Introduction
The identification of distinct cell types that appear to be hierarchically organized in the mammary epithelial glands of healthy women is now well established [
1]. This hierarchy is defined largely by two prospectively separable subsets of cells that generate colonies containing only one or both lineages (myoepithelial and/or luminal) of cells that make up the bulk of the normal mammary gland structure. The bipotent, clonogenic, progenitor-enriched basal cell fraction also contains putative human mammary stem cells identified in xenotransplantation assays [
2,
3]. The ability of human mammary cells to be propagated both
in vitro and
in vivo at limited densities is known to be markedly enhanced by the presence of fibroblast ‘feeders’ [
2,
4,
5]. These and many other studies have shown that fibroblast interactions are important to the growth of mammary epithelial cells [
6-
12]. However, a comprehensive characterization of the mechanisms by which fibroblasts regulate the growth and functional organization of normal mammary epithelial cells has been lacking.
Genome-wide RNA interference (RNAi, small interfering RNA (siRNA)) screens offer an attractive strategy by which to investigate such questions. They have previously been used with success to identify mediators of Ras oncogene-induced senescence, suppressors of p16 gene expression, genes that regulate cell migration and cell survival genes in mammary cells [
13-
16]. This type of investigation is nevertheless dependent on a source of cells that can be obtained in large numbers and readily transfected. Because primary normal mammary epithelial cells, even those derived from human mammoplasties, do not satisfy either of these requirements, we sought an alternative in a clonal diploid isolate of
hTERT-immortalized cells [
17] that we found remains dependent on fibroblast stimulation for its rapid growth when cultured at low density. By combining automated imaging with siRNA screening of these cells, we identified 43 signal-transducing receptors and secreted factors with functionally validated roles in mediating the
in vitro growth of primary normal human mammary epithelial cells.
Discussion
The extracellular factors and transmembrane signals that regulate mammary epithelial growth and differentiation remain poorly understood. In part, this is due to a lack of methods for systematic, genome-wide, genetic and functional interrogation of genes in relation to mammary epithelial growth and differentiation. Although genome-wide functional screens using RNAi methods have proven successful in many similar instances (for example, see [
58,
59]), these have mostly been undertaken with transformed, somatically mutated epithelial cell types, where key extracellular interactions that modulate growth cannot be recapitulated. Important features of the endogenous milieu, such as growth stimulation by fibroblast stroma have been undersampled as a consequence. To overcome this, we isolated and characterized a cloned, diploid, nontransformed mammary epithelial cell line, 184-
hTERT-L9, which retains both fibroblast growth dependence and the capacity to differentiate in three-dimensional growth conditions. We used this cell line in a genome-wide RNAi screen to identify, in a systematic manner, genes required for mammary progenitor cell growth and differentiation.
The 184-hTERT-L9 clone described here is derived from primary mammary epithelium immortalized by hTERT transfection after limited initial passages. The clonal line retains important properties, such as fibroblast-dependent growth and the ability to differentiate in three-dimensional cultures, and is diploid and nontransformed.
Fibroblasts are known to possess an instructive role in regulating mammary epithelial cells in normal development and oncogenesis [
6,
60,
61]. More specifically, the
in vitro growth of bipotent progenitor cells is reliant upon the presence of fibroblasts as feeder cells. The 184-
hTERT-L9 cells mimic the growth of bipotent progenitor cells when plated with increasing densities of irradiated NIH 3T3 feeder cells in a well-defined colony-forming assay. This is not observed in epithelial cell lines with genomic aberrations and/or additional adaptation events, such as MCF10A cells (Figure
2) and transformed epithelial cells derived from malignancies. Thus, 184-
hTERT-L9 cells provided a genomically characterized model system, also amenable to RNAi transfection and image-based, high-content screening, whereby we could replicate
in vitro the fibroblast-dependent growth environment of mammary progenitor cells in order to investigate the signalling pathways involved in regulating mammary epithelial and progenitor cell growth.
Among the 21,121 siRNA pools tested in the primary screen, 2,337 demonstrated statistically significant abrogation of growth to less than 25% of the control condition. This was a stringent selection criterion, given that knockdown of receptors for two of the four defined medium components (insulin, EGF, transferrin and isoproterenol) did not achieve this level of growth inhibition. Surprisingly, GPCR and associated signalling proteins were also found amongst this list, suggesting an underappreciated role of this class of receptors within mammary gland biology.
To identify novel external regulators and signal transducers, we focused our in-depth analysis on cell surface and secreted genes. After rescreening including an independently cloned sister cell to assess reproducibility, and after qPCR assessment for on-target siRNA activity, we identified 47 transmembrane genes for follow-up examination by deconvolution and by assaying growth using independently targeted shRNA constructs (summarized in Table
1). Moreover, we quantified the relative influence of proliferation and apoptosis for each gene, which indicated a general inverse correlation between these two functions.
Table 1
Relative effects of the 47 target genes in two-dimensional and three-dimensional cultures
a
ACE2
| NM_021804 | 22 | 30 | 11 | 5.169 | Lethal |
ADCY4
| NM_139427 | 44 | 17 | 44 | 0.878 | Lethal |
BDKRB2
| NM_000623 | 28 | 44 | 31 | 2.484 | Growth postselection |
BST1
| NM_004334 | 7 | 6 | 14 | 4.406 | Lethal |
CD79A
| NM_001783 | 13 | 16 | 33 | 2.366 | Growth postselection |
COL9A3
| NM_001853 | 16 | 4 | 22 | 3.489 | Lethal |
CTNNA1
| NM_001903 | 37 | 29 | 18 | 4.047 | Lethal |
EFNAA
| NM_005227 | 38 | 37 | 5 | 7.338 | Growth postselection |
FIT1
| NM_203402 | 4 | 22 | No data | No data | Growth postselection |
FL_J30634
| NM_153014 | 15 | 30 | 17 | 4.055 | Growth postselection |
FLOT2
| NM_004475 | 23 | 31 | 41 | 1.618 | Lethal |
FZD2
| NM_001466 | 6 | 25 | 19 | 3.7126 | Lethal |
GPR182
| NM_007264 | 9 | 20 | 32 | 2.483 | Growth postselection |
GPR39
| NM_001508 | 10 | 22 | 26 | 3.159 | Growth postselection |
GPR80
| NM_080818 | 32 | 14 | 27 | 3.137 | Lethal |
HSD17B2
| NM_002153 | 27 | 38 | 2 | 27.328 | Lethal |
KCNJ5
| NM_000890 | 3 | 35 | 42 | 1.494 | Growth postselection |
KTELC1
| NM_020231 | 8 | 17 | No data | No data | Growth postselection |
LGALS1
| NM_002305 | 36 | 7 | 16 | 4.120 | Growth postselection |
LPAR3
| NM_012152 | 1 | 5 | 20 | 3.530 | Growth postselection |
LTBP3
| NM_021070 | 39 | 28 | 9 | 5.291 | Growth postselection |
MMP24
| NM_00690 | 40 | 5 | 8 | 5.447 | Growth postselection |
MMP28
| NM_024302 | 18 | 25 | 37 | 2.075 | Lethal |
NKAIN4
| NM_152864 | 20 | 9 | 21 | 3.505 | Lethal |
NPTX1
| NM_002522 | 45 | 18 | 6 | 7.082 | Lethal |
NTN1
| NM_004822 | 47 | 22 | 10 | 5.175 | Growth postselection |
NTN2L
| NM_006181 | 5 | 30 | 13 | 4.691 | Growth postselection |
OPRS1
| NM_005866 | 29 | 26 | 24 | 3.243 | Lethal |
PARD3
| NM_019619 | 30 | 31 | 15 | 4.221 | Lethal |
PCDHB13
| NM_018933 | 46 | 27 | 43 | 1.214 | Growth postselection |
PDCD1
| NM_00518 | 21 | 36 | 28 | 2.997 | Growth postselection |
PLA2G2F
| NM_022819 | 19 | 22 | 38 | 2.028 | Lethal |
PLUNC
| NM_016583 | 31 | 30 | 23 | 3.280 | Growth postselection |
PROCR
| NM_006404 | 35 | 36 | 1 | 31.637 | Growth postselection |
RHCE
| NM_020485 | 42 | 31 | 35 | 2.209 | Lethal |
RIPK2
| NM_003821 | 41 | 23 | No data | No data | Lethal |
ROBO3
| NM_022370 | 11 | 35 | 39 | 2.012 | Growth postselection |
SAA1
| NM_000331 | 14 | 21 | 12 | 5.167 | Lethal |
SCARB2
| NM_005506 | 34 | 9 | 34 | 2.317 | Lethal |
SEMA3C
| NM_006379 | 12 | 25 | 40 | 1.936 | Growth postselection |
SERPINH1
| NM_001235 | 26 | 32 | 25 | 3.187 | Lethal |
SLC6A4
| NM_001045 | 17 | 29 | 36 | 2.142 | Growth postselection |
SNN
| NM_003498 | 24 | 30 | 3 | 11.523 | Lethal |
TMEM14C
| NM_016462 | 33 | 32 | 7 | 5.884 | Lethal |
TMEM9B
| NM_020644 | 43 | 23 | 30 | 2.556 | Lethal |
TUFT1
| NM_020127 | 25 | 20 | 4 | 7.399 | Lethal |
| | /47 | /47 | /44 | | |
Although these 47 genes have diverse functions, they are strikingly enriched for both GPCRs (LPAR3, FZD2, ADMR, BDKRB2, GPR39, GPR80 and GPR182) (noted in the primary screen) and axonal guidance molecules (SEMA3C, PLXNA2, ROBO3, EFNA4, NTN1 and NTN2L). For many of these genes, we provide the first description of a role in growth regulation or mammary biology.
To better understand the roles of these genes in growth and differentiation (reviewed in [
62]), we assessed the requirement of the 47 validated target genes for growth in three-dimensional culture. The silencing of all but four of the genes (
ADCY4,
PCDHB13,
KCNJ5 and
FLOT2) decreased three-dimensional acinar formation to a level comparable to that seen with PLK1 silencing (which is essential for mitosis). Intriguingly, we have shown that
LPAR3 is required for two-dimensional and three-dimensional mammary growth. Given the established importance of
LPAR1,
LPAR2 and
LPAR3 in mammary tumourigenesis, we wanted to confirm that
LPAR1 and
LPAR2 are indeed irrelevant for growth of normal epithelium (as determined in the primary genetic screen). Colony formation in two-dimensional assays and three-dimensional acinar formation within Matrigel still occur upon silencing of
LPAR1 and
LPAR2, suggesting that they are not required for the growth of normal, nontransformed epithelial cells.
LPAR3 has a greater binding affinity for 2-acyl-LPA with unsaturated fatty acids, whereas
LPAR1 and
LPAR2 are more responsive to saturated acyl chains [
63]. With responsiveness to a similar ligand, it is possible that compensatory redundancy exists between
LPAR1 and
LPAR2.
Some of the genes identified as regulating growth in two dimensions also affected differentiation when epithelial cells were grown in three-dimensions, with
SNN,
HSD17B2 and
PROCR showing greater than tenfold reduction in acinar formation. The requirement for long-term cultures (21 days) and the lethality of the shRNAs for
SNN and
HSD17B2 precluded analysis of these two genes; however, reduction of
PROCR in long-term cultures was associated with absence of lumen formation and disorganized epithelial growth. We quantified the relative effects on epithelial organization and lumen formation and observed that disordered differentiation was also present for two axonal pathfinding associated genes,
EFNA4 and
NTN1, and for
LGALS1 (Additional file
1: Figure S7, Figure
8), with differential effects on polarization and epithelial organization.
PROCR has been implicated as a receptor for protease-cleaved substrates in breast cancer migration [
64] and as a marker of colony-forming cells in malignant cell lines [
65]. Here we show for the first time, to our knowledge, a role in growth and differentiation of primary breast epithelium. Loss of
NTN1 causes disorganization in the terminal end buds, an effect that is proposed to occur through the loss of cellular adhesions [
66]. It has also been shown that implantation of
NTN1-secreting pellets into mammary glands during pregnancy increases the number of alveolar structures that develop [
67]. In the present study, we show a role for
NTN1 in both luminal and bipotent progenitor cell growth in that it was required for colony formation in our
in vitro assays.
Finally, genes required for growth and differentiation are often implicated in tumourigenesis. In this study, we identified a subset of genes that have not previously been implicated in mammary gland growth or development. We sought to determine if expression of these genes correlated with any of the breast cancer subtypes. Significant, nonrandom differences in expression distribution across the 10 METABRIC datasets was seen for 40 of the 47 genes, with several genes (for example, RIPK2, EFNA4 and TMEM9B) differentially expressed in breast cancer subtypes with high proliferation. Furthermore, we were able to demonstrate independent prognostic significance for CD79A (with elevated expression improving survival in two of the ten METABRIC subtypes) and SERPINH1 (with elevated expression decreasing survival in three of the ten METABRIC subtypes). The possible roles of these genes in the tumour subtypes studied requires future functional studies in representative models; however, it is notable that all of the genes studied here are accessible by virtue of solubility or membrane location, making them a practical choice for intervention.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
AB, CE and SA conceived of and designed the experiments. AB, VN, AW, JB, DY, PE and LP performed the experiments. AB, SM, SP, PE, DY, CC and SA analysed the data. LA and CB provided intellectual input on the cloning of 184 polyclonal cells and reviewed the manuscript. SM, SP, LA, CB and CE contributed reagents, materials, and analytical tools. AB, SM and SA wrote the manuscript. All authors reviewed the final draft of the manuscript. All authors read and approved the final manuscript.