High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar.

DNA methylation is environment-sensitive and can mediate stress responses. In long-lived trees, changing environments might cumulatively shape the methylome landscape over their lifetime. However, because high-resolution methylome studies usually focus on single environmental cues, it remains unclear to what extent the methylation responses are generic or stress-specific, and how this relates to their long-term stability. Here, we studied the methylome plasticity of a Populus nigra cv. 'Italica' clone that is widespread across Europe. Adult trees from a variety of geographic locations were clonally propagated in a common garden experiment, and the ramets were exposed to cold, heat, drought, herbivory, rust infection, and salicylic acid treatments. Through comprehensive whole-genome bisulfite sequencing, we analyzed stress-induced and naturally occurring DNA methylation variants. Stress-induced methylation changes predominantly targeted transposable elements. When occurring in CG/CHG contexts, the same regions were often affected by multiple stresses, suggesting a generic response of the methylome. Drought stress caused a distinct CHH hypermethylation response in transposable elements, affecting entire TE superfamilies near drought-responsive genes. Methylation differences in CG/CHG contexts that were induced by stress treatments showed striking overlap with methylation differences observed between trees from distinct geographical locations. Thus, we revealed genomic hotspots of methylation change that are not stress-specific and that contribute to natural DNA methylation variation, and we identified specific transposable element superfamilies that respond to a specific stress with possible functional consequences. Our results underscore the importance of studying the effects of multiple stressors in a single experiment for recognizing general versus stress-specific methylome responses.

Nucleotide-level distance metrics to quantify alternative splicing implemented in TranD.

Advances in affordable transcriptome sequencing combined with better exon and gene prediction has motivated many to compare transcription across the tree of life. We develop a mathematical framework to calculate complexity and compare transcript models. Structural features, i.e. intron retention (IR), donor/acceptor site variation, alternative exon cassettes, alternative 5'/3' UTRs, are compared and the distance between transcript models is calculated with nucleotide level precision. All metrics are implemented in a PyPi package, TranD and output can be used to summarize splicing patterns for a transcriptome (1GTF) and between transcriptomes (2GTF). TranD output enables quantitative comparisons between: annotations augmented by empirical RNA-seq data and the original transcript models; transcript model prediction tools for longread RNA-seq (e.g. FLAIR versus Isoseq3); alternate annotations for a species (e.g. RefSeq vs Ensembl); and between closely related species. In C. elegans, Z. mays, D. melanogaster, D. simulans and H. sapiens, alternative exons were observed more frequently in combination with an alternative donor/acceptor than alone. Transcript models in RefSeq and Ensembl are linked and both have unique transcript models with empirical support. D. melanogaster and D. simulans, share many transcript models and long-read RNAseq data suggests that both species are under-annotated. We recommend combined references.

Shifts in isoform usage underlie transcriptional differences in regulatory T cells in type 1 diabetes.

Genome-wide association studies have identified numerous loci with allelic associations to Type 1 Diabetes (T1D) risk. Most disease-associated variants are enriched in regulatory sequences active in lymphoid cell types, suggesting that lymphocyte gene expression is altered in T1D. Here we assay gene expression between T1D cases and healthy controls in two autoimmunity-relevant lymphocyte cell types, memory CD4+/CD25+ regulatory T cells (Treg) and memory CD4+/CD25- T cells, using a splicing event-based approach to characterize tissue-specific transcriptomes. Limited differences in isoform usage between T1D cases and controls are observed in memory CD4+/CD25- T-cells. In Tregs, 402 genes demonstrate differences in isoform usage between cases and controls, particularly RNA recognition and splicing factor genes. Many of these genes are regulated by the variable inclusion of exons that can trigger nonsense mediated decay. Our results suggest that dysregulation of gene expression, through shifts in alternative splicing in Tregs, contributes to T1D pathophysiology.

Estimating transcriptome complexities across eukaryotes.

BACKGROUND: Genomic complexity is a growing field of evolution, with case studies for comparative evolutionary analyses in model and emerging non-model systems. Understanding complexity and the functional components of the genome is an untapped wealth of knowledge ripe for exploration. With the "remarkable lack of correspondence" between genome size and complexity, there needs to be a way to quantify complexity across organisms. In this study, we use a set of complexity metrics that allow for evaluating changes in complexity using TranD. RESULTS: We ascertain if complexity is increasing or decreasing across transcriptomes and at what structural level, as complexity varies. In this study, we define three metrics - TpG, EpT, and EpG- to quantify the transcriptome's complexity that encapsulates the dynamics of alternative splicing. Here we compare complexity metrics across 1) whole genome annotations, 2) a filtered subset of orthologs, and 3) novel genes to elucidate the impacts of orthologs and novel genes in transcript model analysis. Effective Exon Number (EEN) issued to compare the distribution of exon sizes within transcripts against random expectations of uniform exon placement. EEN accounts for differences in exon size, which is important because novel gene differences in complexity for orthologs and whole-transcriptome analyses are biased towards low-complexity genes with few exons and few alternative transcripts. CONCLUSIONS: With our metric analyses, we are able to quantify changes in complexity across diverse lineages with greater precision and accuracy than previous cross-species comparisons under ortholog conditioning. These analyses represent a step toward whole-transcriptome analysis in the emerging field of non-model evolutionary genomics, with key insights for evolutionary inference of complexity changes on deep timescales across the tree of life. We suggest a means to quantify biases generated in ortholog calling and correct complexity analysis for lineage-specific effects. With these metrics, we directly assay the quantitative properties of newly formed lineage-specific genes as they lower complexity.

Sex-Biased Expression Is Associated With Chromatin State in Drosophila melanogaster and Drosophila simulans.

In Drosophila melanogaster and D. simulans head tissue, 60% of orthologous genes show evidence of sex-biased expression in at least one species. Of these, ∼39% (2,192) are conserved in direction. We hypothesize enrichment of open chromatin in the sex where we see expression bias and closed chromatin in the opposite sex. Male-biased orthologs are significantly enriched for H3K4me3 marks in males of both species (∼89% of male-biased orthologs vs. ∼76% of unbiased orthologs). Similarly, female-biased orthologs are significantly enriched for H3K4me3 marks in females of both species (∼90% of female-biased orthologs vs. ∼73% of unbiased orthologs). The sex-bias ratio in female-biased orthologs was similar in magnitude between the two species, regardless of the closed chromatin (H3K27me2me3) marks in males. However, in male-biased orthologs, the presence of H3K27me2me3 in both species significantly reduced the correlation between D. melanogaster sex-bias ratio and the D. simulans sex-bias ratio. Male-biased orthologs are enriched for evidence of positive selection in the D. melanogaster group. There are more male-biased genes than female-biased genes in both species. For orthologs with gains/losses of sex-bias between the two species, there is an excess of male-bias compared to female-bias, but there is no consistent pattern in the relationship between H3K4me3 or H3K27me2me3 chromatin marks and expression. These data suggest chromatin state is a component of the maintenance of sex-biased expression and divergence of sex-bias between species is reflected in the complexity of the chromatin status.

Sex-biased expression is associated with chromatin state in D. melanogaster and D. simulans.

We propose a new model for the association of chromatin state and sex-bias in expression. We hypothesize enrichment of open chromatin in the sex where we see expression bias (OS) and closed chromatin in the opposite sex (CO). In this study of D. melanogaster and D. simulans head tissue, sex-bias in expression is associated with H3K4me3 (open mark) in males for male-biased genes and in females for female-biased genes in both species. Sex-bias in expression is also largely conserved in direction and magnitude between the two species on the X and autosomes. In male-biased orthologs, the sex-bias ratio is more divergent between species if both species have H3K27me2me3 marks in females compared to when either or neither species has H3K27me2me3 in females. H3K27me2me3 marks in females are associated with male-bias in expression on the autosomes in both species, but on the X only in D. melanogaster . In female-biased orthologs the relationship between the species for the sex-bias ratio is similar regardless of the H3K27me2me3 marks in males. Female-biased orthologs are more similar in the ratio of sex-bias than male-biased orthologs and there is an excess of male-bias in expression in orthologs that gain/lose sex-bias. There is an excess of male-bias in sex-limited expression in both species suggesting excess male-bias is due to rapid evolution between the species. The X chromosome has an enrichment in male-limited H3K4me3 in both species and an enrichment of sex-bias in expression compared to the autosomes.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Variation in leaf transcriptome responses to elevated ozone corresponds with physiological sensitivity to ozone across maize inbred lines.

We examine the impact of sustained elevated ozone concentration on the leaf transcriptome of 5 diverse maize inbred genotypes, which vary in physiological sensitivity to ozone (B73, Mo17, Hp301, C123, and NC338), using long reads to assemble transcripts and short reads to quantify expression of these transcripts. More than 99% of the long reads, 99% of the assembled transcripts, and 97% of the short reads map to both B73 and Mo17 reference genomes. Approximately 95% of the genes with assembled transcripts belong to known B73-Mo17 syntenic loci and 94% of genes with assembled transcripts are present in all temperate lines in the nested association mapping pan-genome. While there is limited evidence for alternative splicing in response to ozone stress, there is a difference in the magnitude of differential expression among the 5 genotypes. The transcriptional response to sustained ozone stress in the ozone resistant B73 genotype (151 genes) was modest, while more than 3,300 genes were significantly differentially expressed in the more sensitive NC338 genotype. There is the potential for tandem duplication in 30% of genes with assembled transcripts, but there is no obvious association between potential tandem duplication and differential expression. Genes with a common response across the 5 genotypes (83 genes) were associated with photosynthesis, in particular photosystem I. The functional annotation of genes not differentially expressed in B73 but responsive in the other 4 genotypes (789) identifies reactive oxygen species. This suggests that B73 has a different response to long-term ozone exposure than the other 4 genotypes. The relative magnitude of the genotypic response to ozone, and the enrichment analyses are consistent regardless of whether aligning short reads to: long read assembled transcripts; the B73 reference; the Mo17 reference. We find that prolonged ozone exposure directly impacts the photosynthetic machinery of the leaf.

GAIT-GM integrative cross-omics analyses reveal cholinergic defects in a C. elegans model of Parkinson's disease.

Parkinson's disease (PD) is a disabling neurodegenerative disorder in which multiple cell types, including dopaminergic and cholinergic neurons, are affected. The mechanisms of neurodegeneration in PD are not fully understood, limiting the development of therapies directed at disease-relevant molecular targets. C. elegans is a genetically tractable model system that can be used to disentangle disease mechanisms in complex diseases such as PD. Such mechanisms can be studied combining high-throughput molecular profiling technologies such as transcriptomics and metabolomics. However, the integrative analysis of multi-omics data in order to unravel disease mechanisms is a challenging task without advanced bioinformatics training. Galaxy, a widely-used resource for enabling bioinformatics analysis by the broad scientific community, has poor representation of multi-omics integration pipelines. We present the integrative analysis of gene expression and metabolite levels of a C. elegans PD model using GAIT-GM, a new Galaxy tool for multi-omics data analysis. Using GAIT-GM, we discovered an association between branched-chain amino acid metabolism and cholinergic neurons in the C. elegans PD model. An independent follow-up experiment uncovered cholinergic neurodegeneration in the C. elegans model that is consistent with cholinergic cell loss observed in PD. GAIT-GM is an easy to use Galaxy-based tool for generating novel testable hypotheses of disease mechanisms involving gene-metabolite relationships.

The metabolome as a biomarker of aging in Drosophila melanogaster.

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age-specific functional traits including fecundity and climbing activity and with age-specific targeted metabolomic profiles. We show that activity levels and metabolome-wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.

Power calculator for detecting allelic imbalance using hierarchical Bayesian model.

OBJECTIVE: Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Methods for testing AI exist, but methods are needed to estimate type I error and power for detecting AI and difference of AI between conditions. As the costs of the technology plummet, what is more important: reads or replicates? RESULTS: We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10, 20, and 30%, respectively, deviation from allelic balance in a condition with power > 80%. A minimum of 960 and 240 allele specific reads divided equally among 8 replicates is needed to detect a 20 or 30% difference in AI between conditions with comparable power. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions.

Long-Term Metabolomics Reference Material.

The use of quality control samples in metabolomics ensures data quality, reproducibility, and comparability between studies, analytical platforms, and laboratories. Long-term, stable, and sustainable reference materials (RMs) are a critical component of the quality assurance/quality control (QA/QC) system; however, the limited selection of currently available matrix-matched RMs reduces their applicability for widespread use. To produce an RM in any context, for any matrix that is robust to changes over the course of time, we developed iterative batch averaging method (IBAT). To illustrate this method, we generated 11 independently grown Escherichia coli batches and made an RM over the course of 10 IBAT iterations. We measured the variance of these materials by nuclear magnetic resonance (NMR) and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as a food source to produce a Caenorhabditis elegans RM for a metabolomics experiment. The metabolite extraction of this material, alongside 41 independently grown individual C. elegans samples of the same genotype, allowed us to estimate the proportion of sample variation in preanalytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

Testcrosses are an efficient strategy for identifying cis-regulatory variation: Bayesian analysis of allele-specific expression (BayesASE).

Allelic imbalance (AI) occurs when alleles in a diploid individual are differentially expressed and indicates cis acting regulatory variation. What is the distribution of allelic effects in a natural population? Are all alleles the same? Are all alleles distinct? The approach described applies to any technology generating allele-specific sequence counts, for example for chromatin accessibility and can be applied generally including to comparisons between tissues or environments for the same genotype. Tests of allelic effect are generally performed by crossing individuals and comparing expression between alleles directly in the F1. However, a crossing scheme that compares alleles pairwise is a prohibitive cost for more than a handful of alleles as the number of crosses is at least (n2-n)/2 where n is the number of alleles. We show here that a testcross design followed by a hypothesis test of AI between testcrosses can be used to infer differences between nontester alleles, allowing n alleles to be compared with n crosses. Using a mouse data set where both testcrosses and direct comparisons have been performed, we show that the predicted differences between nontester alleles are validated at levels of over 90% when a parent-of-origin effect is present and of 60%-80% overall. Power considerations for a testcross, are similar to those in a reciprocal cross. In all applications, the testing for AI involves several complex bioinformatics steps. BayesASE is a complete bioinformatics pipeline that incorporates state-of-the-art error reduction techniques and a flexible Bayesian approach to estimating AI and formally comparing levels of AI between conditions. The modular structure of BayesASE has been packaged in Galaxy, made available in Nextflow and as a collection of scripts for the SLURM workload manager on github (https://github.com/McIntyre-Lab/BayesASE).

Risk Factors for Recurrent Staphylococcus aureus Bacteremia.

BACKGROUND: To understand the clinical, bacterial, and host characteristics associated with recurrent Staphylococcus aureus bacteremia (R-SAB), patients with R-SAB were compared to contemporaneous patients with a single episode of SAB (S-SAB). METHODS: All SAB isolates underwent spa genotyping. All isolates from R-SAB patients underwent pulsed-field gel electrophoresis (PFGE). PFGE-indistinguishable pairs from 40 patients underwent whole genome sequencing (WGS). Acute phase plasma from R-SAB and S-SAB patients was matched 1:1 for age, race, sex, and bacterial genotype, and underwent cytokine quantification using 25-analyte multiplex bead array. RESULTS: R-SAB occurred in 69 (9.1%) of the 756 study patients. Of the 69 patients, 30 experienced relapse (43.5%) and 39 reinfection (56.5%). Age, race, hemodialysis dependence, presence of foreign body, methicillin-resistant Staphyloccus aureus, and persistent bacteremia were individually associated with likelihood of recurrence. Multivariate risk modeling revealed that black hemodialysis patients were nearly 2 times more likely (odds ratio [OR] = 9.652 [95% confidence interval [CI], 5.402-17.418]) than white hemodialysis patients (OR = 4.53 [95% CI, 1.696-10.879]) to experience R-SAB. WGS confirmed PFGE interpretations in all cases. Median RANTES (regulated on activation, normal T cell expressed and secreted) levels in acute phase plasma from the initial episode of SAB were higher in R-SAB than in matched S-SAB controls (P = .0053, false discovery rate < 0.10). CONCLUSION: This study identified several risk factors for R-SAB. The largest risk for R-SAB is among black hemodialysis patients. Higher RANTES levels in R-SAB compared to matched controls warrants further study.

tappAS: a comprehensive computational framework for the analysis of the functional impact of differential splicing.

Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org.

Uncovering hidden genetic variation in photosynthesis of field-grown maize under ozone pollution.

Ozone is the most damaging air pollutant to crops, currently reducing Midwest US maize production by up to 10%, yet there has been very little effort to adapt germplasm for ozone tolerance. Ozone enters plants through stomata, reacts to form reactive oxygen species in the apoplast and ultimately decreases photosynthetic C gain. In this study, 10 diverse inbred parents were crossed in a half-diallel design to create 45 F1 hybrids, which were tested for ozone response in the field using free air concentration enrichment (FACE). Ozone stress increased the heritability of photosynthetic traits and altered genetic correlations among traits. Hybrids from parents Hp301 and NC338 showed greater sensitivity to ozone stress, and disrupted relationships among photosynthetic traits. The physiological responses underlying sensitivity to ozone differed in hybrids from the two parents, suggesting multiple mechanisms of response to oxidative stress. FACE technology was essential to this evaluation because genetic variation in photosynthesis under elevated ozone was not predictable based on performance at ambient ozone. These findings suggest that selection under elevated ozone is needed to identify deleterious alleles in the world's largest commodity crop.

Fibrinogen binding is affected by amino acid substitutions in C-terminal repeat region of fibronectin binding protein A.

Fibronectin-binding protein A (FnBPA), a protein displayed on the outer surface of Staphylococcus aureus, has a structured A-domain that binds fibrinogen (Fg) and a disordered repeat-region that binds fibronectin (Fn). Amino acid substitutions in Fn-binding repeats (FnBRs) have previously been linked to cardiovascular infection in humans. Here we used microtiter and atomic force microscopy (AFM) to investigate adhesion by variants of full-length FnBPA covalently anchored in the outer cell wall of Lactococcus lactis, a Gram-positive surrogate that otherwise lacks adhesins to mammalian ligands. Fn adhesion increased in five of seven FnBPA variants under static conditions. The bond targeting Fn increased its strength with load under mechanical dissociation. Substitutions extended bond lifetime (1/koff) up to 2.1 times for FnBPA-Fn. Weaker adhesion was observed for Fg in all FnBPA variants tested with microtiter. However, mechanical dissociation with AFM showed significantly increased tensile strength for Fg interacting with the E652D/H782Q variant. This is consistent with a force-induced mechanism and suggests that the dock, lock, and latch (DLL) mechanism is favored for Fg-binding under mechanical stress. Collectively, these experiments reveal that FnBPA exhibits bimodal, ligand-dependent adhesive behavior. Amino acid substitutions in the repeat-region of FnBPA impact binding to both ligands. This was unexpected for Fg since all variants have the same A-domain sequence, and the Fg-binding site is distant from the repeat region. This indicates that FnBRs may fold back on the A-domain in a way that impacts the DLL binding mechanism for Fg.

Kinetic Analysis of Hepatic Metabolism Using Hyperpolarized Dihydroxyacetone.

Hyperpolarized carbon-13 magnetic resonance (HP-MR) is a new metabolic imaging method the does not use ionizing radiation. Due to the inherent chemical specificity of MR, not only tracer uptake but also downstream metabolism of the agent is detected in a straightforward manner. HP [2-13C] dihydroxyacetone (DHA) is a promising new agent that directly interrogates hepatic glucose metabolism. DHA has three metabolic fates in the liver: glucose production, glycerol production and potential inclusion into triglycerides, and oxidation in the tricarboxylic acid cycle. Each pathway is regulated by flux through multiple enzymes. Using Duhamel's formula, the kinetics of DHA metabolism is modeled, resulting in estimates of specific reaction rate constants. The multiple enzymatic steps that control DHA metabolism make more simplified methods for extracting kinetic data less than satisfactory. The described modeling paradigm effectively identifies changes in metabolism between gluconeogenic and glycogenolytic models of hepatic function.