You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["AnatomicalEntity"].

Please return the output as a JSON object of the format: {"AnatomicalEntity": ["tumor cells", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Differential expression of drug-induced drug targets
Integrating 4849 CMap arrays with 40,656 drug target relations from STITCH resulted in a set of 1,290 drug-target relations for which a genome-wide cellular response is available. We found that thirteen out of 167 distinct drug targets in this set (8%; 86 drug target relations) are subjected to significant differential expression upon drug treatment (Figure 3) by comparing the drug-induced expression changes of the drug target against all other treatments present in CMap (see methods). We found supporting evidence in the literature for seven out of thirteen (q-value <0.05) significant differential regulations of drug targets shown in Figure 3, confirming the rationale and predictive power of our systematic approach. For the remaining six targets we can predict a hitherto unknown drug-induced differential regulation.
10.1371/journal.pcbi.1000925.g003 Figure 3
Drug-induced differentially regulated drug targets.
Anova is used to assess the significance of the differential expression of drug-induced drug targets against the mRNA changes of the same gene in the population of heterogeneous drug treatments from CMap. The genes are mainly ordered based on their q-values as provided in Table S1. In the scatter plots, inhibitors/activators are labeled in red/green respectively and grey represents all other treatments present in CMap.
The identified, differentially regulated drug targets are enriched in G-protein coupled receptors (GPCRs) (Figure S4), in agreement with previous reports that members of the GPCR family are generally regulated by several mechanisms including receptor desensitization, endocytosis at the protein level and regulation of the cellular receptor content [18], [19]. In the three cancer cell lines used, we observe agonist-induced down-regulation of GPCR mRNAs for beta-2 adrenergic receptor (ADRB2), prostaglandin E2 receptor subtype EP2 and prostaglandin E4 receptor subtype EP4 (Figure 3, Genes 3,4,12), which were previously reported in DDT1 MF-2 smooth muscle cells (ADRB2) and 293-EBNA human embryonic kidney cells (prostaglandin E2/E4 receptor subtypes EP2/EP4) [20], [21]. This indicates that drugs can induce similar feedback loops in a wide variety of cell types. However, we cannot rule out that cross-regulation among signaling pathways may be responsible for the regulation of GPCR mRNAs as it has been described before [22]. For example, it has been shown that a beta adrenergic mRNA-binding protein, ELAV-like protein 1 (ELAVL1) can be induced by ADRB2 agonist or elevated levels of cyclic adenosine monophosphate (cAMP) [22], [23] and destabilizes ADRB2 mRNA. The ELAVL1 protein binds to GPCR mRNAs and recognizes a cognate sequence located at the 3'-UTR of ADRB2, proteinase-activated receptor and M2, M3 muscarinic acetylcholine receptor mRNAs [24], [25]. Therefore cAMP provides cross-talk among GPCR regulatory networks. However, it is shown that intracellular cAMP accumulation is not the only factor contributing to the reduction of ADRB2 mRNA levels [20]. Moreover, we find that GPCR-targeting drugs regulate the transcription of their specific targets (Figure S5). We conclude that in addition to the cross-regulation of G-protein signaling pathways drug target-specific feedback loops are also responsible for the regulation of drug target mRNAs.
In addition to cross-regulation of multiple drugs through the same signaling pathways, promiscuous drugs targeting multiple proteins may cause complex regulatory networks. In order to explore the cross-regulation of drug targets induced by a promiscuous drug, we searched and found that 259 out of 466 total drugs are multi-target drugs and 4 of these drugs act on multiple differentially regulated drug targets upon drug treatment. For example, Podophyllotoxin used in various chemotherapies is known to target both tubulin beta 2C and DNA topoisomerase 2-alpha. The tubulin beta 2C and DNA topoisomerase 2-alpha mRNAs are both down-regulated upon drug treatment in three cell lines (Figure 3, Genes 8,13). Tubulin beta 2C inhibitors induce microtubule depolymerization that leads to the specific down-regulation of tubulin beta 2C mRNAs preventing the translational synthesis and thus the further accumulation of abundant tubulin monomers [26]. Moreover, we found that DNA topoisomerase 2-alpha mRNAs are not down-regulated upon treatment of other tubulin inhibitors (Figure S5). Therefore, we conclude that feedback loops of tubulin beta 2C and DNA topoisomerase 2-alpha are not cross-regulated. Two other examples of multi-target drugs are vorinostat used for the treatment of cutaneous T cell lymphoma and trichostatin A that serves as an antifungal antibiotic. Vorinostat and trichostatin A are considered to be nonspecific histone deacetylase inhibitors. These drugs lead to the up-regulation of histone deacetylase 3 (HDAC3) and down-regulation of histone deacetylase 7 (HDAC7) (Figure 3, Genes 1,5). In this case it is unclear whether there is cross-regulation, although HDAC7 siRNA experiments failed to induce the up-regulation of HDAC3 mRNAs [27], a result that is disfavoring the cross-regulation.
While the above cases only confirm literature reports in other cell lines or tissues, we also identified new cases of drug-induced expression regulation of drug targets. The significant novel findings are the inhibitor-induced down-regulation of calmodulin 1, DNA topoisomerase 2-alpha and up-regulation of endoplasmin, lanosterol 14-alpha demethylase and cAMP-specific phosphodiesterase 4D (Figure 3, Genes 9,8,2,7,10). Lanosterol 14-alpha demethylase is actually an off-target of antifungal drugs that bind the mammalian version with lower affinity than the fungal lanosterol 14-alpha demethylase. Probably, the up-regulation of the mammalian lanosterol 14-alpha demethylase compensates for the undesired inhibition and modulates the adverse effects. On the contrary, we observed a feedback loop that accelerates the down regulation of calmodulin 1 mRNA induced by calmodulin inhibitors. Calmodulin targeting drugs can provide a rapid and effective therapeutic effect, while at the same time small variations of drug concentrations can increase adverse effects. Therefore, it would be interesting to study further the functional effects upon target inhibition of lanosterol 14-alpha demethylase, endoplasmin and calmodulin 1 to elucidate the roles of feedback loops in drug mode of action and adverse effects.
Drug-induced target regulation might be implicated in tolerance development and thus identifying potential target regulation should be an integral part of drug discovery to prevent failures in later stages of clinical trials. For example, we have observed the inhibitor-induced up-regulation of ADRB2 and thymidylate synthetase (TYMS)(Figure 3, Genes 3,11) [28]. TYMS is an essential enzyme for DNA replication/repair and an important drug target in cancerous cells. Indeed, it has been shown that inhibitor-induced TYMS over-expression obstructs the clinical efficiency by inducing tumor drug resistance [29]. In addition to over-expression, down-regulation of drug targets upon agonist treatment may also cause treatment tolerance as observed for ADRB2 long-acting agonist treatment. ADRB2 is a therapeutic target activated to treat the symptoms of asthma. We observe the agonist-induced up-regulation of ADRB2 and already in 2005, the FDA warned patients that ADRB2 might be down-regulated (desensitization) and be unresponsive for asthma treatment due to long-acting agonist exposure [20], [30]. Thus, robustness in biological systems could prevent the applicability of the long-term treatments via positive/negative feedback loops of the drug target affecting the clinical efficiency of drugs in trial and on the market. Drug-induced regulation of drug targets can thus be linked to tolerance development, which restricts the efficiency of clinical treatments where the drug concentration is limited to avoid an excess of adverse drug reactions.
Taken together, we have identified drug-induced differential regulation of drug targets. Due to the limited signal to noise ratio in the data at hand, the identified 8% of all drug targets that show feedback loops has to be seen as a lower limit, i.e. target-regulation appears as a wide-spread biological phenomenon that has to be taken into account during drug development.
{"AnatomicalEntity": ["intracellular", "tissues", "cell", "cancerous cells", "tumor", "DDT1 MF-2 smooth muscle cells", "cellular", "293-EBNA human embryonic kidney cells", "cutaneous T cell lymphoma", "microtubule", "cell lines", "cancer cell lines"]}