen consumption rates were similar in LW and SW crickets . For all substrates, minimum rates of oxygen consumption caused by LEAK were similar between LW and SW crickets  activities of mitochondria were also higher in LW compared to SW crickets  and cytochrome 4Gryllus firmus, we show that flight capability is associated with larger in vivo metabolic capacities, reflected by higher mass‐specific maximal metabolic rates and larger aerobic scopes in long‐winged compared to short‐winged crickets. Since crickets differed in maximal but not resting metabolic rates, our findings suggest that larger metabolic capacities associated with a more active lifestyle can arise through selection that acts independently on resting and maximal metabolic rates. Flight‐capable crickets also had more mitochondria in their dorsal ventral muscles (DVM), a set of bifunctional muscles used during both flight and running, as well as mitochondria that could respire at higher maximal rates. Thus, our results support the conclusion that dispersal by active locomotion drives evolution of maximal capacities for energy production, through tissue‐specific modifications at the organelle and molecular levels.In artificially selected genetic lines of Within other species with life‐history polymorphisms across a broad range of taxa, faster life histories, characterized by fast growth rates, short development time and short lifespans, are often associated with higher metabolic rates  and supports both flight and reproduction, through its roles in nutrient storage, energy mobilization and biosynthesis of reproductive proteins for oogenesis  . Furthermore, if larger ATP production capacities are contributing to a physiological constraint, we expect those high capacities to be lost in LWs along with flight capability, following muscle histolysis. We are currently testing this prediction using older LW crickets.Differences between LW and SW crickets also suggest that maintaining large metabolic capacities may be costly, and therefore contribute to the functional basis of the flight‐oogenesis trade‐off. Consistent with this idea, bioenergetic capacities of mitochondria in the fat body are also larger in LW compared SW crickets in the closely related wing‐polymorphic variable field cricket (In summary, our work adds to a growing body of evidence supporting the hypothesis that life history and metabolic evolution are coupled because metabolism is fine‐tuned to appropriately meet energetic demands set by life‐history strategies. While dispersal capability is seldom considered in studies linking life history and metabolism, we found that selection on flight capability was associated with tissue‐specific modifications of mitochondria, which allowed long‐winged crickets to reach higher maximal metabolic rates during aerobic activity. Based on work here, combined with prior studies using the same genetic lines of crickets (Zera et al., The authors have no conflict of interest to declare.Lisa A Treidel: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing‐ Original Draft, Visualization, Project administration, Funding acquisition; Gessen S. Quintanilla Ramirez: Conceptualization, Investigation, Writing‐ Review & Editing; Dillon J. Chung: Conceptualization, Methodology, Investigation, Writing‐ Review & Editing; Michael A. Menze: Conceptualization, Methodology, Resources, Writing—Review & Editing; José P. Vázquez‐Medina: Conceptualization, Methodology, Resources, Writing—Review & Editing; Caroline M. Williams: Conceptualization, Resources, Writing—Review & Editing, Supervision, Funding acquisition.https://publons.com/publon/10.1111/jeb.13996.The peer review history for this article is available at https://doi.org/10.6078/D1RX3J.This article has earned an Open Data Badge for making publicly available the digitally‐shareable data necessary to reproduce the reported results. The data is available at Appendix S1Click here for additional data file.


Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders generally characterized by repetitive behaviors and difficulties in communication and social behavior. Despite its heterogeneous nature, several metabolic dysregulations are prevalent in individuals with ASD. This work aims to understand ASD brain metabolism by constructing an ASD-specific prefrontal cortex genome-scale metabolic model (GEM) using transcriptomics data to decipher novel neuroinflammatory biomarkers. The healthy and ASD-specific models are compared via uniform sampling to identify ASD-exclusive metabolic features. Noticeably, the results of our simulations and those found in the literature are comparable, supporting the accuracy of our reconstructed ASD model. We identified that several oxidative stress, mitochondrial dysfunction, and inflammatory markers are elevated in ASD. While oxidative phosphorylation fluxes were similar for healthy and ASD-specific models, and the fluxes through the pathway were nearly undisturbed, the tricarboxylic acid (TCA) fluxes indicated disruptions in the pathway. Similarly, the secretions of mitochondrial dysfunction markers such as pyruvate are found to be higher, as well as the activities of oxidative stress marker enzymes like alanine and aspartate aminotransferases (ALT and AST) and glutathione-disulfide reductase (GSR). We also detected abnormalities in the sphingolipid metabolism, which has been implicated in many inflammatory and immune processes, but its relationship with ASD has not been thoroughly explored in the existing literature. We suggest that important sphingolipid metabolites, such as sphingosine-1-phosphate (S1P), ceramide, and glucosylceramide, may be promising biomarkers for the diagnosis of ASD and provide an opportunity for the adoption of early intervention for young children. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that shows up in early childhood and is a lifelong condition. Although the heterogeneous nature of this disorder creates difficulties when diagnosing people, most ASD patients lack verbal and nonverbal communication skills, insist on the same routine, and show restricted and repetitive behaviors . The preA number of studies emphasized that various brain parts, such as the frontal lobes, amygdala, and cerebellum, may show structural and functional differences in ASD and can take part in the pathology of the disorder . For insSeveral central nervous system-based cellular and metabolic dysregulations, such as inflammation, oxidative stress, and mitochondrial dysfunction have been implicated in various neurodegenerative, neuropsychiatric, and neurodevelopmental diseases such as Alzheimer’s disease, schizophrenia, bipolar disease, depression, and ASD . SpecifiAlthough genome-scale metabolic models (GEM) are comparatively new, they are very useful systems biology tools that can provide a holistic model for the whole metabolism and can also be used to decipher the relationship between genotype and phenotype and predict the effects of environmental changes on the metabolism. GEMs have been used for many applications, such as cell factory design for the production of chemicals and prediction of microbial community interactions . AdditioDespite the fact that ASD is a prevalent disorder, the underlying mechanisms behind its pathogenesis are still unknown. The current work aims to investigate to which extent several cellular processes, including neuroinflammation and oxidation stress, in the prefrontal cortex of ASD children are perturbed at the genome-scale. Thus, ASD-specific and neurotypical prefrontal cortex genome-scale metabolic models have been constructed using transcriptomics data to better understand the ASD brain metabolism and consequently to decipher novel biomarkers for ASD. To uncover the ASD-specific metabolic traits, the healthy and ASD-specific models were compared, and the increased neuroinflammatory and oxidative stress-related markers in the ASD model identified. Understanding the metabolic reprogramming in autistic brains not only sheds light on the molecular mechanisms that lead to ASD, but it may also help to direct early intervention strategies.The postmortem prefrontal cortex transcriptome data for autistic people and neurotypical controls aged between 2 and 56 were acquired from the Gene Expression Omnibus database with the ID GSE28475 ,21. The A flowchart of the methodology used in this work can be seen in The RAVEN toolbox  was used2/CMRglu for the young ASD patients and controls [max value between astrocytes and neurons. For the exchange reactions of other essential nutrients, such as amino acids, the cerebrospinal fluid (CSF) concentrations were converted to flux units using CSF flow rates, and these were assumed to be equal to the maximum uptake rates for the corresponding reactions [For the ASD and control models, separate reaction bounds were set if the information was available. The glucose uptake rate was set to the cerebral metabolic rate (CMR) of glucose in the frontal cortexes of the ASD and neurotypical children . The maxcontrols , and thecontrols . The enecontrols . The maxcontrols , glutamicontrols , and glucontrols  reactioneactions .Most steady-state analysis methods, such as flux balance analysis (FBA), require a predefined objective function for the optimization of the problem. For organisms like bacteria, the selection of the objective function is mostly straightforward; they aim to maximize their growth and proliferate. However, for human cells, especially for brain cortex cells, maximization of the growth function is not applicable. Thus, we maximized the reaction rates for the glutaminase, glutamine synthetase, and glutamate carboxylase reactions to provide the replenishment of the glutamine in the neurotransmitter pool through the glutamine–glutamate–GABA cycle. Uniform sampling was used in order to obtain the distribution of attainable flux values rather than one of the optima obtained by the FBA. The lower and upper bounds of the reactions were set to the minimum and maximum flux values obtained through flux variability analysis (FVA) with a loopless solution option by maximizing the glutamine–glutamate–GABA cycle, if possible. A total of 10,000 samples were collected using the artificial centering hit-and-run (ACHR) algorithm within the COBRA Toolbox.All statistical analyses and data visualizations were performed in R software version 3.6.3. Due to the large dataset, effect size estimations were reported instead of the significance of the difference between group means/medians. The effect sizes between samples were determined using Cliff’s d using the effsize R package . The CliThe selection of the flux constraints for model reactions is a significant step in genome-scale modeling. The phenotype of an organism or cell is determined by its environment and thermodynamic limitations, as well as by its genotype. Thus, appropriate constraints should be set for the simulations to obtain a realistic set of solutions. Since the metabolisms of children and adults are dissimilar, the search for the constraints was concentrated on experimental data for children aged 2–14, but if the information was not available, the values obtained from the adult ASD and control cases were used.The extracellular fluid of the brain and spinal cord consists of blood plasma, cerebrospinal fluid (CSF), and interstitial fluid (ISF). The brain tissue is surrounded by ISF, while the CSF fills the brain ventricles and subarachnoid space. The compositions of ISF and CSF are very similar, although they are significantly different from the composition of blood since the blood–brain barrier is a highly selective barrier that allows the passage of small non-polar molecules and lipophilic molecules and blocks the passage of molecules such as neurotransmitters out of the brain . The comThe glucose utilization rates were also determined to be not significantly different between ASD and control children aged between 2 and 18 . The metThe human prefrontal cortex mostly consists of astrocytes, glutamatergic excitatory pyramidal neurons, and GABAergic inhibitory interneurons. A simplified illustration of the exchanges between the astrocytes and neurons can be seen in Mitochondria is the main ATP generation unit of the cells through oxidative phosphorylation and is also responsible for the regulation of reactive oxygen species (ROS). Although only 5% of ASD children are diagnosed with mitochondrial disease, up to 80% of ASD children show signs of mitochondrial dysfunction . MitochoThe brain only constitutes 2% of a human’s body weight, yet it accounts for nearly 20% of the body’s required daily energy. Glucose is the primary energy source for brain cells, and glycolysis, the tricarboxylic acid (TCA) cycle and oxidative phosphorylation are the mechanisms for producing ATP by breaking glucose. Theoretically, a maximum of 36 ATPs can be produced per consumed glucose molecule, two produced by glycolysis, two by the TCA cycle, and 32 by oxidative phosphorylation. Several studies detected that the genes that play a role in oxidative phosphorylation are downregulated in the brains of autistic patients ,44.2+) in the mitochondrial matrix [2+ may create disruptions in the mitochondrial metabolism. Indeed, genetic variants of voltage-gated calcium channels (VGCCs), transmembrane proteins mediating the Ca2+ flux to neurons, were detected in ASD and speculated to be a part of the pathophysiology of ASD [2 secreted for every glucose molecule uptook. Theoretically, 6 CO2 molecules are formed per glucose molecule by complete aerobic respiration, while none are formed after glycolysis. As seen in 2 secretion per glucose uptake for the ASD-specific model is found to be lower compared to the control model with a large effect size, which is an indicator of incomplete aerobic respiration.In our genome-scale metabolic brain model, the attainable flux distributions for the aerobic respiration-related pathways showed that the glycolysis pathway is more active in the ASD model . In paral matrix , and they of ASD . Alteraty of ASD ,49,50. TThe disturbance of the TCA cycle reduces the aerobic respiration rate, and this phenomenon generally results in elevated levels of pyruvate, as well as its derivatives, lactate and alanine . In our The mitochondrial electron transport chain (ETC) is located in the inner membrane of the mitochondria and produces ATP by generating a proton gradient between the mitochondrial matrix to the inner membrane space. ETC consists of 5 complexes: complex I (NADH dehydrogenase), complex II (succinate dehydrogenase), complex III (cytochrome bc1 complex), complex IV (cytochrome c oxidase), and complex V (ATP synthase) . We comp2O2) to GSSG and water. Our results reveal that the total rate of GPx-catalyzed reactions in the cytosol and mitochondria have similar flux ranges in both models, indicating that both the ASD and control models scavenge similar amounts of H2O2  and catalase (CAT) are two other significant antioxidant enzymes. SOD catalyzes the dismutation of superoxide anion  and choline-containing compounds are two indicators of neuroinflammation in the brain and are generally detected via proton magnetic resonance spectroscopy analyses. Activation of glial cells is thought to be correlated with higher inositol and choline-containing compound levels . The actPhospholipases, enzymes hydrolyzing phospholipids to fatty acids, are at the center of the fatty acid mechanism. Specifically, phospholipase A2 (PLA2) breaks the phospholipids into arachidonic acid and polyunsaturated free fatty acids (PUFA), which are further hydrolyzed to eicosanoids such as prostaglandins and leukotrienes. We found that the reaction catalyzed by the PLA2 enzyme has significantly higher fluxes in the control model . A smallProstaglandins and leukotrienes are two important classes of eicosanoids present in inflammatory conditions. Prostaglandin E2 (PGE2) is one of the most abundant prostaglandins in the human body and is a mediator for many biological functions, including inflammation . PGE2 maCompared to other tissues, the nervous system, especially the brain, has the highest lipid content and complexity. Sphingolipids, being one of the major classes of lipids that are essential for eukaryotic cells, are highly enriched in the brain. They have several structural roles in the plasma membranes and are also signaling molecules regulating cellular events such as cell growth, differentiation, senescence, and apoptosis ,89. SphiCeramides participate in important signaling processes, such as inflammation, and can be generated by pro-inflammatory cytokines . CeramidOn a special note, our simulations indicated altered sphingolipid metabolism between the ASD and neurotypical control models and this metabolism needs further research to shed light on the effect of sphingolipids on the pathogenesis of ASD. The research on the relationship between sphingolipid metabolism and ASD has been limited. In fact, the majority of studies on ASD omics have focused on transcriptomics or metabolomics, and it is only recently that lipidomics analyses have been conducted, although lipids constitute 36–66% of the dry weight of the human brain depending on grey or white matter, and nearly one-third of the total lipids are sphingolipids . The incOur simulations on reconstructed ASD-specific and neurotypical control GEMs demonstrated that several pathways, which have already been shown to be disturbed in ASD, generally yield complementary results with literature findings. The energy metabolism is altered in ASD, as indicated by the distinct patterns in glycolysis and the TCA cycle. One of the most prevalent comorbidities of ASD, mitochondrial dysfunction, is also supported by the disparities in the activities of ETC complexes and the increase in the mitochondrial dysfunction biomarkers. In addition, the reactions catalyzed by the oxidative stress marker enzymes, as well as the ones catalyzed by oxidative-stress protective enzymes, showed higher fluxes in ASD, indicating the presence of oxidative stress in ASD. Pro-inflammatory markers are found to have higher fluxes in ASD, whereas contradictory results are found for anti-inflammatory metabolites. Sphingolipid metabolites, especially S1P and ceramide, are promising biomarkers for ASD and should be thoroughly investigated by in vitro experiments.Omics data, such as transcriptomics, proteomics, and metabolomics, enhance the predictive power of systems biology tools such as genome-scale modeling. However, the lack of adequate knowledge about ASD was the most significant challenge encountered throughout this modeling study. Despite the limited number of omics studies conducted on the brains of ASD patients, all of them are post-mortem studies due to inaccessibility to the area, which may have distinctive features compared to ante-mortem analyses. Additionally, the number of studies reporting the enzyme activities in the brain, specifically the prefrontal cortex, is quite low, and in most cases, they conflict with each other. Thus, the neuro-modeling studies, including ours, have inadequate resources to validate the computational results. Furthermore, the heterogeneity of the disease and the complexity of the nervous system hinders the efforts to understand the underlying conditions behind ASD and the impact of altered pathways on the symptoms of ASD. However, customized forecasts of patients’ phenotypic characterization can be improved by employing genome-scale metabolic models. The metabolic models tailored to individual conditions, tissues, and patients shed light on cell-specific changes in the metabolic network, allowing for the prediction of disease metabolism biomarkers. All in all, we may conclude that genome-scale modeling may be beneficial in investigating the disturbed mechanisms in ASD.


To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise. In this article, I describe a modelling framework called Structural Thermokinetic Modelling SKM, ,2) in wh in wh2])Metabolic fluxes in cells are shaped by network structure, reaction thermodynamics, enzymatic rate laws, and enzyme regulation. Flux, metabolite, and protein data provide a detailed picture of metabolism, and computational models can help us answer important biological questions. For example, how do enzyme-inhibiting drugs, enzyme overexpression, or changes in nutrient levels affect a cell’s metabolic state? Will local enzyme perturbations have long-range effects on the fluxes or are they compensated by changes of nearby metabolite concentrations ? To addrClose to a steady state, metabolic dynamics can be approximated by linear models. Metabolic Control Theory ,19,20 M considerStructural Kinetic Modelling, the ORACLE framework ,30,31,32e others ,38. Strue others ,40,41,42If elasticities are variable, uncertain, or unknown, what variability or subjective uncertainty can we expect in a model? To explore this, we can study model ensembles, models in which some model features are given , while others are varied . By sampling variable features from random distributions ,43 and tv vanishes, and the two one-way rates must be equal. More generally, their ratio is given by rG, but dimensionless and with a flipped sign [R and absolute temperature T, is called reaction affinity. The thermodynamic force v vanishes too. Strongly driven reactions, in contrast, have large thermodynamic forces, a negligible backward flux Despite all its merits, ensemble modelling by SKM has one major drawback: it ignores the fact that elasticities are interdependent due to basic physical laws. The net flux in chemical reactions results from a difference of one-way rates ped sign . The quanditions  . A thermodynamically feasible metabolic state serves as a basis for constructing thermodynamically feasible models. Compared to the original SKM, the modelling procedure is more formalised: all model variables (including metabolite concentrations and fluxes) are determined step by step by inserting known values, sampling, or optimisation. The workflow follows a schema that describes the model variables, guarantees physical correctness, and can be used to define probability distributions. By separating network  and schema , models can be built flexibly, and their mathematical relationships are easy to see. This makes STM well-suited for automatic model construction.Studying thermodynamic forces does not only lead to more realistic models, but can also reveal important aspects of cell physiology. The forces do not only determine possible flux directions, but have also effects on reaction rates at given enzyme levels, enzyme demands at given desired fluxes, the reversibility of reactions, the controllability of fluxes, the generation of chemical noise, and its propagation across the network. Since all this affects the proper functioning of cells, the thermodynamic forces in a metabolic network will not be distributed randomly, but are well adjusted to the flux profile. Thermodynamic forces should not be too small because this would reduce enzyme efficiency and create thermodynamic bottlenecks. Force patterns avoiding such bottlenecks can be computed . In contWhile hypotheses about force patterns and their functions are easily made, testing such claims is difficult because the forces cannot be tuned independently: emerging from metabolite concentrations, the forces in a network are under many constraints. Moreover, direct effects of forces may be hard to distinguish from effects of the underlying metabolite concentrations: for example, a high product level will decrease a reaction rate in two ways: by decreasing the driving force and by saturating the enzyme. In the rate law, these effects appear entangled and disentangling them is not easy: if we vary an enzyme concentration experimentally, this changes metabolite levels, fluxes, thermodynamic forces, and enzyme saturation levels at once. STM enables targeted changes in models, for example, varying the thermodynamic forces while keeping all fluxes unchanged and averaging over many possible choices of saturation values. By assessing the results, we can study the effects of thermodynamic forces alone, which casts light on their possible functional roles.Structural Thermokinetic Modelling (STM) is a framework for building kinetic metabolic models with reversible rate laws. It is simple, can flexibly integrate available data, yields consistent models, and can be used for semi-automatic model construction. STM is based on a dependence schema, describing the dependencies between variables by linear or nonlinear functions. In contrast to SKM, elasticities are not directly given by saturation values but depend additionally on thermodynamic forces. Elasticities can be computed from thermodynamic forces and saturation values for a number of rate laws . The resTo construct metabolic states and kinetic models, STM follows the dependence schema : basic vlibrator  and feaslibrator . In the Escherichia coli can be constructed step by step. The model is a modified version of the model from [kM values and metabolite concentrations); for ensemble modelling, we may sample them in the range del from , with addel from  metabolidel from   and contate see . DespiteAside from constructing a single model, we can also build model ensembles with broad parameter distributions. Model ensembles can be used to study the effects of model structure, flux distribution, thermodynamics, or enzyme saturation on metabolic behaviour, and to test which details have significant effects on model outputs. For example, to study the role of thermodynamic forces in flux control, we may generate two model ensembles with the same fluxes, but different forces. Within each ensemble, saturation values are varied and flux control coefficients are computed. By comparing the two ensembles, we can check which control coefficients differ significantly due to the different forces. To assess significance for a single control coefficient, its distributions  are compared between the ensembles (representing different choices of forces). Since a model contains many different control coefficients, the problem of multiple testing must be addressed  see Met.Ensemble models can answer a wide range of questions, e.g., how metabolic dynamics, homeostasis, and control depend on network structure, thermodynamics, enzyme saturation, or regulation. Even with little data, we can study control or synergy coefficients, compute their distributions and correlations, and see which control coefficients differ significantly from zero. All these predictions are probabilistic, reflecting the uncertainties arising due to missing or imprecise data. While building a model ensemble does not require any kinetic data at all, inserting some data  will decrease variability and narrow down the model results. Hence, data that would usually not suffice for model fitting can still be used for model predictions by STM, where their uncertainty ranges can be assessed. The basic STM approach can be extended in various ways. In summary, STM helps us build realistic metabolic models, study their control properties, and assess how these properties vary. The underlying dependence schema shows how model variables depend on each other. Equipped with these methods, we now study the role of thermodynamic forces in metabolic dynamics and control. In the following sections, I show how different aspects of metabolic dynamics—including flux control, linearised metabolic dynamics, enzyme synergisms, and metabolic fluctuations—are shaped by the pattern of thermodynamic forces in the network.When enzyme levels are changing, how will this change the network-wide fluxes? In Flux Balance Analysis (FBA), these effects can be modelled by changing the flux bounds (as a proxy for changing enzyme activities) and re-optimising the fluxes. In MCT, the effects of enzyme changes are directly described by control and response coefficients, and synergy coefficients can be additionally used for more precise approximations  or to acIf a target variable such as ATP or biomass production contributes to cell fitness, enzymes should have a positive effect on this variable: if an enzyme had a negative effect, the cell would benefit from downregulating this enzyme  and woulE. coli model. Metabolite concentrations were determined by thermodynamic balancing of measured concentrations in E. coli as a substitute for human hepatocyte data. Using these flux distributions, I obtained models representing a large number of different flux objectives and checked their control coefficients. For example, with ATP production as the flux objective  were determined by FBA with flux minimisation. In the original paper , for eacWhile enzymes can have a negative flux control , it is hard to imagine that they negatively control their own flux. However, there are examples of this. ycolysis , a high To see this, we can use STM: with saturation values randomly drawn between 0 and 1, about 90 percent of the models show the paradoxical self-repression effect. Thus, this effect does not depend on fine-tuned parameters, but is likely given our network structure, flux distribution, and metabolite concentrations. To further assess the role of the thermodynamic forces, I varied them proportionally see : when brE. coli model). While synergisms can be computed for any perturbation parameters, including concentration variations in the growth medium, here we focus on enzyme pairs. How do STM predictions compare to predictions by FBA? Interactive effects of several enzyme levels (or other parameters) on target variables are called synergisms. In STM, synergy effects are obtained from the second-order response coefficients, also called synergy coefficients . The synergy coefficients depend on three factors , and uncertainty in models due to missing or uncertain data. Notably, the variability and correlations of cell variables are shaped by network structure and enzyme kinetics. Metabolite concentrations and fluxes that vary in time can be described by random fluctuations that emerge, for example, from variable enzyme concentrations, external perturbations, or chemical noise and propagate in the network. All sources of noise have their own dynamics. Fluctuating enzyme concentrations, for example, can be caused intrinsically (by noise in transcription and translation) or extrinsically (by variability in other cell variables) ,63. In tkcat = 10 s−1) catalyses around For an example of temporal fluctuations, we consider chemical noise, one possible cause of metabolic fluctuations. Individual reaction events happen randomly, which can be described by random fluctuations in the reaction rates. A typical enzyme  can be computed similarly: their spectral density matrices The structure, dynamics, regulation, and function of metabolic systems are closely related. If proteins cooperate , this leads to synergisms and may be reflected in epistasis , phylogenetic profiles , co-exprN and elasticity matrix Ec, which is shaped itself by thermodynamics and enzyme saturation as considered in STM. The matrix product NEc yields the Jacobian matrix, which is local and sparse. The inverse of this Jacobian matrix leads to the non-sparse control and response matrices that describe network-wide changes of steady states. Another link between  network structure and (network-wide) metabolic behaviour can be made by the summation and connectivity theorems of MCT [How are network structure, dynamics, and function related precisely? To see this, we note that enzyme control and synergies, metabolite fluctuations, and possibly optimal differential expression patterns  reflect s of MCT ,20: the As we already saw, thermodynamic forces can have various effects on dynamics. Within a reaction, large forces imply a large energy dissipation per flux, and the relatively small one-way fluxes  and dynamic stability  can be subsequently optimised. In a cell, these objectives cannot be controlled separately: external perturbations will change fluxes and stability at the same time. However, STM calculation can tell us about possible trade-offs and about probabilities of finding favourable solutions, and can guide our search for suitable growth media or genetic modifications.How can we predict the effects of enzyme perturbations such as single-enzyme inhibitions or differential expression of many enzymes? Metabolic responses may be quasi-static or dynamic and concern metabolite concentrations and fluxes. Constraint-based models ignore kinetics and replace them by heuristic rules: FBA predicts the most favourable flux changes, while MoMA predicts minimal changes. In both cases, enzyme repression can be modelled by restricting a reaction to smaller fluxes (knock-down) or zero fluxes (knock-out) and computing the perturbed flux distribution by maximising a metabolic objective (in FBA) or minimising the necessary changes in flux (in MoMA). If multiple enzymes are repressed, the flux in a linear pathway depends on the lowest flux bound while the other repressions have no effect. Therefore, in linear pathways, both methods predict buffering interactions. MCT, in contrast, predicts metabolic responses by kinetic models and control coefficients which reflect thermodynamics, enzyme saturation, and regulation and can be determined by STM. Reactions are not simply “reversible” or “irreversible”, like in FBA, but vary gradually between near-equilibrium and strongly forward driven reactions depending on thermodynamic forces. The quantitative effects of these forces, e.g., on the transmission of fluctuations, can be studied systematically. Moreover, an enzyme inhibition may change the control coefficients themselves, which leads to synergisms. MCT captures these synergisms by synergy coefficients.Of course, MCT has its limitations. First, it holds only for small perturbations: with large perturbations, predicted concentrations may become negative. In steady-state MCT, this can be avoided by modelling log-concentrations. In dynamic simulations, we need to consider absolute concentrations, and to avoid negative concentrations heuristic correction functions can be applied. Second, STM assumes that enzyme levels are known and, by default, does not predict enzyme adaptations. In fact, the extra effects of such adaptations are second-order effects and can be neglected if perturbations are small (except for some systems at bifurcation points), or if we focus on dynamics much faster than enzyme changes. However, there are ways to consider enzyme adaptation in our predictions: if they are known from experiments, they can be inserted as enzyme perturbations; otherwise, optimal adaptation may be predicted based on a principle using control coefficients from STM .STM does not only provide a framework for model construction, but it also clarifies physical and statistical dependencies between cell variables. A dependence schema, as in The dependencies and covariances of variables do not only matter in models, but also for cells in reality: kinetic constants or state variables may co-vary between the states of a cell, between cells in a population, during evolution, or between species. Some of this covariation is caused by physical constraints. For example, Haldane relationships between rameters . In geneSTM and SKM implement a retromodelling approach: we first choose a metabolic reference state and then the elasticities, rate laws, and control properties in this state. Retromodelling allows us to define state variables and kinetic constants separately and to study the effects on dynamics. For instance, by varying the thermodynamic forces we may turn reactions into rectifiers for metabolic fluctuations and adjust the stability and control of metabolic states. In unbranched pathways—where pathway fluxes can be computed analytically —these efRetromodelling can be helpful for optimisation with several objectives, e.g., to achieve not only desired fluxes, but also desired control properties such as homeostasis, robustness, and adaptability. We begin with a desired flux distribution and construct, step by step, other state variables and kinetic constants that support this flux distribution. In each step, we can account for different constraints and objectives, e.g., optimal production rates when choosing the flux distribution. This “reverse” approach may help us think differently about optimisation in evolution or biotechnology, how metabolic states can be tuned, and with what protein costs. For example, we may imagine that cells need to optimise, first and foremost, their fluxes and concentrations under standard conditions, and secondly stabilise this state or make it homeostatic or better controllable by tuning the elasticities. To achieve this, evolution may vary thermodynamic and kinetic properties of reactions, tweak saturation values or add regulations, leading to different control patterns. For example, inhibiting an enzyme allosterically may stabilize the metabolic state, but will make the enzyme less efficient. To keep the fluxes unchanged, the lower enzyme efficiency must be compensated by higher enzyme levels. During model construction by STM, such targeted changes can easily be applied. This shows again that STM is not just a tool for model construction, but a framework for thinking about variability and optimality in cell populations, in evolution, or in engineered cells.Model building by STM relies on kinetic and thermodynamic laws and on Metabolic Control Theory. An overview of concepts and formulae is given in In kinetic models, enzyme levels and external metabolite concentrations are usually treated as parameters that determine the steady state. However, fitting such models to fluxes is difficult. Retromodelling reverts this procedure: we start from a flux distribution and construct metabolite concentrations and rate laws around it. We can do this as follows. After choosing the fluxes, the MDF method  can be uN has full row rank.Enzyme kinetics and metabolic control are closely related to thermodynamic forces . In this effects . In modeThe choice of enzyme elasticities reflects some functional trade-offs. At low enzyme saturation, elasticities are high, enzyme efficiency is low, and the enzyme demand per flux  is high. At high saturation, conversely, the reaction rate hardly changes with the substrate level, so the reaction is “stiff”: fluctuations in inflowing substrate cannot be buffered and lead to large fluctuations in substrate levels. Hence, the optimal choice of saturation values (and thus of elasticities) will reflect trade-offs between enzyme demand and favourable control properties.kM instead of If we know the fluxes and concentrations in a metabolic state, how can we find the elasticities? The elasticities depend on the rate laws with many unknown parameters. Modular rate laws see  are geneximation , the kX The scaled elasticities of modular rate laws consist of two terms ,(1)E^ciThe thermodynamic “reversibility” term R and absolute temperature T. For forward reactions near equilibrium E^lirE^lirE^liThe form of the kinetic elasticity term With other rate laws, a kinetic elasticity term hibition  yields aThe terms Equation  shows thEquation  for compEquation  (STM) inquation v=ekcv=ekc(7)vread see (8)E^cjvIn STM, the mathematical structure of a model is described by a dependence schema a that liDuring model construction, model variables can be chosen in various ways: they can be replaced by data values, sampled, fitted, or optimised. Below, we assume that fluxes are fitted to data or determined by FBA, metabolite concentrations are chosen within feasible ranges and in line with the flux directions, and saturation values are sampled. With this set-up, the hardest step is the choice of fluxes, which need to be thermo-physiologically feasible; that is, thermodynamically realisable with metabolite concentrations in given physiological ranges, which also implies that the flux distribution must be loopless . Such flkM values can be converted into saturation values. These values can then be inserted directly, or saturation values can be sampled around them (uniformly or following a beta distribution) to account for uncertainties or missing information [kcat values can be used similarly: in the dependence schema, the kV values, together with equilibrium constants and fluxes, determine the turnover rates e for the same reaction. To match them to data, we can first choose kV values at random and then modify them to obtain a good fit of forward kcat value, enzyme concentration, and flux; or we use a different schema with forward kcat values instead of kV as basic variables.As noted before, in model construction by STM the basic model variables can be sampled, freely chosen, fitted to data, or optimised for a fitness objective. We can use all this to incorporate extra knowledge or data. A consistent set of kinetic constants, as input data for STM, can be obtained by parameter balancing. Parameter balancing is a method for translating incomplete, inconsistent, and uncertain data into complete, consistent sets of model variables ,54. Giveormation . Known kWhen applying STM, I noticed that reference metabolite concentrations and fluxes—and not just the saturation values—are really important for model dynamics because of how they shape the unscaled elasticities. For example, the known metabolite concentrations make the reconstructed threonine pathway model in  work ratFinally, STM may also be used for Bayesian model fitting and to predict biologically optimal states. In these cases, model variables are not sampled but treated as choice variables to be fitted or optimised. Unfortunately, the resulting optimality problems are typically non-convex and hard to solve. Model balancing, which defines simplified convex optimality problems, may be used instead .By repeatedly sampling model parameters, we obtain an ensemble of models that all share the same structure but show different parameter values ,28,33. MThe STM algorithm can be modified in various ways.Cell growth. To model metabolism in growing cells, the formulae must be adapted. For balanced growth at a cell growth rate Kinetic constants as basic variables. Instead of choosing saturation values and concentrations and then computing the constants er 1 see . In pracer 1 see . By sampMultiple steady states. To build a model with multiple steady states, in the metabolic state phase we choose one set of equilibrium constants, but several sets of concentrations and fluxes for the different steady states; in the kinetics phase the constants alancing ,52,54. FEventually, for more realistic models, some of the generic rate laws may be replaced by more detailed rate laws obtained from enzyme assays . For othIf enzymes are perturbed by inhibition or transcriptionally, how will this change the metabolic concentrations and fluxes? Inhibiting an enzyme makes substrates accumulate and products deplete. These changes have further effects, which counteract the original effect and eventually lead to steady-state changes in the entire network. To understand such indirect effects, let us think of a simple example: a reaction perturbed by a single parameter . In MCT, the direct effects on reaction rates are described by parameter elasticities and the further effects (on steady-state metabolite concentrations and fluxes) are described by control coefficients. By multiplying parameter elasticity and control coefficient, we obtain the response coefficient (the sensitivity between parameter perturbation and metabolite concentrations or fluxes). Small perturbations can be treated as additive: the effects of simultaneous perturbations can simply be summed over. A variation For small enzyme perturbations, the effects of enzyme perturbations on fluxes and other target variables can be described by a linear approximation: a variation How can synergisms be predicted from models? We need a model that predicts changes in a metabolic target variable  after single or double perturbations of enzyme concentrations (in kinetic models) or of fluxes . Synergies in kinetic models can be computed numerically: we vary two enzyme levels and simulate the effect on the steady-state fluxes. A first inhibition will change the flux control coefficients of all enzymes, and therefore the effect of a second inhibition. For small perturbations, the synergy effect can be approximated to second order. While the usual first-order response coefficients capture linear effects of single enzymes, the second-order response coefficients (or “synergy coefficients”) capture synergisms of enzyme pairs and second-order effects of single enzymes. Concentrations can be described on log scale to guarantee that concentrations stay positive. Logarithms applied to fluxes would make it impossible for predicted fluxes to change their direction. With logarithmic enzyme changes Different modelling frameworks make different assumptions. FBA tries to maximise the metabolic objective even after a perturbation; MoMA assumes that fluxes show minimal changes despite perturbations, thus behaving homeostatically; MCT instead predicts flux responses from metabolic dynamics, assuming that enzyme concentrations remain constant (as considered below) or are optimally adapted (see ). How arHow are the variability, correlations, and temporal fluctuations of cell variables shaped by network structure, fluxes, and thermodynamic forces? For simplicity, let us first consider static variability between cells and then dynamic fluctuations within a cell—keeping in mind that static variability is just a a limiting case of dynamic fluctuations at frequency zero. To describe a cell population, we may use a single model for all cells, but with differences in the input variables that determine the steady states  and frequency-dependent. The spectral power density Fluctuations in time are described similarly (see  and Supption see . In lineWhat can we learn from the spectral densities about measured variability in cells? In measurement data, noise appears as a variability on a particular time scale, the time resolved in our measurement. If concentration measurements take a second, faster fluctuations will be averaged over and our data can reveal variations only on slower time scales. Hence, what interests us is the variability in a variable, averaged over a time window