cancer metabolome databasecoli genetic stock center

To that end, we applied NCA to resolve TR activity across an independent large compendium of transcriptome data, monitoring gene expression in a panel of 1037 human cancer cell lines30 (Cancer Cell Line Encyclopedia), and identified enzyme levels that correlate with individual TR activity profiles (|Spearman correlation|>0.5, Supplementary Data6). The metabolic demands of cancer cells are coupled to their size and protein synthesis rates. was funded by the Austrian Science Fund (FWF): FWF P26603 and FWF W1224 Doctoral Program BioToPBiomolecular Technology of Proteins. By analyzing the coordinated changes in baseline transcriptome, proteome and metabolome with the aid of a gene regulatory network and model-based fitting analysis, we investigated the bi-directional exchange of signaling information between TRs and metabolic pathways. Different cell lines are seeded in triplicates at low cell density in 96-well microtiter plates, and are grown to confluence within 5 days (37C, 5% CO2). Next, we related intracellular metabolite abundances to measured fluxes by estimating the average correlation with glucose and lactate exchange rates (Fig. Bioinform. Here, we established an in silico framework for generating hypotheses on regulatory interactions between TRs, metabolites and kinases (Fig. Cell Rep. 20, 26662677 (2017). Genome-related datasets within the E. coli Genetic Stock Center 3b, Supplementary Data3). Gray lines represent dynamic changes of all other detected metabolites. 10010015) using a multichannel dispensing pipet, and finally filling each well again with 150L of fresh medium. Databases Curation 2014, bau034 (2014). Chubukov, V., Zuleta, I. 83, 70747080 (2011). The sample plug is delivered directly to the ion source for ionization in negative mode (325C source temperature, 5L/min drying gas, 30psig nebulizer pressure, 175V fragmentor voltage, 65V skimmer voltage). In the more specific cases described above, our analysis of HIF-1A and VHL activities across cell lines recapitulate the action of HIF-1A inhibitor vorinostat47 and HIF-1A inducer imiquimod48 (Fig. To test our hypothesis, we correlated mRNA levels of metabolic enzymes with metabolite abundances, and related enzyme-metabolite correlation to their proximity in the metabolic network. Petrella, B. L. & Brinckerhoff, C. E. PTEN suppression of YY1 induces HIF-2 activity in von Hippel Lindau null renal cell carcinoma. Sci. Nat. Sharifpoor, S. et al. 68. Here, we used our framework to profile the intracellular metabolomes of 54 adherent cell lines from eight different tissue types in the NCI-60 cancer cell line panel. Of note, even if the current knowledge of TR-target genes is incomplete, few gene targets can be sufficient to estimate TR relative activities using this approach. To obtain To study the flow of signaling information between transcriptome and metabolome, we sought to quantify the functional interplay between metabolic phenotypes and different transcriptional programs mediated by the activity of transcriptional regulators (TRs). To this end, raw data were first corrected for instrument drift by normalizing for possible batch/plate effects. J. Biol. Biol. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. no. This work was supported by a Worldwide Cancer Research (WCR-15-1058) project funding to M.Z., K.O. Europe PMC is an archive of life sciences journal literature. Three layers of biological information are integrated using a model-based fitting approach: protein abundance of 100 TRs and 64 kinases, respective TR activities derived from transcriptome datausing NCA and relative levels of 260 metabolites across cell lines (see Methods section). changing maximum flux capacity, or by indirectly affecting substrate availability of proximal metabolic reactions, which can in turn result in local changes of fluxes25,26. The database uses the long-standing Stock Center records (developed and curated by Dr B.J.Bachmann) in describing genotypes of mutant derivatives of E.coli K-12 in terms of alleles, structural mutations, mating type, and plasmids as well as the derivation, names and originators of the strain, and references. But Not With Mutations or or or. The NCI-60 cancer cell lines were obtained from the National Cancer Institute (NCI, Bethesda, MD, USA). Diabetes 59, 16741685 (2010). 2, see Supplementary Note). As a proof of concept, we measured rates of glucose uptake and lactate secretion in each cell line as a proxy for glycolytic flux. These results independently demonstrate the in vivo relevance of the previously inferred in vitro map of TR-metabolite associations, and support its potential clinical applicability to decipher metabolic rearrangements in tumor tissue samples. The plate was imaged to determine the confluence (see below) immediately before and after media change, and after 72h. The starting cell density for metabolomics experiments was then chosen to guarantee a minimum of 20-30% cell confluence after media change, and approx. J. R. Stat. Cell extract samples were analyzed by flow-injection analysis time-of-flight mass spectrometry (FIA-TOFMS) on an Agilent 6550 iFunnel Q-TOF LC-MS System (Agilent Technologies, Santa Clara, CA, USA), as described by Fuhrer et al.14. J. Med. Data are meanstandard deviation across three replicates. This procedure enables selecting only annotated ions exhibiting a linear dependency between measured intensities and extracted cell number (Fig. In brief, a defined sample volume of 5L is injected using a Gerstel MPS2 autosampler into a constant flow of isopropanol/water (60:40, v/v) buffered with 5mM ammonium carbonate (pH 9), containing two compounds for online mass axis correction: 3-Amino-1-propanesulfonic acid, (138.0230374m/z, Sigma Aldrich, cat. 7). This observation suggests that multiple coordinated regulatory mechanisms possibly underlie the post-transcriptional regulation of TR activity. With Mutations and and and. Glunde, K., Bhujwalla, Z. M. & Ronen, S. M. Choline metabolism in malignant transformation. Altogether, these results suggest that phenotypic heterogeneity observed in vitro can emerge as a result of different transcriptional regulatory programs, and that metabolite abundances can be used as intermediate functional readouts linking gene expression profiles to different strategies in allocating metabolic resources for energy generation and growth. The public version of the database includes this . 5a, b), we found that on average TRs are predicted to interact with more kinases than metabolites (Supplementary Fig. e, f The pie chart represents the distribution of metabolites allosterically regulating one or more enzymatic reactions. 2). Nat. We call the resulting association network an augmented TR-gene network. 2e). Fuhrer, T., Heer, D., Begemann, B. Article Genome-scale architecture of small molecule regulatory networks and the fundamental trade-off between regulation and enzymatic activity. Cardoso, F. et al. After this step, we retained 2181ions with a regression p-value below a threshold value of 3.4e-7 (adjusted by the number of cell lines and ions) in at least one cell line, and that showed a significant dependency with the extracted cell number in more than 80% of cell lines (Supplementary Fig. In light of the central regulatory role of TRs in cellular organization, targeting transcriptional regulators is an extremely attractive way to counteract global gene expression changes that underlie cancer survival and development62,63. ECMDB: the E. coli Metabolome Database - PubMed Of note, we found that prior to normalization, the variance across three biological replicates at the same time-point was equally low in cell confluence (median: 7.4% CV) and raw ion intensities (median: 13%, Supplementary Fig. Correspondence to Cell. M.Z. 2). c Volcano plot of metabolic changes at 111h after HIF-1A siRNA transfection. By repeating the analysis of TR-metabolite distance using the augmented TR-gene network, we found an even stronger vicinity between TRs and correlating metabolites (Fig. The contents of the E. coli Genetic Stock Center database and the availability in electronic form of the subset of information most relevant to sequence databases are described. Each plate contains 12 pooled cell extract samples prepared from the 5 different cell lines in each experiment (i.e., batch). Each dot represents the strength (x-axis, median score) and significance (y-axis, q-value, corrected for multiple tests, blue to purple color range) of a TR in mediating in vivo metabolic changes. The herein-proposed workflow for large-scale metabolome profiling is directly applicable to the study of dynamic metabolic responses to external stimuli18, and can scale to larger cohorts that are now within reach of other molecular profiling platforms61. Originally established by Liao et al.29, NCA provides a mathematical framework for reconstructing TR regulatory signals (TR activity) from gene expression profiles. Gossage, L., Eisen, T. & Maher, E. R. VHL, the story of a tumour suppressor gene. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism, $$I_{{\mathrm{i}},{\mathrm{j}},{\mathrm{p}}} = \gamma _{\mathrm{p}} \cdot M_{{\mathrm{i}},{\mathrm{j}}}$$, $$\log \left( {I_{{\mathrm{i}},{\mathrm{j}},{\mathrm{p}}}} \right) = {\mathrm{log}}(\gamma _{\mathrm{p}}) + {\mathrm{log}}(M_{{\mathrm{i}},{\mathrm{j}}})$$, $$\left[ {\begin{array}{*{20}{c}} {I_{{\mathrm{cell}}_1,1}} \\ {I_{{\mathrm{cell}}_1,2}} \\ {I_{{\mathrm{cell}}_1,3}} \\ \ldots \\ {I_{{\mathrm{cell}}_2,1}} \\ {I_{{\mathrm{cell}}_2,2}} \\ {I_{{\mathrm{cell}}_2,3}} \\ \ldots \\ {I_{{\mathrm{cell}}_{\mathrm{c}},{\mathrm{s}}}} \end{array}} \right] = \left[ {\begin{array}{*{20}{c}} {N_{{\mathrm{cell}}_1,1}} & 0 & \ldots & 0 & 1 \\ {N_{{\mathrm{cell}}_1,2}} & 0 & \ldots & 0 & 1 \\ {N_{{\mathrm{cell}}_1,3}} & 0 & \ldots & 0 & 1 \\ \ldots & \ldots & \ldots & \ldots & \ldots \\ 0 & {N_{{\mathrm{cell}}_2,1}} & \ldots & 0 & 1 \\ 0 & {N_{{\mathrm{cell}}_2,2}} & \ldots & 0 & 1 \\ 0 & {N_{{\mathrm{cell}}_2,3}} & \ldots & 0 & 1 \\ \ldots & \ldots & \ldots & \ldots & \ldots \\ 0 & 0 & \ldots & {N_{{\mathrm{cell}}_{\mathrm{c}},{\mathrm{s}}}} & 1 \end{array}} \right] \cdot \left[ {\begin{array}{*{20}{c}} {\alpha _{{\mathrm{cell}}_1}} & {\alpha _{{\mathrm{cell}}_2}} & \ldots & {\alpha _{{\mathrm{cell}}_{\mathrm{c}}}} & \beta \end{array}} \right]$$, $$Z_{\mathrm{\alpha }} = \frac{{\alpha _{{\mathrm{cell}}} - \bar \alpha }}{{\sqrt {\frac{1}{n}\mathop {\sum }\nolimits_{{\mathrm{cell}} = 1}^{\mathrm{n}} \left( {\alpha _{{\mathrm{cell}}} - \bar \alpha } \right)} }},$$, \({\mathrm{S}}_{{\mathrm{TR}}}^{\mathrm{p}}\), \({\tilde{\mathrm{S}}}_{{\mathrm{TR}}}^{\mathrm{p}}\), $$S_{{\mathrm{TR}}}^{\mathrm{p}} = \frac{{{\mathbf{C}}_{{\mathrm{TR}}} \cdot {\mathbf{FC}}^{\mathrm{p}}}}{\mid\mid{{{\mathbf{C}}_{{\mathrm{TR}}}\mid\mid^1}}}$$, $${{p}} - {\mathrm{value}}_{{\mathrm{TR}}} = \frac{{\mathop {\sum }\nolimits_{\mathrm{k}}^{10,000} \left( {\tilde S_{{\mathrm{TR}}}^{\mathrm{p}} \ge S_{{\mathrm{TR}}}^{\mathrm{p}}} \right)}}{{10,000}}$$, https://doi.org/10.1038/s41467-019-09695-9. CAS Growth is continuously monitored by automated acquisition of bright-field microscopy images, and replicate 96-well plates are sampled for metabolome analysis every 24h. To facilitate sampling, increase the throughput and reduce the risk of sample processing artifacts, we collect metabolomics samples directly in the 96-well cultivation plate without prior cell detachment (Supplementary Fig. CGSC Contact Info - Yale University M.Z. Biol. 1g), reflecting a direct dependency between enzyme- and related metabolite levels22,23. & Serpedin, E. SparseNCA: Sparse Network Component Analysis for Recovering Transcription Factor Activities with Incomplete Prior Information. Intersecting these two resources, we assembled a network of 2209 unique genes corresponding to 5490 mRNA probes that match target genes of 728 TRs in the TRRUST database (Supplementary Fig. To quantify the number of extracted cells, confluence was divided by the characteristic cell size (Supplementary Fig. The starting cell density for metabolomics experiments in 96-well plates (Nunc cat.no. 5c). The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. In cell lines where a significant dependency (p-value<3.4e7, Bonferroni-adjusted threshold) of given metabolites abundance with cell number could be robustly determined and exceeded the background noise, relative standard errors of calculated during fitting analysis were below 20% (median: 11%, Supplementary Fig. a Schematic overview of the combined workflow for high-throughput metabolome profiling in adherent cell lines. Heterogeneity of tumor-induced gene expression changes in the human metabolic network. A76109) and hexakis(1H,1H,3H-tetrafluoropropoxy)phosphazine (940.0003763m/z, HP-0921, Agilent Technologies, Santa Clara, CA, USA). Biotechnol. Nature 487, 330337 (2012). In order to validate this association, we monitored dynamic intracellular metabolic changes upon HIF-1A mRNA degradation (i.e., siRNA knockdown) in IGROV1 ovarian cancer cells, exhibiting an average basal level of HIF-1A activity (Supplementary Fig. Here, we ask whether the map of TR-metabolite associations found in vitro recapitulates metabolic rearrangements in an in vivo setting. After an initial growth phase, the medium in each well was renewed on the third day, and the cultures were subsequently monitored for four more days (96h). Cell 60, 195207 (2015). Kawada, J. et al. Thank you for visiting nature.com. Colored squares indicate the significance of the enrichment analysis. Dot size is proportional to the number of TRs that exhibit a significant association (linear regression p-value2.67e4, Bonferroni-adjusted threshold) with the drug, while dot color reflects the estimated average susceptibility of renal cell lines (i.e., kidney) with respect to the remaining seven tissue types. Here, the authors integrate dynamic . Coli Genetic Stock Center - Yale University To generate an empirical network of associations between TRs and metabolites, we systematically correlated the activity of 728 TRs with relative levels of individual metabolites across cell lines (Fig. volume10, Articlenumber:1841 (2019) All data generated or analyzed during this study are included in this published article as Supplementary Data. P-values report on the significance of the difference between TRs and enzyme pdfs (KolmogorovSmirnov test). CellMiner: a web-based suite of genomic and pharmacologic tools to explore transcript and drug patterns in the NCI-60 cell line set. B 64, 479498 (2002). Moreover, by integrating MS readouts at multiple cell densities and time points, the resulting estimates of relative metabolite abundances are invariant to cultivation time and cell densities, enabling the direct comparison between different cell lines and conditions18. 4d). Cascante, M. et al. Metab. Specifically, we optimized each step, from cultivation and extraction to MS analysis, to be compatible with parallel 96-well processing. The size of black circles scales with the number of known TR-gene targets24 in the metabolic pathway. Huang, K.-Y. Moreover, metabolites predicted to affect TR activity are significantly enriched (hypergeometric test, p-value 3.1e4, Supplementary Fig. To this end, we implemented a robust and scalable computational framework that integrates metabolomics profiles with previously published transcriptomics10 and proteomics11 datasets to resolve the flow of signaling information across multiple regulatory layers in the cell. Receiver operating characteristic curve analysis quantifies the likelihood of cell lines from the same tissue type to feature similar molecular profiles. In silico models have proven extremely powerful in finding new allosteric interactions that can regulate enzyme activity53 and in testing their in vivo functionality54, but little progress has been made in the systematic mapping of effectors of TR activity. b Raw MS data for adenosine triphosphate (ATP) in three different cell lines before cell volume correction. A complete description of the experimental procedures used to knockdown the TR HIF-1A in IGROV-1 ovarian cancer cells, and quantify metabolic changes in response to the knockdown can be found in Supplementary Methods. 1 and Supplementary Note). Systems-level analysis of mechanisms regulating yeast metabolic flux. Hence, multiple proximal metabolic intermediates correlating with an individual TRs activity can reveal the functional impact of TRs on overall pathway activity. The Cancer Genome Atlas Research Network. Using Network Component Analysis we calculate the activities of 209 . Bioeng. 1b), that are hence amenable to accurate relative quantification. Only ions annotated to KEGG identifiers are shown. & Zamboni, N. High-throughput, accurate mass metabolome profiling of cellular extracts by flow injectiontime-of-flight mass spectrometry. USA 109, 51275132 (2012). Principal component analysis of relative metabolite abundance per cell revealed a strong trend across the 54 cell lines (PC1, 58.9% explained variance, Supplementary Fig. E-mail Addresses. Cells 19, 650665 (2014). The authors declare no competing interests. Dot color reflects significance (q-value from permutation test) of enzyme-metabolite proximity in the stoichiometric network as compared to the randomized networks. 3). Peer reviewer reports are available. c Pairwise similarity (Spearman correlation) of metabolome profiles among 53 cell lines from the NCI-60 panel. Article b Principal component analysis of dynamic metabolome changes in IGROV1 ovarian cancer cells transfected with three different concentrations (10, 25, and 50nM) of HIF-1A siRNA, and a non-targeting siRNA as negative control. 7). Consistent with this hypothesis, by analyzing the dependency between TR activity and the sensitivity of NCI-60 tumor cell lines to 130 FDA-approved drugs31, we found 392 TRs for which activity significantly (linear regression p-value2.67e4, Bonferroni-adjusted threshold) associates with at least one drug sensitivity profile (Supplementary Data4). All authors contributed to preparing the manuscript. Plate B undergoes the same processing steps, except for the last one, where each well is filled with PBS (pH 7.4, 37C), and the plate is immediately imaged to determine cell confluence for subsequent normalization of MS spectra. Nat. 2). However, evidence linking alterations of cancer metabolism to TR dysfunction is often based on molecular profiling technologies, like transcriptomics and chromatin modification profiling6 or the identification of TR-binding sites upstream of metabolic enzymes3, that dont report on the functional consequences of detected interactions. 8, 13891401 (2009). Finally, the plate is sealed, incubated at 20C for one hour, and subsequently stored at -80C until MS analysis. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in 2i). However, often only few metabolic intermediates of metabolic pathways, typically the end product37, can allosterically regulate TRs. E. coli Genetic Resources at Yale CGSC, The Coli Genetic Stock Center Reinhold, W. C. et al. For each metabolite, we solve the following linear model, including all 54 cell lines: Where Icell c,s is the measured metabolite intensity in sample s of cell line c, Ncell c,s is the number of cells extracted in sample s of cell line c, and s (for each cell line) and are the unknown parameters to be fitted. Because measuring intracellular fluxes is still a major challenge, and these measurements are typically limited to central metabolic pathways, our metabolome profiling technique offers an alternative tool to probe metabolic regulation at a genome-scale and high-throughput in cancer cells. Cite this article. However, this methodology typically requires a large number of null tests to derive an accurate estimate of 0 (estimate of the proportion of true null p-values)65. 3e and Supplementary Fig. Cell. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Internet Explorer). Predicted functional association of TRs to metabolic pathways. c Schematic overview of the computational framework. All metabolites (1633) with at least one known allosteric interaction in human26 are reported in e, while only metabolites predicted to modulate the activity of at least one TR are shown in panel f. Metabolites that regulate more than seven enzymatic reaction are lumped together. Cancer Res. Interestingly, we also found TRs important for the survival and proliferation of cancer cells under nutrient limitation or stress, such as the activating transcription factor 5 (ATF5)34 and the SNF2-related CPB activator protein (SRCAP), a direct regulator of phosphoenolpyruvate carboxykinase 2 (PCK2)35, a gluconeogenic enzyme essential to maintain cell proliferation under limited glucose conditions in cancer cells36. To disentangle the difference in drug sensitivity relating to variable TR activity from those attributable to the tissue of origin, we used a multivariate statistical approach that uncovers the potential association between individual TRs and drug susceptibility. Sugimoto, M. et al. We thank the National Cancer Institute (NCI) for providing the cancer cell lines. 2 and Supplementary Note). To overcome these limitations, we present an innovative and robust workflow enabling large-scale metabolic profiling in adherent mammalian cells alongside with a scalable computational framework to normalize and compare molecular signatures across cell types with large differences in morphology and size (Supplementary Fig. Measured ions were putatively annotated by matching mass-to-charge ratios to a reference list of calculated masses of metabolites listed in the Human Metabolome Database (HMDB) and the genome-scale reconstruction of human metabolism21 (Recon2) within 0.003amu mass accuracy. 40, D809D814 (2012). Protoc. Here, we chart a genome-scale map of TR-metabolite associations in human cells using a combined computational-experimental framework for large-scale metabolic profiling of adherent cell lines. Regulation of cAMP-responsive element-binding protein-mediated transcription by the SNF2/SWI-related protein, SRCAP. Cancer Cell. Sign up for the Nature Briefing: Cancer newsletter what matters in cancer research, free to your inbox weekly. Cancer Metabolomics and the Human Metabolome Database 12). Storey, J. D. A direct approach to false discovery rates. 7 we excluded all detected metabolites with more than 10 missing values across patient samples. Notably, TRs with the highest scores were enriched (permutation test, q-value0.05) for enzyme targets in ubiquinone biosynthesis, insulin signaling, TCA cycle and glycolysis/gluconeogenesis (Fig. 5). The database uses the long-standing Stock Center records (developed and curated by Dr B.J.Bachmann) in describing genotype 3a). Nature 511, 543550 (2014). A community-driven global reconstruction of human metabolism. FGSC: Fungal Genetics Stock Center DB. The significance of the rank distribution of all metabolites within the same KEGG pathway is tested by means of an iterative hypergeometric test, indicating the statistical significance of metabolic intermediates of a common metabolic pathway (e.g., TCA cycle) being distributed toward the top ranking ones. Introduction. Genome-related datasets within the E. coli Genetic Stock Center
How Often Does It Snow In San Bernardino, Pentecostal Churches In Lynchburg, Va, Texas Men's Basketball Roster, Small Beach Wedding Venues Southern California, Articles C