Information

Relationship of RNA-binding proteins to peptidyl-prolyl cis-trans isomerase

Relationship of RNA-binding proteins to peptidyl-prolyl cis-trans isomerase



We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I am studying a Plasmodium gene, known to encode an RNA-binding protein. However a BLAST search brings up mainly peptidyl-prolyl cis-trans isomerases from other species. Why would this be so?


Without seeing the sequence of the RNA-binding protein it is impossible to be sure, but the obvious conclusion is:

The RNA-binding protein is a peptidyl-prolyl cis-trans isomerase.

It has been known since 1989 that peptidyl-prolyl cis-trans isomerase is a multiple-function protein, also exhibiting properties of the cyclophilins, proteins which bind cyclosporin A. The post raised the possibility that it may have other activities and a literature search reveals several more recent papers that indicate cylcophilin A can bind certain RNAs. I list some of the titles:

A nuclear RNA-binding cyclophilin in human T cells (1996) FEBS Letters 398 201-205

Molecular cloning, structure and expression of a novel nuclear RNA-binding cyclophilin-like gene (PPIL4) from human fetal brain (2001) Cytogenet Cell Genet 95 43-47

Cyclophilin A Binds to the Viral RNA and Replication Proteins, Resulting in Inhibition of Tombusviral Replicase Assembly (2013) J. Virol. 87 13330-13342

Structure of RNA-interacting Cyclophilin A-like protein from Piriformospora indica that provides salinity-stress tolerance in plants (2013) Scientific Reports 3 : 3001, DOI: 10.1038/srep03001


Isolation and Characterization of Conotoxin Protein from Conus inscriptus and Its Potential Anticancer Activity Against Cervical Cancer (HeLa-HPV 16 Associated) Cell Lines

Marine snails are abundant sources of biologically important conopeptides with their potential applications in drug development. The present study aimed to identify the potential conopeptides from the venom duct of Conus inscriptus. After extraction conopeptides were characterized by liquid chromatography–mass spectrometric analysis showing totally 29 protein sequences with disulfide linkages of different molecular mass distributed in the range of 387–1536 m/z and the peptides showing mostly to T-superfamily of conotoxins with 78 kDa heat shock proteins. The venom peptides showed six different molecular weight bands (37, 51, 60, 70, 80 and 90 kDa) above 30 kDa after in-gel enzymatic digestion. Furthermore, the conopeptides exhibit potential cytotoxic activity against HeLa-HPV 16 associated, Vero (normal) cell lines and brine shrimp. Fourier transform infra-red spectroscopy analysis confirms the structural and functional groups. The observed results suggests the venom peptides from C. inscriptus as a potential anticancer agent.

This is a preview of subscription content, access via your institution.


Summary

The proteomes expressed at 4°C and 18°C by the psychrophilic Antarctic bacterium Pseudoalteromonas haloplanktis have been compared using two-dimensional differential in-gel electrophoresis, showing that translation, protein folding, membrane integrity and anti-oxidant activities are upregulated at 4°C. This proteomic analysis revealed that the trigger factor is the main upregulated protein at low temperature. The trigger factor is the first molecular chaperone interacting with virtually all newly synthesized polypeptides on the ribosome and also possesses a peptidyl-prolyl cis-trans isomerase activity. This suggests that protein folding at low temperatures is a rate-limiting step for bacterial growth in cold environments. It is proposed that the psychrophilic trigger factor rescues the chaperone function as both DnaK and GroEL (the major bacterial chaperones but also heat-shock proteins) are downregulated at 4°C. The recombinant psychrophilic trigger factor is a monomer that displays unusually low conformational stability with a Tm value of 33°C, suggesting that the essential chaperone function requires considerable flexibility and dynamics to compensate for the reduction of molecular motions at freezing temperatures. Its chaperone activity is strongly temperature-dependent and requires near-zero temperature to stably bind a model-unfolded polypeptide.


Results

Training of the ensemble classifier EffectorP 2.0

EffectorP 1.0 is a Naïve Bayes classifier that was trained on a positive training set of 58 experimentally supported fungal effectors from 16 fungal species. Since its development, additional fungal effectors have been described and, for EffectorP 2.0, we used an expanded training set of 94 secreted fungal effectors from 23 species (Table 1). EffectorP 1.0 predicts 73% of the unseen effectors correctly, which demonstrates its ability to identify novel effectors, but also leaves room for improvement. We set out to investigate whether re-training of EffectorP would improve prediction accuracy.

Species Effector
Melampsora lini AvrM, AvrL567-A, AvrP123, AvrP4, AvrM14, AvrL2-A
Uromyces fabae RTP1
Puccinia graminis f. sp. tritici PGTAUSPE-10-1, AvrSr50
Puccinia striiformis f. sp. tritici PstSCR1, Pec6
Phakopsora pachyrhizi PpEC23
Blumeria graminis f. sp. hordei Avrk1, Avra1, Avra13
Blumeria graminis f. sp. tritici AvrPm2
Cladosporium fulvum Avr9, Avr4, Avr4E, Avr2, Avr5, Ecp1, Ecp2, Ecp4, Ecp5, Ecp6
Leptosphaeria maculans AvrLm6, AvrLm4–7, AvrLm1, AvrLm11
Fusarium oxysporum f. sp. lycopersici Six4, Six3, Six1, Six6, Six2, Six5, Six7, Six8
Magnaporthe oryzae Avr-Pita, Pwl1, Avr-Pia, Bas3, Bas2, Bas4, Bas1, MC69, AvrPiz-t, Avr1-CO39, Avr-Pii, Avr-Pik, Bas107, AvrPib, Iug6, Iug9, Msp1, MoHEG13, MoCDIP1, MoCDIP2, MoCDIP3, MoCDIP4, MoCDIP5, SPD2, SPD4, SPD7, SPD9, SPD10, Bas162, AvrPi9
Rhynchosporium secalis NIP1, NIP2, NIP3
Verticillium dahliae Vdlsc1, Ave1, VdSCP7, PevD1
Ustilago maydis Cmu1, Pep1, Pit2, Tin2, eff1-1, See1
Ustilago hordei UhAvr1
Stagonospora nodorum ToxA, Tox1, Tox3
Botrytis cinerea Nep1
Pyrenophora tritici-repentis ToxB
Laccaria bicolor MiSSP7
Zymoseptoria tritici AvrStb6, Zt6
Colletotrichum graminicola CgEP1, Cgfl
Fusarium graminearum FGL1
Sclerotinia sclerotiorum SsSSVP1
  • Ninety-four fungal effectors were collected from the literature if they had experimental support and did not share sequence homology. Effectors that were not part of the EffectorP 1.0 training set are marked in bold. All sequences are available at: http://effectorp.csiro.au/data.html.

EffectorP 1.0 was trained on a negative set consisting of predicted secreted proteins from the same pathogen species as the known effectors. Thus, the negative training set includes both undiscovered effectors and non-effectors, and therefore poses an unlabelled data classification problem. Although Naïve Bayes classifiers are fairly robust to unlabelled data classification and can tolerate noisy data (Bing et al., 2007 ), other machine learning classifiers might not be able to learn effectively from such sets. To improve predictions, we collected three different subsets of negative training data that are less likely to contain positive instances, i.e. fungal effectors. First, secretomes were predicted from the same fungal pathogen/symbiont species as used in the positive set if they had a publicly available predicted gene set (Table 1). The combined secretome was homology reduced and this resulted in a filtered predicted pathogen secretome of 11 277 proteins. This set will contain both undiscovered effectors and secreted non-effectors, which poses a challenge for machine learning classifiers that traditionally learn from labelled data. Therefore, we applied EffectorP 1.0 to exclude predicted effectors from the secretomes (n = 6138). This procedure removed predominantly small, cysteine-rich proteins from the negative training set (average sequence length, 137 amino acids average cysteine content, 3.55%). We also collected homology-reduced sets of secreted fungal proteins from fungi not pathogenic on plants, namely from 27 saprophyte secretomes (n = 12 939) and from 10 animal-pathogenic fungal secretomes (n = 2763). These sets are less likely to contain plant-pathogenic effectors and were not filtered for EffectorP 1.0-predicted effectors.

As we have large amounts of negative training data (n = 21 840), we used an ensemble learning approach of classifiers that each take a different subset of negative training data and thus provide a different view on classification (Fig. 1). Overall, we chose a total of 50 best-performing models comprising: 10 Naïve Bayes classifiers and 10 C4.5 decision trees that discriminate between fungal effectors and secreted pathogen proteins 10 Naïve Bayes classifiers and 10 C4.5 decision trees that discriminate between fungal effectors and secreted saprophyte proteins and five Naïve Bayes classifiers and five C4.5 decision trees that discriminate between fungal effectors and secreted animal pathogen proteins. In 10-fold cross-validation, the Naïve Bayes classifiers achieve, on average, high sensitivity, whereas the C4.5 decision trees show high specificity (Table S2, see Supporting Information). To generate EffectorP 2.0, we combined these 50 models into an ensemble classifier to utilize their distinct prediction strengths (Fig. 1). Each model has seen a different subset of negative training data and, for a given protein sequence input, returns a probability of whether it is an effector or a non-effector. EffectorP 2.0 returns a final prediction using a voting approach on the predicted probabilities of each model. A protein is classified as an effector if the average probability for the class ‘effector’ is higher than the average probability for the class ‘non-effector’. For each protein in the training set, EffectorP 1.0 utilizes a feature vector that is calculated using amino acid frequencies, amino acid class frequencies, molecular weight, sequence length and protein net charge (Sperschneider et al., 1996 ). EffectorP 2.0 uses an updated feature vector that includes amino acid frequencies, amino acid class frequencies, molecular weight, protein net charge, grand average of hydrophobicity, as well as the averages of surface exposure, disorder propensity, hydrophobicity, bulkiness and interface propensity (Table 2).

Workflow for the EffectorP 2.0 classifier that combines an ensemble of machine learning classifiers. Each classifier Ci has seen a different subset of the negative training data and predicts effectors in unseen data with probability Pi. The probabilities are combined into an overall vote on whether an unseen protein is an effector or non-effector.

Features used in training and classification Method
Frequencies of amino acids (A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y) in the sequence pepstats (Rice et al., 2000 )
Frequencies of amino acid classes in the sequence:
Tiny (A+C+G+S+T)
Small (A+B+C+D+G+N+P+S+T+V)
Aliphatic (I+L+V)
Aromatic (F+H+W+Y)
Non-polar (A+C+F+G+I+L+M+P+V+W+Y)
Polar (D+E+H+K+N+Q+R+S+T+Z)
Charged (B+D+E+H+K+R+Z)
Basic (H+K+R)
Acidic (B+D+E+Z)
Molecular weight
Protein net charge
Grand average of hydropathicity (GRAVY, Kyle and Doolittle, 1982 ) ProtParam (Gasteiger et al., 2005 )
Average of surface exposure (Janin, 1979 ) Amino acid groupings and scales taken from Composition Profiler (Vacic et al., 2007 )
Average of disorder propensity (Dunker et al., 2001 )
Average of hydrophobicity (Fauchere and Pliska, 1983 )
Average of bulkiness (Zimmerman et al., 1968 )
Average of interface propensity (Jones and Thornton, 1997 )

Influential features for effector prediction include protein size, protein net charge as well as the amino acids serine and cysteine

To detect the most discriminative features in the EffectorP 2.0 classification, we analysed the distribution of features for the proteins employed in the training of all 50 models. Four features were found to be different at a significance threshold of P < 10 −5 in distribution between the positive sequence set (effectors) and the negative sequence set (proteins labelled as non-effectors) (Fig. 2). Differences in feature distribution for these four features were also reported previously in the EffectorP 1.0 model as particularly striking (Sperschneider et al., 1996 ), confirming their importance in fungal effector classification. As a group, the effectors exhibit lower molecular weight, a higher percentage of cysteines (C) and a lower percentage of serines (S) than the proteins in the negative sequence set. The distribution of protein net charge for effectors occupies a narrow range around neutral to slightly positive (Fig. 2). We also found significant differences (P < 0.05) in distribution between effectors and the negative sequence set for additional features (Fig. 2). These were depletion in aliphatic amino acids, leucine (L), proline (P), threonine (T), tryptophan (W), disorder propensity and bulkiness, as well as enrichment in basic amino acids, interface propensity, glycine (G), lysine (K) and asparagine (N), for effectors. Only enrichment in tryptophan content in effectors was also reported in the EffectorP 1.0 model.

The most influential features in effector prediction appear to be a small protein size, low serine content, a protein net charge around the neutral range and a high cysteine content. Significant differences (P < 0.05) in distribution between effectors and the negative sequence set for additional features were also observed. These were depletion in aliphatic amino acids, leucine (L), proline (P), threonine (T), tryptophan (W), disorder propensity and bulkiness, as well as enrichment in basic amino acids, interface propensity, glycine (G), lysine (K) and asparagine (N), for effectors. Extreme outliers in the protein net charge plot were removed for clarity (full figure given in Fig. S3, see Supporting Information). All data points are drawn on top of the box plots as black dots. Significance between groups is shown as horizontal brackets and was assessed using t-tests. The lower and upper hinges correspond to the first and third quartiles and the upper (lower) whiskers extend from the hinge to the largest (smallest) value that is within 1.5 times the interquartile range of the hinge. Data beyond the end of the whiskers are outliers.

Machine learning can be a black box learning process where the reasons for an individual prediction are hidden. However, C4.5 decision trees are white box models and their decision-making process is transparent through navigation along tree branches. As examples, we plotted two of the 10 C4.5 decision trees that discriminate between fungal effectors and secreted pathogen proteins (Figs S1 and S2, see Supporting Information). This demonstrates that the decision tree classifiers use a complex set of features and not only the most discriminative features (protein size, protein net charge as well as the amino acids serine and cysteine) for effector classification. In particular, the decision tree in Fig. S2 does not utilize serine content as a feature in classification and still achieves high classification accuracy. Taken together, this analysis confirms the importance of specific combinations of features, as found previously in the EffectorP 1.0 model, but also illustrates that accurate fungal effector prediction machine learning classifiers rely on a diverse set of features.

EffectorP 2.0 improves fungal effector prediction accuracy from secretomes

Machine learning classifiers can overfit/overtrain to memorize the training data, which leads to low accuracy on unseen data. Therefore, independent test sets are important to estimate prediction ability. We collected independent positive and negative test sets to assess the performance of EffectorP 2.0. To estimate the false positive rate, we first used fungal, plant and mammalian proteins with predicted signal peptides that were not extracellular [localization to endoplasmic reticulum, Golgi or membranes or with glycosylphosphatidylinositol (GPI) anchors]. A low false positive rate on these proteins ensures that EffectorP is not merely predicting the presence of a signal peptide. We also used secreted saprophyte proteins as well as fungal proteins from PHI-base (Urban et al., 2007 ) that were annotated as having an unaffected pathogenicity phenotype. Although proteins with an unaffected pathogenicity phenotype are not necessarily non-effectors, we expect to see a low percentage of predicted effectors. A simple classifier based on a small protein size (≤300 amino acids) has a false positive rate of 40.4% on these three sets. A small, cysteine-rich classifier (≤300 amino acids ≥4 cysteines) has a false positive rate of 19%, and EffectorP 1.0 has a false positive rate of 18.3%. EffectorP 2.0 has the lowest false positive rate of 11.2% (Table 3). A combination of EffectorP 1.0 and 2.0, where a protein is a predicted effector only if both classifiers label it as an effector, achieves the lowest false positive rate of 9.4%.

Predicted effectors
Dataset # of proteins EffectorP 2.0 EffectorP 1.0 EffectorP 1.0 and 2.0 Small size classifier Small, cysteine-rich classifier
Fungal saprophyte secreted proteins 24 432 2865 (11.7%) 4774 (19.5%) 2444 (10%) 10 529 (43.1%) 4961 (20.3%)
Fungal, plant and mammalian proteins with signal peptide and localization to endoplasmic reticulum, Golgi, membranes or with glycosylphosphatidylinositol (GPI) anchors 2631 220 (8.4%) 294 (11.2%) 164 (6.2%) 654 (24.9%) 307 (11.7%)
Fungal proteins with unaffected pathogenicity phenotype 938 45 (4.8%) 59 (6.3%) 36 (3.8%) 128 (13.6%) 60 (6.4%)
28 001 3130 (11.2%) 5127 (18.3%) 2644 (9.4%) 11 311 (40.4%) 5328 (19%)
Fungal effector positive training set 94 89 (94.7%) 80 (85.1%) 79 (84%) 88 (93.6%) 53 (56.4%)
Fungal effector independent test set 21 16 (76.2%) 16 (76.2%) 16 (76.2%) 19 (90.5%) 10 (47.6%)
Accuracy 88.8% 81.7% 90.5% 59.8% 80.9%

To assess false negative predictions, we also applied these predictors to the training data of 94 fungal effectors (Table 3). EffectorP 2.0 only predicts five of these proteins as non-effectors: the Phakopsora pachyrhizi effector PpEC23, the Blumeria graminis f. sp. hordei effector Avrk1, the Magnaporthe oryzae effector MoCDIP2, the Ustilago maydis effector eff1-1 and the Colletotrichum graminicola metalloproteinase effector Cgfl. This is an improvement on EffectorP 1.0, which correctly predicted only 80 of the 94 positive examples. However, it is also important to assess overfitting on training data and to use unseen fungal effectors independent from the training set for the validation of the estimated true positive rate. Therefore, we collected 21 effectors (Table 4) that either shared sequence similarity with an effector in the training set and were therefore eliminated in the homology reduction step (Mg3LysM, BEC1054, BEC1011, AvrLm2) or were overlooked during initial literature searches for training the EffectorP 2.0 model (SAD1, CSEP-07, CSEP-09, SIS1, CSEP0055, BEC1019, Bcg1, CSEP0105, CSEP0162, AvrLmJ1, AvrLm3, XylA, Ecp7, PIIN_08944, FGB1, AvrPm3, AvrSr35). On this independent test set, both EffectorP 1.0 and 2.0 show equal performance and correctly predict 76.2% of effectors (Tables 3 and 4). On the total set of 115 effectors, the small size classifier correctly predicts 93% of effectors, but the small, cysteine-rich classifier only correctly predicts 54.8% of effectors. On the combined positive and negative sets, EffectorP 2.0 has the highest accuracy of 88.8% of the four single classifiers. The simple classifier based on a small size has the lowest accuracy of 59.8%, largely because of its high false positive rate (Table 3). The combined EffectorP 1.0/2.0 classifier achieves the highest accuracy of 90.5% because of its low false positive rate. Although the combined EffectorP 1.0/2.0 classifier misses more effectors than EffectorP 2.0 or 1.0, it is a highly stringent method for the prediction of effectors in secretomes. In the following, we assess the prediction abilities of EffectorP 1.0 compared with EffectorP 2.0 in more detail.

Species Effector EffectorP 1.0 (probability) EffectorP 2.0 (probability) Small size classifier Small, cysteine-rich classifier
Sporisorium reilianum SAD1 Effector (0.97) Effector (0.621) Effector Non-effector
Phakopsora pachyrhizi CSEP-07 Effector (0.608) Effector (0.688) Effector Effector
CSEP-09 Effector (0.999) Effector (0.842) Effector Effector
Zymoseptoria tritici Mg3LysM Non-effector (0.556) Non-effector (0.561) Effector Effector
Blumeria graminis f. sp. hordei BEC1054 Effector (0.935) Effector (0.869) Effector Non-effector
BEC1011 Effector (0.974) Effector (0.947) Effector Non-effector
BEC1019 Non-effector (0.986) Non-effector (0.551) Non-effector Non-effector
CSEP0055 Effector (0.649) Effector (0.732) Effector Non-effector
Bcg1 Effector (0.971) Effector (0.896) Effector Non-effector
CSEP0105 Non-effector (0.511) Non-effector (0.595) Effector Effector
CSEP0162 Effector (0.854) Effector (0.693) Effector Effector
Rhizophagus irregularis SIS1 Effector (0.973) Effector (0.611) Effector Non-effector
Leptosphaeria maculans AvrLmJ1 Effector (0.999) Effector (0.727) Effector Effector
AvrLm2 Effector (0.764) Effector (0.578) Effector Effector
AvrLm3 Effector (1.0) Effector (0.91) Effector Effector
Fusarium graminearum XylA Effector (0.882) Effector (0.865) Effector Non-effector
Cladosporium fulvum Ecp7 Effector (0.997) Effector (0.96) Effector Effector
Piriformospora indica PIIN_08944 Non-effector (0.886) Non-effector (0.539) Effector Non-effector
FGB1 Effector (1.0) Effector (0.929) Effector Effector
Blumeria graminis f. sp. tritici AvrPm3 Effector (0.979) Effector (0.913) Effector Non-effector
Puccinia graminis f. sp. tritici AvrSr35 Non-effector (1.0) Non-effector (0.918) Non-effector Non-effector

Sets of infection-induced proteins are enriched for effectors predicted by EffectorP 2.0

Effectors are often induced during infection, and thus the set of genes differentially expressed during infection should be enriched for effectors. However, not all genes that are differentially expressed during infection encode effector proteins, and therefore sets of differentially expressed genes need to be filtered further to detect effectors. We collected 13 gene sets from the literature that were labelled as containing effector candidates based on their induction during infection as well as other criteria (Table 5). For example, a study by Germain et al. ( 2011 ) identified 16 candidate effectors from 1184 small, secreted Melampsora larici-populina proteins. These 16 candidates were selected based on their expression in a haustoria-specific cDNA library and the transcriptome of laser microdissected, rust-infected poplar leaves, as well as their small size of less than 300 amino acids. As another example, Kettles et al. ( 2017 ) selected 63 Zymoseptoria tritici candidate effectors on the basis of being induced during early wheat leaf infection leading up to the transition to the necrotrophic growth phase. In total, four of the 13 sets contained infection-induced effector candidates that were pre-selected based on a small size (≤300 amino acids).

Expression dataset No. of proteins Method Predicted effectors Predicted effectors in secretome Enrichment (Fisher's exact test)
Colletotrichum higginsianum: biotrophy-associated effector candidates (Kleemann et al., 2012 ) 102 Small size 100 (98%) 845 (56.6%) <0.0001
Small, cysteine-rich 46 (45.1%) 412 (27.6%) 0.0003
EffectorP 1.0 73 (71.6%) 490 (32.8%) <0.0001
EffectorP 2.0 49 (48%) 378 (25.3%) <0.0001
Cladosporium fulvum: in planta induced small secreted apoplastic effector candidates (Mesarich et al., 2017 ) 75 Small size
Small, cysteine-rich 70 (93.3%) 272 (25%) <0.0001
EffectorP 1.0 68 (90.7%) 237 (21.8%) <0.0001
EffectorP 2.0 64 (85.3%) 190 (17.5%) <0.0001
Magnaporthe oryzae: genes with ≥50-fold differential expression in biotrophic invasive hyphae (Mosquera et al., 2009 ) 15 Small size 15 (100%) 907 (55.6%) 0.0002
Small, cysteine-rich 9 (60%) 500 (30.7%) 0.0221
EffectorP 1.0 14 (93.3%) 614 (37.7%) <0.0001
EffectorP 2.0 13 (86.7%) 489 (30%) <0.0001
Blumeria graminis f. sp. hordei: Candidates for Secreted Effector Proteins (CSEPs) (Pedersen et al., 2012 ) 491 Small size 347 (70.7%) 426 (58.8%) <0.0001
Small, cysteine-rich 133 (27.1%) 169 (23.3%) NS
EffectorP 1.0 274 (55.8%) 302 (41.7%) <0.0001
EffectorP 2.0 256 (52.1%) 276 (38.1%) <0.0001
Melampsora larici-populina: specific small secreted proteins expressed in haustoria (Petre et al., 2015 ) 24 Small size
Small, cysteine-rich 15 (62.5%) 707 (38.8%) 0.0210
EffectorP 1.0 20 (83.3%) 780 (42.8%) <0.0001
EffectorP 2.0 18 (75%) 752 (41.3%) 0.0013
Melampsora larici-populina: specific small secreted proteins expressed during infection (Germain et al., 2011 ) 16 Small size
Small, cysteine-rich 10 (62.5%) 707 (38.8%) NS
EffectorP 1.0 15 (93.8%) 780 (42.8%) <0.0001
EffectorP 2.0 14 (87.5%) 752 (41.3%) 0.0004
Laccaria bicolor: ectomycorrhiza-regulated small secreted proteins (MiSSPs) (Martin et al., 2008 ) 21 Small size
Small, cysteine-rich 10 (47.6%) 362 (29.2%) NS
EffectorP 1.0 11 (52.4%) 380 (30.7%) NS
EffectorP 2.0 10 (47.6%) 246 (19.9%) 0.0043
Puccinia graminis f. sp. tritici: secreted proteins up-regulated in haustoria (log FC > 10) (Upadhyaya et al., 2015 ) 55 Small size 38 (69.1%) 1223 (64.7%) NS
Small, cysteine-rich 7 (12.7%) 710 (37.6%) NS
EffectorP 1.0 25 (45.5%) 841 (44.5%) NS
EffectorP 2.0 22 (40%) 758 (40.1%) NS
Zymoseptoria tritici candidate effectors (Kettles et al., 2017 ) 63 Small size 56 (88.9%) 426 (42.7%) <0.0001
Small, cysteine-rich 43 (68.3%) 259 (26%) <0.0001
EffectorP 1.0 41 (65.1%) 260 (26.1%) <0.0001
EffectorP 2.0 42 (66.7%) 232 (23.3%) <0.0001
Zymoseptoria tritici candidate effectors with phenotype in Nicotiana benthamiana (Kettles et al., 2017 ) 14 Small size 12 (85.7%) 426 (42.7%) 0.0017
Small, cysteine-rich 10 (71.4%) 259 (26%) 0.0005
EffectorP 1.0 8 (57.1%) 260 (26.1%) 0.0143
EffectorP 2.0 9 (64.3%) 232 (23.3%) 0.0014
Ustilago maydis effector candidates (Tollot et al., 2016 ) 198 Small size 130 (65.7%) 242 (46.8%) <0.0001
Small, cysteine-rich 49 (24.7%) 101 (19.5%) NS
EffectorP 1.0 79 (39.9%) 140 (27.1%) 0.0011
EffectorP 2.0 67 (33.8%) 124 (24%) 0.0106
Leptosphaeria maculans highly expressed early effector candidates (Gervais et al., 2017 ) 49 Small size 44 (89.9%) 514 (49.7%) <0.0001
Small, cysteine-rich 23 (46.9%) 258 (24.9%) 0.0013
EffectorP 1.0 29 (59.2%) 283 (27.3%) <0.0001
EffectorP 2.0 26 (53.1%) 215 (20.8%) <0.0001
Leptosphaeria maculans highly expressed late effector candidates (Gervais et al., 2017 ) 50 Small size 33 (66%) 514 (49.7%) 0.0292
Small, cysteine-rich 16 (32%) 258 (24.9%) NS
EffectorP 1.0 19 (38%) 283 (27.3%) NS
EffectorP 2.0 19 (38%) 215 (20.8%) 0.0073
  • For each expression dataset, the percentage of predicted effector candidates by EffectorP is shown and compared with the percentage of predicted effector candidates in the secretome. The small size classifier is only applied to sets that are not pre-selected based on a small size.

We assessed whether the 13 sets containing infection-induced effector candidates are also enriched for effector candidates predicted by EffectorP 1.0 or 2.0, by a small size classifier or by a small, cysteine-rich classifier when compared with the whole secretome of each species. We did not test the small size classifier on sets containing effector candidates that were pre-selected based on a small size (≤300 amino acids). We found significant enrichments for predicted effector candidates in 12 of 13 sets (92.3%) using EffectorP 2.0 (Table 5). A small, cysteine-rich classifier only returns significant enrichments for predicted effectors in seven of 13 sets (53.9%) and EffectorP 1.0 in 10 of 13 sets (76.9%). A small size classifier returns significant enrichments for predicted effectors in eight of nine sets (88.9%). Surprisingly, we did not observe enrichment for predicted effectors with any of the four classifiers in secreted proteins of P. graminis f. sp. tritici highly up-regulated in haustoria compared with germinated spores (Table 5). This could indicate that rusts might utilize undiscovered effector proteins with different properties to the training set, such as effectors of larger size. This is supported by the recent discovery of AvrSr35, a 578-amino-acid P. graminis f. sp. tritici effector protein (Salcedo et al., 2005 ). Alternatively, haustorial secretomes might contain many non-effectors, such as proteins involved in signalling or in the incorporation of nutrients from the host (Garnica et al., 2005 ). Taken together, although effector function has not been established for all genes in these candidate sets, the enrichment for predicted effectors in infection-induced sets underlines the ability of EffectorP 2.0 to accurately predict unseen effectors.

EffectorP 2.0 reduces the average number of effectors predicted for fungal plant symbionts and saprophytes by 40%

We tested EffectorP 2.0 on predicted secretomes from 93 fungal species, including pathogens and non-pathogens (Table S3, see Supporting Information), and recorded the percentages of secreted proteins that are predicted effectors (Table S4, see Supporting Information). The highest proportions of predicted effectors were found in the obligate biotrophs Melampsora laricis-populina (41.3%), Puccinia graminis f. sp. tritici (40.3%), Blumeria graminis f. sp. hordei (38.1%) and Puccinia striiformis f. sp. tritici (37.6%). Amongst the fungal plant pathogens, the lowest proportions of predicted effectors were recorded for the necrotrophs Heterobasidion annosum (10.4%), Sclerotinia sclerotiorum (13.6%), Botrytis cinerea (13.7%) and Penicillium digitatum (13.9%). Necrotrophic pathogens utilize many secreted PCWDEs to overcome the barrier of the plant cell wall. EffectorP predicts some secreted proteins with enzymatic domains as effectors, such as the Fusarium graminearum xylanase XylA, which has the ability to induce necrosis in wheat independent of its enzymatic activity (Table 4) (Belien et al., 2011 Sella et al., 2016 Sperschneider et al., 1996 ). However, EffectorP has been trained on effectors that predominantly lack recognizable functional domains and interfere with host processes in different ways from PCWDEs which act on the plant cell wall. Therefore, the lower proportions of EffectorP-predicted effectors in necrotrophic fungal pathogen secretomes is expected.

On average, EffectorP 2.0 predicts that plant pathogen secretomes consist of 24.9% effectors and that saprophyte secretomes consist of 11.7% effectors (Tables 5 and 6). EffectorP 2.0 reduces the average number of predicted effectors in fungal plant symbiont and fungal saprophyte secretomes by over 40% when compared with EffectorP 1.0 (Table 6, Fig. 3). Both EffectorP 2.0 and EffectorP 1.0 also predict lower proportions of effectors for fungal animal pathogens than for fungal plant pathogens (Table 6), suggesting that effector repertoires of fungal animal pathogens are different from those of their plant-pathogenic counterparts. One notable exception is the secretome of Enterocytozoon bieneusi, an obligate intracellular parasite (49 predicted effectors, 36% of secretome predicted as effectors). Shortened protein-coding sequences caused by genome compaction have been reported in E. bieneusi (Akiyoshi et al., 2009 ) and might lead to higher than expected false positive predictions. Therefore, we also assessed effector prediction rates for small secreted proteins (<300 amino acids) only. For plant pathogens, EffectorP 2.0 predicts that 47.8% of small secreted proteins are effectors, whereas, for plant symbionts and saprophytes, this is reduced to 29.9% and 26.3%, respectively. This underlines that EffectorP 2.0 does not select effectors based on a small size alone. Small secreted proteins in saprophytes are mostly functionally uncharacterized and might function in a variety of processes unrelated to plant–pathogen interactions. Compared with a small, cysteine-rich classifier, EffectorP 2.0 predicts significantly lower proportions of effectors for plant symbionts and saprophytes, but not for plant pathogens (Fig. 3). This lack of correlation for all groups tested underlines that EffectorP 2.0 does not select effectors based on a small size and a high cysteine content alone, and reflects the reduced false positive rate of EffectorP 2.0.

Average of predicted effectors
Secretomes EffectorP 1.0 EffectorP 2.0 % decrease in predicted effectors (EffectorP 2.0 compared with EffectorP 1.0)
Plant pathogens 338 (29.6%) 284 (24.9%) −16.0%
Fungal symbionts of plants 305 (30.8%) 177 (17.8%) −42.0%
Fungal pathogens of animals 108 (20.9%) 83 (16.1%) −23.2%
Saprophytes 177 (19.5%) 106 (11.7%) −40.1%

Proportions of predicted effectors in fungal secretomes using EffectorP 1.0, EffectorP 2.0, a small size classifier and a small, cysteine-rich classifier. All data points are drawn on top of the box plots as black dots. Significance between groups is shown as horizontal brackets and was assessed using t-tests (NS, not significant *P < 0.05, **P < 0.01 and ***P < 0.001). The lower and upper hinges correspond to the first and third quartiles and the upper (lower) whiskers extend from the hinge to the largest (smallest) value that is within 1.5 times the interquartile range of the hinge. Data beyond the end of the whiskers are outliers.

We then further investigated the properties of effectors that are only predicted by one of the versions of EffectorP, but not by the other, for all 93 secretomes (Table S4). Effector candidates predicted only by EffectorP 2.0 are, on average, of longer sequence length (n = 2304 average sequence length, 229 amino acids) than those that are only predicted by EffectorP 1.0 (n = 8635 average sequence length, 138 amino acids) or by both versions (n = 14 128 average sequence length, 148 amino acids) (Fig. 4). Effector candidates predicted only by EffectorP 1.0 or 2.0 are lower in cysteine content compared with effector candidates predicted by both versions (Fig. 4). We then tested for enrichment and depletion of protein functional classes amongst the effector candidates predicted by EffectorP 1.0 and 2.0 from a total of 24 075 secreted proteins of the 21 plant pathogens (Table 7). The vast majority of effector candidates predicted by either EffectorP 1.0 or 2.0 are proteins without functional annotation. However, we observed that both sets of predicted effector candidates are enriched for proteins with pectate lyase activity, peptidyl-prolyl cis–trans isomerase activity and endopeptidase inhibitor activity (Table 7). Some proteins with peptidyl-prolyl cis–trans isomerase activity have been implicated to function as virulence factors (Unal and Steinert, 2009 ). A cyclophilin with peptidyl-prolyl cistrans isomerase activity functions as a pathogenicity factor in Puccinia triticina (Panwar et al., 2012 ). EffectorP 2.0-predicted effectors are enriched for proteins involved in pathogenesis and defence response (Table 7). However, EffectorP 1.0-predicted effector candidates are also enriched for proteins that do not appear to be related to effector function or to secreted proteins, but rather to intracellular proteins (Table 7), and might reflect the higher false positive rate of EffectorP 1.0, as well as the false positive rate of the signal peptide prediction tools SignalP 3.0 and TargetP.

Differences in sequence length (aas, amino acids) and cysteine content for effectors predicted by different versions of EffectorP. All data points are drawn on top of the box plots as black dots. Significance between groups is shown as horizontal brackets and was assessed using t-tests. The lower and upper hinges correspond to the first and third quartiles and the upper (lower) whiskers extend from the hinge to the largest (smallest) value that is within 1.5 times the interquartile range of the hinge. Data beyond the end of the whiskers are outliers.

Test set: EffectorP 2.0 predicted

Reference set: Secreted pathogen proteins

Test set: EffectorP 1.0 predicted

Reference set: Secreted pathogen proteins


RESULTS

Several proteins from F. alocis D-62D were increased in abundance during coinfection of epithelial cells with P. gingivalis W83.

Because the variations in the pathogenic potential of the F. alocis strains in coculture with P. gingivalis may be related to the relative abundances of specific bacterial protein factors, we examined the proteome of F. alocis during coinfection of epithelial cells with P. gingivalis. As shown in Fig. 1 , approximately 20% to 30% (comparing bars C and F with bar G) more proteins were observed in F. alocis during coinfection with P. gingivalis (P < 0.01). Unlike F. alocis ATCC 35896, the D-62D strain expressed more proteins interacting with P. gingivalis W83 than with P. gingivalis 33277. The proteome modulation of F. alocis during coinfection with P. gingivalis W83 showed a total of 490 proteins with a change in expression of ϡ-fold in contrast to 400 proteins that had a change in expression of 0.5-fold to 1.0-fold. The proteins that were most highly upregulated were classified as hypothetical (13%), regulatory (7%), and transport and binding (6%) proteins and related to cellular processes (6%) and amino acid biosynthesis (6%) ( Fig. 2 ). Several surface adhesion proteins that were upregulated during coinfection include collagen adhesion protein, fibronectin binding protein, calcium binding acid repeat proteins, and hemolysin III calcium binding protein. Furthermore, many hypothetical proteins with cell wall anchor motifs (HMPREF0389_1719, HMPREF0389_00599, HMPREF0389_00019, HMPREF0389_00672, HMPREF0389_1172, and HMPREF0389_1476) were also found in abundance. The hypothetical protein HMPREF0389_00967 was highly (6.4 times) upregulated in coinfection of F. alocis clinical strain D-62D with P. gingivalis W83 ( Table 1 ).

Percentage of protein expressed during coculture or monoculture using various strains of P. gingivalis and F. alocis. HeLa cells were infected with F. alocis ATCC 35896 and D-62D strains (MOI of 1:100 [10 5 epithelial cells]) in monoculture or coculture with P. gingivalis W83 as previously reported (6). Tandem isobaric mass tagging analysis of cocultures and monocultures was carried out using Orbitrap. (*, P < 0.01.) Bar A, P. gingivalis (33277) plus F. alocis (D-62D) versus P. gingivalis (33277) plus F. alocis (ATCC) bar B, P. gingivalis (33277) plus F. alocis (D-62D) versus P. gingivalis (W83) plus F. alocis (D-62D) bar C, P. gingivalis (33277) plus F. alocis (D-62D) versus F. alocis (D-62D) bar D, P. gingivalis (33277) plus F. alocis (ATCC) versus P. gingivalis (W83) plus F. alocis (ATCC) bar E, P. gingivalis (W83) plus F. alocis (D-62D) versus P. gingivalis (W83) plus F. alocis (ATCC) bar F, P. gingivalis (W83) plus F. alocis (D-62D) versus F. alocis (D-62D) bar G, F. alocis (D-62D).

Upregulation in proteome profile showing various protein classes noted during coculture infection with P. gingivalis and Filifactor alocis in epithelial cells. HeLa cells were infected with F. alocis ATCC 35896 and D-62D strains (MOI of 1:100 [10 5 epithelial cells]) in monoculture or coculture with P. gingivalis W83 as previously reported (6). Tandem isobaric mass tagging analysis of cocultures and monocultures was carried out using Orbitrap. The F. alocis proteins were analyzed using MASCOT, and functional analysis was carried out using the UNIPROT proteome database. A, amino acid biosynthesis B, biosynthesis of cofactors, prosthetic groups, and carriers C, cell envelope D, cellular processes E, central intermediary metabolism F, DNA metabolism G, energy metabolism H, fatty acid and phospholipid metabolism I, hypothetical/unassigned/uncategorized/unknown functions J, mobile and extrachromosomal element functions K, protein fate L, purines, pyrimidines, nucleosides, and nucleotides M, regulatory functions N, replication O, transcription P, translation Q, transport and binding R, transposon functions. Protein expression change > 1-fold, n = 490 protein expression change < 1-fold, n = 400.

TABLE 1

F. alocis coinfection with P. gingivalis: relative abundance of proteins with cell wall motif

AnnotationName
HMPREF0389_01580Leukotoxin translocation ATP binding protein
HMPREF0389_00575Fibronectin binding protein
HMPREF0389_00426Type IV pilus assembly protein
HMPREF0389_00816Signal recognition particle protein
HMPREF0389_01719Hypothetical protein
HMPREF0389_00599Hypothetical protein
HMPREF0389_00019Hypothetical protein
HMPREF0389_01532Calcium binding acid repeat protein
HMPREF0389_01139S-layer Y domain-containing protein
HMPREF0389_00672Hypothetical protein
HMPREF0389_01172Hypothetical protein
HMPREF0389_00415Fimbrial assembly protein
HMPREF0389_01657Membrane protein
HMPREF0389_01476Hypothetical protein
HMPREF0389_01477Hemolysin III type calcium binding protein
HMPREF0389_01478Protein export membrane protein
HMPREF0389_01006Collagen adhesion protein

Upregulation of F. alocis proteins involved in host cell signaling.

F. alocis strains ATCC-35896 and D-62D cocultured with P. gingivalis showed upregulation of bacterial proteins that are reported to be involved in eukaryotic transcription ( Table 2 ). Coinfection also showed relative abundances of noncoding RNAs such as clustered regularly interspaced short palindromic repeat (CRISPR) RNA and toxin-antitoxin system proteins. Peptidyl prolyl cis-trans isomerase, an enzyme involved in the histone-modifying pathway, was also upregulated. Relevant gene network data based on host proteome data mining suggest upregulation of host cell signal transduction processes however, analysis also showed downregulation of kinase and protein binding activities. Many vital cellular processes such as phosphorylation, apoptosis, gene expression, cell proliferation, and cell growth were modulated (see Fig. S1 and S2 in the supplemental material).

TABLE 2

F. alocis proteins found in relative abundance during coinfection

Gene IDAnnotationFold change
D-62DATCC
HMPREF0389_ 00905Sodium neurotransmitter symporter family protein3.642.76
HMPREF0389_ 00296PP loop family protein6.381.15
HMPREF0389_ 01047TetR family transcriptional regulator1.751.04
HMPREF0389_ 01180Anti-anti-sigma factor RsbV2.921.09
HMPREF0389_ 00038Hypoxanthine phosphoribosyl transferase1.702.09
HMPREF0389_ 01084Hypothetical protein (containing 𢄧TM receptors with diverse intracellular signaling molecules)1.631.20
HMPREF0389_ 01060GMP synthase1.631.10
HMPREF0389_ 01081Caax aminoprotease family protein1.681.75
HMPREF0389_ 00290Translational regulator LacI family2.381.20
HMPREF0389_ 01109FeS assembly ATPase SufC2.081.50
HMPREF0389_ 01107Iron-regulated ABC-type transporter1.512.5
HMPREF0389_ 00519Hypothetical protein (containing putative zinc ribbon domain)2.183.3
HMPREF0389_ 00052CDP-diacyl glycerol-glycerol 3 phosphate 3 phosphatidyl transferase2.061.90
HMPREF0389_ 00948Phosphoglycerate dehydrogenase1.951.52
HMPREF0389_ 01590Transcriptional regulator AraC family1.551.03
HMPREF0389_ 01398Oxygen-independent coproporphyrinogen III oxidase1.831.08
HMPREF0389_ 01164Hypothetical protein2.281.31
HMPREF0389_ 00472Gamma glutamyl ligase family1.921.40
HMPREF0389_ 00387Pyruvate kinase2.470.74
HMPREF0389_ 00382Hypothetical protein (VCBS domain protein)4.111.13
HMPREF0389_ 00294Ribose ABC transporter permease protein1.742.4
HMPREF0389_ 00212CRISPR-associated protein 2 Cas22.121.4
HMPREF0389_ 00211CRISPR-associated protein 1 Cas11.40.70
HMPREF0389_ 00210CRISPR-associated protein Csn11.0461.00
HMPREF0389_ 01169CRISPR-associated protein Csd10.9600.95
HMPREF0389_ 01170CRISPR-associated protein Cas50.7480.78
HMPREF0389_ 01165CRISPR-associated protein Cas21.301.20
HMPREF0389_ 01167CRISPR-associated protein Cas41.011.00
HMPREF0389_ 00275Peptidyl prolyl isomerase1.2431.0
HMPREF0389_ 0123630S ribosomal protein S183.91.5
HMPREF0389_ 00068Hypothetical protein1.81.1
HMPREF0389_ 00044Glutamate synthase, RluA family2.080.8
HMPREF0389_ 00019Outer membrane protein2.341.8
HMPREF0389_ 00569tRNA delta(2) isopentenyl pyrophosphate/glucosamine 1 phosphate N acetyltransferase1.831.1
HMPREF0389_ 00756Cytidylate kinase1.702.3
HMPREF0389_ 00967Hypothetical protein (CHASE 3 domain)3.106.4
HMPREF0389_ 00243Toxin-antitoxin component ribbon-helix-helix fold protein0.8210.88
HMPREF0389_ 00201Hypothetical protein (HTH3 domain protein)1.381.36
HMPREF0389_ 00275Peptidyl prolyl isomerase1.240.99
HMPREF0389_ 00359Peptidyl prolyl cis-trans isomerase1.241.10

Proteome profile of epithelial cells coinfected with F. alocis and P. gingivalis.

Epithelial cells coinfected with F. alocis and P. gingivalis strains showed activation of several eukaryotic proteins involved in the inflammatory response, cell signaling, and cell death ( Fig. 3 ). The global proteome analysis of the host showed modulation in expression of 209 proteins. Among them, proteins involved in cytoskeleton organization and biogenesis were affected most (17%) followed by proteins involved in the regulation of gene expression and epigenetic modification (9%), regulation of protein transport (7%), transcription initiation (7%), regulation of protein biosynthesis (6%), protein processing (5%), regulation of signal transduction (4%), and cell death-apoptosis (3%). Highly downregulated proteins during coinfection include eukaryotic translation initiation factor, splicing factors, histone protein clusters, and other signaling proteins such as vimentin, prohibitin, and redox proteins. Highly upregulated proteins include the RAS oncogene family proteins, proteins involved in granzyme signaling, and cytoskeletal matrix proteins ( Table 3 ). An Ingenuity pathway analysis showed increased expression of eukaryotic genes involved in antigen presentation, cellular movement, the hematological system, cell trafficking, and inflammatory response (see Fig. S3 in the supplemental material).

Eukaryotic proteome profile showing various protein classes modulated during coculture infection with P. gingivalis and Filifactor alocis. Section 1, GO:0005975 (carbohydrate metabolism) section 2, GO:0006260 (DNA replication) section 3, GO:0006352 (transcription initiation) section 4, GO:0006353 (transcription termination) section 5, GO:0006360 (transcription from RNA polymerase I promoter) section 6, GO:0006417 (regulation of protein biosynthesis) section 7, GO:0006457 (protein folding) section 8, GO:0006512 (ubiquitin cycle) section 9, GO:0006839 (mitochondrial transport) section 10, GO:0007005 (mitochondrion organization and biogenesis) section 11, GO:0007010 (cytoskeleton organization and biogenesis) section 12, GO:0007047 (cell wall organization and biogenesis) section 13, GO:0007166 (cell surface receptor-linked signal transduction) section 14, GO:0007264 (small-GTPase-mediated signal transduction) section 15, GO:0008219 (cell death) section 16, GO:0009890 (negative regulation of biosynthesis) section 17, GO:0009966 (regulation of signal transduction) section 18, GO:0015931 (vesicle organization and biogenesis)section 19, GO:0016071 (mRNA metabolism) section 20, GO:0016481 (negative regulation of transcription) section 21, GO:0016485 (protein processing) section 22, GO:0018193 (peptidyl amino acid modification) section 23, GO:0019932 (secondary messenger-mediated signaling) section 24, GO:0030705 (cytoskeleton-dependent intracellular transport) section 25, GO:0040029 (regulation of gene expression, epigenetics) section 26, GO:0045333 (cellular respiration) section 27, GO:0045454 (cell redox homeostasis) section 28, GO:0051049 (regulation of transport) section 29, GO:0051052 (regulation of DNA metabolism).

TABLE 3

Modulated host proteins during coinfection with F. alocis and P. gingivalis

GeneFold changeAnnotation
Downregulated proteins
   𠀾IF4B𢄣.675Eukaryotic translation initiation factor 4B
    HIST1H1C𢄢.742Histone cluster 1, H1c
    HIST1H1E𢄢.643Histone cluster 1, H1e
    HIST1H2BL𢄢.790Histone cluster 1, H2bl
    PPIA𢄣.307Peptidyl prolyl isomerase A (cyclophilin A)
    VIM𢄢.158Vimentin
    SRSF14.049Serine/arginine-rich splicing factor 1
    PHB Prohibin
    TRAP 1𢄡.035TNF receptor-associated protein 1
    PRDX1𢄡.219Peroxiredoxin 1 and 5
    PRDX5𢄡.201
Upregulated proteins
    NACA2.310Nascent polypeptide-associated complex alpha subunit
    IMPDH22.472Inosine-5′-monophosphate dehydrogenase 2
   𠀺GF31.774AFG3-like protein 2
    RAB 101.128Member of RAS oncogene family
    RAB 7A1.028Member of RAS oncogene family
    ITGB11.356Integrin
    PHB1.260Prohibitin
    VCL2.189Vinculin
    IGAAD Granzyme A signaling proteins
    HSP102.16010-kDa heat shock protein (mitochondrial)
    LDHA1.516Isoform 1 of l -lactate dehydrogenase A chain
1.51246-kDa protein
1.90726-kDa protein
    NACA2.310Nascent polypeptide-associated complex
    KRT11.461Keratin type II cytoskeletal I
    HNRPR1.728Heterogenous nuclear ribonucleoprotein R

Many host cell regulatory proteins that are involved in cytoskeleton integrity were modulated. The integrin beta-1 (ITGB1) genes are downregulated in coculture. The valosin-containing protein (VCP) gene involved in ubiquitin-dependent protein degradation is downregulated in coculture. In both coculture and monoculture of F. alocis, there was upregulation of the poly-β-hydroxybutyrate (PHB) gene involved in negative regulation of cell proliferation (see Fig. S4 in the supplemental material). The vinculin (VCL) gene coding for the membrane cytoskeleton protein vinculin and the voltage-dependent anion channel (VDAC1) gene were upregulated during coinfection. Proteins involved in important cell regulatory networks such as serine/arginine-rich splicing factor 1 (SRSF1), annexin-2 (ANXA2), heat shock proteins (HSPA8, HSP9, and HSPE1), synaptotagmin binding protein (SYNCRIP), eukaryotic initiation factor 4A-1, 19-kDa protein-SRP-dependent translational protein, protein S100A11, and other cytoskeletal proteins such as the lamin A/C proteins were also found to be modulated (see Table S2). There was a generalized downregulation of the actin pathway (see Fig. S5).

In order to identify variations in host cell surface morphology and cell death between coinfection and monoinfection, epithelial cells coinfected with F. alocis and P. gingivalis were subjected to electron microscopy study. Wide morphological variation of the host cell was noted after coinfection compared to monoinfection with either F. alocis or P. gingivalis. The infected epithelial cells show modification of cell surface filopodial projections that were used by both F. alocis and P. gingivalis to adhere to the cell surface and formation of membrane microdomains such as the lipid rafts ( Fig. 4 , panels 1 and 2). Infected cells showed early apoptosis during coinfection compared to monoinfection ( Fig. 4A to ​ toL). L ). Surface modifications of the coinfected epithelial cells were noted in both F. alocis ATCC 35896 and the clinical strain D-62D however, the level of filopodial projections was greater in the F. alocis D-62D strain than in the F. alocis ATCC 35896 strain ( Fig. 4O and ​ andP). P ). Such morphological variations were not noted in monoinfected epithelial cells ( Fig. 4M and ​ andN N ).

Coinfection of P. gingivalis and Filifactor alocis, showing morphological variations during adherence leading to apoptosis of the epithelial cells. Scanning electron microscopy images show modification of the cell surface with filapodial projections in infected epithelial cells that were used by both F. alocis and P. gingivalis to adhere to the cell surface (panels 1 and 2). Infected cells showed early apoptosis during coinfection compared to monoinfection (A to L). Surface morphological variations were not noted in monoinfected epithelial cells (M and N). Filopodial projections were more increased in the F. alocis D-62D strain (O and P). Orange arrows, F. alocis light-green arrows, P. gingivalis dark-green arrows, filapodial projections.

Many host proteins involved in chromatin function and remodeling were downregulated during F. alocis coinfection with P. gingivalis. An overall downregulation of histone cluster proteins and PPIA-peptidyl prolyl isomerase was noted ( Fig. 5 ) ( Table 2 ). The relative expression levels of such proteins were the lowest in F. alocis clinical strain coinfection compared to the type strain coinfection (data not shown). Heterogeneous nuclear ribonuclear protein A2/B1 was found in least abundance during coinfection. Proteins involved in transport and secretory pathways such as the transferrin receptor protein 1 (involved in iron transport), transmembrane emp24 domain-containing protein 10 and dynein (involved in vesicular protein trafficking), surfeit 4, and solute carrier family proteins were also less expressed during coinfection.

Coinfection of Filifactor alocis with P. gingivalis showing downregulation of proteins involved in gene expression and protein synthesis pathways. HeLa cells were infected with the F. alocis ATCC 35896 and D-62D strains (MOI of 1:100 [10 5 epithelial cells]) in monoculture or coculture with P. gingivalis strains as previously reported (6). Tandem isobaric mass tagging analysis of cocultures and monocultures was carried out using Orbitrap. The eukaryotic proteins were analyzed using MASCOT, and functional analysis was carried out using Ingenuity pathway analysis software. The gene ontology classification was used for referencing the proteome. F. alocis coinfection with P. gingivalis showed overall downregulation of histone cluster proteins (histone [Hist] H1, Hist 1H1B, Hist 1H1C, and Hist 1H1E) (shown within the circle), peptidyl prolyl isomerase (PPIA and PPIB), and antioxidant enzymes (PRDX1 and PRDX5). Green, downregulation red, upregulation solid lines, direct interaction dotted lines, indirect interaction.

Proteins that are known to activate oncogenes directly or indirectly were expressed in high abundance during F. alocis coculture. The major proteins that were present in high abundance during coinfection were RAB 7A, RAB10 proteins belonging to the RAS oncogene family, poly(rC) binding protein 2 (PCBP2), voltage-dependent anion channel protein 1, copine-1 (calcium-dependent membrane binding protein), and collagen alpha-2 (V) chain precursor ( Table 3 ).

Many host cell proteins that are involved in cell adhesion and cytoskeletal interactions were upregulated during coinfection of F. alocis with P. gingivalis. Host cytoskeletal proteins such as vimentin, actin, plectin, vinculin, profilin, and transgelin and chaperone proteins such as HSP90 and endoplasmin and proteins such as filamin B and filamin C involved in cell communication were modulated. Signal transduction proteins galectin, proteasome subunit alpha type 6, and 14-3-3 protein theta were relatively less abundant. Inosine 5′ monophosphate dehydrogenase (IMPDH), receptor kinectin, and peroxiredoxins were found to be highly expressed during coinfection (see Table S2).

Oribtrap analysis of the whole proteome of F. alocis coculture with P. gingivalis revealed many upregulated host cell proteins that are involved in apoptosis, cell regulation, and differentiation pathways. Proteins such as prohibitins and Ras-related protein Rab 10 and Ras-related protein Rab 7 SET translocation proteins that are involved in apoptosis and histone binding were upregulated. Also, SRSF1 protein encoded by a proto-oncogene, nascent-polypeptide-associated complex alpha (NACA) protein (transcriptional coactivator), NPM1 (nucleophosmin) involved in apoptosis and tumorigenesis, and CALR (calreticulin) involved in calcium binding and storage were highly upregulated ( Fig. 6 ). Further, our analysis showed upregulation of proteins involved in the ubiquitin proteasome pathway and the granzyme-mediated apoptotic signaling pathway (see Fig. S6 in the supplemental material).

Coinfection of Filifactor alocis with P. gingivalis showing upregulation of proteins involved in cancer and cell death pathways. HeLa cells were infected with F. alocis ATCC 35896 and D-62D strains (MOI of 1:100 [10 5 epithelial cells]) in monoculture or coculture with P. gingivalis strains as previously reported (6). Tandem isobaric mass tagging analysis of cocultures and monocultures was carried out using Orbitrap. The eukaryotic proteins were analyzed using MASCOT, and functional analysis was carried out using Ingenuity pathway analysis software. The gene ontology classification was used for referencing the proteome. Proteins such as prohibitins, Ras-related protein Rab 10, and the Ras-related protein Rab 7 SET translocation protein that are involved in apoptosis and histone binding were upregulated. Also, the SRSF1 protein encoded by a proto-oncogene, NACA protein (transcriptional coactivator), NPM1 (nucleophosmin involved in apoptosis and tumorigenesis), and CALR (calreticulin involved in calcium binding and storage) were highly upregulated. Green, downregulation red, upregulation.

The host metabolic pathways modulated during coinfection are given in Table 4 . Pathways relating to ammonia synthesis, urate biosynthesis, amino acid degradation (valine and aspartate), lipid synthesis, and palmitate and fatty acid synthesis were highly upregulated. There was downregulation of amino acid excretory pathways and transport pathways such as the glutamyl/arginine exchange and tryptophan pathway. Coinfection of F. alocis was shown to affect basic energy pathways such as glycolysis and acetyl coenzyme A (CoA) biosynthesis.

TABLE 4

Metabolome variation in host protein during F. alocis coinfection

Expression category and pathway
Upregulated
   𠀺mmonia production
    Urate biosynthesis through inosine 5′phosphate degradation
    Valine degradation
   𠀺spartate degradation
     l -Asparagine synthesis
    Glutaraldehyde CoA degradation
   �tty acid biosynthesis
    Pentose phosphate pathway
    TCA cycle a
    Pyruvate fermentation to lactate
    Thioredoxin pathway
    Palmitate biosynthesis
    Purine de novo synthesis
Downregulated
    Sodium-independent glutamyl/arginine exchange
     l -Tryptophan transport
    Sucrose degradation
    Peptidyl proline synthesis
    Phosphoprotein synthesis
   �tyl CoA biosynthesis from citrate
    Glycolysis
    NADH repair

Roles of Cyclophilins in Chloroplast

The CsA-sensitive PPIase activity in chloroplasts was first demonstrated in pea by Breiman et al. (1992). Since the characterization of TLP40, a 40 kDa thylakoid lumen cyclophilin from spinach chloroplasts (Fulgosi et al., 1998), proteomics and bioinformatics approaches resulted in the identification of 11 FKBPs and 5 cyclophilins in the chloroplast lumen of Arabidopsis (Edvardsson et al., 2007 Trivedi et al., 2012). TLP40 is a multi-domain cyclophilin that shows PPIase activity and acts as a negative regulator of the thylakoid membrane protein phosphatase (Fulgosi et al., 1998 Vener et al., 1999). This protein plays an essential role in the growth and development of plants since mutations in its Arabidopsis ortholog, AtCYP38, resulted in impaired development of chloroplasts, retarded plant growth, hypersensitivity to light, and enhanced degradation of D1 and D2 components of PSII under high light conditions (Fu et al., 2007 Sirpiö et al., 2008 Vasudevan et al., 2012 Vojta et al., 2019). Together with other immunophilins such as FKBP13 and FKBP20-2, that are required for accumulation of the cytochrome b6f complex and PSII supercomplexes, respectively (Gupta et al., 2002 Lima et al., 2006), AtCYP38, despite lacking PPIase activity, appears to be indispensable for proper biogenesis and maintenance of photosynthetic complexes. On the contrary, impaired functioning of AtCYP20-2, a highly active PPIase and orthologous to the spinach cyclophilin TLP20, had no apparent phenotypic effect, suggesting redundancy in the function of these proteins (Fulgosi et al., 1998 Sirpiö et al., 2009). It has been proposed that while TLP40 performs specialized regulatory function(s), TLP20 might act as a general protein folding catalyst (Edvardsson et al., 2003). The chloroplast stromal protein AtCYP20-3, 65.64 % identical to AtCYP20-2, facilitates the folding of serine acetyltransferase (SAT) that catalyzes the ultimate step in Cys biosynthesis which is important for glutathione formation. The PPIase and folding activities of AtCYP20-3, sensitive to photooxidation and stress-induced ROS, were restored following reduction by photoreduced Trx (Laxa et al., 2007). Mutation in AtCYP20-3 resulted in hypersensitivity to oxidative stress in Arabidopsis (Dominguez-Solis et al., 2008), implying that it enables the Cys-based thiol biosynthesis pathway to adjust to light and stress conditions. Isothermal titration microcalorimetry and gel overlay assays further indicated that AtCYP20-3 interacts with thiol based peroxidases, 2-Cysteine peroxiredoxins (2-CysPrx), which can exist as either dimer or decamer. The dimer form is favored under oxidizing conditions whereas the decamer is formed under reducing conditions. High affinity of AtCYP20-3 for the dimer leads to a decrease in the free dimer concentration. Thus it appears that AtCYP20-3 regulates the critical transition concentration (concentration responsible for dimer-decameric form transition) value of 2-CysPrx, suggesting redox-dependent conformational dynamics of this protein (Liebthal et al., 2016).


TPL2 and tumorigenesis

Initially, TPL2 was identified as a target for provirus integration in Moloney murine leukemia virus (MoMuLV)-induced T cell lymphomas and mouse mammary tumor virus (MMTV)-induced mammary carcinomas in mice [4, 5]. The viral insertion induced the generation of a truncated form of the TPL2 protein, which portrays increased activity and stability with enhanced transformation potential, illustrating the importance of increased TPL2 kinase activity in tumorigenesis [4]. Although TPL2 mutations are rare in human cancers, there is some evidence of mutations that induce TPL2 signal amplification and constitutive kinase activity occurring in breast cancer and lung adenocarcinoma, raising the possibility that TPL2 C-terminal might be a target of mutation in some cancers [5, 36, 37]. However, the number of tumors with TPL2 overexpression and activation is much more significant than the subfraction of tumors with confirmed mutations, suggesting that TPL2 overexpression and activation are the main events associated with increased tumorigenesis [5]. TPL2 overexpression and increased activity are associated with poor prognosis and the progression of several human cancers including skin cancer, prostate cancer, breast cancer, ovarian cancer, hepatocellular carcinoma, colorectal cancer, endometrial cancer, gastric cancer, EBV-related nasopharyngeal carcinoma, anaplastic large-cell lymphoma (ALCL), colitis-associated carcinoma, bladder cancer and cervical cancer (Figure 3) [6, 8, 36, 38-40]. Congruently, TPL2 kinase activity has been implicated in all stages of tumorigenesis, including tumor initiation, tumor promotion and tumor progression where it modulates cell proliferation, stem cell acquisition, angiogenesis, EMT progression, migration, invasion and metastasis [5, 9].

The TPL2 signaling. TPL2 signaling pathway is activated following the stimulation of several receptors including TNFR, IL-1R, TLRs, CD40 and TCR. Activated TPL2 subsequently activates MAPKs signal: ERK1/2, p38α, JNK, and Akt that regulate the activation of several transcription factors: NF-κB, AP1, STAT3, C/EBPβ, CREB, ZEB1 and NFATc1. These transcription factors induce the expression of various genes such as TNFα, IL-6, IL-1β, COX2, VEGF, CXCR4, cyclin D1, HER, EGFR, MMP2, MMP9 and vimentin that are involved in inflammation, cell proliferation and survival, angiogenesis and metastasis. In addition, TPL2 promotes cell survival by inhibiting p27kip expression, deactivating p53 through PP2A activity and through the regulation TACE expression. TPL2 signaling is also involved in mRNA stabilization and protein translation. In the cytoplasm, TPL2-activated ERK1/2 and Akt regulate the mTORC1/S6 signaling pathway that modulates the protein translation of inflammatory factors including TNFα, COX2, CXCL1, IL-1β and IL-18. Activated p38 might also promote mRNA stabilization by activating the MK2. Moreover, activation of RSK1 and MSK1 by ERK1/2 and p38α can promote transcription factor activation, mRNA stabilization and translation. (TPL2: tumor progressive locus 2 TNFR: tumor necrosis factor receptor IL-1R: interleukin 1 receptor TLRs: toll-like receptors CD40: cluster of differentiation 40 TCR: T cell receptor ERK1/2: extracellular signal-regulated kinases 1/ 2 JNK: c-Jun N-terminal kinase Akt: protein kinase B MAPKs: mitogen-activated protein kinases mTORC1: mammalian target of rapamycin complex 1 NF-κB: nuclear factor kappa-light-chain-enhancer of activated B AP1: activator protein 1 STAT: signal transducer and activator of transcription C/EBPβ: CCAAT-enhancer-binding proteins-β CREB: cAMP response element-binding protein ZEB1: zinc finger E-box-binding homeobox 1 NFATc1: nuclear factor of activated T-cells, cytoplasmic 1 TNF: tumor necrosis factor receptor IL: interleukin COX2: cyclooxygenase 2 CXCL: CXC-chemokine ligand CXCR: CXC-chemokine receptor VEGF: vascular endothelial growth factor EGFR, epidermal growth factor receptor MMP: matrix metalloproteinase Pin1: peptidyl-prolyl cis/trans isomerase HER: human epidermal growth factor receptor EGFR: epidermal growth factor receptor RSK1: ribosomal protein S6 kinase 1 MSK1: mitogen- and stress-activated kinase 1 MK2: MAPK activated protein kinase 2).

(Click on the image to enlarge.)

However, suppressed TPL2 expression is also reported in some cancers [9]. For example, reduced TPL2 expression was shown to correlate with poor prognosis and tumor aggressiveness in non-small cell lung cancer (NSCLC) patients [41, 42]. Accordingly, TPL2 -/- mice exposed to the lung carcinogen urethane demonstrated that loss of TPL2 protein promotes increased cell proliferation and apoptosis resistance due to dysregulated p53 signaling [41]. In addition, genetic and epigenetic control mechanisms such as frequency of loss of heterozygosity (LOH) at the TPL2 locus and miR370 upregulation are also associated with TPL2 suppression [41]. However, it is still unclear whether these mechanisms directly contribute to TPL2 suppression in lung cancer. This study also proposed that the oncogenic Ras signaling might contribute to TPL2 suppression in lung cancer through the reduction of NF-κB1 p105 protein [41]. Consistently, urethane-treated NF-κB1 p105 deficient mice, similar to the TPL2 -/- mice, exhibited increased susceptibility to lung cancer that was associated with augmented lung damage, inflammation and K-Ras mutation [43]. Furthermore, the reconstitution of TPL2 expression in NF-κB1 p105 and TPL2 deficient mice was shown to inhibit tumorigenesis, possibly by some unknown mechanisms that suppress oncogenic Ras signaling. Correspondingly, these studies highlight the importance of the TPL2 protein in tumorigenesis and pro-tumorigenic inflammation. Moreover, due to its interaction with NF-κB1 p105 and ABIN2 protein, it is possible that altered TPL2 protein expression might contribute to the aberrant activation or suppression of other signaling pathways, thereby promoting pro-tumorigenic inflammation and tumorigenesis. Hence, further studies investigating the distinguished function of TPL2 protein and TPL2 kinase activity in lung cancer and tumorigenesis are necessary.

Pro- and anti-tumorigenic effects of TPL2 signaling. The TPL2 kinase activity functions as a potent tumor promoter in most cancers, where it is predominantly associated with increased inflammation, malignant transformation, tumor growth, stem cell acquisition, angiogenesis, metastasis and poor prognosis. However, suppressed TPL2 expression is also reported in some cancers like lung cancer and its ablation is associated with increased tumorigenesis in experimental skin cancer and intestinal cancer. It is worth noting that increased tumorigenesis in TPL2 suppressed carcinogen-induced squamous cell carcinoma, carcinogen-induced colitis-associated cancer, lung cancer and sporadic colorectal cancer is mostly dependent on the presence of tumor promoting conditions such as inflammation, tissue damage and NF-κB signaling. (The figure was created with BioRender.com, ©BioRender 2020).

(Click on the image to enlarge.)


Figure 5

Figure 5. Expression of K6W-ubiquitin reduces the levels of periaxin and disrupts the fiber organization. Lenses from WT and high expressing K6W-Ub transgenic newborn (P1) mice were cryosectioned at the equatorial plane. The sections were immunostained with antibody to periaxin and FITC-labeled secondary antibody. F-actin was stained with rhodamine-labeled phalloidin. Confocal photomicrographs were taken using a Leica confocal system. These data indicate that periaxin is down-regulated in most lens fibers of K6W-Ub lenses and the diameter of lens fibers of K6W-ub lens is significantly larger than that of WT lens.


Supporting Information

Figure S1.

Flow chart of the assembly and automated annotation of 454 reads. A) Assembly of 454 reads. Raw reads: chromatograms produced by 454 Titanium sequencing Trimmed reads: reads processed for assembling Singletons: putative transcripts identified by one read Singletons > 200 nt: putative transcripts identified by one read > 200 nucleotide length Isotigs: putative transcripts identified by at least two reads Consensus sequences: non-redundant sequences (singletons + isotigs). B) Automated annotation process. Each consensus sequence, converted to the FASTA format, was searched locally against a nucleotide database downloaded from the NCBI and UniProtUK databases using, respectively, Blast-X and Blast-N. See Material and Methods for more details.

Figure S2.

Functional analysis of differentially expressed transcripts. A) A weighted Venn diagram showing the relative portion in the differentially expressed genes of those with sinusoidal expression patterns (CircWaveBatch analysis). B) The differentially expressed annotated genes (336 consensus sequences) were classified into 12 different functional categories. The diagram shows the proportion of each functional category. C) The 159 annotated transcripts showing sinusoidal oscillatory patterns were grouped into 12 functional categories. Transcripts characterized by a 24-hour (75 out of 159) or a 12-hour (84 out of 159) periodicity of expression are shown separately. See Table S1 for more details.

Figure S3.

Validation of microarray expression values by qRT-PCR. mRNA expression levels are represented by box-and-whisker plots. Normalized qRT-PCR data are expressed as fold changes (FC) relative to the median expression for each time point. 18S rRNA was used as an endogenous control. The microarray expression profile of each gene is shown below the qRT-PCR box plot. Pearson's correlation was calculated to estimate the association between the microarray data and qRT-PCR results (p > 0.7 is considered statistically significant).

Figure S4.

Schematic representation of a normalized cDNA library construction protocol. A combination of two different protocols – the “whole transcriptome amplification (WTA)” and “Duplex-specific nuclease (DSN) normalization” – was adopted. See Material and Methods for more details.

Table S1.

The list of 336 differentially expressed annotated genes grouped into functional categories. Expression levels over a 24-hour cycle are shown. Transcripts identified as cycling by CircWaveBatch V. 3.3 are indicated in bold. Sampling times are indicated. a Annotation = description of the gene b e-value: score of annotation with Blast-N and/or Blast-X c Probe: ID of probe sequence in “Krill 1.1” Agilent microarray platform.

Table S2.

Primers used in quantitative RT-PCR.


Publications by authors named "Kyung Yeon Han"

Samsung Genome Institute, Samsung Medical Center, Seoul 06351, South Korea.

Download full-text PDF

SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells.

Authors:

Genome Res 2018 01 528(1):75-87. Epub 2017 Dec 5.

Samsung Genome Institute, Samsung Medical Center, Seoul 06351, South Korea.

Download full-text PDF

Vertical Magnetic Separation of Circulating Tumor Cells for Somatic Genomic-Alteration Analysis in Lung Cancer Patients.

Authors:

Sci Rep 2016 11 286:37392. Epub 2016 Nov 28.

Samsung Genome Institute (SGI), Samsung Medical Center (SMC), Seoul 06351, Korea.

Download full-text PDF

Highly dense, optically inactive silica microbeads for the isolation and identification of circulating tumor cells.

Authors:

Biomaterials 2016 Jan 2375:271-278. Epub 2015 Oct 23.

Samsung Genome Institute (SGI), Samsung Medical Center (SMC), Seoul 135-710, South Korea Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon 440-746, South Korea. Electronic address:

Download full-text PDF

Escherichia coli EDA is a novel fusion expression partner to improve solubility of aggregation-prone heterologous proteins.

Authors:

J Biotechnol 2015 Jan 5194:39-47. Epub 2014 Dec 5.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Seongbuk-Gu, Seoul 136-713, Republic of Korea. Electronic address:

Download full-text PDF

Synthesis of Mycoplasma arginine deiminase in E. coli using stress-responsive proteins.

Authors:

Enzyme Microb Technol 2014 Sep 2763:46-9. Epub 2014 May 27.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, Republic of Korea. Electronic address:

Download full-text PDF

A stress-responsive Escherichia coli protein, CysQ is a highly effective solubility enhancer for aggregation-prone heterologous proteins.

Authors:

Protein Expr Purif 2014 Sep 17101:91-8. Epub 2014 Jun 17.

Department of Chemical and Biological Engineering, College of Engineering, Korea University, Anam-Ro 145, Seoul 136-713, Republic of Korea. Electronic address:

Download full-text PDF

Fully automated circulating tumor cell isolation platform with large-volume capacity based on lab-on-a-disc.

Authors:

Anal Chem 2014 Apr 3186(8):3735-42. Epub 2014 Mar 31.

Samsung Biomedical Research Institute, Samsung Advanced Institute of Technology, Samsung Electronics Company, Ltd. , 81 Irwon-Ro, Gangnam-Gu, Seoul 135-710, Republic of Korea.

Download full-text PDF

YrhB is a highly stable small protein with unique chaperone-like activity in Escherichia coli BL21(DE3).

Authors:

FEBS Lett 2012 Apr 8586(7):1044-8. Epub 2012 Mar 8.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Gu, Seoul 136-713, Republic of Korea.

Download full-text PDF

A novel Escherichia coli solubility enhancer protein for fusion expression of aggregation-prone heterologous proteins.

Authors:

Enzyme Microb Technol 2011 Jul 2249(2):124-30. Epub 2011 Apr 22.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Seongbuk-Gu, Seoul 136-713, Republic of Korea.

Download full-text PDF

Sensitive and simultaneous detection of cardiac markers in human serum using surface acoustic wave immunosensor.

Authors:

Anal Chem 2011 Nov 2683(22):8629-35. Epub 2011 Oct 26.

Bio Laboratory, Samsung Advanced Institute of Technology, Samsung Electronics Co., Ltd., Yongin-si, Gyeonggi-do, Republic of Korea.

Download full-text PDF

The N-domain of Escherichia coli phosphoglycerate kinase is a novel fusion partner to express aggregation-prone heterologous proteins.

Authors:

Biotechnol Bioeng 2012 Feb 9109(2):325-35. Epub 2011 Sep 9.

Department of Chemical and Biological Engineering, Korea University, 136-713 Anam-Dong 5-1, Seoul, Republic Korea.

Download full-text PDF

Human G-CSF synthesis using stress-responsive bacterial proteins.

Authors:

FEMS Microbiol Lett 2009 Jul 5296(1):60-6. Epub 2009 May 5.

Department of Chemical and Biological Engineering, Korea University, Seoul, South Korea.

Download full-text PDF

Functional fusion mutant of Candida antarctica lipase B (CalB) expressed in Escherichia coli.

Authors:

Biochim Biophys Acta 2009 Mar 241794(3):519-25. Epub 2008 Dec 24.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Gu, Seoul 136-713, South Korea.

Download full-text PDF

Analysis and characterization of hepatitis B vaccine particles synthesized from Hansenula polymorpha.

Authors:

Vaccine 2008 Aug 1326(33):4138-44. Epub 2008 Jun 13.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, South Korea.

Download full-text PDF

Multiple stressor-induced proteome responses of Escherichia coli BL21(DE3).

Authors:

J Proteome Res 2008 May 267(5):1891-903. Epub 2008 Mar 26.

Department of Chemical and Biological Engineering, Korea University, Seoul, South Korea.

Download full-text PDF

Solubility enhancement of aggregation-prone heterologous proteins by fusion expression using stress-responsive Escherichia coli protein, RpoS.

Authors:

BMC Biotechnol 2008 Feb 198:15. Epub 2008 Feb 19.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, South Korea.

Background: The most efficient method for enhancing solubility of recombinant proteins appears to use the fusion expression partners. Although commercial fusion partners including maltose binding protein and glutathione-S-transferase have shown good performance in enhancing the solubility, they cannot be used for the proprietory production of commercially value-added proteins and likely cannot serve as universal helpers to solve all protein solubility and folding issues. Thus, novel fusion partners will continue to be developed through systematic investigations including proteome mining presented in this study.

Results: We analyzed the Escherichia coli proteome response to the exogenous stress of guanidine hydrochloride using 2-dimensional gel electrophoresis and found that RpoS (RNA polymerase sigma factor) was significantly stress responsive. While under the stress condition the total number of soluble proteins decreased by about 7 %, but a 6-fold increase in the level of RpoS was observed, indicating that RpoS is a stress-induced protein. As an N-terminus fusion expression partner, RpoS increased significantly the solubility of many aggregation-prone heterologous proteins in E. coli cytoplasm, indicating that RpoS is a very effective solubility enhancer for the synthesis of many recombinant proteins. RpoS was also well suited for the production of a biologically active fusion mutant of Pseudomonas putida cutinase.

Conclusion: RpoS is highly effective as a strong solubility enhancer for aggregation-prone heterologous proteins when it is used as a fusion expression partner in an E. coli expression system. The results of these findings may, therefore, be useful in the production of other biologically active industrial enzymes, as successfully demonstrated by cutinase.

Download full-text PDF

Transport proteins PotD and Crr of Escherichia coli, novel fusion partners for heterologous protein expression.

Authors:

Biochim Biophys Acta 2007 Dec 41774(12):1536-43. Epub 2007 Oct 4.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, South Korea.

Download full-text PDF

Solubilization of aggregation-prone heterologous proteins by covalent fusion of stress-responsive Escherichia coli protein, SlyD.

Authors:

Protein Eng Des Sel 2007 Nov 3020(11):543-9. Epub 2007 Oct 30.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, South Korea.

Download full-text PDF

Enhanced solubility of heterologous proteins by fusion expression using stress-induced Escherichia coli protein, Tsf.

Authors:

FEMS Microbiol Lett 2007 Sep 3274(1):132-8. Epub 2007 Jul 3.

Department of Chemical and Biological Engineering, Korea University, Seoul, South Korea.

Download full-text PDF

Escherichia coli malate dehydrogenase, a novel solubility enhancer for heterologous proteins synthesized in Escherichia coli.

Authors:

Biotechnol Lett 2007 Oct 529(10):1513-8. Epub 2007 Jun 5.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul, 136-713, South Korea.

Download full-text PDF

Enhanced stability of heterologous proteins by supramolecular self-assembly.

Authors:

Appl Microbiol Biotechnol 2007 May 1475(2):347-55. Epub 2007 Feb 14.

Department of Chemical and Biological Engineering, College of Engineering, Korea University, Seoul, 136-713, South Korea.

Download full-text PDF

A novel approach to ultrasensitive diagnosis using supramolecular protein nanoparticles.

Authors:

FASEB J 2007 May 521(7):1324-34. Epub 2007 Feb 5.

Department of Chemical and Biological Engineering, Korea University, Seoul, South Korea.

Download full-text PDF

Heterologous gene expression using self-assembled supra-molecules with high affinity for HSP70 chaperone.

Authors:

Nucleic Acids Res 2005 833(12):3751-62. Epub 2005 Jul 8.

Department of Chemical and Biological Engineering, Korea University Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-713, South Korea.

Download full-text PDF

Proteome response of Escherichia coli fed-batch culture to temperature downshift.

Authors:

Appl Microbiol Biotechnol 2005 Oct 1368(6):786-93. Epub 2005 Oct 13.

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul, 136-713, South Korea.

Download full-text PDF

Comparative proteome analysis of Hansenula polymorpha DL1 and A16.

Authors:

Proteomics 2004 Jul4(7):2005-13

Department of Chemical and Biological Engineering, Korea University, Anam-Dong 5-1, Sungbuk-Ku, Seoul 136-701, South Korea.


Watch the video: Bogdan Mateescu - RNA binding proteins and exRNAs (August 2022).