Intrauterine growth restriction (IUGR) of the fetus, resulting from placental insufficiency (PI), is characterized by low fetal oxygen and nutrient concentrations that stunt growth rates of metabolic organs. Numerous animal models of IUGR recapitulate pathophysiological conditions found in human fetuses with IUGR. These models provide insight into metabolic dysfunction in skeletal muscle and liver. For example, cellular energy production and metabolic rate are decreased in the skeletal muscle and liver of IUGR fetuses. These metabolic adaptations demonstrate that fundamental processes in mitochondria, such as substrate utilization and oxidative phosphorylation, are tempered in response to low oxygen and nutrient availability. As a central metabolic organelle, mitochondria coordinate cellular metabolism by coupling oxygen consumption to substrate utilization in concert with tissue energy demand and accretion. In IUGR fetuses, reducing mitochondrial metabolic capacity in response to nutrient restriction is advantageous to ensure fetal survival. If permanent, however, these adaptations may predispose IUGR fetuses toward metabolic diseases throughout life. Furthermore, these mitochondrial defects may underscore developmental programming that results in the sequela of metabolic pathologies. In this review, we examine how reduced nutrient availability in IUGR fetuses impacts skeletal muscle and liver substrate catabolism, and discuss how enzymatic processes governing mitochondrial function, such as the tricarboxylic acid cycle and electron transport chain, are regulated. Understanding how deficiencies in oxygen and substrate metabolism in response to placental restriction regulate skeletal muscle and liver metabolism is essential given the importance of these tissues in the development of later lifer metabolic dysfunction. Fetal development and growth are particularly vulnerable to perturbations in the gulation –5. Glucogulation , 7. Consgulation –13. ThesPrenatal development establishes the foundation for global metabolic regulation and substrate utilization in fetal tissues. Glucose, lactate, and amino acids comprise the primary oxidizable substrates used for energy production and tissue growth in the fetus . When thIUGR neonates are born with lower muscle and liver masses –30. The Comprehensive studies evaluating whole-body metabolism and the coordinated tissue-specific responses in IUGR fetuses are beginning to define the impacts of nutrient and oxygen restriction during gestation. However, despite the growing number of experimental models and studies investigating IUGR metabolism, much remains unknown. Given the importance and centrality of mitochondria to substrate utilization and energy balance, the metabolic aberrations observed in IUGR fetuses implies mitochondrial metabolism is adjusted to low nutrient and oxygen concentrations. In this review, we will discuss our current understanding of mitochondrial metabolism in the skeletal muscle and liver of IUGR fetuses and present gaps in the field.via specific carriers. The latter substrate utilization process, known as anaplerosis, is vital to mitochondrial metabolism as it replenishes TCA cycle intermediates. Oxidative phosphorylation is generally limited by the presentation of ADP to the mitochondrion (free ADP), and changes in the concentration of any one substrate affect the utilization of other substrates , amino acid, and fatty acid metabolism intersect in the tricarboxylic acid (TCA) cycle, which requires oxygen to produce the primary cellular energy currency: adenosine triphosphate (ATP). However, utilization of substrates within the mitochondrion is not equal. For example, some metabolites, like lysine and leucine, only enter the TCA cycle only as acetyl-CoA. Alternatively, other substrates, like valine and glutamate, can only enter as TCA cycle intermediates bstrates .2. These reducing equivalents produced from each pass of the TCA cycle are subsequently oxidized at the electron transport chain (ETC) to produce ATP. While acetyl-CoA is the primary substrate for the TCA cycle, the acetyl CoA oxidation capacity of the TCA cycle is dependent upon the availability of TCA cycle intermediates. Specifically, anaplerotic reactions are needed to replace TCA cycle intermediates that are lost through cataplerosis.In the mitochondrion, ATP is synthesized from the oxidation of pyruvate, amino acids, and lipids. To be utilized, these substrates must be transported into the mitochondrion across the outer and inner mitochondrial membranes. Once inside the mitochondrial matrix, metabolic substrates can enter the TCA cycle as acetyl-CoA or as an intermediate and ultimately results in the production of both NADH and FADH2  (i). The integrated nature of the ETC and TCA cycle means the rate of oxygen consumption in the mitochondria is coupled to the phosphorylation potential [log (ATP)/(ADP) (Pi)], which means ATP production and utilization are coupled to oxygen consumption rate (OCR)  . As electe (OCR) –58. As sxidation , 57. ConMeasuring whole-body, tissue, and cellular metabolism requires invasive experiments. As a result, animal models that recapitulate human IUGR are needed to study the link placental insufficiency and the metabolic phenotype of IUGR fetuses. Similar to human IUGR, several animal models of IUGR have been developed that exhibit reductions in lean body mass, lower blood nutrient and/or oxygen concentrations, and develop metabolic syndromes later in life –63.Three well-defined rat models of IUGR show fetal and persistent postnatal metabolic consequences in skeletal muscle and hepatic tissue due to reduced nutrient availability during gestation. Gestational maternal caloric restriction (CR), gestational maternal low protein (LP), and uterine artery ligation (UAL) at embryonic day 19 (E19), each causes significant (10–50%) reductions in lean mass which persist into adulthood , 64–67. Experimental models have been developed to induce IUGR in sheep by either reducing placental structure/function or preventing normal placental development. Models that reduce placental structure or function, such as carunclectomy or placental embolization, mimic placental insufficiency and yield fetuses with 15–66% growth restriction compared to matched controls –81. Modein utero adaptations lead to postnatal metabolic dysfunction where young lambs afflicted with IUGR continue to have lower lean masses and skeletal muscle protein contents, altered glucose metabolism and liver function, and greater adiposity  arises from a disproportionate supply of nutrients along the uterine horn causing growth restriction of 15–20% of piglets from each litter . As a redulthood , 102–104dulthood , 106. IUdulthood , 108. Thdulthood . As aduldulthood , 109.in utero. Thus, in IUGR piglets, the asymmetric growth restriction and metabolic dysfunction indicate defects in mitochondrial function that continue postnatally , and it incorporates into Complex I and lowers the electron flux through the ETC by functioning as an inhibitor of oxidative phosphorylation   production and ADP rephosphorylation and may have significant consequences on basic cellular metabolic rates. The impact of NDUFA4L2 on whole-body metabolism is demonstrated in transgenic mice with NDUFA4L2 overexpression in the skeletal muscle  . Fourth,l muscle . NDUFA4Ll muscle . These al muscle . TherefoReduced ETC flux and ATP production indicate that utilization of oxygen and metabolic substrates is curtailed. Hindlimb oxygen uptake (consumption) relative to mass is 29% lower in IUGR fetuses compared to that in controls, which is consistent with depressed oxidative metabolism . Despiteα-ketoglutarate (via acetyl-CoA) and gluconeogenesis , and increased lactate flux from the skeletal muscle likely counterbalances pyruvate deficits in the IUGR liver  and pyruvate dehydrogenase phosphatases (PDPs), which inactivate or activate PDH, respectively. In IUGR sheep fetuses, the skeletal muscle and liver glycolytic capacity appear normal or increased  and pyruvate dehydrogenase (PDH). PDH converts pyruvate to acetyl CoA, and PC is used for the anaplerotic “backfill” of oxaloacetate. Conversely, lactate dehydrogenase (LDH) facilitates the interconversion of pyruvate and lactate, both of which can be excreted. The pyruvate flux into the mitochondrion is regulated by PDH, and PDH activity is inhibited by its phosphorylation status ncreased , 135. Honcreased 2  is lower in the IUGR sheep skeletal muscle mitochondria Figure 1LDHA mRNA expression which is suspected to increase intrahepatic lactate production  of the MPC1 gene. Here, utilizing a skmKO of MPC1 reduces pyruvate flux through the MPC complex because MPC1 and MPC2 are both necessary for the functional MPC . Moreoveinolysis . The forinolysis Figure 1inolysis . Furtherinolysis . MPC1skmysiology . Due to The metabolism of amino acids supports oxidative phosphorylation and gluconeogenesis through reactions in the mitochondrion. Within the mitochondrial matrix, all 20 standard, proteinogenic amino acids  have metabolic pathways which enter the TCA cycle. Though cysteine, alanine, and histidine do not enter the matrix, they can be converted into pyruvate or other amino acids, which can then be catabolized in the TCA cycle. In normally developing fetuses, amino acids are critical oxidative substrates, and the accretion of amino acids into proteins is an essential component for fetal growth. A common, overlapping feature between human IUGR and animal models of IUGR is reduced placental transfer of certain essential amino acids , 149–152α-ketoglutarate and succinyl-CoA, respectively. Therefore, downregulating the abundances of all of these enzymes establishes a complex substrate regulating system: the oxidation of upstream TCA cycle intermediates, including acetyl CoA, is inhibited by low IDH and OGDH abundances, and the anaplerotic flux of amino acids into downstream TCA cycle intermediates is inhibited by low OGDH and SUCLA2 abundances. In addition to the lower IDH, OGDH, and SUCLA2 concentrations, abundances of enzymes involved in branched-chain amino acid (BCAA) catabolism are lower, which will further reduce TCA cycle activity as BCAAs enter as acetyl CoA (leucine and isoleucine) or succinyl CoA . Not only is TCA cycle activity depressed in IUGR fetal skeletal muscle, but it also appears to be less reliant upon amino acid substrates , Succinate CoA ligase (SUCLA2), and OGDH abundance Figure 1bstrates , 124α-ketoglutarate and glutamate  as well as the mitochondrial redox state. Moreover, the source of gluconeogenic fuels impacts which shuttle is used. If pyruvate is derived from lactate, the cytosolic NADH/NAD+ ratio will remain stable or increased, and the need for cytosolic reducing equivalents will be low, so oxaloacetate transport via the aspartate shuttle will predominate. Conversely, if pyruvate is derived from amino acids, no cytosolic reducing equivalents are produced. However, because gluconeogenesis requires glyceraldehyde-phosphate dehydrogenase, and this enzyme requires NADH, transport of oxaloacetate via the malate shuttle is necessary.Gluconeogenesis relies on oxaloacetate as a substrate. However, the mitochondrial membrane is impermeable to oxaloacetate, and oxaloacetate must be converted to either malate or aspartate for transport Figure 2In IUGR sheep fetuses, the proportions of lactate and amino acids used for hepatic gluconeogenesis is unknown , 142. StCompared to other metabolites discussed lipid metabolism remains relatively unexplored in both normally grown and IUGR fetuses. During pregnancy, maternal triglyceride (TAG) and free fatty acid (FFA) concentrations in the plasma range from 0.2 to 0.3 M for rats, sheep, and humans . Fetal pvia glycolysis (Animal models of IUGR provide valuable insight into the developmental origins of metabolic dysfunction. These models, while diverse in their etiology of IUGR, provide considerable evidence of irregularities in mitochondrial function. Skeletal muscle and liver serve as major metabolic tissues but exhibit distinct metabolic responses to fetal conditions causing IUGR Figure 5ycolysis . HoweverAP was responsible for analysis and interpretation of data, drafting of manuscript, and critical revision. TR, SW, RL and SL were responsible for the interpretation of data and critical revision. All authors contributed to the article and approved the submitted version.NIH RO1 DK084842, NIH T32 HL007249, & R01-DK108910 supported this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Analyses presented in the article were carried out in order to characterize the main parameters of the shaped charge jet formed due to detonation of the PG-7VM warhead. As opposed to the previously published studies in which rolled homogeneous armored steel was mainly used as a target, in the current work the warhead penetration capability was determined against more contemporary high-hardness (500 HB) ARMSTAL 30PM steel armor with precisely determined mechanical properties. The research included experimental depth of penetration tests and their numerical reproduction in the LS-Dyna software. Special attention was paid to factors that could perturbate the shaped charge jet formation process and under- or overestimate its penetration capability. For this reason, warheads were X-ray inspected for structural discrepancies  and properties of materials  were analyzed before the experiments. The numerical model of the warhead was defined more accurately than in previously published studies, since it was based on the real grenade dimensions and its technical documentation. Thanks to this, the depth of penetration of the target made of ARMSTAL 30PM armored steel plates by the shaped charge jet formed from the PG-7VM warhead obtained by numerical simulation was consistent with the experimental results and equaled 278 mm and 280 mm, respectively. The difference between the experimental and numerical value was smaller than 1%, which confirms that the developed methodology of modeling allows users to properly reproduce the PG-7VM shaped charge jet formation and target penetration processes. A verified numerical model of the shaped charge jet penetration into a steel target was used to determine depth of penetration in function of stand-off distance for the PG-7VM warhead. A maximum depth of penetration of about 317 mm was obtained for the stand-off distance of 360 mm, which may indicate the potential direction of modernization of warheads. High-explosive anti-tank (HEAT) warheads ,2,3,4, bRecently developed and technologically advanced military HEAT warheads have sophisticated designs, allowing them to obtain a great penetration capability of armor. Depending on the type  the most powerful HEAT warheads may penetrate up to 1500 mm of rolled homogeneous armor (RHA) . However® software was used to investigate the influence of SC asymmetries on the SCJ characteristics in the work [Effectiveness of HEAT warheads is determined mainly during experimental depth of penetration (DoP) tests. However, currently used measurement techniques, which are based mainly on flash radiography and X-ray pictures of the SCJ at predetermined times, are unable to provide all the necessary data about the entire phenomenon. Although, X-ray pictures may be subsequently used as input data for the analytical models, allowing calculation of the basic SCJ parameters, such a combined experimental–analytical approach is effective only in analysis of the simplest penetration scenarios. For this reason, numerical simulations are often used as a support to experimental analyses. Numerical methods allow researchers to collect more information about the entire process of SCJ formation and penetration, e.g., pressure, density, or velocity distribution at different times. Nowadays, a few different modeling approaches are used in analysis of SCJs. In the work , a Lagrathe work . Two-dimthe work  to simulAlthough, multiple studies on the effectiveness of various HEAT warheads with different numbers of SCs ,22, lineAn additional issue is the relevance of RHA as a reference material simulating passive armors of modern heavy-armored vehicles. RHA has been used as a reference target for over 50 years and its main advantage is that effectiveness of various warheads can be easily compared on the basis of the values of DoP in RHA. However, during those 50 years, technology of production of armored steels has significantly improved and RHA steel was almost completely replaced with new, more durable, and harder steel grades. Strength and hardness of currently offered armored steel is almost twice that of RHA  . Taking In view of the foregoing considerations, the main aim of the work presented in the article was evaluation of the actual PG-7VM warhead performance against the target made of high-hardness (500 HB) ARMSTAL 30PM armored steel  with preEffective and reliable methodology of numerical modeling of shaped charge jet penetration into high-hardness armored steel targets could be used in the future in all kind of optimization studies of both warheads and armor designs.Experimental DoP tests were carried out to determine the warhead penetration capability and to collect data required for further validation of the numerical model of the phenomenon. The experiments were performed at the Military Institute of Armament Technology (MIAT) proving ground in Poland. During the tests, the PG-7VM warheads were placed at an angle of α = 60° in relation to the vector normal to the surface of the target  in orderIn order to increase the reliability of the results before the experiments, the PG-7VM warheads selected for the tests were X-ray inspected for their structural and geometrical discrepancies  to exclude some factors that could perturbate the jet formation and penetration process. The X-ray real-time radioscopy (RTR) diagnostic system (XDS) MU17F 225-9  was used c,d.The X-ray inspection is particularly important because the effectiveness of the PG-7VM warhead is highly dependent on the axial alignment precision of their elements during the manufacturing and assembling process. The most effective (in terms of penetration capability of armor) are warheads in which the dimensional deviations from axial symmetry of liner and explosive do not exceed 0.03 mm. The penetration capability of the warhead significantly decreases with the increase of inaccuracy of production or assembling. Depending on the deformation rate of the liner and the high explosive charge, the process of SCJ formation will be disturbed or will not occur at all. Effects of the warhead’s influence on the target are then comparable to the simple detonation of explosive charge contained in the warhead body. Only the warheads with no discrepancies  required for subsequent definition and validation of the numerical model of the material. The following tests were carried out:Hardness measurements were carried out with the use of a Brinell machine, both parallel and perpendicular to the rolling direction (EN ISO 6506-1). Before the measurements, the surfaces of the plate were polished. Impact strength of ARMSTAL 30PM was tested according to well-known Charpy method (EN 10 045-1). Tests were carried out for the samples with U- and V- shape notches at temperatures of +20 °C and −40 °C, respectively. The geometry of the samples is presented in Mechanical properties of the ARMSTAL 30PM were analyzed on a Zwick Z100 universal testing machine  with servo-hydraulic control. Specimens for the tests were prepared from 4, 6, 8, and 10 mm thick plates using a wire electrical discharge machine. Geometry of the samples prepared for the tests were shown in Triaxial, close to hydrostatic compression in the sample/holder interface zones;Triaxial non-homogeneous compression in the middle layers of the sample;Mixed compressive tensile state in the middle subsurface layers of the sample with a tensile stress in the circumferential direction.Force was applied to the specimen holders with end grippers and their displacements were recorded. Engineering stresses and strains values were calculated on the basis of the force–displacement curves. After necking or barreling of the sample during tension and compression tests, respectively, diversified, three-dimensional stress states in the samples were generated. The barreling effect during compression tests is caused by friction between the sample and the surface of the specimen holders. Various stress states can be distinguished in individual areas of the sample:true—true stress, εtrue—true strain, P—force, Ai—area of actual cross-section of the sample, A0–area of initial cross-section of the sample.Due to barreling and necking effects, determination of the true stresses and strains in the sample requires continuous measurement of the actual cross-section of the sample, e.g., by means of digital image correlation equipment. In the study, the compression and tension processes were recorded with a camera and the values of actual cross-sections of the samples were calculated from the recordings on the basis of the extremal lateral dimension of the tested samples, located usually in the middle of their height, where the maximum deformation occurs. Assuming negligible changes in the volume of the samples, the true stresses and strains were calculated from the following equations:The results of quasi-static compression tests of the ARMSTAL 30PM steel are shown in Mechanical properties of ARMSTAL 30PM obtained during the tests on the samples taken parallel and perpendicular to the rolling direction were similar to what is known about the high uniformity and low anisotropy of the material. The differences in the results obtained for the samples prepared from the plates with different thickness were also very small. The yield strength and ultimate tensile strength of the material exceeded the minimal values that were provided in the manufacturer datasheet . On the PG-7V and PG-7VM warheads are filled with an A-IX-1 explosive composition (phlegmatized RDX), which consists of 94–96% RDX (hexogen) and 4–6% wax. More modern warheads are filled with OKFOL explosive composition, which consist of 96% HMX and 4% wax . ExplosiDifferential scanning calorimetry (DSC) analysis performed with the use of DSC Q100 apparatus of TA Instruments ;Thermogravimetry (TGA) analysis, performed with the use of the TG Q50 apparatus of TA Instruments ;Pycnometric density analysis, performed with the use of the Ultrapyc 1200e by Quanta Chrome Instruments ;Moisture analysis, performed with the use of glass weighing vessels and chamber thermostats with a water jacket;Stability analysis carried out with the use of glass weighing vessels and chamber thermostats with a water jacket.Phlegmatized hexogen was subjected to the following tests:onset—temperature at transformation start; Tmax—temperature at maximum peak, in which the process takes place at the highest rate; Tend—temperature at end of transformation; Δm—mass loss of the tested sample in percents; Q—heat of transformation (J/g). The tests were performed in non-hermetic aluminum vessels (with a lid), in a nitrogen atmosphere, with a heating rate of 10 °C/min.The results of DSC and TGA analyses for the tested samples of phlegmatized hexogen (A-IX-1) are shown in onset temperature of the thermal decomposition process determined with extrapolation were 217.1–219.5 °C and 217.2–221.0 °C for DSC and TGA analyses respectively. The largest differences were observed in the values of the thermal decomposition of hexogen contained in the tested composition (1986–2507 J/g). The decomposition process is a highly exothermic transformation, during which, in the case of non-hermetic vessels, a partial loss of gaseous products may occur (through the upper part of the measuring vessel). The mass loss of the tested explosive samples was in the range from 95.9% to 99.0%. The differences in the recorded mass may result from the amount of wax in a given production batch of A-IX-1 composition. On the basis of the authors experience, it may be assumed that the content of stearin or ceresin in the composition A-IX-1 may vary within ±2%, which was confirmed by the results of TGA analyses.On the basis of the obtained results, it can be concluded that the temperature of the beginning of the melting process of the A-IX-1 explosive composition is within the range of 204.0 to 204.3 °C. The TThe results of the density determination with the pycnometric method, as well as humidity and stability by the weight method, are presented in 3. Actual density of the charge after the pressing process, which is indicated in the PG-7VM warhead technical documentation, is 1.63 g/cm3. Since the results of performed physicochemical tests were similar (even though samples of explosive compositions were extracted from warheads from different production batches and year of manufacture), it can be assumed that that the explosive compositions extracted from the PG-7VM warheads have not undergone excessive aging due to the long storage and should have properties similar to those reported in the manufacturer datasheet. Therefore, the density of the charge was assumed as 1.63 g/cm3, which gives the detonation velocity of about 8200 m/s according to the relationship between the detonation velocity and density of the phlegmatized RDX, available in the literature and presented in The measured density of the A-IX-1 composition extracted from PG-7VM warhead ranged from 1.70 to 1.73 g/cm3. The batch-to-batch differences may be caused by the wax content. The higher the wax content, the lower the density value. The results of the humidity and stability tests for all tested samples were below 0.05% and 0.1%, respectively. However, it should be noted that the density of the charge is always lower than the density of the extracted composition powder due to the limitations of the pressing process. Theoretical density of composition containing 96% of RDX and 4% of wax may reach up to 1.78 g/cmNumerical simulations may constitute an effective tool supporting experimental studies of high-speed phenomena that are inconvenient to analyze with the use of traditional measuring techniques. However, the reliability of the results of simulations is strongly dependent on the accuracy of the model. In the literature, there is a lack of reliable numerical studies related to PG-7VM efficiency. In those that are available, the geometry of the warhead is often modeled in an inappropriate or too simplified way and the results are usually limited to the value of DoP in RHA steel. One of the main objectives of this study was to develop an effective and accurate methodology of modeling shaped charge jet penetration into high-hardness armored steel targets. Numerical simulations were carried out in order to better understand the mechanisms of the phenomenon and to collect data related to SCJ parameters during the SCJ formation and target penetration process.Numerical simulations of the PG-7VM SCJ formation and target penetration processes were carried out in the LS-Dyna software . A threeDiscretization of the simulation components is shown in On the external segments of the model, the boundary condition was applied to prevent artificial stress wave reflections generated at the model boundaries from reentering the model and contaminating the results. In the discretization of the target in order to limit the number of elements, the mesh of the individual armor plates was refined in the SCJ impact point zone. The distance between the neighboring nodes ranged from approximately Δx = Δy = Δz = 0.5 mm in the SCJ impact zone to Δx = Δy = Δz = 2 mm in non-deformed armor areas. The elements of the mesh of other components of the warhead had the size of Δx = Δy = Δz = 0.5 mm.The model of the whole phenomenon is shown in Several contact models, based mainly on the penalty function method  and consMetals—steel , copper , and aluminum alloys ;Plastics—wave shaper, separators, etc.;Explosive;Air filling computational domain.Four general types of materials were modeled in the work:Since commonly used and well-known constitutive relations available in Ls-Dyna software  were useThe popular material model developed by Johnson and Cook (J-C) ,50,51,52n) was defined according to the results of material characterization tests described in chapter 2.2.2. Strain rate hardening (parameter C), thermal sensitivity (parameter m), and the manner of failure of the material  were assumed to be similar to the other high-strength steels of 500 HB class and were adopted on the basis of the works [Values presented in he works ,55,56,57The A-IX-1 explosive composition filling the PG-7VM warhead was described with high-explosive burn (HEB) material model and Jones–Wilkins–Lee (JWL) equations of state (EOS) , which aThe computational domain filled with air was described with a null material model combined with linear polynomial EOS . The matThe results of the DoP experimental tests are shown in Determined average penetration capability of the PG-7VM warhead (DP = 280 mm) constituted the reference value in subsequent numerical reproduction of the SCJ formation and penetration analyses.The results of the simulation of SCJ formation due to the detonation of explosives in the PG-7VM warhead are presented in this chapter. The propagation of the shock front as well as expansion of the detonation products are shown in The influence of the wave shaper on the detonation wave propagation can be clearly seen in the 4–107/s. The liner strains may exceed 1000% under very large hydrodynamic pressures (average pressure of approximately 15–55 GPa). The collapse of the liner material on the axis of the warhead forced a portion of the liner to form a high-velocity (2–8 km/s) jet. Since there was a large velocity gradient between the front (tip) and the rear part (slug) of the jet, it was stretched until it lost its integrity. At the same time, the expansion of the detonation products in the radial direction was evolving  and outer (on the explosive side) surface of the liner  to the base of the cone (about 2 km/s and 0 km/s for the tracer nodes located on the inner and outer surfaces of the liner respectively) a.The radial velocities of the tracer nodes located on the inner and outer surface of the liner were plotted against time in 6–107/s. Therefore, the importance of target strength was significantly reduced in comparison to the impacts at lower velocities (v ≤ 1000 m/s). According to the information available in the literature, the SCJ penetration in armor plate is 10–15% [The results of simulation of SCJ penetration into a steel target are presented in this chapter. The deformations of the components of the simulation at different times after the detonation of the PG-7M warhead are shown in s 10–15%  or even s 10–15%  less thaSince two symmetry planes were used in the simulations and the warhead did not have any discrepancies, the SCJ traveled in a straight line. All the off-axis velocities of the jet particles were caused by the target reaction forces as well as the pressure of subsequent liner elements .The cavity produced in the target was due to the lateral displacement of armor and jet elements by the high pressures that were generated. The penetration depth, hole dimensions and penetration velocities were measured in the simulations .Since in the simulations, two symmetry planes were used, the SCJ impact direction was perpendicular to the surface of the target and the holes in the first three plates did not have the asymmetrical shape like in experiments. The maximal dimensions of the inlet holes in the simulations were 16 × 16 mm. The holes in the other plates were circular with the diameter of approximately 10 mm. The depth of penetration in the simulation was DP = 278 mm. The difference between the experimental and numerical values was 2 mm (≤ 1%). The rear portion of the jet with the velocity lower than 2 km/s did not significantly contribute to the penetration. The final DoP = 278 mm was obtained at time t = 420 µs.Axial velocity and pressure distribution in the SCJ and the target at selected moments of time are presented in A verified numerical model of the phenomenon of PG7-VM SCJ penetration into an ARMSTAL 30PM steel target was used to determine the basic characteristic of the PG-7VM warhead efficiency—DoP as a function of SoD. The relationship is shown in One of the consequences of using axisymmetric formulation is that some phenomena, e.g., lateral SCJ dispersion, could not be properly reproduced and the subsequent SCJ elements hit at the same point of the target as the previous elements. As a result, the plot of DoP as a function of SoD was similar to that of the perfect SC. Only the SCJ breakup mechanisms negatively affected the DoP of the target obtained at large SoD. However, the general tendency was determined and the maximum DoP = 317 mm was obtained for the SoD = 360 mm.f = 0.65, while in the work [f = 0.78.On the basis of the simulation results, it can be concluded that since the penetration crater in the ARMSTAL 30PM target was very deep (about 280 mm) and the SCJ interacted with a great number of target elements, the whole numerical model of the phenomenon may be very sensitive to the values of adopted target material failure criteria, as well as the methods of target discretization (mesh density). In the analyses, the value of effective plastic strain at failure (PSFAIL) was adopted as a target failure criterion. When the effective plastic strain in the element exceeded the defined limiting value of the PSFAIL parameter, the element was removed from the model. A set of additional simulations were carried out in which four different values of PSFAIL in the target material model were adopted . The average value of failure strain was determined during the experimental compression tests and equaled εthe work  the authThe comparison of the simulation results in which different values of effective plastic strain at failure were adopted in the target material model are shown in f = 0.65) or available in the literature (εf = 0.78) [On the basis of the performed calculations, it can be concluded that the model shows a relatively low sensitivity to changes of the PSFAIL value in the range of 0.1–0.6. Increasing the PSFAIL for the target material (ARMSTAL 30PM) by 33% from the value of PSFAIL = 0.6 to PSFAIL = 0.8 reduces the crater depth in steel plates by about 7% (from 280 mm to 260 mm). For higher PSFAIL values, the differences are even lower. The model shows higher sensitiveness when the PSFAIL value is reduced from 0.6 to 0.4. The target elements then erode much faster during the jet penetration process, which results in a complete penetration of the target with a thickness of 300 mm. The target penetration depth equal to DoP = 2 80 mm, i.e., the same as in the experimental tests, was obtained for the value of PSFAIL = 0.63. This value is slightly lower than that determined during the experimental compression tests of ARMSTAL 30PM steel samples (ε = 0.78) .Based on the results of analyses in which different sizes of finite element meshes were used in the target b, it shoTaking into account the slight differences in the obtained calculation results  and a large growth of required computational cost (time required to finish the computation increased from 17 to 38 h), use of elements with the size of Δx = Δy = Δz = 0.25 mm in analysis of this specific phenomenon seems to be unjustified from an economic point of view.In the literature, there is a lack of accurate and reliable research related to the performance of the most common PG-7VM warhead against modern high-hardness armored steels.In the present paper, the penetration capability of the PG-7VM warhead was determined against high-hardness (≥500 HB) ARMSTAL 30PM armored steel. The average depth of penetration of about 280 mm was obtained during the tests.In the present study, much attention was paid to exclude all the factors that could negatively affect (under- or overestimate) results of warhead penetration capability tests. Before the experiments, the presence of structural discrepancies in the warheads were X-ray inspected and material properties of explosive, target, and most important warhead components were analyzed during material characterization tests. The reliability of the results was significantly increased in this way.The numerical model of the warhead was defined more accurately than in previously published studies, since it was based on the real grenade dimensions and its technical documentation. Thanks to this, the difference between the experimental and numerical values of DoP was smaller than 1%, which confirms that developed methodology of modeling allows researchers to properly reproduce the PG-7VM shaped charge jet formation and target penetration processes.Performing numerical simulations facilitates a more precise analysis of the phenomenon and SCJ parameters. A verified numerical model of the PG-7VM SCJ penetration into ARMSTAL 30PM armored steel target was used to determine DoP in function of SoD. Maximum DoP = 317 mm was obtained for the SoD = 360 mm.A combined approach based on experimental tests and developed methodology of modeling of SCJ penetration into high-hardness armored steel targets could be used in the future in all kinds of optimization studies of different HEAT warheads and armor designs.On the basis of literature review as well as performed experimental and numerical analyses the following conclusions can be drawn:


Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size , it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by 13C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use. Anaerobic gut fungi in the early-branching phylum Neocallimastigomycota are found in the digestive tracts of large mammalian herbivores, where they play an integral role in the lignocellulolytic microbiome of their host . These f3–8–2 or a pathway involving pyruvate ferredoxin oxidoreductase that is not supported by extracellular metabolite measurements is used  can be used to address these shortcomings, as they are well suited to act as knowledge base platforms for integrating multi-omic data sets and have been successfully used to drive the engineering of both prokaryotes and eukaryotes –19. More–Neocallimastix lanati. Comparative genomic analyses revealed that N. lanati is metabolically similar to the other sequenced Neocallimastigomycota, including the presence of many CAZymes , suggesting that insights gained from understanding its genome may be generalizable to other species in the clade. Therefore, we used the genome of N. lanati to construct the first genome-scale metabolic model of an anaerobic gut fungus. This fungus is well suited to act as a model system to investigate the metabolism of anaerobic gut fungi because, unlike many of them, it grows relatively well in completely defined (M2) medium (−1 in M2 medium)  of the anaerobic gut fungus ) medium , 20, it  medium) –23, and N. lanati. The model is stoichiometrically consistent as well as mass and charge balanced. Experimental, genomic, transcriptomic, and metabolic flux analysis data were used to build and validate the model, which recapitulates extracellular metabolite production rates and accurately models the observed fungal growth rate. Furthermore, the model refines and expands on previous hypotheses regarding the metabolism of the gut fungal hydrogenosome. Both the model and experimental data suggest that pyruvate formate lyase (PFL) is significantly more active than pyruvate ferredoxin oxidoreductase (PFO) in the hydrogenosome but that hydrogen formation can only occur via the latter pathway. Going forward, this fungus and its associated model can be used to guide efforts to further refine aspects of gut fungal metabolism that remain unclear and direct metabolic engineering strategies. Indeed, model-based analysis could be invaluable in designing stable consortia between anaerobic gut fungi and other industrially utilized organisms—something that has not yet been fully realized  model introduced here, named iNlan20, is composed of 1,018 genes, 1,023 reactions, and 816 metabolites and models the primary metabolism of realized , 24, 25.N. lanati; this isolate was sourced from a fecal pellet of a sheep obtained from an enclosure at the Santa Barbara Zoo (the Index Fungorum identification number is IF557810). While the genome of this fungus is large (N. lanati genome encodes a rich array of carbohydrate-active enzymes (CAZymes) in numbers similar to those reported from other gut fungal genomes (N. lanati deploys both complexed (cellulosomes) and uncomplexed CAZymes through its rhizoidal network  . Here, aCopyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00002-21.5FIG S1N. lanati include numerous glycoside hydrolases (GHs), dockerins (DOCs), and carbohydrate binding modules (CBMs). These domains facilitate the hydrolysis of cellulose- and hemicellulose-based substrates, suggesting that N. lanati has an expansive cellulolytic capability. PL, polysaccharide lyase; EXPN, expansin; GT, glycosyltransferase; CE, carbohydrate esterase. Annotation data for N. lanati can be found at https://mycocosm.jgi.doe.gov/Neolan1/Neolan1.info.html. Download FIG S1, TIF file, 0.2 MB.The CAZyme repertoire (number of annotated domains shown in brackets across the genome) of Copyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. lanati)  revealed that the gut fungi are metabolically similar. Of the 1,023 unique EC numbers identified across these 5 genomes, fewer than ∼3% are unique to each isolate  , which i isolate . This suN. lanati, a manually curated genome-scale metabolic model of N. lanati was built (iNlan20) according to an established protocol for generating high-quality reconstructions /h, while the NGAM function was found to be 2.27 mmol ATP/g (dry weight). While there is a large degree of uncertainty in the fit, these estimates are in line with those of other fungal GEMs  , Bioreaction engineering principles, 3rd edition, 2011, Springer, Berlin, Germany; W. Vongsangnak, A. Klanchui, I. Tawornsamretkit, et al., Gene, 583:121–129, 2016, https://doi.org/10.1016/j.gene.2016.02.028; C. Ye, W. Qiao, X. Yu, et al., BMC Genomics, 16:799, 2015, https://doi.org/10.1186/s12864-015-2042-y) and were assumed sufficiently informative for this model. Increasing concentrations of cellobiose were used to promote faster growth, inferred by pressure accumulation measurements. Metabolite measurements were taken 24 h apart, and the resultant fluxes were used to constrain the model. This constrained model was used to predict the maximum ATP yield given a specific growth rate. See Materials and Methods for more details. Download FIG S3, TIF file, 0.1 MB.Growth- and non-growth-associated maintenance  functions were estimated from experimental data for Copyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the https://github.com/stelmo/iNlan20). N. lanati as well as pathways for mixed-acid fermentation , which are typically found in anaerobic gut bacteria  biosynthesis pathways were found to be complete, with the exception of folate, where no synthesis mechanism of 4-aminobenzoate was found. The heme and biotin biosynthesis pathways appeared to be incomplete; however, the latter vitamin was not found to be essential  in defined medium, suggesting that the pathway is either poorly annotated or that the fungus does not require it for growth. Finally, the model recapitulates the experimentally observed growth rate in defined medium using only the measured flux of glucose (1.5 mmol/g [dry weight]/h) as an input constraint .The major components  of the anabolic metabolism of 2 and formate already identified, as shown in 2 production is not resolved, with literature suggesting either pyruvate ferredoxin oxidoreductase (PFO) or pyruvate formate lyase (PFL) as possible routes . In this case, the function of the membrane-bound subunits of complex 2 and the soluble subunits of the ATP synthase are unknown. In either case, further experimental work is needed to clarify these questions.By combining literature sources, gene annotation, transcriptomic expression, and subcellular localization data, we have included additional pathways in the model of the hydrogenosome for . lanati . Of note. lanati , 33, whi. lanati , 16. Addut fungi . A possiut fungi . The preaginalis , 36 and s ovalis . This rasome see . This prsome see , if thesaginalis , 35. Indmaritima  . Download Copyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the N. lanati genome (similar to what has been found in other H2-producing mitochondria [+ + H2 ↔ H+ + NAD(P)H] operates in the reverse direction in the hydrogenosome (2 and prevents the accumulation of NAD(P)H produced by the malic enzyme in the hydrogenosome. However, in this direction, the reaction is energetically very unfavorable, with a ΔG of ≈34 ± 5.9 kJ/mol assuming physiologically realistic conditions. Therefore, the flux bounds of this reaction in the hydrogenosome were set to reflect the assumption that the hydrogen dehydrogenase only carries flux in the forward, energetically favorable, direction.Taken together, our expanded model of the hydrogenosome includes the core enzymes previously reported in other gut fungal species as well as a speculative bifurcating hydrogenase, ATP synthase, and proton-pumping module composed of the complex 1 and complex 2 enzymes identified in the chondria ). Given genosome , 14, 15.N. lanati did not grow using xylose as its sole carbon source, as has been found in other gut fungi  appeared to be toxic to the fungus. Since cysteine was used to ensure anaerobicity of the medium, we could not test nitrogen source utilization.The curated model was validated using a combination of growth curve, extracellular metabolite, and metabolic flux analysis (MFA) data. Substrate utilization tests were performed on 36 different carbon sources, focusing on metabolites that are present in the digestive tract environment of the anaerobic gut fungi . The quaut fungi . In thisut fungi . Howeverut fungi . It was ut fungi , suggest13C-labeled glucose tracer was used in conjunction with a carbon atom transition model built from the N. lanati metabolic reconstruction. For the MFA model, metabolic degeneracy caused by the ability of the hydrogenosome to metabolize both malate and pyruvate resulted in large bounds on the fluxes involving these metabolites. To circumvent this, the MFA model was constrained to only import pyruvate into the hydrogenosome, based on previous observations  were also used to constrain the MFA model. This resulted in accurate internal metabolic flux measurements based on a statistically significant fit between measured and simulated proteinogenic amino acid labeling patterns (13C measured fluxes were then compared to the fluxes predicted using the GEM with independently measured metabolite flux constraints (P < 0.01)  was also used to experimentally verify the predicted intracellular fluxes of the GEM. A 1,2-rvations . Extracepatterns . These 1straints . Parsimo.01) see . This su10.1128/mSystems.00002-21.9FIG S513C-labeled glucose tracer suggests that the model accurately captures internal cellular fluxes. Normalized parsimonious FBA (pFBA) fluxes and their corresponding measured MFA fluxes were used to compare the accuracy of the model’s predictions of the internal fluxes of N. lanati. The pFBA model was constrained by its exchange fluxes for glucose, H2, lactate, ethanol, formate, and acetate to reflect experimentally observed data. The MFA data were generated by growing N. lanati in M2 medium at 39°C and harvesting the cell pellet during exponential phase. The regression fit is significant (P < 0.01). Further measured flux analysis was conducted as detailed in Materials and Methods and shown in the included MFA model found in the GitHub repository of the model available at https://github.com/stelmo/iNlan20. Download FIG S5, TIF file, 0.06 MB.Metabolic fluxes as measured by a Copyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 production through an associated ferredoxin hydrogenase, as found in the hydrogenosomes of other organisms (N. lanati (There remains uncertainty regarding the presence of pyruvate ferredoxin oxidoreductase (PFO) and its relative importance in the hydrogenosomal metabolism of anaerobic fungi. Earlier enzymatic characterization of hydrogenosomal proteins in Neocallimastigomycota suggested that PFO is the primary route for Hrganisms , 32, 40.rganisms , 15. Therganisms , is signrganisms , 14. Bot. lanati . The modP < 0.05) from unity for N. lanati. This is in agreement with earlier metabolite measurements for Piromyces sp. strain E2, suggesting that PFL is dominant (N. lanati. Since there is no energetic cost associated with using PFO versus PFL (both produce one ATP molecule per pyruvate)  if PFL is metabolically dominant. This is because ethanol and acetate are only produced from PFL (via acetyl coenzyme A) in the cytosol . Since Pdominant . Figure yruvate) , the mododuction , suggestoduction . Since P10.1128/mSystems.00002-21.10FIG S6in vivo. When the H2 flux is constrained, the model accurately captures the experimental data. For panel A, N. lanati was grown in M2 medium at 39°C using the noted cellobiose initial concentration, and the end point metabolite concentrations were measured using the HPLC protocol detailed in Materials and Methods. The molar ratios were then compared as specified. For panel B, 2,000 feasible flux solutions were sampled from the model that was unconstrained and constrained in the H2 flux variable. Download FIG S6, TIF file, 0.08 MB.The molar ratio of produced formate to acetate and ethanol (denoted by the flux ratio in this figure) is not significantly different from unity, confirming that PFL plays a dominant role in the metabolism of the hydrogenosome. (A) Experimentally measured extracellular metabolites indicate that the ratio of formate to acetate and ethanol is close to unity, as has been shown to be the case for other gut fungi . (B) TheCopyright © 2021 Wilken et al.2021Wilken et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2 as an electron sink for the recycling of NADH to NAD+ . The model also dictates that the production of NAD+ shifts from the cytosol to the hydrogenosome when the bifurcating hydrogenase is used, as shown in 2 is predicted to be produced, which is not observed experimentally   . This sue model)  may not e model) , or thate model) . FurtherN. lanati to regulate how NAD+ is regenerated through its mixed acid fermentation pathways, i.e., through a combination of lactate dehydrogenase, acetaldehyde dehydrogenase, and alcohol dehydrogenase. Interestingly, the relative mean error between the predicted flux distributions and experimental measurements of the fermentation products is much more sensitive to constraints placed on acetate production than any other single measured external metabolite flux, as shown in + that can be achieved per mole of pyruvate depending on which mixed-acid fermentation pathway, or combination thereof, is constrained. Without the bifurcating hydrogenase, H2 production does not significantly impact the overall redox balance of the cell. This possibly explains why its constraint has the smallest effect on the flux variability predicted by the model and may allow the cell to fine tune its metabolism to suit the environmental needs, e.g., sugar availability, by up- or downregulating the flux channeled to the hydrogenosome . Download Table S4, DOCX file, 0.01 MB.Flux variability analysis indicates that there is significant variability in the production rates of metabolic products that all lead to near optimal growth rates . SeCompared to the MFA estimates, the model-based flux predictions were the most accurate around the central glycolytic pathways . The lar2 production was expected and observed. However, literature sources indicate that stable gut fungal/H2-consuming methanogen cocultures are readily formed synthetically and observed in nature  and a catalytic rate difference  without compromising growth. In contrast, under more challenging conditions, e.g., when the fungus is using lignocellulose as a carbon source, it might produce more PFO. Alternatively, recent high-resolution growth rate data suggest that gut fungi have highly variable growth rates that fluctuate with environmental conditions . It is nditions . Thus, iDespite these discussed caveats, the iNlan20 shows good agreement with experimental measurements. Due to their nonmodel nature, anaerobic gut fungi are vastly understudied, which presents unique challenges when constructing a metabolic model of these cryptic organisms. Model-based analysis represents a systematic framework that can be used to identify high-impact knowledge gaps and focus experimental attention, and in this case, the model indicates that focus should be directed toward a detailed characterization of the gut fungal hydrogenosome. Within the realm of nonmodel microbes, metabolic modeling is an appealing technique that can be used to speed up the biotechnological translation of anaerobic gut fungi and other nonmodel microbes.N. lanati. While the genome is large, it is relatively unfragmented compared to the genomes of the other sequenced anaerobic gut fungi. Additionally, the genome encodes a large number and diversity of CAZymes, most of which are expressed in the transcriptome. This genome was used to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model, iNlan20, accurately recapitulates the observed growth rate, in vivo fluxes, and substrate consumption and requirement profiles. The model refines and expands on our understanding of gut fungal hydrogenosomal metabolism. We confirm previous findings that suggested that PFL carries more flux than PFO in the hydrogenosome, but an energetically favorable route to hydrogen production still requires the action of PFO. The possible presence of a bifurcating hydrogenase and/or a proton-pumping mechanism suggests that anaerobic fungi may have evolved more complex energy conservation mechanisms that allow them to compete with faster-growing rumen bacteria. Experimental work, likely involving the isolation, purification, and enzymatic characterization  of the hydrogenosome, is necessary to further refine our understanding of its metabolism. This model is well poised to serve as a platform to build a better understanding of these nonmodel organisms. Moreover, the model will serve as a valuable tool to systematically guide future engineering efforts of gut fungi for converting lignocellulose into value-added products.Here, we have introduced a high-quality genome and transcriptome of a novel anaerobic gut fungus, Anaeromyces robustus, Piromyces finnis, and Neocallimastix californiae  annotations to genes. This information was collated into a master metabolic table (see data set S3 in the iNlan20 GitHub repository available at https://github.com/stelmo/iNlan20) and subsequently used to construct the model and assign genes to reactions. Enzyme complexes were assigned by using the “Subunit structure” field in the UniProt database. Protein localization was predicted using DeepLoc (N. lanati were conducted as part of this study (described later). These omics data sets were used to assign a confidence score to each gene in the model of N. lanati. Gaps in the model of N. lanati were filled by inspecting the EC assignments found for each other anaerobic fungus, as well as the GEMs of E. coli and S. cerevisiae, using the approach described above and looking for homologous genes in the genome of N. lanati /h of glucose was used in all simulations. Flux balance analysis was used to simulate the genome-scale metabolic model of N. lanati using the COBRA Toolbox and COBRApy  were generated by sampling from the model and constraining the objective function to be within 90% of the optimum found by FBA. This threshold was set to reflect the assumption that the gut fungi need to maintain a high growth rate to compete with faster growing bacteria in their native microbiome  MycoCosm database . This iniforniae  as well rain C1A , 52, andtrain E2  were alstrain E2 . Briefly DeepLoc . Reactio DeepLoc , and spe DeepLoc . Transcr. lanati , 58. The. lanati  was usedion data , 60. Theft model . Specifift model . The cur COBRApy , 63. Flucrobiome . ExampleN. lanati was grown at 39°C in sealed Hungate tubes (10-ml liquid volume) or 70-ml serum bottles (40-ml liquid volume) in both undefined complex medium C (MC)   and compm C (MC) , with 10eadspace  was usedN. lanati was isolated from the feces of a sheep located at the Santa Barbara Zoo according to an established protocol , and 2-mercaptoethanol , for 1 h at 50°C. After centrifugation, the supernatant was gently extracted twice with 24:1 chloroform–iso-amyl alcohol. The upper phase was removed, adjusted to one-tenth volume with 3 M potassium acetate, and gently mixed, and the gDNA was precipitated with iso-propanol. Subsequently, the gDNA was collected by centrifugation, washed with 70% ethanol, air dried for 20 min, and dissolved thoroughly in 1× Tris-EDTA (TE) buffer at room temperature. The purified gDNA was shipped to the JGI, where it was sequenced and annotated. Briefly, 10 μg of genomic DNA was sheared to approximately 15 to 20 kb using Megaruptor3 (Diagenode). The sheared DNA was treated with DNA Prep to remove single-stranded ends and then with DNA damage repair mix, followed by end repair, A tailing, and ligation of PacBio overhang adapters using SMRTbell template prep kit 1.0 (Pacific Biosciences). The final library was size selected with BluePippin (Sage Science) at a 10-kb cutoff size and purified with AMPure PB beads. PacBio Sequencing primer v3 was then annealed to the SMRTbell template library, and sequencing polymerase was bound to them using a Sequel binding kit 3.0. The prepared SMRTbell template libraries were then sequenced on a Pacific Biosystem’s Sequel sequencer using 1M v3 SMRT cells, and version 3.0 sequencing chemistry with 10-h movie run times. Subsequently, the main assembly consisted of 62.05× of PacBio read coverage  and was assembled using MECAT version 1.8; the resulting sequence was polished using ARROW (version 2.2.3). The assembled genome was annotated using the JGI annotation pipeline. The genome is available at https://mycocosm.jgi.doe.gov/Neolan1.protocol . Fungal protocol . Brieflyhttps://github.com/TransDecoder/TransDecoder/wiki) was used to find the highest likelihood coding regions in the transcriptome. The transcript abundance was estimated using Kallisto  oligonucleotides. mRNA was fragmented using divalent cations and high temperature. The fragmented RNA was reversed transcribed using random hexamers and SSII (Invitrogen) followed by second-strand synthesis. The fragmented cDNA was treated with end repair, A tailing, adapter ligation, and 10 cycles of PCR. The prepared libraries were quantified using KAPA Biosystem’s next-generation sequencing library quantitative PCR (qPCR) kit and run on a Roche LightCycler 480 real-time PCR instrument. The libraries were then multiplexed into pools, and sequencing was performed on the Illumina NovaSeq 6000 sequencer using NovaSeq XP v1 reagent kits and S4 flow cell and according to a 2-by-150 indexed run protocol. This transcriptome may be found at g and filtered using a 0.22-μm syringe filter into HPLC vials. The samples were run on an Agilent 1260 Infinity  using a Bio-Rad HPX-87H column . Samples were run at two column conditions to effectively separate all the fermentation products  mixed gas at 1, 2, and 5% (mole basis). Fatty acid composition and extracellular amino acid concentrations were measured on a liquid chromatograph-mass spectrometer (LC-MS) using an Agilent Polaris 3 C18-ether 150-by-3.0 mm (part number [no.] A2021150X030) column. Total run time was 13 min and injection volume was 5 μl with a column temperature of 30°C and flow rate of 0.3 ml/min. The MS was run in negative ionization mode, with the gas temperature at 230°C and a flow rate of 12 liters/min. Standards were made to bracket the expected concentration ranges of the fatty acids.Liquid samples for high-performance liquid chromatography (HPLC) analysis were stored in microcentrifuge tubes at −20°C for batch analysis. Sulfuric acid (0.5 M) was added to the samples (1 in 100 volumes), vortexed, and allowed to mix at room temperature for 5 min. Thereafter, the samples were centrifuged for 5 min at 21,000 × products , 72. Sucproducts . StandarN. lanati was experimentally determined in triplicates for each major component . Cultures grown in serum bottles on medium C, with cellobiose as the carbon source, were harvested during exponential phase for each measurement. DNA and RNA were immediately isolated from the wet cell pellet using a previously established CTAB protocol  in medium M2 with cellobiose as the carbon source. Pressure accumulation was measured twice daily to calculate the fungal growth rate . After the last measurement, the fungal cell pellets were harvested by centrifugation, lyophilized, and weighed. The estimated growth rate for each sample was then used to extrapolate the dry cell mass at the respective time points  functions were estimated from experimental data. Briefly, five triplicate sets of Hungate tubes with various concentrations of cellobiose  in medium M2 were inoculated with 1 ml each  from a single serum bottle of wth rate . Liquid e points . The flue points .13C]glucose as the sole carbon source in medium 2 at an initial concentration of 5 g/liter were used for the labeling experiment. Each Hungate tube was passaged during exponential growth phase, after which, the cell pellet and remaining media were frozen at −20°C for later processing. The medium was analyzed for glucose and fermentation products using the HPLC protocol described above. The pellets were lyophilized, after which GC-MS measurements were used to quantify the isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose as described previously . Similarly, the vitamin requirements of N. lanati were tested by individually removing each vitamin in medium 2 , and a standard protocol was used to visualize the presence of electrochemical gradients. Briefly, JC-1 was dissolved in dimethyl sulfoxide  and frozen until use. Dye aliquots were thawed and added to cultures of anaerobic gut fungal zoospores using final dye concentrations of 1 μg/ml. Zoospores were incubated with JC-1 for 30 min anaerobically in standard M2 medium at 39°C. After incubation, cultures were filtered onto 3-μm polycarbonate membranes  with a nitrocellulose backing filter . Cells were counterstained with 4′,6-diamidino-2-phenylindole  and mounted on glass slides using an antifade mounting solution composed of 4:1 Citifluor  to Vectashield . Prepared slides were placed on ice and imaged immediately using a Zeiss Axiovert M200 fluorescence microscope .https://mycocosm.jgi.doe.gov/Neolan1/Neolan1.info.html.The full genome, raw sequencing data, assembly, predicted genes, and annotations are available at


The structure of the metabolic network is highly conserved, but we know little about its evolutionary origins. Key for explaining the early evolution of metabolism is solving a chicken–egg dilemma, which describes that enzymes are made from the very same molecules they produce. The recent discovery of several nonenzymatic reaction sequences that topologically resemble central metabolism has provided experimental support for a “metabolism first” theory, in which at least part of the extant metabolic network emerged on the basis of nonenzymatic reactions. But how could evolution kick-start on the basis of a metal catalyzed reaction sequence, and how could the structure of nonenzymatic reaction sequences be imprinted on the metabolic network to remain conserved for billions of years? We performed an in vitro screening where we add the simplest components of metabolic enzymes, proteinogenic amino acids, to a nonenzymatic, iron-driven reaction network that resembles glycolysis and the pentose phosphate pathway (PPP). We observe that the presence of the amino acids enhanced several of the nonenzymatic reactions. Particular attention was triggered by a reaction that resembles a rate-limiting step in the oxidative PPP. A prebiotically available, proteinogenic amino acid cysteine accelerated the formation of RNA nucleoside precursor ribose-5-phosphate from 6-phosphogluconate. We report that iron and cysteine interact and have additive effects on the reaction rate so that ribose-5-phosphate forms at high specificity under mild, metabolism typical temperature and environmental conditions. We speculate that accelerating effects of amino acids on rate-limiting nonenzymatic reactions could have facilitated a stepwise enzymatization of nonenzymatic reaction sequences, imprinting their structure on the evolving metabolic network. The evolutionary origins of metabolism are largely unknown. This study shows that the prebiotically available proteinogenic amino acid cysteine can promote the metabolism-like rate-limiting formation of ribose-5-phosphate, suggesting that early metabolic pathways could have emerged thought the stepwise enzymatization of non-enzymatic reaction sequences. Cellular metabolism is constituted by a highly conserved and ancient biochemical system, the metabolic network. The size and complexity of the metabolic network are major constraints in the evolution of cells. In prokaryotic cells, this network typically involves up to two-thirds of the genes encoded in their genome and can consist of several hundred enzymes that interconvert thousands of different metabolites –5. DespiThe evolutionary origins of the metabolic network remain largely unknown –14. One A second difficulty in the origins of metabolism is to explain the event of the multistep reaction sequences that are characteristic for the metabolic network. These could emerge from autocatalytic chemical cycles, but these are not evolvable . Second,More recently, the discovery of metabolism-like nonenzymatic chemical reactions that are driven by simple, prebiotically abundant metal ions has shed a new light on the evolution of early enzymes, as well as suggests an alternative solution to the end product problem. In the absence of enzymes, intermediates could simply be formed by nonenzymatic reactions . NonenzyThe stepwise enzymatization model generates a series of testable hypotheses. First, it requires the existence of nonenzymatic, or environmental, reaction sequences that resemble modern metabolic pathway structures, a continuity scenario already put forward by de Duve and Morowitz ,33. Seve.Second, the stepwise enzymatization model requires all the reactions to occur in the same reaction condition, i.e., without condition step changes, so that the selection process can occur. Indeed, most of the nonenzymatic glycolytic and PPP-like reactions are accelerated and gain efficiency upon the addition of ferrous iron, the most abundant transition metal in Archean sediment In this study, we test another condition for the stepwise enzymatization model. In order for a metal catalyzed reaction network to form a basis for the evolution of metabolism, simple components that later form enzymes, amino acids, or peptides need to interact with the reaction sequence and favorably change its reaction properties. We chose the nonenzymatic network of glycolytic and PPP-like reactions ,44. We tAn in vitro screening was devised in order to assess the influence of proteinogenic amino acids on the metabolism-like nonenzymatic reactivity of glycolytic and PPP intermediates ,48. A to1H-NMR spectroscopy, which is frequently applied in this field due to its high identification precision. 1H-NMR, however, suffers from signal suppression when Archean sediment typical iron concentrations are present in the reaction mixtures and hence fails to detect a broad spectrum of transient, iron-promoted, metabolism-like nonenzymatic reactions that are increasingly considered as a basis for early metabolic evolution [To account for the situation that early metabolic enzymes likely produced lower amounts of metabolites compared to highly evolved, modern metabolic enzymes , we chosvolution ,46. Sampvolution . The mixvolution . To captU test, α = 0.10).Several sugar phosphate products formed and indicated a metabolism-like topology of a nonenzymatic reaction network . OverallAs one can expect from the nature of chemical reactions, a rate can be changed either by the direct interaction, i.e., of an amino acid with the sugar phosphates, or by indirect effects, like changes in pH. A number of reactions were indeed accelerated or slowed by the positively charged amino acids  as well as by negatively charged ones (aspartate and glutamate). These rate changes were hence possibly explained by their ability to change their protonation level in response to shifts in the pH, an important factor in the nonenzymatic reactivity of sugar phosphates  S1 Fig)S1 Fig). Sulfur-containing amino acids had their most striking effect on the nonenzymatic reactivity of 6-phosphogluconic acid (6PG), a central metabolite of the PPP. In many modern cells, 6PG participates in a rate-limiting metabolic reaction that forms ribose-5-phosphate, which is then further metabolized and forms, among others, the RNA and, subsequently, the DNA backbone. In water, and in the presence of all other amino acids, 6-phosphogluconate was stable and did not yield any significant concentration of ribose-5-phosphate or other intermediates of the PPP . In the We next determined whether cysteine, methionine, or both sulfur-containing species were responsible for enhancing 6-phosphogluconate transformations. We found that the effects were exclusively explained by cysteine . As reacIn order to get insights into the nature of this reaction, we first tested a panel of sulfur-containing cysteine analogues to explore the impact of chirality, chain length, functional group, and oxidation state on the enhancement of the nonenzymatic reaction from 6-phosphogluconate. Considering closest similarity and commercial availability, we chose the following molecules: L- and D-forms of cysteine , D/L-homocysteine and reduced glutathione (for molecular or chain-size effects), 3-mercaptopropionic acid, cysteamine, β-mercaptoethanol , and L-cystine, cysteine sulfonic acid, and oxidized glutathione (oxidized forms of cysteine thiol group). Ribose-5-phosphate formation rate was most affected by the oxidation state of the sulfur and, to a lesser extent, by the nature of the other pendant functional groups . Oxidize2), arguing that the formation of ribose-5-phosphate was robust despite approximately 1,000-fold difference in O2 concentration between these conditions at the given timescale, when cysteine remains relatively stable  and cysteine, respectively  is paramagnetic and it distorts and suppresses the signal intensities of closely proximal protons from adjacent molecules, in part by reducing the spin–lattice relaxation times of coupled nuclei (T1)  even at relatively low molar fractions of Fe (II) )  became consistently more pronounced in the presence of 6-phosphogluconate, and vice versa, even if differences of proton behavior along a given molecule were subtle ) . In eith Fe(II)) . This in Fe(II)) , consist Fe(II)) . These oe subtle . Converse subtle , support1H-NMR spectroscopy showed a statistically significant decrease  in the diffusion coefficients of both 6-phosphogluconate and cysteine when these were exposed simultaneously to increasing concentrations of Fe(II) in the presence of a relatively bulky, nonchelating Fe(II) ligand such as phenanthroline , so that reaction monitoring was limited to the much more sensitive LC–SRM technology.Altogether, these data would evoke a cooperative interaction between cysteine, Fe(II) and the 6-phosphogluconate, which would form an assembly that could presumably underpin the catalysis. Molecular diffusion measurements carried out by diffusion ordered spectroscopy (DOSY) throline . This coAt 70°C, temperature is assumed to be a major driver of the net reaction kinetics. There are plausible arguments that suggest that the origin of metabolism could have happened under thermophilic conditions, as thermodynamic barriers are easier to overcome ,52. HoweEa of 69 kJ mol−1 . For the same range of temperatures, only very limited formation of ribose-5-phosphate was observed in the absence of the amino acid and the metal, due to a higher energy barrier, estimated to be around 87 kJ mol−1 , the solution was incubated for up to 3 days, depending on temperature, to allow enough time for detectable product formation. Cysteine and Fe(II) did promote the formation of ribose-5-phosphate in a whole range of mild temperatures, even though the reaction rate slowed with decreasing temperatures, in agreement with the Arrhenius equation . The leakJ mol−1 .Interestingly, temperature-driven changes in reaction rate inversely correlated with an increased specificity of forming the signal corresponding to ribose-5-phosphate , S7 Fig.The historical biochemistry textbook has primarily explained the function of metabolism with the need for the biosynthesis of the cellular building blocks, like amino acids, nucleotides, and lipids. This view has put specific emphasis on what has become known as “anabolism,” the biosynthesis of biomolecules. If metabolism had no other function than providing these biomolecules, a variety of separate chemical processes would have hence contributed to the early forms of the metabolic network, as long as they produced the products required. Consequently, many investigations into the origins of metabolism during abiogenesis have focused on generating nucleotides, amino acids, and other biomolecules at a high concentration ,53. To gHowever, the detailed study into metabolism’s topology, given a vast amount of information that has become available about biochemical systems, draws a more differentiated picture. For the evolution of cells, it does not only matter which metabolites are formed, but also through which reaction sequences they are formed ,55,56. MIndeed, there are different possibilities on how the early metabolism was supplied with its substrates, but only one of these scenarios suggests that all life essential metabolites had to accumulate to high levels in the environment before life could start. Clues are provided by the modern metabolic network, in which anabolism and catabolism are highly intertwined. They are driven by a largely overlapping set of biochemical reactions, indicating anabolism and catabolism evolved in parallel rather than one after the other ,3,66. InRecently, increasing evidence has emerged pointing that the structure of the metabolic network could have been directly primed by nonenzymatic metabolic interconversions, in particular those driven by ferrous iron. Fe(II) is believed to have reached up to millimolar concentrations in Archean aquatic environments including the Earth’s early oceans, as the reduced form of iron was stable in the reducing preoxygenation atmosphere and is highly water soluble ,68,69. NThe motivation for our study was to gain insights into an unsolved question of early metabolic evolution. How could environmentally catalyzed reaction sequences underlie or favor the evolution of enzyme-catalyzed pathway structures? How could the topology of a nonenzymatic reaction sequence become encoded in an enzyme-catalyzed pathway? Previously named the “end product problem,” it is unlikely that multiple metabolic enzymes came into being at once before a selectable product can be formed. However, the metabolic control theory suggests a path out of this dilemma. The performance of any reaction sequences can be improved upon altering the chemistry of the most rate-limiting steps. This situation suggests a gradual stepwise enzymatization of previous nonenzymatic reaction sequences. Once one reaction is improved to the extent it is no longer rate limiting, the selection pressure would shift to the next reaction . An arguThis model allows us to speculate about the point in time when first enzymes were selected. The metabolic network evolved before LUCA; the colonization of different niches required a functional metabolic system. On the other end of the spectrum, the evolution of modern enzymes required an at least rudimentary transcription and translation machinery in place. In turn, the transcription and translation machinery required the metabolites produced by metabolism. We can hence speculate that the rudimentary transcription and translation machinery was supplied by nonenzymatic reactions and that modern enzymes were selected and sequentially populated the nonenzymatic reaction sequences in parallel evolution. The properties of nonenzymatic chemical reactions instead are not subject to Darwinian selection and have hence not changed during the timeline of evolution.A testable requirement of this model is that simple components that provide the foundation for the selection of enzymes need to be able to interact with the nonenzymatic reaction sequence and, if any, improve their rate-limiting steps. The ability of amino acids to be both enhancers and products of the primordial metabolic network at the same time offers beneficial feedback mechanisms ,72. NetwThe fact that we observed such properties for cysteine is important for at least 2 reasons. Firstly, cysteine has been previously implicated in the nonenzymatic catalysis of glycolysis and in particular in acetyl-CoA formation, even though such study used large amounts of reagents and remained mainly qualitative . SecondlRegarding the chemical mechanisms, the role of cysteine could be that of a nucleophile as observed in the case of the enzyme aldehyde dehydrogenase . Althoug−) in the medium leading to precipitation of the metal catalyst as oxides, in addition to causing a destabilization of cysteine. Furthermore, in the case of the metal-free version of ribose-5-phosphate formation, a higher pH would deplete the availability of free protons that are required for the catalyst. Hence, a mildly acidic media favors solubilized metal species while still retaining the nucleophilicity of the ligands. Given that the isoelectric point of cysteine is close to pH 5, we speculate that the deprotonated carboxylic group could coordinate with Fe(II), in addition to the coordination by the thiol group. From our investigation with different cysteine analogues, we indeed observe that both carboxylic and thiol groups are more critical for reactivity than the amino group.It may seem puzzling that the optimum conditions for the reaction occur at mildly acidic pH given that cysteine nucleophilicity would increase with the pH. However, a higher pH also increases the concentration of hydroxyl . UPLC/MS grade acetonitrile (AcCN) and water were purchased from Greyhound Chemicals (UK)  measurements. The incubation time points were specifically set for each metabolite based on its previously reported stability [Samples consisting of individual sugar phosphates dissolved (in the μM range) in 200 μL aqueous solutions (either in 50 mM phosphate or UPLC/MS grade water) were prepared fresh. These were placed in 2-ml screw thread amber autosampler glass vials  and sealed in an anaerobic chamber  following 3 vacuum/Ntability ,44; thesSamples were analyzed for sugar phosphate content by an LC–MS/MS system, using a triple-quadrupole device (Agilent 6460) operating in SRM mode : a least squares linear production model, a saturating hyperbolic model or sigmoidal Gompertz function, or a mixed model comprising a first linear phase of maximum  growth rate and a slow exponential decay. Rates, when reported, are shown in terms of the maximum  slopes of the reaction kinetics fits.MS/MS data were analyzed with the quantitative MassHunter software (Agilent Technologies), with manual supervision of the automated signal-peak integration. Several replicates of sugar phosphate external-standard dilution series allowed translating signal intensities into absolute concentrations. This was achieved with less accuracy in the case of E4P due to its broad, low-intensity peak, as described in previous work  and beca2O) containing 0.05 mM trimethylsilylpropanoic acid (TSP) as internal standard, sodium azide and 50 mM sodium phosphate at a pH 5. FeCl2 was added at concentrations of 0, 0.1, 0.2 and 1 mM. NMR experiments were carried out on freshly prepared samples using an AVANCE II+  NMR spectrometer operating at 500.13 MHz for the 1H frequency using a 5 mm TXI probe. Basic 1D spectra were collected using a solvent suppression pulse sequence based on a one-dimensional version of the nuclear Overhauser effect spectroscopy (NOESY) pulse sequence to saturate the residual 1H water signal . A total of 128 transients were collected into 16 K data points over a spectral width of 12 ppm at 320 K.Samples containing a final metabolite concentration of 20 mM were prepared in deuterated water (DT1) were measured using the Inversion recovery pulse sequence, 180-τ-90, with pulse spacing τ having the following values: 0.01 seconds, 0.02 seconds, 0.03 seconds, 0.05 seconds, 0.1 seconds, 0.25 seconds, 0.5 seconds, 0.75 seconds, 1 seconds, 1.5 seconds, 2 seconds, 4 seconds, 8 seconds, 15 seconds, 20 seconds, and 25 seconds. The pulse repetition time was set at 20 seconds. A total of 8 transients were collected into 16 K data points over a spectral width of 20 ppm at 320 K.Longitudinal relaxation times (Diffusion coefficients were measured using a bipolar gradient pulse pair with a spoil gradient pulse (stebpgp1s), with 16 incremental steps in the gradient strength linearly ramped from 2% to 95% of the maximum gradient strength. Moreover, 16 scans per increment step were collected into 16K data points over a spectra width of 19 ppm at 320 K. The gradient pulse length was set at 1,000 μs, while the time between pulses at 0.1 seconds. The pulse repetition time was set at 2 seconds.1D NMR spectra were processed using TopSpin v.3.2 . Free induction decays were Fourier transformed following multiplication by a line broadening of 1 Hz, and referenced to TSP at 0.0 ppm. Spectra were automatically phase and baseline corrected. For the relaxation times and diffusion coefficients, data were analyzed using the T1/T2 Relaxation module present in TopSpin. All experiments were carried out at least by triplicate.S1 Figp = 0.1). “All mixed” stands for samples containing all 20 amino acids at a total concentration of 400 μM . Data points shown in gray (N = 3 independent experiments)  are shown for different prototypic reactions: diverse transformations accelerated by the sulfur-containing amino acids cysteine and methionine, some of which are negatively affected by at least another group of amino acids (top 3 panel rows); F1,6BP dephosphorylation, enhanced by negatively charged amino acids, with antagonistic effects from positively charged amino acids (fourth set of panels); a pentose-phosphate isomerization, where the role of cysteine and methionine is actually as inhibitors (bottom panel row). Asterisks indicate reaction rates that were significantly different—either higher (green) or lower (red)—than in the aqueous control without amino acids (last column of panels)  see .(TIF)Click here for additional data file.S2 Fig(A) Concentrations of sugar phosphate products detected by LC/MS after 6-hour incubation of 100 μM 6PG at 70°C in 50 mM phosphate solution at different pHs, either in the absence (purple lines) or presence (green lines) of 400 μM cysteine. The optimum pH range where the reaction enhancement by cysteine was larger is highlighted as a shaded, gray region where applicable. A control consisting of 100 μM 6PG in unbuffered (aqueous) conditions, as well as a negative control without 6PG are shown for comparison. Data shown as mean ± SD  (see (B) Same protocol was followed but in the presence of a metal ion, 200 μM FeCl2, either without (purple lines) or with 400 μM cysteine co-present (green lines). Data shown as mean ± SD  (see ons) see . (B) Samons) see . 6PG, 6-(TIF)Click here for additional data file.S3 Fig(A) Time evolution of cysteine concentration during incubation in 50 mM phosphate solution at different pHs. Lines correspond with exponential decay fits. Corresponding rates are shown in (B): median ± IQR (N ≥ 3) (see (C) For those conditions of intermediate pH, cysteine time courses were reanalyzed both in absence and presence of increasing concentrations of the substrate, 6PG, with just very slight changes detected. Corresponding rates are shown in (D): median ± IQR (N ≥ 3) (see ≥ 3) see . (C) For≥ 3) see . In all ≥ 3) see . 6PG, 6-(TIF)Click here for additional data file.S4 Fig(A) Representative chromatograms obtained by LC–SRM after 6-hour incubation under control conditions without amino acids (−), or in the presence of serine or cysteine, showing how serine, unlike cysteine, has no significant effect on product formation versus control conditions (N = 3 independent experiments) (see (B) The concentration of R5P formed after 6 hours is shown—relative to the 400 μM cysteine condition—for isomers or close homologues bearing the same functional groups ), structural analogues differing in one or several functional groups , and oxidized cysteine derivatives ), evidencing the importance of the thiol group. Error bars represent mean ± SD (N = 3)  see . (B) The= 3) see . 6PG, 6-(TIF)Click here for additional data file.S5 Fig2 but at the same total concentration, 150 μM. (A) Detailed time courses in R5P formation are shown. Lines represent best hyperbolic fits. N = 3 per condition (see (B) Estimated initial rates (error bars: mean ± SD) are plotted as a function of the ratio between both additives  Estives see . The gra(TIF)Click here for additional data file.S6 Fig2+ broadens the proton signal peaks of closely proximal molecules in solution (in this case solvent is 50 mM phosphate pH 5 in D2O). (A) Chemical structures of glucose 6-phosphate (G6P) and methionine, with positions of resonant protons labeled. (B) Representative 1H-NMR spectra of solutions containing 20 mM G6P + 20 mM cysteine or 20 mM 6PG + 20 mM methionine were compared with those of a solution with the key components of the reaction: 20 mM 6PG + 20 mM cysteine. Addition of 1 mM FeCl2 (spectra in red) just slightly affected the individual species when analyzed separately, but extensively distorted the peaks of 6PG and cysteine when these were combined in the same solution, in contrast to the other 2 cases with either G6P or methionine where peak definition remained almost unaltered. This suggests a fairly specific cysteine–6PG interaction that makes them more likely to Fe2+ binding. Peak numbers correspond with proton labels in (A). In all cases, representative examples are shown from at least N = 3 independent experiments  (see Fease) see . 6PG, 6-(TIF)Click here for additional data file.S7 Fig2, and the formation of different sugar phosphate products was monitored over time. Shown is the cumulative product concentration normalized to the total product quantified at each time by targeted LC/MS. As temperature decreases, the loss of specificity in R5P formation detected at higher temperatures ceases and R5P becomes the predominant sugar phosphate product, also at long term . Time ranges are the same as in main N = 3 independent experiments Click here for additional data file.S8 Figin situ. X could be a lewis acid like Fe2+ or protonated amino group from the amino acid. The pentose phosphate sugar product could be either D-Ribose-5-phosphate or D-Arabinose 5-phosphate, which are diastereomers and cannot be distinguished by LC/MS. 6PG, 6-phosphogluconate; LC/MS, liquid chromatography–mass spectrometry; R5P, ribose-5-phosphate.Oxidant could be an electron acceptor such as oxygen or Fe(III) generated (TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S1 DataThe data for experiments summarized in the figures are included in a single Excel file as individual worksheets for panels in Figs (XLSX)Click here for additional data file.S2 DataSummary of nonenzymatic interconversion rates obtained by LC–SRM and models used for fitting experimental data. See individual rates in (XLSX)Click here for additional data file.S1 Text1H-NMR T1 measurements across proton peaks in different setups.Details of materials and reagents used and the cysteine quantitation procedure are described as Supporting information methods, followed by notes discussing pH-dependent product formation and the role of cysteine functional groups, and Supporting information tables with additional data on trace Fe quantitation and (PDF)Click here for additional data file.