Comparative Analysis of Heat Shock Extracts vs. Standard Extracts

To investigate the effects of altered cultivation conditions on the composition of S30 extracts, the cultivation and extract preparation protocol for S30^+D as described in 3.6.1 was employed. For the comparison, the S30^+D(1-4) from the previous chapter was used and three biological replicates of S30^+D-S, hereafter referred to as S30^+D-S^1-3, were prepared according to the scheme illustrated in Figure 9, A and are described in detail (3.6.1). The cultivation for S30^+D-S^1-3 included a heat shock at 42 °C for 30 min and simultaneous exposure to 3% (v/v) EtOH (SOS response*).

Figure 9: General parameters of S30 extracts used comparative analysis of S30^+D and S30^+D-S extracts. Workflow of cultivation conditions, S30 extract preparation and extract performance is illustrated. (A) Flowchart of different S30 (S30^+D and S30^+D-S) extract preparation procedures (as described in more detail in 3.6.1). (B) Growth curves of E. coli A19 as observed under different fermentation conditions used for S30^+D and S30^+D-S extract, respectively. (C) Performance evaluation of S30^+D and S30^+D-S extract as determined under standard expression conditions using sGFP as a quantitative reporter and was carried out in CECF mode as described in (3.6.4). The error bars represent the standard deviation of three measurements. *The SOS-response was induced by addition of 3% (v/v) EtOH to the culture broth and simultaneous heat shock at 42 °C for 30 min.

Inclusion of the SOS response at the tail of the fermentation led to a slightly different growth curve and longer fermentation time; the growth curve is depicted in Figure 9, B. The

expression efficiency of S30^+D-S^1-3 extracts is reduced compared to S30^+D(1-4) , with a yield of 2.75 mg mL^-1 and 4.5 mg mL^-1 for S30^+D-S^1-3 and S30^+D(1-4), respectively, for sGFP (Figure 9, C). Despite the lower expression efficiency of S30^+D-S^1-3,it was hypothesized that these extracts may exhibit beneficial properties for the expression of proteins that have been shown to be difficult-to-express due to e.g. higher chaperone dependency.

Figure 10: Strategy for quantitative GeLC-MS/MS analysis of S30^+D vs. S30^+D-S extract. The S30^+D(1) and S30^+D-S^1-3 extracts were individually labelled using ICPL strategy by heavy and light label. The individual biological replicates S30^+D-S¹, S30^+D-S² and S30^+D-S³ were eachmixed with the respective sample of S30^+D(1) and then separated on protein-level by 1D-SDS-PAGE. The lane was then divided into 12 equal fractions (F1-F12) and proteins were in-gel digested (trypsin) followed by extraction. Peptides extracted from F1-F12 were further separated by nanoHPLC (Reversed Phase C18 column) and individual elution fractions (450 per gel-slice (F1-F12) were analyzed by MALDI-TOF-MS/MS to identify and quantify protein of both sample relative to each other.

In order to quantify the changes of the S30^+D-S^1-3 proteome relative to the standard S30^+D(1) extract proteome, an ICPL labelling strategy was employed (see Figure 10). In this process, the lysine residues of all denatured proteins of S30^+D extract were labelled with a heavy label, while the proteins of S30^+D-S^1-3 were labelled with a light label. Subsequently, the labelled extracts were mixed and separated using 1D-SDS-PAGE and analyzed as described previously using bottom-up proteomics. The light and heavy labelled peptides co-eluting during LC separation were analyzed simultaneously in one MS¹ spectrum as duplets with 6 Da difference in mass, whereas their relative intensities to each other were compared to identify up-, down- and non-regulated peptides. Protein identification was based on MS² data acquired in a second step.

The MS² data was then searched against the E. coli K12 reference proteome (Uniprot ID: UP000000625 as downloaded on 19^th of January 2017). Statistical means were applied to reach a FDR on protein- and peptide level of 1%. The dataset was analyzed in triplicate

(biological replicates: S30^+D-S¹ vs. S30^+D(1) (=B01_T01); S30^+D-S² vs. S30^+D(1) (=B02_T01);

S30^+D-S³ vs. S30^+D(1) (=B03_T01), whereas S30^+D-S¹ vs. S30^+D(1) was additionally analyzed as technical replicate by swapping the labels (=B01_T02). Peptide ratios were determined by Quantitation Toolbox (Mascot Distiller v2.6, Matrix Science, UK), using the quality thresholds as described in detail in 3.7.4.

With an FDR on protein-level of 1% in in the individual datasets B01_T01, B01_T02, B02_T01 and B03_T01, a number of 724, 724, 806 and 761 proteins were found. The technical replicates B01_T01 and B01_T02 shared 655 proteins (82.6%), and 69 proteins (8.7%) were uniquely identified in each technical replicate. The high number of shared proteins in the two technical replicates suggests good technical reproducibility of the quantitative proteome analysis.

Next, the determined peptide ratios were normalized to median=0 in log2-space and visualized using a box plot (Figure 11, A). The plot shows that the median of all technical and biological replicate datasets is on a line around log2=0, suggesting that the datasets are not skewed and the distribution of data points, including the outliers, is comparable in all datasets.

The correlation plot in Figure 11, B confirmed the overall high correlation between datasets, with Pearson coefficients between 0.738  x  0.826 for biological replicates, and 0.874 for the two technical replicates.

Figure 11: Evaluation of technical and biological replicates by Box plot and correlation plot. (A) Box plot of technical replicates (T01-T02) and biological replicates (B01-B03) after normalization to median=0 in log2 space. (B) Correlation plot of biological and technical replicates to identify problematic and outlier datasets. The Pearson correlation factor between each dataset is indicated.

Having confirmed the technical reproducibility, the technical replicate B01_T02 (using swapped labels in contrast to the other biological replicates) was omitted from further analysis to avoid any bias towards the biological replicate #1.

In the datasets B01_T01, B02_T02 and B03_T01 a total of 901 proteins were identified.

Among these datasets, 622 (69%) proteins were found in all three replicates, and 146 (16.3%) were found in at least two datasets. 25 (2.8%), 63 (7%) and 45 (5%) proteins were uniquely identified in B01_T01, B02_T01 and B03_T01, respectively (Figure 12, A). The fact that most

proteins (85.3%) were repeatedly detected in at least two biological replicates reinforces the high technical and biological reproducibility.

Figure 12: Venn Diagram to identify common and unique proteins in different samples. (A) Number and percentage of common und unique proteins identified in three biological replicates B01_T01, B02_T01 and B03_T01 (S30^+D-S^1-3 dataset). Proteins that were identified in at least two biological replicates are marked with an asterisk (*). (B) Number and percentage of common and unique proteins as identified in S30-S extracts and standard S30^+D extracts. As basis for the comparison proteins were chosen that were found in at least two biological replicates in either the S30^+D-S^1-3 dataset (indicated with one asterisk *) or in dataset S30^+D(1-4) (indicated with two asterisks **).

Proteins that were identified at least twice in S30^+D-S^1-3 (referred to as S30^+D-S^1-3*) were then compared to proteins that were identified at least twice the previous dataset S30

+D(1-4) (referred to as S30^+D(1-4)**) using a Venn diagram (Figure 12, B). The analysis shows that most proteins (690 or 68%) appeared in both analyses. Since the S30^+D(1-4) analysis was performed without labelling, resulting in a less complex sample and thus higher sensitivity in the proteome analysis, it is not surprising that more proteins were identified.

The proteins identified uniquely either in S30^+D-S^1-3* or S30^+D(1-4)** were further analyzed using the Cytoscape plugin ClueGO (Marker List: Escherichia coli [562, 511145];

Ontology: Biological Process-GOA-3023 as download on 18^th of November, 2016), to test if any biological pattern would arise when integrating the uniquely identified genes to GO terms.

The 246** proteins identified in S30^+D(1-4)** did not result in any significantly enriched terms (pV0.01), thus supporting the idea that they were present due to technical differences in the analysis process. In contrast, within the 78* uniquely identified proteins in S30^+D-S^1-3*, the GO terms response to temperature stimulus (GOID 9266) and organonitrogen compound metabolic process (GOID 1901564) were significantly enriched (pV0.01). In particular, the enrichment of the GO term response to temperature stimulus in S30^+D-S^1-3* implies that some proteins are not detectable in standard S30^+D(1-4) extract and are only expressed after the induction of the

SOS response for S30^+D-S^1-3* extract. These uniquely identified proteins are potentially of high interest, as they are highly up-regulated but probably not covered by the quantitative proteomics approach, as no corresponding labelled peptide from S30^+D(1-4) may be present to compare peak intensities of the two samples. These uniquely identified proteins were therefore be matched after analyzing the quantitative data.

After individual analysis, the datasets B01_T01, B02_T01 and B03_T01 were combined and further analyzed using Quantitative Proteomics p-Value Calculator (QPPC) to identify significantly regulated proteins based on an implementation of a permutation test [101, 112]. The calculated pVs were transformed using the negative logarithm to the base 10 and plotted against fold change transformed using natural logarithm to visualize regulated proteins as Volcano plot (Figure 13, A).

Figure 13: Quantitative proteome analysis of S30^+D(1) vs. S30^+D-S^1-3 extract. (A) Volcano plot of quantitative proteome analysis of S30^+D(1) vs.

S30^+D-S^1-3. 57 proteins were found down-regulated (left, red) and 27 proteins were up regulated (right, blue) in S30^+D-S^1-3 relative to S30^+D(1) extract, whereas a total of 458 proteins of 901 identified proteins were quantified with at least two peptides. Only proteins that were showing a fold change ≥1.5 (dashed vertical line) and were above significance level (pV≤0.05) (dashed horizontal line) as determined by Quantitative Proteomics p-Value Calculator (QPPC) [101, 112] were considered significantly regulated. (B) Bar diagram illustrating reproducibility of three biological replicates (S30^+D-S^1-3). The majority of proteins (>96%) were quantified in at least two biological replicates. Only 3.2% were uniquely quantified in one biological replicate. Numbers besides the bars represent the percentage and the number of quantified proteins.

Regulated proteins appear, due to the nature of data transformation, in the left and right upper corner of the plot, and the proteins that were regulated under both criteria (pV cut-off of 0.05 and regulated more than 1.5-fold) were labelled with the corresponding GeneID.

According to this analysis, 57 proteins were down-regulated (Table 10) and 27 proteins were up-regulated (Table 11) in S30^+D-S^1-3 relative to S30^+D(1). Only proteins that were quantified in at least one biological replicate and with at least two peptides were incorporated in the Volcano plot. Therefore, the Volcano plot covers all quantified proteins, where 64.8% (=297 proteins) were found in all three replicates, 31.7% (=145 proteins) in two replicates, and 3.5% (=16 proteins) in one replicate.

As mentioned before, the 78 proteins exclusively identified in S30^+D-S^1-3 (Figure 12, B) are potentially highly regulated proteins, but may be missed in the quantitative analysis due to the lack of labelled counterpart in S30^+D(1-4) extract. The proteins rpoH, spy, hslR, fruB and aroB, which are among the most up-regulated proteins in the quantitative analysis (see Figure 13), are borderline cases. The availability of quantitative data for these proteins suggests that, even though a labelled counterpart of these proteins must be present in S30^+D(1), the amount was not sufficient for identification based on MS² spectra. Therefore, their identity was inferred from the labelled peptide present in S30^+D-S^1-3 extract. In order to cover potentially highly regulated proteins as well, uniquely identified but not quantified proteins that were assigned to GO term response to temperature stimulus (GOID 9266) (namely dtd, hflC, hflK, pspA, raiA, and yajL) were included in further analyses, as their expression was likely induced during the heat shock applied in S30^+D-S^1-3 extract preparation.

In order to get an impression of their function, up- and down-regulated proteins listed in Table 10 and Table 11 were integrated using the Cytoscape plugin ClueGO to assign Gene ID to GO terms and KEGG pathways. This analysis could determine whether certain proteins were significantly enriched in comparison to a reference set of proteins (Uniprot ID:

UP000000625, 4315 sequences as downloaded on 19^th of January 2017) that represents all proteins that could theoretically be detected in the sample.

The 57 down-regulated proteins were analyzed using the ontology sets BiologicalProcess-GOA (as downloaded on 18^th of November 2016) and KEGG (as downloaded 21^st of November 2016). Out of 57 proteins, 53 (=92.98%) were functionally annotated in the selected ontology sets, and after applying the selection criteria (GO terms/pathways with pV  0.01; kappa score > 0.4; minimum number of genes 3 and minimum percentage 4%; GO level 3-8; statistical test: Fishers Exact Test for enrichment (right-sided hypergeometric test); correction for multiple test: Bonferroni step down), 47 proteins (=82.46%) were associated with representative GO-terms/pathways, where the assignment was based on all evidence levels except inferred from electronic annotation (IEA).

Table 10: Down-regulated proteins in S30^+D-S^1-3 relative to S30^+D(1) as determined by QPPC.

Table 10 (continued): Down-regulated proteins in S30^+D-S^1-3 relative to S30^+D(1) as determined by QPPC.

The down-regulated proteins were assigned to several GO terms/KEGG pathways with oversignificant pV, as depicted in Figure 14, A, and were functionally grouped based on their kappa scores to five major groups (pie chart in Figure 14, B). Overview terms were chosen based on the most significant GO term assigned in Figure 14, A.

The network visualization in Figure 14, C shows the individual gene products assigned to the individual terms, wherein only the most significant terms are labelled (compare Figure 14, B).

The edges demonstrate furthermore the high connectivity and high number of shared proteins between the terms. Only the term RNA degradation is separated from the other terms.

In summary, it appears that mostly biosynthetic pathways for nucleotides (IMP metabolic process) and amino acids (cellular amino acid metabolic process) as well as energy generative pathways such as citrate cycle, glyoxylate and dicarboxylate metabolism are down-regulated in S30^+D-S^1-3 in comparison to S30^+D(1). These pathways may negatively affect the supply of energy during CFPS reactions. In contrast, the downregulation of the RNA degradation machinery in these extracts should enhance stability of e.g. T7 generated mRNA during CFPS.

The 27 up-regulated proteins listed in Table 11 and the proteins uniquely identified in S30^+D-S^1-3 previously assigned to the term response to temperature stimulus, (dtd, hflC, hflK, pspA, raiA, and yajL), were also functionally integrated. The same functional analysis, including the parameters for selection for down-regulated proteins was performed, except that only the ontology source BiologicalProcess-GOA (as downloaded on 18^th of November 2016) was used.

Table 11: Up-regulated proteins in S30^+D-S^1-3 relative to S30^+D(1) as determined by QPPC.

All 33 proteins were functionally annotated in the selected ontology source, and 25 (=75.76%) of them were represented by GO-terms after applying the selection criteria. The representative GO terms including the number/percentage of assigned Gene IDs per term are illustrated as bar diagram in Figure 15, A, and the major GO terms are grouped in the pie chart (Figure 15, B) with the most significant GO term used as label for the group term. The assigned Gene IDs to individual GO terms including their connectivity are illustrated as network in Figure 15, C. According to the analysis (and as expected after induction of a heat shock response), mostly proteins associated with response to temperature stimulus and the closely associated GO term chaperone mediated proteins folding were among the most significant. In particular, the chaperones GroS, GroL, DnaK and Spy

Figure 14: Functional analysis of down-regulated proteins in S30^+D-S^1-3 relative to S30^+D(1) using the Cytoscape plugin ClueGO based on the proteins listed in Table 10. (A) Assigned GO terms and KEGG terms including the percentage of identified proteins per term. The number behind the bar represents the absolute number of proteins assigned per term and the ** indicates an oversignificant term pV (<0.001). (B) Summary of terms to functional groups, where the most significant GO term defines the group term. The double asterisk ** indicates an oversignificant group pV (<0.001). (C) GO terms and assigned gene products with most significant GO terms as nodes and the edges represent the kappa score or connectivity between pathways (0.4). The node size represents significance and the node is colored according to the functional grouping as shown in (A and B) only the label of the most significant term is shown.

Figure 15: Functional analysis of up-regulated proteins in S30^+D-S^1-3 relative to S30^+D(1) using the Cytoscape plugin ClueGO based on the proteins listed in Table 11. (A) Assigned GO terms and including the percentage of identified proteins per term. The number behind the bar represents the absolute number of proteins assigned per term and the ** indicates an oversignificant term pV (<0.001). (B) Summary of terms to functional groups, where the most significant GO term defines the group term. The double asterisk ** indicates an oversignificant group pV (<0.001). (C) GO terms and assigned gene products with GO terms as nodes and the edges represent the kappa score or connectivity between pathways (0.4). The node size represents significance and the node is colored according to the functional grouping as shown in (A and B) and only the label of the most significant term is shown.

were upregulated in S30^+D-S^1-3 relative to S30^+D(1) between 3-30 fold (compare Table 11). Thus, chaperones and other proteins induced in response to unfolded proteins (e.g. proteases) are significantly more abundant in S30^+D-S^1-3 extracts and may provide a beneficial folding environment for the expression of difficult-to-express proteins.