Article | Open | Published:

Transcriptomic response of breast cancer cells to anacardic acid


Anacardic acid (AnAc), a potential dietary agent for preventing and treating breast cancer, inhibited the proliferation of estrogen receptor α (ERα) positive MCF-7 and MDA-MB-231 triple negative breast cancer cells. To characterize potential regulators of AnAc action, MCF-7 and MDA-MB-231 cells were treated for 6 h with purified AnAc 24:1n5 congener followed by next generation transcriptomic sequencing (RNA-seq) and network analysis. We reported that AnAc-differentially regulated miRNA transcriptomes in each cell line and now identify AnAc-regulated changes in mRNA and lncRNA transcript expression. In MCF-7 cells, 80 AnAc-responsive genes were identified, including lncRNA MIR22HG. More AnAc-responsive genes (886) were identified in MDA-MB-231 cells. Only six genes were commonly altered by AnAc in both cell lines: SCD, INSIG1, and TGM2 were decreased and PDK4, GPR176, and ZBT20 were increased. Modeling of AnAc-induced gene changes suggests that AnAc inhibits monounsaturated fatty acid biosynthesis in both cell lines and increases endoplasmic reticulum stress in MDA-MB-231 cells. Since modeling of downregulated genes implicated NFκB in MCF-7, we confirmed that AnAc inhibited TNFα-induced NFκB reporter activity in MCF-7 cells. These data identify new targets and pathways that may account for AnAc’s anti-proliferative and pro-apoptotic activity.


A number of plants produce anacardic acid (AnAc) which is a mixture of 6-alkylbenzoic acid congeners1. Previously, we showed that a specific congener, AnAc 24:1n5, acts as a concentration-dependent mixed agonist/antagonist of estrogen receptor (ERα)-induced proliferation and transcription and inhibits ERα-estrogen response element (ERE) binding by interacting with the DNA binding domain (DBD), thus acting as a nuclear receptor alternate site modulator (NRAM)2. AnAc 24:1n5 also inhibited MDA-MB-231 triple negative breast cancer (TNBC) cell proliferation, although at a higher IC50 and via an unknown mechanism2. We reported that the expression of endogenous estrogen-regulated genes, i.e., TFF1, CCND1, and CTSD, was inhibited by AnAc 24:1n5 in breast cancer cell lines2. However, because AnAc affects multiple molecular targets (reviewed in3) and since we detected an ERα-independent inhibition of TNBC cell proliferation by AnAc 24:1n5, we suspect additional unknown molecular targets, independent of ERα, are altered by AnAc in these cells. Gene expression profiling is used in drug development to understand and predict the activity of novel therapeutic compounds in pre-clinical settings. Transcriptome analysis using bioinformatics tools gives an overview of biological processes and pathways affected by a ‘drug’; thus providing new insights about the potential cellular targets and mechanisms of action of that ‘drug’. Identification of such targets using RNA-seq would be beneficial in identifying AnAc-regulated pathways and targets in both luminal A breast cancer and in TNBC which primarily affects premenopausal women with a predominance in women of African and Hispanic ancestry4,5.

In previous work using RNA-seq analysis of AnAc-treated MCF-7 and MDA-MB-231 cells we identified 69 and 37 AnAc-regulated miRNAs, respectively6. MetaCore enrichment analysis revealed that no miRNAs were downregulated by AnAc in both cell lines while two miRNAs were increased by AnAc in both cell lines: miR-612 and miR-20b with the common gene ontology (GO) process “cellular response to inorganic substance”6.

The goal of the study reported here was to use RNA-seq to identify alterations in mRNA target transcript levels in the same representative ERα-positive and TNBC breast cancer cell lines after AnAc 24:1n5 treatment. AnAc up- or down- regulated divergent and common mRNA transcripts in MCF-7 and MDA-MB-231 cells. These results provide an overview of the processes and targets of AnAc in representative ERα+ and TNBC breast cancer cells in vitro.

Results and Discussion

RNA-seq analysis of AnAc-regulated RNAs

To identify primary transcriptome changes in AnAc 24:1n5 (hereafter AnAc)-treated MCF-7 (ERα+) and MDA-MB-231 TNBC cells, cells were treated with the previously established IC50 concentrations of AnAc for MCF-7 (13.5 µM) and MDA-MB-231 (35.0 µM)2 prior to RNA isolation6. We note that AnAc has no overt effect on the viability of either cell line or cellular bioenergetics at that time2,7. The treatment duration was selected since primary gene targets have been identified in MCF-7 cells with a 6 h treatment8 and because the goal was to identify early transcriptome changes in response to AnAc in each cell line. For target analysis, only transcripts that showed a log2 fold-change greater than 1 (or −1 for repressed mRNAs) were included9.

Differentially expressed genes (DEGs) were identified for four pairwise comparisons (MCF-7 control vs. MCF-7 AnAc-treated; MDA-MB-231 control vs. MDA-MB-231 AnAc-treated; MCF-7 and MDA-MB-231 control vs. MCF-7 and MDA-MB-231 AnAc-treated; MDA-MB-231 control and AnAc-treated vs. MCF-7 control and AnAc treated) using cufflinks and cuffdiff 6,10,11. Table 1 shows the number of DEGs in each comparison. More genes were significantly changed in response to AnAc in MDA-MB-231 cells vs MCF-7 cells (Fig. 1). These data suggest selectivity of AnAc-induced transcriptional perturbations between these cell lines.

Table 1 Differentially expressed genes (DEGs).
Figure 1

Enrichment analysis of RNA-seq data. Differentially expressed genes were identified in pairwise comparisons: MCF7 AnAc vs. MDA-MB-231 AnAc using the Tuxedo Suite of programs including Cufflink-Cuffdiff2. The Venn diagrams show the number of common and differentially expressed genes significantly downregulated (A) and upregulated (B). Pathway analysis was performed using GeneGo Pathways Software (MetaCore). The pathways identified for each comparison are listed in the order provided by MetaCore analysis.

DEGs for each comparison were used for further analysis of enriched GO:BP12,13 and KEGG Pathways14 using CategoryCompare15. Table 2 and Supplementary Tables 1 and 2 list the top enriched GO:BP terms with p-value cutoff 0.001 for each of the four pairwise comparisons of DEGs while Supplementary Table 3 lists the top enriched KEGG pathways identified in AnAc-treated vs. control for each cell line. None of the top five GO terms for DEGs from (MCF-7 control and AnAc-treated) vs. (MDA-MB-231 control and AnAc-treated) (Supplementary Table 1) overlapped with those previously identified using Agilent microarrays to identify differential gene expression between non-treated MCF-7 vs. MDA-MB-231 cells16. The difference in these results may reflect changes in the rank order of differentially expressed genes of cell lines treated with AnAc or may reflect a difference in methodological approaches to analyze transcriptomes.

Table 2 Top enriched GO:BP terms for DEGs from MCF-7 and MDA-MB-231 AnAc vs. MCF-7 and MDA-MB-231 control using CategoryCompare.

For MCF-7 cells, only one GO term was identified for DEGs in AnAc cells: “Cellular response to acid chemical” with four genes in that pathway (Supplementary Table 2). In contrast, for AnAc-treated MDA-MB-231 cells five GO:BP terms were identified with 15–27 genes/GO:BP term and GO:BP terms related to the endoplasmic reticulum (ER) stress (ERS) and the unfolded protein response (UPR) as well as cholesterol and sterol biosynthetic responses (Supplementary Table 2). Since AnAc 24:1n5 inhibits cell proliferation in both cell lines after 24 h (18 h longer than the treatment here)2 these gene changes/pathways suggest mechanisms by which AnAc achieves its anti-proliferative effects differ between the two cell lines. Cholesterol and sterol biosynthesis take place in the ER and thus, the identification of these GO terms suggest that AnAc targets the ERS signaling pathway that is a survival factor in cancer17,18. Others reported that targeting MAPK-activation of the ERS response in TNBC cells, including MDA-MB-231, induces apoptosis19. We reported that 24 h treatment with 10–25 µM AnAc stimulates basal oxygen consumption and proton leak and reduces mitochondrial reserve in both MCF-7 and MDA-MB-231 cells, hallmarks of the apoptotic response7.

MetaCorenetwork enrichment analysis of the DEGs identified in AnAc-treated MCF-7 vs. MDA-MB-231 cells identified both cell line-specific and common enrichment pathways (Fig. 1) and GO processes (Supplementary Figure 1). MetaCore shortest direct pathways analysis of AnAc-regulated genes in MCF-7 cells suggests that increased JNK (MAPK8–10) is associated with higher ERV6 (TEL1) and decreased STIM1 associating with reduced EGR1 that associates with lower TGM2 (Supplementary Figure 2). Further discussion of these genes follows.

AnAc-downregulated genes in common to MCF-7 and MDA-MB-231 cells

AnAc treatment downregulated three genes (SCD, INSIG1, and TGM2) in both MCF-7 and MDA-MB-231 cells (Fig. 1). Hence, we would expect this downregulation to be ERα-independent. The third of the top 10 common downregulated pathways was “Regulation of lipid metabolism” (Fig. 1), which relates to SCD and INSIG1. The top GO processes identified were “response to fatty acid, triglyceride metabolic process”, and “regulation of steroid metabolic processes” (Supplementary Figure 1). Aberrant activation of lipid biosynthesis is involved in the early stages of breast cancer development (reviewed in20). Further, cell migration, invasion, and angiogenesis are all associated with increased SREBP-coordinated lipid biosynthesis20, results which may help to explain the more general, i.e., ERα-independent, breast cancer cell inhibition demonstrated by AnAc. We modeled the roles of the three AnAc-downregulated genes (SCD, INSIG1, and TGM2) and one of the three commonly AnAc-upregulated genes (PDK4) in lipid biosynthesis in Fig. 2. Each gene is discussed individually below. Supporting this model, ginkgolic acid (an AnAc congener from Ginkgo biloba) that suppresses pancreatic cancer cell viability, colony formation, migration, and invasion while increasing apoptosis, was reported to inhibit expression of enzyme targets involved in lipid biogenesis21.

Figure 2

Modeling roles of four AnAc-regulated genes in MCF-7 and MDA-MB-231 cells. AnAc treatment reduced SCD, INSIG1, and TGM2 and increased PDK4 in both MCF-7 and MDA-MB-231 cells. PDK4 phosphorylates and inhibits pyruvate dehydrogenase (PDH), which would be expected to decrease acetyl CoA. SCD-1 (SCD, stearoyl-CoA desaturase-1) is a key rate-limiting enzyme for the synthesis of monounsaturated fatty acids. Endogenously synthesized monounsaturated fatty acids are metabolized by diacylglycerol acyltransferase (DGAT) to synthesize triglycerides (TG) or by acyl-CoA:cholesterol acyltransferase (ACAT) for cholesterol esters (CE) synthesis. INSIG1 anchors sterol regulatory element-binding protein (SREBP)/cleavage-activating protein (SCAP) in the endoplasmic reticulum (ER) membrane. SREBP-1 upregulates SCD and FASN transcription. TGM2 (transglutaminase 2) has various functions described in the text including activation of NFκB, which in turn regulates TGM2 expression. NFκB and proinflammatory cytokines, elevated in breast cancer, activate ER stress and SREBP-1.

AnAc reduced SCD (stearoyl-CoA desaturase, also called SCD1) transcript levels in both MCF-7 and MDA-MB-231 cells, suggesting an ERα-independent effect. However, different mechanisms may be responsible for SCD downregulation by AnAc in each cell line. For example, E2 stimulates SCD transcription by increasing transcription of SREBP-1C in MCF-7 cells22; thus, it is possible that the ERα-dependent NRAM activity of AnAc2 in MCF-7 contributes to SCD inhibition. Whereas an ERα-independent activity in MDA-MB-231 cells (or both cell lines) may be involved in the observed decrease in SCD transcript expression. SCD is anchored in the ER where it catalyzes the production of monounsaturated fatty acids (MUFAs, primarily oleic acid, oleate and palmitoleate) that are essential for membrane biogenesis in cancer cell proliferation20. Interestingly, oleic acid promotes proliferation in a number of breast cell lines, including MCF-7 and MDA-MB-23123. Importantly, oleic acid was also shown to inhibit apoptosis while palmitic acid (a precursor of oleic acid, Fig. 2) increased apoptosis in MDA-MB-231 cells24. SCD was also one of the most downregulated genes in primary breast cancer cells treated with 5 µM curcumin, another anticancer phytochemical25. SCD protein, not mRNA, was inhibited by cis−9, trans-11 and trans−10, cis−12 conjugated linoleic acid (CLA) isomers (45 μM) in MDA-MB-231 cells, but the mechanism was not identified26. A recent study demonstrated that SCD is essential for viability in three out of the four TNBC cell lines studied, including MDA-MB-231, that showed high sensitivity to SCD depletion27. Localized and systemic SCD deficiency causes ERS by increasing peroxisome proliferator active receptor ϒ (PPARϒ) Coactivator 1α (PGC-1α) and activates UPR (reviewed in28). “Apoptosis and survival: ERS response pathway” was upregulated by AnAc specifically in MDA-MB-231 cells (Fig. 1).

Upregulation of SCD in B16F10 mouse melanoma cells contributed to tumor formation and metastasis in vivo and CAY10566, a selective SCD inhibitor (IC50 ~7 nM), reduced lung metastasis in vivo29. That paper reported high SCD was associated with shorter disease free survival (DFS) in skin cutaneous and uveal melanoma, renal clear cell carcinoma, and pancreatic adenocarcinoma29. We used BreastMark30 and KM plotter31 to examine the correlation of SCD transcript expression and DFS in breast tumors (Supplementary Fig. 2). These analyses reveal that high SCD correlates with lower DFS in all breast and luminal A tumors, but does not reach statistical significance in TNBC, perhaps due to a lower number of tumor samples analyzed (Supplementary Fig. 2C). While the mechanism of AnAc inhibition of SCD expression reported here is unknown, the SCD promoter binds and is upregulated by AP1, C/EBPα, LXR, TR, SREBP1, NF1, NFY, SP1, C/EBPα, PPARα and PPARγ32, possible targets of AnAc action. Although 13 miRNAs were predicted to target the 3-UTR33, few have been experimentally validated. miRNAs downregulating SCD by direct interaction with its 3′UTR include miR-125b34, miR-199a-3p35, miR-212-5p36, and miR-27a37. None of these miRNAs were upregulated by AnAc with a 6 h treatment of MCF-7 or MDA-MB-231 cells6. Further studies will be necessary to delineate the mechanism for SCD downregulation in both cell lines.

AnAc inhibited INSIG1 (Insulin Induced Gene 1) expression in MCF-7 and MDA-MB-231 cells (Fig. 1), again implying an ERα-independent mechanism. INSIG-1 anchors sterol regulatory element-binding protein (SREBP)/cleavage-activating protein (SCAP) in the ER membrane prior to its glycosylation or cholesterol binding which reduces its affinity to INSIG-1 allowing movement of SCAP/SREBP to the Golgi. Subsequent proteolytic activation of SREBP leads to its nuclear localization and upregulation of genes important in the uptake and synthesis of fatty acids, cholesterol, and phospholipids38. INSIG1 is a direct target of SREBP39. Supporting a role for INSIG1 in cell viability, knockdown of INSIG1 inhibited ZR-75-1 and MDA-MB-468 breast cancer and MCF-10A immortalized normal breast epithelial cell viability40. A methanol extract of black cohash (40 µg/ml) first stimulated (6 h) and then inhibited (24 h) INSIG1 transcript expression in MDA-MB-453 breast cancer cells41. In contrast, gemcitabine, a nucleoside analog used to treat breast cancer, stimulated INSIG1 expression in MCF-7 and MDA-MB-231 cells with MCF-7 cells showing higher INSIG1 than MDA-MB-231 cells42.

AnAc reduced TGM2 (transglutaminase 2) transcript levels in MCF-7 and MDA-MB-231 cells. TGM2 is a tumor and stem cell survival factor in breast and other cancers43,44. TGM2 has intrinsic and Ca2+dependent kinase activity and phosphorylates target proteins involved in cell proliferation and/or apoptosis45. TGM2 results in constitutive activation of NFκB via the noncannonical pathway, creating a feedback loop where NFκB upregulates TGM2 expression46. The increased NFκB and TGM2 results in drug-resistance and increased cancer stemness47. Knockdown of TGM2 in MDA-MB-231 cells reversed epithelial to mesenchymal transition (EMT) and stimulated doxetaxel-induced apoptosis48. Overexpression of TGM2 in MCF-10A cells inhibited basal oxygen consumption rate (OCR) and stimulated glycolysis as measured by extracellular acidification (ECAR) whereas TGM2 knockdown in MCF-7 cells had the opposite effect49. Interestingly, we reported that AnAc stimulates basal OCR in both MCF-7 and MDA-MB-231 cells7, a result correlating with the reduction in TGM2 transcript detected here.

Genes uniquely inhibited by AnAc in MCF-7 cells downstream of NFκB

Of the 44 gene transcripts identified as downregulated by AnAc in MCF-7 cells, 19 were matched to genes, 12 were protein-coding genes, and 13 are unannotated (Table 2). The canonical network analysis of the 19 genes downregulated by AnAc in MCF-7 generated by pathway enrichment analysis in MetaCore is shown in Fig. 3. The pathways and GO processes identified by MetaCore in the AnAc-downregulated genes in MCF-7 cells are shown in Fig. 1 and the pathway enrichment analysis of networks associated with DEGs in MCF-7 is shown in Supplementary Fig. 3. The top network for AnAc-downregulated genes centers on Acyl-CoA synthetase, ACSL6, APBECH3, CDIP, and EGR1 (Supplementary Fig. 4). MetaCore transcription factor network analysis identified 30 transcription factors in the DEGs in MCF-7 cells including CREB, p53, ESR1 (ERα), and RelA/NFκB (Supplementary Fig. 5). AnAc was previously reported to inhibit NFκB activation in KBM-5 cells50.

Figure 3

AnAc downregulated genes canonical pathway map for MCF-7 cells generated by MetaCore. Networks identified were: 1) SCD, LXRα, Insulin, Norepinephrine, IGF-1; 2) miR-22, CDIP; 3)MALL, NCOA2, E2 cytoplasm, hyaluronic acid extracellular, ESR1 (nuclear); 4) MRLC, CaMK II, STIM1, CARACM1, Ca; 5) uPAR, fibrinogen, BDKRB2, C2b, alpha-X/beta-2 integrin. All objects with the blue circle are downregulated by AnAc. The lines are connections that have been documented in the literature with green lines indicating canonical pathways.

AnAc inhibits tumor necrosis factor α (TNFα)-stimulated NFκB in MCF-7 cells

The pathway enrichment analysis of networks associated with downregulated genes in AnAc-treated MCF-7 cells (Supplementary Fig. 6) suggests involvement of NFκB. MCF-7 cells have low NFκB activity51. We examined if AnAc would inhibit TNFα-stimulated NFκB luciferase reporter activity in transiently transfected MCF-7 cells (Fig. 4). Consistent with the DEGs identified in RNA-seq analysis of AnAc-treated MCF-7 cells and with the AnAc inhibition of TGM2 that stimulates NFκB expression and activity (modeled in Fig. 2), AnAc inhibited TNFα-stimulated NFκB luciferase reporter activity (Fig. 4). We reported that AnAc inhibits NFκB target gene CCND1 expression in MCF-7 cells2 and AnAc reduced CCND1 in MDA-MB-231 cells (Supplementary Table 5), results in agreement with the antiproliferative activity of AnAc.

Figure 4

AnAc inhibits TNFα-induced NFκB luciferase reporter activity in transiently transfected MCF-7 cells. MCF-7 cells were transfected with a NFκB response element luciferase reporter and a Renilla reporter for 48 h. Cells were treated with 10 ng/ml TNFα EtOH (vehicle control, open bar, and 1–40 µM AnAc for 6 h before performing dual luciferase assay. Values are the average of three separate wells in one experiment ± SEM. *p < 0.01 versus EtOH control (open bar).

Genes downregulated in MCF-7 cells by AnAc

We hypothesized that the ERα antagonist activity of AnAc6 might be involved in the decrease of selected gene transcripts in AnAc-treated MCF-7 cells and not in MDA-MB-231 cells. Based on our data and the literature reports cited below, we suggest that this hypothesis may support the downregulation of ZNF462, MALL (BENE), and EGR1 transcript expression by AnAc in MCF-7 cells.

AnAc inhibited ZNF462 expression in MCF-7 cells (Table 3). ZNF462 was identified as a putative target of miR-210 which is upregulated by HIF-1α in pancreatic cancer52. A search in the NURSA Transcriptomine database53 revealed that both E2 and 4-OHT increase transcript levels of ZNF462 in MCF-7 cells. Thus, the NRAM activity of AnAc with ERα2 may be responsible for the observed decrease in ZNF462 expression.

Table 3 Genes significantly inhibited in MCF-7 cells after 6 h. of 13.5 µM anacardic acid (AnAc) treatment.

AnAc inhibited MALL (BENE) expression in MCF-7 cells (Table 3). MALL is a member of the proteolipid family that localizes in glycolipid- and cholesterol-enriched membrane rafts and it interacts with CAV-1. A search in the NURSA Transcriptomine database53 revealed that both E2 (100 nM, 12 h) and fulvestrant (100 nM, 12 h) inhibited MALL transcript expression in MCF-7 cells, a result that seems contradictory for an ERα-mediated response, but not one mediated by GPER1 that binds E2 and fulvestrant as agonists with Kd = 3–6 and 10–100 nM, respectively54. No references regarding the regulation or function of MALL in breast or other cancers were found in PubMed.

AnAc inhibited EGR1 expression in MCF-7 cells (Table 3). EGR1 is a member of the immediate-early gene group of transcription factors whose transcription is rapidly increased by E2 in MCF-7 cells55 and deleted in ERα- breast tumors where it is thought to be a tumor suppressor56. 4-OHT suppresses E2-simulated EGR1 transcription in MCF-7 cells57. Thus, the repression of EGR1 expression by AnAc in MCF-7 cells may reflect its ability to block E2-induced gene transcription by inhibiting ERα-DNA binding2.

AnAc had the greatest inhibitory activity on RBMS1 (also called MSSP, MSSP-1) transcript expression in MCF-7 cells (Table 3). RBMBS1/MSSP1 has been implicated in DNA replication, gene transcription, cell cycle progression and apoptosis58. Increased RBMBS1/MSSP-1 was associated with cisplatin resistance in ovarian cancer cells59. A search in the NURSA Transcriptomine database53 revealed that E2 inhibited RBMS1 expression in MCF-7 cells. Another dietary anticancer agent, bromelain, a mixture of proteolytic enzymes found in pineapples (reviewed in60) also downregulated RBMS1 expression in MCF-7 cells61. Thus, two dietary phytochemicals inhibit RBMS1 expression in MCF-7 cells. RBMS1 is downregulated by miR-38362, but miR-383 was not regulated by AnAc treatment of MCF-7 cells6. It is unknown how RBMS1 downregulation by AnAc in MCF-7 cells contributes to AnAc’s antiproliferative/pro-apoptotic activity in these cells.

No information about RNU4-2, TSPAN33 (PEN, an alternative protein name), GATA6-AS1, RN7SL389P, CDIP1, RNU5B-1, or RNU4-1 in breast or other cancers was found in PubMed. A search in the NURSA Transcriptomine database53 revealed no reports of RNU4-2, TSPAN33, GATA6-AS1, RN7SL389P, RNU5B-1, or RNU4-1, in mammary gland/human transcriptome data sets curated in that collection. However, CDIP1 was repressed by 30 pM E2 in MCF-7 cells with 48 h of treatment whereas ERα knockdown increased CDIP1 expression in MCF-7 cells63, implying a possible role for E2-ERα in reducing CDIP1 transcript levels. CDIP is a key downstream effector of p53‐dependent apoptosis64. In contrast to our findings with AnAc, phytochemicals (xanthones) from Garcinia increased CDIP transcript expression in NCI-H1650 lung adenocarcinoma cells which correlated with antiproliferative activity65.

AnAc inhibited STIM1 and LAM2 expression in MCF-7 cells (Table 3). STIM1 is an ER Ca2+ sensor that triggers Ca2+ influx by activating store-operated calcium entry and is involved in the TGF-β-induced suppression of cell proliferation66. Inhibition of STIM1 expression by TGF-β in MCF-7 and MDA-MB-231 cells inhibited cell proliferation66. LAMC2 is a subunit of the basement membrane protein laminin-332 that interacts with CD44 on the membrane of breast cancer cells, stimulates cell migration, and is regarded as a typical cancer invasion marker corresponding with poor patient prognosis67. Thus, the inhibition of LAMC2 by AnAc provides a potential mechanism for the anti-proliferative activity of AnAc in breast cancer cells in vitro.

AnAc inhibited SAMD9 expression in MCF-7 cells (Table 3). SAMD9 has antiproliferative activity in H1299 lung adenocarcinoma cells in vitro and in tumor xenografts in vivo68. Further, SAMD9 expression is lower in breast tumors than normal breast69. A search in the NURSA Transcriptomine database53 revealed that knockdown of ERα coactivator SRC-1/NCOA1 increased SAMD9 in LY2 endocrine-resistant cells derived from MCF-7 cells70. However, we did not detect any change in NCOA1 transcript expression in our RNA-seq data of AnAc-treated MCF-7 cells. The inhibition of putative tumor suppressor SAMD9 expression by AnAc seems to conflict with AnAc’s anti-cancer activity in MCF-7 cells. However, this may be a time-dependent effect since we analyzed gene expression after only 6 h of AnAc treatment.

LncRNA MIR22HG is inhibited by AnAc in MCF-7 cells

AnAc reduced MIR22HG lncRNA transcript in MCF-7 cells (Table 3). PTGES3 is a long non-coding RNA (lncRNA) and is the host gene for miR-22 that functions as a tumor suppressor by repressing CDK6, CCNA2, SP1, and PTGES3 (p23) (reviewed in71). Chemical stressors (24 h) 100 µM cycloheximide, 100 µM hydrogen peroxide, 1 µM cadmium nitrate, or 100 nM arsenic trioxide stimulated MIR22HG expression in human-induced Pluripotent Stem Cell (hiPSC) line 201B772. Ischemia increased MIR22HG in a mouse hind limb ischemia model73 and MIR22HG was downregulated in human lung adenocarcinomas74. miR-22 directly targets ESR1 (ERα)75. Further studies will be needed to address the role of inhibition of MIR22HG in downstream effects of AnAc in MCF-7 cells.

Genes downregulated by AnAc in MDA-MB-231 cells

AnAc inhibited the expression of 378 genes in MDA-MB-231 cells (Table 1, Supplementary Table 4). The top 10 downregulated pathways identified by MetaCore enrichment analysis is shown in Fig. 1. Not surprisingly since AnAc inhibits MDA-MB-231 cell proliferation2, the top two pathways involve cell cycle regulation. Intriguingly, third on the list is “Transcription: Ligand-dependent activation of the ESR1/SP pathway”. Although MDA-MB-231 cells are ERα-, they were reported to express ERβ protein76. Recent studies indicate that activation of peroxisome proliferator activated receptor (PPAR) δ and inhibition of PPARγ stimulate ERα expression in ERα- mouse mammary tumors77,78. We did not detect ESR1 or any of the PPAR genes among those regulated by AnAc in MDA-MB-231 cells. A PubMed search for RNA-seq studies in MDA-MB-231 cells treated with an ‘anti-cancer drug’, found only one report79. A comparison of the genes regulated by the ruthenium-derived compound NAMI-A anti-metastasis compound in MDA-MB-231 cells and AnAc-regulated DEGs identified three common genes: HES1, RIPK4 (downregulated by AnAc, upregulated by NAMI-A) and SPRY1 (upregulated by AnAc and NAMI-S) (Supplementary Table 5 and79). These data confirm that anticancer compounds work through distinct processes in MDA-MB-231 cells.

Network analysis of the top 50 downregulated genes in AnAc-treated MDA-MB-231 cells using Dijkstra’s shortest paths algorithm calculating the shortest direct paths with 2 steps. This analysis shows connections of a number of downregulated genes to each other and to p53, ERR1, and ARF-2/c-Jun (Fig. 5). The function of the top 18 genes downregulated by AnAc in MDA-MB-231 cells, based on statistical evaluation, are summarized in Supplementary Table 6.

Figure 5

MetaCore network analysis of the top 50 genes downregulated by AnAc in MDA-MB-231cells. Shown is Dijkstra’s shortest paths algorithm calculating the shortest directed paths between the top 50 genes downregulated by AnAc in MDA-MB-231 with two steps in the path and genes fitting this model were arranged by cellular location. The network of up-regulated genes centers on p53, ERR1, and ART-2/c-Jun. The MetaCore legend is shown at the right.

Three genes upregulated by AnAc in both MCF-7 and MDA-MB-231 cells

AnAc stimulated ZBTB20, PDK4, and GPR176 expression in MCF-7 and MDA-MB-231 cells (Fig. 1). We would expect these increases to be ERα-independent. MetaCore analysis identified only one pathway in common for these three upregulated genes: “Transcription Sirtuin6 (SIRT6) regulation and function”. SIRT6, an established chromatin regulatory protein, is a tumor suppressor that has three enzymatic activities: deacetylase, ADP-ribosyltransferase, and de-fatty-acylase80. SIRT6 overexpression inhibited breast cancer stem cell biogenesis in cells with a PI3K mutation and murine PyMT mammary tumor progression in vivo81. Hence if ZBTB20, PDK4, and GPR176 indeed stimulate SIRT6 function, e.g., by increasing transcription, stabilizing the protein, or increasing its activity by increasing NAD+ and free FAs80, this could provide a mechanism by which AnAc inhibits MCF-7 and MDA-MB-231 viability. Further study will be needed to examine AnAc-mediated metabolic changes in these cells. Figure 6 is model of the potential cellular functions of the three AnAc-upregulated genes common to MCF-7 and MDA-MB-231 breast cancer cells.

Figure 6

Modeling the potential cellular roles of three genes upregulated by AnAc in MCF-7 and MDA-MB-231 cells. As indicted in Fig. 1, MetaCore analysis identified only one pathway for these three common upregulated genes: Transcription Sirtuin6 (SIRT6) regulation and function. AnAc increased GPR176 in both cell lines. GPR176 is a GZ-coupled receptor that decreases cAMP, which would be expected to decrease PKA, both of which would maintain SIRT6 protein by preventing its ubiquitinylation and proteasome-mediated degradation (dashed arrow,+). AnAc increased ZPTB20, a transcriptional repressor that inhibits FOXO1 transcription. SIRT6 deacetylates FOXO1 which decreases its nuclear localization, hence reducing glycolysis, pentose phosphate pathway, lipid metabolism, and cancer stem cell biogenesis. AnAc increased PDK4 in both MCF-7 and MDA-MB-231 cells. PDK4 phosphorylates and inhibits pyruvate dehydrogenase (PDH), which would be expected to decrease acetyl CoA, possibly inhibiting the TCA cycle, oxidative phosphorylation, and FA biosynthesis. Taken together, the observed gene changes are commensurate with the observed ability of AnAc to inhibit the proliferation of these two breast cancer cell lines.

ZBTB20 (also called DPZF, HOF, and ZNF288) is a member of the POK (POZ and Krüppel) family of transcriptional repressors82. ZBTB20 is upregulated in HCC82 and non-small cell lung cancer (NSCLC)83, but downregulated in primary prostate cancer samples and metastases84. ZBTB20 was among the genes downregulated in primary breast tumors from patients treated with aromatase inhibitors, either letrozole or anastrozole for 2 weeks prior to surgery85. In NSCLC, ZBTB20 downregulated FOXO1 by binding to its 5′ promoter83. Although FOXO1 was not among the AnAc-regulated genes in MCF-7 cells, FOXO1 was upregulated by AnAc in MDA-MB-231 cells (Supplementary Table 5), a result opposite of what might be anticipated if an AnAc-mediated increase in ZBTB20 inhibits FOXO1 expression. However, SIRT6 deacetylates FOXO1 leading to its export from the nucleus to the cytoplasm and hence inhibiting its transcriptional activity, as well as glycolysis, the pentose phosphate pathway, lipid metabolism, and breast cancer stem cell biogenesis81,86. ZBTB20 was recently identified as a tumor suppressor that cooperates with PTEN to prevent malignant progression in prostate cancer84. We did not detect PTEN among the AnAc-regulated genes in either cell line. Together these studies suggest that an increase in tumor suppressor ZBTB20 may contribute to the anti-proliferative activity of AnAc in breast cancer cells.

When active, PDK4 phosphorylates and inhibits pyruvate dehydrogenase (PDH) which converts pyruvate to acetyl-CoA for the TCA cycle or fatty acid biosynthesis. Thus, an increase in PDK4 would decrease glucose carbon flux into the TCA cycle and lipid biosynthesis, consistent with the three common downregulated genes modeled in Fig. 2. Estrogen-related receptor gamma (ERRγ, ESRRG) is a major activator of PDK487,88, but we did not find ESRRG among the AnAc-regulated genes in either cell line. PDK4 expression was higher in TAM-resistant MCF-7 cells than parental MCF-7 cells and siPDK4 sensitized the cells to growth inhibition by fulvestrant89. Increased PDK4 results in an increase in mitochondrial ROS88 and ERS90, findings commensurate with MetaCore-identified upregulated pathway 2: “Apoptosis and Survival: ERS response” in MDA-MB-231 cells (Fig. 1).

AnAc increased GPR176, orphan G-protein coupled receptor (GPCR), in both MCF-7 and MDA-MB-231 cells. GPR176 is an evolutionarily conserved, vertebrate class A orphan GPCR that acts in a ligand-independent manner and can repress adenyl cyclase91. A sequence-structure based alignment of known GPCRs to identify putative ligand associations for orphan GPRs posited free fatty acids as ligand for GPR17692. This association raises an interesting speculation that AnAc may activate GPR176. Since an increase in active GPR176 would be expected to decrease cAMP, and hence decrease active PKA, we wondered if cAMP regulates SIRT6. Indeed an increase in cAMP-activated PKA reduced SIRT6 by promoting its ubiquitin-proteasome-mediated degradation93. This is modeled in Fig. 6. Taken together, the observed common gene changes in response to AnAc correspond with the observed ability of AnAc to inhibit the proliferation of these two breast cancer cell lines.

Genes uniquely increased by AnAc in MCF-7 cells

Of the 36 gene transcripts increased by AnAc in MCF-7 cells (Table 1, Fig. 1), 12 were protein-coding genes that were not affected by AnAc in MDA-MB-231 cells (Table 4). Only 2 genes have been reported to be regulated by ERα; hence, both ERα and ERα-independent mechanisms are likely involved in the AnAc-regulated changes in their expression specifically in MCF-7 cells. MetaCore network analysis of these AnAc-upregulated genes identified only one direct interaction between JNK and ETV6 (Supplementary Fig. 7). When analyzed by pathway enrichment using two steps, MetaCore calculated ‘hub proteins’ including Vimentin, JNK, PKA, and GSK3beta (Supplementary Fig. 8).

Table 4 AnAc-regulated lncRNAs in MDA-MB-231 cells.

The mixed agonist/antagonist activity of AnAc in MCF-7 cells2 may play a role in the increase in transcript level of VIM (vimentin), TMC5, a multi-pass membrane protein, ETV6, and MAPK10 (Table 5). This suggestion is based on data in the NURSA Transcriptomine database53 that revealed E2 inhibited VIM, TMC5, ETV6, and MAPK10 expression in MCF-7 cells. We reported that AnAc reduced miR-378g in MCF-7 cells6 and note that miR-378 targets VIM94, suggesting a possible mechanism for the increase in VIM by relief of repression. ETV6 is a dominant-acting cancer gene that appears to be a site of frequent genomic rearrangements in human breast tumors, but is not amplified95. The mechanism for AnAc regulation of ETV6 and MAPK10 will require further investigation.

Table 5 Genes uniquely upregulated by anacardic acid in MCF-7 cells after 6 h of treatment with 13.5 µM.

Genes uniquely increased by AnAc in MDA-MB-231 cells

AnAc uniquely upregulated 503 genes in MDA-MB-231 and not in MCF-7 cells (Table 1, Fig. 1, and Supplementary Table 5). The top 10 upregulated pathways identified by MetaCore enrichment analysis are shown in Fig. 1 and the top 10 GO processes in Fig. 2. Notably, the ERS response pathway was increased by AnAc in MDA-MB-231 cells. Network analysis of the top 50 upregulated genes using Dijkstra’s shortest paths algorithm calculating the shortest direct paths with one step shows connections of a number of upregulated genes to each other and to STAT3, STAT5, NFκB, AP1, and AR in MDA-MB-231 cells (Fig. 7). The top 10 genes upregulated uniquely by AnAc in MDA-MB-231 cells and their roles in the ER stress response are summarized in Supplementary Table 7.

Figure 7

MetaCore network analysis of the top 50 genes upregulated by AnAc in MDA-MB-231cells. Shown is Dijkstra’s shortest paths algorithm calculating the shortest directed paths between the top 50 genes downregulated by AnAc in MDA-MB-231 with one step in the path and genes fitting this model were arranged by cellular location. The network of up-regulated genes shows a number of nodes including c-MYC, C/EBP, XBP1, E4BP4, AP-1, NFκB, STAT3, STAT5, and PIM1.

When cellular stresses perturb energy levels, the redox state, or Ca2+ concentrations, unfolded proteins accumulate triggering the unfolded protein response (UPR) and proteins aggregate, contributing to ERS96. Related to the ERS response, AnAc increased the transcript levels of HSPA5, SLC3A2, IER3, ERRFI1, HERPUD1, PLIN2, MT1X, MYC, PPP1R15A, DDIT3, XBP1, TRIB3, SEC. 24D, DUSP1, GADD45A, AMIGO2, and GDF15 in MDA-MB-231 cells (Supplementary Table 7). The UPR inhibits protein translation, induces expression of chaperones, and exports misfolded proteins to the cytosol for degradation. If the UPR fails to relieve the stress, the function of the UPR switches from promoting cell survival to promoting cell death17,97, which we speculate may be involved in the observed AnAc-mediated inhibition of cell viability and increased apoptosis2.

AnAc regulated lncRNAs in MDA-MB-231 cells

Data from the Encyclopedia of DNA Elements (ENCODE) Project Consortium indicate that the human genome encodes >28,000 long noncoding RNAs (lncRNAs) that are transcribed by RNA pol II, capped and polyadenylated and expressed in a tissue-specific manner (reviewed in98). lncRNAs are involved in regulating numerous biological processes including roles as scaffolds, decoys or signals, cis- and trans- regulation of transcription, antisense interference, imprinting genomic loci, shaping chromosome conformation and allosterically regulating enzymatic activity (reviewed in99). Currently, the rate of lncRNA discovery outpaces lncRNA characterization; thus, relatively few lncRNAs are fully characterized. Long intervening noncoding RNAs (lincRNAs) are a subset of lncRNAs and are transcribed from thousands of loci preferentially found within 10 kb of protein-coding genes (reviewed in100). lncRNAs are involved in cotranscriptional regulation and act to bridge proteins and chromatin.

AnAc significantly downregulated thirteen and upregulated five lncRNAs in MDA-MB-231 cells (Table 4). These changes are likely due to changes in the entire transcriptome in response to AnAc. MetaCore enrichment analysis only included MIR24-2, reflecting the paucity of information included in this database on lncRNAs. The GO Processes and Diseases identified by MetaCore analysis are shown in Supplementary Fig. 9A and B. Only three of the lncRNAs (all downregulated by AnAc) were found in PubMed: CFLAR-AS1, UBL7-AS1, and MIR210HG (Table 4). Future studies will be needed to address the roles of these lncRNAs in mediating MDA-MB-231 cellular responses to AnAc.

qPCR validation of selected changes in AnAc-regulated genes

We selected SCD, STIM1, EGR1, CDIP, INSIG1, MIR22HG, and CPT1A for validation by qPCR. Based on the effect of AnAc on each of these genes detected by RNA-seq, we expected SCD and INSIG1 to be decreased in both MCF-7 and MDA-MB-231 cells while STIM1, EGR1, and CDIP would be inhibited in MCF-7, but not MDA-MB-231 cells and CPT1A would be increased in MDA-MB-231 and not significantly regulated in MCF-7 cells. AnAc inhibited STIM1, CDIP, and MIR22HG transcript expression in both cell lines with a greater effect in MCF-7 than MDA-MB-231 cells (Fig. 8). AnAc inhibited SCD, STIM1, and CPT1A in MCF-7 cells, but not in MDA-MB-231 cells. The inhibitory effect of AnAc on SCR, STIM1, EGR1, MIR22HG, and CPT1A was significantly greater in MCF-7 than MDA-MB-231 cells. In fact, AnAc stimulated CPT1A in MDA-MB-231 cells. Overall, qPCR results generally confirmed the RNA-seq data except that EGR1 was inhibited in both MCF-7 and MDA-MB-231 cells.

Figure 8

qPCR validation of changes in AnAc target genes. MCF-7 and MDA-MB-231 cells were grown in hormone-depleted medium for 48 h prior to 6 h treatment with vehicle control (ethanol EtOH) 13.5 and 35 µM AnAc, respectively. mRNA expression of the indicated genes relative to GAPDH is shown. Bars are the average of 6 samples from two separate experiments. Statistical evaluation was performed by two way ANOVA followed by Bonferroni post hoc test. *p < 0.05 versus the EtOH vehicle control; ** versus the same gene in MCF-7 AnAc.


The major goal of this study was to identify the global effects of AnAc on the RNA transcriptome of two well-characterized cell lines representing luminal A, ERα+ (MCF-7) and TNBC (MDA-MB-231) breast cancer. We have provided the comprehensive mRNA and lncRNA sets for each cell line and defined their common and cell-specific expression. Notably, AnAc regulated more transcripts in MDA-MB-231 than MCF-7 cells. Only three genes were commonly down- and up-regulated, respectively, by AnAc in both cell lines. The cell-specific and common up- and down-regulated genes were characterized using the MetaCore Gene Ontology (GO) enrichment analysis algorithm. Top among the downregulated enrichment pathways were Development, Unsaturated fatty acid biosynthesis, and Immune response in MCF-7 and Cell cycle and Transcription: ligand-dependent activation of the ESR1/SP pathway in MDA-MB-231 cells, respectively. ERα-dependent and independent pathways are suggested to be involved in the AnAc-mediated transcriptome responses. Top among the upregulated enrichment pathways were Neurophysiological process and Immune response: MIF-JAB1 signaling in MCF-7 cells and PDE4 regulation of cyto/chemokine expression in arthritis and apoptosis and survival: Endoplasmic reticulum stress response pathway in MDA-MB-231 cells. Only one pathway was identified for the three common upregulated genes: Transcription Sirtuin6 regulation and function. qPCR confirmed AnAc regulation of seven genes. Our results suggest that AnAc regulates common and different pathways in ERα+ MCF-7 and MDA-MB-231 TNBC cells.



AnAc 24:1n5 was purified to greater than 95% as previously reported2,101. AnAc 24:1n5 was dissolved in ethanol (EtOH); thus, EtOH was used as a vehicle control.

Cell culture and treatments

MCF-7 and MDA-MB-231 cells were purchased from American Type Tissue Collection (ATCC, Manassas, VA) and were used within 9 passages from ATCC. MCF-7 and MDA-MB-231 cells were grown as described previously6 prior to a 6 h treatment with established IC50 concentrations of AnAc 24:1n5: 13.5 µM for MCF-7 and 35.0 µM for MDA-MB-231 cells2.

For mRNA RNA-seq

RNA was isolated from three separate experiments for each cell line and treatment as previously reported6. The Truseq Stranded mRNA kit (Illumina) was used to prepare mRNA libraries from 1 µg total RNA. Libraries were confirmed on the Agilent 2100 Bioanalyzer and quantitated using the Illumina Library Quantification Kit, ABI Prism qPCR Mix from Kapa Biosystems and the ABI7900HT real-time PCR instrument at the University of Louisville Center for Genetics and Molecular Medicine (CGeMM) DNA Sequencing Core Facility. 76 cycle single read sequencing was performed with the 500 High-output v2 (75cycle) sequencing kit on the Illumina NextSeq. 500 instrument. The sequence reads were mapped to the human reference genome, version GRCh37.1 (hg19) using the mapping algorithm tophat11 version 2.0.2. The expression levels were quantified at loci specified by the annotation found within the ENSEMBL release 73 gene description file (Homo_sapiens.GRCh37.73.gtf) using cufflinks version 2.2.1. Contributions to the annotation file from both ribosomal RNA (rRNA) and mitochondrial RNA (mtRNA) were removed from the gtf file prior to use. Differential analyses between the specified conditions was performed using cuffdiff version 2.2.1. The raw data were uploaded in the Gene Expression Omnibus (GEO) database as GSE78011.

Differential Gene Expression Analysis

The analysis was similar to that used to identify miRNAs regulated by AnAc in these cell lines6 and that data analysis pipeline is shown in Supplementary Figure 10. The number of raw reads, number of reads after trimming, and number of reads successfully aligned for each of the samples is provided in Supplementary Table 8. Aligned RNA-seq reads were assembled according to the hg19.gtf annotation file (downloaded from ENSEMBL102) using Cufflinks (version 2.2.1)11. For each comparison, both cufflinks assemblies were merged, and the resulting merged gtf file serves as the transcript input for differential gene expression analysis in Gene Ontology and KEGG pathways (below). For three of the comparisons, a p-value cutoff ≤0.05 was used to determine differential expression. For the MCF-7 vs. MDA-MB-231 comparison, differential genes were determined using a q-value of ≤0.01 and a |FC| ≥2. Differentially expressed genes (DEGs) for each comparison were used for further analysis of enriched Gene Ontology Biological Processes (GO:BP)12,13 and KEGG Pathways14 using categoryCompare15. The Entrez gene ID for each DEG was obtained from the human Entrez IDs database downloaded from NCBI. Tables for the enriched GO:BP and KEGG pathways were generated as text files from four lists of DEGs with a unique Entrez gene ID.

In silico MetaCore network analysis

Pathway and network analysis of differentially expressed genes was performed in MetaCore version 6.27 (GeneGO, Thomson Reuters, New York, N.Y.)103.

Luciferase assay

To analyze NFκB activity, MCF-7 cells were transiently transfected with pGL4.32[luc2P/NF-κB-RE/Hygro] (Promega, Madison, WI) containing five copies of a NFκB response element and pGL4-hRluc-TK (Renilla, Promega) for 48 h and treated with 10 ng/ml TNFα and 0–40 µM AnAc 24:1n5 for 6 h before performing dual luciferase assay (Promega). Firefly luciferase was normalized by Renilla luciferase. Values are the average of three separate wells in one experiment ± SEM.

RNA isolation, RT-PCR and quantitative real-time PCR (qPCR) was performed essentially as described previously in MCF-7 and MDA-MB-231 cells treated with vehicle control (EtOH) or AnAc 24:1n5 (13.5 and 35 µM, respectively) for 6 h6. PCR Primers were synthesized by Integrated DNA Technologies (Coralville, IA) and sequences used were are listed in Supplementary Table 9. GAPDH was used as a reference for normalization104. qPCR was performed in triplicate using ABI Viia 7 (LifeTechnologies). Fold change relative to vehicle-treated, control cells was estimated by the comparative threshold cycle (Ct) method (2−ΔΔCT)105.

Data availability statement

Raw sequencing data files obtained from our analysis are available at GEO: accession number GSE78011. All data analyzed during this study are included in this published article (and its Supplementary Information files).

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Schultz, D. J., Wickramasinghe, N. S. & Klinge, C. M. In Recent Advances in Phytochemistry Vol. 40 (ed J. Romeo) 131–156 (2006).

  2. 2.

    Schultz, D. J. et al. Anacardic acid inhibits estrogen receptor alpha-DNA binding and reduces target gene transcription and breast cancer cell proliferation. Mol. Cancer Ther. 9, 594–605 (2010).

  3. 3.

    Hemshekhar, M., Sebastin Santhosh, M., Kemparaju, K. & Girish, K. S. Emerging Roles of Anacardic Acid and Its Derivatives: A Pharmacological Overview. Basic Clin. Pharmacol. Toxicol. 110, 122–132, (2012).

  4. 4.

    Brewster, A. M., Chavez-MacGregor, M. & Brown, P. Epidemiology, biology, and treatment of triple-negative breast cancer in women of African ancestry. Lancet Oncol 15, e625–634, (2014).

  5. 5.

    Newman, L. A., Reis-Filho, J. S., Morrow, M., Carey, L. A. & King, T. A. The 2014 Society of Surgical Oncology Susan G. Komen for the Cure Symposium: Triple-Negative Breast Cancer. Ann. Surg. Oncol. 22, 874–882, (2015).

  6. 6.

    Schultz, D. J. et al. Genome-wide miRNA response to anacardic acid in breast cancer cells. PLOS ONE 12, e0184471, (2017).

  7. 7.

    Radde, B. N., Alizadeh-Rad, N., Price, S. M., Schultz, D. J. & Klinge, C. M. Anacardic Acid, Salicylic Acid, and Oleic Acid Differentially Alter Cellular Bioenergetic Function in Breast Cancer Cells. J. Cell. Biochem., (2016).

  8. 8.

    Hah, N. & Kraus, W. L. Hormone-regulated transcriptomes: Lessons learned from estrogen signaling pathways in breast cancer cells. Mol. Cell. Endocrinol. 382, 652–664, (2014).

  9. 9.

    Campbell, J. D. et al. Assessment of microRNA differential expression and detection in multiplexed small RNA sequencing data. RNA 21, 164–171, (2015).

  10. 10.

    Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53, (2013).

  11. 11.

    Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protocols 7, 562–578, (2012).

  12. 12.

    Harris, M. A. et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32, D258–261, (2004).

  13. 13.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29, (2000).

  14. 14.

    Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28, 27–30 (2000).

  15. 15.

    Flight, R. M. et al. categoryCompare, an analytical tool based on feature annotations. Front Genet 5, 98, (2014).

  16. 16.

    Wu, Q. et al. Analysis of the miRNA-mRNA-lncRNA networks in ER+ and ER− breast cancer cell lines. J Cell Mol Med 19, 2874–2887, (2015).

  17. 17.

    Hetz, C., Martinon, F., Rodriguez, D. & Glimcher, L. H. The unfolded protein response: integrating stress signals through the stress sensor IRE1alpha. Physiol. Rev. 91, 1219–1243, (2011).

  18. 18.

    Chevet, E., Hetz, C. & Samali, A. Endoplasmic Reticulum Stress–Activated Cell Reprogramming in Oncogenesis. Cancer Discovery 5, 586 (2015).

  19. 19.

    Ghosh, S. et al. Cross-talk between Endoplasmic Reticulum (ER) Stress and the MEK/ERK Pathway Potentiates Apoptosis in Human Triple Negative Breast Carcinoma Cells: Role Of A Dihydropyrimidone, Nifetepimine. J. Biol. Chem. 290, 3936–3949, (2015).

  20. 20.

    Baenke, F., Peck, B., Miess, H. & Schulze, A. Hooked on fat: the role of lipid synthesis in cancer metabolism and tumour development. Dis Model Mech 6, 1353–1363, (2013).

  21. 21.

    Ma, J. et al. Ginkgolic acid suppresses the development of pancreatic cancer by inhibiting pathways driving lipogenesis. Oncotarget 6, 20993–21003, (2015).

  22. 22.

    Belkaid, A., Duguay, S. R., Ouellette, R. J. & Surette, M. E. 17beta-estradiol induces stearoyl-CoA desaturase-1 expression in estrogen receptor-positive breast cancer cells. BMC Cancer 15, 440, (2015).

  23. 23.

    Hardy, S., St-Onge, G. G., Joly, E., Langelier, Y. & Prentki, M. Oleate Promotes the Proliferation of Breast Cancer Cells via the G Protein-coupled Receptor GPR40. J. Biol. Chem. 280, 13285–13291, (2005).

  24. 24.

    Hardy, S., Langelier, Y. & Prentki, M. Oleate Activates Phosphatidylinositol 3-Kinase and Promotes Proliferation and Reduces Apoptosis of MDA-MB-231 Breast Cancer Cells, Whereas Palmitate Has Opposite Effects. Cancer Res. 60, 6353–6358 (2000).

  25. 25.

    Colacino, J. A., McDermott, S. P., Sartor, M. A., Wicha, M. S. & Rozek, L. S. Transcriptomic profiling of curcumin-treated human breast stem cells identifies a role for stearoyl-coa desaturase in breast cancer prevention. Breast Cancer Res. Treat., (2016).

  26. 26.

    Choi, Y., Park, Y., Storkson, J. M., Pariza, M. W. & Ntambi, J. M. Inhibition of stearoyl-CoA desaturase activity by the cis-9,trans-11 isomer and the trans-10,cis-12 isomer of conjugated linoleic acid in MDA-MB-231 and MCF-7 human breast cancer cells. Biochem. Biophys. Res. Commun. 294, 785–790, (2002).

  27. 27.

    Peck, B. et al. Inhibition of fatty acid desaturation is detrimental to cancer cell survival in metabolically compromised environments. Cancer & metabolism 4, 6, (2016).

  28. 28.

    AlJohani, A. M., Syed, D. N. & Ntambi, J. M. Insights into Stearoyl-CoA Desaturase-1 Regulation of Systemic Metabolism. Trends in Endocrinology & Metabolism 28, 831–842, (2017).

  29. 29.

    Liu, G. et al. Lung fibroblasts promote metastatic colonization through upregulation of stearoyl-CoA desaturase 1 in tumor cells. Oncogene 37, 1519–1533, (2018).

  30. 30.

    Madden, S. F. et al. BreastMark: an integrated approach to mining publicly available transcriptomic datasets relating to breast cancer outcome. Breast Cancer Res 15, R52, (2013).

  31. 31.

    Gyorffy, B. et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res. Treat. 123, 725–731, (2010).

  32. 32.

    Paton, C. M. & Ntambi, J. M. Biochemical and physiological function of stearoyl-CoA desaturase. American Journal of Physiology - Endocrinology And Metabolism 297, E28–E37 (2009).

  33. 33.

    Wu, X. et al. The evolutionary pattern and the regulation of stearoyl-CoA desaturase genes. BioMed research international 2013, 856521, (2013).

  34. 34.

    Cheng, X. et al. Critical role of miR-125b in lipogenesis by targeting stearoyl-CoA desaturase-1 (SCD-1)1. J. Anim. Sci. 94, 65–76, (2016).

  35. 35.

    Tan, Z. et al. miR-199a-3p affects adipocytes differentiation and fatty acid composition through targeting SCD. Biochem. Biophys. Res. Commun. 492, 82–88, (2017).

  36. 36.

    Guo, Y. et al. miR-212-5p suppresses lipid accumulation by targeting FAS and SCD1. J. Mol. Endocrinol. 59, 205–217, (2017).

  37. 37.

    Zhang, M., Sun, W., Zhou, M. & Tang, Y. MicroRNA-27a regulates hepatic lipid metabolism and alleviates NAFLD via repressing FAS and SCD1. Scientific reports 7, 14493, (2017).

  38. 38.

    Cheng, C. et al. Glucose-Mediated N-glycosylation of SCAP Is Essential for SREBP-1 Activation and Tumor Growth. Cancer Cell 28, 569–581, (2015).

  39. 39.

    Shao, W. & Espenshade Peter J. Expanding Roles for SREBP in Metabolism. Cell Metabolism 16, 414–419, (2012).

  40. 40.

    Hilvo, M. et al. Novel theranostic opportunities offered by characterization of altered membrane lipid metabolism in breast cancer progression. Cancer Res. 71, 3236–3245, (2011).

  41. 41.

    Einbond, L. S. et al. Gene expression analysis of the mechanisms whereby black cohosh inhibits human breast cancer cell growth. Anticancer Res. 27, 697–712 (2007).

  42. 42.

    Hernández-Vargas, H. et al. Gene expression profiling of breast cancer cells in response to gemcitabine: NF-κB pathway activation as a potential mechanism of resistance. Breast Cancer Res. Treat. 102, 157–172, (2007).

  43. 43.

    Eckert, R. L. et al. Transglutaminase is a tumor cell and cancer stem cell survival factor. Mol. Carcinog. 54, 947–958, (2015).

  44. 44.

    Bravaccini, S. et al. New biomarkers to predict the evolution of in situ breast cancers. BioMed research international 2014, 159765, (2014).

  45. 45.

    Wang, Y., Ande, S. R. & Mishra, S. Phosphorylation of transglutaminase 2 (TG2) at serine-216 has a role in TG2 mediated activation of nuclear factor-kappa B and in the downregulation of PTEN. BMC Cancer 12, 277, (2012).

  46. 46.

    Eckert, R. L. et al. Transglutaminase regulation of cell function. Physiol. Rev. 94, 383–417, (2014).

  47. 47.

    Brown, K. D. Transglutaminase 2 and NF-κB: an odd couple that shapes breast cancer phenotype. Breast Cancer Res. Treat. 137, 329–336, (2013).

  48. 48.

    He, W., Sun, Z. & Liu, Z. Silencing of TGM2 reverses epithelial to mesenchymal transition and modulates the chemosensitivity of breast cancer to docetaxel. Experimental and therapeutic medicine 10, 1413–1418, (2015).

  49. 49.

    Kumar, S., Donti, T. R., Agnihotri, N. & Mehta, K. Transglutaminase 2 reprogramming of glucose metabolism in mammary epithelial cells via activation of inflammatory signaling pathways. Int. J. Cancer 134, 2798–2807, (2014).

  50. 50.

    Sung, B. et al. Anacardic acid (6-nonadecyl salicylic acid), an inhibitor of histone acetyltransferase, suppresses expression of nuclear factor-{kappa}B-regulated gene products involved in cell survival, proliferation, invasion, and inflammation through inhibition of the inhibitory subunit of nuclear factor-{kappa}B{alpha} kinase, leading to potentiation of apoptosis. Blood 111, 4880–4891, (2008).

  51. 51.

    Litchfield, L. M., Appana, S. N. & Klinge, C. M. COUP-TFII inhibits NFkappaB activation in endocrine-resistant breast cancer cells. Mol.Cell. Endocrinol. 382, 358–367 (2014).

  52. 52.

    Chen, W. Y. et al. Induction, modulation and potential targets of miR-210 in pancreatic cancer cells. Hepatobiliary & pancreatic diseases international: HBPD INT 11, 319–324 (2012).

  53. 53.

    Ochsner, S. A. et al. Transcriptomine, a web resource for nuclear receptor signaling transcriptomes. Physiol Genomics 44, 853–863, (2012).

  54. 54.

    Prossnitz, E. R. & Arterburn, J. B. International Union of Basic and Clinical Pharmacology. XCVII. G Protein-Coupled Estrogen Receptor and Its Pharmacologic Modulators. Pharmacol. Rev. 67, 505–540, (2015).

  55. 55.

    Chen, C. C., Lee, W. R. & Safe, S. Egr-1 is activated by 17beta-estradiol in MCF-7 cells by mitogen-activated protein kinase-dependent phosphorylation of ELK-1. J. Cell. Biochem. 93, 1063–1074 (2004).

  56. 56.

    Ronski, K. et al. Early growth response gene 1 (EGR1) is deleted in estrogen receptor-negative human breast carcinoma. Cancer 104, 925–930 (2005).

  57. 57.

    Wardell, S. E., Kazmin, D. & McDonnell, D. P. Research Resource: Transcriptional Profiling in a Cellular Model of Breast Cancer Reveals Functional and Mechanistic Differences Between Clinically Relevant SERM and Between SERM/Estrogen Complexes. Mol. Endocrinol. 26, 1235–1248, (2012).

  58. 58.

    Niki, T., Galli, I., Ariga, H. & Iguchi-Ariga, S. M. MSSP, a protein binding to an origin of replication in the c-myc gene, interacts with a catalytic subunit of DNA polymerase alpha and stimulates its polymerase activity. FEBS Lett. 475, 209–212 (2000).

  59. 59.

    Roberts, D. et al. Identification of genes associated with platinum drug sensitivity and resistance in human ovarian cancer cells. Br. J. Cancer 92, 1149–1158, (2005).

  60. 60.

    Pillai, K., Akhter, J., Chua, T. C. & Morris, D. L. Anticancer property of bromelain with therapeutic potential in malignant peritoneal mesothelioma. Cancer Invest. 31, 241–250, (2013).

  61. 61.

    Fouz, N., Amid, A. & Hashim, Y. Z. Gene expression analysis in MCF-7 breast cancer cells treated with recombinant bromelain. Appl. Biochem. Biotechnol. 173, 1618–1639, (2014).

  62. 62.

    Yin, M. et al. Transactivation of microRNA-383 by Steroidogenic Factor-1 Promotes Estradiol Release from Mouse Ovarian Granulosa Cells by Targeting RBMS1. Mol. Endocrinol. 26, 1129–1143, (2012).

  63. 63.

    Gong, P. et al. Transcriptomic analysis identifies gene networks regulated by estrogen receptor alpha (ERalpha) and ERbeta that control distinct effects of different botanical estrogens. Nucl Recept Signal 12, e001, (2014).

  64. 64.

    Brown, L. et al. CDIP, a novel pro-apoptotic gene, regulates TNFalpha-mediated apoptosis in a p53-dependent manner. EMBO J. 26, 3410–3422, (2007).

  65. 65.

    Xu, L. et al. Screening Active Compounds from Garcinia Species Native to China Reveals Novel Compounds Targeting theSTAT/JAK Signaling Pathway. BioMed research international 2015, 910453, (2015).

  66. 66.

    Cheng, H., Wang, S. & Feng, R. STIM1 plays an important role in TGF-beta-induced suppression of breast cancer cell proliferation. Oncotarget 7, 16866–16878, (2016).

  67. 67.

    Sato, H., Higashi, S. & Miyazaki, K. Amino-terminal fragments of laminin γ2 chain stimulate migration of metastatic breast cancer cells by interacting with CD44. Clin. Exp. Metastasis 32, 405–415, (2015).

  68. 68.

    Ma, Q., Yu, T., Ren, Y.-Y., Gong, T. & Zhong, D.-S. Overexpression of SAMD9 suppresses tumorigenesis and progression during non small cell lung cancer. Biochem. Biophys. Res. Commun. 454, 157–161, (2014).

  69. 69.

    Li, C. F. et al. Human sterile alpha motif domain 9, a novel gene identified as down-regulated in aggressive fibromatosis, is absent in the mouse. BMC Genomics 8, 92, (2007).

  70. 70.

    McCartan, D. et al. Global Characterization of the SRC-1 Transcriptome Identifies ADAM22 as an ER-Independent Mediator of Endocrine-Resistant Breast Cancer. Cancer Res. 72, 220–229, (2012).

  71. 71.

    Vidyasekar, P. et al. Genome Wide Expression Profiling of Cancer Cell Lines Cultured in Microgravity Reveals Significant Dysregulation of Cell Cycle and MicroRNA Gene Networks. PLoS One 10, e0135958, (2015).

  72. 72.

    Tani, H., Onuma, Y., Ito, Y. & Torimura, M. Long Non-Coding RNAs as Surrogate Indicators for Chemical Stress Responses in Human-Induced Pluripotent Stem Cells. PLoS ONE 9, e106282, (2014).

  73. 73.

    Voellenkle, C. et al. Implication of Long noncoding RNAs in the endothelial cell response to hypoxia revealed by RNA-sequencing. Scientific reports 6, 24141, (2016).

  74. 74.

    Li, D. S., Ainiwaer, J. L., Sheyhiding, I., Zhang, Z. & Zhang, L. W. Identification of key long non-coding RNAs as competing endogenous RNAs for miRNA-mRNA in lung adenocarcinoma. Eur. Rev. Med. Pharmacol. Sci. 20, 2285–2295 (2016).

  75. 75.

    Xiong, J. et al. An estrogen receptor alpha suppressor, microRNA-22, is downregulated in estrogen receptor alpha-positive human breast cancer cell lines and clinical samples. FEBS J 277, 1684–1694, (2010).

  76. 76.

    Bowers, L. W. et al. Obesity Suppresses Estrogen Receptor Beta Expression in Breast Cancer Cells via a HER2-Mediated Pathway. PLoS One 10, e0145452, (2015).

  77. 77.

    Glazer, R. I. PPARdelta as a Metabolic Initiator of Mammary Neoplasia and Immune Tolerance. PPAR research 2016, 3082340, (2016).

  78. 78.

    Yuan, H. et al. PPARdelta induces estrogen receptor-positive mammary neoplasia through an inflammatory and metabolic phenotype linked to mTOR activation. Cancer Res. 73, 4349–4361, (2013).

  79. 79.

    Bergamo, A. et al. RNA-seq analysis of the whole transcriptome of MDA-MB-231 mammary carcinoma cells exposed to the antimetastatic drug NAMI-A. Metallomics: integrated biometal science 7, 1439–1450, (2015).

  80. 80.

    Tasselli, L., Zheng, W. & Chua, K. F. SIRT6: Novel Mechanisms and Links to Aging and Disease. Trends Endocrinol Metab 28, 168–185, (2017).

  81. 81.

    Ioris, R. M. et al. SIRT6 Suppresses Cancer Stem-like Capacity in Tumors with PI3K Activation Independently of Its Deacetylase Activity. Cell Reports 18, 1858–1868, (2017).

  82. 82.

    Wang, Q. et al. Zinc finger protein ZBTB20 expression is increased in hepatocellular carcinoma and associated with poor prognosis. BMC Cancer 11, 271, (2011).

  83. 83.

    Zhao, J. G., Ren, K. M. & Tang, J. Zinc finger protein ZBTB20 promotes cell proliferation in non-small cell lung cancer through repression of FoxO1. FEBS Lett. 588, 4536–4542, (2014).

  84. 84.

    de la Rosa, J. et al. A single-copy Sleeping Beauty transposon mutagenesis screen identifies new PTEN-cooperating tumor suppressor genes. Nat. Genet. 49, 730–741, (2017).

  85. 85.

    Mackay, A. et al. Molecular response to aromatase inhibitor treatment in primary breast cancer. Breast Cancer Research 9, R37, (2007).

  86. 86.

    Zhang, P. et al. Tumor suppressor p53 cooperates with SIRT6 to regulate gluconeogenesis by promoting FoxO1 nuclear exclusion. Proc. Natl. Acad. Sci. USA 111, 10684–10689, (2014).

  87. 87.

    Cai, Q., Lin, T., Kamarajugadda, S. & Lu, J. Regulation of glycolysis and the Warburg effect by estrogen-related receptors. Oncogene 32, 2079–2086, (2013).

  88. 88.

    Kamarajugadda, S. et al. Glucose Oxidation Modulates Anoikis and Tumor Metastasis. Mol. Cell. Biol. 32, 1893–1907, (2012).

  89. 89.

    Walter, W. et al. Altered regulation of PDK4 expression promotes antiestrogen resistance in human breast cancer cells. SpringerPlus 4, 689, (2015).

  90. 90.

    Lin, Y. C. et al. Metformin sensitizes anticancer effect of dasatinib in head and neck squamous cell carcinoma cells through AMPK-dependent ER stress. Oncotarget 5, 298–308, (2014).

  91. 91.

    Doi, M. et al. Gpr176 is a Gz-linked orphan G-protein-coupled receptor that sets the pace of circadian behaviour. Nat Commun 7, (2016).

  92. 92.

    Kakarala, K. K. & Jamil, K. Sequence-structure based phylogeny of GPCR Class A Rhodopsin receptors. Mol. Phylogenet. Evol. 74, 66–96, (2014).

  93. 93.

    Kim, E. J. & Juhnn, Y. S. Cyclic AMP signaling reduces sirtuin 6 expression in non-small cell lung cancer cells by promoting ubiquitin-proteasomal degradation via inhibition of the Raf-MEK-ERK (Raf/mitogen-activated extracellular signal-regulated kinase/extracellular signal-regulated kinase) pathway. J. Biol. Chem. 290, 9604–9613, (2015).

  94. 94.

    Deng, Z. et al. The Intermediate Filament Vimentin Mediates MicroRNA miR-378 Function in Cellular Self-renewal by Regulating the Expression of the Sox2 Transcription Factor. J. Biol. Chem. 288, 319–331, (2013).

  95. 95.

    Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54, (2016).

  96. 96.

    Schönthal, A. H. Endoplasmic reticulum stress and autophagy as targets for cancer therapy. Cancer Lett. 275, 163–169 (2009).

  97. 97.

    Clarke, R. et al. Endoplasmic Reticulum Stress, the Unfolded Protein Response, Autophagy, and the Integrated Regulation of Breast Cancer Cell Fate. Cancer Res. 72, 1321–1331, (2012).

  98. 98.

    Huarte, M. The emerging role of lncRNAs in cancer. Nat. Med. 21, 1253–1261, (2015).

  99. 99.

    Quinn, J. J. & Chang, H. Y. Unique features of long non-coding RNA biogenesis and function. Nat Rev Genet 17, 47–62, (2016).

  100. 100.

    Ulitsky, I. & Bartel David P. lincRNAs: Genomics, Evolution, and Mechanisms. Cell 154, 26–46, (2013).

  101. 101.

    Schultz, D. J., Olsen, C., Cobbs, G. A., Stolowich, N. J. & Parrott, M. M. Bioactivity of anacardic acid against Colorado potato beetle (Leptinotarsa decemlineata) larvae. J. Agric. Food Chem. 54, 7522–7529 (2006).

  102. 102.

    Flicek, P. et al. Ensembl 2014. Nucleic Acids Res 42, D749–755, (2014).

  103. 103.

    Bolser, D. M. et al. MetaBase–the wiki-database of biological databases. Nucleic Acids Res 40, D1250–1254, (2012).

  104. 104.

    Muluhngwi, P. et al. Tamoxifen differentially regulates miR-29b-1 and miR-29a expression depending on endocrine-sensitivity in breast cancer cells. Cancer Lett. 388, 230–238, (2017).

  105. 105.

    Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 3, 1101–1108 (2008).

  106. 106.

    Hu, H. B., Jie, H. Y. & Zheng, X. X. Three Circulating LncRNA Predict Early Progress of Esophageal Squamous Cell Carcinoma. Cell. Physiol. Biochem. 40, 117–125, (2016).

  107. 107.

    L’Abbate, A. et al. t(15;21) translocations leading to the concurrent downregulation of RUNX1 and its transcription factor partner genes SIN3A and TCF12 in myeloid disorders. Mol Cancer 14, 211, (2015).

  108. 108.

    Lin, J. et al. The long noncoding RNA landscape in hypoxic and inflammatory renal epithelial injury. Am J Physiol Renal Physiol 309, F901–913, (2015).

  109. 109.

    Min, W. et al. Long Noncoding RNA miR210HG as a Potential Biomarker for the Diagnosis of Glioma. PLoS One 11, e0160451, (2016).

  110. 110.

    Karpathakis, A. et al. Prognostic Impact of Novel Molecular Subtypes of Small Intestinal Neuroendocrine Tumor. American Association for Cancer Research 22, 250–258 (2016).

  111. 111.

    Khor, G. H., Froemming, G. R., Zain, R. B., Abraham, T. M. & Lin, T. K. Involvement of CELSR3 Hypermethylation in Primary Oral Squamous Cell Carcinoma. Asian Pacific journal of cancer prevention: APJCP 17, 219–223 (2016).

  112. 112.

    Permuth, J. B. et al. Exome genotyping arrays to identify rare and low frequency variants associated with epithelial ovarian cancer risk. Hum. Mol. Genet., (2016).

  113. 113.

    Pärssinen, J. et al. Identification of differentially expressed genes after PPM1D silencing in breast cancer. Cancer Lett. 259, 61–70, (2008).

  114. 114.

    De Braekeleer, E. et al. ETV6 fusion genes in hematological malignancies: A review. Leuk. Res. 36, 945–961, (2012).

  115. 115.

    Ying, J. et al. Epigenetic disruption of two proapoptotic genes MAPK10//JNK3 and PTPN13//FAP-1 in multiple lymphomas and carcinomas through hypermethylation of a common bidirectional promoter. Leukemia 20, 1173–1175 (2006).

  116. 116.

    Biton, S. & Ashkenazi, A. NEMO and RIP1 Control Cell Fate in Response to Extensive DNA Damage via TNF-α Feedforward Signaling. Cell 145, 92–103, (2011).

  117. 117.

    Hurvitz, S. A. et al. In vitro activity of the mTOR inhibitor everolimus, in a large panel of breast cancer cell lines and analysis for predictors of response. Breast Cancer Res. Treat. 149, 669–680, (2015).

Download references


We thank Brandie N. Radde for performing the initial cell treatments and RNA isolation for RNA-seq. The research was supported in part by a pilot grant from the University of Louisville Center for Genetics and Molecular Medicine to C.M.K. and D.J.S.; and by an Internal Research Grant from the Office of the Executive Vice President for Research and Innovation of the University of Louisville to C.M.K.; E.C.R. and the KBRIN Bioinformatics core are supported by NIH/NIGMS grant P20 GM103436 (Nigel Cooper, PI); A.K. was supported by a NIH 5 T35 DK072923 (Carolyn M. Klinge, PI), S.L.V. was supported by the University of Louisville Summer Research Opportunity Program (SROP).

Author information

D.J.S. purified the anacardic acid used in experiments; A.K., N.A-R., S.L.V., and P.M. performed experiments; C.M.K. and D.J.S. designed experiments; E.C.R. performed the bioinformatic analysis; C.M.K. performed statistical and MetaCore analyses; C.M.K. wrote the manuscript with editing by E.C.R. and D.J.S.

Competing Interests

The authors declare no competing interests.

Correspondence to Carolyn M. Klinge.

Electronic supplementary material

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.