Introduction

Down syndrome (DS) is relatively common in the human population (ca. 1 in 700 live births)(de Graaf et al. 2017) and results from trisomy of chromosome 21 (T21). T21 manifests in myriad ways, including an increased propensity for autoimmune and neurological disorders, as well as elevated incidence of leukemia (Hasle et al. 2000; Antonarakis 2017; Antonarakis et al. 2020). Notably, T21 also results in chronic immune dysregulation associated with hyperactivation of interferon (IFN) signaling (Sullivan et al. 2016; Araya et al. 2019; Waugh et al. 2019).

The chronic, hyperactive IFN response in DS can be attributed, at least in part, to the fact that four IFN receptors are encoded on chromosome 21: IFNAR1 and IFNAR2 for Type I IFNs (e.g., IFNβ, IFNγ), IFNGR2 for IFNγ, and the Type III subunit IL10RB for IFNλ, which also serves as a subunit of the IL10 receptor. Numerous pathologies associated with DS, including autoimmune-related disorders, are considered direct consequences of chronic IFN pathway activation (Madan et al. 2006; Marild et al. 2013; Araya et al. 2019; Waugh et al. 2019; Bush et al. 2021). For these reasons, hyperactive IFN signaling lies at the heart of DS pathophysiology (Waugh et al. 2023), and therapeutic strategies to dampen IFN responses are being tested in clinical trials (NCT04246372, NCT05662228).

We recently demonstrated that the Mediator kinases (CDK8 and its paralog CDK19) are drivers of inflammatory responses to the universal cytokine IFNγ (Steinparzer et al. 2019). This discovery has implications for DS, because hyperactive IFN signaling underlies many DS symptoms and because Mediator kinases are promising targets for molecular therapeutics, in part due to low toxicity of a selective inhibitor in mouse models (Pelish et al. 2015). Many studies have linked Mediator kinase activity to immune system function and inflammatory responses (Bancerek et al. 2013; Chen et al. 2017; Johannessen et al. 2017; Yamamoto et al. 2017; Guo et al. 2019; Steinparzer et al. 2019; Hofmann et al. 2020; Freitas et al. 2022), and CDK8/CDK19 inhibition can suppress autoimmune disease in animal models (Akamatsu et al. 2019). Mediator kinases target the STAT transcription factor (TF) family (Bancerek et al. 2013; Poss et al. 2016) and activation of JAK/STAT pathway TFs (e.g. STAT1, IRF1) is blocked upon Mediator kinase inhibition in IFNγ-stimulated human or mouse cells (Steinparzer et al. 2019). Collectively, these results suggest that Mediator kinase inhibition could mitigate chronic, hyperactive IFN signaling in T21.

Mediator is a 26-subunit complex that regulates RNA polymerase II (RNAPII) transcription genome-wide (Richter et al. 2022). Mediator is recruited to specific genomic loci through interactions with sequence-specific, DNA-binding TFs, and Mediator interacts with the RNAPII enzyme at transcription start sites. Through these distinct interactions, Mediator enables TF-dependent control of RNAPII function. CDK8 or CDK19 can reversibly associate with Mediator as part of a 4-subunit “Mediator kinase module” that contains additional subunits MED12, MED13, and CCNC (Luyties and Taatjes 2022).

Mediator kinases have not been studied in the context of DS, and a goal of this project was to define their roles in the context of IFNγ signaling, using donor-derived cell lines. We also sought to address other fundamental questions regarding Mediator kinase function that remained largely unexplored. For instance, it is not known whether Mediator kinases impact pre-mRNA splicing that is coupled to RNAPII transcription, despite evidence that CDK8 and/or CDK19 phosphorylate splicing regulatory proteins (Poss et al. 2016). Furthermore, metabolic changes are at least as important as transcriptional changes in driving biological responses (Rinschen et al. 2019; Zhu and Thompson 2019), but how Mediator kinases impact metabolism in human cells remains incompletely understood (Zhao et al. 2012; Galbraith et al. 2017). Finally, cytokines serve as key drivers of IFNγ responses, and despite links between CDK8/CDK19 function and inflammation (Chen et al. 2017; Steinparzer et al. 2019; Freitas et al. 2022), it is largely unknown whether Mediator kinases impact cytokine levels.

Using a combination of approaches, we have identified novel and diverse mechanisms by which Mediator kinases control IFNγ responses under both basal and IFNγ-stimulated conditions. Beyond effects on the RNAPII transcriptome, our results indicate that Mediator kinases act by governing TF activation and suggest that they selectively control gene expression through pre-mRNA splicing; furthermore, regulation of cytokine and metabolite levels contributes to CDK8/CDK19-dependent control of inflammatory signaling, including downstream activation of nuclear receptors through changes in lipid metabolites. Taken together, our findings identify Mediator kinases as therapeutic targets that could mitigate immune system dysregulation in individuals with DS.

Results

Experimental overview: transcriptomics, metabolomics, and cytokine screens

The goals of this project were to compare and contrast transcriptional and metabolic changes in the following experimental contexts: i) T21 vs. D21, ii) ±IFNγ stimulation, iii) ±Mediator kinase inhibition, and iv) IFNγ stimulation + Mediator kinase inhibition. For T21 vs. D21 comparisons, we selected lymphoblastoid cells from the Nexus Biobank that were derived from siblings matched in age (3- or 5-year old) and sex (male). We used the natural product cortistatin A (CA) to inhibit CDK8 and CDK19 (Pelish et al. 2015). CA is the most potent and selective Mediator kinase inhibitor available (Clopper and Taatjes 2022); for example, kinome-wide screens showed no off-target kinase inhibition even at 1 µM, a concentration 5000-times greater than its measured K_D of 0.2 nM (Pelish et al. 2015). Throughout this project, we used CA at a concentration of 100 nM, to minimize potential off-target effects. Immunoblots confirmed CA-dependent inhibition of STAT1 S727 phosphorylation, a known CDK8 substrate (Figure 1 – figure supplement 1A).

Fig 1.

Prior to completion of RNA-seq experiments, we probed the timing of the IFNγ response at IFN target genes GBP1 and IRF1 by RT-qPCR (Figure 1 – figure supplement 1B). Based upon these results, we chose the 4-hour time point for RNA-seq (see Methods). We also completed experiments in T21 cells after 24h ±CA. An overview of the RNA-seq experiments is shown in Figure 1A, with data for all genes across all conditions in Figure 1 – source data 1.

Large-scale, untargeted metabolomic analyses were completed 24h post-IFNγ stimulation in D21 and T21 cells. This time point was chosen to approximate a “steady state” following IFNγ treatment and to allow time for metabolic adaptations to occur; this time point was also consistent with prior metabolite analyses in IFN-treated cells (Wang et al. 2018; Lee et al. 2021b). An overview of the metabolomics experiments is shown in Figure 1A, with raw data for all identified metabolites across conditions in Figure 1 – source data 2.

Down syndrome has been described as a “cytokinopathy” based upon evaluation of blood plasma from individuals with or without T21. Arrays screening 29 different cytokines showed elevated levels in Down syndrome individuals (Malle et al. 2023). Based upon these and other results, we sought to directly measure cytokine levels to assess IFNγ and CA-dependent effects. Therefore, we completed a series of cytokine screens (n=105 different cytokines) in T21 and D21 cells (Figure 1 – figure supplement 1C), using the same experimental design as the metabolomics experiments (basal conditions, +CA, +IFNγ, or +CA+IFNγ). Biological replicate cytokine screens were completed for each condition after 24h treatment; measurements for all cytokines in both replicates can be found in Figure 1 – source data 3.

The T21 transcriptome, metabolome, and cytokine levels are consistent with hyperactive IFN signaling

For RNA-seq, biological triplicate experiments were completed, and a normalization (Hunter et al. 2023) was performed to account for the potential 1.5-fold expression differences from chromosome 21 in T21 cells (Hwang et al. 2021). Comparison of gene expression in T21 vs. D21 cells revealed massive differences, as expected (Figure 1 – figure supplement 1D; Figure 1 – source data 1). Gene set enrichment analysis (GSEA)(Subramanian et al. 2005) revealed significant up-regulation of inflammatory pathways (e.g. IFNγ, IFNα, Complement, TNFα) in T21 vs. D21 cells (Figure 1 – figure supplement 1E; Figure 1 – source data 4), consistent with prior reports (Sullivan et al. 2016; Sullivan et al. 2017; Malle et al. 2023). Furthermore, the “upstream regulators” identified from Ingenuity Pathway Analysis (IPA) (Kramer et al. 2014) predicted activation of numerous inflammatory markers in T21 cells (e.g. IFNγ, IFNα, TNF, TGFβ1, NFκB), based upon the RNA-seq results (Figure 1 – figure supplement 1F; Figure 1 – source data 5). Negatively enriched pathways in T21 cells (GSEA Hallmarks, Figure 1 – figure supplement 1E, Figure 1 – source data 4) reflected proliferative gene expression programs (e.g. MYC targets, G2M checkpoint); in agreement, we observed slower growth rates for T21 cells compared with their D21 counterparts.

Untargeted metabolomics experiments identified and quantified over 600 biochemicals representing every major metabolic pathway (Figure 1 – source data 2). We observed evidence that inflammatory pathways were activated in T21 cells under basal conditions (IPA results, Figure 1 – figure supplement 1G; Figure 1 – source data 5), and 196 biochemicals increased and 214 decreased in T21 vs. D21 (Figure 1 – figure supplement 2A, B; Figure 1 – source data 2). Pathways related to fatty acid transport, growth, and energy metabolism were reduced in T21 cells (vs. D21; Figure 1 – figure supplement 1G). Supporting these general trends, the metabolomics data revealed T21-specific changes in glycolysis, nucleotide and lipid biosynthesis, and other fundamental cellular processes (Figure 1 – figure supplement 2B; Figure 1 – source data 2). Metabolic distinctions between T21 and D21 cells will be described in more detail later, in the context of IFNγ treatment and/or Mediator kinase inhibition. An overview of metabolic pathways that are relevant for this study is shown in Figure 1B.

As with the RNA-seq and metabolomics data, the cytokine screen results implicated chronic activation of the IFN response in T21 cells under basal conditions; for example, levels of many cytokines were elevated in T21 vs. D21 cells (Fig 1C, left column). Furthermore, treatment with IFNγ appeared to “normalize” cytokine levels in T21 vs. D21 cells (Fig 1C, right column), such that many cytokines were detected at roughly equal levels in IFNγ-stimulated T21 and D21 cells.

T21-specific transcriptional, metabolic, and cytokine changes are reflected in population-level data

For practical reasons, the transcriptomics, metabolomics, and cytokine screening experiments completed here precluded parallel examination of additional genetically distinct donor-derived T21 and D21 cell lines (but see below). A basic observation from past studies was that the transcriptomes of T21 individuals are not only tissue- and cell type-specific, but also reflect individual-to-individual differences within the same cell types (Sullivan et al. 2016; Hwang et al. 2021). However, we hypothesized that the general trends in our sibling-matched T21 vs. D21 lines would show some commonality with large-scale cohort studies.

We analyzed RNA-seq data from whole blood samples from individuals with DS (n=304) or euploid (i.e. D21; n=96) controls (Waugh et al. 2023), generated by the Human Trisome Project (www.trisome.org, NCT02864108). The data revealed upregulation of the IFNγ response, Complement, cytokine production, and other inflammatory pathways in T21 individuals (vs. D21; Figure 1 – figure supplement 2C; Figure 1 – source data 4). Approximately ten percent of differentially expressed genes were shared between the Human Trisome Project cohort study (T21 vs. D21; whole blood transcriptomes from individuals varying in age and sex) and our RNA-seq results from sibling-matched T21 and D21 lymphoblastoid cell lines (Figure 1 – figure supplement 2D), consistent with individual-to-individual variation within the human population (Sullivan et al. 2016; Hwang et al. 2021). Notably, the shared “core sets” of genes reflected prominent trends in our T21/D21 RNA-seq comparisons. For example, up-regulated genes represented cytokine production and other inflammatory pathways, whereas down-regulated genes involved nucleotide and fatty acid metabolism, splicing, GPCR signaling, and proliferation. These pathway trends are well-represented in our RNA-seq and metabolomics data (e.g. Figure 1 – figure supplement 1E, G; Figure 1 – source data 6) and additional data supporting these T21-specific alterations will be described in more detail later.

We also used the IFN score metric (Galbraith et al. 2023) to evaluate our sibling-matched T21 and D21 lines. The IFN score considers expression of 18 genes from a set of 382 genes comprising the IFNγ, IFNα, and inflammatory response GSEA Hallmarks gene sets. It has been used to evaluate differences between whole blood transcriptomes in a cohort of 502 participants (n=356 T21, n=146 D21). As shown in Figure 1 – figure supplement 2E, the sibling-matched T21 line had an elevated IFN score, consistent with comparative trends from a large cohort of whole blood transcriptomes of T21 or D21 individuals (Galbraith et al. 2023).

A recent analysis of blood plasma cytokine levels, measuring 29 cytokines from DS individuals (n=21) or age-matched euploid controls (n=10) identified elevated levels of approximately 10 to as many as 22 of these 29 cytokines (Malle et al. 2023). In agreement, at least 13 of the 22 cytokines (59%) were elevated in our T21 vs. D21 comparison (red font in Fig 1C). Collectively, the RNA-seq and cytokine data from cohorts of DS individuals indicate that the two model cell lines evaluated here, derived from age- and sibling-matched D21 and T21 individuals, broadly reflect population-wide trends.

Mediator kinase inhibition tempers T21 inflammatory pathways under basal conditions

We next asked whether Mediator kinases influenced the up-regulation of IFNγ signaling in T21 vs. D21 cells. We first describe how Mediator kinase inhibition impacted T21 or D21 cells under basal conditions.

Transcriptome: As shown in Figure 2A, Figure 2 – figure supplement 1A-D, and Figure 1 – source data 4, CA caused similar changes to gene expression programs in T21 and D21 cells, although some T21-specific differences were evident (Figure 2 – figure supplement 1C, D). CA treatment up-regulated 445 genes in D21 (331 in T21; 210 shared) and down-regulated 233 genes (191 in T21, 112 shared; Padj 0.01). GSEA showed downregulation of MYC targets, consistent with prior studies in CDK8-depleted cells (Sooraj et al. 2022). Downregulation of the inflammatory “TNFα signaling via NFκB” pathway was also observed in CA-treated D21 and T21 cells (Figure 2 – figure supplement 1C, D); decreased expression of the NFKB1 and NFKB2 transcripts themselves likely contributed to this effect (Figure 1 – source data 1). Regulation of NFκB transcriptional programs has been previously linked to Mediator kinases (Chen et al. 2017) although direct control of NFκB transcription levels has not been reported. Activation of GSEA Hallmarks related to mTOR signaling, cholesterol and fatty acid metabolism were common in CA-treated D21 and T21 cells (Figure 2 – figure supplement 1C, D), in agreement with prior studies in other cell types (Poss et al. 2016; Audetat et al. 2017; Andrysik et al. 2021). The genes significantly up-regulated in these pathways are highlighted in Figure 2 – figure supplement 1E; the data indicate that Mediator kinases broadly regulate cholesterol and fatty acid biosynthesis, and this was further confirmed by the metabolomics data (see below).

Fig 2.

We next completed an “upstream regulators” analysis (IPA; Figure 2 – figure supplement 1F, Figure 1 – source data 5) based upon the RNA-seq data. In agreement with the GSEA results, factors regulating cholesterol homeostasis (e.g. SCAP, SREBF1/2, INSIG1), fatty acid metabolism (e.g. ACSS2, FASN), and inflammation (e.g. NFKB1, MYD88, TLR3) were significantly altered by CA treatment in T21 cells (Fig 2B; Figure 2 – figure supplement 1F), with similar results in D21 (Figure 2 – figure supplement 1F). The IPA results also implicated CA-dependent repression of pro-inflammatory TFs specifically in T21 cells, including RELA (NFκB complex), FOXO3, and ILF3 (Figure 2 – figure supplement 1F; Figure 1 – source data 5), consistent with repression of chronic basal IFN signaling upon Mediator kinase inhibition.

Metabolome: CA-dependent changes in the metabolomes of D21 and T21 cells were strikingly distinct. This is likely due to i) the longer time frame used for metabolomics analyses (24h vs. 4h for RNA-seq), and ii) the massive “pre-existing” basal metabolome differences in T21 vs. D21 cells (Figure 2 – source data 1). Direct comparison of T21 vs. D21 metabolomes (basal conditions) identified major differences in glycolysis, the pentose phosphate pathway, the citric acid cycle, various categories of lipids, and nucleotide biosynthesis pathways (Figure 1 – figure supplement 2A, B). Here, we will focus on the T21 vs. D21 metabolic differences that relate to pathways that were impacted by Mediator kinase inhibition.

In Figure 2C, we highlight examples of CA-dependent increases in anti-inflammatory lipid metabolites. In T21 cells selectively, CA treatment broadly increased levels of long chain mono- and poly-unsaturated FA (PUFA; Fig 2C), including oleic acid, eicosapentaenoate (EPA), docosapentaenoate (DPA), docosahexaenoate (DHA), and various other χο3 and χο6 PUFAs (Figure 2 – source data 2). These metabolites act as signaling molecules by binding to extracellular G-protein coupled receptors (GPCRs) to trigger anti-inflammatory signaling cascades (Basil and Levy 2016; Husted et al. 2017; Kimura et al. 2020; Jordan and Werz 2022), as shown in the simplified schematic in Figure 2D. Similar to LCFAs, endocannabinoids were broadly elevated in CA-treated T21 cells (vs D21; Fig 2C). Endocannabinoids mediate anti-inflammatory effects in part through PPAR TFs (Alhouayek and Muccioli 2014); for example, oleoylethanolamide (OEA) and oleamide are PPARα ligands (Fu et al. 2003; Roy et al. 2016) and OEA can suppress pro-inflammatory NFκB signaling (Yang et al. 2016).

Consistent with lipid-dependent activation of LXR and PPAR nuclear receptors, the RNA-seq results showed evidence of CA-dependent LXR and PPAR activation specifically in T21 cells (Fig 2B; Figure 2 – figure supplement 1F), which correlated with the elevated levels of LCFAs, desmosterol, oleamide, and endocannabinoids in CA-treated T21 cells (Fig 2C). Although metabolites were measured 24h after CA treatment, these data suggest that altered lipid metabolites influence LXR and PPAR function within 4.5h; moreover, evidence for PPAR activation in CA-treated T21 cells was also observed from RNA-seq data 24h after CA treatment (see below). Collectively, these results implicate lipid metabolites as key “downstream” regulators of an anti-inflammatory transcriptional response to Mediator kinase inhibition. Importantly, this link between lipid metabolites and LXR or PPAR activation was observed in T21 but not D21 cells, suggesting that this CA-dependent effect occurs in the context of hyperactive IFN signaling.

Cytokine: CA treatment was also shown to be anti-inflammatory based upon the cytokine screen results. Under basal conditions, the levels of about two dozen cytokines changed in response to CA treatment (Fig 2E). In D21 cells, the anti-inflammatory cytokine IL10 increased upon Mediator kinase inhibition, in agreement with prior reports in other cell types (Johannessen et al. 2017), whereas the pro-inflammatory cytokines GMCSF, IL3, and MIP1α/β (a.k.a. CCL3/4) decreased; an exception was TNFα. Similar results were obtained in T21 cells; however, CA treatment reduced the levels of pro-inflammatory cytokines in T21 cells that were not reduced in D21 (e.g. IL32, IL33, IL34), although CXCL10 and GMCSF were exceptions. The selective CA-dependent decrease in IFNγ in T21 cells under basal conditions further supports the chronic high baseline IFN signaling in these cells (Sullivan et al. 2016). Note that some cytokines cannot be clearly defined as either pro- or anti-inflammatory and their roles may be cell type-specific (Dinarello 2007).

Because cytokine protein levels were measured 24h after CA treatment, we hypothesized that their altered levels would partially correspond to gene expression changes that were measured earlier, at 4h post-treatment. Indeed, this was observed for many cytokines impacted by Mediator kinase inhibition, as shown in Figure 2F. (Note that expression changes for cytokine receptors are also shown.)

Metabolic effects persist in transcriptome of CA-treated T21 cells

We next assessed whether CA-dependent suppression of inflammatory gene expression programs would persist beyond the 4.5-hour timeframe in T21 cells. This was motivated in part by the metabolomics data (t=24h), which showed evidence for CA-dependent activation of nuclear receptors and anti-inflammatory GPCRs (Fig 2D). We completed biological triplicate RNA-seq experiments 24h after CA treatment (or DMSO controls; Figure 2 – figure supplement 2A). As shown in Figure 2 – figure supplement 2B, GSEA Hallmarks showed little evidence for suppression of anti-inflammatory pathways, in contrast to the results in T21 cells after 4.5h CA treatment. This was further supported by GSEA with the GO Biological Processes (GOBP) pathway set (Figure 2 – figure supplement 2C), which includes more gene sets (n=7641 for GOBP vs. n=50 for Hallmarks; see Methods) and more with relevance to IFN signaling and inflammation.

In contrast to IFN and inflammatory pathways, gene sets related to cholesterol and lipid metabolism remained activated in CA-treated cells after 24h, consistent with the RNA-seq data at the shorter 4.5h timeframe. Moreover, the t=24h RNA-seq results showed evidence for sustained activation of SREBP as well as PPAR and LXR in CA-treated cells, a result that was inferred from the metablomics data (Fig 2D). For instance, IPA upstream regulators showed activation of “PPARA:RXRA coactivator” and NR1H3/LXRA (Fig 2 – figure supplement 2D). This coincided with elevated levels of PPAR and LXR ligands (e.g. OEA, oleamide, desmosterol), as measured by metabolomics 24hr after CA treatment (Fig 2C, D; Figure 2 – source data 2).

We also noted that the GSEA results (Fig 2 – figure supplement 2B) showed evidence for down-regulation of oxidative phosphorylation in CA-treated T21 cells (RNA-seq t=24h), a result consistent with a prior proteomics study we completed in HCT116 cells ±CA (Poss et al. 2016). To probe further, we measured the oxygen consumption rate (OCR) in D21 or T21 cells ±CA (t=24h). As shown in Figure 2 – figure supplement 3A, B, OCR was reduced in CA-treated D21 or T21 cells, and reached statistical confidence in T21 cells. The reduced OCR in CA-treated T21 cells tracked with reduced mRNA levels for oxidative phosphorylation genes, measured by RNA-seq (Fig 2 – figure supplement 3C).

In summary, a comparison of the RNA-seq results from 4.5h vs. 24h CA-treated T21 cells showed evidence for both transient and durable transcriptional responses. Genes associated with inflammation were suppressed after 4.5h CA treatment, but this suppression generally did not persist to 24h, at least in the cell line evaluated here. By contrast, genes associated with cholesterol and lipid metabolism were elevated after 4.5h CA treatment and remained elevated at 24h, and nuclear receptors PPAR and LXR showed evidence of continued activation 4-24h after Mediator kinase inhibition.

Similar CA-dependent anti-inflammatory effects in different donor T21 cell line

Although completing parallel analyses across many cell lines was impractical, we did complete RNA-seq experiments in a different T21 donor cell line. Biological triplicate RNA-seq experiments were performed in a non-sibling T21 line ±CA (t=4.5h). Similar to the “sibling-matched” T21 line, CA-treated cells showed elevated expression of genes in the cholesterol homeostasis, fatty acid metabolism, and peroxisome pathways (Figure 2 – figure supplement 4A, B). Moreover, GSEA completed with the GOBP set showed suppression of pathways related to IFN signaling and inflammation (Figure 2 – figure supplement 4C, D). In fact, the CA-dependent suppression was amplified in this “non-sibling” T21 line, which may reflect higher basal IFN signaling. IPA upstream regulators results (Figure 2 – figure supplement 4E) also showed evidence for CA-dependent suppression of IFN signaling, with negative z-scores for pro-inflammatory TFs (e.g. STAT2, IRF1, NFKB1) and cytokines (e.g. IFNG, IFNA2, IFNL1). Conversely, SREBP TFs were activated, as were LXR and PPAR, similar to the sibling-matched T21 line (Figure 2 – figure supplement 4E). Taken together, the transcriptomics data from this different T21 cell line showed robust CA-dependent suppression of inflammatory pathways, with evidence for CA-dependent activation of SREBP, PPAR, and LXR TFs, consistent with the results from the different sibling-matched T21 line.

Mediator kinase inhibition antagonizes IFNγ-induced transcriptional, metabolic, and cytokine responses

We next evaluated the transcriptomic, metabolomic, and cytokine data from IFNγ-treated cells. As shown in Figure 3 – figure supplement 1A, the transcriptional response to IFNγ was broadly similar in the sibling-matched T21 and D21 cells; however, T21 cells showed higher basal expression of inflammatory genes. This can be seen upon comparison of gene expression in T21 vs. D21 cells under basal (Figure 3 – figure supplement 1B) or IFNγ-stimulated conditions (Figure 3 – figure supplement 1C; see also Figure 3 – figure supplement 1D, E). Only 10 genes showed greater induction in T21 cells in response to IFNγ, whereas 68 genes had reduced induction in T21 compared with D21 (Figure 3 – figure supplement 1F; Figure 1 – source data 1). Pathway analysis (GO Biological Processes) of these 68 genes showed predominant roles in IFN signaling and inflammatory responses (Figure 3 – figure supplement 1G; Figure 1 – source data 6). This result is consistent with hyperactive IFN signaling in T21 cells (Sullivan et al. 2016), such that induction upon exogenous IFNγ stimulation was reduced compared with D21 cells. Next, we focus on how Mediator kinase inhibition influenced transcriptional, metabolic, and cytokine responses in IFNγ-treated D21 and T21 cells.

Fig 3.

Transcriptome: The regulatory roles of the Mediator kinases CDK8 and CDK19 are context- and cell type-specific, and their functions appear to be especially important for initiating changes in gene expression programs, such as during an acute stimulus (Luyties and Taatjes 2022). As shown in Figure 3A, many of the mRNAs responsive to IFNγ (activated or repressed) were impacted in the opposite way in CA + IFNγ-treated D21 or T21 cells (see also Figure 3 – figure supplement 2A, B). This CA-dependent antagonism of IFNγ-response genes is best illustrated with difference heatmaps that compared levels of IFN-dependent transcriptional changes with IFN +CA conditions (i.e. IFN vs. Ctrl – IFN+CA vs. Ctrl; Figure 3 – figure supplement 2C, D). An example at the CXCL9 gene locus is shown in Figure 3B; CXCL9 is a pro-inflammatory cytokine. GSEA also reflected CA-dependent suppression of the transcriptional response to IFNγ. In both D21 and T21 cell lines, IFNγ response and other inflammatory pathways were down-regulated with CA treatment compared with IFNγ alone (Fig 3C, D; Figure 3 – figure supplement 2E; Figure 1 – source data 4). Similar results were obtained with an assessment of inflammatory pathways using IPA (Figure 3 – figure supplement 2F; Figure 1 – source data 5). Thus, Mediator kinase inhibition blocked normal transcriptional responses to IFNγ, consistent with prior studies in MEFs and human cancer cells (Bancerek et al. 2013; Steinparzer et al. 2019). The up-regulated Hallmark pathways included cholesterol homeostasis and fatty acid metabolism (Fig 3C, D; Figure 1 – source data 4). A set of factors from the IPA upstream regulators analysis underscores this result, identifying TFs and enzymes involved in cholesterol and lipid metabolism in CA-treated cells T21 cells, (Fig 3E-G), with similar results in D21 (Figure 3 – figure supplement 2G, H; Figure 1 – source data 5). Furthermore, an array of pro-inflammatory TFs were induced in IFNγ-treated T21 and D21 cells, as expected; however, activation of these same TFs was blocked in CA-treated cells (D21 and T21; Fig 3F, G; Figure 3 – figure supplement 2G, H). These results suggest that inhibition of CDK8/CDK19 suppresses IFNγ responses, in part, through inhibition of pro-inflammatory TF activity, in agreement with prior experiments (Steinparzer et al. 2019).

To probe further, we compared the GSEA “leading edge” IFN response gene set from a whole blood transcriptome dataset generated by the Human Trisome Project (Waugh et al. 2023) with the CA-responsive genes in our T21 cell line. As shown in Figure 3 – figure supplement 2I, 86% of these genes (69 out of 80) overlapped with those identified as elevated in the Human Trisome Project dataset, reflecting a common transcriptional response to IFNγ. Among these IFN-responsive genes were many whose expression decreased upon CA treatment, including STAT1, IRF1, GBP4, MX2, XCL1, and CXCL9. Moreover, CA treatment decreased the IFN score (Galbraith et al. 2023) in IFN-stimulated T21 cells (Figure 3 – figure supplement 2J). These results reveal that Mediator kinase inhibition suppresses transcriptional responses that are typically hyperactivated in DS individuals.

Metabolome: Consistent with the RNA-seq results, metabolic changes in D21 and T21 cells reflected a CA-dependent suppression of IFNγ-induced inflammation. CA treatment reversed metabolic changes induced by IFNγ alone (Fig 4A-C). For example, LCFAs, endocannabinoids, oleamide, desmosterol, and bile acids were reduced in D21 cells under IFNγ-stimulation conditions, consistent with the anti-inflammatory roles of these metabolites (Alhouayek and Muccioli 2014; Basil and Levy 2016; Husted et al. 2017; Kimura et al. 2020; Jordan and Werz 2022). By contrast, their levels were elevated in CA-treated D21 cells. Compared with D21, the effects of Mediator kinase inhibition were reduced in scope in IFNγ-treated T21 cells, which may reflect the elevated basal IFN signaling in T21 cells (Fig 4A-C; Figure 4 – source data 1). In Figure 4C we highlight the CA-dependent elevation of desmosterol and oleamide (LXR and PPAR ligands, respectively), and the CA-dependent decline in quinolinic acid, a tryptophan derivative that is typically elevated in DS (Powers et al. 2019). Collectively, the data summarized in Figure 4A-C are consistent with CA-dependent anti-inflammatory effects.

Fig 4.

Cytokines: Compared with basal conditions, many more cytokines showed CA-responsiveness in the context of IFNγ stimulation, and a majority of the changes countered IFNγ-induced inflammatory responses (Figure 4 – figure supplement 1A). This CA-dependent antagonism of IFNγ cytokine responses is illustrated in the difference heatmaps shown in Figure 4D, E. Levels of many pro-inflammatory cytokines were reduced in IFNγ + CA-treated D21 and T21 cells, including CXCL9, C5, IL33, IL1α, and others (Figure 4 – figure supplement 1A).

Because cytokine levels were measured 24h after treatment, we hypothesized that their altered levels would at least partially correspond to gene expression changes measured earlier, at 4h post-treatment. This was observed for many IFNγ-induced cytokines impacted by Mediator kinase inhibition, as shown in Figure 4 – figure supplement 1B. (Note that expression changes for cytokine receptors are also shown.) Collectively, the cytokine screen demonstrated that inhibition of Mediator kinase function generally downregulated pro-inflammatory cytokines while upregulating anti-inflammatory cytokines (e.g IL10, LIF, IL19). In the context of Down syndrome, this could potentially mitigate pathological immune system hyperactivation.

Mediator kinases regulate splicing in pathway- and cell type-specific ways

As Mediator-associated kinases, CDK8 and CDK19 are established regulators of RNAPII transcription (Luyties and Taatjes 2022); however, their potential impact on splicing has not been examined. Over 95% of human mRNAs are alternatively spliced and defects in this process contribute to errors in gene expression in human diseases (Scotti and Swanson 2016). We compared alternative splicing events using rMATS (Shen et al. 2014), separately evaluating all RNA-seq replicates (biological triplicates; Fig 1A). The data revealed 741 differential exon skipping events in T21 cells (vs. D21) under basal conditions (Fig 5A, B; Figure 5 – source data 1), with approximately equal numbers of increased exon inclusion (n=382) and increased exon skipping events (n=359). A smaller number of other alternative splicing events (e.g. intron retention) were also detected (Figure 5 – figure supplement 1A). We observed similar trends in IFNγ-treated T21 and D21 cells, in which a greater number of exon skipping events (n=418 vs. n=296) occurred in IFNγ-stimulated T21 cells vs. D21 cells (Figure 5 – figure supplement 1B, C; Figure 5 – source data 1).

Fig 5.

To determine whether Mediator kinase activity might influence splicing, we next evaluated CA-treated D21 and T21 cells. We found that CA treatment had a substantial impact on splicing, in both D21 and T21 cells (Fig 5C, Figure 5 – figure supplement 1D). We identified 432 or 444 sites with altered exon skipping events (D21 or T21, respectively) in CA-treated cells compared with controls (ΔPSI [percent spliced in]≥ 0.2, Padj < 0.05; Fig 5C, Figure 5 – figure supplement 1E; Figure 5 – source data 1), and CA treatment increased inclusion of alternative exons more frequently than it increased their skipping in both lines (n=239 vs. 193 in D21; n=256 vs. 188 in T21). Similarly in IFNγ-treated D21 and T21 cells (Fig 5D, Figure 5 – figure supplement 1F; Figure 5 – source data 1), Mediator kinase inhibition favored exon inclusion (n=178 in D21; 213 in T21) over exon skipping (n=130 in D21; 148 in T21).

To assess whether the exon inclusion events sensitive to Mediator kinase inhibition were selective for specific gene sets, we completed pathway analyses (IPA) across all conditions tested (Fig 5E-G, Figure 5 – figure supplement 1H, I; Figure 1 – source data 5). To increase statistical power, we grouped exon skipping events together (i.e. alternative exon inclusion events + alternative exon skipping events). Thus, the T21 vs. D21 comparison included 741 events, D21 +CA vs. Ctrl included 432 events, and so on. The results revealed that splicing changes triggered by Mediator kinase inhibition were cell type specific (T21 vs. D21) and selectively impacted inflammatory and metabolic pathway genes. As shown in Figure 5E, genes associated with inflammatory signaling (red highlighted pathways) showed evidence of differential splicing in CA-treated T21 cells (vs. D21) during IFNγ stimulation. In addition, pyrimidine salvage and biosynthesis genes were alternatively spliced in CA-treated T21 cells (vs. DMSO; Fig 5G). These pathway-specific effects in CA-treated cells were consistent with gene expression changes (e.g. GSEA Hallmark gene sets, Figure 1 – source data 4) and metabolic changes observed in CA-treated cells. For instance, the pyrimidine biosynthesis intermediates N-carbamoylaspartate, dihydro-orotate, and orotate were markedly depleted (1.4 – 3 fold) in CA-treated T21 cells (vs. DMSO; Figure 1 – source data 2). The data in Figure 5G implicate CA-dependent splicing changes as a contributing factor.

Altered TF function underlies metabolic and anti-inflammatory effects in Mediator kinase-inhibited T21 cells

Sequence-specific, DNA-binding TFs are a major target of Mediator kinases, based upon quantitative phosphoproteomics data (Poss et al. 2016). To broadly assess how TF activity changed in response to Mediator kinase inhibition, we completed PRO-seq experiments in control vs. CA-treated T21 cells. When sequenced at high depth (≥60M reads/replicate), PRO-seq allows reliable detection of short, bidirectional transcripts that center around consensus TF binding sites. Studies from numerous labs have shown that these bidirectional transcripts represent “signatures” of active TFs bound to these sites (Azofeifa et al. 2018; Wang et al. 2019; Kristjansdottir et al. 2020; Rubin et al. 2021). Similarly, loss of bidirectional transcription indicates reduced TF activity.

Because our primary interest was CA-dependent suppression of hyperactive IFN signaling in DS, we completed PRO-seq experiments in T21 cells under basal conditions, ±CA treatment. Based upon prior experiments (Steinparzer et al. 2019), PRO-seq was completed after 45 minutes CA treatment (or DMSO control). We then applied TFEA (Transcription Factor Enrichment Analysis), a well-tested method to globally assess TF activity from PRO-seq data (Rubin et al. 2021). As shown in Figure 6A, CA treatment broadly shut down pro-inflammatory TFs in T21 cells (e.g. IRF4, IRF8, SPIB, BACH1, RELA; examples shown in Fig 6B), consistent with the “downstream” RNA-seq (4.5h), cytokine (24h), and metabolomics data (24h). Furthermore, it was evident that CA treatment rapidly repressed the p53 family of TFs (Fig 6A, B; Figure 6 – source data 1). Although best known as a tumor suppressor, p53 represses cholesterol and lipid biosynthesis pathways, in part by blocking activation of SREBP (Moon et al. 2019). Rapid suppression of p53 by Mediator kinase inhibition is consistent with the metabolic changes observed in CA-treated T21 cells (Fig 2C). Moreover, the RNA-seq data implicated SREBP TF activation in CA-treated T21 cells (vs. DMSO controls) at later time points (4-24h), consistent with de-repression of SREBP following CA-dependent inhibition of p53 activity. From these results, we conclude that altered TF function is a major contributor to i) CA-dependent metabolic changes and ii) suppression of pro-inflammatory IFN signaling in T21 cells.

Fig 6.

Discussion

Previous studies have shown that Mediator kinases regulate transcriptional responses to inflammatory stimuli (e.g. IFNγ or TNFα) in a variety of model systems (Chen et al. 2017; Steinparzer et al. 2019). Because individuals with Down syndrome have chronic, hyperactive IFN signaling (Sullivan et al. 2016), we hypothesized that Mediator kinase inhibition would antagonize inflammatory signaling cascades in T21 cells. This hypothesis was supported by the experimental results described here, which showed that Mediator kinase inhibition antagonizes inflammatory pathways at the transcriptional, metabolic, and cytokine protein levels. To maintain focus on Mediator kinase activity, we avoided knockdown or knockout experiments in this study, in part because CDK8/CDK19 proteins serve additional functions that are not kinase-dependent (Poss et al. 2016; Audetat et al. 2017; Sooraj et al. 2022; Chen et al. 2023). Collectively, our results expand upon established concepts for CDK8 and CDK19 (e.g. regulation of IFNγ signaling) and reveal new insights about their impact on TF activation, metabolic and cytokine responses, as well as their effect on pre-mRNA splicing. Our results also establish Mediator kinases as potential therapeutic targets to antagonize hyperactive IFN signaling in DS.

We acknowledge that individual-to-individual genetic variation could contribute to the differences we observe in our T21 vs. D21 comparisons (Sullivan et al. 2016; Hwang et al. 2021). We chose cell lines from sibling donors (both males of similar age) to minimize this possibility. Moreover, cross-referencing our results with data collected from large cohorts of individuals of varying age and sex (with/without T21) showed broadly similar trends in transcript (Waugh et al. 2023), metabolite (Powers et al. 2019), and cytokine levels (Malle et al. 2023) in the T21/D21 comparisons. Although our multi-omics T21/D21 comparisons yielded new insights, Mediator kinases were a focus of this study, and the T21/D21 comparisons primarily served as benchmarks to assess CDK8/CDK19 function in the context of DS and IFNγ signaling. A limitation of this study is that only one additional donor-derived T21 cell line was evaluated; furthermore, the transcriptomic, metabolomic, and cytokine data were collected at select time points, such that some temporal trends in IFNγ responses can only be inferred (see below).

CDK8 and CDK19 kinase activity impacts core metabolic pathways

Metabolomics has been described as the most direct readout of cell state, yielding a “front line” assessment of the biochemicals that drive all cellular processes (Rinschen et al. 2019). The role of Mediator kinases in human cell metabolism is poorly understood. As a whole, our metabolomics data revealed the most striking CDK8/CDK19 effects on nucleotide biosynthesis and lipid homeostasis. Nucleotide levels (purines and pyrimidines) and their intermediates were broadly reduced in CA-treated cells whereas different classes of lipids were generally elevated (Figure 2 – source data 2).

The gene expression changes (RNA-seq, 4h or 24h) triggered by Mediator kinase inhibition tracked with these “downstream” metabolic changes (24h) (e.g. Fig 2B, Figure 2 – figure supplement 1E). For example, nucleotide biosynthesis mRNAs were down-regulated whereas lipid metabolism mRNAs were up-regulated in CA-treated cells; this was reflected in the GSEA Hallmarks, with positive enrichment for pathways such as cholesterol homeostasis, bile acid metabolism, and FA metabolism and negative enrichment for DNA repair pathways, which contains many nucleotide biosynthesis genes. Pyrimidine metabolism genes were also alternatively spliced in CA-treated cells. The differential timing of the transcriptomics and metabolomics experiments helps link CA-dependent metabolic changes to gene expression changes; however, we cannot exclude other factors and pathways. For example, reduced levels of nucleotides can trigger cellular compensation through up-regulation of FA levels (Schoors et al. 2015). Consequently, elevated levels of FA and other lipids may result, in part, from compensatory mechanisms triggered by CA-dependent reductions in nucleotide levels.

Pathway-specific regulation of splicing by Mediator kinases

Despite widespread study of Mediator kinases as regulators of RNAPII transcription, their potential function in splicing has not been addressed. We hypothesized that CDK8 and/or CDK19 may impact splicing based upon prior results that identified NAB2, SRRM2, and KDM3A as high-confidence CDK8/CDK19 substrates in human cells (Poss et al. 2016); each of these proteins has been linked to regulation of splicing (Gautam et al. 2015; Soucek et al. 2016; Baker et al. 2021). Our analysis revealed that exon skipping was dependent upon the kinase function of CDK8 and/or CDK19; increased inclusion or skipping of hundreds of alternative exons was observed in CA-treated cells (D21 or T21), and this was observed under both basal and IFNγ-stimulated conditions (Fig 5C, D).

Pathway analysis of the CA-sensitive alternative splicing events identified gene sets associated with signaling pathways known to be regulated by Mediator kinases, such as TGFβ and IFN signaling (Fig 5E). Interestingly, CA-dependent alternative splicing of these gene sets occurred specifically in IFNγ-stimulated T21 cells (not D21); moreover, other pathways were selectively affected by Mediator kinase inhibition in cell type and context-specific ways. For instance, mTOR pathway genes in CA-treated D21 cells and pyrimidine metabolism genes in T21 cells (Fig 5F, G).

Taken together, these results suggest that Mediator kinases regulate pre-mRNA splicing, which adds to the mechanisms by which CDK8 and CDK19 can influence gene expression. Our data further suggest that kinase-dependent splicing regulation occurs in cell type- and context-specific ways, similar to other known regulatory functions for CDK8 and CDK19 (Luyties and Taatjes 2022). However, the effects of CDK8/CDK19 on splicing appear to be modest, especially compared with CDK7, a different transcription-associated kinase (Rimel et al. 2020). The mechanisms by which CDK8 and/or CDK19 influence pre-mRNA splicing remain unclear, but are likely to involve both direct kinase-dependent regulation of splicing factors (e.g. through phosphorylation of NAB2 or SRRM2) and indirect regulation through modification of DNA-binding TFs (Rambout et al. 2018), which are common Mediator kinase substrates (Poss et al. 2016). Differential expression of splicing regulatory proteins in CA-treated cells may also contribute, although the time frames of our analyses (RNA-seq at 4h post-treatment) minimize this possibility.

Mediator kinase inhibition activates nuclear receptors through lipid metabolites

The anti-inflammatory lipids that were elevated in CA-treated cells included oleamide, desmosterol, endocannabinoids such as oleoylethanolamide, and PUFAs such as EPA, DPA, DHA and various other μ3 or μ6 PUFAs. These metabolites act as signaling molecules, at least in part, through binding nuclear receptors or GPCRs such as FFAR1, FFAR4, and GPR119, to initiate a cascade of anti-inflammatory responses (Fu et al. 2003; Basil and Levy 2016; Husted et al. 2017; Kimura et al. 2020; Jordan and Werz 2022). Our RNA-seq results (4h and 24h) confirmed CA-dependent activation of nuclear receptors PPAR and LXR, but only under conditions with elevated IFN signaling (e.g. T21 cells or D21 +IFNγ). As expected, PPAR and LXR activation tracked with elevated levels of their ligands (e.g. oleamide, endocannabinoids, desmosterol), as measured by metabolomics. These results reveal an unexpected mechanistic link between Mediator kinase activity and nuclear receptor function, and suggests potential regulation of GPCR function as well (Fig 7). Metabolic changes are generally not considered when evaluating the biological function of transcription-associated kinases such as CDK8 and CDK19. To our knowledge, this is the first demonstration that kinase inhibition can subsequently activate transcription factors through metabolic changes.

Fig 7.

Mediator kinase inhibition antagonizes IFN signaling

Independently and collectively, our transcriptomics, metabolomics, and cytokine screen data revealed that Mediator kinase inhibition suppressed pro-inflammatory signaling. This was most evident during IFNγ stimulation but was also apparent under basal conditions, especially in T21 cells due to their high “baseline” activation of IFN signaling pathways. We acknowledge that metabolites and cytokines can have variable, dynamic, and context-specific effects on inflammatory signaling. For example, sphingolipids (Maceyka and Spiegel 2014) and bile acids may serve pro- or anti-inflammatory roles depending upon cell type (Chiang and Ferrell 2019), and cytokine responses are complex, in part because cytokines regulate each other (e.g. MIP3α/CCL20 is down-regulated by IL10). For this study, a more straightforward metric for an anti-inflammatory change was whether CA treatment countered the effect of IFNγ stimulation alone. This was observed in a large number of cases across all experiments.

Given the well-established roles for CDK8 and CDK19 as regulators of RNAPII transcription (Luyties and Taatjes 2022), the majority of CA-dependent effects result from gene expression changes. This hypothesis is also consistent with phosphoproteomics data that identified TFs and other RNAPII regulatory factors as high-confidence targets of CDK8/CDK19 in human cells (Poss et al. 2016). The staggered timing of our RNA-seq (4h or 24h), metabolomics (24h), and cytokine screen experiments (24h) allowed inference of direct vs. indirect effects of Mediator kinase inhibition. Moreover, TFEA results in T21 cells (PRO-seq, 45min) provided insights about CA-dependent changes in TF activity. As expected, we observed changes in anti-inflammatory transcriptional responses over time, which likely reflects compensatory responses to Mediator kinase inhibition.

Taken as a whole, our results suggest that under conditions of IFN pathway stimulation, CDK8/CDK19 inhibition initiates a cascade of events that mediate anti-inflammatory responses (Fig 7). At short timeframes (45min), Mediator kinase inhibition suppressed activation of a cohort of IFN-responsive, pro-inflammatory TFs in T21 cells (Fig 6). Consistent with these results, mRNA levels of pro-inflammatory genes were suppressed in CA-treated T21 cells (Figure 2 – figure supplement 2C-E), as were protein levels of pro-inflammatory cytokines (Fig 2E). Similar CA-dependent effects were observed with exogenous IFNγ stimulation in both T21 and D21 cells (Fig 3 & 4). TFEA also identified p53 as rapidly suppressed in CA-treated T21 cells (t=45min); p53 not only blocks cholesterol and lipid biosynthesis (e.g. to suppress growth), it also blocks SREBP activation (Moon et al. 2019). Suppression of p53 function in T21 cells could therefore activate SREBP and elevate cholesterol and lipid biosynthesis. In agreement, RNA-seq data (t=4h or 24h) identified SREBP (IPA upstream regulators), cholesterol homeostasis and fatty acid metabolism (GSEA Hallmarks) as activated upon Mediator kinase inhibition. SREBP TFs activate cholesterol and lipid biosynthesis pathways, and metabolomics data revealed elevated levels of anti-inflammatory lipids in CA-treated cells during IFNγ signaling (e.g. D21 cells +IFNγ; T21 cells at basal conditions). Among these elevated lipids, oleamide, desmosterol, and PUFAs are nuclear receptor agonists that activate PPAR and LXR (Yang et al. 2006; Sun and Bennett 2007; Spann et al. 2012; Roy et al. 2016); PPAR and LXR, in turn, broadly control fatty acid biosynthesis (Schultz et al. 2000), including PUFAs such as DPA, DHA, and EPA, whose levels were consistently elevated in CA-treated cells. In this way, Mediator kinase inhibition can robustly and durably elevate lipid metabolites, with downstream consequences for inflammatory signaling and nuclear receptor function (Fig 7).

Concluding remarks

Although the factors that contribute to the DS condition are complex (Antonarakis et al. 2020), it has been described as both an interferonopathy (Sullivan et al. 2016) and a cytokinopathy (Malle et al. 2023). An emerging theme in DS research is that individuals with DS could benefit from therapeutic strategies that diminish chronic, hyperactive IFN signaling (Sullivan et al. 2017; Tuttle et al. 2020; Waugh et al. 2023). Through multi-omics evaluation of CA-dependent effects in T21 cells, we have demonstrated that Mediator kinase inhibition antagonizes hyperactive IFN signaling, via diverse mechanisms that extend beyond transcription (Fig 7). Consequently, selective inhibition of Mediator kinase function represents a potential therapeutic strategy to address chronic inflammation and its associated co-morbidities in DS.

Materials & Methods

Cell culture

Immortalized lymphoblastoid cell lines derived from age- and sex-matched sibling pairs (one D21: TIC0002294, one T21: TIC0001678) were obtained from the Nexus Biobank. Cells were cultured in RPMI medium (Gibco, 23400062) supplemented with 20% fetal bovine serum (Peak Serum Inc, PS-FB3), 1x GlutaminePlus (R&D Systems, R90210), and 1x Penicillin-Streptomycin (Gibco, 15140-163), on low-attachment flasks at 37C and 5% CO₂. Due to the lower proliferation rate of T21 cells compared to D21 cells, T21 cells were seeded at twice the density of D21 cells for all experiments unless otherwise noted. For all cortistatin A treatments, the compound remained in the media during IFNγ stimulation.

Western blotting

RIPA lysis buffer (Thermo Scientific, 89900) was used to generate cell lysates; all lysis was performed at 4°C in the presence of Halt™ Protease and Phosphatase Inhibitor Cocktail, EDTA-free (100X) (Thermo Scientific, 78441) and Benzonase® endonuclease (Millipore Sigma, 101697). The BCA Protein Assay Kit (Pierce, 23227) was used to measure and normalize protein concentrations across samples. Protein samples were boiled for 5 minutes at 95°C in 4x Laemmli Sample Buffer (Bio-Rad, 1610747) and run on SDS-acrylamide gels, followed by transfer to nitrocellulose membranes. Blocking was performed using either 5% milk in TBS-T or in 5% bovine serum albumin for phospho-antibodies.

Antibodies used are as follows: anti-STAT1 (RRID: AB_2198300; Cell Signaling Technology, 9172), anti-phospho STAT1 S727 (RRID: AB_2197983; Cell Signaling Techology, 9177), and anti-GAPDH (RRID: AB_1080976; GeneTex, GTX100118). HRP-conjugated anti-rabbit secondary antibody was used for detection (Invitrogen, 31480) and protein was visualized using Immunobilon Western Chemilumiescent HRP Substrate (Millipore, WBKLS0500) on an ImageQuant LAS 4000 (GE Healthcare). Intensity values were quantified and normalized using ImageJ.

RT-qPCR

Cells were seeded and allowed to recover for 24 hours prior to being treated with 10 ng/mL interferon gamma (Gibco PHC4031) or vehicle (40 mM Tris pH 7.4). Total RNA was isolated using TRizol (Invitrogen, 15596026) according to the manufacturer’s instructions, quantified with a Qubit® 3.0 using the RNA High Sensitivity (HS) kit (Invitrogen™, Q32855), and 100 ng of RNA from each sample was converted to cDNA using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, 4368813) following the manufacturer’s instructions. cDNA was diluted to 0.25 ng/μL for all samples, and amplification was performed using Sybr Select Master Mix (Thermo Fisher Scientific, 4472908) on a BioRad CFX384 Real Time PCR System. ΔΔCT values were calculated using GAPDH as a loading control. Treatments, RNA isolation, and RT-qPCR were performed in biological duplicate. The primers used for qPCR are as follows - GAPDH Forward: ACCACAGTCCATGCCATCAC, GAPDH Reverse: TCCACCACCCTGTTGCTGTA, GBP1 Forward: GTGCTAGAAGCCAGTGCTCGT, GBP1 Reverse: TGGGCCTGTCATGTGGATCTC, IRF1 Forward: GAGGAGGTGAAAGACCAGAGC, IRF1 Reverse: TAGCATCTCGGCTGGACTTCGA.

To probe the timing of the RNA-seq experiments, we completed RT-qPCR at various time points as shown in Figure 1 – figure supplement 1B. The 4h time point (post-IFNγ) was chosen because robust induction of GBP1 and IRF1 mRNA was observed. Whereas mRNA levels remained high at 6h, we selected the 4h time point because it would better represent the primary transcriptional response; moreover, the 4h time point was consistent with prior transcriptomics studies of IFNγ response in mammalian cells (Steinparzer et al. 2019).

Metabolomics

For each treatment condition, six flasks of cells were seeded and allowed to recover for 24 hours prior to treatment. Cells were pre-treated with 0.1% DMSO or 100 nM cortistatin A for one hour, then treated with 40 mM Tris, pH 7.4 or 10 ng/mL interferon gamma (Gibco, PHC4031). After 24 hours of interferon gamma treatment, cells were harvested and snap-frozen in liquid nitrogen. Sample preparation was carried out by Metabolon (Durham, North Carolina, USA) using a previously published workflow (Ford et al. 2020). Samples were prepared using the automated MicroLab STAR® system from Hamilton Company, and several recovery standards were added prior to the first step in the extraction process for QC purposes. To remove protein, dissociate small molecules bound to protein or trapped in the precipitated protein matrix, and to recover chemically diverse metabolites, proteins were precipitated with methanol under vigorous shaking for 2 min (Glen Mills GenoGrinder 2000) followed by centrifugation. The resulting extract was divided into five fractions: two for analysis by two separate reverse phase (RP)/UPLC-MS/MS methods with positive ion mode electrospray ionization (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI, and one sample was reserved for backup. Samples were placed briefly on a TurboVap® (Zymark) to remove the organic solvent.

All methods utilized a Waters ACQUITY ultra-performance liquid chromatography (UPLC) and a Thermo Scientific Q-Exactive high resolution/accurate mass spectrometer interfaced with a heated electrospray ionization (HESI-II) source and Orbitrap mass analyzer operated at 35,000 mass resolution. The sample extract was dried then reconstituted in solvents compatible to each of the four methods. Each reconstitution solvent contained a series of standards at fixed concentrations to ensure injection and chromatographic consistency. One aliquot was analyzed using acidic positive ion conditions, chromatographically optimized for more hydrophilic compounds. In this method, the extract was gradient eluted from a C18 column (Waters UPLC BEH C18-2.1x100 mm, 1.7 µm) using water and methanol, containing 0.05% perfluoropentanoic acid (PFPA) and 0.1% formic acid (FA). Another aliquot was also analyzed using acidic positive ion conditions, however it was chromatographically optimized for more hydrophobic compounds. In this method, the extract was gradient eluted from the same afore mentioned C18 column using methanol, acetonitrile, water, 0.05% PFPA and 0.01% FA and was operated at an overall higher organic content. Another aliquot was analyzed using basic negative ion optimized conditions using a separate dedicated C18 column. The basic extracts were gradient eluted from the column using methanol and water, however with 6.5mM Ammonium Bicarbonate at pH 8. The fourth aliquot was analyzed via negative ionization following elution from a HILIC column (Waters UPLC BEH Amide 2.1x150 mm, 1.7 µm) using a gradient consisting of water and acetonitrile with 10mM Ammonium Formate, pH 10.8. The MS analysis alternated between MS and data-dependent MSn scans using dynamic exclusion. The scan range varied slighted between methods but covered 70-1000 m/z.

Identification of metabolites was performed through automated comparison of the ion features in experimental samples to a reference library of chemical standard entries (Dehaven et al. 2010), and are based on three criteria: retention index within a narrow RI window of the proposed identification, accurate mass match to the library +/- 10 ppm, and the MS/MS forward and reverse scores between the experimental data and authentic standards. The MS/MS scores are based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. While there may be similarities between these molecules based on one of these factors, the use of all three data points can be utilized to distinguish and differentiate biochemicals.

Statistical and pathway analysis of metabolomics data

Two types of statistical analyses were performed: (1) significance tests and (2) classification analysis. Standard statistical analyses were performed in Array Studio on log-transformed data. For analyses not standard in Array Studio, the R program (http://cran.r-project.org/) was used. Following log transformation and imputation of missing values, if any, with the minimum observed value for each compound, Welch 2-sample t test was used as a significance test to identify biochemicals that differed significantly (p < 0.05) between experimental groups. An estimate of the false discovery rate (q-value) was calculated to take into account the multiple comparisons that normally occur in metabolomic-based studies. Classification analyses included principal component analysis (PCA), hierarchical clustering, and random forest.

Pathway analysis of metabolomic data was performed using the Ingenuity Pathway Analysis software (Qiagen) (Kramer et al. 2014). For metabolomic analysis, a total of 566 out of 675 metabolites could be mapped using one of the following identifiers: CAS registry number, Human Metabolome Database (HMDB) identification, KEGG, PubChem CID, or RefSeq number; metabolites not used in IPA analysis can be found in Figure 1 – source data 2. Metabolomic pathway analysis used only mapped metabolites with a p-value of <0.1 when making pathway predictions.

Seahorse metabolic assays

Cells for metabolic assays were seeded 24 hours prior to treatments and allowed to recover, then were treated with either vehicle or Cortistatin A at a final concentration of 100 nM. After 24 hours of treatment, cells were collected, washed, and resuspended in Seahorse XF RPMI Assay Media (Agilent, 103576) supplemented with 10 mM glucose (Agilent, 103577-100), 1 mM pyruvate (Agilent, 103578-100), and 2 mM glutamine (Agilent, 103579-100). Cells were counted and for each cell line 100,000 cells were seeded per well on XF24 cell culture plates that had been pre-coated with poly-D-lysine (Gibco, A3890401). The plates were spun down at 300xg for 3 minutes using the minimum brake setting, and cells were then allowed to recover for 30-60 minutes in a humidified, non-CO₂ incubator set to 37°C. Compounds from the Mito Stress Test Kit (Agilent, 103015-100) were used at the following final concentrations: 1.5 uM Oligomycin, 1 uM FCCP, 5 uM Rotenone/Antimycin A. All assays were performed on an Agilent Seahorse XFe24 Analyzer (XFe version 2.6.3.5), and data was processed using the Agilent Wave software (version 2.6.3). Two to three technical replicates of each treatment condition were plated per assay, and three biological replicates of each assay were completed on different days. Across all three biological replicates, measurements from 2 wells that did not exhibit a drop in OCR of at least 30% following Oligomycin injection were considered outliers and were removed from analysis. Technical replicates from each assay were averaged, and the average OCR values for each treatment condition at each time point were normalized relative to the first baseline measurement taken of vehicle treated cells. Relative values for OCR at each time point, for each treatment condition were averaged between all three biological replicates.

RNA-seq

Cells were pre-treated with 0.1% DMSO or 100 nM cortistatin A for 30 minutes, then treated with 40 mM Tris, pH 7.4 or 10 ng/mL interferon gamma (Gibco, PHC4031). After four hours, total RNA was isolated from D21 or T21 cells using TRizol (Invitrogen, 15596026) as specified by the manufacturer and quantified with a Qubit® 3.0 using the RNA High Sensitivity (HS) kit (Invitrogen™, Q32855). 1 μg of total RNA with a RIN number of ≥8 was used for RNA-seq library prep. Libraries were constructed using Universal Plus™ mRNA-Seq library preparation kit with NuQuant® (Tecan, 0520). Library construction and sequencing were performed at the Genomics Shared Resource (CU Anschutz). Paired-end libraries (151 bp x 151 bp) were sequenced on the Illumina NextSeq 6000 platform (Firmware version 1.26.1, RTA version: v3.4.4, Instrument ID: A00405).

For the non-sibling T21 and sibling T21 t=24h RNA-seq experiments, cells were treated with 0.1% DMSO or 100 nM cortistatin A for 4.5 hours (non-sibling T21) or 24 hours (sibling T21). After treatment, total RNA was isolated using TRizol (Invitrogen, 15596026) as specified by the manufacturer and quantified with a Qubit® 3.0 using the RNA High Sensitivity (HS) kit (Invitrogen™, Q32855). 200 ng of total RNA with a RIN number of ≥8 was used for RNA-seq library prep. Libraries were constructed using the Zymo-Seq RiboFree Total RNA Library Kit (Zymo Research). Paired-end sequencing reads of 150 nucleotides were generated on a NovaSeq X (RTA v4.6.7, Serial Number LH00407, Firmware v1.2.0.28691) and de-multiplexed using bcl2fastq.

RNA-seq computational analysis

For differential gene expression analysis, the workflow was as follows: adaptor sequences were trimmed from the RNA-seq raw fastq files using BBDuk (https://sourceforge.net/projects/bbmap/) and mapped using HISAT2(Kim et al. 2019). Gene counts were generated using featureCounts(Liao et al. 2014), and differential expression analysis was performed using DESeq2 (Anders and Huber 2010). When there were multiple transcripts per gene, those with the highest FPKM were kept for analysis. For inter-genotype (T21 vs D21) comparisons, expression of genes on chromosome 21 was normalized to ploidy. While ERCC RNA Spike-In Mix (Invitrogen™, 4456740) was added to isolated RNA samples prior to sequencing, ERCC gene counts were variable across vehicle treated samples even after accounting for read depth, and when using multiple normalization methods and a linear regression. As such, the median of ratios method native to DESeq2 was used to generate size factors and normalize samples. Qiagen ingenuity pathway analysis (IPA) version 90348151 (Kramer et al. 2014), GSEA 4.2.3 (Subramanian et al. 2005), and Gene Ontology analysis (Ashburner et al. 2000) were used for identification of activated and inhibited pathways. For IPA analysis, a total of 27,647 out of 28,265 genes could be mapped using Entrez Gene Symbols. For cross-genotype (i.e. T21 vs D21) pathway analysis, only genes with a fold-change > |1.25| and adjusted p-value <0.1 were used. For within-genotype (i.e. treatment effects) pathway analysis, only genes with an adjusted p-value <0.1 were used.

For analysis of RNA-seq data collected from Sibling T21 cell line at 24 hours ±CA, and Non-Sibling T21 at 4.5 hours ±CA, batch correction was applied to gene counts using Surrogate Variable Analysis (Leek et al. 2019) as initial principal component analysis revealed inter-replicate variability as a stronger source of variation than kinase inhibition. However, differential expression analysis demonstrated that the same pathways were predicted to be up- or downregulated regardless of whether batch correction was performed. Gene Set Enrichment analysis was performed by generating a list of genes with differential expression across treatments/genotypes, ranked by the negative log10 adjusted p-value and the sign (positive or negative) of log2(fold-change). Ranked gene lists were loaded onto GSEA (v4.2.3) and GSEAPreranked was run with the following settings – Number of permutations: 1000, Collapse/Remap: No_collapse, Enrichment statistic: weighted, Max size: 500, Min size: 15.

For splicing analysis, the workflow was as follows: duplicates were removed and adaptors trimmed using the bbTools function (v39.01), trimmed reads were mapped uniquely against the hg38 genome using HISAT2 (v2.1.0) (Kim et al. 2019), mapped reads were processed with rMATS (v4.0.1) (Shen et al. 2014), and results were filtered based on FDR < 0.05, absolute(IncLevelDifference) < 0.2, and ≥2 reads/replicate. Sashimi plots were generated from rMATS results using a modified script based on ggsashimi.py.

The IFN score was calculated as described (Galbraith et al. 2023) using normalized gene counts from DESeq2. The score is calculated with 18 genes: BPGM, CCL5, IFI27, GMPR, IRF7, CMKLR1, IFITM3, GZMA, FCGR1A, USP18, PLSCR1, CXCL9, IFI44, CD274, CXCL10, ISG15, IFI44L, RSAD2. For each gene, the mean and standard deviation of the D21 replicates was calculated. Then the measured level of each sample was subtracted by the mean of the D21 replicates and divided by the standard deviation of the D21 replicates to create a standardized gene value. For each sample, the sum of the standardized values was taken to get the IFN score.

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE220652 or GSE263239. Code used to process and visualize data can be found at https://github.com/kira-alia/Cozzolino2023.

Cytokine screen

Cells were pre-treated with 0.1% DMSO or 100 nM cortistatin A for 30 minutes, then treated with 40 mM Tris, pH 7.4 or 10 ng/mL interferon gamma (Gibco, PHC4031). After 24 hours, cells were lysed in RIPA buffer (Thermo Scientific, 89900) supplemented with Halt™ Protease and Phosphatase Inhibitor Cocktail, EDTA-free (100X) (Thermo Scientific, 78441) and Benzonase® endonuclease (Millipore Sigma, 101697). Lysate concentrations were determined using the Pierce™ BCA Protein Assay Kit (Thermo Scientific, 23225), and 250 μg of protein from each condition was incubated with a membrane array from the Proteome Profiler Human XL Cytokine Array Kit (R&D Systems, ARY022B). Membranes were processed according to the manufacturer’s instructions and imaged using an ImageQuant LAS 4000 (GE Healthcare). Background-subtracted technical replicate values for each cytokine in each condition were quantified using ImageJ and averaged giving a normalized intensity value. The relative intensity was compared on a per cytokine basis and was statistically assessed using a one-way ANOVA test between the eight total conditions across two biological replicates.

PRO-seq

Cells were treated with 0.1% DMSO or 100 nM cortistatin A for 75 minutes before being transferred into a lysis buffer consisting of final concentrations of 10 mM Tris pH 7.4, 2 mM MgCl₂, 3 mM CaCl₂, 0.5% NP-40, 10% glycerol, 1 mM DTT, 1x Halt^TM Protease and Phosphatase Inhibitor Cocktail (Thermo Scientific, 78441), and 2 U/μL SUPERase•In™ RNase Inhibitor (Invitrogen, AM2696). Nuclei were isolated and cleaned through repeated centrifugation and washes, and were counted, aliquoted, and snap-frozen in using liquid nitrogen in a freezing buffer consisting of 50 mM Tris pH 8.3, 5 mM MgCl₂, 40% glycerol. 0.1 mM EDTA, and 4 U/μL SUPERase•In™ RNase Inhibitor (Invitrogen, AM2696). Nuclear run-ons were performed for 3 minutes at 37°C on a Groovin’ Tubes thermoshaker (Boekel Scientific, 270500), on a mixture of 10 million human and 100,000 Drosophila melanogaster nuclei per replicate. PRO-seq library preparation was performed as described (Fant et al. 2020). Library sequencing was performed at the Cornell Genomics Facility (Cornell Institute of Biotechnology) on the Illumina NextSeq 2000 platform (Instrument ID VH00231).

PRO-seq data analysis

Raw fastq files from the sequencing pipeline described above were trimmed and mapped using the NascentFlow pipeline (DOI 10.17605/OSF.IO/NDHJ2). Bidirectional sites of transcription in the human genome (assembly hg38) were identified using the BidirectionalFlow pipeline (https://github.com/Dowell-Lab/Bidirectional-Flow), which runs TFIT (Azofeifa and Dowell 2017). Bidirectional calls across all replicates and conditions relevant to this study were merged using muMerge (https://github.com/Dowell-Lab/mumerge) and used as an annotation file for running Transcription Factor Enrichment Analysis (Rubin et al. 2021). Example traces of differentially active sites of bidirectional transcription were plotted using pyGenomeTracks (Lopez-Delisle et al. 2021).

The data in this publication have been deposited in NCBI’s Gene Expression Omnibus and are accessible through GEO Series accession number GSE249549. Code used to process and visualize data can be found at https://github.com/kira-alia/Cozzolino2023.

Acknowledgements

We thank A. York (Yale University) and P. Kovarik (University of Vienna) for helpful comments on the manuscript; we thank Matt Shair (Harvard) for providing Cortistatin A. We acknowledge the Linda Crnic Institute for Down Syndrome Research, particularly the Human Trisome Project Biobank and the Nexus Biobank, for providing the cells used in this work. We thank Theresa Nahreini for cell culture assistance. We acknowledge the Flow Cytometry Shared Core (RRID: S10ODO21601) at UC-Boulder and the Genomics Shared Resource (RRID: SCR_021984) at UC-Anschutz. This work was supported by the NIH (R01 AI156739 to DJT; R35 GM139550 to DJT; R01 HD100935 to DLB; R01 AI50305 to JME), and the Global Down Syndrome Foundation (JME) and the Anna & John J. Sie Foundation (JME, KC, MCSC). K.C. was supported in part by the NIH (T32 GM065103); K.M. was supported by the Arnold and Mabel Beckman Foundation and the NSF (MCB-1818147).

Competing interests

R.D.D. is a founder of Arpeggio Biosciences; J.M.E. has provided consulting services for Eli Lilly and Gilead, and serves on the advisory board of Perha Pharmaceuticals.