Progress in physiologically based pharmacokinetic-pharmacodynamic models of amino acids in humans.

PURPOSE OF REVIEW: Amino acids are critical to health, serving both as constituents of proteins and in signaling and metabolism. Amino acids are consumed as nutrients, supplements, and nutraceuticals. Much remains to be learned about amino acid function. Physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) modeling is an emerging tool for studying their complex biology. This review highlights recent PBPK-PD models developed to study amino acid physiology and metabolism and discusses their potential for addressing unresolved questions in the field. RECENT FINDINGS: PBPK-PD models provided several insights. They revealed the interplay between the mechanisms by which leucine governs skeletal muscle protein metabolism in healthy adults. The models also identified optimal dosing regimens of amino acid supplementation to treat sickle-cell disease and recurrent hypoglycemia, and to minimize drug side effects in seizure disorders. Additionally, they characterized the effects of novel anticancer drugs that seek to deprive cancer cells of amino acids. Future models may inform treatment strategies for sarcopenia, characterize distinctions between animal- and plant-based nutrition, and inform nutrient-drug interactions in Parkinson's disease. SUMMARY: PBPK-PD models are powerful tools for studying amino acid physiology and metabolism, with applications to nutrition, pharmacology, and their interplay.

ADP is the dominant controller of AMP-activated protein kinase activity dynamics in skeletal muscle during exercise.

Exercise training elicits profound metabolic adaptations in skeletal muscle cells. A key molecule in coordinating these adaptations is AMP-activated protein kinase (AMPK), whose activity increases in response to cellular energy demand. AMPK activity dynamics are primarily controlled by the adenine nucleotides ADP and AMP, but how each contributes to its control in skeletal muscle during exercise is unclear. We developed and validated a mathematical model of AMPK signaling dynamics, and then applied global parameter sensitivity analyses with data-informed constraints to predict that AMPK activity dynamics are determined principally by ADP and not AMP. We then used the model to predict the effects of two additional direct-binding activators of AMPK, ZMP and Compound 991, further validating the model and demonstrating its applicability to understanding AMPK pharmacology. The relative effects of direct-binding activators can be understood in terms of four properties, namely their concentrations, binding affinities for AMPK, abilities to enhance AMPK phosphorylation, and the magnitudes of their allosteric activation of AMPK. Despite AMP's favorable values in three of these four properties, ADP is the dominant controller of AMPK activity dynamics in skeletal muscle during exercise by virtue of its higher concentration compared to that of AMP.

Self-initiated lifestyle interventions lead to potential insight into an effective, alternative, non-surgical therapy for mitochondrial disease associated multiple symmetric lipomatosis.

BACKGROUND: A 56-year-old female, diagnosed as a carrier of the mitochondrial DNA mutation (MTTK c.8344A > G) associated with the MERRF (myoclonic epilepsy with ragged red fibers) syndrome, presented with a relatively uncommon but well-known phenotypic manifestation: severe multiple symmetric lipomatosis (MSL). After surgical resection of three kilograms of upper mid-back lipomatous tissue, the patient experienced a significant decline in her functional capacity and quality of life, which ultimately resulted in her placement on long-term disability. METHODS: Dissatisfied with the available treatment options centered on additional resection surgeries, given the high probability of lipoma regrowth, the patient independently researched and applied alternative therapies that centred on a carbohydrate-restricted diet and a supervised exercise program. RESULTS: The cumulative effect of her lifestyle interventions resulted in the reversal of her MSL and her previously low quality of life. She met all her personal goals by the one-year mark, including reduced size of the residual post-surgical lipomas, markedly enhanced exercise tolerance, and return to work. She continues to maintain her interventions and to experience positive outcomes at the two-year mark. INTERPRETATION: This case report documents the timing and nature of lifestyle interventions in relation to the reversal in growth pattern of her previously expanding and debilitating lipomas. The profound nature of the apparent benefit on lipoma growth demonstrates the intervention's potential as a new feasible non-surgical therapy for mitochondrial-disease-associated MSL, and justifies its systematic study. We also describe how this case has inspired the care team to re-examine its approach to involved patients.

Attenuated thermoregulatory, metabolic, and liver acute phase protein response to heat stroke in TNF receptor knockout mice.

Tumor necrosis factor (TNF) is considered an adverse mediator of heat stroke (HS) based on clinical studies showing high serum levels. However, soluble TNF receptors (sTNFR; TNF antagonists) were higher in survivors than nonsurvivors, and TNFR knockout (KO) mice showed a trend toward increased mortality, suggesting TNF has protective actions for recovery. We delineated TNF actions in HS by comparing thermoregulatory, metabolic, and inflammatory responses between B6129F2 (wild type, WT) and TNFR KO mice. Before heat exposure, TNFR KO mice showed ~0.4°C lower core temperature (T(c); radiotelemetry), ~10% lower metabolic rate (M(r); indirect calorimetry), and reduced plasma interleukin (IL)-1α and sIL-1RI than WT mice. KO mice selected warmer temperatures than WT mice in a gradient but remained hypothermic. In the calorimeter, both genotypes showed a similar heating rate, but TNFR KO maintained lower T(c) and M(r) than WT mice for a given heat exposure duration and required ~30 min longer to reach maximum T(c) (42.4°C). Plasma IL-6 increased at ~3 h of recovery in both genotypes, but KO mice showed a more robust sIL-6R response. Higher sIL-6R in the KO mice was associated with delayed liver p-STAT3 protein expression and attenuated serum amyloid A3 (SAA3) gene expression, suggesting the acute phase response (APR) was attenuated in these mice. Our data suggest that the absence of TNF signaling induced a regulated hypothermic state in the KO mice, TNF-IL-1 interactions may modulate T(c) and M(r) during homeostatic conditions, and TNF modulates the APR during HS recovery through interactions with the liver IL-6-STAT3 pathway of SAA3 regulation.

Linking proteomic and transcriptional data through the interactome and epigenome reveals a map of oncogene-induced signaling.

Cellular signal transduction generally involves cascades of post-translational protein modifications that rapidly catalyze changes in protein-DNA interactions and gene expression. High-throughput measurements are improving our ability to study each of these stages individually, but do not capture the connections between them. Here we present an approach for building a network of physical links among these data that can be used to prioritize targets for pharmacological intervention. Our method recovers the critical missing links between proteomic and transcriptional data by relating changes in chromatin accessibility to changes in expression and then uses these links to connect proteomic and transcriptome data. We applied our approach to integrate epigenomic, phosphoproteomic and transcriptome changes induced by the variant III mutation of the epidermal growth factor receptor (EGFRvIII) in a cell line model of glioblastoma multiforme (GBM). To test the relevance of the network, we used small molecules to target highly connected nodes implicated by the network model that were not detected by the experimental data in isolation and we found that a large fraction of these agents alter cell viability. Among these are two compounds, ICG-001, targeting CREB binding protein (CREBBP), and PKF118-310, targeting ß-catenin (CTNNB1), which have not been tested previously for effectiveness against GBM. At the level of transcriptional regulation, we used chromatin immunoprecipitation sequencing (ChIP-Seq) to experimentally determine the genome-wide binding locations of p300, a transcriptional co-regulator highly connected in the network. Analysis of p300 target genes suggested its role in tumorigenesis. We propose that this general method, in which experimental measurements are used as constraints for building regulatory networks from the interactome while taking into account noise and missing data, should be applicable to a wide range of high-throughput datasets.

Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling.

Multiplexed bead-based flow cytometric immunoassays are a powerful experimental tool for investigating cellular communication networks, yet their widespread adoption is limited in part by challenges in robust quantitative analysis of the measurements. Here we report our application of mixed-effects modeling for the normalization and statistical analysis of bead-based immunoassay data. Our data set consisted of bead-based immunoassay measurements of 16 phospho-proteins in lysates of HepG2 cells treated with ligands that regulate acute-phase protein secretion. Mixed-effects modeling provided estimates for the effects of both the technical and biological sources of variance, and normalization was achieved by subtracting the technical effects from the measured values. This approach allowed us to detect ligand effects on signaling with greater precision and sensitivity and to more accurately characterize the HepG2 cell signaling network using constrained fuzzy logic. Mixed-effects modeling analysis of our data was vital for ascertaining that IL-1α and TGF-α treatment increased the activities of more pathways than IL-6 and TNF-α and that TGF-α and TNF-α increased p38 MAPK and c-Jun N-terminal kinase (JNK) phospho-protein levels in a synergistic manner. Moreover, we used mixed-effects modeling-based technical effect estimates to reveal the substantial variance contributed by batch effects along with the absence of loading order and assay plate position effects. We conclude that mixed-effects modeling enabled additional insights to be gained from our data than would otherwise be possible and we discuss how this methodology can play an important role in enhancing the value of experiments employing multiplexed bead-based immunoassays.

Multi-pathway network analysis of mammalian epithelial cell responses in inflammatory environments.

Inflammation is a key physiological response to infection and injury and, although usually beneficial, it can also be damaging to the host. The liver is a prototypical example in this regard because inflammation helps to resolve liver injury, but it also underlies the aetiology of pathologies such as fibrosis and hepatocellular carcinoma. Liver cells sense their environment, including the inflammatory environment, through the activities of receptor-mediated signal transduction pathways. These pathways are organized in a complex interconnected network, and it is becoming increasingly recognized that cellular adaptations result from the quantitative integration of multi-pathway network activities, rather than isolated pathways causing particular phenotypes. Therefore comprehending liver cell signalling in inflammation requires a scientific approach that is appropriate for studying complex networks. In the present paper, we review our application of systems analyses of liver cell signalling in response to inflammatory environments. Our studies feature broad measurements of cell signalling and phenotypes in response to numerous experimental perturbations reflective of inflammatory environments, the data from which are analysed using Boolean and fuzzy logic models and regression-based methods in order to quantitatively relate the phenotypic responses to cell signalling network states. Our principal biological insight from these studies is that hepatocellular carcinoma cells feature uncoupled inflammatory and growth factor signalling, which may underlie their immune evasion and hyperproliferative properties.

Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli.

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone.

Transforming growth factor beta depletion is the primary determinant of Smad signaling kinetics.

A cell's decision to growth arrest, apoptose, or differentiate in response to transforming growth factor beta (TGF-beta) superfamily ligands depends on the ligand concentration. How cells sense the concentration of extracellular bioavailable TGF-beta remains poorly understood. We therefore undertook a systematic quantitative analysis of how TGF-beta ligand concentration is transduced into downstream phospho-Smad2 kinetics, and we found that the rate of TGF-beta ligand depletion is the principal determinant of Smad signal duration. TGF-beta depletion is caused by two mechanisms: (i) cellular uptake of TGF-beta by a TGF-beta type II receptor-dependent mechanism and (ii) reversible binding of TGF-beta to the cell surface. Our results indicate that cells sense TGF-beta dose by depleting TGF-beta via constitutive TGF-beta type II receptor trafficking processes. Our results also have implications for the role of the TGF-beta type II receptor in disease, as tumor cells harboring TGF-beta type II receptor mutations exhibit impaired TGF-beta depletion, which may contribute to the overproduction of TGF-beta and a consequently poor prognosis in cancer.

Decoding the quantitative nature of TGF-beta/Smad signaling.

How transforming growth factor-beta (TGF-beta) signaling elicits diverse cell responses remains elusive, despite the major molecular components of the pathway being known. We contend that understanding TGF-beta biology requires mathematical models to decipher the quantitative nature of TGF-beta/Smad signaling and to account for its complexity. Here, we review mathematical models of TGF-beta superfamily signaling that predict how robustness is achieved in bone-morphogenetic-protein signaling in the Drosophila embryo, how changes in receptor-trafficking dynamics can be exploited by cancer cells and how the basic mechanisms of TGF-beta/Smad signaling conspire to promote Smad accumulation in the nucleus. These studies demonstrate the power of mathematical modeling for understanding TGF-beta biology.

Activation of Mps1 promotes transforming growth factor-beta-independent Smad signaling.

The primary intracellular mediators of TGF-beta signaling are the Smad proteins. Phosphorylation of R-Smad at the C-terminal SSXS motif by the activated TGF-beta type I receptor kinase triggers a conformation change in R-Smad and facilitates complex formation between R-Smad and Smad4, which shuttle into the nucleus where they interact with DNA and other transcription factors to regulate gene expression. In an attempt to identify proteins interacting with activated Smad signaling complex, we discovered that Mps1, a protein kinase that plays important roles in normal mitotic progression and mitotic checkpoint signaling, co-purifies with this complex. We demonstrated that Smad2 and Smad3 but not Smad4 are substrates of Mps1 in vitro and in vivo. Mps1 phosphorylates Smad2 and Smad3 at the SSXS motif in their C-terminal regions in vitro and in vivo. Disruption of microtubule networks by nocodazole activates Mps1 and promotes TGF-beta-independent activation of Smad signaling. We found that Mps1 is involved in turning on Smad signaling by phosphorylating R-Smads. Our results reveal a novel functional link between Mps1 and Smads in a non-canonical Smad signaling pathway.

Overexpression of membrane-associated fatty acid binding protein (FABPpm) in vivo increases fatty acid sarcolemmal transport and metabolism.

Fatty acid translocase (FAT/CD36) is a key fatty acid transporter in skeletal muscle. However, the effects on fatty acid transport by another putative fatty acid transporter, plasma membrane-associated fatty acid binding protein (FABPpm), have not been determined in mammalian tissue. We examined the functional effects of overexpressing FABPpm on the rates of 1) palmitate transport across the sarcolemma and 2) palmitate metabolism in skeletal muscle. One muscle (soleus) was transfected with pTracer containing FABPpm cDNA. The contralateral muscle served as control. After injecting the FABPpm cDNA, muscles were electroporated. FABPpm overexpression was directly related to the quantity of DNA administered. Electrotransfection (200 microg/muscle) rapidly induced FABPpm protein overexpression (day 1, +92%, P < 0.05), which was further increased during the next few days (days 3-7; range +142% to +160%, P < 0.05). Sarcolemmal FABPpm was comparably increased (day 7, +173%, P < 0.05). Neither FAT/CD36 expression nor sarcolemmal FAT/CD36 content was altered. FABPpm overexpression increased the rates of palmitate transport (+79%, P < 0.05). Rates of palmitate incorporation into phospholipids were also increased +36%, as were the rates of palmitate oxidation (+20%). Rates of palmitate incorporation into triacylglycerol depots were not altered. These studies demonstrate that in mammalian tissue FABPpm overexpression increased the rates of palmitate transport across the sarcolemma, an effect that is independent of any changes in FAT/CD36. However, since the overexpression of plasmalemmal FABPpm (+173%) exceeded the effects on the rates of palmitate transport and metabolism, it appears that the overexpression of FABPpm alone is not sufficient to induce completely parallel increments in palmitate transport and metabolism. This suggests that other mechanisms are required to realize the full potential offered by FABPpm overexpression.