In order to validate a gel free quantitative proteomics assay for the model methylotrophic bacterium Methylobacterium extorquens AM1, we examined the M. Extorquens AM1 proteome under single carbon (methanol) and multi-carbon (succinate) growth, conditions that have been studied for decades and for which extensive corroborative data have been compiled. In total, 4,447 proteins from a database containing 7,556 putative ORFs from M. Extorquens AM1 could be identified with two or more peptide sequences, corresponding to a qualitative proteome coverage of 58%. Statistically significant non-zero (log 2 scale) differential abundance ratios of methanol/succinate could be detected for 317 proteins using summed ion intensity measurements and 585 proteins using spectral counting, at a q-value cut-off of 0.01, a measure of false discovery rate.
The results were compared to recent microarray studies performed under equivalent chemostat conditions. Extorquens AM1 studies demonstrated the feasibility of scaling up the multidimensional capillary HPLC tandem mass spectrometry approach to a prokaryotic organism with a proteome more than three times the size of microbes we have investigated previously, while maintaining a high degree of proteome coverage and reliable quantitative abundance ratios.
1 IntroductionMethylotrophy describes the ability to metabolize one carbon (C 1) sources such as methanol for use as a sole source of carbon and energy. C 1 compounds can be converted to either biomass or carbon dioxide, accumulating cellular formaldehyde in the process.
Methylotrophy has been studied intensively in recent decades because of its relevance for the global carbon cycle and its applicability in biotechnological detoxification and production processes ,. Among the best characterized model organisms for the study of one carbon metabolism is the facultatively methylotrophic Methylobacterium extorquens AM1, which is able to grow on C 1 substrates (e.g. Methanol) and also several C 2, C 3 and C 4 compounds, such as ethanol, pyruvate and succinate -. The special metabolic properties of Methylobacterium species appear to contribute to their widespread distribution in the environment -, and specifically in the phytosphere. The numerical predominance of the epiphytic species M.
Extorquens in the plant colonization process was shown to be dependent on its ability to make use of methanol as a carbon source. Although a wealth of information about the enzymes and regulators involved in C 1 metabolic pathways has been provided through genomic, genetic, enzymatic, metabolic flux and modelling studies, and more recently metabolic profiling analysis and in planta experiments , -, a number of enzymes and their functions remain uncharacterized. To identify the remaining unknown functions required for methylotrophic growth, recent studies have taken a more global approach to understanding the metabolism and physiology of M. Extorquens, including 2-D gel proteomic and whole genome microarray studies based on the existing gapped genome sequence of 6.7 Mbp. The first proteome level experiments with M. Extorquens were performed using 2-D gel electrophoresis and peptide mass fingerprinting by Laukel and coworkers, yielding 229 identified proteins. This study provided valuable information about the protein adaptations M.
Extorquens undergoes when grown on synthetic medium with methanol as a sole carbon source in comparison to succinate, describing 68 proteins as induced under methylotrophic conditions, although a number of primary oxidation, C 1 transfer and formate oxidation proteins could not be detected using the gel-based approach.In order to obtain a more comprehensive model of methylotrophy in M. Extorquens, we developed a non-gel proteomic assay using a MudPIT - approach under the assumption that such an assay would provide a more complete and quantitative description of the proteome than would be possible using other methods. The assay was applied to experiments using chemostat grown cultures with methanol or succinate as substrates, respectively, and an ORF database based on the same annotation used for the global transcription studies. This work was also a necessary step on the road towards future work planned for the analysis of model microbial communities or consortia, in that it involved analysis of a much larger and more technically challenging proteome relative to what we have worked with in the past.
2.2 Linear ion trap tandem mass spectrometryThe proteolytic digests were further analyzed in duplicate or triplicate per biological replicate, using a biphasic 2-D capillary HPLC system, coupled to an LTQ linear ion trap mass spectrometer (Thermo Fisher Scientific, San Jose, CA, USA), as described ,. Briefly, samples were analyzed on a Michrom Magic 2002 HPLC (Michrom, Auburn, CA, USA) modified in-house for capillary column operation, using a 75 μm inner diameter biphasic column which consisted of a 3 cm strong cation exchange section followed by a 10 cm reversed phase C18 section. The column was coupled to the LTQ mass spectrometer through an in-house built electrospray ionization interface.
The apparatus was similar to that originally described by Washburn et al. The peptides were eluted by ammonium acetate solutions (0, 10, 25, 50, 100, 250 and 500 mM), followed by reverse phase gradients of 5-12% in 1 min, 12% B for 9 min, 12-40% B in 50 min, 40-80% B in 11 min, 80% B for 10 min and 80-5% B in 5 min.
Solvent B: 99.5% acetonitrile, 0.5% acetic acid (v/v). The MS 1 scan range was 400-2000 m/z. After each main beam (MS 1) scan, the 10 most intense ions above threshold were selected for CID scans with one CID scan collected for each of the precursor ions. Default parameters under the Xcalibur 1.4 data acquisition software (Thermo Fisher) were used, with the exception of an isolation width of 3.0 m/z units and a normalized collision energy of 40%. 2.3 SEQUEST and DTASelectSEQUEST database searching and DTASelect filtering were performed as described ,. Briefly, product ion (MS 2) mass spectra were searched using TurboSEQUEST Cluster Version 3.2 (Thermo-Finnigan) on a 16-CPU computing cluster (Denali Advanced Integration, Seattle, WA, USA) against a concatenated fasta database that included M. Extorquens proteins, the human subset of the nrdb depleted of all virus sequences, with reversed sequence decoy versions of the M.
Extorquens and human database appended. The concatenated, 91 Mb database comprised a total of 203,065 protein sequences, 7,556 of which belonged to the M.
Extorquens database. A gapped genome sequence (6.5 × coverage; ) was used to generate the M. Extorquens protein fasta database, corresponding to the DNA sequence database underlying the transcriptomics results by Okubo and Skovran et al. The DTASelect Version 1.9 filtering criteria chosen comprised fully tryptic peptides and ΔCn/Xcorr values for different peptide charge states of 0.08/1.9 for +1, 0.08/2.0 for + 2, and 0.08/3.3 for +3. All redundant spectra detected for each sequence were retained (t = 0 in DTASelect). Two peptides unique to a particular ORF were required for positive identification. Unique means that the peptide sequence could only be found in one ORF entry in the concatenated database.
Filemaker Pro 18
Prior to final archiving in PRIDE , the DTASelect 1.9 filter data for all protein identifications will be posted on the corresponding author's website. Summary and for the complete proteomic dataset can be found at the same URL, along with conversion tables to allow use of the data with more recent annotations, as they become available. And also contain an abbreviated version of the microarray data previously published. An abbreviated table of the most relevant proteomic and microarray results with respect to methylotrophy, is included in the electronic supplement to this paper, along with explanatory notes for all tables, supplementary figures, equations ( t-test and G-test) and additional detail regarding the normalization and multiple hypothesis testing procedures described below. 2.5 Data processing for estimation of protein abundance ratios from protein intensity and spectral counting valuesDetailed descriptions of the Visual Basic, R and Filemaker scripts applied to estimate protein abundances on the basis of summed protein intensity and spectral count values are given in previous work by Xia et al. Summed protein intensity refers to the summation of all processed parent ion intensity measurements (MS 1) for which a confirming CID spectrum (MS 2) exists according to the DTASelect filter files mentioned above in Section 2.3.
Additional R source code used specifically for the present work is given in the electronic supplement. Briefly, a Visual Basic 2005 program (IntensityMaker Version 2.0) was applied to convert raw data files into plain text intensity files, which were, together with the DTAselect filter file containing spectral counting information, imported into a relational FileMaker Pro 8 database as separate tables. A FileMaker script application (QuantScript, Version 1.0, March 2006) combined these two streams of information into ion intensity based protein abundance ratios for methanol/succinate. The protein level summed signal intensity method used the summed intensity values in MS 1 from all unique peptides identified for a given protein in order to calculate the abundance ratios for each protein given in. Proteomics dataMicroarray dataBiological process / proteinGene name, functional descriptionSpectr. Count ratio MeOH/Succin (log 2) b)Sum (log 2) c)q-value d)Avg. Ratio MeOH/Succin (log 10)Avg.
A)Included in Table 2 are those proteins showing the 33 highest observed methanol vs. Succinate ratios, with q-values. E)SAM δ=1.25: Significance Analysis of Microarrays score, as previously published. See the supplementary notes for - for more detail regardins the SAM score.For spectral counting, the redundant numbers of peptides uniquely associated with each ORF were summed from the DTAselect filter table ,. This is a measurement of frequency of occurrence, not signal strength. After normalization, a ratio of summed spectral count values was calculated for each protein. Spectral counting is a frequency measurement that has been demonstrated in the literature to correlate with protein abundance -.
For a discussion contrasting the strengths and weaknesses of spectral counting, summed signal intensity and other methods for generating protein abundance ratios for whole microbial cell studies, see the recent review by Xia et al. 2.6 Experimental design, data normalization and significance testingThe overall experimental design involved two complete biological replicates as described above (Section 2.2) for each of the two nutrient conditions, methanol ( MeOH1, MeOH2) and succinate ( Succin1, Succin2). Each methanol replicate was compared to each succinate replicate, yielding four sets of abundance ratios. This design functions for non-label quantitation in a manner analogous to stable isotope “flip” replicates for metabolic labeling proteomic studies or dye swap replicates in a transcription microarray analysis. Each biological replicate consisted of the mean value of normalized total counts and total intensity calculated for each protein observed in the technical replicates.
This design allowed for estimation of variance associated with technical replicates as well as sources of variance associated with different chemostat experiments (biological replicates). Global normalization of the data was based on the counts or summed intensity observed for the most abundant biological replicate. After normalization the average summed intensity or average spectral count was calculated.
For spectral counting, the G-test was used with each ratio determination, as we have published previously , followed by calculation of G Total, as per the method described in Sokal and Rohlf. The global G-test functions much like analysis of variance (ANOVA), partitioning variance into that associated with the individual spectral count comparisons for a given ORF (within group variation) and that associated with significant deviation from the null hypothesis in terms of the overall trend in the four abundance ratios.
For each value of G Total, a p-value was calculated as in our previous work. The uncorrected p-value was used as an input into the R package QVALUE , yielding a measure of the quantitative false discovery rate , the q-value. For the summed intensity measurements, a paired t-test was employed, n = 4. The t-test was used to generate a global p-value over all four comparisons. As with the G-test results, the p-values were input into the QVALUE R package using the default parameters. A significance level of q = 0.01 or lower was chosen as a cut-off for both methods, based on the criterion of achieving a best balance between false positive and false negative errors. A q-value of zero in the tables means that the calculated value was less than the smallest value that could be calculated in R, a number that is platform dependent and equal to 2.22 × 10 -16 for the work reported here.
As a check on the reasonableness of the q cut-off, the lists of significantly changed proteins were checked against well defined regions of random error determined experimentally from the random scatter about zero expression change generated from LOWESS curves defining error boundaries in technical replicates , analogous to the M versus A plots commonly used in the microarray community. The q-value is appropriate for multiple hypothesis testing for thousands of proteins in a way that has proven to be more realistic and not as overtly conservative for global protein abundance data, relative to other, more established approaches to the multiple hypothesis problem. The q-value associated with each global abundance ratio in - and main text can be informally defined here as the minimum false discovery rate when rejecting the null hypothesis of no significant change in M. Extorquens protein abundance. For a more technical description of the false discovery rate , and the q-value as originally developed for transcription microarray data, see Storey and Tibshirani and the references contained therein. Project overview, showing (A), the experimental design; (B), an outline of the analytical procedure; and (C), the number of protein relative abundances called as significantly changed by the two calculation methods and the observed overlap of 129 proteins called as changed by both approaches.The scatter plots and correlation analyses shown in - were performed using the standard, un-weighted linear regression routine in R on the log-transformed data (R Version 2.2.1, ). More detail regarding the normalization method, G-test, t-test, q-value and color coding for the supplemental data tables and figures can be found in the on-line supplement under Notes and explanatory material for -.
Scatter plots and linear regression for (A), the log2 protein abundance ratios calculated by summed signal intensity versus spectral counting for 762 proteins judged to be significantly changed by either method; (B), the log 2 mRNA abundance ratios versus the log2 spectral count proteomic abundance ratios for 585 ORFs showing significant change in protein abundance; and (C), the log 2 mRNA abundance ratios versus the log 2 summed signal intensity proteomic abundance ratios for 317 ORFs showing significant change in protein abundance. 3.1 Qualitative proteome coverageIn all biological replicates we qualitatively identified 4,447 proteins out of 7,556 ORFs found in the Methylobacterium extorquens AM1 first draft genome annotation. Specifically, 3,644 proteins were detected in MeOH1, 3,170 in MeOH2, 3,584 in Succin1 and 3,401 proteins in Succin2.
The overall qualitative proteome coverage was 58%. Comprehensive lists of all identified proteins, abundance ratios, and a subset of proteins specifically associated with C 1 metabolism are contained in - and can be downloaded as described in Section 2.3. The twenty most abundant proteins observed by spectral counting are shown in. In general, gene names are more widely recognized than annotation specific ORF numbers, thus we have used gene or gene product names wherever possible, and numbers from the 2002 annotation cited above (designated as RMQ numbers) when a name has not been assigned or is ambiguous. At this time there is no consensus categorization of the RMQ numbers by functional category, so such a summary presentation will have to wait until the final ORF designations and functional annotation become available, see Section 2.3.
ProteinFunctional descriptionGene nameSpectr. Counts log 2 MeOH b)Spectr. 3.2 Reproducibility of biological replicatesand illustrate the reproducibility of the biological replicates for succinate and methanol. The statistical analysis as well as manual inspection of the proteomic results support the observation that no obvious trends could be associated with individual chemostat runs or MudPIT analyses, beyond one technical replicate in which twice as much sample was injected into the MudPIT instrumentation relative to all others.
This type of systematic error is corrected at the normalization stage of the calculations and had little effect on the final global abundance ratio calculations. The random errors associated with the abundance ratios judged to be significantly different from zero on a log 2 scale tended on average to be in the range of 15% to 35% RSD, towards the low end of this range for spectral counting and the high end for summed signal intensity. 3.3 Abundance change detection and proteome coverageThere is a clear relationship among the number of pre-fractions, sampling rates at the level of raw mass spectral data collection, proteome coverage and power to detect abundance change: more pre-fractionation and (or) faster data acquisition rates yield more coverage that equals higher power and thus lower quantitative FNRs (false negative rates). Non-label quantitative approaches such as spectral counting and summed signal intensity are especially sensitive to the coverage issue, but even with metabolic stable isotope labeling the same relationship holds true ,. Because the sampling rate of the mass spectrometer is fixed under our experimental protocols, necessary improvements in throughput to maintain acceptable coverage were accomplished for the proteome of M. Extorquens by doubling the number of pre-fractions collected prior to the 2-D capillary HPLC/mass spectrometry analysis from five to 10 for each biological replicate (Section 2.1), relative to our previous published work with less complex microbial proteomes ,. Spectral counts are very stable measurements, but can often be invariant with real abundance change in experiments where the true abundance change is known.
The methods employed here for both spectral counting and summed signal intensity are low in power (1-FNR) and thus suffer from inherently higher quantitative FNRs compared to the technology employed for the microarray analysis. This is in part a reflection of the greater time and expense associated with global expression proteomics relative to performing a similar experiment at the transcription level, which necessarily limits complete biological replicates to a number that can control for false discovery rate at an acceptable level, but that leaves quantitative FNRs unknown but presumed to be relatively high. 3.4 Overview of relative abundance changesThe differences between the two growth conditions were described by statistical evaluation of the log 2 abundance ratios (Sections 2.6, 3.2, 3.3 and additional notes in the supplement) and are summarized over the entire observed M.
Extorquens proteome in the tables mentioned above. Summarizes the spectral counting abundance ratios observed for a subset of proteins most highly over-expressed under methanol, with q-values. 3.5 C 1 metabolism and associated metabolic pathwaysThe enzymatic requirements of M.
Extorquens for growth on one carbon substrates have been recently reviewed. During both methanol and succinate growth, the overall highest abundance values in our dataset were found for the large subunit of methanol dehydrogenase (MxaF).
However, the spectral counts for this protein under methanol growth conditions were still significantly higher relative to succinate. This finding correlates well with the microarray data and with previous findings that MxaF can comprise a large fraction of the total cellular protein content under methanol growth conditions ,. Similarly to the array data, we found the methanol dehydrogenase small subunit, MxaI, up-regulated, as was the methanol utilization protein PqqE, which is involved in coenzyme pyrroloquinoline quinone (PQQ) synthesis, the cofactor for methanol dehydrogenase. Except for PqqA, a small protein important for PQQ synthesis, and Orf17, which is involved in H 4MPT biosynthesis, all components of the primary oxidation, C 1 transfer and related cofactor biosynthesis modules were identified in the proteomics dataset.For the formaldehyde activating enzyme Fae, we detected high spectral counts under both C 1 and multi-carbon conditions, with the highest relative abundance observed for methanol grown cells, corroborating the microarray data and previous proteomics. Fae has been shown to catalyze the condensation of the toxic intermediate formaldehyde with H 4MPT.Consistent with the 2-D gel electrophoresis study by Laukel and coworkers and the microarray data, we did not detect significant abundance change for methylene-H 4MPT dehydrogenase B (MtdB). Apart from MtdB, all the primary oxidation and C 1 transfer proteins discussed below were found to be methanol-induced according to the proteomics data, thus showing several differences between the proteomics and array studies.
Methenyl-H 4MPT cyclohydrolase (Mch) was only slightly over-expressed, as well as the gamma-subunit of the Formyl-H 4MPT transferase/hydrolase protein complex (FhcA, B, C, D). While the microarray data showed no significant change for mch and fhc, Laukel and colleagues reported both Mch and the Fhc subunits A, B and C as induced under methanol, a finding which corroborates previous enzyme essays. For the H 4F-linked C 1 transfer-proteins, formyl-H4F ligase (FtfL), methenyl-H 4F cyclohydrolase (Fch) and methylene-H 4MPT/methylene-H 4F dehydrogenase (MtdA), their genes were up-regulated during methanol growth in the microarray data and the proteomics showed the same trend.Two of the formate dehydrogenase complexes (Fdh1A, B and Fdh2A, B, C, D) were up-regulated under methanol, which is again consistent with the array data. The abundance ratios for Fdh2 were the highest in the entire dataset, but this may have more to do with molybdenum added to the growth medium than with methanol induction.
The activity of Fdh2 in electron transport and energy production during methylotrophic growth is molybdenum-dependent. Only a few peptides were detected for the alpha chain of the newly identified formate dehydrogenase 4 (Fdh4, RMQ08549 and RMQ08550), which were not enough to determine a quantitative change in abundance. The fdh3 genes and gene products showed no statistically significant evidence for differential abundance.In agreement with the microarray data and the proteomics study by Laukel et al. , high methanol/succinate ratios were calculated for almost all components of the serine cycle (in particular, MtkA, MtkB, Sga and Mcl).
The serine cycle regulator QscR was strongly down-regulated under methanol conditions. The only known functions of QscR are as an activator of methylotrophy genes and as an autorepressor ,. Studies are currently underway investigating a potentially wider regulatory role for QscR.Serine hydroxymethyltransferase (GlyA), which catalyzes the interconversion of serine and glycine yielding methylene-H 4F, was methanol-induced in the proteome and microarrays. This metabolite is thought to be important in the metabolism of methanol in M. Extorquens and is the major source of glycine and one-carbon units in non-methylotrophs like Escherichia coli. A mutant lacking in functional GlyA was able to grow normally in succinate, suggesting this protein is not essential for growth under multi-carbon conditions. Likewise, the first enzyme in serine biosynthesis, phosphoglycerate dehydrogenase, showed strong induction in the arrays and proteomics under succinate conditions.
Up-regulation of the glycine cleavage system and of the downstream enzyme methylene-H 4F reductase in the array and proteomics data also suggested the importance of C 1 dependent functions in succinate grown cells. However, a reversible and thus glycine synthesizing activity of the glycine cleavage system has not been reported in prokaryotes.For the serine cycle enzyme enolase, proteomics results showed a statistically significant increased abundance under methanol growth, as observed previously. However, the microarrays did not show significant differential expression of the mRNA. High abundance values were observed for the acetoacetyl-CoA reductase PhaB, and lower values for the regulator PhaR.
In addition, we detected Poly 3-hydroxyalkanoate polymerase (PhaC), the PHB granule associated proteins Gap11 and Gap20 and also two proteins associated with PHB degradation (Hbd and RMQ09431) with a spectral count ratio indicating succinate induction. PhaA induction was recently demonstrated during the phyllosphere colonization phase of M.
Extorquens. 3.6 Central intermediary metabolismA clear up-regulation was observed for the enzymes of the citric acid cycle under succinate growth, in agreement with the array data. This is not surprising since one half of the citric acid cycle, comprising citrate synthase, aconitase and the subsequent oxidative decarboxylation steps, is only necessary to provide glutamate for cellular biosynthesis during growth on methanol.Malate dehydrogenase (Mdh) was detected as succinate-induced in the proteomics data but not significantly regulated in the transcriptomics. Although less abundant, membrane-associated Malate:quinone oxidoreductase (Mqo) was observed as clearly induced under multi-carbon conditions, in agreement with the transcription arrays and the previous proteomics study. Apart from donating to the electron transfer chain, Mqo has been shown to support the malate dehydrogenase function, thus keeping the citric acid cycle functioning during growth on organic acids, which favors high oxaloacetate concentrations ,. The citric acid cycle fueling pyruvate dehydrogenase complex (PdhA, B, C) was strongly induced under succinate, as were proteins for gluconeogenic and anaplerotic enzymes like phosphoenolpyruvate carboxykinase (Pck), pyruvate phosphate dikinase (Pdk), phosphoenolpyruvate synthase (Pps) and malic enzyme. The latter has been proposed as the main producer of pyruvate in M.
Extorquens based on enzyme activities. 3.7 NAD(P)H metabolism and electron transportThe subunits of NADH:ubiquinone oxidoreductase (NuoA-N) were all up-regulated in the transcription data under succinate conditions, which is consistent with a previous metabolic model and several mutant studies emphasizing the importance of this enzyme complex for multi-carbon growth. However, the proteomics data only showed significant relative abundance increases under succinate for NuoB, F and G, while NuoI was increased under methanol. We could not obtain good quality mass spectra for NuoM, N, K and H. This might indicate an artifact due to the membrane location of this multi-subunit complex.
The number of predicted proteolytic fragments recovered from membrane bound proteins, especially those that are intrinsic or that have extensive hydrophobic domains, is usually lower compared to proteins found in less hydrophobic environments. Lower recoveries imply less reliable abundance ratios, as discussed in Section 3.3.The increase in abundance of the NADP-transhydrogenase PntA, B under succinate was also observed in the transcription arrays and in the 2-D gel proteomics. These findings are in physiological agreement with the need for M. Extorquens to balance its redox state during growth on multi-carbon sources, where a surplus of reduction equivalents, e.g.
From the citric acid cycle, must be compensated by reducing NADP through NADH oxidation, thus providing reducing power for anabolic reactions, such as gluconeogenesis. Concluding remarksValidating the 2-D capillary HPLC/tandem MS assay for M. Extorquens was straightforward, given the amount of information that has been collected for this organism. The high frequencies of spectral counts for proteins of previously established high abundance observed under both methanol and succinate conditions were consistent with this body of work. For purposes of validation, the situation is less clear in the case of the abundant outer membrane protein RMQ12418 and several ORFs with unknown functions and high observed spectral count values.The gene products showing different quantitative trends in the microarrays and the proteomics data may be of interest for further investigation, e.g. Discrepancies concerning the C 1 transfer enzymes Mch and FhcA, B, C, D; the detection of enolase as methanol-induced on the proteome level in two independent studies, but not in the microarrays; malate dehydrogenase as succinate-induced in the proteome, but not in the array data; and contradictory observations with respect to ferredoxin and proteins involved in iron uptake and siderophore biosynthesis.Perhaps most importantly in terms of future work, the M.
Extorquens AM1 proteome reported here demonstrates the feasibility of extending the “bottom up” shotgun proteomics approach to more complex whole cell microbial proteomes in a reproducible and quantitative fashion. This suggests the possibility of further extension to model microbial communities or consortia, while retaining a high degree of proteome coverage for multiple organisms, up to a total of approximately 10,000 proteins.
Beyond that level of complexity, the proteomics infrastructure described in Sections 2.1-2.6 would not provide adequate sampling depth for reliable quantitation. A major increase in throughput would be required, which would necessarily require improvements to several aspects of the analytical scheme, including separations, mass spectrometry and subsequent data processing. Fortuitously for the proteomics field, these are, at least in part, solved problems at the technical level. One would thus expect, for example, to see increasing use of multiple mass spectrometers operating in parallel with samples from a single experiment to increase throughput, a tactic that has been used in industrial settings for many years. Of greater concern is the pressing need for development of publicly available proteomics software designed to routinely accommodate such a scale of data acquisition and subsequent processing.
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All equipment is connected to back-up generator power and monitored 24/7 by a Rees alarm system.Biospecimen Banking - Freezer - Liquid NitrogenTaylor Wharton LABS 40K Liquid Nitrogen Storage System(2) Liquid nitrogen biospecimen storage system. Holds up to 40,000 1ml samplesBiospecimen Banking - Freezer - -80 degreesThermo Scientific HERAfreezer 24 cu.80 degree freezer(3) Biospecimen Storage at -80 degrees. Holds up to 50,000 1ml samplesBiospecimen Banking - RefrigeratorLABREPCO FUTURA Platinum Series 52 cu. Glass door refrigerator(3) Biospecimen Storage Refrigerators at 4 degrees.
4 degree hinged glass door refrigeratorRobotic Workstation - Sample PreparationQiagen QiaCubeDNA, RNA or Protein PurificationBioBank, Tumor Tissue Bank. (last updated 2019-01-09 15:24:52)Contact: Location: CORO Center West, Suite 404Affiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionTesting SystemsBose Corp Biaxial ELF 3200 material test systemForce Testers (Tension, Compression, Pull Testers). (last updated 2019-01-17 14:03:20)Contact: Location: Pharmacy BuildingAffiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionBioinformatics AnalysisContact:ServiceWe provide bioinformatics consultation and services to the Rhode Island INBRE community and to the general scientific community of Rhode Island. In addition to traditional bioinformatic services, our Core is expanding to 3D/VR/AR visualization services in conjunction with the URI 3D Visualization Laboratory. We are currently developing 3D/VR visualization apps for a variety of platforms including smartphones, Vive/SteamVR, and others. We also have access to the 3D printer at the URI 3D Visualization Core for printing of marcomolecules at a reasonable price.Biorepository Core. (last updated 2019-01-17 13:42:21)Contact: Location: 130 Plain Street Providence, RI 20903Affiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionTechnical Assistance - Biostatistical ConsultingContact:ServiceThe Biostatistics Core at Rhode Island Hospital offers centralized biostatistics consultation services to clinical, basic, and translational researchers.
We provide, or can often steer, investigators to a broad array of resources during all phases of research. During planning, the core can provide high quality trial design, including final sculpting of specific aims and hypotheses, measure selection, power analysis, data analysis planning, and grant/proposal preparation. At the beginning of the project, the core can assist in construction of REDCap data collection tools, databases, and data management protocols, and provide help navigating approvals for special technology needs, such as purchasing non-standard equipment. Throughout the trial, the core can provide decision support, assessing the impact of potential design additions or changes on final statistical inference. It can serve on or help find individuals to serve on data and safety monitoring boards (DSMB).
At the conclusion of the trial, the core can assist with implementation of the data analysis plan, either by providing access to contemporary statistical analysis software and coaching, or by conducting analyses for or with investigators. During reporting/publication preparation, the core can assist in write-up, data visualization, and integration of interpretations in the broader context of the project, and help address comments from peer reviewers.Bradley Hasbro Children's Research Center Resources.
(last updated 2019-04-04 15:03:56)Contact: Location: Bradley HospitalAffiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionChild Development and Mental Health ResearchContact:Clinical Research Resources, Child DevelopmentThe Bradley Hasbro Children's Research Center (BHCRC) encompasses a broad spectrum of research programs that share a commitment to studying the impact of psychological factors on the growth and development of children and their families. BHCRC investigators are exploring new insights into the genetic roots of autism; finding pediatric bio-behavioral markers of bipolar disorder; creating effective therapies for OCD; devising effective prevention strategies for adolescent sexual risk behaviors and obesity; and much more. (last updated 2019-01-08 09:36:09)Contact: Location: Biomed Center Instrument/Service/Resource UnitManufacturer/DescriptionFunctionHerbariumDried and pressed plant specimens mounted on sheets of archival paper. Plant data.The collection includes around 100,000 plant specimens and is an important depository of Rhode Island and New England collections. It is also rich in western and southern North American plants and includes special sets of historically valuable specimens from 19th and early 20th century western US expeditions. Among other important collections, the herbarium also includes a full set of Charles Wright's Cuban plants (1856-1867) and a unique and classic collection of CarexBrown University Oncology Group (BRUOG).
(last updated 2017-12-08 09:40:58)Contact: Location: 233 Richmond Street, Providence, RI 02903Affiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionBrown University Oncology Group (BRUOG)Contact:Clinical Trials Support, Oncology ResearchThe Brown University Oncology Research Group (BrUOG) mission is to improve cancer care through the implementation of innovative, multidisciplinary cancer clinical trials. BrUOG provides the infrastructure for the efficient development and implementation of these trials, which are created by Brown University faculty. BrUOG’s administrators and physicians provide support for the initial study concept and validation of trial design, and are responsible for trial administration, safety monitoring, data analysis, and the presentation and publication of findings.
The trials sponsored by BrUOG investigate novel, cutting-edge applications of chemotherapy, biologic agents and other cancer treatments. Clinical trials are available for a broad range of disorders in hematology/oncology and these include treatment for cancers of the breast, brain, lung, gastrointestinal tract, skin, and prostate as well as trials in leukemia and lymphoma.Bryant Unistructure Science Spaces. (last updated 2018-01-17 10:34:29)Location: Zecchino PavilionAffiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionCardiovascular Research Center (CVRC)Contact:Clinical Research Resources, CardiovascularThe mission of the CVRC is to unravel fundamental mechanisms of heart disease and to discover novel cardiac therapies. The research focus of CVRC investigators is on the molecular mechanisms of rhythm disorders of the heart (cardiac arrhythmias), sudden cardiac arrest, heart enlargement (hypertrophy) and heart failure.
CVRC facilities include a sophisticated invasive animal catheterization and electrophysiology laboratory, and a brand new state-of-the-art research facility that is used for the studying of genetically modified mouse and rabbit models of cardiac diseases. These studies include cellular electrophysiology, optical mapping, single cell analyses of calcium transients and contractility, sophisticated imaging tools, as well as biochemical, molecular and cell biological experimental approaches. An important goal of the CVRC is to enhance and promote education and training in molecular cardiology and life sciences.Vascular Disease Research CenterContact:Clinical Research Resources, VascularThe Vascular Disease Research Center at Rhode Island Hospital develops clinical trials and basic science research, and has a demonstrated track record in obtaining funding for these initiatives from the federal government through the National Institutes of Health (NIH). The center also coordinates a number of Food and Drug Administration (FDA). We can help investigators seeking to develop ideas in the vascular disease arena into fundable grant applications, either formally with center involvement or informally. We also can serve in this capacity for the medical device or drug industry, helping to plan and execute all aspects of FDA studies.
Our mission is to advance scientific understanding and improve public health by developing and performing pivotal clinical and basic scientific research in peripheral vascular disease.CCRI Biology Labs. (last updated 2016-06-16 10:29:30)Contact: Location: Warwick, RIAffiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionAutoclaveConsolidated Stills & Sterilizers 24D36Sterilization. Chamber size is 24inch x36inch, Volume 9.4 cu. (last updated 2018-03-28 10:48:31)Contact: Location: The Providence VA Medical CenterAffiliated Centers:, Instrument/Service/Resource UnitManufacturer/DescriptionFunctionAnalyzer - Cell MetabolismSeahorse Biosciences - XF Extracellular Flux AnalyzerXF instruments analyze mitochondrial respiration and glycolysis in live cells, generating data in just minutes. XF Technology uses solid-state sensors to simultaneously measure both oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in every well. All platforms support injection of up to 4 unique compounds per well.Sensing equipment - Electrical Cell Impedance Sensing (ECIS)Applied BioPhysics, Model 100Measures cell impedance using altering currents across cell monolayers and measuring the change in electrical potential. Impedance is calculated using Ohm's law.Sensing equipment - Electrical Cell Impedance Sensing (ECIS)Applied BioPhysics, Model ECIS Z ThetaMeasures cell impedance using altering currents across cell monolayers and measuring the change in electrical potential.
Impedance is calculated using Ohm's law.Imaging System - Slide ScannerAperio ScanScope CS Digital Slide Scanner CSBrightfield 5 slide scanner with two workstations with Aperio brightfield imaging analysis software.Imaging System - Slide ScannerAperio Scan Scope FL Fluorescent Digital Slide Scanner5 slide scanner with Indica Labs fluorescence imaging analysis software.Flow Cytometer - Magnetic-Activated Cell Sorter (MACS)Miltenyi MACSQuant Analyzer 7 Flow CytometerThe MACSQuant Analyzer is a powerful benchtop flow cytometer for highly sensitive, multiparameter cell analysis. MACSQuant Analyzers offer fully automated instrument setup via pre-set calibration and compensation programs.
Casio phone mate ta 120 manual. Automated startup, shutdown, and cleaning cycles make for hassle-free instrument housekeeping. The MACSQuantify Software provides a powerful and easy-to-use user interface for instrument control, data acquisition, and fully automated analysis.EvaporatorBuchi RotaVapor R215 EvaporatorRotary evaporator for removing solvents.Imaging System - EchocardiographVisualSonics, Vevo 2100 Imaging SystemA high-frequency, high-resolution digital imaging platform with linear array technology and Color Doppler Mode.
(last updated 2017-12-08 09:46:37)Contact: Location: 55 Claverick Street Providence, RI 02903Affiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionCenter for International Health ResearchContact:Clinical Research Resources, Tropical Infectious DiseasesThe Center's mission is to address these urgent global health challenges, the Center for International Health Research (CIHR) at Rhode Island Hospital was founded. The center's mission is to understand the pathogenesis of tropical infectious diseases, specifically malaria and schistosomiasis, and to harness this knowledge to design improved treatments and vaccines. ChildrenTo combat these infections, the center is engaged in studies integrating community-based epidemiologic studies in endemic countries with laboratory-based, basic science investigations. The center's research approach is rooted in the belief that the discoveries of tomorrow will come at the intersection of field and lab science.Center for Neurorestoration and Neurotechnology (CfNN) Resources. (last updated 2017-12-07 15:05:08)Contact: Location: The Providence VA Medical CenterAffiliated Centers: Instrument/Service/Resource UnitManufacturer/DescriptionFunctionCenter for Neurorestoration and Neurotechnology (CfNN)Contact:Clinical Research Resources, Neurorestoration and NeurotechnologyThe Center for Neurorestoration and Neurotechnology (CfNN) is a collaboration between the Providence VA Medical Center, Brown University, Butler Hospital, Lifespan, and Massachusetts General Hospital. CfNN unifies distinguished researchers and clinicians to advance and translate neurotechnology to restore lost function. Through its three focus areas and three support cores, CfNN seeks to develop, test and implement new therapies and technologies that restore function for Veterans with disorders affecting the nervous system.Restoring Communication and MobilityClinical Research Resources, Neurorestoration and NeurotechnologyResearch in this CfNN Focus Area aims to restore function for Veterans with severe communication or movement impairment resulting from amyotrophic lateral sclerosis (ALS), spinal cord injury (SCI), stroke, seizure disorders or disorders of consciousness.
With the direct research participation of Veterans and others, this research aims to improve the independence and well-being of those with severe motor disability through the development of novel neurotechnologies and innovative medical approaches based on fundamentally new views of the neural processes underlying these conditions.
'While My Guitar Gently Weeps', 'Something' and 'Here Comes The Sun' are exciting live versions, not original Beatles recordings.Among my many favorites on Greatest Hits are: 'Blow Away', Handle With Care' (Traveling Wilburys Vol. Torrent george harrison greatest hits album. 1), 'All Things Must Pass', 'Give Me Love'; plus lesser known songs such as 'Any Road' and 'Never Get Over You', both from the Brainwashed album. It contains the vast majority of notable songs from George's solo career and is far more comprehensive than the other 'hits' albums available.This album contains 40 songs of which only three are from the Beatles era. The album also contains two less familiar soundtrack selections 'Cheer Down' and 'I Don't Want To Do It' (written by Bob Dylan).I held back one star due to the omission of several signature Harrison early releases, especially 'I'd Have You Anytime', 'If Not For You' and 'Isn't It a Pity' from the All Things Must Pass album.
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