Myd88 is required for disease development in a primary Sjögren's syndrome mouse model.

Sjögren's syndrome (SS) is an autoimmune disease that often results in diminished exocrine gland function. SS patients also experience systemic disease manifestations, including hypergammaglobulinemia and pulmonary and renal pathoses. MyD88 is a ubiquitously expressed adaptor molecule used by all immune cells that is required for IL-1 receptor (IL-1R), IL-18R, and most TLR signaling. The precise role of MyD88 in SS has not been evaluated, although this adaptor is critical for development of lupus, a related autoimmune disease. This study tested the hypothesis that Myd88-mediated signaling is required for local and systemic SS manifestations. To this end, we generated NOD.B10Sn-H2b /J (NOD.B10) mice that are deficient in Myd88 (NOD.B10 Myd88-/- ). We found that NOD.B10 animals that lack Myd88 show reduced exocrine and extraglandular inflammation. Moreover, these animals are protected from loss of salivary flow. Splenocytes from NOD.B10 Myd88-/- mice did not up-regulate activation markers or secrete IL-6 in response to a Myd88-dependent agonist, although BCR signaling remained intact. Finally, IgM, IgG, and anti-nuclear autoantibodies were reduced in NOD.B10 Myd88-/- mice compared with the parental strain. These data demonstrate that Myd88 is a crucial mediator of local and systemic SS disease manifestations.

Systemic manifestations of primary Sjögren's syndrome in the NOD.B10Sn-H2b/J mouse model.

Animal models that recapitulate human disease are crucial for the study of Sjögren's Syndrome (SS). While several SS mouse models exist, there are few primary SS (pSS) models that mimic systemic disease manifestations seen in humans. Similar to pSS patients, NOD.B10Sn-H2b/J (NOD.B10) mice develop exocrine gland disease and anti-nuclear autoantibodies. However, the disease kinetics and spectrum of extra-glandular disease remain poorly characterized in this model. Our objective was to characterize local and systemic SS manifestations in depth in NOD.B10 female mice at early and late disease time points. To this end, sera, exocrine tissue, lung, and kidney were analyzed. NOD.B10 mice have robust lymphocytic infiltration of salivary and lacrimal tissue. In addition, they exhibit significant renal and pulmonary inflammation. We identified numerous autoantibodies, including those directed against salivary proteins. In conclusion, the NOD.B10 model recapitulates both local and systemic pSS disease and represents an excellent model for translational studies.

Belief propagation in genotype-phenotype networks.

Graphical models have proven to be a valuable tool for connecting genotypes and phenotypes. Structural learning of phenotype-genotype networks has received considerable attention in the post-genome era. In recent years, a dozen different methods have emerged for network inference, which leverage natural variation that arises in certain genetic populations. The structure of the network itself can be used to form hypotheses based on the inferred direct and indirect network relationships, but represents a premature endpoint to the graphical analyses. In this work, we extend this endpoint. We examine the unexplored problem of perturbing a given network structure, and quantifying the system-wide effects on the network in a node-wise manner. The perturbation is achieved through the setting of values of phenotype node(s), which may reflect an inhibition or activation, and propagating this information through the entire network. We leverage belief propagation methods in Conditional Gaussian Bayesian Networks (CG-BNs), in order to absorb and propagate phenotypic evidence through the network. We show that the modeling assumptions adopted for genotype-phenotype networks represent an important sub-class of CG-BNs, which possess properties that ensure exact inference in the propagation scheme. The system-wide effects of the perturbation are quantified in a node-wise manner through the comparison of perturbed and unperturbed marginal distributions using a symmetric Kullback-Leibler divergence. Applications to kidney and skin cancer expression quantitative trait loci (eQTL) data from different mus musculus populations are presented. System-wide effects in the network were predicted and visualized across a spectrum of evidence. Sub-pathways and regions of the network responded in concert, suggesting co-regulation and coordination throughout the network in response to phenotypic changes. We demonstrate how these predicted system-wide effects can be examined in connection with estimated class probabilities for covariates of interest, e.g. cancer status. Despite the uncertainty in the network structure, we demonstrate the system-wide predictions are stable across an ensemble of highly likely networks. A software package, geneNetBP, which implements our approach, was developed in the R programming language.

Identification of consistent functional genetic modules.

It is often of scientific interest to find a set of genes that may represent an independent functional module or network, such as a functional gene expression module causing a biological response, a transcription regulatory network, or a constellation of mutations jointly causing a disease. In this paper we are specifically interested in identifying modules that control a particular outcome variable such as a disease biomarker. We discuss the statistical properties that functional networks should possess and introduce the concept of network consistency which should be satisfied by real functional networks of cooperating genes, and directly use the concept in the pathway discovery method we present. Our method gives superior performance for all but the simplest functional networks.

Analysis of IgM antibody production and repertoire in a mouse model of Sjögren's syndrome.

This study tested the hypothesis that B cells from salivary tissue are distinct in terms of proliferative capacity, immunoglobulin M secretion, repertoire, and autoantibody enrichment in Sjögren's syndrome. We sorted purified B cells from the spleen, cervical lymph nodes, and submandibular glands of a primary Sjögren's syndrome mouse model (Id3(-/-)). Enzyme-linked immunospot and proliferation assays were performed with stimulated B cells. We single-cell sorted B cells from the spleen, cervical lymph nodes, and submandibular gland tissue from Sjögren's syndrome mice and sequenced immunoglobulin M heavy-chain variable regions. Finally, autoantigen arrays were performed using immunoglobulin M derived from sera, cervical lymph nodes, spleens, and submandibular gland tissue of Id3(-/-) animals. Results suggest B cells from salivary tissue of Sjögren's syndrome mice are similar to those from secondary immune sites in terms of proliferative and secretory capacity. However, differences in repertoire usage, heavy chain complementarity-determining region 3 length, mutational frequency, and N region addition were observed among B cells derived from submandibular gland, cervical lymph node, and spleen tissue. Moreover, autoantigen array data show immunoglobulin M from salivary B cells have enriched specificity for Ro (Sjögren's syndrome A) and La (Sjögren's syndrome B). All together, these data suggest salivary B cells have unique repertoire characteristics that likely influence autoantigen binding and contribute to Sjögren's syndrome disease in a tissue-specific manner.

Lentivirus Live Cell Array for Quantitative Assessment of Gene and Pathway Activation during Myogenic Differentiation of Mesenchymal Stem Cells.

Stem cell differentiation involves multiple cascades of transcriptional regulation that govern the cell fate. To study the real-time dynamics of this complex process, quantitative and high throughput live cell assays are required. Herein, we developed a lentiviral library of promoters and transcription factor binding sites to quantitatively capture the gene expression dynamics over a period of several days during myogenic differentiation of human mesenchymal stem cells (MSCs) harvested from two different anatomic locations, bone marrow and hair follicle. Our results enabled us to monitor the sequential activation of signaling pathways and myogenic gene promoters at various stages of differentiation. In conjunction with chemical inhibitors, the lentiviral array (LVA) results also revealed the relative contribution of key signaling pathways that regulate the myogenic differentiation. Our study demonstrates the potential of LVA to monitor the dynamics of gene and pathway activation during MSC differentiation as well as serve as a platform for discovery of novel molecules, genes and pathways that promote or inhibit complex biological processes.

Arsenic responsive microRNAs in vivo and their potential involvement in arsenic-induced oxidative stress.

Arsenic exposure is postulated to modify microRNA (miRNA) expression, leading to changes of gene expression and toxicities, but studies relating the responses of miRNAs to arsenic exposure are lacking, especially with respect to in vivo studies. We utilized high-throughput sequencing technology and generated miRNA expression profiles of liver tissues from Sprague Dawley (SD) rats exposed to various concentrations of sodium arsenite (0, 0.1, 1, 10 and 100mg/L) for 60days. Unsupervised hierarchical clustering analysis of the miRNA expression profiles clustered the SD rats into different groups based on the arsenic exposure status, indicating a highly significant association between arsenic exposure and cluster membership (p-value of 0.0012). Multiple miRNA expressions were altered by arsenic in an exposure concentration-dependent manner. Among the identified arsenic-responsive miRNAs, several are predicted to target Nfe2l2-regulated antioxidant genes, including glutamate-cysteine ligase (GCL) catalytic subunit (GCLC) and modifier subunit (GCLM) which are involved in glutathione (GSH) synthesis. Exposure to low concentrations of arsenic increased mRNA expression for Gclc and Gclm, while high concentrations significantly reduced their expression, which were correlated to changes in hepatic GCL activity and GSH level. Moreover, our data suggested that other mechanisms, e.g., miRNAs, rather than Nfe2l2-signaling pathway, could be involved in the regulation of mRNA expression of Gclc and Gclm post-arsenic exposure in vivo. Together, our findings show that arsenic exposure disrupts the genome-wide expression of miRNAs in vivo, which could lead to the biological consequence, such as an altered balance of antioxidant defense and oxidative stress.

A novel characterization of the generalized family wise error rate using empirical null distributions.

We present a novel characterization of the generalized family wise error rate: kFWER. The interpretation allows researchers to view kFWER as a function of the test statistics rather than current methods based on p-values. Using this interpretation we present several theorems and methods (parametric and non-parametric) for estimating kFWER in various data settings. With this version of kFWER, researchers will have an estimate of kFWER in addition to knowing what tests are significant at the estimated kFWER. Additionally, we present methods that use empirical null distributions in place of parametric distributions in standard p-value kFWER controlling schemes. These advancements represent an improvement over common kFWER methods which are based on parametric assumptions and merely report the tests that are significant under a given value for kFWER.

From small studies to precision medicine: prioritizing candidate biomarkers.

There are still many open questions in data-analytic research pertaining to biomarker development in the era of personalized/precision medicine and big data. Among them is the question of what constitutes best practice for the extraction of prioritized lists of candidate biomarkers from smaller studies that are 'hypothesis generating' in nature. A recent comparison of methods to detect patient-specific aberrant expression events in small- to medium-sized (10 to 50 samples) studies provides results that favor the use of outlying degree methods. See related Research, http://genomemedicine.com/content/5/11/103.

Human papillomavirus and tobacco use in tongue base cancers.

Human papillomavirus 16 (HPV-16) infection and tobacco use are associated with human oropharyngeal cancers. We conducted a study of the role of HPV and tobacco use in base of the tongue (BOT) cancers. DNA from 34 such cancers was subjected to HPV-16 and HPV-18-specific polymerase chain reaction analysis. Demographic and clinicopathologic data were obtained from each patient's medical record. HPV-16 was detected in 68% of tumors. Tobacco use was the only factor found to be significantly associated with HPV status. Tumors from 100% of patients who had never used tobacco tested positive for HPV, compared with only 56% of those who had ever used tobacco (Fisher exact test, p = 0.024). All tumors were associated with either tobacco use or HPV infection. These findings are consistent with the hypothesis that either tobacco use or HPV infection is necessary to the etiology of BOT tumors, and they suggest that tongue base carcinoma may be prevented by combining HPV vaccination with tobacco avoidance.

Differential copy number aberrations in novel candidate genes associated with progression from in situ to invasive ductal carcinoma of the breast.

Only a minority of intraductal carcinomas of the breast give rise to stromally invasive disease. We microdissected 206 paraffin blocks representing 116 different cases of low-grade ductal carcinoma in situ (DCIS). Fifty-five were pure DCIS (PD) cases without progression to invasive carcinoma. Sixty-one cases had a small invasive component. DNA was extracted from microdissected sections and hybridized to high-density bacterial artificial chromosome arrays. Array comparative genomic hybridization analysis of 118 hybridized DNA samples yielded data on 69 samples that were suitable for further statistical analysis. This cohort included 20 pure DCIS cases, 25 mixed DCIS (MD), and 24 mixed invasive carcinoma samples. PD cases had a higher frequency of DNA copy number changes than MD cases, and the latter had similar DNA profiles compared to paired invasive carcinomas. Copy number changes on 13 chromosomal arms occurred at different rates in PD versus MD lesions. Eight of 19 candidate genes residing at those loci were confirmed to have differential copy number changes by quantitative PCR. NCOR2/SMRT and NR4A1 (both on 12q), DYNLRB2 (16q), CELSR1, UPK3A, and ST13 (all on 22q) were more frequently amplified in PD. Moreover, NCOR2, NR4A1, and DYNLRB2 showed more frequent copy number losses in MD. GRAP2 (22q) was more often amplified in MD, whereas TAF1C (16q) was more commonly deleted in PD. A multigene model comprising these candidate genes discriminated between PD and MD lesions with high accuracy. These findings suggest that the propensity to invade the stroma may be encoded in the genome of intraductal carcinomas.

The genomic relationship between primary breast carcinomas and their nodal metastases.

We screened the whole tumor genome to identify DNA copy number gains and losses that discriminate between primary breast carcinomas (MP) and their nodal metastases (ML). Six candidate genes were confirmed by quantitative PCR to have differentially distributed copy number changes. Three of the genes (ERRγ, DDX6, and TIAM1) were more commonly amplified in nodal metastases. Principal component analysis revealed that MP-ML pairs varied markedly in their genomic divergence. The latter was larger in PR-negative tumors. Nodal metastases may form early or late in the development of breast carcinomas and PR-negative tumors may metastasize earlier or are genomically less stable.

A new normalizing algorithm for BAC CGH arrays with quality control metrics.

The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose "SmoothArray", a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package "aCGHplus," this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.

Identification of metastasis-associated breast cancer genes using a high-resolution whole genome profiling approach.

PURPOSE: We employed a whole genome tumor profiling approach in an attempt to identify DNA copy number alterations (CNAs) and new candidate genes that are correlated with the metastatic potential of a primary breast carcinoma and with progression at the metastatic site. METHODS: Fifty-four small (≤ 2 cm), high grade, ER-positive, formalin-fixed invasive ductal carcinomas were suitable for whole genome profiling analysis. Twenty-four of them did not form metastases within 5-10 years (unmatched primaries, UP). Thirty tumors had at least one synchronous axillary lymph node metastasis (matched primaries, MP; matched lymph node metastases, ML). Genomic DNA was hybridized to high density (19k) BAC arrays. Statistical analysis revealed differential distributions of CNAs between UP and MP and between MP and ML, respectively. We selected 27 candidate genes for validation experiments using quantitative (Q-)PCR of genomic DNA. For tetraspanin TSPAN1, we studied mRNA expression levels in a separate cohort of primary breast carcinomas and in breast cell lines. RESULTS: Matched primary (MP) tumors had a threefold higher rate of DNA copy number losses compared to UP tumors. In the UP-MP comparison, 186 BACs were differentially amplified or deleted. Most of them were localized to chromosomes 7p, 16q and 18q. In the MP-ML comparison, 131 BACs showed differential CNAs. Most of them were localized to chromosomes 1q and 20. By Q-PCR, seven candidate genes could be confirmed to show differential distributions of CNAs. TSPAN1 was amplified in UP and deleted in MP tumors. The gene was markedly downregulated in ER-negative and high-grade breast cancers. CONCLUSIONS: Metastasizing tumors had a higher rate of deletions, suggesting possible inactivation of metastasis suppressor genes. We provide preliminary evidence that TSPAN1 may be another important breast cancer suppressor gene belonging to the tetraspanin superfamily.

iGenomicViewer: R package for visualisation of high dimension genomic data.

While the technologies for high dimensional data have been advancing, a lack of adequate visualisation tools to accommodate the results and inability to integrate multiple sources of data has emerged. The move towards multi-disciplinary work and collaborative research impresses the need for visualisation and analysis tools that are platform independent and customisable. iGenomicViewer through the use of customisable tool-tips that may include links and images, allows for a greater level of data integration for genomic data in a variety of formats. The iGenomicViewer is a freely available R software which allows users to generate interactive, platform-independent plots of genomic data.

Long tandem repeats as a form of genomic copy number variation: structure and length polymorphism of a chromosome 5p repeat in control and schizophrenia populations.

OBJECTIVES: Genomic copy number variations (CNVs) are a major form of variation in the human genome and play an etiologic role in several neuropsychiatric diseases. Tandem repeats, particularly with long (>50 bp) repeat units, are a relatively common yet underexplored type of CNV that may significantly contribute to human genomic variation and disease risk. We therefore carried out a pilot experiment to explore the potential role of long tandem repeats as risk factors in psychiatric disorders. METHODS: A bacterial artificial chromosome-based array comparative genomic hybridization (aCGH) platform was used to examine CNVs in genomic DNA from 34 probands with schizophrenia or schizoaffective disorder. RESULTS: The aCGH screen detected an apparent deletion on 5p15.1 in two probands, caused by the presence in each proband of two low copy number (short) alleles of a tandem repeat that ranges in length from fewer than 10 to greater than 50 3.4 kb units in the population examined. Short alleles partially segregate with schizophrenia in a small number of families, though linkage was not significant. An association study showed no significant difference in repeat length between 406 schizophrenia cases and 392 controls. CONCLUSION: Although we did not demonstrate a relationship between the 5p15.1 repeat and schizophrenia, our results illustrate that long tandem repeats represent an intriguing type of genetic variation that have not been studied in earlier connection with psychiatric illness. aCGH can detect a small subset of these repeats, but systematic investigation will require the development of specific arrays and improved analytical methods.

Estimating the arm-wise false discovery rate in array comparative genomic hybridization experiments.

Array Comparative Genomic Hybridization (aCGH) is an array-based technology which provides simultaneous spot assays of relative genetic abundance (RGA) levels at multiple sites across the genome. These spot assays are spatially correlated with respect to genomic location and, as a result, the univariate tests conducted using data generated from these spot assays are also spatially correlated. In the context of multiple hypothesis testing, this spatial correlation complicates the question of how best to define a 'discovery' and consequently, how best to estimate the false discovery rate (FDR) corresponding to a given rejection region. One can quantify the number of discoveries as the total number of spots for which the spot-based univariate test statistic falls within a given rejection region. Under this spot-based method, separate but correlated discoveries are identified. We show via a simulation study that the method of Benjamini and Hochberg (1995) can provide a reasonable estimate of the spot-wise FDR, but these results require that the simulated spot assays are categorized as true or false discoveries in a particular way. However, laboratory researchers may actually be interested in estimating a 'regional' FDR, rather than a 'local' spot-wise FDR. We describe an example of such circumstances, and present a method for estimating the (chromosome) arm-wise False Discovery Rate. In this framework, one can quantify the number of discoveries as the total number of chromosome arms for which at least one spot-based test statistic falls into a given rejection region. Defining the discoveries in this way, both the biological and testing objectives coincide. We provide results from a series of simulations which involved the analysis of preferentially re-sampled spot assay values from a real aCGH dataset.

Putative null distributions corresponding to tests of differential expression in the Golden Spike dataset are intensity dependent.

BACKGROUND: We provide a re-analysis of the Golden Spike dataset, a first generation "spike-in" control microarray dataset. The original analysis of the Golden Spike dataset was presented in a manuscript by Choe et al. and raised questions concerning the performance of several statistical methods for the control of the false discovery rate (across a set of tests for differential expression). These original findings are now in question as it has been reported that the p-values associated with the tests of differential expression for null probesets (i.e., probesets designed to be fold change 1 across the two arms of the experiment) are not uniformly distributed. Two recent publications have speculated as to the reasons the null distributions are non-uniform. A publication by Dabney and Storey concludes that the non-uniform distributions of null p-values are the direct consequence of an experimental design which requires technical replicates to approximate biological replicates. Irizarry et al. identify four characteristics of the feature level data (three related to experimental design and one artifact). Irizarry et al. argue that the four observed characteristics imply that the assumptions common to most pre-processing algorithms are not satisfied and hence the expression measure methodologies considered by Choe et al. are likely to be flawed. RESULTS: We replicate and extend the analyses of Dabney and Storey and present our results in the context of a two stage analysis. We provide evidence that the Stage I pre-processing algorithms considered in Dabney and Storey fail to provide expression values that are adequately centered or scaled. Furthermore, we demonstrate that the distributions of the p-values, test statistics, and probabilities associated with the relative locations and variabilities of the Stage II expression values vary with signal intensity. We provide diagnostic plots and a simple logistic regression based test statistic to detect these intensity related defects in the processed data. CONCLUSION: We agree with Dabney and Storey that the null p-values considered in Choe et al. are indeed non-uniform. We also agree with the conclusion that, given current pre-processing technologies, the Golden Spike dataset should not serve as a reference dataset to evaluate false discovery rate controlling methodologies. However, we disagree with the assessment that the non-uniform p-values are merely the byproduct of testing for differential expression under the incorrect assumption that chip data are approximate to biological replicates. Whereas Dabney and Storey attribute the non-uniform p-values to violations of the Stage II model assumptions, we provide evidence that the non-uniformity can be attributed to the failure of the Stage I analyses to correct for systematic biases in the raw data matrix. Although we do not speculate as to the root cause of these systematic biases, the observations made in Irizarry et al. appear to be consistent with our findings. Whereas Irizarry et al. describe the effect of the experimental design on the feature level data, we consider the effect on the underlying multivariate distribution of putative null p-values. We demonstrate that the putative null distributions corresponding to the pre-processing algorithms considered in Choe et al. are all intensity dependent. This dependence serves to invalidate statistical inference based upon standard two sample test statistics. We identify a flaw in the characterization of the appropriate "null" probesets described in Choe et al. and we provide a corrected analysis which reduces (but does not eliminate) the intensity dependent effects.

aCGH local copy number aberrations associated with overall copy number genomic instability in colorectal cancer: coordinate involvement of the regions including BCR and ABL.

In order to identify small regions of the genome whose specific copy number alteration is associated with high genomic instability in the form of overall genome-wide copy number aberrations, we have analyzed array-based comparative genomic hybridization (aCGH) data from 33 sporadic colorectal carcinomas. Copy number changes of a small number of specific regions were significantly correlated with elevated overall amplifications and deletions scattered throughout the entire genome. One significant region at 9q34 includes the c-ABL gene. Another region spanning 22q11-q13 includes the breakpoint cluster region (BCR) of the Philadelphia chromosome. Coordinate 22q11-q13 alterations were observed in 9 of 11 tumors with the 9q34 alteration. Additional regions on 1q and 14q were associated with overall genome-wide copy number changes, while copy number aberrations on chromosome 7p, 7q, and 13q21.1-q31.3 were found associated with this instability only in tumors from patients with a smoking history. Our analysis demonstrates there are a small number of regions of the genome where gain or loss is commonly associated with a tumor's overall level of copy number aberrations. Our finding BCR and ABL located within two of the instability-associated regions, and the involvement of these two regions occurring coordinately, suggests a system akin to the BCR-ABL translocation of CML may be involved in genomic instability in about one-third of human colorectal carcinomas.

Diabetes-altered gene expression in rat skeletal muscle corrected by oral administration of vanadyl sulfate.

Treatment with vanadium, a representative of a class of antidiabetic compounds, alleviates diabetic hyperglycemia and hyperlipidemia. Oral administration of vanadium compounds in animal models and humans does not cause clinical symptoms of hypoglycemia, a common problem for diabetic patients with insulin treatment. Gene expression, using Affymetrix arrays, was examined in muscle from streptozotocin-induced diabetic and normal rats in the presence or absence of oral vanadyl sulfate treatment. This treatment affected normal rats differently from diabetic rats, as demonstrated by two-way ANOVA of the full array data. Diabetes altered the expression of 133 genes, and the expression of 30% of these genes dysregulated in diabetes was normalized by vanadyl sulfate treatment. For those genes, the ratio of expression in normal animals to the expression in diabetic animals showed a strong negative correlation with the ratio of expression in diabetic animals to the expression in diabetic animals treated with vanadyl sulfate (P = -0.85). The genes identified belong to six major metabolic functional groups: lipid metabolism, oxidative stress, muscle structure, protein breakdown and biosynthesis, the complement system, and signal transduction. The identification of oxidative stress genes, coupled with the known oxidative chemistry of vanadium, implicates reactive oxygen species in the action of this class of compounds. These results imply that early transition metals or compounds formed from their chemical interactions with other metabolites may act as general transcription modulators, a role not usually associated with this class of compounds.