We next addressed how each regulator contributes to the generation of specific cell states. We first automatically defined the two major states induced by the five pathogen components using non-negative matrix factorization (NMF) (19
)and the original array data (SOM
). This procedure identified two major expression components (termed ‘metagenes’): one predominantly determined by genes from the ‘inflammatory-like’ program and the other by genes from the ‘antiviral like’ programs (). Next, we quantified the effects of each regulator's knockdown on these two states (, Fig. S15A, Table S9
), by classifying the nCounter expression measurements following a regulator's perturbation (19
Finally, we used a regulator ranking score (SOM
) to assign 33 (8 known) genes as regulators of the inflammatory state and 33 (15 known) genes as regulators of the anti-viral state. This accurately classified the known activators of the inflammatory response (e.g.
the NFκB factors Rela, Nfkbiz, Nfkb1, , yellow in the inflammatory metagene
) and of the antiviral response (e.g.
Stat1, Stat2, Stat4, Irf8, Irf9 , yellow in the viral metagene
). Although all perturbation experiments were conducted only under LPS stimulation (a bacterial component), we correctly classified factors known to mediate the response to other stimuli. 34 additional regulators were associated with both responses, suggesting that a single regulator can control genes in either state depending on the differential timing of regulator activation, its level, or combinatorial regulation. Notably, for 12 of the transcription factors examined, we found an enriched cis
-regulatory element in the appropriate metagene (SOM
On the basis of the NMF scores (Table S9
), we identified an inflammatory subnetwork (Fig. S15B
), an anti-viral subnetwork (, S15C
), and several fine-tuning subnetworks that affect smaller numbers of genes from both responses (Figs. S15D, S16, SOM
). The inflammatory subnetwork (Fig. S15B
) consisted of three regulatory modes: dominant activators (Cebpb, Bcl3, Cited2) which induce more inflammatory targets than anti-viral ones; cross-inhibitors (Nfkbiz, Nfkb1, Atf4, Pnrc2) which induce inflammatory genes while repressing anti-viral ones, and specific activators (Runx1, Plagl2), that only target inflammatory genes. We observed that dominant activators mostly regulate effectors, whereas regulators are primarily controlled by cross-inhibitors.
The core regulatory circuits controlling the inflammatory and anti-viral responses
Focusing on the network architecture, we found multiple feed-forward circuits in this response, where an upstream regulator controls a target gene both directly and indirectly through a secondary regulator (21
, and Tables S10, S11
). The majority (76%, 4892 of 6444) of these feed-forward circuits were found to be coherent (21
); having the same direct and indirect effect on the regulated gene. The vast majority (80%) are type I loops (22
)with all-positive regulation (e.g.
NFKBIZ activates E2F5 and both activate IL6). Such feed-forward circuits respond to persistent rather than transient stimulation, protecting the system from responding to spurious signals, as was shown for one circuit in LPS-stimulated macrophages (23
). Our finding suggests that coherent feed-forward loops, especially class I (21
), are a general design principle in this system and may physiologically impact this response.
In the anti-viral sub-network, we identified a two-tiered regulatory circuit combining feed-forward and feed-back loops (, Table S11
). This circuit has at the top the anti-viral regulators Stat1 and Stat2, which regulate a full complement of anti-viral reporters. The second-tier regulators Timeless, Rbl1 and Hhex are controlled by Stat1 and 2 and most likely form coherent feed-forward loops that target specific sub-sets of genes. Timeless, Rbl1 and Hhex also feed-back and promote the expression of the Stat regulators. This circuit is repressed through the cell cycle regulator and RNA binding protein Fus (24
), acting as a single dominant inhibitor of 43 viral genes.
Finally, we derived a core network incorporating the regulators with the most substantial impact on each response, on the basis of the number, magnitude, and logic of targets that each regulator affects (SOM
). The core network () has 24 regulators, 13 of which have previously been identified as key factors regulating the inflammatory or anti-viral responses, while 11 have not been previously implicated in either response. Of these, 19 are transcription factors, three are chromatin modifiers, and two are RNA binding proteins. The regulators apparently distinguish the two programs through cross-inhibition (, gray lines) or dominant activation (). The core network also explains how differential expression of secreted factors is specified, leading to the activation and migration of appropriate cell types for different pathogens (25
) (Fig. S17, SOM
Embedded within the many known regulators of the anti-viral response (, S15C
), we found a large set of regulators not previously associated with this response. These included several known regulators of the cell cycle and the circadian rhythm, including Rbl1, Jun, RB, E2F5, E2F8, Nmi, Fus, and Timeless, several of which were placed in our core network. This suggests that a cell cycle regulatory circuit was co-opted to function in the anti-viral response in DCs (with no observable effect on cell cycle progression, Fig S18
). Since we identified these anti-viral regulatory relations in perturbation experiments using DCs stimulated with the bacterial component LPS, we silenced four regulators (TIMELESS, RBL1, JUN and NMI) following exposure to the viral component polyI:C. Each of the four regulators strongly impacted the antiviral program, more than was observed under LPS stimulation (), and affected genes (e.g.
Type I IFNs) whose expression is polyI:C-specific. Nmi affected a smaller set of genes, consistent with the model's prediction. These results demonstrate our ability to correctly predict function in unobserved conditions.
Although most anti-viral genes are induced following stimulation with the bacterial component LPS, a few critical ones are expressed specifically in polyI:C stimulation, or follow distinct patterns in each stimulus. In response to viral infection cells induce the production of interferon beta1 (IFNB1), a crucial mediator of the antiviral response. Because high levels of IFNB1 may be deleterious to the host if infected by specific bacteria (26
), we predicted that specific mechanisms insulate IFNB1's regulation from the response to LPS. Indeed, although IFNB1 expression was induced in the first two hours of stimulation with LPS, this expression declined at subsequent time points, in contrast to its sustained induction following polyI:C treatment (). Our model suggested that three regulators known to affect chromatin remodeling (24
) are IFNB1 repressors in LPS (): the Polycomb complex subunit Cbx4 (27
), Fus (24
), and the DNA methyltransferase Dnmt3a (28
). Cbx4 appeared to confer antiviral specificity to IFNB1 induction as it is induced within the first two hours of PAM and LPS treatment but not by polyI:C (), and Cbx4 knockdown caused induction of IFNB1 mRNA and protein during LPS treatment (, Fig. S19A
), but had no effect on the induction of the chemokine Cxcl10, a polyI:C and LPS-induced gene (Fig. S19B
). Cbx4 knockdown did not affect IFNB1 during PAM activation (), when the anti-viral response is not induced. Combined with evidence for chromatin changes around the Ifnb1 locus and its closest neighbor gene, Ptplad2 (Fig. S20A
), which has a similar dependence on Cbx4, these data are consistent with an effect by Cbx4 on local chromatin organization (Figs. S20B, C
). Cbx4 knockdown affected few genes (~120 up-regulated and ~120 down-regulated genome-wide, Table S12
). Because most up-regulated genes show a precise temporal pattern in unperturbed cells akin to that of Cbx4– they are induced quickly and return to basal level by 2-4 hours (Fig. S21 A-F
), we conclude that a chromatin modifier can act like a transcription factor controlling the precise expression of specific genes in the regulatory program.
The polycomb component Cbx4 selectively restricts IFNB1 production under bacterial perturbations
Taken together, our results suggest a model of a transcriptional negative feedback loop, controlling IFNB1 expression in LPS stimulation, wherein the induced pro-inflammatory regulator and chromatin modifier Cbx4 represses transcription by modifying the chromatin in the Ifnb1 locus, generating the specificity needed to drive inflammatory versus the anti-viral response (). The Type I coherent feedforward loop formed by Cbx4 and Dnmt3a () is consistent with a delayed repression of IFNB1. Since neither regulator carries a sequence-specific DNA binding domain, the factors responsible for their guidance to the Ifnb1 locus remain unknown.