**4. GKRP modulates the impact of GK activity on glucose and lipid homeostasis**

GKRP is the best-known regulator of the hepatic GK at the post-transcriptional level. Therefore, impairments in GKRP should affect GK and consequently glucose metabolism, since GK plays a central role in glucose homeostasis. Nevertheless, mutations in the GKRP gene (Gckr) that caused disease or alterations in glucose metabolism have never been described until now. Recently, several whole-genome analysis have associated polymorphisms in the Gckr gene with fast hypoglycemia and increased serum triglyceride in humans, even though these subjects have reduced risk to type 2 diabetes (Køster, 2005; Sparsø, 2007; Vaxillaire, 2008; Orho-Melander, 2008 & Beer, 2009). The mechanism underlying this phenotype seems to be a reduction in GK inhibition by the variant regulatory protein (Beer, 2009). But, before exploring this issue it should be convenient to consider some aspects of GKRP biology.

Although GKRP research has been focused in the liver, there are evidences that the GKRP protein is also present in hypothalamic neurons (Schuit, 2001; Alvarez, 2002 & Roncero, 2009). GK/GKRP system in the hypothalamus could play a role in glucose-sensing important for the regulation of energy homeostasis by balancing energy intake, expenditure and storage. On the other hand, there is some controversy in the literature as to whether GKRP also regulates GK in pancreatic β-cells. The vast majority of studies state that GKRP is not expressed in rodent β-cells. However, it has been demonstrated that human islets express GKRP at very low levels (Beer, 2009). This issue should be revisited because of the recent publication of several genome-wide association studies that associate GK, GCKR, G6PC2, MTNR1B with type 2 diabetes risk linked to β-cell function (Reiling, 2009 & Bonetti, 2011). Whether, β-cell GK function is affected directly by a hypothetic pancreatic GKRP, or indirectly by liver GKRP impaired activity, still needs clarification. Another question that remains to be resolved is whether GKRP is also expressed and functional in other GK expressing cells, for instance, in the gut and in the pituitary gland.

Consequently, when considering studies of genome-wide association, mutant GKRP protein might affect GK activity in the brain, in the liver and perhaps in the β-cell. Therefore it is difficult to explain the phenotype only taking into account the hepatic GK/GKRP system. The same occurs with the characterization of GKRP-deficient mice (Farrelly, 2002 & Grimsby, 2000). GKRP knock-out mice models, whether heterozygous or homozygous, had normal weight. Interestingly focusing in liver analysis, those mice displayed reduced production of hepatic GK protein while having the same levels of GK mRNA than control animals, and GK protein was localized exclusively in the cytoplasm. That showed the importance of hepatic GKRP in stabilizing and protecting the intracellular GK pool. These animal models exhibited impaired postprandial glycemic control, with lower hepatic glycogen content and lack of inhibition of PEPCK-C gene expression, albeit with no

All in all, our review of the literature together with our own results on the subject will convey that pGK-overexpression in the liver, independent of zonation, will result in changes in glycaemia but with the risk of non-desirable lipid alterations and insulin resistance. However, several undetermined factors influence the results obtained in GK overexpression studies, reinforcing the concept that hepatic GK is a key regulator of whole-body homeostasis, so that little changes in its activity and/or in its regulation affect glucose and

GKRP is the best-known regulator of the hepatic GK at the post-transcriptional level. Therefore, impairments in GKRP should affect GK and consequently glucose metabolism, since GK plays a central role in glucose homeostasis. Nevertheless, mutations in the GKRP gene (Gckr) that caused disease or alterations in glucose metabolism have never been described until now. Recently, several whole-genome analysis have associated polymorphisms in the Gckr gene with fast hypoglycemia and increased serum triglyceride in humans, even though these subjects have reduced risk to type 2 diabetes (Køster, 2005; Sparsø, 2007; Vaxillaire, 2008; Orho-Melander, 2008 & Beer, 2009). The mechanism underlying this phenotype seems to be a reduction in GK inhibition by the variant regulatory protein (Beer, 2009). But, before exploring this issue it should be convenient to

Although GKRP research has been focused in the liver, there are evidences that the GKRP protein is also present in hypothalamic neurons (Schuit, 2001; Alvarez, 2002 & Roncero, 2009). GK/GKRP system in the hypothalamus could play a role in glucose-sensing important for the regulation of energy homeostasis by balancing energy intake, expenditure and storage. On the other hand, there is some controversy in the literature as to whether GKRP also regulates GK in pancreatic β-cells. The vast majority of studies state that GKRP is not expressed in rodent β-cells. However, it has been demonstrated that human islets express GKRP at very low levels (Beer, 2009). This issue should be revisited because of the recent publication of several genome-wide association studies that associate GK, GCKR, G6PC2, MTNR1B with type 2 diabetes risk linked to β-cell function (Reiling, 2009 & Bonetti, 2011). Whether, β-cell GK function is affected directly by a hypothetic pancreatic GKRP, or indirectly by liver GKRP impaired activity, still needs clarification. Another question that remains to be resolved is whether GKRP is also expressed and functional in other GK

Consequently, when considering studies of genome-wide association, mutant GKRP protein might affect GK activity in the brain, in the liver and perhaps in the β-cell. Therefore it is difficult to explain the phenotype only taking into account the hepatic GK/GKRP system. The same occurs with the characterization of GKRP-deficient mice (Farrelly, 2002 & Grimsby, 2000). GKRP knock-out mice models, whether heterozygous or homozygous, had normal weight. Interestingly focusing in liver analysis, those mice displayed reduced production of hepatic GK protein while having the same levels of GK mRNA than control animals, and GK protein was localized exclusively in the cytoplasm. That showed the importance of hepatic GKRP in stabilizing and protecting the intracellular GK pool. These animal models exhibited impaired postprandial glycemic control, with lower hepatic glycogen content and lack of inhibition of PEPCK-C gene expression, albeit with no

expressing cells, for instance, in the gut and in the pituitary gland.

**4. GKRP modulates the impact of GK activity on glucose and lipid**

lipid metabolism.

**homeostasis**

consider some aspects of GKRP biology.

noteworthy loss in insulin secretion or changes in fasting blood glucose concentrations. Moreover, when challenged with a high-sucrose/high-fat diet the knock-out and normal mice gained body weight at a similar rate but the knock-out mice were hyperglycaemic and hyperinsulinemic. Importantly, no changes in plasma triglycerides and non-esterified fatty acids were observed in basal conditions as well as with a high-sucrose/high-fat diet. In summary, absence of GKRP results in decreased hepatic GK protein content, affecting glucose metabolism without disturbing lipid parameters.

On the other hand, GKRP gain of function in the liver has also been assessed. In vitro studies with HepG2 cells simultaneously transduced with an adenoviral vector expressing GKRP and another adenoviral vector for GK had significantly elevated GK protein and activity levels compared with cells transduced with the GK adenovirus alone (Slosberg, 2001). These data suggest that GKRP serves to stabilize and protect a pool of GK protein (i.e., extend half-life), and is consistent with data obtained in GKRP knock-out studies. But in vivo studies revealed a more complicated situation. Adenoviral-mediated hepatic overproduction of GKRP in mice with high-fat diet-induced diabetes resulted in 23% decrease in GK enzymatic activity. Although reduction of GK activity is commonly associated to diabetes, hepatic GKRP-expressing mice had improved fasting and glucoseinduced glycaemia with a concomitant increase in insulin sensitivity and TAG levels, and a decrease in leptin levels. A possible explanation for discrepancies between in vivo and in vitro results on GK levels when overexpressing GKRP is that GK expression in vivo is influenced by insulin and other physiological regulators. To understand how decreased GK activity improved type 2 diabetes phenotype in this model, a possibility is that GK activity may be applied in a more efficient manner toward metabolizing blood glucose. The subcellular compartmentalization by scaffolding proteins of enzymes or signaling proteins into clusters is often used as a means of increasing system efficiencies.

Coming back to genome-wide studies that associate Gckr with fast hypoglycemia and high triglycerides, Beer et al reported that P446L-GKRP has reduced regulation by physiological concentrations of fructose-6-phosphate, resulting indirectly in increased GK activity (Beer, 2009). They predicted that this increased GK activity in the liver enhanced glycolytic flux, promoting hepatic glucose metabolism and elevating concentrations of malonyl-CoA, a substrate for *de novo* lipogenesis, providing a mutational mechanism for the reported association of this variant with raised triglycerides and lower glucose levels. However, their predictions are conflictive with in vivo studies by Slosberg et al (Slosberg, 2001), since GKRP gain of function reduced hepatic GK activity and also resulted in a decrease of blood glucose levels accompanied by an increase of blood triglycerides. Therefore, any other undetermined factor/s must exist to really understand the complex physiology of the GK/GKRP system. Another possibility is that brain P446L-GKRP and β-cell P446L-GKRP (if existent) may exert determinant influences on phenotype.

Another study that may bring light to this issue, relates to defects in glucokinase translocation identified in Zucker diabetic fatty (ZDF) (Fujimoto, 2004 & Shin, 2007). Although having normal GK protein content, GK was predominantly localized in the nucleus regardless of plasma glucose and insulin levels. Nevertheless, sorbitol restored GK translocation. Clearly, there must be two distinct mechanisms bringing about the dissociation of GK from GKRP. How they are related and what differentiate them are questions currently under investigation. Since this defect was discovered in early stage of diabetes, it could cause of the progression to diabetes seen in the adult ZDF rat. Consistently, a MODY-2 mutation in the Gck gene has been reported to increase the physical

Liver Glucokinase and Lipid Metabolism 253

and oxidative metabolism and not through the pentose phosphate pathway that would favor lipid biosynthesis. This hypothesis is reinforced by results published by Wu et al (Wu, 2005) in which adenovirus expression of wild-type GK in the liver activate the pentose phosphate pathway, in marked contrast to the overexpression of the kinase domain from PFK-2 that stimulates flux through the glycolytic pathway. Surprisingly, GK-A456V transfected animals showed a marked increase in glucose-6-phosphatase. GK overexpression in perivenous hepatocytes does not significantly affect Glc6Pase expression, suggesting that zonation is an

Transfecting GK-A456V in type 1 diabetic mice induced with streptozotocin, also caused an important reduction of diabetic hyperglycemia without dyslipidemia, in contrast with GK overexpression. Again, an induction of glucose-6-phosphatase transcription was observed in the liver GK-A456V -expressing animals (Figure 7) (Vidal-Alabró; publication pending).

**0.0**

\*

**0.5**

**Free fatty acids (mM)**

*Ucp2*

glycaemia, serum triglyceride and free fatty acid levels, 48 h post-injection of the plasmid for

gluconeogenic and lipolysis genes from liver was analyzed by Real-Time PCR. Calculations were done following ΔΔCt algorithm (Applied Biosystems), using β-microglobulin gene expression as a housekeeping gene. (C) The same for lipogenic genes. \* p<0.05, # p<0.05 vs control and pGK, and & p<0.001 vs control and p<0.05 vs pGK-A456V, as determined by

Our results lead us to consider the physiology of glucose-6-phosphatase in the context of glucose and lipid metabolism. Glucose-6-phosphatase dephosphorylates glucose-6 phosphate in the endoplasmic reticulum to obtain glucose, as the last step in the

*Fasn*

*Mod1*

*Srebf*

*Nr1h3*

*Chrebp*

pControl pGK pGK-A456V

**1.0 \***

important experimental variable not sufficiently addressed to date in the field.

**0**

**50**

**Triglyceride (mg / dL)**

**100 \***

**0**

**0.0 0.5 1.0 1.5 2.0 2.5 3 6**

**mRNA (relative units)**

One-way ANOVA.

**B**

*Pck1*

\*

*Glc6p*

*Slc2a2*

*Ppargc1*

*Cpt1a*

Fig. 7. Study of GK-A456V expression in the liver of type 1 diabetic mice. (A) Shows

the GK-A456V gene. Columns represent media ± standard error. (B) Expression of

\* \* **&** \*

**250**

**Glycemia (mg / dL)**

**A**

**\***

**#**

**500**

interaction of GK and GKRP (García-Herrero, 2007). But, again these data are in conflict with other studies that reported some new GK mutations causing MODY-2 that reduced GK inhibition by GKRP (Veiga-da-Cunha, 1996; Gloyn, 2005 & Sagen, 2006). Once more, it is difficult to draw conclusions, but the importance of proper GK/GKRP function on metabolism and disease is reinforced, as subtle changes in its activity and/or regulation lead to contrary phenotypes.

Several naturally occurring activating mutations have been described that are localized at the same region where synthetic GK activators bind (Kamata, 2004; Heredia, 2006 & Matschinsky, 2009). Both activating mutations and synthetic activators stabilize the open conformation of the GK protein, resulting in higher affinity for glucose and a reduction of the interaction between GK and GKRP, since the super-open conformation of the enzyme (inactive) is not possible. In humans, activation of GK by naturally occurring mutations is associated to persistent hyperinsulinemic hypoglycemia of the infancy (PHHI), syndrome with a heterogeneous phenotype even in the same family but generally with a normal lipid profile. On the other hand, GK activation through administration of GK activation drugs has been tested for their potential in the therapy of type 2 diabetes, considering principally their capacity to increase glucose-stimulated insulin release at the β-cell (Grimsby, 2003; Brocklehurst, 2004; Efanov, 2005; Leighton, 2005; Coope, 2006 & Matschinsky, 2009). Wholebody effects of glucokinase activator drugs demonstrated a dose-dependent reduction of glycaemia, associated with increased insulin secretion in the pancreas and net glucose uptake in the liver. Besides, the administration of a GK activator prevented the development of diabetes in a diet-induced obesity animal model (Grimsby, 2003). Surprisingly, most in vivo studies with GK activators drugs do not show the lipid profile (Grimsby, 2003; Brocklehurst, 2004; Efanov, 2005; Leighton, 2005 & Coope, 2006), except one where treatment of ob/ob mice with GK activator PSN-GK1 did not produce any significant change blood lipids (Fyfe, 2007).

With all this puzzling background, we intended to study the expression of an activated mutant form of GK with the aim to decipher the metabolic consequences in the liver of having a GK not regulated by GKRP, with theoretical antidiabetic properties. Particularly we proposed the overexpression of glucokinase A456V (identified in patients of persistent hyperinsulinemic hypoglycemia of the infant), with a S0.5 for glucose of 3 mM instead of 8 mM for the wild-type enzyme (Christesen, 2002), and without GKRP regulation (Heredia, 2006). We postulated that GK-A456V overexpression (also as a model for the liver-specific consequences of activating drugs on GK) could increase glucose uptake compared with the wild-type enzyme at equal levels of expression, whilst the metabolic fate of glucose might be different from that of wild-type GK due to its different capacity of interaction with other regulating proteins (GKRP and maybe PFK-2).

By means of hydrodynamic gene transfer of an expression plasmid for GK-A456V in healthy mice, we have been able to demonstrate that the perivenous overexpression of GK-A456V results in a sustained improvement in blood glucose, insulinemia and glucose tolerance, in the absence of dyslipidemia or hepatic lipidosis nor long-term insulin resistance (Vidal-Alabró; publication pending). Importantly, GK-A456V protein levels were similar to GKcontrol group, suggesting GK-A456V stability although not being directly regulated by GKRP. Its mechanism of action could be explained by its lower *S0.5* for glucose, so that glucose uptake is stimulated in later phases after ingestion (post absorptive phase) and during early fasting. It is tempting to speculate that glucose taken-up in perivenous liver, both in postprandial and post-absorptive periods, could be directed towards the glycolytic

interaction of GK and GKRP (García-Herrero, 2007). But, again these data are in conflict with other studies that reported some new GK mutations causing MODY-2 that reduced GK inhibition by GKRP (Veiga-da-Cunha, 1996; Gloyn, 2005 & Sagen, 2006). Once more, it is difficult to draw conclusions, but the importance of proper GK/GKRP function on metabolism and disease is reinforced, as subtle changes in its activity and/or regulation lead

Several naturally occurring activating mutations have been described that are localized at the same region where synthetic GK activators bind (Kamata, 2004; Heredia, 2006 & Matschinsky, 2009). Both activating mutations and synthetic activators stabilize the open conformation of the GK protein, resulting in higher affinity for glucose and a reduction of the interaction between GK and GKRP, since the super-open conformation of the enzyme (inactive) is not possible. In humans, activation of GK by naturally occurring mutations is associated to persistent hyperinsulinemic hypoglycemia of the infancy (PHHI), syndrome with a heterogeneous phenotype even in the same family but generally with a normal lipid profile. On the other hand, GK activation through administration of GK activation drugs has been tested for their potential in the therapy of type 2 diabetes, considering principally their capacity to increase glucose-stimulated insulin release at the β-cell (Grimsby, 2003; Brocklehurst, 2004; Efanov, 2005; Leighton, 2005; Coope, 2006 & Matschinsky, 2009). Wholebody effects of glucokinase activator drugs demonstrated a dose-dependent reduction of glycaemia, associated with increased insulin secretion in the pancreas and net glucose uptake in the liver. Besides, the administration of a GK activator prevented the development of diabetes in a diet-induced obesity animal model (Grimsby, 2003). Surprisingly, most in vivo studies with GK activators drugs do not show the lipid profile (Grimsby, 2003; Brocklehurst, 2004; Efanov, 2005; Leighton, 2005 & Coope, 2006), except one where treatment of ob/ob mice with GK activator PSN-GK1 did not produce any significant

With all this puzzling background, we intended to study the expression of an activated mutant form of GK with the aim to decipher the metabolic consequences in the liver of having a GK not regulated by GKRP, with theoretical antidiabetic properties. Particularly we proposed the overexpression of glucokinase A456V (identified in patients of persistent hyperinsulinemic hypoglycemia of the infant), with a S0.5 for glucose of 3 mM instead of 8 mM for the wild-type enzyme (Christesen, 2002), and without GKRP regulation (Heredia, 2006). We postulated that GK-A456V overexpression (also as a model for the liver-specific consequences of activating drugs on GK) could increase glucose uptake compared with the wild-type enzyme at equal levels of expression, whilst the metabolic fate of glucose might be different from that of wild-type GK due to its different capacity of interaction with other

By means of hydrodynamic gene transfer of an expression plasmid for GK-A456V in healthy mice, we have been able to demonstrate that the perivenous overexpression of GK-A456V results in a sustained improvement in blood glucose, insulinemia and glucose tolerance, in the absence of dyslipidemia or hepatic lipidosis nor long-term insulin resistance (Vidal-Alabró; publication pending). Importantly, GK-A456V protein levels were similar to GKcontrol group, suggesting GK-A456V stability although not being directly regulated by GKRP. Its mechanism of action could be explained by its lower *S0.5* for glucose, so that glucose uptake is stimulated in later phases after ingestion (post absorptive phase) and during early fasting. It is tempting to speculate that glucose taken-up in perivenous liver, both in postprandial and post-absorptive periods, could be directed towards the glycolytic

to contrary phenotypes.

change blood lipids (Fyfe, 2007).

regulating proteins (GKRP and maybe PFK-2).

and oxidative metabolism and not through the pentose phosphate pathway that would favor lipid biosynthesis. This hypothesis is reinforced by results published by Wu et al (Wu, 2005) in which adenovirus expression of wild-type GK in the liver activate the pentose phosphate pathway, in marked contrast to the overexpression of the kinase domain from PFK-2 that stimulates flux through the glycolytic pathway. Surprisingly, GK-A456V transfected animals showed a marked increase in glucose-6-phosphatase. GK overexpression in perivenous hepatocytes does not significantly affect Glc6Pase expression, suggesting that zonation is an important experimental variable not sufficiently addressed to date in the field.

Transfecting GK-A456V in type 1 diabetic mice induced with streptozotocin, also caused an important reduction of diabetic hyperglycemia without dyslipidemia, in contrast with GK overexpression. Again, an induction of glucose-6-phosphatase transcription was observed in the liver GK-A456V -expressing animals (Figure 7) (Vidal-Alabró; publication pending).

Fig. 7. Study of GK-A456V expression in the liver of type 1 diabetic mice. (A) Shows glycaemia, serum triglyceride and free fatty acid levels, 48 h post-injection of the plasmid for the GK-A456V gene. Columns represent media ± standard error. (B) Expression of gluconeogenic and lipolysis genes from liver was analyzed by Real-Time PCR. Calculations were done following ΔΔCt algorithm (Applied Biosystems), using β-microglobulin gene expression as a housekeeping gene. (C) The same for lipogenic genes. \* p<0.05, # p<0.05 vs control and pGK, and & p<0.001 vs control and p<0.05 vs pGK-A456V, as determined by One-way ANOVA.

Our results lead us to consider the physiology of glucose-6-phosphatase in the context of glucose and lipid metabolism. Glucose-6-phosphatase dephosphorylates glucose-6 phosphate in the endoplasmic reticulum to obtain glucose, as the last step in the

Liver Glucokinase and Lipid Metabolism 255

Anderka, O., Boyken, J., Aschenbach, U., Batzer, A., Boscheinen, O. & Schmoll, D. (2008).

Beer, N.L., Tribble, N.D., McCulloch, L.J., Roos, C., Johnson, P.R., Orho-Melander, M. &

Bollen, J., Keppens, S. & Stalmans, W. (1998). Specific features of glycogen metabolism in the

Bonetti, S., Trombetta, M., Boselli, M.L., Turrini, F., Malerba, G., Trabetti, E., Pignatti, P.F.,

Brocklehurst, K. J., Payne, V.A., Davies, R.A., Carroll, D., Vertigan, H.L., Wightman, H.J.,

Budker, V.G., Subbotin, V.M., Budker, T., Sebestyén, M.G., Zhang, G. & Wolff, J.A. (2006).

Cha, J.Y. & Repa, J.J. (2007). The liver X receptor (LXR) and hepatic lipogenesis. The

Christesen, H.B., Jacobsen, B.B., Odili, S., Buettger, C., Cuesta-Munoz, A., Hansen, T.,

Coope, G.J., Atkinson, A.M., Allot, C., McKerrecher, D., Johnstone, C., Pike, K.G., Holme,

Denechaud, P.D., Bossard, P., Lobaccaro, J.A., Millatt, L., Staels, B., Girard, J. & Postic, C.

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Biophysical characterization of the interaction between hepatic glucokinase and its regulatory protein. *The Journal of Biological Chemistry,* Vol.283, No. 46, (November

Gloyn, A.L. (2009). The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. *Human Molecular Genetics*, Vol 18, No. 21, (November 2009), pp.

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Bonora, E. & Bonadonna, R.C. (2011). Variants of GCKR affect both β-cell and kidney function in patients with newly diagnosed type 2 diabetes: the Verona newly diagnosed type 2 diabetes study 2. *Diabetes care,* Vol. 34, No. 5, (May 2011),

Aiston, S., Waddell, I.D., Leighton, B., Coghlan, M.P. & Agius, L. (2004). Stimulation of hepatocyte glycose metabolism by a novel small molecule glucokinase activators.

Mechanism of plasmid delivery by hydrodynamic tail vein injection. II. Morphological studies. *The journal of gene medicine,* Vol. 8, N. 7, (July 2006), pp. 874-

carbohydrate-response element-binding protein is a target gene of LXR. *The Biochemical Journal,* Vol.282, No. 1, (January 2007), pp. 743-751, ISSN 0264-6021 Chen, G., Liang, G., Ou, J., Goldstein, J. L. & Brown, M.S. (2004). Central role for liver X

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Brusgaard, K., Massa, O., Magnuson, M.A., Shiota, C., Matschinsky, F.M & Barbetti, F. (2002). The second activating glucokinase mutation (A456V): implications for glucose homeostasis and diabetes therapy. *Diabetes,* Vol. 51, No. 4, (April 2002), pp.

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gluconeogenic pathway. Its transcription is regulated by insulin, so that it is repressed in fed state and induced during fasting. However, glucose induces transcription of this enzyme although the physiological significance of this induction is still not resolved (Nordlie, 2010). Finally glucose-6-phosphatase deficiency causes severe hyperlipidemia and hepatic steatosis (Bandsma, 2002, 2008), therefore giving rise that this enzyme may also participate or influence the GK/GKRP system in the regulation of hepatic glucose fate. To support this hypothesis, Reiling and colleagues described combined effects of single-nucleotide polymorphisms in GK, GKRP and glucose-6-phosphatase on fasting plasma glucose and type 2 diabetes (Reiling, 2009). Therefore, it is a field that needs further exploration.


**Preface to "High Performance Functional Bio-based Polymers for Skin-contact Products"**

The personal care, cosmetic and biomedical industries deal with high-value and/or large-volume consumption of polymer-based products that are often derived from fossil sources. Although several bio-based alternatives have been the subject of recent research, more effort is still needed to increase their specific functionalities and performances in order to proceed with their true translation into the market. Recently, many researchers, such as those involved in the European POLYBIOSKIN project funded by BBI JU, are working in the field of biomaterials with anti-microbial, anti-inflammatory and anti-oxidant properties, as well as biobased materials, which are renewable and biodegradable in the environment. Both types can be transformed into final products by innovative technologies, allowing for the control of bulk and surface properties down to the nanostructure. By merging such biomedical functionalities and environmental aspects, new research could have a great impact on skin-contact biomaterials. The present issue aims to gather research and review papers where the attention paid to health and environmental impact is efficiently integrated, thus considering both source and final waste management.

> **Maria Beatrice Coltelli, Serena Danti** *Editors*


**Author details**

Robert L. Drury1,2,3

1 ReThink Health/Canary Systems, Bainbridge Island, USA

2 Royal Medical Society, London, UK

3 Institute for Discovery, University of Wisconsin, Madison, USA

\*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.

*New and Emerging Technologies for Integrative Ambulatory Autonomic Assessment… DOI: http://dx.doi.org/10.5772/intechopen.104092*


2.1.2. Sample Surface Modification

After printing, the PEEK and CFR-PEEK disc samples for wettability, roughness, microstructure, and biological tests were divided into three groups: as-printed (untreated) group, polished group, and sandblasted group (*n* = 12 per group). The untreated group included the directly printed samples, without any surface treatment. For the polished and sandblasted groups, all the discs were manually polished with a series of SiC abrasive papers up to P4000 (Buehler, Lake Bluff, IL, USA) by a polisher (Buehler, Coventry, UK). Then, the samples of the sandblasted group were further modified using a sandblasting machine (P-G 400, Harnisch + Rieth, Winterbach, Germany) with 120 μm alumina (Al2O3) particles (Cobra, Renfert, Hilzingen, Germany) under the pressure of 0.1 MPa at a distance of 50 mm for 15 s.

All Ti disc surfaces were modified using the same processes as for PEEK and CFR-PEEK samples by a series of SiC abrasive papers (1200, 2500, 4000 grit, Buehler, Lake Bluff, IL, USA) by a polisher (Buehler, Coventry, UK). After polishing, all the samples were cleaned with deionized (DI) water by an ultrasonic cleaner (Sonorex Super RK102H, Bandelin, Berlin, Germany) to remove residual Al2O3 particles on the sample surfaces.

#### *2.2. Mechanical Properties Test*

Mechanical tests were carried out using an electro-hydraulic servo mechanical testing machine (CMT4304, MTS Corp., Eden Prairie, MN, USA) according to ISO standards. ISO 527-1: 2012 (Plastics—Determination of tensile properties), ISO 178: 2010 (Plastics—Determination of flexural properties), and ISO 604: 2002 (Plastics—Determination of compressive properties) were applied for the tensile, bending, and compressive tests, respectively [29–31]. Six samples were tested for each batch with a 1 mm/min testing speed, and the test was performed at an ambient temperature of 20 ◦C.

#### *2.3. Surface Characterization*

To determine the surface morphology, samples of PEEK and CFR-PEEK from the untreated, polished, and sandblasted groups (*n* = 2 per group) were sputtered with a 20 nm thick Au–Pd coating (SCD 050, Baltec, Lübeck, Germany) and characterized by a scanning electron microscope (SEM) (LEO 1430, Zeiss, Oberkochen, Germany) at 200× and 2000× magnification.

The surface topography of the discs (*n* = 6 per group) was analyzed by a profilometer (Perthometer Concept S6P, Mahr, Göttingen, Germany). For each sample, 121 profiles were measured over a 3 mm × 3 mm area. The arithmetic mean height (Sa) and root mean square height (Sq) were calculated based on these topographies by software (MountainsMap Universal 7.3, Digital Surf, Besançon, France).

The water contact angle (WCA) was measured at room temperature on six samples per group using a drop shape analyzer (DSA 10-MK 2, Kruess, Hamburg, Germany). Drops of 2 μL of distilled water were deposited on the respective disc surfaces using an automatic pipette. After 20 s wetting time, the contact angle at the air–water–substrate interface was quantified from the drop geometry using DSA software (version <IP_ADDRESS>, Kruess, Hamburg, Germany).

#### *2.4. Biological Tests*

Biological tests consisted of an extract test and a direct contact test to analyze the cytotoxicity and of the investigation of cell attachment to the different samples (*n* = 9 per group). Two materials (PEEK and CFR-PEEK) with three different surfaces each (untreated, polished, and sandblasted) were tested. In each test, *n* = 3 samples were used for each surface modification. All tests were performed three times in independent experiments. Directly before the biological tests, samples were ultrasonically cleaned with DI water for 15 min and sterilized with 70% ethanol and 100% ethanol (15 min each). Subsequently, the samples were dried on filter paper in a sterile workbench (Lamin Air HB2472, Burgdorf, Switzerland).

### 2.4.1. Cell Culture

L929 fibroblasts (DSMZ GmbH, Braunschweig, Germany) were cultured in DMEM medium (21063-029, Gibco, Paisley, UK) containing 10% fetal bovine serum (FBS, Life Technologies, Carlsbad, CA, USA), 1% penicillin/streptomycin (15140-122, Life Technologies Co., Carlsbad, CA, USA), and 1% GlutaMAX (Life Technologies Co., Paisley, UK) in 75 cm2 sterile cell culture flasks (Costar, Corning, Tewksbury, MA, USA). The cells were maintained in an incubator under a humidified atmosphere with 5% CO2 at 37 ◦C. The DMEM culture medium was renewed twice a week. When cells reached confluence, Trypsin (GIBCO, Paisley, UK) was used to detach cells from the bottom of flasks, and 1/10 of the total cells were transferred into a new flask.

#### 2.4.2. Test for In Vitro Cytotoxicity

The in vitro cytotoxicity test of PEEK and CFR-PEEK was performed by an extract method based on ISO 10993-5 [32]. Extracts were derived from soaking the samples with DMEM cell culture medium for 24 h at 37 ◦C. The ratio between the sample surface area and extraction vehicle volume was 3 cm2/mL. In the meantime, the cells were precultured for 24 h. The seeding concentration of L929 cells was 30,000 cells/cm2 in 200 μL DMEM medium per well in a 96-well plate (Cellstar 655180, Greiner Bio-One, Frickenhausen, Germany). After 24 h, the culture medium was removed from the cells and replaced by 150 μL extracts obtained from the respective sample groups. Three concentrations of each extract were tested: (a) undiluted (150 μL extracts), (b) 1:3 diluted with medium (50 μL extracts + 100 μL medium), and (c) 1:10 diluted (15 μL extracts + 135 μL medium). Ti samples were used as the negative control, and copper (Cu) samples were used as the positive control. After culturing for an additional 24 h, the extracts in all groups were replaced by 100 μL fresh DMEM medium to avoid artifacts in the following assay caused by blue color in the Cu extracts. The cytotoxicity was quantitatively analyzed by CCK-8 assay (Dojindo Molecular Technologies, Inc., Rockville, MD, USA). The volume of CCK-8 solution added to each test well was 10 μL. After incubating for 2 h, the optical density (OD) value was measured by a microplate ELISA reader (Tecan F50, Tecan Austria, Groedig, Austria) at 450 nm wavelength. The metabolic activity of L929 cells in the different test groups in comparison to the negative control was calculated according to the following formula:

$$\text{Cell metabolism activity (\%)} = \left(\text{OD}\_{\text{t}} - \text{OD}\_{\text{b}}\right) / \left(\text{OD}\_{\text{nc}} - \text{OD}\_{\text{b}}\right) 100\%,\tag{1}$$

where the OD value is the absorbance value of the respective test group (ODt), blank control group (ODb), and negative control group (ODnc).

#### 2.4.3. Cell Adhesion and Spreading

L929 cells were seeded on PEEK, CFR-PEEK, and Ti samples in 12-well plates (REF 3512, Costar, Kennebunk, ME, USA) with a density of 30,000 cells/cm2 and incubated in 2.4 mL DMEM medium at 37 ◦C and 5% CO2. After incubation for 24 h, cell adhesion was terminated by rinsing with Hank's balanced salt solution (HBSS, Biochrom AG, Berlin, Germany). Adhering cells were vital stained for 10 min in a solution of 25 μg/mL fluorescein diacetate (FDA) and 1.25 μg/mL ethidium bromide (EB) (Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany) in HBSS. For each sample, a minimum of six typical surface areas of every magnification (25×, 100×, 200×, and 400×) was documented by an Optishot-2 fluorescence microscope (Nikon, Tokyo, Japan) equipped with a digital camera (550D, Canon, Tokyo, Japan). Cell adhesion and spreading were assessed by measuring the density of the vital-stained cells (cells/cm2) and the mean area of sample surface covered by cells (% of Ti) using a photo editing software (ImageJ, v1.8.0, National Institutes of Health, Bethesda, MD, USA).

#### *2.5. Statistical Analysis*

SPSS Version 21 (SPSS INC, Chicago, IL, USA) was used for analyzing the data. Shapiro–Wilk and Levene tests were applied to assess the assumptions of data normal distribution and homogeneity of variances. The results of the mechanical properties of each parameter were tested using the Student's *t*-test of unpaired data with equal variance. One-way analysis of variance (ANOVA) was used for the cell density, and cell adhesion and spreading followed by Tukey post-hoc test (α = 0.05). The contact angle and roughness data were analyzed by Kruskal–Wallis analysis (α = 0.05) for the disobedience of the data normality or homogeneity of variances.