A genomic survey of proteases in Aspergilli
- Sebnem Ozturkoglu Budak†1, 2, 3,
- Miaomiao Zhou†1, 3,
- Carlo Brouwer1,
- Ad Wiebenga1, 3,
- Isabelle Benoit1, 3,
- Marcos Di Falco4,
- Adrian Tsang4 and
- Ronald P de Vries1, 3Email author
© Ozturkoglu Budak et al.; licensee BioMed Central Ltd. 2014
Received: 3 February 2014
Accepted: 18 June 2014
Published: 25 June 2014
Proteases can hydrolyze peptides in aqueous environments. This property has made proteases the most important industrial enzymes by taking up about 60% of the total enzyme market. Microorganisms are the main sources for industrial protease production due to their high yield and a wide range of biochemical properties. Several Aspergilli have the ability to produce a variety of proteases, but no comprehensive comparative study has been carried out on protease productivity in this genus so far.
We have performed a combined analysis of comparative genomics, proteomics and enzymology tests on seven Aspergillus species grown on wheat bran and sugar beet pulp. Putative proteases were identified by homology search and Pfam domains. These genes were then clusters based on orthology and extracellular proteases were identified by protein subcellular localization prediction. Proteomics was used to identify the secreted enzymes in the cultures, while protease essays with and without inhibitors were performed to determine the overall protease activity per protease class. All this data was then integrated to compare the protease productivities in Aspergilli.
Genomes of Aspergillus species contain a similar proportion of protease encoding genes. According to comparative genomics, proteomics and enzymatic experiments serine proteases make up the largest group in the protease spectrum across the species. In general wheat bran gives higher induction of proteases than sugar beet pulp. Interesting differences of protease activity, extracellular enzyme spectrum composition, protein occurrence and abundance were identified for species. By combining in silico and wet-lab experiments, we present the intriguing variety of protease productivity in Aspergilli.
Proteases form a complex family of enzymes that possess different catalytic mechanisms with various active sites and divergent substrate specificities [1, 2]. Proteases hydrolyze peptides in aqueous environments [3, 4] and for years this ability has been utilized in industrial processes like food processing, waste treatment, textiles/detergent applications, and photography/chemical processing [5–9]. Proteases can be classified into four major groups: aspartic, cysteine, metallo and serine proteases . Protease inhibitors for each of these classes have been described . These inhibitors regulate the activity of proteases by binding to the enzyme and eliminating unwanted proteolysis [11, 12]. In recent years, proteases and protease inhibitors have gained additional interests in many health related areas as e.g. pathogenic agents by allergy, asthma and obese related illness . Proteases have been recognized as the most important industrial enzymes accounting for about 60% of the total enzyme market .
Proteases can be obtained from animal, plant and microbial sources . However, microorganisms are the most important sources for industrial applications [3, 4] due to their high yield and productivity and a wide range of biochemical and catalytic properties . The genus Aspergillus represents a diverse group of filamentous ascomycetous fungi , including human, animal and plant pathogens, but also species with a major role in industrial biotechnology . Several Aspergillus species have the ability to produce a variety of proteases [17–22].
In this study we have performed a genome survey of several Aspergilli based on the protein sequences of verified proteases and Pfam domains. Curated putative proteases were fed to a combination of protein subcellular localization (SCL) predictors to identify the potentially secreted proteins. The results of this in silico comparative secretomics were then tested by enzyme activity assays and proteomic experiments on samples from cultures grown on wheat bran and sugar beet pulp. Protease inhibitors were used to determine the contribution of the various protease classes to the total protease activity. Finally, by combining comparative genomes, proteomics and enzymology tests, we demonstrate the intriguing variety of protease productivity in the Aspergilli.
Genome mining and extracellular protein clustering
Summary of putative proteases in Aspergilli
A. fumigatus Af293
A. flavus NRLL 3357
A. oryzae RIB40
A. terreus NIH 2624
N. fischeri CBS 544.65
A. nidulans FGSC A4
A. niger ATCC 1015
Six different protein SCL predictors were applied to all 2146 putative proteases. By using majority vote 335 proteins were considered extracellular, among which 277 were in the original AspGD protease clusters (Additional file 3).
Further classification of proteases was determined by combined manual literature search and Pfam annotations. At the end, most putative proteases were classified into four major groups, namely amino, aspartic, metallo and serine, while the remaining genes formed the miscellaneous group (Additional file 1).
Effect of wheat bran and sugar beet pulp on extracellular protease induction in Aspergilli
Extracellular proteases in 7 Aspergilli confirmed by proteomics
Putative proteases identified by proteomics in 7 Aspergilli on wheat bran and sugar beet pulp
From all identified proteins, 93 were found on both WB and SBP, while 38 were found uniquely on WB and only two (dipeptidyl-peptidase Afu4g09320 [51, 63] and neutral protease I NFIA_099860) were found uniquely on SBP. Twenty-five out of these 133 identified proteases were not predicted to be extracellular according to our combined SCL predictions. Some of them may be secreted through alternative (non-classical) secretion systems, as suggested for the spermidine synthase (AO090012000528) from A. oryzae.
Intriguingly, contradictions were also found when delving deeper into the protease production profile of individual species. For example, A. terreus has the second largest amount of proteases identified in WB (24) whereas only 11 proteins were found in SBP. However, the protease activity in SBP was only around 20% less than in WB (Additional file 4). In N. fischeri a lower than average number of proteins was detected by proteomics, but the protease activity was amongst the highest of all species. In A. fumigatus only 3 additional proteins (15% extra quantity by spectral counts) were identified in WB compared to SBP, but the overall protease activity in WB was 40% higher. These findings demonstrate that the total protease activity does not only depend on the total production of proteases. This is likely due to the fact that the enzyme assays measure the combined activity of the available proteases. As they have different specific activities, the total activity is not equal to the sum of the protein amount. For instance, high abundance of a protease with a low specific activity may affect the overall protease activity less than moderate abundance of a protease with a high specific activity.
Closer examination of the produced protease activity using protease inhibitors
In order to elucidate the extracellular protease composition in more detail, a series of inhibitor specificity tests was performed. Most of the proteases that were identified in Aspergilli could be classified into the following major groups: amino, aspartic, metallo and serine. Based on literature, the main inhibitor of each group was Bestatin [64, 65], Pepstatin [66–68], Ethylenediaminetetraacetic acid (EDTA) [69, 70] and phenylmethanesulfonylfluoride (PMSF) , respectively (Table 2, Additional file 4). These inhibitors were added to the supernatants and protease activities were compared to those without inhibitors (Additional file 5).In general, for all samples protease activity was found to be inhibited predominantly by PMSF (43.48-67.12% decrease of activity). Lesser inhibition of activity was detected with EDTA (1.56-40.05%), Bestatin (1.29-14.92%) and Pepstatin (2.24-28.40%) (Figure 1B).
For PMSF inhibition the ratio of decrease was similar in all species (55 ± 12%), even though A. niger has the lowest overall protease activity and N. fischeri one of the highest (Figure 1 and Additional file 5). No significant difference of PMSF inhibited activities was found between WB and SBP in A. oryzae, A. nidulans, A. terreus, N. fischeri and A. niger. Although the occurrence and abundance of serine proteases were different in the samples, PMSF inhibited around half of the protease activity in all samples (Figure 1B, Additional file 5). Nevertheless, some of the prevalently produced serine protease clusters may be responsible for at least half of total enzyme activity in these species regardless of carbon source differences. Examples could be OG-30 that contains the lysosomal Pro-Xaa carboxypeptidase ProtA (56689) [39, 47], OG-31 that contains the dipeptidyl peptidase II (214460) , OG-33 that contains the carboxypeptidase CpyI (AO090701000220)  and OG-34 that contains the tripeptidyl-peptidase TppA (AO090011000235)  (Table 2).
EDTA was the second best inhibitor, but a large variation of inhibited activity was detected (1.56-40.05%). A. terreus and A. oryzae showed the highest activity inhibition in WB samples. A. nidulans also showed a significant inhibition effect (~20%), though only a small difference between WB and SBP was detected. The enzyme activity in these species did not show strong correlation to their protease profiles. In A. terreus minor amounts of metallo proteases were identified by the proteomics experiments. The spectral counts in WB were comparatively lower than in SBP, even though the activity inhibition was much higher in WB than in SBP, suggesting higher specific activity of metallo proteases present in WB. Alkaline protease AN7962  was the only metallo protease detected in A. nidulans cultures by proteomics. The spectral counts of this protein in WB were two-fold higher than in SBP. EDTA showed an equal effect on this protein with both substrates. In A. oryzae the main metallo proteases identified were neutral protease I (AO090011000036), neutral protease II (AO090010000493) and the leucine aminopeptidase (AO090011000052) . They showed higher total abundance in WB than SBP, and the inhibition effects confirmed this.
Among all species, A.oryzae and A.niger showed the highest activity inhibition when pepstatin was added to the supernatant produced with WB, indicating the presence of aspartic proteins in these species.In A. niger Aspergillopepsin A (PepA, 201655)  was found to be the most dominant protease, with a four-fold higher abundance in WB than in SBP.
Bestatin mainly inhibits the activity of amino-protease/peptidases. Adding Bestatin to the supernatants showed minor inhibition of protease activities (<10%). This was intriguing because a rather high presence of amino proteases was identified in A. terreus, A. nidulans, A. fumigatus, A. flavus and N. fischeri by proteomics. The putative aminopeptidase OG-4 [33, 36] (AN8445, AO090003000354, ATEG_09137, AFL2G_02631, NFIA_001250, and Afu3g00650) was the most abundant amino protease regardless of the carbon source in all species except A. niger (Figure 3).
We have performed sets of heterogeneous tests on Aspergillus species using two complex substrates as carbon sources, aiming to construct a snapshot of fungal life that reflects the variation in protease productivity in different species. In contrast to commonly reported genome-scale protease analysis results [27, 48, 62], besides comparative genomics and proteomics we also included the analysis of enzymatic measurements, which provided further elucidation on the composition of extracellular protease spectra.
By comparative genomics, a rather even distribution (around 3%) of putative proteases was detected in Aspergillus genomes despite genome size variations (Table 1). Among species, the proportions of proteins in each specific subgroup were also consistent, namely 25 ± 1% serine, 18 ± 1% metallo, 8 ± 1% amino and 5% aspartic proteases. Further ortholog clustering revealed only a very low number of extracellular “orphan” genes (9 putatively extracellular genes that have no homologs in the other six species included in this analysis). In fact, more than 60% of the extracellular putative proteases clusters were found to be ubiquitous by containing at least one gene per species. Moreover, the major extracellular protease regulator PrtT was also found to have a single presence per Aspergillus genome (except for A. nidulans) [51, 72–75]. This might have brought assumptions that in during evolution, moderate divergence of protease genes has occurred in this genus since most of the encoding genes were well conserved at sequence level and only a small number of species-specific genes was identified. If this hypothesis applies, the production rate of extracellular proteases in all Aspergillus species should follow the distribution of encoded genes and have similar influence of the regulator prtT, meaning even protein count and quantitative measurement should be detected by proteomics. However, large variations in protein occurrence and abundance were found, indicating more profound mechanisms might be playing important roles.
For example, A. flavus and N. fischeri contain almost identical percentages of putative extracellular proteases in the genomes, but when cultivated on the same carbon sources a double amount of proteases and even higher abundance were identified in A. flavus. Should the protease productivity in Aspergilli follow the distribution of protease encoding genes, the production of each specific subgroup of protease would be consistent among categories and species. Indeed at least one semi-ubiquitous protease ortholog group of genes were identified for each sub-category of proteases on at least one of the substrates, such as OG-4 (lap2 amino protease, AN8445) , OG-9 (pepE aspartic protease, AN2903) , OG-16 (neutral metallo protease I, AO090011000036)  and OG-30 (ProtA serine protease, 56689) . Moreover, a larger number of serine proteases were identified in all species, which correlates with the serine protease encoding genes being the largest subgroup of proteases in Aspergilli. However when quantitative measurements (abundance) were taken into account this correlation was absent because the most abundant individual proteases were never in the serine group, neither did the sum of abundances of the total serine group per species make this the dominant group (Additional file 4). In A. flavus (AFL2G_02631), A. fumigatus (Afu3g00650), and A. oryzae (AO090003000354) the most abundant protease belong to the amino protease group, while in A. nidulans (AN7962) and A. terreus (ATEG_04941) the most abundant proteases were metallo proteases. In the other species aspartic proteases (201655 and NFIA_073740) were more abundant. Taking A. niger as an example, the highest amount of serine proteases were indeed identified in the supernatant. However, based on comparative genomics the second most abundant group should be the metallo proteases, but no metallo protease was detected by proteomics on either substrate, which could possibly indicate that some of the proteases of the other classes also require metal ions [76, 77]. The second most abundant group detected in A. niger were the aspartic proteases, including pepA (213261) , opsA (211797)  and opsB (53364) . This demonstrated that even on the same substrate protease occurrence and abundance in Aspergillus species can differ significantly.
Although in industrial applications the productivity of proteases usually refers to the production rate of proteases per time per unit, in this study we aim to construct a snapshot of Aspergilli life style which reflects the protease production mechanisms, therefore the productivity measurements of proteases did not only include the occurrence or abundance of proteins but also the enzyme activities.
Summarizing the comparison results of genomics, proteomics and enzymology tests, a general trend was detected. WB induced higher total protease activity, richer proteomics profiles and more protein abundance than SBP. This strongly suggests that in Aspergilli, carbon source difference is the most important factor that influences protease productivity (see Additional file 6 for monosaccharide composition of WB and SBP and  for the composition of amino acids). This was further confirmed by the fact that using glucose (minimal medium +3% glucose) or glucose plus casein (minimal medium +1% glucose + 1% casein) only low protease induction could be detected in A. nidulans while sampled at the same time point as the WB or SBP cultures (data not shown).
While outside the scope of this study, it should be mentioned that it has been frequently reported that proteases are largely produced upon environment-induced cell lysis/damage , especially with sugar or nitrogen depletion [33, 36, 79, 80]. In our analysis, WB-based substrates showed higher protease activity as well as profiles than SBP-based substrates. This may indicate that WB cultivation resulted in a faster growth rate and earlier sugar depletion, and has therefore promoted an earlier production of proteases [36, 81]. To further reveal the mechanisms behind Aspergillus protease productivity, aspects such as sugar consumption and fungal growth rate should be taken into account in future studies.
Besides amino, aspartic, metallo and serine proteases, a certain amount of ubiquitin and trypsin proteases were also detected by proteomics. The specificity of these proteases was not tested due to the unavailability of inhibitor kits. Although very low abundance was found for these proteins, these proteins may also take part in the total extracellular protease activity in Aspergilli.
Other factors may also cause variability between individual Aspergillus species. pH has been reported to be one of these factors [46, 82, 83] and some of the data of this study supports this assumption. For example, AN6888 (pepA) has been reported to be an acidic protease  and was not detected in A. nidulans (pH 7 on WB and 8 on SBP). In contrast, the ortholog of this protein in A. niger (201655) had high abundance (pH = 5-6) [39, 49, 75, 80].
Finally, even though 6 well known protein SCL predictors were employed in order to guarantee the accuracy of extracellular protease prediction, improvements could still be made for secretome prediction. Among all six used tools the prediction rate varied largely. The WoLF-PSORT prediction fitted best with the proteomics results, while Multi-LOC was most different from this (data not shown). Interestingly, although with low area abundance 25 proteases were detected extracellularly by proteomics that lack a translocation signal peptide. Most of them were found in A. flavus and A.terreus (7 proteins each species), 3 were found in A. oryzae and the rest disseminated among the other species. If this was not caused by cell lysis or leakage, these proteins can be considered as indications of alternative secretion systems in Aspergilli. Hardly any of these proteins were correctly predicted by the SCL predictors we used. Hence, this study may also be of value as a testing or training set to improve currently existing prediction methods.
We have performed a series of in silico and biological experiments to gain understanding of protease production in Aspergilli. According to the results of comparative genomics Aspergillus species contain a similar proportion of protease encoding genes with serine proteases as the biggest group. The proteomics and enzymatic experiments generally confirm this composition, as serine proteases indeed make up the largest subcategory in the protease spectrum across the species. Furthermore, taking carbon source differences into account, wheat bran resulted in a higher induction of proteases than sugar beet pulp. An interesting variation of total protease activity, composition of the protease spectrum, and their abundance were observed between the species. The broadest set of proteases was found in A. flavus, while the highest overall protease abundance was found in A. niger, and the highest protease activity was detected for A. fumigatus in wheat bran and for N.fischeri in sugar beet pulp. It is very likely that even cultivated in an identical environment, the tested Aspergillus species were experiencing different physiology when sampled at the same time point. Concerning the high protein sequence conservation level (1E-20, sequence coverage 85%) among clustered proteases, it is likely that the variation of protease productivity is caused by more complicated mechanisms such as gene regulation related to environmental changes by carbon source differences [35, 44, 46] but not by enzymatic differences between the orthologous proteases themselves.
Genome mining, clustering and extracellular protein prediction
The pre-calculated protease clusters in AspDG were retrieved from the Aspergillus10-way-comparative database. Additional homologs were added to the clusters by homolog searches using majority vote of BLASTP , Jaccard  (cutoff E-value e-20 and alignment coverage 85%) and OrthoMCL (E-value 1E-10, inflation level 1 and sequence coverage 40%) results. Gene models were double checked with manual curation combining literature searches.
Six protein subcellular localization (SCL) predictors, Phobius , SignalP , PrediSi , CELLO , MultiLOC  and WoLF-PSORT [91, 92] were used to predict the extracellular proteases. Default settings of each SCL predictor were used, with the species parameter as “Eukaryotic” or “Fungi”. Majority votes were applied to combine the results of each SCL prediction.
Protease inhibitor information extraction and other bioinformatics analysis
The specific enzyme inhibitor information was retrieved by AspGD gene annotation repository and literature researches. Protein functional domain prediction was performed by HMMER v.3.0  using the complete Pfam-A and Pfam-B models  (data retrieved from Pfam database, version November 2012) with the trust cutoff and the gathering cutoff. The resulting Pfam predictions were pooled.
Strains and media
The fungal strains used in this study are listed in Table 1. All strains were grown on Malt Extract Agar and incubated at 30°C for 3–4 days until good sporulation had occurred. Spores were harvested by gentle agitation in 10 ml ACES (acid buffer) and solutions were taken into sterile tubes. Twenty times dilution of each solution were counted using a haemocytometer (Burker-Turk) under microscope (Axioplan, Zeiss). Liquid media was prepared in 250 ml conical flasks containing 50 ml Minimal medium (MM) . Five different culture conditions were prepared for the determination of protease activity in different mediums. Below substrates were added into 250 ml conical flasks containing 50 ml MM and a) 1% wheat bran, b) 1% wheat bran +1% glucose, c) 1% sugar beet pulp, d) 1% glucose + 1% sugar beet pulp and e) 1% glucose + 1% casein. All prepared media were autoclaved at 121°C for 20 min. For each strain, sterile liquid culture media were inoculated with 5× 108 spore/ml in 250 ml erlenmeyer flask and incubated for 72 h at 30 0C on a shaker at 250 rpm for the production of proteases. During the growth of fungi, 2 ml of aliquots were taken from cultures at 48 h, 72 h and 96 h. Those were centrifuged and used for all the experiments. Cultures were established in duplication for biological repetition and triplicated for technical repetition. The pH of most samples on was 7 except for A. nidulans on SBP (pH = 8) and A. niger on WB (pH = 5-6) and SBP (pH = 4-5).
Protease activity assay
A pilot experiment was performed on A.nidulans growing on WB, WB + Glc, SBP and SBP + Glc and protease activities were measured on 48, 72 and 96 h post-inoculation. From the analysis the best day with highest protease activity was found to be day 3 (72 h post-inoculation, Additional file 7).
For all experiments, protease activities of the cultures were measured after 72 h post-inoculation in liquid-state fermentation. 2 ml samples were taken from flasks and centrifuged at 14000 × ρ for 10 min (Eppendorf Centrifuge, 5417R). Supernatant was separated after centrifugation and stored at -20°C until the measurements of protease activity.
The protease activity assay was performed according to the procedures mentioned in protocol of Pierce Fluorescent Protease Assay Kit (Kit number: 23266, Pierce Biotechnology, Thermo Scientific, USA). The levels of protease activity in the supernatants of 7 strains over 72 h were compared using a fluorescein isothiocyanate (FITC)-labeled casein assay according to the manufacturer’s instructions. Fluorescence of the samples were measured by optical density (OD) using the plate reader (Fluostar Optima, BMG LABTECH) with excitation at 485 nm and emission at 530 nm to determine protease activity. The enzyme activity was expressed as micromoles of trypsin released per minute per milligram of total protein in culture filtrate (unit: U/mg, 1 μmol trypsin min-1).
pH 7.2 was required for the Pierce in light of the TBS solution stability. A pilot experiment was performed testing this kit on pH 4, 6, and 8 (Additional file 8). According to the result of this test, pH 6 was selected for protease activity measurements.
Inhibition of proteases
Protease inhibitors were prepared to give final concentrations of 50 mM for PMSF and EDTA (Sigma), 1 mM for Pepstatin A and Bestatin (Sigma) as instructed by the manufacturer.
2 μL protease inhibitors were added into the assay mixture and incubated for 60 min at room temperature prior to performing the assay. Culture supernatants treated alone was used as negative control. Each assay was performed in triplicate. All measurements were performed under pH 6.
Neutral carbohydrate composition
Neutral carbohydrate composition of wheat straw and sugar beet pulp was analysed according to Englyst  using inositol as an internal standard. Samples were treated with 72% (w/w) H2SO4 (1 h, 30°C) followed by hydrolysis with 1 M H2SO4 for 3 h at 100°C and the constituent sugars released were derivatised and analysed as their alditol acetates using gas chromatography (GC).
Protein from 3 ml of incubation medium was precipitated with cold TCA/Acetone. Protein sample determination was carried out with the RCDC kit assay (BioRad, Mississauga, Ont). Five ug. of protein was incubated in 100 mM ammonium bicarbonate, 0.1% AALS II (Morgantown, WV) and 5 mM dithiothreitol for 30 min. followed by the addition of Iodoacetamide to a final 25 mM concentration and incubated for an additional 30 min at 37 Deg. C. 200 ng of trypsin was added to each sample and the solution totaling 70 ul was incubated for 18 hr at 37 deg C. The digestion solutions were acidified with trifluoroacetic acid (1% final) then put through two rounds of desalting using C18 ziptips™ (Millipore, Billerica, MA). Eluted peptides were dried in a SpeedVac and resuspended in a 60 ul solution of 5% ACN, 0.1% FA and 4fmol/ul of predigested Bovine Serum Albumin (Michrom, Auburn, CA) used as an internal standard.
Five ul of peptide digest was loaded onto 15 cm × 75 μm i.d PicoFrit column (New Objective, Woburn, MA) packed with Jupiter 5 μm, 300 Å, C18 resin (Phenomemex, Torrance, CA) connected in-line with a Velos LTQ-Orbitrap mass spectrometer (Thermo-Fisher, San Jose, CA). Peptide separation was done using a linear gradient generated by an Easy-LC II Nano-HPLC system (Thermo-Fisher) using a mixture of solvent A (3% ACN:0.1% FA) and solvent B (99.9% ACN:0.1%FA). The gradient started at 1% B, was set to reach 27% B in 85 min, ramped to 52% B in 15 min and 90% B in 5 min then held at 90% for 5 min.
The mass spectrometer used was a Velos LTQ-Orbitrap (Thermo-Fisher, San Jose, CA). The capillary voltage on the nanospray source was adjusted to get the best spraying plume at 10% B and typically ranged from 1.9 to 2.1 kV. MS survey scan spanning the 350 to 2000 m/z range was done at 60000 resolution. The top 10 doubly, triply or quadruply charged ions with intensity higher that 5000 counts were considered candidates to undergo CID MS/MS fragmentation in the LTQ-Velos ion trap. Quantification was based on MS precursor ion signal using the precursor ion detection workflow from Proteome Discoverer Quant 1.3 (Thermo-Fisher). Briefly, extracted ion chromatograms were generated to compute the peptide area value associated to each identified precursor ion. A Protein Area value is subsequently calculated as the average of the three most intense, distinct, peptides assigned to a protein. Protein area values were expressed as a fold value of the protein area value calculated for Bovine Serum Albumin (BSA) which was spiked as an internal standard in each individual sample. For spectral count-based comparisons, the number of assigned spectra for each protein was reported as a fold value of the total number of spectra assigned to BSA in each sample.
Bioinformatics data processing
LC-MSMS data was processed using Proteome Discoverer Quant 1.3 (Thermo-Fisher) and spectral data was searched against Aspergillus protein databases downloaded from the Aspergillus Genome Database (AspGD). Search parameters used were 0.80 Da for fragment ion tolerance of and 10.0 ppm for parent ion tolerance, fixed iodoacetamide cysteine modification and variable methionine oxidation. Quantification was based on MS precursor ion signal using the precursor ion detection workflow from Proteome Discoverer Quant 1.3 (Thermo-Fisher). Briefly, extracted ion chromatograms were generated to compute the peptide area value associated to each identified precursor ion. A Protein Area value is subsequently calculated as the average of the three most intense, distinct, peptides assigned to a protein. Protein area values were expressed as a fold value of the protein area value calculated for Bovine Serum Albumin (BSA) which was spiked as an internal standard in each individual sample. For spectral count-based comparisons, the number of assigned spectra for each protein was reported as a fold value of the total number of spectra assigned to BSA in each sample. Confidence filters were applied to satisfy a 1% FDR at the Peptide and Protein level. Protein grouping was applied so as to satisfy the principles of parsimony. The normalized protein areas of a protein were used as the measurement of abundance level of the protein. The abundance of a protein represents the productivity of the protein in an organism under that specific circumstance while measured. The areas are also used as the measurement of protease productivity.
The unique counts of peptides to each identified protein were used as evidences of the occurrence of the protein. For any protein that has more than one uniquely mapped peptide it is considered occurred in the supernatant. The total amount/number of proteases in a sample was calculated by the sum of proteins which have more than one uniquely mapped peptide.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository  with the dataset identifier PXD000982.
Availability of supporting data
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository  with the dataset identifier PXD000982.
The authors would like to acknowledge Yagmur Karacelik for her help during protease activity analyses. SOB was supported with IDB Merit PhD-Scholarship Programme for High Technology (MSP). MZ was supported by a grant from the Netherlands Organisation for Scientific Research (NWO) and the Netherlands Genomics Initiative 93511035 to RPdV. This work was carried out on the Dutch national e-infrastructure with the support of SURF Foundation (e-infra130078).
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