Physiological activation of B cells is not mediated by a single factor, but by a mixture of different stimuli and pathway activations (Galibert et al., 1996; Rousset et al., 1992). To achieve highest variability in signaling pathway activation, Myclowand Mychighcells were each stimulated with 40 different combinations of α-IgM, CD40L, IGF-1, CpG and IL10, each in two different concentrations (see Table 19, p. 28). Due to the high number of samples experiments were performed in 10 batches each consisting of an unstimulated Myclowand Mychighsample, one single simulations and three combinatorial stimulations per Myc status leading to 100 samples in total. Thereby, stimulations were distributed randomly to min-imize batch effects. From all stimulations, samples were taken for RNASeq, intracellular metabolome analysis and BrdU staining 24 h after stimulation. In cooperation with the Biostatistical Institute of the University of Regensburg, effects of one single stimulus or the combination of two stimuli on gene expression, metabolism and proliferation of Myclowcells were calculated by linear regression. Notably, higher combinations of stimuli (for example combination of three stimuli) were not considered as they would have led to overfitting of the data. Stimulations of Mychighwere needed to increase the statistical power and minimize cross validation errors. MYC expression levels were considered as independent factors and ef-fects of stimuli on Myclowcells were predicted by setting the Myc coefficient and Myc/stimuli interaction terms to zero in the linear regression model. Therefore, only effects of the stim-uli on Myclowcells on global gene expression, metabolism and proliferation are presented below.
3.3.1 Stimuli combinations induce cooperative changes in global gene expression and metabolome in Myclowcells
Linear regression analyis of the spike-in normalized RNASeq data of the stimulated P493-6 cells revealed that all stimuli used in this study increased the expression of the majority of genes, while nearly no genes were downregulated (Figure 10A). Values of gene expression up-regulation greatly differed between single stimuli. While IGF-1 and CD40L mildly increased global gene expression, α-IgM, CpG and IL10 stimulted cells showed an even increased ex-pression of the same genes. Investigating stimuli combinations, strong positive synergistic effects on global gene expression could be observed after CD40L+IGF-1 and IL10+CpG stimulation. In this cases the combinations of stimuli resulted in a stronger increase in gene expression than the simple addition of the single stimulation effects. As IGF-1 and CD40L showed only minor effects on gene expression in single stimulations, their combination just increased gene expression to a level comparable to α-IgM or CpG alone. In contrast, IL10 and CpG already showed strong positive effects on gene expression leading in combination to the highest changes in expression levels of all calculated stimuli combinations. In contrast to these two combinations, the majority of stimuli combinations showed additive or synergistic negative effects on gene expression. In the last case, a lesser increase in gene expression than
Figure 10: Linear regression analysis reveals cooperative effects of stimuli on global gene expression and metabolite abundancy.
(A) Heatmap of regression coefficients of single stimuli and the interaction terms of two stimuli on gene expression in Myclowcells. Myclowand Mychighcells were stimulated withα-IgM, CD40L, IGF-1, CpG and IL10 according to Table 19 (p. 28), RNASeq was performed and data was normalized ondrosophilacell spike-in. Coefficients and interaction terms of stimuli and Myc were calculated by linear regression analysis and coefficients of Myc and Myc interactions terms were set to zero. Red color indicates increased gene expression for single stimulations. For combinations of stimuli the following color code is used: blue = synergistic negative effects, white = additive effects, red = synergistic positive effects. (B) Heatmap of regression coefficients of intracellular metabolite abundancy (pmol/1·106 cells) measured by mass spectrometry of samples in A. Analysis was performed analog to A. Same color code is used. Scaled regression coefficients of A and B are provided as digital data (Supplemental Table 1+2, Appendix, p. 119).
the theoretical addition of the single stimuli was observed. This negative interactions on gene expression are a hint for negative influences between the different signaling pathways or a saturation of a common used pathway.
Interestingly, a comparable pattern of effects could be observed in changes of intracellu-lar metabolite abundancy measured by mass spectrometry (Figure 10B). Strongest changes were mediated by CpG stimulation alone, followed by IL10 and α-IgM. Again, CpG and IL10 combination showed largest changes in intracellular metabolite abundancy based on a positive synergistic effect.
In summary, a quantitative but not qualitative difference induced by the different stimuli was observed on global gene expression. This quantitative response was further modified by costimulation with a second stimulus, mostly resulting in additive or less than additive
3.3.2 Stimulation induced cell cycle entry correlates with metabolic changes
Due to the qualitative equal patterns of global gene expression and metabolism changes and the increase in S-phase shown for single stimuli (Figure 6), a strong correlation of both factors to cell proliferation was considered. To test if metabolic changes correlated with cell cycle changes in Myclowcells, supernatant metabolite turnover and intracellular metabo-lite abundancy were plotted against the number of cells in S-phase 24 h after stimulation.
Combination of stimuli resulted in a variety of S-phase values and as expected, most of the measured metabolites correlated with these (data provided at the ). Most of all, glutamine and glucose uptake and lactate secretion fitted the number of cells that entered S-phase (Figure 11).
Thereby, an increase of replication and metabolism comparable to unstimulated Mychighcells was achieved by a definded set of stimuli combinations. This positive correlation was also reflected in the abundancy of TCA intermediates represented by intracellular citrate levels (Figure 12A). In contrast, a negative correlation between intracellular aspartate levels and cell cycle entry was observed (Figure 12B). Interestingly, while nearly all measured metabo-lites correlated with the number of cells in S-phase, alanine levels were only increased in Mychighcells but not in any of the stimulations in Myclowcells (Figure 12C). This difference indicates specific metabolic reprogramming in Mychighcells.
Figure 11: Extracellular turnover of metabolites correlates with cellular replication.
Correlation of daily metabolite turnover from cell culture media and corresponding number of cells in S-phase of unstimulated Myclow, Mychighand stimulated Myclowcells 24 h after stimulation. Shown are consump-tion of glucose (A), glutamine (B) and secreaconsump-tion of lactate (C) from ten replicates of Myclow(dark blue), nine replicates of Mychigh(red) and 40 different stimulations of Myclowcells (light blue) according to Ta-ble 19 (p. 28). Pearson correlation coefficients (r-values) are shown in the lower right. Further S-phase and metabolotite measurements are provided as digital data (Supplemental Table 3, Appendix, p. 119).
Figure 12: Intracellular metabolites differentially correlate to cellular replication rates.
Correlation of intracellular metabolites and corresponding number of cells in S-phase of unstimulated Myclow, Mychighand stimulated Myclowcells 24 h after stimulation. Shown are intracellular levels of citrate (A), aspartate (B) and alanine (C) from ten replicates of Myclow(dark blue), nine replicates Mychigh(red) and 40 different stimulations of Myclowcells (light blue) according to Table 19 (p. 28). Pearson correlation coefficients (r-values) are shown in the lower right. Further S-phase and metabolotite measurements are provided as digital data (Supplemental Table 3, Appendix, p. 119).
3.3.3 S-phase entry of Myclowcells is mainly driven by combined IL10 and CpG stimulation
As descibed above, IL10+CpG stimulation synergistically increases metabolite abundancy and global gene expression in Myclowcells (Figure 10). To reveal the effect of stimuli com-bination on proliferation in Myclowcells linear regression analysis of BrdU incorporation, representing the number of cells in S-phase, was performed. Thereby, the regression analysis was able to predict BrdU incorporation after single stimulation comparable to the values observed above (Figure 6B). Additionally, linear regression analysis predicted a strong in-crease in BrdU incorporation for IL10+CpG and CpG+a-IgM stimulation (Figure 13). In contrast, a less than additive S-phase entry was predicted for most of the other stimulation combinations and total incorporation was not further increased than only CpG stimulation alone.
As described in the introduction a synergistic effect of CpG+BCR was analyzed in B cells before (Bernasconiet al., 2003). In contrast, no synergy of IL10 and CpG on gene expression, metabolism or proliferation was described so far. Therefore, stimulations of Myclowcells with IL10+CpG were chosen for further detailed analysis in this thesis work.
Figure 13: IL10+CpG stimulation synergistically induce S-phase entry in Myclowcells.
Linear regression analysis of BrdU incorporation 24 h after stimulation of P493-6 according to Table 19 (p.
28). Regression coefficients for single stimuli and combinations were calculated as described before. Intersect of regression is shown as Myclowctrl line. Single stimuli are shown by their coefficients, combinations by the addition of the coefficients of the two single stimuli plus their interaction term. Expected additive effects of two stimuli are shown as dashed line. Observed effects under the line represent negative synergistic effects, above positive synergism. Absolute linear regression coefficients are shown in Table 30 (Appendix, p. 117)