Transcription factor analysis in APPPS1 mice

One potential mechanism that might cause a deregulation of gene expression is a modification of TF activity. To identify potential TFs that might act as drivers for differential gene expression or that could be targeted to reinstate a healthy gene expression, I performed a TF binding site pre-diction analysis on the previously described RNAseq dataset using Pscan (Zambelli et al., 2009).

A list of differentially expressed genes is parsed into Pscan which then screens the promoters for each gene and tries to align those to an implemented set of 263 TF binding sites, or motifs. A score is computed for each motif:promoter-pair and a p-value is given for each motif. For a more solid analysis, p-values were transformed into adjusted p-values. Transcription factors with an adjusted p-value≤0.05 and a motif length≥8 were considered as significant and scores≥0.95 were considered as likely true positive motif:promoter-pairs.

Due to the large overlap of differentially expressed genes in 4 and 8 months old transgenic mice, the majority of significant TFs found by Pscan is shared between the age groups (seefigure 4.23A, p.84). A substantial number of highly significant TFs can be related to immune response, i.e.

STAT1, SPI1, IRF1, or NFκB1 (see table 4.2, p.86). Thus, results from Pscan do nicely match data from functional pathway analysis. Figure 4.23B and C (see p.84) shows the coverage of differ-entially expressed genes targeted by significant TFs. A core-set of TFs was defined covering TF families known to function in neuronal plasticity and neural immune response, namely ETS, GATA, IRF, KLF, NFκB and STAT. A detailed list of TFs covered by Pscan and included in the core-set can be seen in table 7.17 (see p.136). 74-80% of all differentially genes significant in 4 months old mice are likely to be regulated by at least one of the TFs included in the core-set consistently for all brain regions. The coverage only marginally changes in 8 months old transgenic mice.

Only approximately 6 to 12% of genes expressed in either age group or brain region can not be linked to one of the TFs included in Pscan.

In the ACC and CA1, most of the genes that carried a motif belonging to the core-set of TFs were likely targeted by members of the ETS or KLF family. Only a very small fraction of genes might be regulated by proteins from the GATA family. The ETS is regulating a larger fraction of differen-tially expressed genes in the DG. Inside that TF family, the most significant hit is SPI1. Members

genes targeted by this family of TFs might not be identified by the described analysis yet.

1.5 months 4 months 8 months

ACC CA1 DG

/ core-set targets non-core-set targets unassigned genes Figure 4.23–Overrepresented transcription factors in APP/PS1 transgenic mice:

A)Overrepresented TFs in 1.5 (yellow), 4 (red), and 8 (blue) months old APP/PS1 transgenic mice compared to wildtype mice identified by Pscan. The top 20 significant motifs identified for 8 months old mice were chosen for plotting. Dotted lines resemble the threshold for significance (=0.05).

B/C)Pie-chart: Percentage of differentially expressed genes for 4 (top) and 8 (bottom) months old mice with motifs for either one of the core-set TFs, non-core-set TFs and those genes that do not carry any of the motifs included in the analysis (unassigned). Bar charts next to each pie chart resemble the relative coverage of target genes for each respective TF family included in the core-set.

The activity of TFs depends on different parameters. Some TFs might form complexes with other proteins, some need to undergo post-translational modifications to become active. Another im-portant parameter is the pure abundance of a respective TF which is mainly driven by gene expression itself. Thus, I checked for the level of gene expression from the core-set TFs in AP-P/PS1 transgenic mice. As shown infigure 4.24, only a small fraction of genes coding for core-set TFs is differentially expressed or mildly deregulated (|log²foldchange| ≤0.5). Gene expression per se is stronger affected in 8 months old mice compared to 4 months old mice as previously shown infigure 4.5B (see p.63) and core-set TF coding genes are also following that pattern as the number of significant genes is higher in 8 months old mice. The core-set TFs affected in their gene expression are largely shared among the brain regions and included Irf1, Spi1 and Stat1 (all regions), Elf1, Fli1, Nfκb1, RelA, Stat3 and Stat6 (ACC and CA1), Klf4, and Stat4 (both exclusive for the ACC). The only significantly downregulated transcription factor in the given datasets is Stat4. All differentially expressed core-set TFs can be related with immune response. Addition-ally, NfκB and Stat3 were also implicated with neuronal signaling. Altogether, upregulation of the identified core-set TFs further induces immune response observed in APP/PS1 mice.

significant: & mildly deregulated: &

ACC CA1 DG

4months

2 2

27

1 2

28

31

8months 5

6 20

5 4 22

3

28

Figure 4.24–Differentially expressed transcription factors in APP/PS1 mice:

Numbers of differentially expressed and mildly deregulated genes corresponding to core-set TFs in 4 (red, top) and 8 (blue, bottom) months old APP/PS1 mice. Transcription factor coding genes not affected in their gene expression are shown in grey.

Table 4.2– Biological function of deregulated Transcription factors

TF effect reference

Elf1 induction of immune response Sharrocks (2001); Wang et al.

(1993a)

FLI1 TGF-βsignaling; tumor suppression Hahm et al. (1999); Sharrocks (2001)

IRF1 activation of immune response, tumor suppression Miyamoto et al. (1988); Xie et al.

(2003)

KLF4 induction of apoptosis, tumor suppression (El-Karim et al., 2013; Rowland et al., 2005)

NFκB proliferation, cell survival, neuronal plasticity, induction of immune response

Brantley et al. (2001); Hayden et al. (2006); Mattson and Caman-dola (2001)

RELA NFκB subunit p65 see NFκB

SPI1 differentiation of microglia Sharrocks (2001); Suzuki et al.

(2003)

STAT1 induction of immune response and apoptosis Frank et al. (1999); Takagi et al.

(2002)

STAT3 neuronal signaling, immune suppression Chiba et al. (2009); Yu et al. (2007) STAT4 induction of immune response (Deng et al., 2004; Wurster et al.,

2000)

STAT6 induction of immune response (Goenka and Kaplan, 2011;

Wurster et al., 2000)