alexa Proteomics
ISSN: 2155-9821
Journal of Bioprocessing & Biotechniques
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Fermentation Monitoring of a Co-Culture Process with Saccharomyces cerevisiae and Scheffersomyces stipitis Using Shotgun Proteomics

Eric L Huang and Mark G Lefsrud*
Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, Quebec, H9X 3V9, Canada
Corresponding Author : Mark G Lefsrud
Bioresource Engineering, McGill University
21111 Lakeshore Road, Ste-Anne-de-Bellevue
Quebec, H9X 3V9, Canada
Tel: 514-398-7967
Fax: 514-398-8387
Email: [email protected]
Received October 27, 2013; Accepted January 02, 2014; Published January 09, 2014
Citation: Huang EL, Lefsrud MG (2014) Fermentation Monitoring of a Co- Culture Process with Saccharomyces cerevisiae and Scheffersomyces stipitis Using Shotgun Proteomics. J Bioprocess Biotech 4:144.doi:10.4172/2155-9821.1000144
Copyright: © 2014 Huang EL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and and source are credited.

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Abstract

Co-culture processes present the opportunity to produce value-added products from economical raw materials, but there lacks a high-throughput fermentation monitoring technique to study the temporal physiology of fermentation organisms in co-culture processes. In this study, we applied shotgun proteomics to investigate a co-culture process of Saccharomyces cerevisiae and Scheffersomyces stipitis, and we monitored the fermentation until glucose depletion. Three time points were taken for proteomics analysis at 11.5 hour, 18.5 hour and 32 hour, representing transition into diauxic shift. Using label-free quantitation, we observed cellular dynamic within 20-hour time frame. We distinguished the proteome between two yeasts, and the most abundant proteins of S. stipitis and S. cerevisiae contained expected processes of glycolytic enzymes, histones, heat shock proteins, ribosomal proteins and F1F0-ATPase. After glucose depletion, we identified up-regulations of S. stipitis malate synthase and isocitrate lyase as key enzymes in glyoxylate cycle and gluconeogenesis. Increased expression of S. stipitis histone 2B was observed in diauxic shift, and histone acetylation was suggested by up-regulation of acetyl-CoA synthetase. Without the presence of xylose, we observed induction of NAD(P)H-dependent D-xylose reductase (Xyl1p) as early as 11.5-hour before glucose depletion. We also observed the expression of D-xylulose reductase after glucose depletion without xylose induction. Further study is needed to investigate the cause of derepression signals for xylose oxo-reductive pathway. Without cellulose induction, the up-regulation of S. stipitis endo-1,4-beta-glucanase suggested S. stipites’ strategy in diversifying carbon choices after glucose repression. This research demonstrated the application of shotgun proteomics in high-throughput monitoring of complex co-culture system and able to elucidate the temporal physiology of S. stipitis.

Keywords
Saccharomyces cerevisiae; Scheffersomyces stipitis; Pichia stipitis; Shotgun proteomics; Co-Culture; Mudpit; Fermentation monitoring
Introduction
Co-culture processes present the opportunity to establish stable and profitable biotechnological bioprocesses and produce valueadded products from economical raw materials such as agricultural residues [1]. Mixed culture systems have demonstrated promise in hydrogen, methane, ethanol and polyhydroxyalkanoates productions from renewable resource, increasing potential revenue and reducing environmental impacts [2-4]. The use of controlled mixed fermentation using Saccharomyces and non-Saccharomyces yeasts has been implemented in winemaking to alter both chemical and the aromatic composition of wines [5]. The synergistic interaction between different yeasts provides a tool for the implementation of new fermentation technologies [5]. In biofuel research, the co-culture process of Saccharomyce cerevisiae and Scheffersomyces stipitis (Pichia stipitis) has been extensively studied with attempts to achieve a simple, one-batch process to ferment glucose and xylose using lignocellulosic feedstock [6,7]. The co-culture process poses technical obstacles such as low ethanol yields associated with different optimal oxygen transfer rate required by each organism. Novel fermentation apparatus using two fermentors and two microfiltration modules to achieve optimal fermentation condition for each yeast [7]. However, before optimizing a co-culture process such as ones described by Taniguchi et al. [7], there lacks a robust, high-throughput fermentation monitoring technique to monitor the temporal physiology of each organism in more complex process such as co-culture fermentation. In recent years, fermentation technology has turned ‘-omics’ for solutions in process development [8]. Proteomics quantification of yeast high gravity ethanol fermentations was investigated to determine the dynamic of metabolic pathways (e.g. the deactivation of secondary metabolites pathway) [9]. Shotgun proteomics can provide the physiological backgrounds needed for nonmodel organisms such as S. stipitis and allow observation of the global profile of the organism's proteome. The analysis of the proteome allows the evaluation of cellular processes at greater depth with a much simpler technique than 2D-PAGE-MS [10]. In this study we demonstrated the application of shotgun proteomics in monitoring a co-culture process using S. cerevisiae S288C and S. stipitis CBS 6054. The yeast proteome of three time points were analyzed based on metabolic and regulatory pathways. Without the presence of xylose, we observed the induction of xylose reductase (Xyl1p, S. stipitis) as early as 11.5-hour before glucose depletion, and D-xylulose reductase (Xyl2p, S. stipitis) as early as 18.5- hour after glucose depletion. . We distinguished the proteome between two yeasts, identified the most abundant proteins of S. stipitis and S. cerevisiae, and determine the dynamic of S. stipitis proteome during the co-culture process.
Materials and Methods
Microorganism and culture condition
Saccharomyces cerevisiae S288C and Scheffersomyces stipitis (syn. Pichia stipitis CBS 6054) were obtained from American Type Culture Collection (ATCC, Manassas, VA, USA) and maintained on YPD or YPX agar slants accordingly. YPX substituted xylose with glucose in YPD. Pre-inoculums of S. cerevisiae and S. stipitis were grown separately at 26°C, 100 rpm and harvested at mid-exponential phase. Two biological replicates of the co-culture process were carried out in a 250mL Erlenmeyer flask with 100mL working volume at 26°C and 100rpm. The medium composition for the co-culture process was 20g/L glucose, 20g/L peptone and 10g/L yeast extract.
Fermentation parameters analysis
Glucose, ethanol, acetic acid and glycerol were measured using enzymatic kits (Megazyme, Wicklow, Ireland). OD610 was monitored throughout growth with a UV-VIS spectrophotometer. Colonyforming unit (CFU) was used to obtain viable yeast counts of S. stipitis and S. cerevisiae. S. stipitis and S. cerevisiae were enumerated by adapting methods as described in Laplace and colleagues [11]. In brief, S. stipitis and S. cerevisiae were distinguished from each other by plating on medium containing erythromycin (1g/L), 1% yeast extract, 2% peptone, 2% glycerol or 2% glucose. S. stipitis was able to grow on media containing erythromycin (1 g/L) with 2% glycerol, whereas S. cerevisiae was inhibited by the mitochondrial inhibitor.
Cells lysis and protein extraction
Three protein samples from each biological replicate were taken at 11.5 hour, 18.5 hour and 32 hour. ~10 mg of wet-mass cell pellet for each sample was obtained and processed through single tube cell lysis and protein digestion [12,13]. The protein concentrations after the extraction were ~6 mg/mL for each sample after extraction. Tris/10mM CaCl2 at pH 7.6 and 6M Guanidine/10mM Dithiothreitol (DTT) (Sigma-Aldrich Canada, Oakville, Ontario) were used to lyse cells and extrude proteins. In brief, the mixture was placed on the rocker for the first hour and vortexed every 10 minutes, and then incubated for 12 hours at 37°C. The mixture was spun down at 10,000 g. The supernatant was discarded and the pellet was diluted with 6-fold 50mM Tris/10mM CaCl2. ~5 μg of sequencing grade trypsin (Promega, Madison, WI, USA) was added to each sample and digested for 12 hours at 37°C by gentle rocking. The same amount of trypsin was added a second time and incubated at 37°C for another 12 hours. 1M DTT was added to a final concentration of 20mM and incubated for another hour with gentle rocking at 37°C followed by centrifugation at 10,000 g for 10 minutes. The supernatant was kept and samples were cleaned and desalted via solid phase extraction with Sep-Pak Plus cartridges (Waters Limited, Mississauga, Ontario), concentrated, solvent exchanged, filtered, aliquoted and frozen at -80°C [14].
2D-LC MS/MS
Each extracted and digested sample was analyzed twice for technical replicate in 2D-LC MS/MS. Samples were loaded onto the back column of split phase 2D column [15] (~3-5cm Luna SCX 5 μm and ~3- 5cm C18 Aqua 5μm 100A) (Phenomenex, Torrance, CA) using a pressure cell and then connected to a front column with integrated nanospray tip (~15 cm of Aqua C18 Aqua 5μm 100A, Phenomenex, Torrance, CA; 150 μm with 15 μm tip, New Objective, Woburn, MA). Samples were analyzed via 2-D LC-nanoESI-MS/MS on a LTQ XL (ThermoFisher Scientific, San Jose, CA) as previously described [13]. Samples were analyzed via 13-hr runs (6 steps with 1 wash step) [14,16].
Proteome informatics
Proteome Discoverer 1.1 (ThermoFisher Scientific, San Jose, CA) and SEQUEST algorithm [17] was used to correlate peptides spectra with S. stipitis protein sequence (filtered models containing 5839 entries downloaded from the Joint Genome Institute; accessed on 05/15/2011) and S. cerevisiae protein sequence (5884 entries downloaded from the Saccharomyces Genome Database; accessed on 05/15/2011) [6,16]. Proteome Discoverer 1.1 was used to filter SEQUEST results and sort peptides into protein identification based on modified method of Verberkmoes et al. [17] (minimum Xcor of at least 1.5 [+1], 2.3 [+2], 2.8 [+3]; minimum probability score of at least 2 [+1], 7 [+2] and 7.5 [+3]; minimum two fully tryptic peptides) [13]. We enabled protein grouping, counted only rank 1 peptides and only in the top scored proteins. Each protein identified had at least one unique peptide. Reverse protein sequences were included in the search database to estimate the overall false-positive rates of protein identification (falsepositive rate = 2[nrev/(nrev + nreal)]*100; nrev = the number of peptides identified from the reverse database; nreal = the number of peptides identified from the real database) [18].
Data analysis
KEGG and GO annotations of S. cerevisiae and S. stipitis (also KOGs) were downloaded from Saccharomyces Genome Database and Joint Genome Institute [19,20]. The identified proteomes were examined manually with each biological function. In-house Perl scripts were written to perform the functional counts of GOs and KOGs. The metabolic pathways were visualized using KEGG Mapper [21]. For comparison of protein abundance levels, normalized spectral abundance factors (as label free estimation of relative protein abundance) (NSAF) were determined as described in Florens et al. [22]. This method estimates protein abundance by first dividing the spectral count for each protein by the protein length and then dividing this number by the sum of all length-normalized spectral counts for each organism and multiplying by 100 [17,22]. Differentially expressed proteins between 11.5- hour and 32-hour were determined using spectral count and the software QSpec with log. fold change of 3 and up-regulated FDR = 0.01 [23].
Results and Discussions
Overview of proteome
This study represented the first attempt to monitor the temporal physiological profile of a co-culture process using S. cerevisiae and S. stipitis with shotgun proteomics. Key metabolites, including ethanol, glucose, acetic acid and glycerol were measured and monitored throughout the co-culture fermentation (Figure 1 and 2). The fermentation lasted for 36 hour in both biological replicates. The highest ethanol yielded ~9 g/L. We observed low acetic acid (< 0.5 g/L) throughout the process, but glycerol was observed at moderate level (<1.15 g/L). Glucose was consumed to near depletion (< 1 g/L) at ~16 hour, indicating diauxic shift for both organisms. We inoculated batch co-culture fermentation with the pre-inoculums and obtained initial cell density of 8.07x105 CFU for S. cerevisiae and 7.23x105 CFU for S. stipitis in biological replicate 1 at time zero. In biological replicate 2 we obtained initial cell density of 7.87 x105 CFU for S. cerevisiae and 1.10 x106 CFU for S. stipitis. Before 11.5 hour, both S. cerevisiae and S. stipitis multiplied at near equal rate. In biological replicate 1, S. stipitis outgrew S. cerevisiae, reaching 1.06x108 CFU for S. stipitis and 6.20x107 CFU for S. cerevisiae at the end of the fermentation. However, in biological replicate 2, the population of S. stipitis and S. cerevisiae fluctuated back and forth, failing to determine the dominant specie. This study further demonstrated the challenge in producing consistent batches in co-culture process. Based on the growth curve of each organism, the maximal growth rates of both S. cerevisiae and S. stipitis occurred at 11.5 hour and the maximal glucose consumption rate was also observed at 11.5 hour, corresponding to the exponential growth phase of both yeasts. We collected proteomics samples from two flask fermentations at three time points (11.5 hour, 18.5 hour and 32 hour) to monitor the co-culture process. The shotgun approach was used to identified and matched peptide mass data to the sequenced S. stipitis and S. cerevisiae sequence databases. In biological replicate 1, combining all time points and technical replicates for each time point, we identified a total number of 1390 proteins throughout the co-culture process (650 proteins from S. stipitis and 740 proteins from S. cerevisiae (Table 1). In biological replicate 2, combining all time points and technical replicates for each time point, we identified a total number of 1499 proteins throughout the co-culture process (666 proteins from S. stipitis and 833 proteins from S. cerevisiae; Table 1). The false discovery rates of 2D-LC-MS/MS runs were calculated between 0.16% to 0.57%. Table 1 summarized spectral counts and the CFU for each organism in each time point in both biological replicates. We observed that although both S. cerevisiae and S. stipitis CFU were at approximately 1:1 ratio (~2.5x107 CFU each) at 11.5 hour, the total peptides counts were not equal between two yeasts. The difference between total peptides counts demonstrated the difference in cell size and mass between S. stipitis and S. cerevisiae, where S. stipitis is smaller than S. cerevisiae in cell mass per cell. S. stipitis exhibited higher CFU than S. cerevisiae during the fermentation, but higher spectral counts in S. cerevisiae were observed. Looking at the spectral counts, CFU and protein identified, we found an increase in S. cerevisiae CFU did not necessary result in identifications of higher proteins and spectral counts, but an increase in S. stipitis CFU resulted in higher spectral counts and minor increase in numbers of protein identification. This implied other variables, which could be time dependent, interacted with spectrum identification capacity of the mass spectrometry. One possible explanation could be the competition for electro-spray ionization between S. cerevisiae and S. stipitis peptides. Another explanation was the difference in extraction and digestion efficiency between two organisms. We calculated normalized spectral abundance factors (NSAFs) to estimate the relative protein abundance. By comparing the technical replicates of each dataset, we obtained highly reproducible results in each biological replicate, with the linear regression line fell between R2 = 0.81 and 0.89 for S. cerevisiae in biological replicate 1(as illustrated in Figure 3 for 18.5 hour time point) and between R2 = 0.85 and 0.90 for S. stipitis (data not shown).
Most abundant proteins in s. cerevisiae and s. stipitis
Proteomics data was further analyzed below by using both biological replicates if not stated. Similar to the most abundant proteins found using GFP fusion library and high-throughput flow cytometry, S. cerevisiae glycolytic enzymes were among the most abundant proteins in the cell from exponential to diauxic shift [24]. These included pyruvate kinase (Cdc19p), 3-phosphoglycerate kinase (Pgk1p), glyceraldehyde-3-phosphate dehydrogenase (Tdh1p, Tdh2p, Tdh3p), enolase II (Eno2p) and fructose 1,6-bisphosphate aldolase (Fba1p). Alcohol dehydrogenase (Adh1p) and F1 sector of mitochondrial F1F0 ATP synthase (Atp2p) were also highly abundant in S. cerevisiae proteome, indicating the importance of oxidative phosphorylation. The fact that the other enzymes of the tricarboxylic acid cycle were not switched off during fermentative growth can be explained by their involvement in amino acid biosynthesis, gluconeogenesis, heme biosynthesis or de novo fatty acid synthesis.
The most abundant proteins identified in S. stipitis also came from glycolysis and gluconeogenesis. Similar to S. cerevisiae, the glycolytic enzymes observed in most abundance included glyceraldehyde-3- phosphate dehydrogenase (Tdh2p and Tdh3p), enolase I (Eno1p), phosphoglycerate mutase (Gpm1.1p), fructose-bisphosphate aldolase (Fba1p), phosphoglycerate kinase (Pgk13p) and pyruvate kinase (Pyk1p). S. stipitis cells also produced high amount of alcohol dehydrogenase (Adh1p), which reduces acetaldehyde to ethanol. We observed high abundance of F1F0-ATPase complex alpha and beta subunits (Atp1p, Atp2p) located in mitochondrial cellular components. We also identified high abundance of mitochondrial malate dehydrogenase (Mdh1p) involved in tricarboxylic acid (TCA) cycle, indicating respiro-fermentative. Proteins involved in chromatin assembly, protein translation and folding were also high in abundance. These included heat shock protein (HSP70 family Ssa2p), histone H4 and 2B (Hhf1p and Htb2.1p) and ribosomal proteins (L14B, 40S S14-B, S7-A and 60S L4360S). The resemblance in both S. cerevisiae and S. stipitis proteomes showed that highly abundant proteins were housekeeping proteins and essential for viability. In yeast cells, glycolytic enzymes constitute a major fraction (30 to 60%) of the soluble proteins [25], and in rapidly growing cells, 60% of total transcription is devoted to ribosomal RNA [26].
Functional categorization and analysis of s. cerevisiae and s. stipitis proteome
A comparison between S. stipitis and other more related yeast such as C. albicans would almost certainly show a higher degree of conservation, but because regulation has been best studied in S. cerevisiae, that is a standard frame of reference. Therefore in this study regulation of S. stipitis was inferred and compared with S. cerevisiae if not otherwise stated. The proteome of S. cerevisiae and S. stipitis observed in the co-cultural process contained housekeeping processes such as oxidative phosphorylation, glycolysis, non-oxidative branch of the pentose phosphate pathway, gluconeogenesis, biosynthesis of amino acids and aminoacyl-tRNA, protein synthesis and proteolysis, fatty acid metabolism and cell division. An attempt was made to categorize S. stipitis proteome based on GO terms for direct comparison between S. stipitis and S. cerevisiae, but the incomplete nature of the GO entries for S. stipitis proved impractical (data not shown). The incomplete nature of the GO annotations for S. stipitis directed us to categorize S. stipitis proteome based on KOGs. In analyzing the S. stipitis proteome, the majority of KOGs functions fell into the categories of carbohydrate metabolism, protein synthesis and modification. Technical replicates of each time point showed reproducible counts of each KOGs term. Even with the higher counts observed in later time points, each KOGs category still constituted the same relative ratio to the whole data set (data now shown). The majority of GO terms also fell into protein synthesis and carbohydrate metabolism in S. cerevisiae. The ratio of each category relative to the whole data set was consistent between time points. Technical replicate of each time point also showed reproducibility. All time points shared similar distribution of biological function in terms of KOGs for S. stipitis and GO Biological terms for S. cerevisiae.
Distinctive physiology in temporal proteome
In analyzing the proteome of S. stipitis, certain stress proteins were observed before diauxic shift, and these included thioredoxin peroxidase (acts as antioxidant) and glycogen synthase (Gsy1p). The expression of Gsy1p was observed as early as 11.5 hour in both S. cerevisiae and S. stipitis. It was possible that heat shock, nitrogen starvation or the 2% glucose medium (insufficient glucose) caused the de-repression of Gsy1p [27,28]. Earlier studies suggested S. cerevisiae and S. stipitis shared similar amino acid biosynthesis pathway. In S. stipitis proteome, we observed proteins involved in a list of amino acid biosynthesis pathways that are under general amino acid controls before diauxic shift. These included acetolactate synthase (Ilv2p), dihydroxyacid dehydratase (Ilv3p, 32 hour only), mitochondrial ketolacid reductoisomerase (Ilv5p) and 3-deoxy-D-arabino-heptulosonate 7-phosphate synthase isoenzyme (Aro4p). Hans and colleagues observed the most drastic alternations of intracellular amino acid pools occurred during the diauxic shift and at the entry into the stationary phase [29]. We also observed principal enzyme of glyoxylate cycle before diauxic shift, including malate synthase (Mls1.1p) and isocitrate lyase (Icl1p), a key enzyme for growth on ethanol and acetate in yeast. Earlier studies in S. cerevisiae showed that ICL1 expression is tightly regulated and growth on ethanol requires the glyoxylate pathway for replenishing C4 compounds to the tricarboxylic acid cycle. ICL1 was also induced by growth on ethanol and repressed by growth on glucose [30]. The most peculiar finding in our co-culture process was the observation of the enzymes involved in oxo-reductive pathway for xylose utilization. We observed NAD(P)H-dependent D-xylose reductase (Xyl1p) as early as 11.5- hour before glucose depletion. We also observed D-xylulose reductase as early as 18.5-hour after glucose depletion. Xylose was not presented in the media (confirmed by enzymatic assay). We observed aldehyde dehydrogenase (Ald5p) at 32 hour, but S. stipitis was not known to produce acetate. The function of Ald5p remained to be examined. Our metabolic data supported the expression of DLglycerol- 3-phosphatase (Gpp1p) and suggested anaerobic and osmotic stress for S. stipitis. Other proteins which suggested stress included Gph1p, glycogen (starch) synthase (Gsy1p), thiol-specific antioxidant protein (Tsa1p), NADPH-dependent methylglyoxal reductase (Gre2p) and thioredoxin reductase (Prx1p) [31,32].
Dynamic of S. stipitis during fermentation
Using the spectral counts and the software QSpec (log. fold change cutoff = 3; FDR up-regulated = 0.01) [23], we identified up-regulations of six S. stipitis proteins and one S. cerevisiae protein from 11.5 hour to 32 hour (Table 2). During the diauxic shift at 32-hour, we observed up-regulation of S. stipitis malate synthase and isocitrate lyase, the key enzymes involved in glyoxylate cycle. The depletion of glucose and the transition to growth on ethanol and acetate also triggered the up-regulation of S. stipitis phosphoenolpyruvate carboxykinase in gluconeogenesis. Interestingly, S. stipitis histone 2B did not exhibit down-regulation and increased histone acetylation was suggested by up-regulation of acetyl-coA synthetase. With glucose repression no longer existed at 32 hour, we also observed up-regulation of S. stipitis endo-1,4-beta-glucanase, a family 5 glycoside hydrolase, without cellulose in the growth media. Another unexpected finding in our proteomics data was the observation of the expressed enzymes involved in oxo-reductive pathway of xylose utilization in S. stipitis. NAD(P)H-dependent D-xylose reductase (Xyl1p) and D-xylose reductase (Xyl2p) are repressed under glucose culture [33]. However, we observed S. stipitis Xyl1p expression as early as 11.5- hour before glucose depletion. We also observed S. stipitis Xyl2p expression as early as 18.5 hour after glucose depletion. Xylose was not present in the media (confirmed by enzymatic assay) and the mechanism for signal derepression of these two enzymes is unknown. Earlier studies have suggested that reducing ATP production can also bring about a partial derepression of xylose assimilation [34]. Further study is needed to investigate and verify the derepression signal for Xyl1p and Xyl2p. We hypothesize that the derepression signal came from the effect of co-culture, which could be a competitive advantage for S. stipitis [35] as a hedging bet to utilize other potential carbon sources. Along with the up-regulation of S. stipitis endo-1,4-beta-glucanase without cellulose induction, our data demonstrated S. stipites strategy in diversifying potential carbon sources once glucose no longer existed.
Conclusions
In this study, we demonstrated a shotgun proteomics to monitor a co-culture processing with S. cerevisiae and S. stipitis. Shotgun proteomics successfully distinguished, identified distinct physiology of both yeast during the co-culture process, and able to elucidate the temporal profile of S. stipitis proteome. The most abundant proteins of S. stipitis and S. cerevisiae came from the expected process such as glycolytic enzymes, histones, heat shock proteins, ribosomal proteins and F1F0-ATPase. Without the presence of xylose, the induction of NAD(P)H-dependent D-xylose reductase and D-xylulose reductase were unexpected. Up-regulation of S. stipitis endo-1,4-beta-glucanase, histone 2B and acetyl-coA synthetase provided insight into the dynamics of non-model yeast S. stipitis during the co-culture process.
Acknowledgements
We thank Brian Dill and Collin Olivier for giving us detailed parameters of datadepended and LTQ XL setup. Funding of the project came from McGill University start up fund and Canadian Foundation for Innovation (CFI-IOF grant #23635).
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