Biochemical Model Research Articles

Contribution of Proteins and Peptides to the Impact of a Soy Protein Isolate on Oxidative Stress and Inflammation-Associated Biomarkers in an Innate Immune Cell Model.

The innate and adaptative immune systems are involved in the regulation of inflammatory and oxidative processes and mediators such as reactive oxygen species (ROS) and nitric oxide (NO). The exacerbated action of these players results in an oxidative stress status and chronic inflammation, which is responsible for the development of non-communicable diseases (NCDs). By modulating these mediators, bioactive compounds in food can exert a key role in the prevention of several NCDs. Among these compounds, soybean proteins and peptides such as lunasin have been considered to be among the most promising. The aim of this study was to obtain and characterize a soluble protein-enriched extract from a commercial soybean protein isolate and fractionate it into different fractions through ultrafiltration. Their antioxidant and immunomodulatory properties were then evaluated using biochemical and cell models. A total of 535 proteins (from 282 protein groups) were identified in the extract, in which the presence of the peptide lunasin was confirmed. The enrichment of this peptide was achieved in the 3-10 kDa fraction. The protective effects against the oxidative stress induced by LPS in the macrophage model could have been mediated by the radical scavenging capacity of the peptides present in the soybean samples. Under basal conditions, the extract and its ultrafiltered fractions activated macrophages and induced the release of NO. However, under challenged conditions, the whole extract potentiated the NO-stimulating effects of LPS, whereas the fraction containing 3-10 kDa peptides, including lunasin, counteracted the LPS-induced NO increase. Our findings suggest a promising role of soybean protein as an ingredient for functional foods and nutraceuticals aimed at promoting health and preventing oxidative stress and/or immune-alteration-associated diseases.

Motif2Mol: Prediction of New Active Compounds Based on Sequence Motifs of Ligand Binding Sites in Proteins Using a Biochemical Language Model.

In drug design, the prediction of new active compounds from protein sequence data has only been attempted in a few studies thus far. This prediction task is principally challenging because global protein sequence similarity has strong evolutional and structural implications, but is often only vaguely related to ligand binding. Deep language models adapted from natural language processing offer new opportunities to attempt such predictions via machine translation by directly relating amino acid sequences and chemical structures to each based on textual molecular representations. Herein, we introduce a biochemical language model with transformer architecture for the prediction of new active compounds from sequence motifs of ligand binding sites. In a proof-of-concept application on inhibitors of more than 200 human kinases, the Motif2Mol model revealed promising learning characteristics and an unprecedented ability to consistently reproduce known inhibitors of different kinases.

Improving the cotton simulation model, GOSSYM, for soil, photosynthesis, and transpiration processes

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM’s predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil–plant–atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m−2 day−1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m−2 day−1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

The flexible and iterative steps within the NHEJ pathway

Cellular and biochemical studies of nonhomologous DNA end joining (NHEJ) have long established that nuclease and polymerase action are necessary for the repair of a very large fraction of naturally-arising double-strand breaks (DSBs). This conclusion is derived from NHEJ studies ranging from yeast to humans and all genetically-tractable model organisms. Biochemical models derived from recent real-time and structural studies have yet to incorporate physical space or timing for DNA end processing. In real-time single molecule FRET (smFRET) studies, we analyzed NHEJ synapsis of DNA ends in a defined biochemical system. We described a Flexible Synapsis (FS) state in which the DNA ends were in proximity via only Ku and XRCC4:DNA ligase 4 (X4L4), and in an orientation that would not yet permit ligation until base pairing between one or more nucleotides of microhomology (MH) occurred, thereby allowing an in-line Close Synapsis (CS) state. If no MH was achievable, then XLF was critical for ligation. Neither FS or CS required DNA-PKcs, unless Artemis activation was necessary to permit local resection and subsequent base pairing between the two DNA ends being joined. Here we conjecture on possible 3D configurations for this FS state, which would spatially accommodate the nuclease and polymerase processing steps in an iterative manner. The FS model permits repeated attempts at ligation of at least one strand at the DSB after each round of nuclease or polymerase action. In addition to activation of Artemis, other possible roles for DNA-PKcs are discussed.

HLA-DO promotes Treg differentiation by altering antigen presentation

Abstract HLA-DO in human (H2-O in mice) (DO) is a highly conserved non-classical MHC class II accessory molecule. Unlike the main Class II peptide editor HLA-DM, expression of DO is restricted to the thymic medulla, B cells and certain dendritic cell subsets. DO forms a stable complex with HLA-DM and requires DM for trafficking to MIIC. Using biochemical experiments, our lab has previously discovered that DO has differential effects on editing peptides of different sequences. DO enhances binding of DM-resistant peptides, and reduces the binding of DM-sensitive peptides to the HLA-DR1 molecules. Changes in the quantities of either DM-resistant of DM-sensitive peptides could therefore lead to alterations in both thymic negative selection, and peripheral activation of CD4 T cells. Using single-cell RNA-sequencing (scRNA-seq) of peripheral CD4 T cells from unimmunized DO knock-out (KO) mice we found an increased frequency of “activated” CD4 T cells, in support of our biochemical model. This increased frequency corresponds with a decrease in “naïve” CD4 T cells in both scRNA-seq and FACs analysis. Interestingly, scRNA-seq analysis confirmed our previous findings of elevated regulatory T cells (Treg) levels in DO-KO mice. Genetic analysis showed that unimmunized DO-KO Tregs also have a more activated phenotype. These findings suggests that absence of DO generates a more stimulatory in vivo cellular environment for CD4 T cells, likely due to altered antigen presentation. Research supported by grants from NIH (R01 AI130210, R01 AI121174 and R37 AI060040)

Biochemical and histological insights of 1,4-polyisoprene isolated from Sphenocentrum jollyanum pierre (menispermaceae) stem in wound healing activity in streptozotocin-induced diabetic rats.

Sphenocentrum jollyanum whole stem extract is used traditionally in combination with its leaves to treat chronic wounds and also ameliorate conditions that exacerbate wounds such as diabetes mellitus. The study isolated the major wound healing bioactive compound from the non-polar fraction of S. jollyanum extract and evaluated the in vivo wound healing activity of a 0.10% w/w 1,4-polyisoprene-based ointment in streptozotocin-induced diabetic rats. The major bioactive constituent of S. jollyanum was isolated using a wound healing activity-guided approach and characterized the compound using 1D and 2D-NMR spectroscopic techniques. The wound healing activity study adopted both excision (wound contraction) and incision (biochemical) models. In the excision model, the 1,4-polyisoprene caused 99% wound closure and restored the excised wound on day 12. On the 6th and 12th post-wounding days, 1,4-polyisoprene caused a significant (p<0.001) elevation in the tensile strength (486g) of the incision wound compared with the control (388g). The biochemical (hexosamine and hydroxyproline) and antioxidant/inflammatory (ascorbic acid, superoxide dismutase, and glutathione peroxidase) parameters increased significantly while malondialdehyde was down-regulated in the wounds treated with 1,4-polyisoprene compared with control. The histological analysis of tissue sections taken from the edge and center of the wounds at 0-12 days post wounding revealed an increased tissue regeneration, accelerated collagen formation, and epidermal regeneration without edema or inflammation on the 12th day. The major wound healing constituent of S. jollyanum is 1,4-polyisoprene and the study has provided a new class of compounds for further optimization.

Acute and reversible modulation of kidney proximal tubule endocytic capacity by fluid shear stress

The proximal tubule (PT) of the kidney is comprised of cells that are uniquely specialized for efficient apical uptake of albumin and other proteins that escape the glomerular filtration barrier. These proteins bind to the multiligand receptors megalin and cubilin and are internalized via clathrin-dependent endocytosis into apical early endosomes that rapidly mature into larger apical vacuoles (AVs). Dissociated ligands are delivered from AVs to lysosomes for degradation, while receptors are collected into dense apical tubules for recycling to the cell surface. When cells are grown under continuous fluid shear stress (FSS), the endocytic uptake is approximately 9-fold greater than static cells. Additionally, cells cultured in this manner rapidly and reversibly adjust their endocytic capacity in response to changes in FSS. We hypothesize flow-dependent modulation of endocytic capacity enables PT cells in vivo to preserve uptake efficiency in response to changes in glomerular filtration. We are combining biochemical, imaging, and mathematical modeling approaches to determine the mechanism(s) by which cells chronically and acutely modulate endocytic uptake. Increased levels of megalin transcripts (~2x) and protein (~4x) is insufficient to account for the large increase in endocytic uptake observed in cells cultured under FSS vs static conditions. We used cell surface biotinylation experiments to compare the fraction of megalin at the surface, the fraction endocytosed, and the half-life of surface-tagged megalin under FSS and static conditions. These data were used to adapt a kinetic model that describes kinetics of megalin biosynthesis, trafficking, and degradation in cells. Overall, the cells grown under FSS have a greater endocytic rate and recycling rate compared with static grown cells. By contrast, megalin degradation is faster than recycling in static-grown cells. Colocalization of megalin with known markers of apical compartments will enable us to extend our model to determine whether changes in endosomal maturation rates contribute to these differences. Additionally, cells cultured under continuous FSS rapidly and reversibly adjust their endocytic capacity in response to changes in shear stress. We hypothesize flow-dependent modulation of endocytic capacity enables PT cells in vivo to preserve uptake efficiency in response to changes in glomerular filtration. We are currently adapting our model to determine how acute changes in shear stress trigger alterations in endocytic uptake. National Institute of Health: 5T32GM133353-03, R01 DK118726, R01 DK125049 This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Bioactive Peptides from African Yam (AVIAIMF and GPADPF) and Taro (NGDF and NGNW) Reveal Multifunctional Antidiabetic Effects Using Biochemical and Cellular Models

Bioactive Peptides from African Yam (AVIAIMF and GPADPF) and Taro (NGDF and NGNW) Reveal Multifunctional Antidiabetic Effects Using Biochemical and Cellular Models

Maternal and fetal outcomes in acute hepatic porphyria: A Swedish National Cohort Study.

Current knowledge of pregnancy and perinatal outcomes in women with acute hepatic porphyria (AHP) is largely based on biochemical disease models, case reports, and case series. We performed a nationwide, registered-based cohort study to investigate the association between maternal AHP and the risk of adverse pregnancy and perinatal outcomes. All women in the Swedish Porphyria Register with confirmed AHP aged 18 years or older between 1987 and 2015 and matched general population comparators, with at least one registered delivery in the Swedish Medical Birth Register were included. Risk ratios (RRs) of pregnancy complications, delivery mode and perinatal outcomes were estimated and adjusted for maternal age at delivery, area of residency, birth year and parity. Women with acute intermittent porphyria (AIP), the most common form of AHP, were further categorized according to maximal lifetime urinary porphobilinogen (U-PBG) levels. The study included 214 women with AHP and 2174 matched comparators. Women with AHP presented with a higher risk for pregnancy-induced hypertensive disorder (aRR 1.73, 95% CI 1.12-2.68), gestational diabetes (aRR 3.41, 95% CI 1.69-6.89), and small-for-gestational-age birth (aRR 2.08, 95% CI 1.26-3.45). In general, RRs were higher among women with AIP who had high lifetime U-PBG levels. Our study shows an increased risk for pregnancy induced hypertensive disease, gestational diabetes, and small for gestational age births for AHP women, with higher relative risks for women with biochemically active AIP. No increased risk for perinatal death or malformations was observed.

MPK12 in stomatal CO2 signaling: function beyond its kinase activity.

Protein phosphorylation is a major molecular switch involved in the regulation of stomatal opening and closure. Previous research defined interaction between MAP kinase 12 and Raf-like kinase HT1 as a required step for stomatal movements caused by changes in CO2 concentration. However, whether MPK12 kinase activity is required for regulation of CO2 -induced stomatal responses warrants in-depth investigation. We apply genetic, biochemical, and structural modeling approaches to examining the noncatalytic role of MPK12 in guard cell CO2 signaling that relies on allosteric inhibition of HT1. We show that CO2 /HCO3 - -enhanced MPK12 interaction with HT1 is independent of its kinase activity. By analyzing gas exchange of plant lines expressing various kinase-dead and constitutively active versions of MPK12 in a plant line where MPK12 is deleted, we confirmed that CO2 -dependent stomatal responses rely on MPK12's ability to bind to HT1, but not its kinase activity. We also demonstrate that purified MPK12 and HT1 proteins form a heterodimer in the presence of CO2 /HCO3 - and present structural modeling that explains the MPK12:HT1 interaction interface. These data add to the model that MPK12 kinase-activity-independent interaction with HT1 functions as a molecular switch by which guard cells sense changes in atmospheric CO2 concentration.

A tractable physical model for the yeast polarity predicts epistasis and fitness.

Accurate phenotype prediction based on genetic information has numerous societal applications, such as crop design or cellular factories. Epistasis, when biological components interact, complicates modelling phenotypes from genotypes. Here we show an approach to mitigate this complication for polarity establishment in budding yeast, where mechanistic information is abundant. We coarse-grain molecular interactions into a so-called mesotype, which we combine with gene expression noise into a physical cell cycle model. First, we show with computer simulations that the mesotype allows validation of the most current biochemical polarity models by quantitatively matching doubling times. Second, the mesotype elucidates epistasis emergence as exemplified by evaluating the predicted mutational effect of key polarity protein Bem1p when combined with known interactors or under different growth conditions. This example also illustrates how unlikely evolutionary trajectories can become more accessible. The tractability of our biophysically justifiable approach inspires a road-map towards bottom-up modelling complementary to statistical inferences. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Kashuri Fundo Decomposition Method for Solving Michaelis-Menten Nonlinear Biochemical Reaction Model

In most of the real life problems, we encounter with nonlinear differential equations. Problems are made more understandable by modeling them with these equations. In this way, it becomes easier to interpret the problems and reach the results. In 1913, the basic enzymatic reaction model introduced by Michaelis and Menten to describe enzyme processes is an example of nonlinear differential equation. This model is the one of the simplest and best-known approaches of the mechanisms used to model enzyme-catalyzed reactions and is the most studied. For most nonlinear differential equations, it is very difficult to get an analytical solution. For this reason, various studies have been carried out to find approximate solutions to such equations. Among these studies, those in which two different methods are used by blending attract attention. In this study, a blended form of the Kashuri Fundo transform method and the Adomian decomposition method, so-called the Kashuri Fundo decomposition method, is used to find a solution to the Michaelis-Menten nonlinear biochemical reaction model in this way. This method has been applied to the biochemical reaction model and an approximate solution has been obtained for this model without complex calculations. This shows that the hybrid method is an effective, reliable, simpler and time-saving method in reaching the solutions of nonlinear differential equations.

Integrability Analysis of the Smallest 3D Biochemical Reaction Model

In this paper the complex dynamics of a smallest biochemical system model in three-dimensional systems with the reaction scheme. This model is described by a system of three nonlinear ordinary differential equations with five positive real parameters, are analyzed and studied. We present a thorough analysis of their invariant algebraic surfaces and exponential factors and investigate the integrability and nonintegrabilty of this model. Particularly, we show the non-existence of polynomial, rational, Darboux and local analytic first integrals in a neighborhood of the equilibrium. Moreover, we prove that, the model is not integrable in the sense of Bogoyavlensky in the class of rational functions.

Study of the antiaging effects of bird's nest peptide based on biochemical, cellular, and animal models

Study of the antiaging effects of bird's nest peptide based on biochemical, cellular, and animal models

Do Dosiomic Features Extracted From Planned 3-Dimensional Dose Distribution Improve Biochemical Failure-Free Survival Prediction: an Analysis Based on a Large Multi-Institutional Data Set

The objective of this work was to investigate whether including additional dosiomic features can improve biochemical failure-free survival prediction compared with models with clinical features only or with clinical features as well as equivalent uniform dose and tumor control probability. This retrospective study included 1852 patients who received diagnoses of localized prostate cancer between 2010 and 2016 and were treated with curative external beam radiation therapy in Albert, Canada. A total of 1562 patients from 2 centers were used for developing 3 random survival forest models: Model A included only 5 clinical features; Model B included 5 clinical features, equivalent uniform dose, and tumor control probability; and Model C considered 5 clinical features and 2074 dosiomic features derived from the planned dose distribution of the clinical target volume and planning target volume with further feature selection to determine prognostic features. No feature selection was performed for models A and B. Two hundred ninety patients from another 2 centers were used for independent validation. Individual model-based risk stratification was examined, and the log-rank tests were performed to test statistically significant differences between the risk groups. The 3 models' performances were evaluated using Harrell's concordance index (C-index) and compared using one-way repeated-measures analysis of variance with post hoc paired t test. Model C selected 6 dosiomic features and 4 clinical features to be prognostic. There were statistically significant differences between the 4 risk groups for both training and validation data sets. The C-index for the out-of-bag samples of the training data set was 0.650, 0.648, and 0.669 for models A, B, and C, respectively. The C-index for the validation data set for models A, B, and C was 0.653, 0.648, and 0.662, respectively. Although gains were modest, Model C was statistically significantly better than models A and B. Dosiomics contain information beyond common dose-volume histogram metrics from planned dose distributions. Incorporation of prognostic dosiomic features in biochemical failure-free survival outcome models can lead to statistically significant although modest improvement in performance.

Interaction of an anticancer benzopyrane derivative with DNA: Biophysical, biochemical, and molecular modeling studies

BackgroundSIMR1281 is a potent anticancer lead candidate with multi- target activity against several proteins; however, its mechanism of action at the molecular level is not fully understood. Revealing the mechanism and the origin of multitarget activity is important for the rational identification and optimization of multitarget drugs. MethodsWe have used a variety of biophysical (circular dichroism, isothermal titration calorimetry, viscosity, and UV DNA melting), biochemical (topoisomerase I & II assays) and computational (molecular docking and MD simulations) methods to study the interaction of SIMR1281 with duplex DNA structures. ResultsThe biophysical results revealed that SIMR1281 binds to dsDNA via an intercalation-binding mode with an average binding constant of 3.1 × 106 M−1. This binding mode was confirmed by the topoisomerases' inhibition assays and molecular modeling simulations, which showed the intercalation of the benzopyrane moiety between DNA base pairs, while the remaining moieties (thiazole and phenyl rings) sit in the minor groove and interact with the flanking base pairs adjacent to the intercalation site. ConclusionsThe DNA binding characteristics of SIMR1281, which can disrupt/inhibit DNA function as confirmed by the topoisomerases' inhibition assays, indicate that the observed multi-target activity might originate from ligand intervention at nucleic acids level rather than due to direct interactions with multiple biological targets at the protein level. General significanceThe findings of this study could be helpful to guide future optimization of benzopyrane-based ligands for therapeutic purposes.

Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system

Context-dependency of mammalian transcriptional elements has hindered the quantitative investigation of multigene expression stoichiometry and its biological functions. Here, we describe a host- and local DNA context-independent transcription system to gradually fine-tune single and multiple gene expression with predictable stoichiometries. The mammalian transcription system is composed of a library of modular and programmable promoters from bacteriophage and its cognate RNA polymerase (RNAP) fused to a capping enzyme. The relative expression of single genes is quantitatively determined by the relative binding affinity of the RNAP to the promoters, while multigene expression stoichiometry is predicted by a simple biochemical model with resource competition. We use these programmable and modular promoters to predictably tune the expression of three components of an influenza A virus-like particle (VLP). Optimized stoichiometry leads to a 2-fold yield of intact VLP complexes. The host-independent orthogonal transcription system provides a platform for dose-dependent control of multiple protein expression which may be applied for advanced vaccine engineering, cell-fate programming and other therapeutic applications.

Soliton phenomena in the process of the functioning of the heart

The biochemical model explains the intricate mechanisms of psychobiological life. He still cannot explain what the transition from inanimate to living matter is all about. Where is the threshold and what is its essence, what role do biochemical processes play in the coherence of the soma with consciousness and its impact on the soma and vice versa? A similar problem is with other mental processes, their nature does not fit into the biochemical model of life and is inexplicable on the basis of biochemical interactions, again it is much easier to describe it in the light of quantum processes - including wave physics. It is similar to the functioning of the heart or other organs, where only the biochemical processes of the cell are considered, ignoring the bioelectronic processes. Man is not only a purely biological construct but also contains the basis of biochemical, bioelectronic, information, and cybernetic processes that are responsible for shaping the psychobiological processes of man. Contemporary biosystems in science are considered at the level of corpuscular structures, ignoring energy and information structures. By shifting the cognitive emphasis towards energy and information structures, the organism can be perceived as a quantum generator of information: electromagnetic, soliton, acoustic, spin and bioplasma. This bioelectronic construction creates homo electronics with his electronic personality.

When nitrate treatment wins the battle against microbial reservoir souring but loses the war

The injection of seawater into a hydrocarbon reservoir (seawater flooding), as an oil recovery method, triggers microbial reservoir souring, through which microorganisms consume sulfate ions and produce hydrogen sulfide, which is hazardous, corrosive, and detrimental to the environment if enters production wells. A strategy to mitigate this problem is to add nitrate or nitrite to the injection seawater, also known as nitrate or nitrite treatment. This study investigates nitrate treatment in a simulated real-world sector scale hydrocarbon reservoir in the Danish North Sea through a non-isothermal multi-phase multi-component bio-chemical model and field observations. The common expectation is that the higher the concentration of nitrate or nitrite in the injection seawater the less hydrogen sulfide production. However, the results of this study show that slowing down the microorganisms (or mitigation through nitrate treatment) may cause higher hydrogen sulfide production from production wells. Put differently, not only the total amount of hydrogen sulfide generated inside the reservoir matters, but also the location of the generation zone and its distance from production wells matter. Therefore, it is possible that insufficient amounts of nitrate cause the generation zone to move toward the production wells. Hence, more hydrogen sulfide is produced during the lifetime of the reservoir. Moreover, considering laboratory and field scale measurements, in the presence of coupled multi-physics processes, the growth rates at the field scale are significantly lower than those observed in the laboratory experiments, under in-situ conditions, utilizing actual reservoir fluid samples.

Biochemical identification of prepubertal boys with Klinefelter syndrome by combined reproductive hormone profiling using machine learning.

Klinefelter syndrome (KS) is the most common sex chromosome disorder and genetic cause of infertility in males. A highly variable phenotype contributes to the fact that a large proportion of cases are never diagnosed. Typical hallmarks in adults include small testes and azoospermia which may prompt biochemical evaluation that typically shows extremely high FSH and low/undetectable inhibin B serum concentrations. However, in prepubertal KS individual biochemical parameters are largely overlapping those of prepubertal controls. We aimed to characterize clinical profiles of prepubertal boys with KS in relation to controls, and to develop a novel biochemical classification model to identify KS before puberty. Retrospective, longitudinal data from 15 prepubertal boys with KS and data from 1475 controls were used to calculate age- and sex-adjusted standard deviation scores (SDS) for height and serum concentrations of reproductive hormones and used to infer a decision tree classification model for KS. Individual reproductive hormones were low but within reference ranges and did not discriminate KS from controls. Clinical and biochemical profiles including age- and sex-adjusted SDS from multiple reference curves provided input data to train a 'random forest' machine learning (ML) model for detection of KS. Applied to unseen data, the ML model achieved a classification accuracy of 78% (95% CI, 61% - 94%). Supervised ML applied to clinically relevant variables enabled computational classification of control and KS profiles. The application of age- and sex-adjusted SDS provided robust predictions irrespective of age. Specialized ML models applied to combined reproductive hormone concentrations may be useful diagnostic tools to improve the identification of prepubertal boys with KS.