Several lines of research pointed to an important effect of oxygen tension on cell differentiation (41, 42). Hypoxia reversibly arrested stem cells in an undifferentiated state (42), while low oxygen tensions have also been used to maintain the pluripotency of different progenitors (43). Therefore, it is not excluded that hypoxia by maintaining a quiescence phenotype of putative endothelial progenitor cells suppresses their endothelial differentiation toward ECFCs especially if the microenvironment is devoid of pro-neovascularization clues. It is plausible that in ischemic tissues—once the pro-angiogenic environment is established—growth factors that are essential for EC differentiation such as VEFG, FGF, or HGF in conjunction with pro-angiogenic myeloid cells such as circulating angiogenic cells would eventually overcome the hypoxia-induced inhibition of differentiation of EPCs toward EC phenotype.

The initial outgrowth of PB-ECFCs was also inhibited by hypoxia, but to a much lesser extent than that of CB-ECFCs. The difference in the ability of CB-MNCs and PB-MNCs to generate primary clones under normoxia and hypoxia suggests that beside oxygen tension other factors play an important role in the initial outgrowth of ECFCs from MNCs. The microenvironment which imprint stem and progenitor cells during fetal and postnatal life can accounted for by observed differences between CB-MNCs and PB-MNCs in their ability to generate ECFCs colonies. Furthermore, to the best of our knowledge, there is no information about the circulation time of CB- and PB-endothelial progenitors. Hence, we cannot exclude that PB-endothelial progenitors have been exposed to the oxygenated milieu of the blood for a longer period than their CB counterparts. Once isolated and cultured, ECFCs obtained from umbilical cord blood differ from PB-ECFCs with respect to proliferation (43) gene expression (44–46), and in vivo vessel formation (46, 47).

# Hypoxia and the Propagation of Subcultured ECFCs

Hypoxia not only inhibited the initial outgrowth of ECFCs from CB- and PB-MNCs but also has an impact to the clonal and proliferative ability of subcultured cells. Interestingly, the CB-ECFCs displayed a small reduced proliferation at 1% O2 which is in agreement with previously reports (23, 24), while similar data have also been reported very recently on PB-ECFCs (25). The whole genome sequencing data also showed a change in metabolic pathways and in cell cycle genes during exposure to hypoxia comparable with that of human foreskin MVECs (36). While the metabolic adaptation helps the cell to overcome the limitation of energy supply, the suppression of cell cycle genes likely contributes to the hypoxia-induced reduction of ECFC proliferation. Hypoxia-driven HIF-1α activation with subsequent proliferation cell cycle arrest in G1/S phase and induction of apoptosis has been reported as a mechanism that restricts the growth of EC obtained from post-natal tissues (48). Whatever is the transcriptomic background of observed hypoxia-induced cell proliferation in CB- and PB-ECFCs, future investigation is warranted.

During neovascularization, ECFC proliferation matches the growth of the vascular tree. Our finding that hypoxia reduces ECFC proliferation is contra-intuitive suggesting that other factors that play a crucial role during in vivo neovascularization and are accountable for proper in vivo multiplication of EC are absent in our in vitro hypoxic assay. Indeed, rapid neovascularization reported in animal studies of addition of VEGF to ECFCs (49) as well as co-implantation of ECFCs with myeloid cells (50) or MSCs (51) pinpoint to the necessity to include these clues during in vitro assaying in order to unravel the true behavior of ECFCs under hypoxia.

### TABLE 2 | Hypoxia pathway analysis.

*The KEGG pathways involved in angiogenesis and metabolism with a p* < *0.05 are show. Red is upregulated and green is downregulated.*

### Endothelial Tube Formation by PB-ECFCs

Hypoxia not only reduced proliferation, but also inhibited endothelial tube formation by PB-ECFCs into a fibrin matrix. This is in line with earlier observations on CB-ECFCs (22, 24). The observed inhibition of tube formation in hypoxia was highly similar to the inhibition observed in hypoxic human microvascular EC, which was largely corrected after inhibition of HIF-2α (29, 30). Hookham et al. (24) found that Placental growth factor (PlGF) was a major player in the inhibition of tube formation by hypoxic CB-ECFCs. How PlGF exerts its effect is not yet clear. As it binds to VEGFR-1 and NRP-1 (52), it may on the one hand prevent quenching of VEGF-A by VEGFR-1 (53), and at the other hand withdraw NRP-1 from assisting cis-oriented VEGFR-2 endocytosis which is required for VEGFR-2 signaling (54). Only the latter would contribute to inhibition of tubule formation. In our experimental conditions tubule formation was fully inhibited by anti-u-PA antibodies (33) or si-uPAR (34), which suggest that migration/invasion plays a dominant role in tube formation in the fibrin matrix. A comparable mechanism has been observed in tubule formation by human microvascular endothelial cells, a process that depended on migration/invasion independent of proliferation (35).

The suppression of ECFC sprouting and proliferation by hypoxia seems contra-intuitive, but it may reflects the monolayer stabilizing properties of endothelial cells induced by HIF-2α. This response normally balances the sprouting-inducing effect of HIF-1α (29). In an hypoxic environment the surrounding nonendothelial tissue cells mainly support the HIF-1α part of this balance. However, the suggestion of a HIF-2α-suppressed ECFC sprouting contrasts to a very recent study of He et al. (25), who demonstrated by specific inhibition that inhibition of HIF-1α, but not HIF-2α, could overcome the hypoxia-induced inhibition of angiogenesis. This conclusion, which is opposite to studies on microvascular endothelial cells (29, 30), needs further evaluation and underpinning.

Altogether, there is firm consensus that hypoxia suppresses proliferation and sprouting of CB- and PB-ECFCs in vitro, but how the variety of suggested mediators participate and interact requires further elucidation. Notwithstanding, the crucial question raised by our findings is "why is there poor outgrowth of circulating ECFCs in hypoxia, while expansion of endothelial cells would be essential for vessel repair or new vessel forming abilities in hypoxic conditions?" One cannot exclude that this reflects the fact that ECFCs were studied in isolation, while the presence of many hypoxic tissue cells may shift the balance more into pro-angiogenic direction. Alternatively, one may anticipate that the highly proliferation of ECFCs can in particular contribute to new vessels with improved tissue circulation at the interface of the hypoxic tissue and circulating blood. If non-perfused vessel-like structures would be made in the center of a chronic hypoxic environment, it would take much energy without improvement of blood and oxygen supply. However, under these conditions, alternative sources of "endothelial precursor cells" e.g., the quiescent resident endothelial cells in vessels (55) or endothelial cells derived from erythro-myeloid progenitors (56) might be involved. Getting more insight in all these alternatives is needed to improve the use of endothelial precursor cells and ECFCs in regenerative medicine.

### ETHICS STATEMENT

The collection of cord and peripheral blood was approved and conducted according to the guidelines by the Medical Ethics Committee of the VU University medical center in Amsterdam, Netherlands.

### AUTHOR CONTRIBUTIONS

DT prepared, performed and evaluated experiments (on PB-ECFCs) and made initial draft of the manuscript. LD-V

### REFERENCES

prepared, performed and evaluated experiments (on CB-ECFCs) and made initial draft of the manuscript. MvW performed experiments. HB was involved in supervising and evaluating experiments, corrected the manuscript. PK planned and supervised experiments, evaluated and assembled data, corrected the manuscript. VvH planned and supervised, final coordination of the manuscript.

### FUNDING

This work was supported by Grant 1.6 of the Netherlands Institute for Regenerative Medicine (NIRM).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed. 2018.00356/full#supplementary-material

statement on nomenclature. Stem Cells Transl Med. (2017) 6:1316–20. doi: 10.1002/sctm.16-0360

via HIF-1alpha signaling. Respir Physiol Neurobiol. (2018) 247:87–95. doi: 10.1016/j.resp.2017.09.013

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Tasev, Dekker-Vroling, van Wijhe, Broxterman, Koolwijk and van Hinsbergh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Endoglin as an Adhesion Molecule in Mature and Progenitor Endothelial Cells: A Function Beyond TGF-β

Elisa Rossi 1,2 \*, Carmelo Bernabeu3 and David M. Smadja1,2,4,5

1 Université Paris Descartes, Sorbonne Paris Cité, Paris, France, 2 Inserm UMR-S1140, Paris, France, 3 Centro de Investigaciones Biológicas, Consejo Superior de Investigaciones Científicas, Centro de Investigación Biomédica en Red de Enfermedades Raras, Madrid, Spain, 4 Department of Hematology, AP-HP, Hôpital Européen Georges Pompidou, Paris, France, 5 Laboratory of Biosurgical Research, Carpentier Foundation, Hôpital Européen Georges Pompidou, Paris, France

Endoglin (ENG) is a transmembrane glycoprotein expressed on endothelial cells that functions as a co-receptor for several ligands of the transforming growth factor beta (TGF-β) family. ENG is also a recognized marker of angiogenesis and mutations in the endoglin gene are responsible for Hereditary Hemorrhagic Telangiectasia (HHT) type 1, a vascular disease characterized by defective angiogenesis, arteriovenous malformations, telangiectasia, and epistaxis. In addition to its involvement in the TGF-β family signaling pathways, several lines of evidence suggest that the extracellular domain of ENG has a role in integrin-mediated cell adhesion via its RGD motif. Indeed, we have described a role for endothelial ENG in leukocyte trafficking and extravasation via its binding to leukocyte integrins. We have also found that ENG is involved in vasculogenic properties of endothelial progenitor cells known as endothelial colony forming cells (ECFCs). Moreover, the binding of endothelial ENG to platelet integrins regulate the resistance to shear during platelet-endothelium interactions under inflammatory conditions. Because of the need for more effective treatments in HHT and the involvement of ENG in angiogenesis, current studies are aimed at identifying novel biological functions of ENG which could serve as a therapeutic target. This review focuses on the interaction between ENG and integrins with the aim to better understand the role of this protein in blood vessel formation driven by progenitor and mature endothelial cells.

### Edited by: Peter S. Steyger,

Oregon Health and Science University, United States

### Reviewed by:

Eleni Papakonstantinou, Aristotle University of Thessaloniki, Greece Valerie Kouskoff, University of Manchester, United Kingdom Michelle Letarte, Hospital for Sick Children, Canada

> \*Correspondence: Elisa Rossi<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Translational Medicine, a section of the journal Frontiers in Medicine

Received: 27 June 2018 Accepted: 14 January 2019 Published: 30 January 2019

### Citation:

Rossi E, Bernabeu C and Smadja DM (2019) Endoglin as an Adhesion Molecule in Mature and Progenitor Endothelial Cells: A Function Beyond TGF-β. Front. Med. 6:10. doi: 10.3389/fmed.2019.00010 Keywords: endoglin, endothelial progenitors, EPC, ECFCs, HHT1, integrins, TGF-β

# ENDOGLIN STRUCTURE AND FUNCTION

Endoglin (ENG; also known as CD105) is a type I transmembrane glycoprotein predominantly expressed in endothelial cells (ECs) (1, 2) and endothelial colony-forming cells (ECFCs) (3). It is known as an essential co-receptor for the transforming growth factor β (TGFβ) family, playing an important role in angiogenesis. At the cell surface, ENG associates with TGF-β type I receptors, including the ECs-specific ALK1 (activin receptor-like kinase 1) and the ubiquitous ALK5, as well as to a TGF-β type II receptor. In this receptor complex, ENG and ALK1 are able to bind to bone morphogenetic protein 9 (BMP9; also known as growth differentiation factor 2 [GDF2]), a member of the TGF-β family, with much higher affinity than to TGF-β1 and mediate its proliferation signal (4–7). Upon ligand binding, the TGF-β signaling receptor complex phosphorylates members of the Smad family of transcription factors which, in turn, undergo nuclear translocation to regulate gene expression (**Figure 1A**).

In addition to the membrane bound form of endoglin, a circulating form of the ENG ectodomain, was found to be released upon a C-terminal cleavage of ENG by matrix metalloproteinase 14 (MMP-14) (8, 9). Increased levels of circulating endoglin have been detected at early stages of preeclampsia (5, 10) in hypertensive or diabetic patients (11) and in certain cancer patients (12), suggesting its role as a predictive biomarker in these pathologies. Interestingly, a detailed characterization of plasma from women with preeclampsia has revealed that circulating endoglin can be complexed within exosomes, rather than as individual soluble endoglin (13).

A pioneer article on human ENG described that this glycoprotein is present on ECs as a dimer (two subunits of ∼90-kDa), each displaying the tripeptide arginine-glycineaspartic acid (RGD) in the extracellular region of the protein (1). The RGD motif and homologous sequences in mouse and pig are recognition motifs for cell surface integrins present in extracellular matrix proteins such as fibronectin, tenascin, thrombospondin, vitronectin, von Willebrand factor as well as fibrinogen and prothrombin (14–16). Integrinmediated adhesion is involved in hemostasis, thrombosis, and inflammation, processes in which the endothelium plays a critical role. Therefore, the presence of the RGD motif within a hydrophilic environment (1) and its accessibility in the 3D structure of ENG (7), clearly suggest the involvement of ENG in integrin binding, as postulated in early studies (17, 18). In fact, endothelial endoglin binds to leukocyte integrins, allowing leukocyte extravasation (19) suggesting a possible novel function for ENG independent of TGF-β signaling (**Figures 1B,C**). Several lines of evidence support the role of ENG in leukocyte trafficking: (i) ENG expression is markedly up regulated in endothelial cells of inflamed tissues with an associated inflammatory cell infiltrate (19); (ii) ENG is up-regulated in the post-ischemic kidney and ENG-haploinsufficient mice are protected from renal ischemiareperfusion injury, due to a reduction of cellular inflammatory responses (20); (iii) Arterial, venous, and capillary endothelia in lymphoid organs are highly reactive with anti-ENG antibodies and a marked staining pattern is observed in high endothelial venules (21); and (iv) Although ENG is present throughout the vascular endothelium, its expression, as compared to veins or arteries, is stronger in capillaries, where most leukocyte infiltration to organs occurs.

In addition to leukocytes, other cell types present in the circulatory system have also revealed an integrin-mediated adhesion activity toward endothelial endoglin (22–24) (**Figures 1D,E**). Thus, endoglin plays a role in integrin-mediated adhesion of vascular mural cells to endothelium (23). Also, a new role for endoglin in αIIbβ3 integrin-mediated adhesion of platelets to the endothelium, conferring resistance of adherent platelets to detachment has been described (24) (**Figure 1E**).

# VASCULAR PATHOPHYSIOLOGY OF ENDOGLIN IN HEREDITARY HEMORRAGIC TELANGECTASIA (HHT)

Hereditary Hemorrhagic Telangiectasia (HHT) is an autosomal dominant disease with a prevalence of one case per 5,000–8,000 individuals (25, 26). HHT patients develop muco-cutaneous lesions known as telangiectasia in the nose, mouth, and gastrointestinal tract, as well as larger AVMs in major organs such as the lung, liver, and brain (25). The telangiectasia are comprised of fragile vessels that are susceptible to rupture and hemorrhage, which means that HHT patients can suffer recurrent anemia following frequent and severe bleeding episodes. Mutations in the endoglin gene (ENG) are responsible for HHT type 1 (HHT1) (27), while mutations in activing receptor-like kinase (ACVRL1 alias ALK1) are responsible for HHT2 (2, 27). Taken together, HHT1 and HHT2 account for more than 80% of all HHT cases, whereas a small number of patients show mutations in SMAD4 (MADH4) or in BMP9 gene (GDF2), which are responsible for less common HHT variants such as a combined syndrome with juvenile polyposis (JP-HHT) and HHT5, respectively (28, 29). Remarkably, all of these genes code for proteins involved in the TGF-β family signaling pathways (30). Nowadays, the most widely accepted hypotheses for AVMs formation in HHT1 and HHT2 involves a "first hit" (gene haploinsufficiency) based on the loss of one functional allele Eng+/− (HHT1) or ALK1+/− (HHT2), which causes a 50% reduction in protein expression, followed by a "second hit" based on an angiogenic trigger such as inflammation, hypoxia or vascular injury (31). Together, these events may induce a deficient endothelial function of ENG that could result in HHT vascular lesions. The relevant role of endoglin in the pathophysiology of the vascular system is illustrated by the phenotype of different HHT1 animal models (32). While endoglin heterozygous mice are viable and reproduce the vascular phenotype of HHT patients, total loss of ENG expression leads in mice to cardiovascular defects and embryonic death by mid-gestation (33–35). A conditional knock out mouse model for HHT1 has revealed that the arteriovenous malformations (AVMs), induced upon ENG loss, appear to be the result of delayed vascular remodeling and inappropriate ECs proliferation responses (36). In ENG null mice, vasculogenesis does not seem affected, suggesting that ENG is not required for initial differentiation of ECs or formation of a primitive vascular plexus. The yolk sac of Eng−/− embryos has enlarged fragile vessels, while the embryo proper shows cardiac cushion defects and delayed maturation of major vessels (34). The reason why the total loss of ENG expression leads to cardiovascular defects and embryonic death is not completely understood. Some authors have postulated that the primary defect is in the heart (37), while others suggest that the loss of smooth muscle cells (SMCs) coverage of the endothelium could play a decisive role in the lethality of Eng−/− animals (34).

FIGURE 1 | Hypothetical model of Endoglin and its role as TGF-β co-receptor and adhesion molecule in different physiological contexts. (A) Canonical pathway that implicates Eng as a co-receptor of TGF-β in ECs. Bone morphogenetic protein 9 (BMP9), and other members of the TGF-β family, bind to an EC receptor complex composed by the type I (R-I) receptor named ALK1 and the type II (R-II) receptor, both exhibiting serine/threonine kinase activity, as well as the auxiliary receptor endoglin. Upon ligand binding, the R-II transphosphorylates ALK1, which subsequently propagates the signal by phosphorylating the receptor-regulated Smad (R-Smad) family of proteins Smad1/5/8. Phosphorylated R-Smads form heteromeric complexes with Smad4, translocating into the nucleus to regulate the transcriptional activity of different target genes. BMP9, Endoglin, ALK1 and Smad4 proteins are encoded by GDF2, ENG, ACVRL1 and MADH4 genes, whose pathogenic mutations give rise to HHT5, HHT1, HHT2, and JPHT, respectively (30). (B) Endothelial endoglin as an adhesion molecule involving its RGD sequence when binding to cell surface integrins from leukocytes, VMCs or platelets. (C) Leukocyte transmigration through the vessel endothelium. Under inflammatory conditions, different soluble factors are released leading to leukocyte adhesion and transmigration through ECs. This process is mediated, at least, by the interaction between leukocyte integrin α5β1 and endothelial endoglin involving the specific recognition of the RGD motif in Eng (22). (D) Adhesion between endothelial cells and vascular mural cells (VMCs). This intercellular association involves, at least, the interaction between integrin α5β1 in VMCs and endothelial endoglin via the specific recognition of the RGD motif in Eng (23). (E) Platelet-dependent hemostasis. Endothelial endoglin via its RGD binds to platelet integrins αIIbβ3 and α5β1, contributing to stabilize platelets adhesion to endothelium (24).

### ENDOGLIN INTERACTING PARTNERS IN THE VASCULATURE

Previous studies reported that endothelial ENG is associated with several proteins critically involved in endothelial cell adhesion, proliferation, migration, angiogenesis, and vascular permeability, including integrins (38), zyxin (39), zyxin-related protein 1 (ZRP-1) (40), VEGF receptor type 2 (VEGFR-2) (41), betaarrestin 2 (42), and vascular endothelial-cadherin (VE-cadherin) (43). It has been shown that VE-cadherin interacts with several components of the TGF-β receptor complex, including ENG, ALK1, and the TGF-β type II receptor TGFBR2 in adherents junctions. Of note, ENG, TGFBR2, and ALK1 also interact with p21-activated kinase (PAK-1), GTP-binding proteins, and Ras-related C3 botulinum toxin substrate 2 (Rac-2), involved in endothelial barrier maintenance, cytoskeletal remodeling and cell migration (44). Using in vitro ECs lines derived from ENG homozygous null, heterozygous, and control mouse embryos, Jerkic and Letarte have reported that ENG-deficient mouse embryonic ECs are hyper-permeable and unresponsive to stimulation by VEGF and TGF-β1, factors that regulate vascular permeability (45). Interestingly, VE-cadherin is barely detected in ENG-deficient ECs, a finding that could explain the EC junction destabilization and impairment of TGFβ signaling. Furthermore, permeability alterations in Eng+/− mice are in line with findings in patients with Hereditary Hemorrhagic Telangiectasia type 1 (HHT1). Thus, ECs from HHT1 patients show a disorganized actin cytoskeleton prone to cell breaking with changes in shear stress and blood pressure (46). This reorganization of actin filaments in HHT1 might lead to vessel hemorrhages and eventual disappearance of the capillary network, as reported in this disorder. In line with the pathogenic hypothesis of an endothelial dysfunction in HHT1, the involvement of ENG in integrin-mediated trans-endothelial leukocyte trafficking was proposed (31). Accordingly, following inflammation triggering, the extracellular region of ENG binds to leukocyte integrin α5β1 and promotes leukocyte transmigration (31). In contrast, a different experimental approach showed an increased gut inflammation and myeloid infiltration in colitis Eng+/− vs. control mice (47). Overall, this novel role of ENG may contribute to a better understanding of the inflammation process and the high incidence of infections in HHT patients (48). Because pericytes embrace the endothelium to regulate the blood retinal barrier, the role of endothelial endoglin in this context has also been analyzed. Thus, vascular permeability studies carried out in vivo showed that retinas of Eng+/− heterozygous mice displayed higher retinal permeability than that of Eng+/+ mice, suggesting a destabilization of the endothelial barrier function due to ENG haploinsufficiency (23). The importance of the interaction between ECs and vascular mural cells is also underlined by studies showing that thalidomide stabilizes the vasculature in a mouse model of HHT1 (49). Indeed, pericyte recruitment and stabilization appears as a therapeutic strategy to reduce the severity of epistaxis in HHT (50). Thus, ENG, directly, or as a component of the TGF-β receptor complex may regulate ECs integrity, whereas its absence may result in vascular hyper-permeability, thus contributing to the fetal lethality associated with the homozygous null genotype (39, 40).

Although this review focuses on the role of endoglin in ECs, it is worth mentioning that endoglin is also expressed at lower levels in other cell types, some of which present in the vasculature as vascular SMCs, activated monocytes or mesenchymal cells (18, 51–53). However, a review on the endoglin role in these cell types deserves an independent study.

### ENDOGLIN AND ENDOTHELIAL COLONIES FORMING CELLS (ECFCS)

The formation of blood vessels from mesoderm is driven by the recruitment of undifferentiated cells which can differentiate into endothelial lineage. In the adult, populations of bone marrowderived endothelial progenitor cells (EPCs) are mobilized into the circulation by stimuli such as estrogen and VEGF (54). Interestingly, human EPCs could derive from very small embryonic like stem cells (55) that can also give rise to hematopoietic cells (56). Two populations of EPCs have been differentially characterized from circulating endothelial progenitors: (i) early EPCs that exhibit a hematopoietic profile with a genomic fingerprint similar to monocytes; and (ii) ECFCs, also known as blood outgrowth endothelial cells (BOECs), which are considered late EPCs with an endothelial-like genomic profile (57). ECFCs display an intrinsic tube forming capacity in vitro and in vivo and they are involved in blood vessel formation and vascular repair (58, 59). For example, upon vascular injury, tissue perfusion of blood flow involves not only angiogenic sprouting of ECs from nearby intact vessels, but also the recruitment circulating ECFCs allowing the formation of new blood vessels (60). Acting at the interface between blood and tissues, ECFCs have a remarkable ability for migration and proliferation, being key cells in angiogenesis and vascular remodeling. It is therefore not surprising that perturbations in these critical ECFCs functions contribute to several vascular pathologies. Accordingly, ECFCs have opened a new area for treatment of cardiovascular diseases using cell therapy, advancing in the knowledge of vessel reconstruction ability in postnatal life. In this line, much effort has been recently devoted to identify novel molecular targets within ECFCs able to enhance their angiogenic potential. ECFCs are positive for endoglin (CD105), VE-cadherin (CD144), CD31, VEGFR2 (KDR), EGFL7, and CD146, and negative for CD45 and CD14 (58, 59). Among these, ENG is emerging as an interesting therapeutic target based on its involvement in angiogenesis and vascular remodeling (46, 61).

Many strategies of therapeutic revascularization, based on the administration of growth factors or stem/progenitor cells from diverse sources including EPCs, have been proposed and are currently being tested in patients with peripheral arterial disease or cardiac diseases (62). Some pretreatment of ECFCs with erythropoietin (63), fucoidan (64), soluble CD146 (65), tumor necrosis factor-α (TNF-α) (66), or platelet lysates (67, 68) have also been proposed to improve the therapeutic efficacy of in vivo administered ECFCs. Interestingly, integrins represent a major molecular determinant of EPCs function. More specifically, integrin α4β1 is a key regulator of EPCs retention and/or mobilization from the bone marrow, while integrins α5β1, α6β1, αvβ3, and αvβ5 are involved in EPCs homing, invasion, differentiation and paracrine factor production (69). The involvement of ECFCs in angiogenesis has been shown to be essential for ischemic disease recovery (70, 71). Furthermore, the differentiation of mesenchymal stem cells (MSCs) into mural cells appears to be stimulated by their co-culture with ECFCs, whereas overexpression of the Notch ligand Jagged1 in ECs further enhanced the differentiation of MSC into pericytes (72). This cooperation prompted some authors to postulate an improvement of ECFCs efficiency by co-injecting ECFCs and SMC progenitors (73). Therefore, a combined treatment of ECFCs with mesenchymal stem/progenitor cells (MSCs/MPCs) appears to operate synergistically, enhancing neovascularization compared to either individual cell population (74, 75). Recently, we proposed a role for ENG in the ECFCs-MSCs interplay involved in the revascularization process (61). Indeed, ENG silencing in ECFCs markedly inhibited ECFCs adhesion to MSCs in vitro, without affecting MSCs differentiation into perivascular cells or other mesenchymal lineages. Mesenchymal stem cells (MSCs) increase muscle recovery of ECFC in HLI model and we found that ENG-silenced ECFCs co-injected with MSCs in mice abolish beneficial effects of MSCs leading to decreased vessel density and foot perfusion upon ischemia. Our results suggest ENG involvement in the crosstalk between ECFCs and MSCs, leading to vessel formation and stabilization (51) (**Figures 2A,B**).

As postulated in **Figure 2C**, three steps may be involved during the formation of stable vessels by ECFC and MSC and/or perivascular cells: (i) Mutual paracrine effects by VEGF and/or PDGF-BB (76, 77). (ii) ECFCs-driven perivascular maturation of MSC and/or SMC progenitors via the notchdependent pathway; and (iii) Adhesion between ECFCs and perivascular cells in an ENG-dependent manner. The relevance of endothelial ENG adhesive properties in mature vessel formation in mice (61) is in agreement with a previous report on the active role of human ENG in the adhesion between mature ECs and vascular mural cells (23).

Relevance of ENG in ECFC has been demonstrated also in ECFCs isolated directly from HHT1 patients. Indeed, these ECFCs have disorganized and depolymerized actin fibers and impaired tube formation, as compared to healthy ECFCs (46). The molecular basis for the abnormal behavior of HHT1 ECFCs appears to be mediated, at least in part, by the regulatory role of endoglin and we can speculate via its interaction with zyxin family members involved in the actin cytoskeletal organization as it was proposed for endothelial cells (39, 40). Thus, the ENG cytoplasmic domain binds to zyxin and ZRP1, which concentrate at focal adhesions within actin polymerization points. Consequently, the decrease of ENG levels in HHT1 ECFCs could be related to the cytoskeleton alteration.

# CONCLUSION

Blood vessel formation and remodeling are essential processes in the maintenance of tissue homeostasis and function, and therefore, their alteration causes a variety of pathologic conditions. ENG involvement in the function of mature and progenitor endothelial cells is a hot topic not completely understood and developed yet. Recent findings underline the importance of ENG not only as a co-receptor for several ligands of the TGF-β family, but also as a molecule involved in cell adhesion, which can regulate ECFCs behavior in the context of angiogenic processes. It is recognized that in mature ECs, ENG plays a key role in angiogenesis and blood vessel homeostasis, becoming a potential therapeutic target for pro- and anti-angiogenic approaches in the treatment of diseases such as HHT, cancer, preeclampsia, diabetes complications or post-ischemic disease. While ENG expression is relatively low in quiescent ECs, it is upregulated in the active endothelium involved in angiogenesis and vessel remodeling and permeability. In these processes, endothelial ENG regulates ECs proliferation, migration, and actin cytoskeletal organization, leukocyte extravasation, and interaction with mural cells. In addition, a role for ENG in platelet-endothelium interaction, including the resistance of adherent platelets to shear under inflammatory conditions, was also recently proposed, opening new perspectives on ENG functions.

This review highlights the emerging roles of endothelial ENG as a cell adhesion molecule in mature and progenitor endothelial cells interacting with vascular mural cells, leukocytes,

### REFERENCES

and platelets, independently or in parallel to its TGF-β coreceptor role.

While evidence-based therapeutics are coming into play in the traditionally empirical base for endoglin related protein derivatives, future studies of ENG in endothelial progenitor cells may pave the way for a better understanding of its function in the vascular system and for the development and understanding of the use of these immature cells as a cell therapy product.

### AUTHOR CONTRIBUTIONS

ER wrote the manuscript. CB provided helpful suggestions and contributed to writing the manuscript. DS wrote the manuscript and provided funding support.

telangiectasia associated with mutations in MADH4 (SMAD4). Lancet (2004) 363:852–9. doi: 10.1016/S0140-6736(04)15732-2

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Rossi, Bernabeu and Smadja. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Selection of a Real-Time PCR Housekeeping Gene Panel in Human Endothelial Colony Forming Cells for Cellular Senescence Studies

Kiran J. McLoughlin, Edoardo Pedrini, Meabh MacMahon, Jasenka Guduric-Fuchs and Reinhold J. Medina\*

*Centre for Experimental Medicine, School of Medicine, Dentistry, and Biomedical Science, Queen's University Belfast, Belfast, United Kingdom*

Endothelial Colony Forming Cells (ECFCs) represent a subset of endothelial progenitors with well-documented vasoreparative capacity. However, cellular senescence, which occurs due to aging, diabetes, smoking, or tissue inflammation, renders these cells dysfunctional. Therefore, there is growing interest in studying expression of senescence markers in ECFCs. RT-qPCR is the most commonly used technique to quantify gene expression and the proper choice of reference genes used for data normalization is critical for accurate quantification. It has been reported that the expression of commonly used housekeeping genes is often unstable in senescence. To identify the most suitable reference genes for ECFC senescence studies, we analyzed a microarray dataset, which compared the gene expression between proliferating and senescent ECFCs. In addition to replicative senescence, the data included X-ray-induced and Etoposide-induced senescence. We used the geNorm algorithm to identify the most stable genes across all studied conditions. Gene Ontology analysis found that the most stable genes belonged to the KEGG category of Genetic Information Processing. The optimal combination of housekeeping genes for ECFC senescence was found to include four ribosomal protein genes; RPL13, RPL31, RPL37, and RPL30. The RT-qPCR validation confirmed that normalization with our novel panel was more sensitive in identifying senescence markers compared to commonly used genes such as ACTB, UBC, and GAPDH.

### Edited by:

*Enrico Capobianco, University of Miami, United States*

### Reviewed by:

*Petra Hruba, Institute for Clinical and Experimental Medicine (IKEM), Czechia Nicholas Ekow Thomford, University of Cape Coast, Ghana*

\*Correspondence:

*Reinhold J. Medina<EMAIL_ADDRESS>
### Specialty section:

*This article was submitted to Translational Medicine, a section of the journal Frontiers in Medicine*

Received: *12 November 2018* Accepted: *04 February 2019* Published: *11 March 2019*

### Citation:

*McLoughlin KJ, Pedrini E, MacMahon M, Guduric-Fuchs J and Medina RJ (2019) Selection of a Real-Time PCR Housekeeping Gene Panel in Human Endothelial Colony Forming Cells for Cellular Senescence Studies. Front. Med. 6:33. doi: 10.3389/fmed.2019.00033*

Keywords: housekeeping gene, ECFCs, senescence, RT-qPCR, computational biology

# 1. INTRODUCTION

Endothelial progenitor cells (EPCs) are defined as cells circulating in blood with the capacity to differentiate into endothelial cells and contribute to new blood vessel formation. There has been controversy and confusion in the literature when studying EPCs. We and others have proposed nomenclature guidelines for precise terminology (1). A subtype of EPC which is clearly defined by their immunophenotype and functional properties are endothelial colony forming cells (ECFCs). ECFCs exhibit both progenitor and endothelial characteristics and have been considered the bona fide EPC (2). There is agreement that ECFCs have remarkable vasoreparative potential and consequently represent an ideal candidate for cell therapy (3–5). ECFCs are isolated as small clusters of cells and therefore, require in vitro cell number amplification from tens to millions to meet numbers needed for a cell therapy. Our data demonstrated that this is feasible, and we can expand ECFC numbers from 100,000 to 2.5 billion cells in 14 days. Although ECFCs have significant proliferative potential, they have a Hayflick limit and undergo replicative senescence (6). In order to characterize the senescence program in ECFCs at the gene expression level, there is a requirement to optimize and validate reference genes. While microarray and RNA-seq are high-throughput technologies that allow genome-wide assessment of transcriptomes, reverse transcriptase real time quantitative polymerase chain reaction (RT-qPCR) remains the most frequently used methodology for small scale studies of gene expression. RT-qPCR is extremely sensitive, has a broad dynamic range, is fast and highly reproducible (7). Despite these advantages, RT-qPCR accuracy is highly dependent on the choice of reference genes (8). These internal controls, also known as housekeeping genes, are used as the normalization factor and therefore its expression ideally should not be affected by experimental treatments. Poor choice of housekeeping genes has a major impact on results and could lead to generation of misleading information. Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines have indicated the need for validation of the housekeeping gene choice to ensure stable expression across the experimental settings (9). The general assumption that "classical" housekeeping genes are appropriate is not justified because it has been demonstrated that the expression GAPDH and βactin change under certain in vitro experimental conditions, as well as with respect to source material. For example, Glare and colleagues found that β-actin and GAPDH expression is reduced in both broncoalveolar lavage fluid cells and endobronchial biopsy tissue in asthmatics compared to healthy controls (10, 11). In particular, several studies have highlighted that the expression of common housekeeping genes is often unstable in aging and cellular senescence (12–14). Therefore, considering that aging is associated with major metabolic and structural changes in cell phenotypes, it is critical to identify the most stable gene normalizers for senescence studies. Here, we established and validated a panel of housekeeping genes for cellular senescence studies using ECFCs.

# 2. MATERIALS AND METHODS

### 2.1. ECFC Isolation and Characterization

ECFCs were obtained from human umbilical cord blood with appropriate maternal consent and under ethical approval in accordance with the Declaration of Helsinki. The mononuclear cell fraction was isolated by density gradient fractionation. Umbilical cord blood was diluted using Alsever's solution (Sigma-Aldrich) and carefully layered on Histopaque-1077 (Sigma-Aldrich) in a 1:1 ratio. The fraction at the interphase between the Histopaque and the plasma was carefully removed using a transfer pipette and resuspended in EGM-2 (Lonza Ltd.) supplemented with 20% fetal bovine serum (Hyclone), and plated in 24-well NUNC tissue culture plates precoated with rat tail collagen type I (BD Biosciences). ECFCs were characterized by immunophenotyping for CD31, CD105, CD14, and CD45 (eBioscience) using an Acoustic Focusing Cytometer (Attune NxT, Life Technologies) following already established methodology (5). For all experiments, ECFCs at passage 9 were used.

### 2.2. Induction of Senescence

Both replicative and stress-induced senescent ECFCs were generated for this study. Stress-induced senescence was induced by treating ECFCs with 1 µM Etoposide (Sigma) for 4 days. Media was replaced with fresh EGM-2 supplemented with 10% fetal calf serum (Gibco) and ECFCs were cultured for an additional 4 days to establish senescence, before RNA was extracted using Maxwell RSC automated RNA extraction system (Promega). For X-ray induced senescence cells were treated with 10 Gy and cultured for 5 days to allow establishment of senescence. For replicative-induced senescence, cells were allowed to grow until their Hayflick limit was reached. Quiescence was induced via contact inhibition by allowing cells to reach and maintain 100% confluency for 5 days before RNA was extracted using Maxwell RSC automated RNA extraction system (Promega).

### 2.3. Microarray

Total RNA was extracted using miRNeasy Mini RNA extraction kit (Qiagen). Total RNA from each sample was quantified by the NanoDrop ND-1000 and RNA integrity was assessed by Agilent 2100 Bioanalyser. The microarray hybridization was performed by Arraystar (Rockwille, US) following established protocols. Microarray data was deposited in GEO with accession number GSE125792.

# 2.4. Quantitative Real Time PCR

RNA was reverse transcribed with the High-Capacity cDNA Reverse Transcription kit (ThermoFisher). Primer sequences shown in **Table S1** were designed using Primer BLAST and experimentally validated; only primers matching the MIQE guidelines (**Table S2**) were used (9). RT-qPCR was performed on Roche LightCycler 480 with the Power SYBR-green PCR Master Mix (ThermoFisher), following the manufacturer instructions. Amplification reactions were done in three technical replicates. Following a polymerase activation step at 95◦C for 15 min, 40 cycles, including denaturation at 94◦C for 10 s; annealing at 58◦C for 10 s, and amplification at 72◦C for 10 s were performed. Fluorescent signals generated during PCR amplification were monitored and analyzed with LightCycler 480 software (Roche). The expression level was estimated using 35 cycles as limit of detection (LOD). For both microarray data and RT-qPCR data, 3 biological replicates were used; however, different biological replicates were used between techniques (i.e., microarray used 3 different biological replicates to those used for RT-qPCR). Therefore, independent samples were used for RT-qPCR validation because this provides higher confidence to generalize results as 6 different biological replicates tested using two techniques yielded similar results.

### 2.5. GeNorm Implementation

Stability of gene expression was assessed using the selectHK() function form the NormqPCR package (15). The source code applied is presented below:

```
# library(BiocInstaller)
# biocLite("NormqPCR")
library(NormqPCR)
input_file <- "input.csv"
df_test <- read.csv(paste0("~/folder/",
input_file))
# identify the numeric varible
# df_test<-df_test[c(1:100,nrow(df_test)),]
id_test <- unlist(lapply(df_test,is.numeric))
mat2 <- as.matrix(df_test[-nrow(df_test),
id_test])
str(mat2)
raw2 <- new("qPCRBatch", exprs = mat2,
 featureCategory =
            as.data.frame(array("OK",
             dim=dim(mat2))))
sampleNames(raw2) <- colnames(df_test)[-1]
featureNames(raw2) <- as.character
(df_test[,1][-nrow(df_test)])
head(exprs(raw2))
t <- Sys.time()
out <- selectHKs(raw2, method = "geNorm",
               Symbols = featureNames(raw2),
                minNrHK = 2, log = T)
d<-Sys.time()-t
# print the time enlapsed
d
saveRDS(object = out,file = paste0("~/out_",
input_file,".rds"))
```
# 2.6. Statistical Analysis

Data handling and statistical analysis was done using R (16). Unless otherwise specified, the alpha value for statistically significant was set to 5%. The tests were performed using the default parameters and in a two-sided manner. Where appropriate, p-values were corrected for multiple testing. To calculate the confidence interval of the correlation coefficient, the cor.test() function from the "stats" package (16) has been used.

# 3. RESULTS

### 3.1. Characterizing Gene Stability From the ECFC Transcriptome in a Cellular Senescence Investigation Using geNorm

To identify ECFC genes with the most stable expression during cellular senescence, we analyzed a microarray dataset of non-senescent and senescent ECFCs. Senescence programs investigated were replicative, X-ray induced, and Etoposideinduced. Since microarray and RT-qPCR are two different techniques in terms of dynamic range and limit of detection (17), and to avoid the selection of candidate housekeeping genes that were difficult to detect within our RT-qPCR experimental setup, we decided to filter the dataset focusing on genes with high expression. After normalization, the microarray dataset provided values for average expression of 18,667 probes (**Figure 1A**). We screened for the level of expression by RT-qPCR along the overall distribution defined by the microarray. Based on these results (**Figure 1A** bottom panel), we decided to use the top 9,000 probes for further analysis. This threshold was applied as it was both a combination of the halfway point of the microarray data and the point at which transcripts could be unequivocally assessed in vitro using RT-qPCR. We applied the geNorm algorithm using the normalized expression level of the probes in the microarray. GeNorm is an algorithm developed by Vandesompele and colleagues which can be used to assess the level of variation in gene expression between different experimental conditions. It works by taking the standard deviation of the genes between experimental conditions and comparing it to the other genes within the dataset. The least stable gene is then excluded and the process is repeated, until two genes remain at the end. Full details can be found in Vandesompele et al. (18). It is important to highlight that the geNorm algorithm provides a stability score, which is inversely proportional to the common idea of stability; therefore, a lower value depicts higher stability. GeNorm indicated that 8616 probes had stability values lower than 1; and 4,444 transcripts had stability values lower than 0.5 (**Figure 1B**). Furthermore, we confirmed results by visualizing the expression of some genes at different stability ranking levels. As expected, by moving down the ranking, both inter- and intra- variability of the gene expression increased (**Figure 1B** bottom panel). Interestingly, we noticed a general trend in the dataset; if a gene is more highly expressed, then it seems more likely to be ranked as stable as shown by the dotted red line (**Figure 1C**). This is also highlighted by the MA plot showing that genes with the higher expression (above 15 variance stabilizing transformation VST units) are the most stable (**Figure 1D**). When choosing and designing a panel of housekeeping genes, the most stable transcripts from different biologically relevant categories (defined by GO term) were identified from microarray analysis using the geNorm algorithm and validated with PCR. Transcripts with middle and low stability were also sampled at random in order to establish variances in expression based on differing stability values. In summary, we applied the well-established geNorm algorithm to a subset of ECFC senescence microarray dataset to screen for stable targets.

### 3.2. Most Stable Genes Belong to Genetic Information Processing

Starting from the geNorm-based results, we used the top 1000 genes, ranked by stability among all senescence samples, to perform GO analysis (19, 20), using Cellular Component domain. We found that GOs for cytosolic large and small ribosomal subunits were among the most enriched cellular components in terms of stability (**Figure 2A**). To further characterize the identity of the most stable genes, we also performed enrichment analysis using KEGGs terms (21, 22). We found that mRNAs coding for proteins involved in genetic information processing, which include ribosomal protein genes, were among the most stable ECFC genes in the dataset used (**Figure 2B**).

from microarray data. The red line defines the arbitrary threshold used to filter the dataset. In the lower panel, the qPCR amplification plots for some genes along the distribution are shown. Genes with expression level close to the threshold are close to the LOD of our experimental setup. (B) Stability vs. ranking obtained from geNorm. In the lower panel, the distributions of expression levels (from the microarray) for some genes along the ranking are shown. Genes with lower rank have higher within and between treatment variability in gene expression. (C) Scatter plot of stability vs. expression (from microarray). The dotted line shows the trend in the dataset; genes with a higher expression level seem to have a lower stability score i.e., more stable. (D) MA plot showing level of expression vs. expression fold change. GeNorm stability score depicted using a colored scale.

### 3.3. Four Ribosomal Protein Genes RPL13, RPL31, RPL37, and RPL30 Provide the Optimal Combination of Housekeeping Genes to Normalize Gene Expression in ECFC Senescence Studies

To refine our ECFC housekeeping gene selection and establish the ideal number of housekeeping genes to be used in a panel, we designed and successfully validated RT-qPCR primers for 28 target genes (**Table S1**), including targets from geNorm screening of microarray data plus "classical" housekeeping genes i.e., ACTB, GAPDH, and UBC. Those were further tested using different RNA samples including non-senescent, quiescent, and senescent ECFCs. RT-qPCR results were analyzed with geNorm and ranked according to their stability (**Figure 3A**). As expected, this independent experiment confirmed ribosomal genes as the most stable and validated RPL13, RPL31, RPL37, RPL30, and RPS6 as the top 5 most stable genes among

FIGURE 2 | Gene Ontology analysis for most stable genes. (A) GO enrichment analysis. The table shows the top 10 GO terms for the Cellular Component domain, using the top 1,000 most stable genes in the dataset. Ribosomal proteins seem to be the most enriched component within the most stable genes. (B) Proteomap of the most stable genes. The terms shown in the Voroni plot are based on the KEGG Pathways gene classification. Each gene is shown by a polygon and functionally related proteins are arranged in common regions.

the 28 candidates. In addition, since MIQE (9) guidelines recommend to use at least two housekeeping genes for accurate normalization of gene expression in qPCR experiments; we used the geNorm algorithm to calculate the variability provided by the normalization factor using different number of housekeeping genes. Interestingly using our data and housekeeping candidates, we found that, as expected, the higher the number of genes used, the lower the variability; but from our 28 candidates, we reached a maximum low variability with 17 genes; as using more showed increased variability. In addition, the pairwise variation using the top two housekeeping genes was already below the suggested threshold of 0.15. Nevertheless, results from geNorm variability highlighted that there was an evident improvement in the pairwise variation using 4 genes in the definition of a normalization factor; however, there was a minimal improvement when using 5 instead of 4 genes (**Figure 3B**). This is also supported by comparing the correlation of the NFs using 3 vs. 4 genes and 4 vs. 5 genes **Figure S1**).

# 3.4. The Normalization Factor From Our Housekeeping Panel Is Different to the Normalization Factor Using Classical Housekeeping Genes

Although we followed stringent criteria to select optimal housekeeping genes for ECFC senescence studies, it is difficult to define the "real normalizer"; therefore, we decided to compare our set of candidate housekeeping genes with a set of "classical" housekeeping genes including ACTB, UBC, and GAPDH in terms of their normalization factor. The objective was to test if our set of housekeeping genes provides a normalization factor which is not correlated with the one obtained by using the classical housekeeping genes (therefore different). Correlation analysis demonstrated that the normalization factor using our set of selected housekeeping genes is not significantly correlated with any normalization factor provided by other classical approaches (ACTB alone, GAPDH alone, UBC alone and ACTB GAPDH and UBC altogether) (**Figure 4A**). Consequently, considering that

ordered by stability (starting from the most stable; by row; top to bottom), expressed as Ct of three biological replicates (for each biological replicate, three technical replicates were produced). The horizontal red line is the estimate of the average expression for the studied gene in the specific treatment group. The vertical red line is the 95% confidence interval of the estimate of the average expression. The variability within and between treatment increase moving from top-left to bottom-right. Ctrl, control ECFCs; Qui, quiescent ECFCs; Etop, Etoposide-induced senescent ECFCs. (B) Bar plot showing sample variability by the number of genes used for calculating the normalization factor (NF).

our set of genes has been proven to be more stable compared to the classical ones, we can conclude that our normalization factor is more reliable than the one provided by classical housekeeping genes alone and combined. This accuracy is further underscored by different results found within our experimental setup using ECFC and senescence/quiescence in vitro models. The expression of UBC was significantly downregulated in quiescent ECFCs when using our housekeeping gene panel (**Figure 4B**). Similarly, we found significant upregulation of UBC gene expression in senescent ECFCs, confirming UBC as an unsuitable housekeeping gene for senescence and quiescence studies in ECFCs. This is in agreement with previously published data from another group investigating cell size and gene expression; which reported similar changes in UBC expression in both A549 cells and primary human fibroblasts (23). In addition, we evaluated gene expression of IGFBP7 and IL6, which are components of the senescent associated secretory phenotype (SASP) and were expected to be upregulated. While both, our new and the classical housekeeping panels identified a significant upregulation of IGFBP7, only our new housekeeping panel was able to detect a significant upregulation of IL6 (**Figure 4C**). Taken together, we were able to validate our set of new housekeeping genes as highly accurate for ECFC senescence studies.

### 4. DISCUSSION

We have shown a workflow for the identification of housekeeping genes for RT-qPCR using an unbiased approach starting from a transcriptomic dataset. Considering the growing number of omic dataset freely available online, we demonstrate that relying on classically reported housekeeping gene is not always accurate. Moreover, with this approach, it is possible to identify candidates for specific cell types and biological processes that are not yet validated for RT-qPCR experiments. Here, we have used such

FIGURE 4 | Normalization factor using "classic" housekeeping genes does not correlate with our new geNorm-based housekeeping panel.(A) The upper panel shows the scatter plots comparing normalization factors (NFs) from single genes (UBC, GAPDH, ACTB) or the geometric average of the expression of the three (classic) vs. the new NF based on the genes identified by this study (RPL13, RPL31, RPL37, RPL30). The lower panel shows the estimate of the Pearson correlation r (the black dot) and its 95% confidence interval (vertical black line) for each of the scatter plots above. All the confidence intervals include zero (horizontal black line), therefore we can conclude there is not enough evidence of a correlation between any of the classical NFs and the new NF. (B) Level of expression (qPCR) for UBC as dCt using the new NF. The upper panel shows the expression in three biological replicates (three independent ECFC clones) in control vs. quiescence (matched samples). The lower panel shows the expression in four biological replicates (four independent ECFC clones) in young vs. late passage (LP) (matched samples). The horizontal red line corresponds to the estimate of the average expression in the treatment group. The vertical red line is the 95% confidence interval of the estimate of the average expression. (C) Level of expression (qPCR) for IGFBP7 (upper panel) and IL6 (lower panel) as dCt, using either the new NF (right side) or the classical NF (left side) in four biological replicates (matched samples). IGFBP7 is significantly upregulated (as expected comparing LP vs young cells) using both the classical NF and the new NF. IL6 is significantly upregulated (as expected comparing LP vs young cells) only using the new NF. The horizontal red line corresponds to the estimate of the average expression in the treatment group. The vertical red line is the 95% confidence interval of the estimate of the average expression. \**p* <0.05; ns, not significant.

analytical workflow to identify the optimal housekeeping panel for studies investigating ECFCs and cellular senescence. The use of a non-stable reference gene leads to incorrect results. As established by MIQE guidelines (9), reference gene validation must be carried out for every experiment and every type of sample, whether treated or not. In this paper, we propose a panel of the most stable genes to be used in ECFCs using different means of inducing senescence. This study has identified and validated the genes RPL13, RPL31, RPL37, and RPL30 as the most stable in ECFCs for senescence studies. Here, we report a significant decrease in expression of the classical housekeeping gene UBC under quiescence, coupled with a significant increase under senescence, when using the novel panel. This is in agreement with previous work showing that UBC expression is reduced in fibroblasts under quiescence (23) further strengthening the argument that housekeeping genes must be optimized for each experimental condition and cell type to ensure correct interpretation of results. The optimal number of housekeeping reference genes needed for analysis of ECFCs in senescence studies was established. Although MIQE guidelines published in 2009 recommend using at least two housekeeping genes, a systematic review found that the average number of housekeeping genes used across 130 studies between 2010 and 2015 was 1.2 (13). This highlights that while MIQE guidelines are widely accepted, there is a major lack of compliance. In terms of recommendations for future studies, we feel that the use of at least three housekeeping controls is a relatively modest request to ensure that RT-qPCR data investigating senescence mechanisms in ECFCs is accurately analyzed. The mechanistic and organelle-based locations of candidate housekeeping genes was characterized in detail. We found that the majority of identified stable genes were located within the KEGG category genetic information processing, which include also genes coding for ribosomal proteins. This information highlights why a similar workflow as the outlined in this report, should be used to identify future candidates when experimental parameters or cell types change. For example, our housekeeping panel, although validated for senescence studies, will not be ideal for investigations into pathologies focussing on ribosomal proteins or genes. We want to emphasize that there is no perfect "universal" housekeeping gene control for every condition; for any experimental setup, it is essential to first identify the most effective controls. There is little available information in the literature about housekeeping genes for ECFCs; however, a recent publication identified a selection of optimal housekeeping genes for differentiating ECFCs from umbilical cord blood mononuclear cells on scaffold delivery systems (24). This investigation yielded a panel of genes different to ours (YWHAZ, GAPDH, UBC) although the experimental conditions did not investigate senescence and only utilized seven common candidate genes. To our knowledge, this is the first investigation which has used microarray data as a starting point for identifying reference gene candidates in ECFCs. Microarrays have been used before for identifying a shortlist of novel candidate genes in grapevine exposed to water and heat stress (25). In this study, a method was established for cleaning raw microarray data by excluding genes below the threshold of detection for qPCR and those genes which would be unsuitable due to high variation (measured by observing the standard deviation and excluding those of which the standard deviation was unacceptable). This improves the screening process in two ways: Firstly, it allows the identification of robust candidates by excluding those which are unsuitable. Secondly, it reduces the computational burden required to run the algorithm for identifying genes. This is a particular disadvantage of geNorm, where the algorithm follows a polynomial time kinetic (**Figure S2**). This study will contribute to the current field by providing an in-depth methodology for identifying potential housekeeping candidates for ECFCs from microarray data. Although the authors chose senescence as the focus for the analytical pipeline, the methodology is transferable to other cell types, pathologies, and experimental settings.

### REFERENCES

Other cell types, such as human umblical vein endothelial cells (HUVECs) and human mesenchymal stem cells (hMSCs), have already undergone rigorous housekeeping gene selection from a broad range of sources (26, 27) including microarrays. As such, it is important that ECFCs are held to the same rigorous standards. As ECFCs are a promising candidate for cellular therapy applications, it is paramount to determine the mechanisms which may disrupt their function in pathological conditions. For example, it is important to study ECFCs in the context of hypoxia, high glucose, inflammation, oxidative stress, among others. Since senescence of endothelial progenitors is a major component driving vascular aging, this study decided to focus on housekeeping genes for ECFCs when evaluating their senescence programme. Results shown here have practical applications for future studies investigating ECFC senescence. This study also recommends screening transcriptomic changes as a valid strategy to identify precise reference controls in any future ECFC studies.

# AUTHOR CONTRIBUTIONS

KM and EP contributed equally to this manuscript. KM and EP carried out the experiments. JG-F generated samples for the microarray. RM, KM, and EP wrote the manuscript with support from JG-F. MM provided technical advice. RM conceived the original idea.

# FUNDING

This work was supported by the Leverhulme Trust RPG-2015- 357, the Juvenile Diabetes Research Foundation 5-CDA-2014- 225-A-N (JDRF), the National Eye Research Centre SAC 016, and the Medical Research Council (MRC).

### ACKNOWLEDGMENTS

We thank the Belfast Health and Social Care Trust, in particular, Dr. Inez Cooke and Katie Corry for the constant support and assistance.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed. 2019.00033/full#supplementary-material

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geometric averaging of multiple internal control genes. Genome Biol. (2002) 3:RESEARCH0034. doi: 10.1186/gb-2002-3-7-research0034

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 McLoughlin, Pedrini, MacMahon, Guduric-Fuchs and Medina. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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**Author details**

Thomas F. O'Dwyer1,2, Bashir Ghanim1,2, Ronan Courtney2,3, Ashlene Hudson2,3, J. Tony Pembroke1,2 and John G. Murnane4 \*

1 Department of Chemical Sciences, School of Natural Sciences, University of Limerick, Limerick, Ireland

2 Bernal Institute, University of Limerick, Limerick, Ireland

3 Department of Biological Sciences, School of Natural Sciences, University of Limerick, Limerick, Ireland

4 School of Engineering, University of Limerick, Limerick, Ireland

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Sustainable Treatment of Acidic and Alkaline Leachates from Mining and Industrial… DOI: http://dx.doi.org/10.5772/intechopen.103889*


**Author details**

Zulfiqar Ahmad1\*, Arshad Ashraf2 and Mohsin Hafeez3

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Earth Sciences, Quaid-i-Azam University, Islamabad, Pakistan

2 National Agricultural Research Center, Islamabad, Pakistan

3 Bureau of Meteorology, Brisbane, Australia


**1. Introduction**

Cell cytotoxicity refers to the ability of certain chemicals or mediator cells to destroy living cells. The cytotoxicity is a very important aspect, as destruction of healthy living cells around the wound will have a negative impact on the healing process. Cytotoxicity is the general quality of being toxic to cells, and can be caused by chemical stimuli, exposure to other cells (NK or T cells for example), or physical/environmental conditions (radiation exposure, temperature or pressure extremes, etc.) [1]. Chemical toxicity can occur in many ways, but we hypothesize that it can be broadly classified into two major categories: disruption of specific biomolecular targets or pathways (e.g., receptor agonist/antagonist effects and enzyme activation/inhibition), or generalized disruption of cellular machinery that can lead to cell stress and cytotoxicity [2]. Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Chemical toxicity can occur in disruption of specific biomolecular targets or pathways (e.g., receptor agonist/antagonist effects and enzyme activation/inhibition), or generalized disruption of cellular machinery that can lead to cell stress and cytotoxicity. Cell-disruptive processes include protein, DNA, or lipid reactivity; physicochemical disruption of proteins or membranes (e.g., by surfactants); or processes such as apoptosis, oxidative stress response, mitochondrial disruption, endoplasmic reticulum (ER) stress, microtubule disruption, or heat shock response [2]. Treating cells with a cytotoxic compound can result in a variety of cell fates. By using a cytotoxic compound, healthy living cells can either be induced to undergo necrosis (accidental cell death) or apoptosis (programmed cell death). Whereas apoptotic cell death is slower, more orderly, and is genetically controlled, the cells may undergo necrosis, in which they rapidly loss membrane integrity and die rapidly as a result of cell lysis. The cells can stop actively growing and dividing (a decrease in cell viability). Cytostasis is a special category of cytotoxicity, wherein cells remain alive but fail to grow and divide [1]. Cell death/cytotoxicity cannot be the sole causal driver of this phenomenon. Some cytotoxicity may be driven by physicochemical factors, such

DOI: 10.5772/intechopen.77244

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

as protein denaturation or reactivity, which would affect both the cell-free and cell-based assays. Another possibility is very low-affinity non-covalent binding to receptors, enzymes, etc. that only occurs at very high concentrations [2].

or chemical substances should be helpful in creating proper different models for extrapolation to low doses. In addition, biomarkers of exposure are gaining importance as tolls in the cytotoxicity research. The detection of the cytotoxic chemicals in humans may be useful in assessing human exposure or cellular injury. Also, understanding specific mechanisms may be useful in identifying the potential target tissues *in vivo* because cell types have different

Introductory Chapter: Cytotoxicity

5

http://dx.doi.org/10.5772/intechopen.77244

Today, we need to understand the cytotoxicity that particular cells, organs, and organism are facing and identifying specific treatment interventions to address their unique needs both at macro- and micro-levels. The scope of this book goes precisely toward this direction. Each chapter offers the ways of intervention to address some of the most pressing cytotoxic chemi-

Cytotoxicity book is a web based resource, encompassing some of the cytotoxicity natural and different chemical substance, such as natural coumarins, colchicine alkaloids, titania nanosheets, asbestos fiber, nanomaterials, nanocrystals, and composites, and curcumin

"Cytotoxicity" is an essential reading to all medical students, biologist, biochemist, and professionals involved in the field of toxicology. The book is an useful and ideal guide for novice researchers interested in learning research methods to study cytotoxic bioactive compounds.

Department of Biology, Art and Science Faculty, Adnan Menderes University, Central

[1] ACEA Biosciences Inc. 2015. Available from: https://www.aceabio.com/applications/

[2] Judson R, Houck K, Martin M, Richard AM, Knudsen TB, Shah I, Little S, Wambaugh J, Setzer RW, Kothya P, Phuong J, Filer D, Smith D, Reif D, Rotroff D, Kleinstreuer N, Sioes N, Xia M, Huang R, Crofton K, Thomas RS. Analysis of the effects of cell stress and cytotoxicity on *in vitro* assay activity across a diverse chemical and assay space. Toxicological

[3] Li W, Zhou J, Xu Y. Study of the *in vitro* cytotoxicity testing of medical devices (review).

capacities to handle different types of chemicals.

loaded copolymer encapsulated ZnO nanocomposites.

Address all correspondence to<EMAIL_ADDRESS>
cals of our time.

**Author details**

Tülay Aşkin Çelik

**References**

Campus, Aydin, Turkey

cytotoxicity/27.02.2018

Sciences. 2016;**152**(2):323-339

Biomedical Reports. 2015;**3**:617-620

#### **1.1. Cytotoxicity**

Cytotoxicity is one of the most important methods for biological evaluation as it has a series of advantages, along with the preferred and mandatory items. Given this information, the ability to accurately measure cytotoxicity can prove to be a very valuable tool in identifying compounds that might pose certain health risks in humans [3]. The cytotoxicity test is one of the most important indicators of the biological evaluation system *in vitro* to observe the cell growth, reproduction and morphological effects by chemicals, and with the progress of modern cell biology; experimental methods to evaluate cytotoxicity are also continuously being developed and improved [3].


**3.2 CNT based electrolyte in DSSC**

In recent years, carbon-based materials in ionic liquids have been investigated as a potential alternative for traditional liquid electrolytes for DSSC application. An effective electrolyte has low viscosity, low vapor pressure, high diffusion coefficient, high electrochemical, and thermal stability. Conventional liquid redox electrolyte has low viscosity and high diffusion coefficient. However, liquid redox electrolyte uses a volatile solvent, which causes a problem in the commercialization of DSSC, such as cell leakage of electrolyte, performance degradation, hightemperature instability, and pressure build-up after in the fabricated cell due to the volatile solvent. Also, liquid electrolyte creates obstacles for the flexible structure and large-scale solar cell. Quasi-solid-state electrolytes based on ionic liquids can easily solve the drawbacks of liquid electrolyte efficiently. Organic hole conducting materials, p-type inorganic semiconductors, or different nano-components (i.e., graphene, CNTs) were diffused into ionic liquids to the sensitized quasi-solid-state electrolyte for DSSC application. **Figure 9** shows a basic schematic diagram of a CNT based electrolyte for DSSC.


**1. Introduction**

For concrete box-girder bridges, integral parts of the structural health monitoring (SHM) system include the structural state assessment system and the real-time warning system. A necessary prerequisite to achieve a reliable state assessment and real-time warning is the clear understanding of environmental load e ffects on the structure, especially the temperature related e ffects. In fact, the temperature e ffects have been investigated by many researchers. It is considered that the stress generated by the nonlinear temperature distribution is usually equivalent to the live load, and the temperature-induced stress is significant in the concrete structure [1]. Taysi et al. [2] studied the thermal characteristics of concrete under the influence of temperature variation on box-girder utilizing experiments and finite element simulation. Their work highlighted the distribution of thermal di fference

and its influencing factors. Besides, Catbas et al. [3] discovered that temperature effects possess an important impact on the reliability of full bridges by analyzing a vast amount of monitored data.

In order to analyze the influence of temperature effects on structure assessment [4,5], Huang et al. [6] applied Kalman filtering and Kalman cointegration to identify the damage and recognized that external effects (e.g., temperature) may mask the changes induced by structural damage. Moreover, Liang et al. and Li et al. [7,8] carried a sensitivity study based on the structural fundamental frequencies in which it has been found that variations in ambient temperature might lead to the misjudgment of structural health conditions. Their work emphasized on temperature effects, but the observation and analysis of temperature distributions need to be further investigated.

The temperature distribution is the state of temperature at various positions inside and outside a structure at a certain time [9]. Preliminary studies of the temperature effects only considered the general temperature effects. Xu et al. [10] examined more than 8 years' temperature displacements using mean values of different temperatures sensors and obtained the statistical law of temperature change. Xu et al. [11] studied the change of structural dynamic response using uniform temperature rise and fall models. Nevertheless, they all ignored the difference in temperature distribution.

To further study the temperature stress, the nonlinear effect should be taken into consideration, when horizontal and vertical temperature gradients exist [12,13]. The external factors affecting the temperature distribution of concrete structures are solar radiation, nighttime cooling, cold air flow, wind, rain, snow, and other meteorological factors [1]. The internal factors that affect the temperature distribution of the concrete are mainly determined by the thermophysical properties of the concrete and the geometrical dimensions of the components. The spatial heterogeneity and time-dependent nature of temperature distribution make it difficult to determine the exact relationship between temperature and structural response [14]. Considering the monitored response data of a real bridge inevitably includes the influences of live load and environmental factors, making the problem more complicated.

To explore the temperature influences, separation of the temperature-induced part in data should be performed beforehand. For instance, Chenet al. [15] used the linear fitting method to determine the relationship between ambient temperature and temperature-induced strain while Hedegaard et al. [16] separated the time-dependent deformations from the temperature-related deformations by means of linear regression. They all achieved the purpose of separating the temperature-induced parts in the raw structural response data.

Previously, some scholars have noticed discrepancies in different temperature history data of steel bridges. For example, Zhou et al. [17] analyzed the lateral temperature distribution and temperature time history of the steel box-girder, and found a difference existed in the lateral temperature distribution of the box-girder at the same time. Brownjohn et al. [18] revealed that temporal and spatial temperature variations dominate displacement in long-span bridges. Meanwhile, the difference can reach 5 and 12 ◦C in winter and summer seasons, respectively. Furthermore, Brownjohn et al. [19] noticed there exists a time-lag effect due to thermal inertia effects, giving us a clear insight into the temperature time-lag effect.

To account for this temperature time-lag phenomenon, Zhao et al. [20] selected the first five principal components as the main components to determine the overall response of the -structure. Moreover, Guo et al. [21] noted that displacement data and temperature data for a steel box-girder cable-stayed bridge represented a lag time of approximately 45 min. After directly shifting the temperature data by 45 min, they found the correlation between the two was significantly improved. However, the temperature distribution and the temperature-induced effects of concrete small box-girder bridges still need to be further investigated.

Based on the above analysis, this study establishes that there is a significant time-lag effect in concrete box-girder bridges. This effect can be handled by a phase shift method, which is illustrated in a case study using field monitoring (strain and temperature) data. As a result, the aims of this paper include: (1) Investigate the time-lag phenomenon and its basic characteristics of concrete box-girder bridges; (2) cope with the issue of insufficient correlation between temperature data and strain data.


**6. Use and Interpretation of the Model**

The processing of the three indices is read simultaneously on a star (or radar) diagram that identifies three reference scenarios, three directions, and three uncertain situations for which the model is unable to provide an answer. Situations are defined as uncertain when the model is unable to provide exhaustive indications or when all three indicators share the same value (Figure 2).

**Figure 2.** Situations are defined as uncertain when the model is unable to provide exhaustive indications because: (**a**) the three indices have a maximum value, (**b**) the three indices have a minimum value, and (**c**) the three indices have equal value.


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Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/56909