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Abstract Background The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs. Findings We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix. Conclusions We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.

Abstract Background Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information. Description Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach. Conclusion This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

Abstract Background Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Abstract Background Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines. Methods Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch t test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest. Results Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known JUNB, FOS and TP63, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as SERPINE1 and MMP7, are known to regulate metastasis. Twenty-one genes including VEGFA and ID2 have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis. Conclusion The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.

Abstract Background Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. Results Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. Conclusions Performance analysis refined the results from benchmarks published previously. We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. Availability The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Many large European rivers have undergone multiple pressures that have strongly impaired ecosystem functioning at different spatial and temporal scales. Global warming and other environmental changes have favored the success of invasive species, deeply modifying the structure of aquatic communities in large rivers. Some exogenous species could alter trophic interactions within assemblages by increasing the predation risk for potential prey species (top-down effect) and limiting the dynamics of others via resource availability limitation (bottom-up effect). Furthermore, large transboundary rivers are complex aquatic ecosystems that have often been poorly investigated so that data for assessing long-term ecological trends are missing. In this study, we propose an original approach for investigating long-term combined effects of global warming, trophic resource decrease, predation risk, and water quality variations on the trait-based structure of macroinvertebrate and fish assemblages over 26 yr (1985-2011) and 427-km stretch of the river Meuse (France and Belgium). The study of temporal variations in biological, physiological, and ecological traits of macroinvertebrate and fish allowed identifying community trends and distinguishing impacts of environmental perturbations from those induced by biological alterations. We provide evidence, for this large European river, of an increase in water temperature (close to 1°C) and a decrease in phytoplankton biomass (-85%), as well as independent effects of these changes on both invertebrate and fish communities. The reduction of trophic resources in the water column by invasive molluscs has dramatically affected the density of omnivorous fish in favor of invertebrate feeders, while scrapers became the major feeding guild among invertebrates. Macroinvertebrate and fish communities have shifted from large-sized organisms with low fecundity to prolific, small-sized organisms, with early maturity, as a response to increased predation pressure., (© 2017 by the Ecological Society of America.)

Sex differentiation in fish is a highly labile process easily reversed by the use of exogenous hormonal treatment and has led to environmental concerns since low doses of estrogenic molecules can adversely impact fish reproduction. The goal of this study was to identify pathways altered by treatment with ethynylestradiol (EE2) in developing fish and to find new target genes to be tested further for their possible role in male-to-female sex transdifferentiation. To this end, we have successfully adapted a previously developed bioinformatics workflow to a meta-analysis of two datasets studying sex reversal following exposure to EE2 in juvenile rainbow trout. The meta-analysis consisted of retrieving the intersection of the top gene lists generated for both datasets, performed at different levels of stringency. The intersecting gene lists, enriched in true positive differentially expressed genes (DEGs), were subjected to over-representation analysis (ORA) which allowed identifying several statistically significant enriched pathways altered by EE2 treatment and several new candidate pathways, such as progesterone-mediated oocyte maturation and PPAR signalling. Moreover, several relevant key genes potentially implicated in the early transdifferentiation process were selected. Altogether, the results show that EE2 has a great effect on gene expression in juvenile rainbow trout. The feminization process seems to result from the altered transcription of genes implicated in normal female gonad differentiation, resulting in expression similar to that observed in normal females (i.e. the repression of key testicular markers cyp17a1, cyp11b, tbx1), as well as from other genes (including transcription factors) that respond specifically to the EE2 treatment. The results also showed that the bioinformatics workflow can be applied to different types of microarray platforms and could be generalized to (eco)toxicogenomics studies for environmental risk assessment purposes.

Sex steroids play a key role in triggering sex differentiation in fish, the use of exogenous hormone treatment leading to partial or complete sex reversal. This phenomenon has attracted attention since the discovery that even low environmental doses of exogenous steroids can adversely affect gonad morphology (ovotestis development) and induce reproductive failure. Modern genomic-based technologies have enhanced opportunities to find out mechanisms of actions (MOA) and identify biomarkers related to the toxic action of a compound. However, high throughput data interpretation relies on statistical analysis, species genomic resources, and bioinformatics tools. The goals of this study are to improve the knowledge of feminisation in fish, by the analysis of molecular responses in the gonads of rainbow trout fry after chronic exposure to several doses (0.01, 0.1, 1 and 10 μg/L) of ethynylestradiol (EE2) and to offer target genes as potential biomarkers of ovotestis development. We successfully adapted a bioinformatics microarray analysis workflow elaborated on human data to a toxicogenomic study using rainbow trout, a fish species lacking accurate functional annotation and genomic resources. The workflow allowed to obtain lists of genes supposed to be enriched in true positive differentially expressed genes (DEGs), which were subjected to over-representation analysis methods (ORA). Several pathways and ontologies, mostly related to cell division and metabolism, sexual reproduction and steroid production, were found significantly enriched in our analyses. Moreover, two sets of potential ovotestis biomarkers were selected using several criteria. The first group displayed specific potential biomarkers belonging to pathways/ontologies highlighted in the experiment. Among them, the early ovarian differentiation gene foxl2a was overexpressed. The second group, which was highly sensitive but not specific, included the DEGs presenting the highest fold change and lowest p-value of the statistical workflow output. The methodology can be generalized to other (non-model) species and various types of microarray platforms.

This study aimed to investigate the male-to-female morphological and physiological transdifferentiation process in rainbow trout (Oncorhynchus mykiss) exposed to exogenous estrogens. The first objective was to elucidate whether trout develop intersex gonads under exposure to low levels of estrogen. To this end, the gonads of an all-male population of fry exposed chronically (from 60 to 136 days post fertilization--dpf) to several doses (from environmentally relevant 0.01 µg/L to supra-environmental levels: 0.1, 1 and 10 µg/L) of the potent synthetic estrogen ethynylestradiol (EE2) were examined histologically. The morphological evaluations were underpinned by the analysis of gonad steroid (testosterone, estradiol and 11-ketotestosterone) levels and of brain and gonad gene expression, including estrogen-responsive genes and genes involved in sex differentiation in (gonads: cyp19a1a, ER isoforms, vtg, dmrt1, sox9a2; sdY; cyp11b; brain: cyp19a1b, ER isoforms). Intersex gonads were observed from the first concentration used (0.01 µg EE2/L) and sexual inversion could be detected from 0.1 µg EE2/L. This was accompanied by a linear decrease in 11-KT levels, whereas no effect on E2 and T levels was observed. Q-PCR results from the gonads showed downregulation of testicular markers (dmrt1, sox9a2; sdY; cyp11b) with increasing EE2 exposure concentrations, and upregulation of the female vtg gene. No evidence was found for a direct involvement of aromatase in the sex conversion process. The results from this study provide evidence that gonads of male trout respond to estrogen exposure by intersex formation and, with increasing concentration, by morphological and physiological conversion to phenotypic ovaries. However, supra-environmental estrogen concentrations are needed to induce these changes.

We propose to make use of the wealth of underused DNA chip data available in public repositories to study the molecular mechanisms behind the adaptation of cancer cells to hypoxic conditions leading to the metastatic phenotype. We have developed new bioinformatics tools and adapted others to identify with maximum sensitivity those genes which are expressed differentially across several experiments. The comparison of two analytical approaches, based on either Over Representation Analysis or Functional Class Scoring, by a meta-analysis-based approach, led to the retrieval of known information about the biological situation - thus validating the model - but also more importantly to the discovery of the previously unknown implication of the spliceosome, the cellular machinery responsible for mRNA splicing, in the development of metastasis.

Although ovine stifle models are commonly used to study osteoarthritis, meniscal pathology and cruciate ligament injuries and repair, there is little information about the anatomy of the joint or techniques for synovial injections. The objectives of this study were to improve anatomical knowledge of the synovial cavities of the ovine knee and to compare intra-articular injection techniques. Synovial cavities of 24 cadaver hind limbs from 12 adult sheep were investigated by intra-articular resin, positive-contrast arthrography, computed tomography (CT) arthrography and gross anatomical dissection. Communication between femoro-patellar, medial femoro-tibial and lateral femoro-tibial compartments occurred in all cases. The knee joint should be considered as one synovial structure with three communicating compartments. Several unreported features were observed, including a communication between the medial femoro-tibial and lateral femoro-tibial compartments and a latero-caudal recess of the lateral femoro-tibial compartment. No intermeniscal ligament was identified. CT was able to define many anatomical features of the stifle, including the anatomy of the tendinous synovial recess on the lateral aspect of the proximal tibia under the combined tendon of the peroneus tertius, extensor longus digitorum and extensor digiti III proprius. An approach for intra-articular injection into this recess (the subtendinous technique) was assessed and compared with the retropatellar and paraligamentous techniques. All three injection procedures were equally successful, but the subtendinous technique appeared to be most appropriate for synoviocentesis and for injections in therapeutic research protocols with less risk of damaging the articular cartilage., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Background: The quantity of microarray data available on the Internet has grown dramatically over the past years and now represents millions of Euros worth of underused information. One way to use this data is through co-expression analysis. To avoid a certain amount of bias, such data must often be analyzed at the genome scale, for example by network representation. The identification of co-expression networks is an important means to unravel gene to gene interactions and the underlying functional relationship between them. However, it is very difficult to explore and analyze a network of such dimensions. Several programs (Cytoscape, yEd) have already been developed for network analysis; however, to our knowledge, there are no available GraphML compatible programs., Findings: We designed and developed gViz, a GraphML network visualization and exploration tool. gViz is built on clustering coefficient-based algorithms and is a novel tool to visualize and manipulate networks of co-expression interactions among a selection of probesets (each representing a single gene or transcript), based on a set of microarray co-expression data stored as an adjacency matrix., Conclusions: We present here gViz, a software tool designed to visualize and explore large GraphML networks, combining network theory, biological annotation data, microarray data analysis and advanced graphical features.

Lipid rafts are cholesterol-rich cell signaling platforms, and their physiological role can be explored by cholesterol depletion. To characterize transcriptional changes ongoing after lipid raft disruption in epidermal keratinocytes, a cell type that synthesizes its cholesterol in situ, we performed whole-genome expression profiling. Microarray results show that over 3,000 genes are differentially regulated. In particular, IL-8, urokinase-like plasminogen activator receptor, and metalloproteinases are highly upregulated after cholesterol extraction. Quantitative reverse transcriptase PCR validation and protein release measurements demonstrate the physiological relevance of microarray data. Major enriched terms and functions, determined by Ingenuity Pathways Analysis, identify cholesterol biosynthesis as a major function, illustrating the specificity of keratinocyte response toward cholesterol depletion. Moreover, the inflammatory skin disorder atopic dermatitis (AD) is identified as the disease most closely associated with the profile of lipid raft-disrupted keratinocytes. This finding is confirmed in skin of AD patients, in whom transcript levels of major lipid raft target genes are similarly regulated in lesional atopic skin, compared with non-lesional and normal skin. Thus, lipid raft disruption evokes typical features of AD, thereby suggesting that lipid raft organization and signaling could be perturbed in atopic keratinocytes.

Background: Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information., Description: Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach., Conclusion: This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms., Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets., Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

Background: Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines., Methods: Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch t test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest., Results: Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known JUNB, FOS and TP63, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as SERPINE1 and MMP7, are known to regulate metastasis. Twenty-one genes including VEGFA and ID2 have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis., Conclusion: The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.

Background: Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods., Results: Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality., Conclusions: Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better., Availability: The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

Background: Given the availability of full genome sequences, mapping gene gains, duplications, and losses during evolution should theoretically be straightforward. However, this endeavor suffers from overemphasis on detecting conserved genome features, which in turn has led to sequencing multiple eutherian genomes with low coverage rather than fewer genomes with high-coverage and more even distribution in the phylogeny. Although limitations associated with analysis of low coverage genomes are recognized, they have not been quantified., Results: Here, using recently developed comparative genomic application systems, we evaluate the impact of low-coverage genomes on inferences pertaining to gene gains and losses when analyzing eukaryote genome evolution through gene duplication. We demonstrate that, when performing inference of genome content evolution, low-coverage genomes generate not only a massive number of false gene losses, but also striking artifacts in gene duplication inference, especially at the most recent common ancestor of low-coverage genomes. We show that the artifactual gains are caused by the low coverage of genome sequence per se rather than by the increased taxon sampling in a biased portion of the species tree., Conclusions: We argue that it will remain difficult to differentiate artifacts from true changes in modes and tempo of genome evolution until there is better homogeneity in both taxon sampling and high-coverage sequencing. This is important for broadening the utility of full genome data to the community of evolutionary biologists, whose interests go well beyond widely conserved physiologies and developmental patterns as they seek to understand the generative mechanisms underlying biological diversity.

The genome sequences of three Brucella biovars and of some species close to Brucella sp. have become available, leading to new relationship analysis. Moreover, the automatic genome annotation of the pathogenic bacteria Brucella melitensis has been manually corrected by a consortium of experts, leading to 899 modifications of start sites predictions among the 3198 open reading frames (ORFs) examined. This new annotation, coupled with the results of automatic annotation tools of the complete genome sequences of the B. melitensis genome (including BLASTs to 9 genomes close to Brucella), provides numerous data sets related to predicted functions, biochemical properties and phylogenic comparisons. To made these results available, alphaPAGe, a functional auto-updatable database of the corrected sequence genome of B. melitensis, has been built, using the entity-relationship (ER) approach and a multi-purpose database structure. A friendly graphical user interface has been designed, and users can carry out different kinds of information by three levels of queries: (1) the basic search use the classical keywords or sequence identifiers; (2) the original advanced search engine allows to combine (by using logical operators) numerous criteria: (a) keywords (textual comparison) related to the pCDS's function, family domains and cellular localization; (b) physico-chemical characteristics (numerical comparison) such as isoelectric point or molecular weight and structural criteria such as the nucleic length or the number of transmembrane helix (TMH); (c) similarity scores with Escherichia coli and 10 species phylogenetically close to B. melitensis; (3) complex queries can be performed by using a SQL field, which allows all queries respecting the database's structure. The database is publicly available through a Web server at the following url: http://www.fundp.ac.be/urbm/bioinfo/aPAGe.

We cloned XYL1, a Scytalidium acidophilum gene encoding for an acidophilic family 11 xylanase. The XYL1p protein was expressed in Pichia pastoris using the pPICZalphaA expression plasmid. The secreted protein was purified by TAXI affinity column chromatography. The purified XYL1p showed an optimum activity at pH 3.2 and 56 degrees C. The Michaelis-Menten constants were determined.