In order to examine the adhesion of integrins in the presence of HA, dual-functionalized surfaces were prepared following the previously described procedure. This was achieved by conjugation of end-alkylated HA to an OEG layer, containing varying amounts of EG6N3 and EG3OH and a constant ratio of 40% EG6RGD. The immobilization of the OEG layers caused a constant frequency change, fluctuating around −24.25 ± 2.33 Hz, independently of the ratio of EG6N3 with general low dissipation values (0.96 × 10−6 ± 1.12 × 10−6 ). Directly after immobilization of the OEG layer, end-alkylated HA was conjugated to the EG6N3 in this OEG layer as indicated by the slight decrease in frequency and increase in dissipation, respectively (**Figures 4A,B**). Observation of the unspecific attachment of bare liposomes to these surfaces shows a constantly low level with a frequency change of −5.06 ± 6.36 Hz and dissipation change of 2.31 × 10−6 ± 2.49 × 10−6 . These signal changes indicate that conjugated HA reduces the unspecific attachment of pure liposomes, which has been detected on the surfaces presenting the RGD motif alone (**Figure 4C**).

Finally, the effect of the dual-functionalized surface on the adhesion of integrins was investigated. It was observed that the frequency and dissipation changes caused by the binding of proteoliposomes declines with a higher EG6N3 content and consequently higher HA conjugation degree. At an initial EG6N3 ratio of 30%, only slight shifts in frequency and dissipation are detected indicating that the adhesion of proteoliposomes fails.

Even at lower ratios of HA between 0.01 and 30%, fewer proteoliposomes attach to the surface (**Figure 4D**). Thus, it can be concluded that minimal amounts of conjugated HA are sufficient to prevent the attachment of proteoliposomes to a surface with a constant RGD motif density. In this experiment, sHA molecules with a radius of gyration of around 5.3 nm were immobilized. Therefore, each conjugated HA molecule covers several molecules in the OEG layer including RGD binding motifs.

### CONCLUSION

fphys-09-01022 August 3, 2018 Time: 18:24 # 7

Within this study a biomimetic model was established to address the challenge of co-presenting HA and the RGD motif to study integrin binding. The adhesion of αIIbβ3 integrin, which was reconstituted in sphere-shaped lipid vesicles devoid of HA binding proteins, was quantified on HA and the RGD motif functionalized on an otherwise inert background. It is demonstrated that an RGD density of 40% has to be exceeded to achieve a prominent binding of proteoliposomes. Moreover, it is also concluded that even marginal amounts of HA are sufficient to significantly impact integrin attachment. The dissipation changes indicate that the proteoliposomes maintain an intact vesicle shape. Frohnmayer et al. (2015a,b) demonstrated that the adhesion of similarly prepared proteoliposomes on fibrinogen-coated surfaces, a protein which contains the RGD motif, results in decreased frequency and increased dissipation values within the same order of magnitude monitored in this study.

It would be especially beneficial to use these well-defined dualfunctionalized surfaces to investigate the behavior of various cell lines in future studies. Especially, cancer cell lines are highly interesting since it is known that HA is highly enriched in their ECM. These cell-ECM interactions additionally involve binding of HA with receptors such as CD44 and RHAMM which are

#### REFERENCES

associated with tumor progression and metastasis (Liang et al., 2016; Morath et al., 2016; Rankin and Frankel, 2016).

### DATA AVAILABILITY

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

### AUTHOR CONTRIBUTIONS

HB, BM, and CZ designed and conceived the experiments. BM performed preliminary experiments. CZ performed the experiments and analyzed the data. CZ and HB wrote the paper.

#### ACKNOWLEDGMENTS

We thank Christine Mollenhauer for the extraction of integrins, Dorothea Brüggemann for fruitful discussions and Prof. Tanaka for providing the NanoZS for DLS measurements. Generous support by Prof. Joachim Spatz and the Max Planck Society is gratefully acknowledged.

nanoprobes. Biomaterials 29, 4709–4718. doi: 10.1016/j.biomaterials.2008. 08.038

on terminally functionalized. Langmuir 13, 3404–3413. doi: 10.1021/la96 04341

**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 Zapp, Minsky and Boehm. 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.

# Generation and Neuronal Differentiation of hiPSCs From Patients With Myotonic Dystrophy Type 2

Paola Spitalieri1 \*, Rosa V. Talarico1 , Michela Murdocca1 , Luana Fontana1 , Marzia Marcaurelio1 , Elena Campione2 , Roberto Massa3 , Giovanni Meola4 , Annalucia Serafino5 , Giuseppe Novelli1,6, Federica Sangiuolo1† and Annalisa Botta1 \* †

1 Medical Genetics Section, Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy, 2 Division of Dermatology, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy, 3 Division of Neurology, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy, 4 Department of Biomedical Science for Health, Policlinico San Donato (IRCCS), University of Milan, Milan, Italy, 5 Institute of Translational Pharmacology, Italian National Research Council, Rome, Italy, 6 Istituto Neurologico Mediterraneo (IRCCS), Pozzilli, Italy

#### Edited by:

Cesare Gargioli, Università degli Studi di Roma Tor Vergata, Italy

#### Reviewed by:

Emanuele Berardi, Ku Leuven University, Belgium Chiara Mozzetta, Sapienza Università di Roma, Italy

#### \*Correspondence:

Paola Spitalieri<EMAIL_ADDRESS>Annalisa Botta<EMAIL_ADDRESS>†These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

Received: 29 March 2018 Accepted: 02 July 2018 Published: 27 July 2018

#### Citation:

Spitalieri P, Talarico RV, Murdocca M, Fontana L, Marcaurelio M, Campione E, Massa R, Meola G, Serafino A, Novelli G, Sangiuolo F and Botta A (2018) Generation and Neuronal Differentiation of hiPSCs From Patients With Myotonic Dystrophy Type 2. Front. Physiol. 9:967. doi: 10.3389/fphys.2018.00967 Human induced pluripotent stem cells (hiPSCs)-patient specific are an innovative tool to reproduce a model of disease in vitro and summarize the pathological phenotype and the disease etiopathology. Myotonic dystrophy type 2 (DM2) is caused by an unstable (CCTG)n expansion in intron 1 of the CNBP gene, leading to a progressive multisystemic disease with muscle, heart and central nervous dysfunctions. The pathogenesis of CNS involvement in DM2 is poorly understood since no cellular or animal models fully recapitulate the molecular and clinical neurodegenerative phenotype of patients. In this study, we generated for the first time, two DM2 and two wild type hiPSC lines from dermal fibroblasts by polycistronic lentiviral vector (hSTEMCCA-loxP) expressing OCT4, SOX2, KLF4, and cMYC genes and containing loxP-sites, excisable by Cre recombinase. Specific morphological, molecular and immunocytochemical markers have confirmed the stemness of DM2 and wild type-derived hiPSCs. These cells are able to differentiate into neuronal population (NP) expressing tissue specific markers. hiPSCs-derived NP cells maintain (CCTG)n repeat expansion and intranuclear RNA foci exhibiting sequestration of MBNL1 protein, which are pathognomonic of the disease. DM2 hiPSCs represent an important tool for the study of CNS pathogenesis in patients, opening new perspectives for the development of cell-based therapies in the field of personalized medicine and drug screening.

Keywords: myotonic dystrophy type 2 (DM2), human induced pluripotent stem cells (hiPSCs), intranuclear foci, neuronal population (NP), muscleblind-like 1 and 2 (MBNL1 and 2)

## INTRODUCTION

Myotonic dystrophies (DMs) represent a group of autosomal dominant multisystemic diseases (Harper, 2001), including DM type 1 and type 2 and manifesting highly variability in term of age at onset, severity of the symptoms, and clinical pictures. Myotonic dystrophy type 1 (DM1, OMIM 160900) is one of the most common forms of muscular dystrophy in adults with a prevalence of

**155**

1/8000 worldwide (Harper et al., 2002). It is caused by expansion of a CTG trinucleotide repeat in the non-coding region of myotonic dystrophy protein kinase gene (DMPK; OMIM 605377), a gene located on chromosome 19q13.3 encoding a protein kinase (Brook et al., 1992; Fu et al., 1992; Mahadevan et al., 1992). Myotonic dystrophy type 2 (DM2, OMIM 602688) results from an unstable tetranucleotide CCTG repeat expansion in intron 1 of the nucleic acid-binding protein (CNBP) gene (previously known as zinc finger 9 gene, ZNF9; OMIM 116955) on chromosome 3q21 (Liquori et al., 2001). The true prevalence of DM2 is still uncertain since DM2 is likely under diagnosed, not only due to the unfamiliarity with the disorder by most clinicians but also to the heterogeneous phenotype and the rather aspecific onset.

The disease mechanism proposed for the pathogenesis of DMs is complex and involves a common RNA gain-of-function mechanism in which the CUG and CCUG repeats cause the accumulation of the expanded transcripts into nuclear RNA foci (Wojciechowska and Krzyzosiak, 2011) which, in turn, sequester and deregulate RNA-binding proteins, such as MBNL1 and MBNL2. These events lead to the impaired alternative splicing (AS) and expression of aberrant embryonic protein isoforms in adult tissues and thus, the spliceopathy observed in DMs is indicated as the main cause of the multisystemic features of the diseases (Ranum and Day, 2004). In addition to spliceopathy, RNA toxicity may trigger further pathogenic mechanisms including dysregulation of miRNAs and RNAi metabolism, RAN translation, defects in protein translation/turnover, activation of cellular stress and apoptotic pathways (Loro et al., 2010; Botta et al., 2013a,b; Meola and Cardani, 2015). To date, defects in the AS of several genes have been reported in DM1 brain, including the glutamate NMDA receptor1, APP, and MAPT genes (Caillet-Boudin et al., 2014). Although, MBNL2 appears to be a crucial factor in the aberrant splicing of the nervous tissues (Charizanis et al., 2012; Goodwin et al., 2015), little is known about links between the molecular defects and the various CNS symptoms in DM1 patients.

Despite caused by similar mutations, DM1 and DM2 are clinically distinct diseases: they differ for the age at onset, pattern of muscle weakness and absence of congenital and childhood-onset of DM2. Different studies confirm central nervous system (CNS) involvement in DM1 and to a lesser extent in DM2, indicating that DMs can be considered, beyond muscle, true brain disorders (Meola and Sansone, 2007). The cerebral involvement of DM1 patients has been associated with difficulties in executive functions, visuospatial/constructive abilities, memory, facial emotion recognition, and psychomotor delay. In the most severe cases of DM1 (congenital/juvenile form), mental retardation has also been described (Machuca-Tzili et al., 2005; Schara and Schoser, 2006). Furthermore, approximately half of these young patients also have autism spectrum disorders, the frequency of which is related to the number of CTG repeat (Ekstrom et al., 2008). Brain involvement in DM2 is more controversial. There are only few studies regarding the neuropsychological involvement in DM2 pathology and each includes small number of patients (Minnerop et al., 2011). Cognitive manifestations in DM2 include problems with organization, concentration, and word finding which are considerably milder than in DM1 (Romeo et al., 2010). A specific type of "avoidant" personality trait and a significant impairment in executive functions have been observed in both DMs (Meola et al., 2003). Recently, a significant dysexecutive syndrome and impairment of episodic verbal memory have been reported on a large cohort of DM2 patients (Peric et al., 2015), reflecting of frontostriatal and temporal lobe dysfunctions. In contrast to DM1, DM2 has not been associated with developmental abnormalities and thus does not cause severe childhood symptoms. This difference likely explains why no mental retardation similar to that reported in congenital and juvenile forms of DM1 has been described in DM2 patients. The characterization of CNS involvement in DM2 is an evolving field of research, however, no cellular or animal models fully recapitulate the molecular and clinical phenotype of DM2 patients, particularly their brain involvement. The lack of accessibility of neural tissue (both living and post-mortem), combined with difficulties in culturing of these cell types in vitro, emphasizes the need for a DM2 cellular system capable to reproduce the mechanisms underlying the CNS impairment in patients.

Human induced pluripotent stem cells (hiPSCs) offer a nearly limitless potential for disease modeling, thanks to their great self-renewal and wide differentiation capacity, coupled with the relative ease of producing patient-specific cells. hiPSCs, carrying mutations implicated in disease, make possible the generation of a wide range of cell types in culture (Yanovsky-Dagan et al., 2015; Spitalieri et al., 2016). Neuronal stem cells (NSCs) derived from Embryonic Stem cells (ESCs) or from hiPSCs have been already generated for DM1, demonstrating their ability to recapitulate important features of the disease, including RNA foci, MBNL sequestration and transcriptional/splicing defects (Marteyn et al., 2011; Denis et al., 2013; Xia and Ashizawa, 2015; Xia et al., 2015; Gourdon and Meola, 2017; Spitalieri et al., 2018).

In this study, we established, for the first time two DM2 and two healthy hiPSC lines as control from human dermal fibroblasts (HDFs), using lentiviral polycistronic vector containing Yamanaka's four factors (OCT4, SOX2, KLF4, and c-MYC). These cells are able to self-renew indefinitely and also to differentiate into neuronal population (NP) maintaining the major specific DM2 hallmarks, such CCTG repeat expansion and CCUG-containing intranuclear RNA foci, which are pathognomonic the disease. The development of a hiPSCsbased platform represents an important tool to study the neurodegenerative and neuromuscular nature of the DM2 mutation and to identify molecular targets for drug design in repeat expansion disorders.

#### MATERIALS AND METHODS

#### Skin Biopsy and Culture of Human Dermal Fibroblasts (HDFs)

Two DM2 patients, one male and one female, with a molecular diagnosis of DM2, and two age and sex-matched

healthy donors (wild type, WT) have been recruited for dermal biopsy (**Table 1**). Before participation, informed written consent was obtained. The project was approved by The Committees on Health Research Ethics of Tor Vergata Hospital (2932/2017) and in accordance with the Declaration of Helsinki. Skin biopsies (6 mm in diameter) have been digested with 2 mg/mL DISPASE (Sigma-Aldrich, St. Louis, MO, United States) clean off any adipose and epidermal tissue for overnight at +4 ◦C. Tissues have been processed into 0.5 mm cubes, treated with 1 mg/mL COLLAGENASE type I (Sigma-Aldrich, St. Louis, MO, United States) for 4 h at 37◦C and successively placed onto 0.1% gelatin-coated (from porcine skin Type A; Sigma-Aldrich, St. Louis, MO, United States) 35-mmculture plates and cultured in primary culture medium DMEM (Sigma-Aldrich, St. Louis, MO, United States) with 15% fetal bovine serum (FBS) (Gibco, Waltham, MA, United States), 1 mM L-Glutamine (Gibco, Waltham, MA, United States), 1% penicillin/streptomycin (Thermo Fisher Scientific, Waltham, MA, United States), 1% non-essential aminoacid solution (Gibco, Waltham, MA, United States), 0.1 mM β-mercaptoethanol (Gibco, Waltham, MA, United States). Fibroblasts growing out from the dermal tissue have been expanded in conventional serum-containing culture media until passage 2.

#### hiPS Cells Reprogramming

A single lentiviral "stem cell cassette" has been used, flanked by loxP-sites (hSTEMCCA-loxP), and encoding for all four reprogramming factors (OCT4, SOX2, KLF4, and c-MYC) in a single polycistonic vector (Spitalieri et al., 2015, 2018). Approximately 1.5 × 105 HDFs have been seeded on plastic in 35 mm culture plates and infected in DMEM, containing 15% FBS. The medium has been replaced after 16 h with hiPSCs medium: Dulbecco's Modified Eagle's medium-F12 (DMEM/HAM's F12) (Gibco, Waltham, MA, United States) with 20% knock out serum replacement (KSR) (Gibco, Waltham, MA, United States), 1 mM L-glutamine, 1% penicillin/streptomycin, 1% non-essential amino acid solution, 0.1 mM β-mercaptoethanol and 10 ng/ml of basic Fibroblast Growth Factor (bFGF) (PeproTech, London, United Kingdom) and changed every 2–3 days. hiPSCs colonies have been picked 20–25 days post-infection on the basis of morphology and expanded by plating on mitomycin C-treated MEFs in hiPSCs medium (Sigma-Aldrich, St. Louis, MO, United States). Successively, hiPSCs have been manually picked, passaged on human embryonic stem cell-qualified Matrigel-coated plates (0.05 mg/mL) (Corning, NY, United States) and cultured under feeder-free condition in mTeSR1 medium with Y-27632 ROCK inhibitor (StemCell, Canada), mantaining the stability over 20 and more passages.

#### Neural Differentiation of hiPSCs

For neural induction, a modified method previously described was used (Chambers et al., 2009). hiPSCs were cultured in neural induction medium combining dual ALK inhibition (SB + LDN:10 µm SB431542 (Tocris Bioscience, Bristol, United Kingdom) and 0.2 µm LDN193189 (Stemgent, United Kingdom) in KSR media. For differentiating neuronal cells into neurons, increasing amounts of N2 (Gibco, Waltham, MA, United States) (25, 50, and 75%) was added to the KSR media maintaining LDN193189. On day 5–9 sonic hedgehog (SHH; Curis, Lexington, MA United States) was added in N2 media followed by the addition on day 9 of BDNF (PeproTech, London, United Kingdom), ascorbic acid (Sigma-Aldrich, St. Louis, MO, United States) and FGF8 (PeproTech, London, United Kingdom) and maturated on days 12–30 with BDNF, ascorbic acid, GDNF (PeproTech, London, United Kingdom), TGFβ3 (Sigma-Aldrich, St. Louis, MO, United States), cAMP (Sigma-Aldrich, St. Louis, MO, United States) for doparminergic subtype and retinoic acid (Sigma-Aldrich, St. Louis, MO, United States) for motor neuron cells.

#### Alkaline Phosphatase (AP) and Immunofluorescence (IF) Analyses

Alkaline phosphatase staining was performed with the Vector Red Substrate Kit (Vector Laboratories, Burlingame, CA, United States1 ) according to the manufacturer's protocol. For IF analysis, cells were fixed in 4% paraformaldehyde and incubated with the appropriate primary antibodies against OCT4 (Stemgent, United Kingdom), SSEA-4 (Stemgent, United Kingdom), TRA1-60 (Stemgent, United Kingdom), TRA1-81 (Stemgent, United Kingdom), NESTIN (Millipore, Burlington, NJ, United States), PAX6 (Abcam, Cambridge, United Kingdom), OLIG2 (Millipore, Burlington, NJ, United States), CHAT (Millipore, Burlington, NJ, United States), TH (Abcam, Cambridge, United Kingdom), TUJ1 (Covance, Princeton, NJ, United States), MAP2 (Sigma-Aldrich, St. Louis, MO, United States), GFAP (Millipore, Burlington, NJ, United States). Appropriate AlexaFluor 488 or 568 secondary antibodies were incubated for 1 h. Nuclei have been counterstained with 4,6- diamidino-2-phenylindole (DAPI; Sigma-Aldrich, St. Louis, MO, United States). Samples have been observed and acquired by fluorescence microscope (Zeiss Axioplan 2 microscope, Thornwood, NY, United States).

#### RNA Fluorescence in Situ Hybridization (RNA-FISH) and IF Staining

Intranuclear foci containing (CCUG)exp RNA were dectected in hiPSCs and NPs from DM2 patients using via RNA fluorescence in situ hybridization (FISH) using Cy3 labeled (CAGG)10 DNA probe (Bonifazi et al., 2006). The cells have been fixed with 4% paraformaldehyde (PFA), permeabilized using 0.3% Triton 100-X, then blocked with 3% BSA and incubated with primary antibodies OCT4, TUJ1, MAP2, GFAP, ISLET1 (Abcam, Cambridge, United Kingdom), LIM3 (Millipore, Burlington, NJ, United States), and MBNL1 (3A4, Santa Cruz Biotechnology,

1www.vectorlabs.com

#### TABLE 1 | Description of samples used in this study.

fphys-09-00967 July 27, 2018 Time: 13:20 # 4

#### TABLE 2 | List of primers and TaqMan probes used in this study.

Dallas, TX, United States) labeled with AlexaFluor 488 or 568 secondary antibody. Nuclei have been counterstained with 4,6- diamidino-2-phenylindole. Samples have been observed by confocal microscopy (LEICA TCS SP5). Up to 50 serial sections (each section about 1 µm in thickness), taken from the top to the bottom along the Z axis, have been used to obtain a 3D reconstruction of each analyzed field.

#### Gene Expression and Splicing Analysis

RNA from HDFs, hiPSCs and NP was extracted by TRIzol (ThermoFisher Scientific, Waltham, MA, United States) following manufacturer's instructions and reverse-transcribed with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, United States) for cDNA syntesis. Both SYBR Green and TaqMan chemistry with reference genes have been used to quantify mRNA expression. Oligonucleotide sequences and TaqMan probes used are listed in **Table 2**. RT-PCR for MBNL1 selected target (FNIP1, MARK2, PPIP5K1, KIF13A, FMNL3, CD47, PALM, ITGA6 genes) was carried out as reported in Venables et al. (2013). Electrophoresis of the PCR products for splicing analysis was carried out on a 2.5% agarose gel.

### Methylation Sensitive High Resolution Melting (MS-HRM) Analysis of the OCT4 Gene Promoter

Bisulphite treatment of DNA converts all unmethylated cytosines to uracil, leaving methylated cytosines unaltered. Genomic DNA (2 µg in 20 µl) extracted from HDFs and hiPSCs cell lines was treated with EZ DNA Methylation-Gold Kit (Zymo Research, Irvine, CA, United States) according to the manufacturer's instructions. After bisulfite conversion, the genomic DNA was quantified by the Qubit R 2.0 Fluorometer (Invitrogen, Carlsbad, CA, United States) according to the manufacturer's instructions. The bisulphite converted DNA was then used for PCR amplification of OCT4 gene promoter regions with primers designed using Methyl Primer Express Software v1.0 (Applied Biosystem). Primers (OCT4-BSP) were designed to amplify the promoter and exon 1 from −234 to +46 base pairs (NR\_034180) (Shi et al., 2013; **Table 2**). PCR amplification and HRM analysis of bisulphite-converted DNA was carried out on 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, United States), as previously described (Spitalieri et al., 2015).

#### Genotyping at the DM2 Locus

Genomic DNA was extracted from DM2 hiPSCs and NPs using Wizard Genomic DNA Purification Kit (Promega, Madison, WI, United States). Molecular characterization of the (CCTG)n expansion in the CNBP gene was obtained by a Long-Range PCR based protocol followed by oligospecific hybridization with a (CCTG)5 radioactively labeled probe (Botta et al., 2006).

#### Statistical Analysis

All values provided for RT-qPCR experiments and for foci analysis are from independent experiments and are reported as mean ± SEM. Each cell line (10 from 2 WT donors and 10 for 2 DM2 donors) has been tested in triplicate and elaborated as mean ± SEM, then for each donor a mean of these value has been further elaborated. Data have been compared using the two-tailed Student's t-test, for independent samples.

### RESULTS

#### Generation and Characterization of DM2 and WT hiPSCs

HDFs from two DM2 patients and two healthy controls (WT) were reprogrammed using a single lentiviral "stem cell cassette" (hSTEMCCA-loxP), containing all defined genes necessary to obtain hiPSCs (Spitalieri et al., 2015, 2018). Thirty-eight hiPSC clonal lines from two healthy donors (WT) and 20 hiPSC clonal lines from two DM2 patients have been generated and 5 lines/each donor have been used (**Table 1**), molecular and IF analyses have been performed between passages p20 and p23 in culture. All hiPSCs exhibited an undifferentiated morphology with compact refractile, defined borders and high rate of proliferation. As shown in **Figure 1**, cells expressed alkaline phosphatase activity (AP), the transcription factor OCT4 and surface stemness markers such as TRA 1–60, SSEA-4, TRA 1– 81. The success of reprogramming has been also confirmed by RT-qPCR analysis, carried out using primers designed to specifically detect endogenous reprogramming transcription factors (OCT4, NANOG, SOX2, KLF4, c-MYC, REX-1) and to elude any possible contamination of exogenous transcripts expressed by the hSTEMCCA-loxP lentiviral vector. The results revealed a clear expression of embryonic marker genes in both DM2 and WT hiPSCs, compared to those detected in human embryonic stem cells (HUES-3) (**Figure 2**). No chromosomal aberrations in DM2 and WT hiPSCs have been observed by karyotype analysis (data not shown). Moreover, RNA-FISH, combined with IF analysis, revealed the presence of intranuclear CCUG-containing foci in OCT4-positive hiPSCs derived from DM2 patients, thus confirming the retention of expanded sequences accumulated within nuclei of pluripotent cells. No foci were detected in the healthy controls-derived hiPSCs (**Figure 3**).

### Methylation Analysis of the OCT-4 Promoter

Methylation Sensitive High Resolution Melting (MS-HRM) analysis has been used to examine if the OCT4 expression observed in hiPSCs lines was correlated with the hypomethylation of minimal promoter and exon 1 of the gene. Eleven CpG dinucleotides have been analyzed within this region. The aligned melt curve profiles of standard with different ratios of methylated-to-unmethylated templates (0, 10, 25, 50, 80, and 100%), HDFs and hiPSCs lines are displayed in **Figure 4**. The methylation pattern of the OCT4 region analyzed differs significantly between HDFs and hiPSCs lines. The percentage of OCT4 methylation in both WT and DM2 HDFs (values ranging from 70 to 90%) resulted to be significantly higher respect to WT and DM2 hiPSCs (values ranging from 20 to 40%; p < 0.005) (**Figure 4**). These data are in agreement with the activation of the OCT4 gene that occurs along the HDFs to hiPSCs reprogramming process.

#### MBNL1 and MBNL2 Are Involved in Pluripotent Stem Cell Reprogramming of WT and DM2 Lines

Previous studies have shown that both MBNL1 and MBNL2 have a lowest relative mRNA level in ESCs/hiPSCs compared to other cells and tissues (Han et al., 2013; Venables et al., 2013), confirming a conserved and prominent role for both genes in ESC-differential AS. In fact, MBNL proteins are shown to negatively regulate an ESC-differential AS network that controls pluripotency and reprogramming (Han et al., 2013). We therefore performed a gene expression analysis by RT-qPCR in order to quantify mRNA levels of MBNL1 and MBNL2 genes in our WT and DM2 HDF and hiPSC lines. Results clearly showed a variation in the expression of these genes among the four analyzed cell lines, independently from their genotypes.

condition in mTeSR1 medium, showing undifferentiated morphology, similar to hES cells, with compact refractile and defined borders. hiPSCs express alkaline phosphatase (AP) and are positive for stem cell markers OCT4 (green), TRA1-60 (red), SSEA-4 (green), and TRA1-81 (red). 4,6-Diamidino-2-phenylindole (DAPI) nuclear staining in blue (right column in A,B). Scale bars = 200 µm.

In particular, RT-qPCR assay revealed that HDFs express the higher levels of MBNL1 and MBNL2 transcripts (average values of DM2 and WT cell lines: 0.648 ± 0.07 and 0.603 ± 0.03, respectively) compared to hiPSCs (average values of DM2 and WT cell lines: 0.115 ± 0.04 and 0.206 ± 0.07, respectively) (**Figure 5A**). These data confirm that MBNL2, as well as MBNL1, is differently regulated during hiPSCs reprogramming and plays a central and negative regulatory role in pluripotency. However, comparison of the MBNL1 and MBNL2 expression pattern in WT (C3 and C4) and DM2 (D1 and D2) HDFs and hiPSCs did not revealed any significant differences.

We then focused our attention on a set of genes (FNIP1, MARK2, PPIP5K1, KIF13A, FMNL3, CD46, PALM, and ITGA6), whose AS is controlled by the MBNL proteins during the HDFs to hiPSCs transition (Venables et al., 2013). As expected, a classical HDFs to hiPSCs switch-like splicing pattern has been observed for all the gene analyzed in both DM2 and WT cell lines (**Figure 5B**). Again, no significant differences have been observed among DM2 and WT cells, thus implying that the MBNLs sequestration into CCUGcontaining ribonuclear foci does not perturb splicing changes upon the induction of the original HDFs into the pluripotent state.

#### Differentiation and Characterization of hiPSCs-Derived Neural Population (NPs)

Neuronal differentiation was performed using EB-based approach by DUAL inhibition of SMAD signaling, obtained with the addition of SB431542/LDN to achieve full neural conversion from WT and DM2 hiPSCs into NPs. The expression of neural stem cell marker, NESTIN, and dorsal forebrain marker, PAX6, showed a profile of neural induction after the first 12 days of the differentiation process, as revealed by immunofluorescence analysis (**Figure 6A**). Following the addition of several factors such as BDNF, ascorbic acid, TGFβ3, GDFN, retinoic acid, and cAMP to neural precursors, at day 25 of differentiation, we observed a mixed population of cells expressing different neuronal markers specific for neurons, astrocytes, oligodendrocytes, early dopaminergic cells and early motor neuron precursors. After day 25, we obtained neuronal networks formed by precursors of oligodendrocytes and dopaminergic cells, as demonstrated by

positive staining of OLIG2 and Tyrosine Hydroxylase (TH) antigen, respectively (**Figure 6A**). Cells also showed positivity for choline acetyltransferase marker (CHAT), one of the enzymes involved in the synthesis of the neurotransmitter acetylcholine (**Figure 6A**). The differentiation process has verified according to the protocol described in **Figure 6B**.

In order to test if NPs from DM2 patients retain the main molecular hallmark of the disease after the differentiation process, we evaluate the presence of intranuclear CCUG-containing RNA foci in cells expressing known neuronal markers. RNA-FISH, combined with IF analysis in DM2 and WT NPs, showed ribonuclear foci only in DM2 cells, positive to neuron-specific marker TUJ1, dendritic marker MAP2 and astrocytic marker GFAP (**Figure 7**). A further differentiation was obtained by adding retinoic acid until the appearance of motor neuron cells, in which ISLET1 and LIM3 markers together with RNA-foci have been detected by RNA-FISH. No foci were present in WT motor neurons (**Figure 8**). The quantification of ribonuclear foci in DM2 hiPSCs and NPs showed that the number of foci appears to be directly related with the neuronal differentiation. As shown in **Figure 9A**, the percentage of cells containing a number of foci/nucleus >5 was significantly higher in DM2 NPs respect to DM2 hiPSCs (82 and 20% of hiPSCs, respectively). We then wanted to verify if the observed increase in the number of CCUG-containing RNA foci could be related to the DM2 mutation size and/or CNBP expression. LR-PCR analysis on DNA extracted from DM2 hiPSCs and NPs revealed that in neural cells the number of CCTG repetition increases compared to hiPSCs in both lines (**Figure 9B**). Conversely, RT-qPCR showed variable expression levels of CNBP transcript with no significative differences between hiPSCs (median value normalized to β-actin gene: 0.203 ± 0.01) and NP DM2 cell lines (median value normalized to β-actin gene: 0.166 ± 0.04) (**Figure 9C**).

To further validate the differentiation capacity of DM2 and WT hiPSCs into NPs, we measured the expression

∗P < 0.005.

FIGURE 5 | RT-qPCR analysis of MBNL1 and MBNL2 gene and alternative splicing assay. (A) RT-qPCR assay for MBNL1 and MBNL2 genes expression in the HDFs to hiPSCs transition of WT and DM2 lines. Gene expression levels reported are an average of the WT and DM2 cell lines normalized to β-actin used as reference gene. (B) Eight-switch-like splicing changes of genes regulated by MBNL1 protein have been assessed by conventional RT-PCR analysis during the HDFs to hiPSCs transitions in WT and DM2 lines. The upper and lower bands on each gel represent the long (exon-included) and short (exon-omitted) isoforms, respectively.

FIGURE 6 | Timeline of hiPSCs differentiation into NPs. (A) Immunostaining has been performed using antibodies specific to neural stem cell markers NESTIN (green) and PAX6 (green) and to specific markers for NP as OLIG2 (red), CHAT (green), and TH (green). 4,6-Diamidino-2-phenylindole (DAPI) nuclear staining in blue. Scale bars = 50 and 25 µm. (B) Neuronal differentiation is obtained by SMAD signaling modulation via treatment with SB431542/LDN. The timing of differentiation represents the day at which several factors have been added and the crucial stages of differentiation from hiPSCs to NPs through the neural induction.

(MAP2, green), and astrocytes marker GFAP (green) performed on DM2 and WT NPs. 4,6-Diamidino-2-phenylindole (DAPI) nuclear staining (blue). Image show the presence of CCTG RNA foci only in DM2 nuclei. Scale bars = 25 µm.

levels of specific neural markers by RT-qPCR. This analysis revealed that NESTIN, an early marker of neural stem cell, resulted to be less expressed respect to markers of mature neurons, such as astrocytes and oligodendrocytes in both DM2 and WT cells. Specifically, after day 25th induction of neuronal and astrocytic differentiation resulted in transcriptional boost of lineage specific markers (i.e., MAP2, GFAP, peripherin). At the same time point of the in vitro differentiation, we also observed an evident expression level of both oligodendrocyte transcription factor OLIG2, playing a crucial role in the neurogenesis, and of HB9, an early motor neuron marker (p < 0.01) (**Figure 10**). These data demonstrated a robust patterning response in SB431542/LDNtreated neural progeny and derivation of relevant neuron subtypes after short differentiation periods (∼25–30 days) compared to 40–50 days necessary for inducing rapid and complete neural conversion, as already reported (Perrier et al., 2004; Lee et al., 2007).

### DISCUSSION

The majority of study in DM2 have been focused on the muscle pathology, but less is known about the impact of the CCTG expansion on the rest of the body. CNS symptoms, such as intellectual disability, behavioral issues and day time sleepness also have a large impact on patient's quality of life. However, the CNS is especially difficult to study as most material can only be obtained post-mortem. In this study we report, for the first time, that neural cells can be obtained by DM2 patient-specific hiPSCs, opening new avenues for modeling this complex multisystemic disease.

We obtained two hiPSCs from a male and a female affected by DM2. The analyses have been performed by comparing them to WT hiPSCs age- and sex-matched (**Table 1**). No isogenic lines were produced, but in order to improve the statistical significance, 5 cell lines from each patients, for a total of 10 lines DM2 and 10 lines WT, were always compared, considering cell-to-cell variability. In this way, even if partially, we have circumvented the limitation due to the lack of isogenic cells, in which a gene targeting approach has reversed the pathological phenotype (Spitalieri et al., 2018). The differentiation protocol used in the present study has considered the treatment with small molecules, SB and LDN that, effectively enhanced the differentiation of hiPSCs and changed their state toward a transitional EB state, inducing in vitro neural maturation. As previously described, the inhibition of BMP and TGF-β signaling by the small molecules early during hiPSCs differentiation selectively blocks endodermal and mesodermal cell fates. This induces default neural specification, termed dual-SMAD inhibition, dramatically enriches neural ectodermal directly from pluripotent cells (Chambers et al., 2009; Sances et al., 2016). The following steps correspond to the neutralization through dual-SMAD inhibition, caudalization and ventralization through SHH activation by SHH protein and the consequent neural determination by neutrophic factors such as BDNF, GDNF, TGFβ3, and others for promoting neural maturation and survival. The robustness and modularity of the above strategy beyond hiPSCs differentiation offered an efficient defined and valid platform for the rapid generation of neural cell types including somatic motor neurons and dopamine neurons, as demonstrated by expression analyses of neural markers. Moreover, after differentiation no difference in term of differentiation efficiency has been observed among DM2 and WT cells. NPs obtained in this study typically contain heterogeneous cell progeny containing original neural stem cells (NSCs), including NSCs themselves and their progeny (Hawes and Pera, 2006), showing a similar response to induction factors during time-course.

The main pathognomonic molecular hallmark of DM2, represented by the presence intranuclear accumulation of CCUG-containing foci which sequester MBNL1 protein, are also conserved after neuronal differentiation. This suggests that RNA toxicity underlay CNS dysfunction in DM2 patients and allowed us to consider our hiPSCs-derived NP cells

bars = 25 µm.

as a good disease model. Interestingly, in DM patients the most severely affected tissues are those mainly formed by non-cycling cell populations (i.e., myotubes, neurons, and cardiomyocytes), on the contrary, self-renewing tissues are much less affected (Giagnacovo et al., 2012). This observation indicates that in these cells foci formation and the sequestration factors essential for RNA splicing and processing would be a continuous and progressive phenomenon, eventually leading to the onset of disease symptoms. The quantification of ribonuclear foci during the neural differentiation of our DM2 hiPSCs showed that the number of foci increased after the neuronal differentiation and is not related to the number of passages in culture which is maintained after hiPSCs to NP transition. This observation is in accordance with previous

data indicating that the number and density of ribonuclear foci progressively increase in cells that stop dividing and undergo differentiation (Cardani et al., 2009). Nevertheless, the expression level of the CNBP transcript does not differ significantly between hiPSCs and NP from DM2 patients. It is therefore possible that in non-cycling NP cells ribonuclear foci do not undergo relocation and degradation occurring during mitosis and progressively increase in number and size, due to a continuous accumulation of both expanded RNAs and protein factors.

Another important aspect still unknown in DM2 is the dynamic of the (CCTG)n expansion in different cell types and the mechanism causing the (CCTG)n expansion and/or contraction. The CCTG repeats expansion is highly unstable and tends to increase with age, while, differently from DM1, it usually decreases on transmission to the next generation being shorter in the children (Meola and Cardani, 2015). The cause for the instability is probably related to an Alu-mediated mechanism developed into the large CCTG expanded tract or to unequal crossing over (Dere et al., 2004; Kurosaki et al., 2012). This instability leads to the somatic mosaicism that gives rise to intra-tissue, inter-tissue, and cell-type variability over a patient's lifetime. Differently from DM1, the CCTG repetition number is not associated with phenotype severity, age, onset of disease. All together, these observations could explain some distinct features of DM2 such as the lack of a congenital form and anticipation and the later onset of symptoms (Udd et al., 2003). Interestingly the determination of the CCTG repetition number in our DM2 cells lines showed an increased in the expansion size during the neural differentiation. Of course, we are aware of all the limitations in determining the DM2 mutation size by LR-PCR, however, this analysis indicates a trend toward the expansion in our neuronal cell model which could be further investigated at molecular level.

Consistent with previous studies, we found that the MBNL1 and MBNL2 genes are expressed at minimal levels in hiPSCs compared to fibroblasts, confirming that both MBNL proteins negatively regulate pluripotency and reprogramming (Han et al., 2013). The direct consequences of MBNL downregulation in hiPSCs is the switch-like splicing pattern of a set of genes whose AS is controlled by the MBNL proteins during the HDFs to hiPSCs transition (Venables et al., 2013). Interestingly, no differences have been observed in the splicing profile these MBNL-dependent genes in WT and DM2 cell lines. Consistently, once established, either DM2 and WT hiPSCs grew in a similar pattern with a high nuclear to cytoplasm ratio, expressed stem cells markers and had stem cells features of self-renewal and pluripotency. Taken together, our results indicate that sequestration of the MBNL proteins into the ribonuclear foci of DM2 cells does not exert a toxic effect on the reprogramming and differentiation capacity into NPs of hiPSCs.

Multiple RNA-based approaches have been investigated to induce the downregulation of the DMPK expanded transcripts (Thornton et al., 2017) and recently CRISPR/CAS9 genome editing technology has been successful applied as a gene therapy approach for DM1 (Provenzano et al., 2017). The DM2-hiPSCs generated in our work could represent a powerful tool to study the potential role of genome editing and RNA-based therapeutic approaches also for DM2 pathology. Moreover, the detection of specific markers expressed in DM2 NPs could allow to follow the time-course of neurogenesis gaining further insights into the development of brain impairment in patients. The understanding/elucidation of the functional alteration affecting DM2 neural cells would help the finding of the more suitable pharmacological intervention to correct such abnormalities and would thus lay the bases for a future patient-tailored therapy.

#### AUTHOR CONTRIBUTIONS

AB, FS, PS, and GN conceived the experiments. PS, RT, MiM and MaM conducted the experiments. EC, GM, and RM selected patients, LF and AS analyzed the data. AB, FS, and PS wrote the manuscript. All authors discussed and reviewed the manuscript.

#### ACKNOWLEDGMENTS

Authors thank Graziano Bonelli for expert technical help.

### REFERENCES

fphys-09-00967 July 27, 2018 Time: 13:20 # 13

Harper, P. S. (2001). Myotonic Dystrophy, 3rd Edn, London: WB Sauders.

**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.

The handling editor declared a shared affiliation, though no other collaboration, with several of the authors at time of review.

Copyright © 2018 Spitalieri, Talarico, Murdocca, Fontana, Marcaurelio, Campione, Massa, Meola, Serafino, Novelli, Sangiuolo and Botta. 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.

# Dysfunction of Sister Chromatids Separation Promotes Progression of Hepatocellular Carcinoma According to Analysis of Gene Expression Profiling

#### Baozhen Sun1 , Guibo Lin2 , Degang Ji1 , Shuo Li1 , Guonan Chi2 and Xingyi Jin2 \*

1 Department of Hepatopancreatobiliary, China-Japan Union Hospital of Jilin University, Changchun, China, 2 First Department of Neurosurgery, China-Japan Union Hospital of Jilin University, Changchun, China

#### Edited by:

Cesare Gargioli, Università degli Studi di Roma Tor Vergata, Italy

#### Reviewed by:

Gabriele Grassi, University of Trieste, Italy Fabiola Moretti, National Council of Italy, Italy

> \*Correspondence: Xingyi Jin

<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

Received: 23 February 2018 Accepted: 10 July 2018 Published: 27 July 2018

#### Citation:

Sun B, Lin G, Ji D, Li S, Chi G and Jin X (2018) Dysfunction of Sister Chromatids Separation Promotes Progression of Hepatocellular Carcinoma According to Analysis of Gene Expression Profiling. Front. Physiol. 9:1019. doi: 10.3389/fphys.2018.01019 Despite studying the various molecular mechanisms of hepatocellular carcinoma (HCC), effective drugs and biomarkers in HCC therapy are still scarce. The present study was designed to investigate dysregulated pathways, novel biomarkers and therapeutic targets for HCC. The gene expression dataset of GSE14520, which included 362 tumor and their paired non-tumor tissues of HCC, was extracted for processing by the Robust multi-array average (RMA) algorithm in the R environment. SAM methods were leveraged to identify differentially expressed genes (DEGs). Functional analysis of DEGs was performed using DAVID. The GeneMania and Cytohubba were used to construct the PPI network. To avoid individual bias, GSEA and survival analysis were employed to verify the results. The results of these analyses indicated that separation of sister chromatids was the most aberrant phase in the progression of HCC, and the most frequently involved genes, EZH2, GINS1, TPX2, CENPF, and BUB1B, require further study to be used as drug targets or biomarkers in diagnosis and treatment of HCC.

#### Keywords: separation of sister chromatids, HCC, biomarker, SAM, GSEA

### INTRODUCTION

Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related deaths worldwide and its incidence continues to rise (Mittal and El-Serag, 2013). It mainly arise from hepatitis B virus (HBV) or hepatitis C virus (HCV) infections, and patients with cirrhosis have more opportunities to get HCC (El-Serag, 2002; Umemura et al., 2009). The limited knowledge on the molecular mechanisms of HCC contribute to poor prognosis and ineffective therapy, which leaves liver transplantation as the best choice of management (Ho et al., 2015; Turato et al., 2017). However, recurrence following transplantation is also associated with an unfavorable prognosis (Spinzi and Paggi, 2008; Sposito et al., 2013). Moreover, surgical intervention is ineffective in patients diagnosed at advanced stages of HCC (Rich et al., 2017). Therefore, new therapies for HCC are direly needed.

Although some molecular events that facilitate the progression to HCC have been investigated, the effective drug targets and potential biomarkers for early treatment and diagnosis of HCC are still unclear (Blum, 2005; Cha and Dematteo, 2005). Therefore, identifying the

dysregulated pathways and hub genes involved in this process would allow us to identify patients with HCC as early as possible. Findings from previous studies that have focused on this area are limited due to small sample sizes, resulting in an incomplete understanding of HCC (Jia et al., 2007; Lin et al., 2014; Yin et al., 2017).

In this study, we extracted the gene expression profiles from the GEO database of 362 HBV-related HCC tumors and their paired non-tumor tissues which are mostly accompany with liver cirrhosis. The significance analysis of microarrays (SAMs) algorithm was used to screen the differential expressed genes (DEG), which was performed for pathway enrichment and generation of PPI network. After that, hub genes were identified by GeneMANIA and CytoHubba in Cytoscape. Furthermore, we performed a gene set enrichment analysis (GSEA), which evaluates microarray data at the level of gene sets, to overcome the limitation of individual gene analysis. In the meantime, survival analysis was leveraged, using the TCGA database, to assess the risks of hub gene expression. Finally, five most significantly hub genes were verified by qPCR and IHC in human HCC to confirm the results.

### RESULTS

### Microarray Data Collection and Processing

The BioConductor package, Simpleaffy, was used for quality control and normalization of the microarray raw files (**Supplementary Figure S1**).

## Identification of DEGs

To identify DEGs from the tumor and non-tumor tissue of HCC patients, we used the SAM method at the delta 14.31, with the FDR < 0.1%. A total of 862 genes were identified as DEGs, including 553 up-regulated and 309 down-regulated genes (**Figure 1**).

## Functional Analysis of DEGs

Gene Ontology (GO) enrichment analysis revealed that GO terms were most significantly enriched in cell division (p = 1.09E-20), sister chromatid cohesion (p = 2.79E-13), mitotic nuclear division (p = 1.62E-11) and DNA replication (p = 1.34E-09; **Figure 2**). Reactome Pathway enrichment analysis showed that the separation of sister chromatids was the most significantly affected phase in HCC (**Figure 3**), which was in accordance with the results of the GO enrichment analysis.

### PPI Network Analysis of DEGs

A PPI network with 873 nodes and 84,272 edges was generated via the GeneMANIA plugin, around the DEGs. A global metric was utilized in the determination of hub proteins, through the Cytohubba plugin. Following this, the relationship between the 20 top-ranked proteins was mapped, based on the MCC as shown in **Figure 4**. The majority of these were cell division-related genes, such as TOP2A, GINS1, BUB1B, TPX2, and CENPF. The 20 topranked proteins were all up-regulated DEGs.

### GSEA and Leading-Edge Analysis

In order to further confirm the molecular mechanisms of HCC in the whole transcriptome, GSEA of the gene expression profile data from 362 tumor and paired nontumor tissue of HCC patients was performed, based on the GO biological process. The results revealed that the most significant biological processes that were enriched were cell division-related processes, including GO\_REGULATION\_OF\_ CELL\_DIVISION (FDR = 0.052), GO\_REGULATION\_ OF\_MEIOTIC \_CELL\_CYCLE (FDR = 0.050), GO\_REGULATION\_ OF\_NUCLEAR\_DIVISION (FDR = 0.051), GO\_SISTER\_ CHROMATID\_COHESION (FDR = 0.051), GO\_REGULATION\_OF\_ CHROMOSOME\_ORGANIZATION (FDR = 0.043), and GO\_SISTER\_ CHROMATID\_SEGREGATION (FDR = 0.033), which are shown in **Figure 5A**. Leading-edge analysis was used to find the hub genes which appeared frequently in related gene sets, and the results showed that AURKB appeared in five gene sets, while BUB1B, CDC20, FBXO5, DLGAP5, ESPL1, BIRC5, BUB3, BUB1, CENPE, CENPF, and MAD2L1 appeared in four gene sets (**Figures 5B,C**).

#### Kaplan–Meier Survival Analysis

SurvExpress was engaged to explore the relationship between the hub genes and the survival of HCC patients in silico. Finally, survival analysis, based on clinical information from the TCGAliver cancer datasets, revealed that the high expression of EZH2, GINS1, and TPX2 correlated positively with higher risk, CENPF and BUB1B were quite the contrary (**Figure 6**).

## Validation of Hub Genes by qPCR in Human HCC

Five most significantly hub genes, EZH2, GINS1, TPX2, CENPF, and BUB1B, were successfully validated by qPCR in 30 paired human HCC tissues which have no difference with the analysis results of gene expression profiling (**Figure 7**).

#### Immunohistochemistry

IHC was employed to validate the results from bioinformation analysis, which revealed the strong expression of five hub genes in HCC vs. the control group. However, CENPF and BUB1B were not outstandingly in our result, the individual difference may affect the outcome to some extent (**Figure 8**).

## DISCUSSION

In the present study, we explored the molecular mechanisms of HCC between tumor and non-tumor tissues, using bioinformatics analysis. Our results indicated that the separation of sister chromatids was the most significantly dysregulated pathway during the transition from cirrhosis to HCC, with the up-regulation of 12 hub genes.

One of the most miraculous events in the human cell cycle is the concurrent separation of 46 pairs of sister chromatids. Since this irreversible separation is highly monitored and regulated, neither damage to the genome, nor errors in chromosome alignment, can be easily rectified after separation (Nasmyth, 1999). The initiation of chromosomal segregation was supervised by the spindle assembly checkpoint (SAC), which ensures the genomic stability during mitosis (Dai, 2009). Defects in sister chromatid segregation could lead to aneuploidy (Panigrahi and Pati, 2009) and promote chromosome instability (CIN) during mitosis (Schvartzman et al., 2010). In addition, CIN may contribute to tumor initiation and/or progression, which has been demonstrated in cell lines (Zhang et al., 2004; Vader and Lens, 2008), mice models (Bernal et al., 2002; Dawlaty et al., 2008), and human tumors (Kronenwett et al., 2004; Carter et al., 2006). In the Sgo1± mouse model, mitotic error-induced CIN was shown to be an important early event in HCC development (Kronenwett et al., 2004). Furthermore, 120 HCC with 195 markers (Nagai et al., 1997), and 48 HCC with 275 chromosomal markers (Boige et al., 1997), indicated that CIN appears widely in HCC. Aneuploidy is a major manifestation of CIN and is seen in over 75% of cancers, and is also considered essential for tumorigenesis, by some biologists (Duesberg and Li, 2003). Therefore, dysregulation of sister chromatids separation might contribute to the initiation and progression of human HCC.

Based on the results of the PPI analysis by GeneMania and Cytohubba, 20 top-ranked proteins from 862 DEGs are thought to participate in the core pathway of HCC, such as TOP2, GINS1, EZH2, TTK2, CDK1, BUB1B, TPX2, CENPF, and MAD2L1. Furthermore, the high expression of GINS1, EZH2, and TPX2, correlates with high-risk in HCC, as confirmed by the survival analysis. BUB1B, TPX2, and CENPF appeared most often in related gene sets, which were demonstrated by GSEA at the level

leading edge genes in sets A and B. (C) The bar graph shows each gene and the number of subsets in which it appears.

of gene sets. Therefore, EZH2, GINS1, TPX2, CENPF, and BUB1B are thought to be hub genes in HCC and are discussed below.

Epigenetic regulation of gene expression, particularly hypermethylation, plays an important role in tumorigenesis (Jones and Baylin, 2002). EZH2 is the enzymatic subunit of Polycomb repressive complex 2 (PRC2), which methylates H3K27, resulting in silence of the associated tumor suppressor genes (Margueron and Reinberg, 2011; Momparler et al., 2012). EZH2 was up-regulated and expressed in many solid cancers, and YY1 can recruit EZH2 and suppress NFkB function in hepatitis B virus-dependent HCC (Chase and Cross, 2011). Additionally, EZH2 is clinically associated with tumor progression and multiple metastatic features, and epigenetically restrained a subset of miRNA in human HCC (Au et al., 2012). Thus, EZH2 may be regarded as a potential therapeutic target, and a few of compounds have been already investigated as inhibitors of

EZH2, in pre-clinical studies (Knutson et al., 2012, 2013; McCabe et al., 2012; Qi et al., 2012; Kim et al., 2013).

GINS1/PSF1 is a subunit of the GINS complex, which is involved in the DNA replication fork and the initiation of chromosome replication (Labib and Gambus, 2007). Research suggests that GINS1 and/or other GINS complex subunits are upregulated in some types of cancers and possess some tumorigenic characteristics (Hokka et al., 2013; Zhang et al., 2015; Zhou et al., 2015). GINS1 is expressed at high levels in HCC tissues, which is associated with more aggressive tumors and worse prognosis. Moreover, in a mouse xenograft model, high levels of GINS1 expression correspond to high proliferative activity, transplantation potential, and metastatic capability (Nagahama et al., 2010). In contrast, knockdown of GINS1 expression led to inhibited tumor growth by disrupting DNA replication and chromosomal segregation, and promoted apoptosis, particularly early apoptosis (Nagahama et al., 2010; Zhou et al., 2015). These findings may make GINS1 a potential theranostic target in the future.

For many years, the function of TPX2 has been studied in mitosis and spindle assembly because of the chromatinmediated TPX2/Importinα-β/Ran signal and its control of Aurora A kinase (Asteriti et al., 2010). The location of TPX2 is at the long arm of chromosome 20, at position 20q11, which is often amplified in HCC and other tumors (Knuutila et al., 1998; Hodgson et al., 2003; Scotto et al., 2008; Beroukhim et al., 2010). The elevated TPX2 expression results in dysregulation of spindle formation and balanced chromosome segregation, by over activation of Aurora-A, which could lead to unscheduled phosphorylation of downstream targets. TPX2 knockdown inhibits cell proliferation and AKT signaling, and decreases the MMP2 and MMP9 expression in HCC cell lines (Liu et al., 2014). Clinical sample analysis also indicates that TPX2 expression is associated with the tumor–node– metastasis stage, tumor numbers, and tumor differentiation in the HCC tissues (Liu et al., 2015). Moreover, TPX2 inactivation experiments indicated anti-proliferative effects in cancer cells, suggesting the potential value of TPX2 as an anti-cancer

target (Fenner et al., 2005; Warner et al., 2009; Li et al., 2010).

CENPF is a large coiled-coil protein whose expression and subcellular localization was cell cycle-dependent, and undergoes rapid degradation at the end of cell division. It is reported that CENPF plays a major role in kinetochore assembly, regulation of chromosome segregation, and control of SAC activity. CENPF may be a potential proliferation marker in the clinical diagnosis of HCC (Ma et al., 2006). CENPF is overexpressed in HCC (Kim et al., 2012) and other tumors (Varis et al., 2006). Additionally, the up-regulated CENPF expression has been shown to contribute to the proliferation of HCC rather than acting as a trigger for malignant cell growth. Consequently, CENPF could be an indicator of tumorigenesis, especially at early stages of HCC (Zhang et al., 2001).

BUB1B is a key component in the SAC protein family, which has been proven to be involved and upregulated in multiple human cancers (Seike et al., 2002; Shichiri et al., 2002; Gupta et al., 2003; Yamamoto et al., 2007; Fu et al., 2016). In mitosis, BUB1B accumulates cyclin B in G2 phase, by binding to CDC20 to inhibit APC/C activity and prolonging the checkpoint signaling by kinase activity at kinetochores (Malureanu et al., 2009). A human study reported that the role of BUB1B was to facilitate accurate chromosome segregation and maintain chromosomal stability, to suppress cancer (Hanks et al., 2004). However, the phosphorylated BUB1B, which is tightly regulated through its own activation and subcellular localization (Bin et al., 1998; Li et al., 1999), was elevated in the SV40 Tag-derived prostate cancer models (Guo et al., 2006). The contradiction of BUB1B, between its role in suppressing cancer and upregulating cancers, is demonstrated in varying reports of cancer-associated missense and nonsense mutations in BUB1B, in several cancers (Cahill et al., 1998, 1999; Myrie et al., 2000; Saeki et al., 2002). However, the true mechanism of BUB1B in cancers remains to be elucidated.

In conclusion, we collectively analyzed the molecular mechanisms of human HCC through interpretation of the functions and PPI network of DEGs, which were confirmed by GSEA and survival analysis. In doing so, we ascertained the molecular genetic differences between tumor and nontumor tissues of HCC, which suggest that separation of sister chromatids may have the most important influence on initiation and progression of human HCC. Errors in this process contribute to CIN and aneuploidy which were thought to be responsible for tumorigenic progression in human cells. The hub genes we found may be useful as biomarkers for diagnosis and prognosis or in tailoring treatment in human HCC. Finally, additional studies are needed to confirm the findings of these experiments.

## MATERIALS AND METHODS

### Microarray Data Collection and Processing

The gene expression dataset of GSE14520 was obtained from NCBI GEO database1 which is based on the Affymetrix Human Genome U133A 2.0 Array. 362 tumor and paired non-tumor tissues of HCC patients were collected for analysis of genome microarrays. The detail of every HCC patient was shown in **Supplementary Table S1**. The Simpleaffy package was used to read the raw data and perform quality control and normalization by Robust multi-array average (RMA) algorithm in the R environment (Wilson and Miller, 2005). The mean gene expression was considered in multiple probe sets with one name.

### Identification of Differentially Expressed Genes (DEGs)

The DEGs in tumor and non-tumor tissues of HCC patients were determined using SAM (Grace and Nacheva, 2012). We used two class unpaired comparison analysis with t-statistics, and permutations of 103 . SAM uses permutations of repeated measurements to estimate the percentage of genes identified by chance, and the false discovery rate (FDR).

### Functional Analysis of DEGs

Gene Ontology (GO) and Reactome Pathway enrichment analyses were performed using DAVID to explore the biological processes and signaling pathways in which the DEGs were involved (Ashburner et al., 2000; Huang et al., 2009; Fabregat et al., 2018). The enrichment results were visualized in a new R visualization package called GOPlot (Walter et al., 2015).

### PPI Network Analysis of DEGs and the Screening of Hub Proteins

A PPI network analysis was performed to evaluate physical relationships between the proteins encoded by the DEGs. The GeneMania (Montojo et al., 2010) and Cytohubba (Chin et al., 2014) were used to construct the PPI network based on coexpression, physical interactions, and genetic interactions.

### Gene Set Enrichment Analysis (GSEA) and Leading-Edge Analysis

To further investigate the biological characteristics of HCC, we performed GSEA assay in non-tumor and tumor groups with permutations of 104 in the GO biological process, from the Molecular Signature Database (MSigDB). A leading-edge analysis was performed to elucidate hub genes of HCC according to the results of Reactome Pathway enrichment (Subramanian et al., 2005).

### Kaplan–Meier Survival Analysis

Kaplan–Meier analysis was performed with the online multicancer biomarker validation tool, SurvExpress, in the TCGA-liver cancer datasets containing 422 samples, using the hub genes as an input (Aguirre-Gamboa et al., 2013).

### Sample Collection

Thirty HBV-related HCC and their paired non-tumor tissues were collected from August 2014 to December 2017 at China-Japan Union Hospital of Jilin University. All the specimens were dealing with liquid nitrogen after surgical resection and stored at −80◦C. Three independent pathologists made the decision about the diagnosis of HCC and assessed the samples with HE staining.

### Quantitative RT-PCR

Total RNA of 30 HCC samples was extracted using Trizol (Invitrogen) as described everywhere, qRT-PCR was performed by One-Step qPCR Kit (Invitrogen) and CFX ConnectTM Real-Time System (BIO-RAD) following manufacturer's instructions. The data of qPCR were processed by 11Ct method with normalizing to GAPDH as the reference gene. The sequence of primers was shown in **Supplementary Table S2**.

#### Immunohistochemistry

The 4 µm thick sections cut from formalin-fixed, paraffinembedded HCC tissue were used for IHC of hub genes as described before (Ho et al., 2012). The primary antibodies against

1http://www.ncbi.nlm.nih.gov/geo/

Frontiers in Physiology | www.frontiersin.org

EZH2 (No. 191080, Abcam), BUB1B (No. 183496, Abcam), CENPF (No. 223847, Abcam), TPX2 (11741-1-AP, Proteintech) and GINS1 (No. D161403, Sangon Biotech) were employed for IHC. Image Pro Plus 6.0 (Media Cybernetics, Bethesda, MD, United States) was employed to measure the positive area of hub genes for quantitative analysis.

#### Statistical Analysis

fphys-09-01019 July 25, 2018 Time: 16:53 # 9

All data are shown as mean ± SDs except for otherwise indicated. Significance was determined with two-tailed t-test when comparing the variance from HCC to the adjacent nontumor tissue. GraphPad Prism 6 software (GraphPad Software, La Jolla, CA, United States) was used for analysis. A P < 0.05 is considered significant.

#### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of CIOMS. The protocol was approved by the institutional review boards of the China-Japan Union Hospital

#### REFERENCES

of Jilin University. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

### AUTHOR CONTRIBUTIONS

XJ conceived and designed the study. BS collected, analyzed the data, and wrote the manuscript. GL, DJ, SL, and GC revised the manuscript. All authors read and approved the manuscript.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2018.01019/full#supplementary-material

FIGURE S1 | (A) Data before normalization. (B) Data after normalization.

TABLE S1 | Samples detail.

TABLE S2 | qPCR primers used for tissues specific expression analysis.

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**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 Sun, Lin, Ji, Li, Chi and Jin. 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.

# Toward Morphologically Relevant Extracellular Matrix in Vitro Models: 3D Fiber Reinforced Hydrogels

Ashok Williams1,2† , James F. Nowak1† , Rachel Dass1,2, Johnson Samuel1 and K. L. Mills1,2 \*

1 Department of Mechanical, Aerospace, Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States, 2 Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, United States

The extracellular matrix (ECM) is known to play an important role in the health of cells and tissues. Not only are chemical signals transmitted via bonds and tightly controlled diffusion, but the structure of the ECM also provides important physical signaling for the cells attached to it. The structure is composed of a mesh of fibrous proteins, such as collagen, embedded in a hydrated gel matrix of glycosaminoglycans. To study cell behavior with respect to the combined morphology and mechanics of such matrices is not currently possible with the types of 3D cell culture matrices available. Most of the cell culture matrices are single-phase bio- or polymeric hydrogels. Therefore, here we developed a continuous hybrid manufacturing process to make fiber-reinforced composite hydrogels. A far field electrospinning process was used to deposit the fibrous component with the aid of guiding electrodes; and a gravity-assisted, droplet-based system controlled the rate of addition of the cell-laden hydrogel component. The addition of the fibrous component slightly increased the elastic modulus of the pure hydrogel. The cells that were embedded into the fiber-reinforced hydrogels were viable for 8 days. The cells were randomly placed in the matrix such that some had no contact to the fibers and others were initially in proximity to fibers. The cells with no contact to fibers grew into spheroidal clusters within the hydrogel, and those in proximity to the fibers spread out and grew along the fibers showing that the fiber-reinforced hydrogels are able to control cell behavior with morphological cues.

#### Edited by:

Giancarlo Forte, International Clinical Research Center (FNUSA-ICRC), Czechia

#### Reviewed by:

Antonio Rinaldi, Energy and Sustainable Economic Development, Italy Pamela Mozetic, International Clinical Research Center (FNUSA-ICRC), Czechia

\*Correspondence:

K. L. Mills<EMAIL_ADDRESS>
†These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

> Received: 06 April 2018 Accepted: 02 July 2018 Published: 24 July 2018

#### Citation:

Williams A, Nowak JF, Dass R, Samuel J and Mills KL (2018) Toward Morphologically Relevant Extracellular Matrix in Vitro Models: 3D Fiber Reinforced Hydrogels. Front. Physiol. 9:966. doi: 10.3389/fphys.2018.00966 Keywords: extracellular matrix, electrospinning, hydrogel, mechanobiology, cell-ECM interaction

### INTRODUCTION

The extracellular matrix (ECM) of connective tissues is composed of two distinctly different morphological components. One of these is a mesh of proteinaceous fibers, such as collagen, fibronectin, and elastin. The other is a highly hydrated gel composed of glycosaminoglycans. Together, they provide structural, biophysical, and biologically active support to the cells within this microenvironment (Theocharis et al., 2016). During the progression of solid tumors, the ECM is altered through increased fibrous protein deposition, reorganization, and cross-linking (Lu et al., 2012). The result is a stiffening, or desmoplasia, of the surrounding tissue (Croft et al., 2004). Pathologically desmoplastic tissues are a major contributor to the biomechanical properties of the tumor microenvironment (Berger et al., 2017). Although this phenomenon is frequently observed, the influence of these mechanical changes on cellular behavior toward the progression of the disease is not fully understood.

**180**

Although the mechanobiology of tumor cells is well researched (Makale, 2007), it is often done so in 2D Petri dishes where interactions of the tumor cells with the 3D fiber and gel composite structure of the ECM are ignored. This has lead to a lack of fundamental understanding of the combined effects of matrix stiffening and ECM fiber density on tumor growth. Currently, many investigations utilize reconstituted ECM proteins to mimic the physiology in vivo (Narayanan et al., 2009). Although these systems provide 3D microenvironments with native bioactivity, they lack mechanical relevance, as these biomaterials cannot produce scaffolds stiff enough to match relevant physiological or pathophysiological mechanics. Chemical modifications have been used to increase stiffness, but they introduce confounding variables that make it difficult to correctly isolate only the effects of fibers (Erikson et al., 2008). Furthermore, these techniques are unable to control the density of fibrous proteins to correctly isolate their influence on cellular behaviors. Thus, it has been a nontrivial pursuit to produce a system that can capture both morphology and mechanical properties of the evolving tumor microenvironment.

Our main goal, therefore, was to create a 3D in vitro ECM model comprised of both fiber and hydrogel components into which cells may be embedded. Furthermore, the fiber density in this model must be readily tunable to capture tissue morphologies from healthy to cancerous. A common method for producing polymeric nano- to micro-scale fibers, often used in cell–material interaction studies, is electrospinning. Typical electrospinning setups produce aligned or random mats of densely packed fibers up to generally a few hundred microns in thickness. Whereas such 2D mats may be used to explore, for example, the contact guidance behavior of cells (Nisbet et al., 2008), they do not capture the 3D nature of the in vivo environment. It has proven to be challenging to extend the electrospinning process from easily producing 2D mats to producing 3D fiber networks. Researchers have sought to expand upon the electrospun mats to create hybrid fiber-gel 3D matrices. One method has been to build layered electrospun fiber mat-hydrogel structures (McCullen et al., 2010; Jang et al., 2013; Xu et al., 2013).  This structured approach, however, leads to an imposed layered morphology, which will a priori influence the way the cells behave. To our knowledge, only one other research group has attempted to randomly mix electrospun polymeric fibers with a hydrogel matrix. Coburn et al. (2011) report manually collecting fibers that were spun into an organic solvent and mixing them with a poly(ethylene glycol) diacrylate (PEG-DA) solution before UV exposure to cure the PEG-DA gel.

Here, we report a proof-of-principle study in which we developed a continuous hybrid manufacturing process capable of creating a uniformly random fiber-reinforced hydrogel composite matrix. Without the need for layering or postelectrospinning mixing, we demonstrate the ability to tune the fiber density between samples over a wide range. The process has been designed to incorporate cells into the hydrogel component

during production, therefore, fully embedding the cells in the 3D matrix.

### MATERIALS AND METHODS

#### Manufacturing System

In order to create three-dimensional fiber-reinforced hydrogels, as opposed to mats of hydrogel-soaked electrospun fibers, we developed a continuous hybrid manufacturing process (**Figure 1**). The process involves the combination of farfield electrospinning of polymer fiber reinforcement with droplet deposition of the cell-infused hydrogel solution. In order to allow for the combined deposition of both the fiber reinforcement and cell-infused hydrogel droplets, the farfield electrospinning printhead was angled at 30◦ off of the z-axis, while the path of the cell-infused hydrogel droplets was collinear to the z-axis. In addition, the build reservoir was also rotated about the z-axis. The major premise behind our hybrid manufacturing process is that the wavefront created by the hydrogel droplet impact (Worthington, 1882; Rein, 1993; Manzello and Yang, 2002; Yarin, 2006; Kavehpour, 2015), when combined with rotation of the reservoir, will result in the effective three dimensional distribution of the fibers.

The sequential steps of the droplet impact cycle to create the 3D fiber networks within the hydrogel matrix are as follows (**Figure 2**):

It should be noted the rotational motion of the build reservoir augments the mechanisms at play in Steps 2 and 3. This additional energy not only mixes the electrospun fibers in the fluid (Step 2) but it also introduces a new deposition zone for the eletrospun fibers and hydrogel solution (Step 3).

The components of the manufacturing system include the far-field electrospinning print head the electrospinning solution syringe pump (R99, Razel, St. Albans, VT, United States), two positive-polarity, high-voltage power supplies (ES-50P, Gamma High Voltage, Ormond Beach, FL, United States), the hydrogel dispensing printhead, the hydrogel solution syringe pump (R99, Razel, St. Albans, VT, United States) with syringe heating pad and controller (New Era Pump Systems, Farmingdale, NY, United States), and build reservoir on a rotary stepper motor stage (maximum rotational speed of 30 revolutions per minute). The manufacturing system was placed inside of an environmental enclosure (**Figure 3**) constructed from polypropylene, due to its high electrical resistivity and chemical resistance. The following section describes the components and process methodology of the system in detail.

#### Electrospinning Process

The electrospinning process utilized a far-field setup, with guiding electrodes located underneath the spinneret needle. The spinneret design consists of a round, aluminum-6061- T6 alloy manifold (where the polymer solution was fed from a syringe pump). The spinneret voltage was applied to the manifold via a positive-polarity, high-voltage power supply in order to charge the polymer solution and bluntended needle (21-gauge by 12 mm long, stainless steel).

TABLE 1 | Electrospinning process parameters.

The round shape of the manifold was selected so that it would better transmit the electric field to the collection substrate.

The use of guiding electrodes has been used in previous electrospinning experiments to reduce the spread of the fiber deposition (Deitzel et al., 2001; Lee et al., 2012; Martinez-Prieto

et al., 2015). With the addition of these guiding electrodes (i.e., copper rings), a second positive-polarity, high-voltage power supply was required to allow for the independent control of both the spinneret voltage (applied to the needle), and the focusing electrode voltage (applied to the rings). Here, the guiding electrodes were two solid copper rings (rolled diameter 75 mm, tube diameter of 4 mm), mounted concentric to the manifold and needle assembly. The rings were rolled to a ring diameter of 75 mm. The two copper rings were located 40 and 60 mm, respectively, below the bottom surface of the manifold.

The electrospinning process parameters used in this setup are listed in **Table 1**. The ground electrode, required for electrospinning, was a round sheet of aluminum foil affixed to the bottom of the build reservoir (a 100 mm diameter polystyrene Petri dish).

#### Hydrogel Dispensing

The hydrogel dispensing system was a gravity-assisted, dropletbased deposition system. The hydrogel solution was supplied to a dispensing head located directly above the build reservoir through the use of a syringe pump. The syringe pump supplied pressure to force a droplet to grow at the tip of a tapered polypropylene dispensing needle (14-gauge, 1.6 mm inside diameter). The height at which the hydrogel dispensing head was located above the build reservoir dictated the velocity (and energy) of the impacting droplet. This was set constant at a height of 45 cm above the build reservoir, which resulted in a droplet velocity of approximately 3 m/s. When embedding cells, they were suspended in the hydrogel at 40◦C (see section "Hydrogel" below) and aspirated into the syringe just before the manufacturing process began. The cellinfused hydrogel solution was maintained at a temperature of 40◦C in the syringe using the syringe heater. The entire manufacturing process for one sample lasts approximately 3 min.

#### Electrospinning and Hydrogel Materials Electrospinning Solution

Polycaprolactone (PCL) (Mn = 80,000, Sigma-Aldrich, St. Louis, MO, United States) was used as the material for the electrospun fiber-reinforcement. A concentration of 17 wt.%.

FIGURE 4 | (A) Schematic of the micro-indentation system used to characterize the mechanical properties of the fiber-reinforced hydrogels. (B) Schematic and image of the 3 mm diameter glass bead and the view of the camera as it indents the hydrogel surface. (C) Samples of characteristic force-indentation depth curves (dashed lines) and their corresponding Hertz equation fits for fiber-reinforced hydrogels with 10% fiber concentration (green), 5% fiber concentration (red), and pure agarose (blue).

was dissolved in glacial acetic acid (Sigma-Aldrich, St. Louis, MO, United States). While being biocompatible, PCL suffers from exhibiting hydrophobic properties, which can inhibit cell adhesion (Liverani and Boccaccini, 2016; Mirhosseini et al., 2016). In order to improve these wetting properties, pluronic acid F-127 (concentration 1.0 wt.%) was added to the electrospinning solution. The solution was mixed for 12–18 h at 60◦C.

#### Hydrogel

Low gelling temperature agarose (Sigma-Aldrich, St. Louis, MO, United States) was dissolved in incomplete McCoys 5A (Gibco) cell culture medium. For these experiments, the concentration of agarose used was 0.3% w/v to simulate the native ECM stiffness of the cells being used (Mills et al., 2011, 2014; Yu et al., 2011). When creating cell-laden matrices, cells were mixed into the agarose solution at a density of 80,000 cells/mL immediately prior to deposition. After the agarose was deposited, the sample was placed in the refrigerator (4◦C) for 30 min to hasten gelation. After gelling, the sample was washed in incomplete medium three times for 5 min. The incomplete medium was then replaced with supplemented medium and the sample placed in the incubator. The washing steps removed traces of the solvent needed to create the fibers and brought the sample to physiological pH.

#### Cell Culture and Staining

Human colon cancer cells (HCT 116) were embedded in the fiber-reinforced hydrogels. Once cells were embedded, the samples were hydrated with McCoys 5A cell culture medium supplemented with 10% fetal bovine serum, and 1% penicillin, streptomycin, and amphotericin. Samples were incubated at 37◦C and 5% CO2. The cell-culture medium was changed every 48 h.

After 8 days of incubation and observation, cell-laden fiber-reinforced hydrogel samples were fixed and stained for fluorescence imaging with the confocal microscope. First, the samples were washed three times for 5 min each in phosphate buffered saline (PBS). They were then fixed overnight at 4◦C in 4% paraformaldehyde. After three more 5 min washes in PBS, the samples were permeabilized in 0.2% Triton X-100 detergent for 30 min followed by three further 5 min washes in PBS. The samples were blocked in 1% bovine serum albumin solution for 30 min before incubating in rhodamine phalloidin (1:75 dilution in PBS) to stain F-actin. The nuclei were then stained with Hoechst (0.2 µg/mL, Hoechst 33342, Thermo Fisher) at room temperature in dark for 4 h.

#### Microscopy

#### For Fiber Characterization

To obtain clean cross-sections for imaging, the fiber-reinforced hydrogel samples were dehydrated and cooled prior to sectioning. The samples were dehydrated in a series baths, of increasing ethanol concentration, over a period of 24 h. The sample was first placed in a 33% ethanol bath for 8 h, a 66% ethanol bath for 8 h, and finally in a 100% ethanol bath for 8 h. Then, the samples were cooled in a refrigerator (2–4◦C) for 12 h. Finally, the samples were mechanically sectioned using a tungsten-carbide cutter. The cross-sections of these samples were imaged using an optical microscope (Zeta 20, Zeta Instruments, San Jose, CA, United States), at a magnification of 50×. Cross-sectional images were then analyzed in ImageJ image analysis software to measure the fiber diameter distribution, and fiber number density of the produced samples.

#### For Cell Morphological Characterization

For the cell-incorporated fiber-reinforced hydrogels, an inverted optical microscope (Axio Vert.A1, Zeiss, Jena, Germany) was used to take images daily. Ten images were captured per sample in random regions, while making sure to document cell behavior near or on fibers and away from fibers. Fluorescence images of cells and fibers were acquired with a laser scanning confocal microscope (LSM 510 Meta, Zeiss, Jena, Germany).

(CellScale Biomaterials Testing, Waterloo, ON, Canada; **Figure 4A**). The hydrogels were indented with a 3 mm glass bead, which was glued to the end of cantilevered steel microbeams of 0.2032 or 0.4064 mm diameter (**Figure 4B**). Indentation force, F, and depth, δ, were continuously calculated during the experiment based on the deflection of the indenter end of the cantilevered beam (measured optically) and the piezo-controlled z-displacement of the cantilevered beam's fixed end. Experimental force-indentation depth curves were fit to determine the sample's elastic modulus, E, using the Hertz contact model for a spherical indenter (Yang et al., 2007):

$$F = \frac{4}{3} \frac{E R^{1/2}}{\left(1 - \upsilon^2\right)} \delta^{3/2}$$

where R is the radius of the indenter and ν is the Poisson's ratio, which was assumed to be 0.49 for the hydrogels (**Figure 4C**).

When testing, fiber-reinforced hydrogel samples were kept in 60 mm Petri dishes with the walls carefully removed, and the entire sample and microbeam were submerged in PBS. The indentation and retraction rates were the same and kept constant between tests at 4 µm/s. The maximum indentation depth was 10% of the height of the sample.

#### Mechanical Characterization

Micro-indentation tests were performed using a high-precision piezo-electric actuator controlled microcompression system

FIGURE 8 | The fiber reinforced hydrogel appearance at macro-scale (left, 100 mm diameter Petri dish) and at increasingly smaller micro-scales (middle, scale bar 100 µm; right, scale bar 50 µm). In the middle image, cell clusters appear in focus (white arrow head) or out of the focus plane (white arrow); oddly shaped cell clusters at this magnification often indicate cell attachment to fibers (red arrow). In the right image, a higher-magnification, confocal image of cells attached to fibers at a junction where fibers cross (red is F-actin, blue is autofluorescence of the fibers).

#### RESULTS

#### Fiber-Reinforced Hydrogels Microscale Morphology

The use of a syringe pump allows for the volumetric flow rate of the hydrogel solution to be tailored to allow for varying fiber densities in the final fiber-reinforced hydrogel samples. Varying the volumetric flow rate of the hydrogel solution between 3.8 and 0.9 mL/min, with the electrospinning rate held constant at 0.1 mL/min, we observed nominal fiber densities between 2.5 and 10%, respectively (**Figure 5**).

Representative samples at the different fiber densities were selected for cross-sectioning and imaging (**Figure 6A**). From these images we observed the morphology of the fibrous network and whether it was deposited uniformly through the thickness of the sample, we also measured the fiber diameter (**Figure 6B**)

from fibers and on fibers directly after seeding on day 0 and on days 4 and 8. Scale bar is 50 µm.

and fiber number density (**Figure 6C**). Visually we observe (**Figure 6A**) that the fibrous network uniformly penetrates the full imaged depth of the samples. The network appears to be comprised of randomly oriented fibers, both in-plane (observed as bright lines) and out-of-plane (observed as bright dots).

**Figure 6B** displays a histogram of the measured fiber diameters pooled across all the samples, consisting of varying fiber concentrations produced (n = 9 samples). The distribution of fiber diameter followed a lognormal distribution, with a mean and standard deviation of 2.40 and 0.98 µm, respectively. No significant changes in fiber diameter were seen across samples with different fiber concentrations, as the electrospinning parameters (listed in **Table 1**), and electrospinning solution (detailed in section Electrospinning Solution) remained constant throughout the production of all samples.

The fiber number density was also computed for the samples produced at different fiber concentrations. **Figure 6C** presents value of the fiber number density (i.e., number of fibers per unit area), seen in the optical microscopy images. A total of three optical microscopy images were analyzed for each fiber concentration on the plot. As the fiber concentration increases, the fiber number density also increases. Using this process, the fiber number density increase up to ∼850 fibers per sq. mm at the highest fiber concentration (10%) that was created.

#### Mechanical Characterization of the Fiber-Reinforced Hydrogels

At least three samples were produced of pure 0.3% agarose hydrogel and fiber-reinforced hydrogels with fiber densities of 2.5, 5, and 10% for mechanical testing by micro-indentation. The elastic moduli for pure 0.3% agarose and 2.5% fiberreinforced hydrogel were found to be 385 ± 13 Pa and 368 ± 28 Pa, respectively, and not significantly different from one another. Upon the incorporation of higher concentrations of fibers, however, the elastic modulus rose to 1,500 ± 387 Pa with 5% fiber concentration and 3,830 ± 466 Pa with 10% fiber concentration (**Figure 7**). At least four indentation

FIGURE 10 | Measurements of cell cluster development over 8 days. (A) The growth of cell clusters was quantified as their projected area and (B) their morphology as their aspect ratio (mean ± standard error). For cell clusters growing away from fibers, N0 = 35, N4 = 43, N6 = 27, and N8 = 12; for cell clusters growing on fibers, N4 = 3, N6 = 4, N8 = 7. Significance was tested using one-way ANOVA with Tukey's post hoc test for multiple comparisons (∗p < 0.05, ∗∗p < 0.001).

measurements per sample (12 total per condition) were collected for the averages and standard errors presented here.

### Cell Growth and Morphological Characterization

For this proof of principle study we chose an approximately 4% fiber concentration as we found this was an optimal density that maximized the probability of cell–fiber interaction while maintaining optical transparency of the matrix to obtain microscope images of the cells (**Figure 8** and Supplementary Figure 1). In order to initiate cell-fiber contact, a cluster of cells must be embedded in proximity to a fiber or bundle of fibers. Growth and morphology of the embedded HCT-116 cell clusters were monitored over an 8 days period in the fiber-reinforced hydrogels (**Figures 9**, **10**). Characteristic images of cell clusters growing away from and on the fibers within the hydrogels show the obvious effects of the fibers on growth morphology (**Figures 9**, **11**). We quantified the growth rates of the cell clusters as their projected area in the micrographs and their morphology as their projected aspect ratio over 8 days of growth (**Figure 10**). Initially (day 0), the embedded cell clusters are not apparently attached to fibers; therefore, no data is recorded for that day and condition. Cell clusters that are away from fibers and on fibers both exhibit steady growth (**Figure 10**, left). The most significant effect of the presence of the fibers on cell growth, however, is the morphology. Whereas cell clusters away from the fibers maintain an approximately spherical morphology (**Figure 10**, right; **Figure 11**, top rows), the clusters on fibers extend out along the fibers and project a more elongated shape over time (**Figure 10**, right; **Figure 11**, bottom rows). Since the probability of a cell cluster being embedded in contact with a fiber is relatively low, there are significantly fewer measurements for "on fiber" clusters as compared to "away from fibers" clusters. After the 8 days growth period, the samples were fixed and stained with either Hoechst (to visualize the nuclei) or RFP phalloidin (to visualize the F-actin network). Although the characterization of cell growth and morphology has been presented here for the HCT-116 colon cancer cell line, the applicability of the method

to a second cell line (MDA-MB-231 breast adenocarcinoma) is presented in the Supplementary Materials (Supplementary Figure 2).

#### DISCUSSION

Morphological guidance cues play an important role in determining cell behavior, from spreading and proliferation to apoptosis. It has been recognized for some time the need to culture cells in environments that more closely resemble their native 3D, soft, and structured ECM than 2D, stiff Petri dish plastic. However, the commonly used bio- and synthetic polymeric hydrogels lack the important protein fiber structures that are present in the body. Furthermore, the protein fiber structure is known to become significantly denser and stiffer due to increased deposition and cross-linking in diseases like fibrosis and cancer. Since no in vitro cell culture matrix is available that combines both the fibrous and gel components of the ECM, nor the ability to tune the fiber density and mechanical properties of the matrix, our major goal here was to produce such a matrix and demonstrate cell viability and morphological differences in it.

Here, we developed a continuous hybrid manufacturing process to make fiber-reinforced composite hydrogels. A far field electrospinning process was used to deposit the fibrous component with the aid of guiding electrodes; and a gravityassisted, droplet-based system controlled the rate of addition of the cell-laden hydrogel component. An environmental enclosure was designed in which to carry out the manufacturing of the samples to avoid contamination. For these proof of principle experiments, we used PCL for the electrospun fibers and 0.3% agarose for the gel matrix due to their ease of use and cost effectiveness, but other commonly electrospun materials and hydrogels may also be implemented with little effort.

Cells were introduced into the fiber-reinforced hydrogels by suspending them in the hydrogel solution prior to its droplet deposition. The cells were therefore randomly placed in the matrices, sometimes lying near to or on fibers and sometimes away from fibers. We tracked how the cells grew and responded with respect to their position in the gels. Obvious differences could be seen between cells that were growing away from fibers, and cells growing along fibers. Such distinctive phenotypic differences were expected. Agarose is a bioinert, non-cell-adhesive hydrogel that only provides tissue-mechanicsmimicking structural support for cell growth. Therefore, it was not surprising that cell clusters that were only in contact with this gel maintained a compacted spheroidal shape. The use of other, bioactive hydrogels would add another layer of complexity to this system. With little to no modification of the manufacturing

#### REFERENCES

Berger, A. J., Linsmeier, K. M., Kreeger, P. K., and Masters, K. S. (2017). Decoupling the effects of stiffness and fiber density on cellular behaviors via an interpenetrating network of gelatin-methacrylate and collagen. Biomaterials 141, 125–135. doi: 10.1016/j.biomaterials.2017. 06.039

system, other thermo-sensitive gelling materials may be used (e.g., gelatin; Supplementary Figure 3). Cells that were embedded near to or in contact with the PCL fibers, however, displayed a completely different phenotype. They attached to the PCL fibers, spreading out along them in long thin bundles. This demonstrated the ability to control cell behavior with a randomly embedded, 3D polymeric fiber mesh embedded in a hydrogel matrix. This will be a useful tool to study how cells interact with fibrotic and desmoplastic tissues, which are known to be part of the pathophysiology of tumor growth and metastasis. Excessive fibrous structures in and surrounding the tumor are known, for example, to provide highways upon which tumor cells migrate (Condeelis and Segall, 2003).

Continuous improvement of this manufacturing process will allow greater control over fiber density, and possibly fiber orientations, within the hydrogel matrix. Such fully tunable matrices will provide mechanobiologists a useful tool with which to study the interactions of cells with the ECM and determine their influence in health and disease.

#### AUTHOR CONTRIBUTIONS

AW performed cell experiments, analyzed data, and wrote sections of the manuscript. JN designed and built the hybrid manufacturing process, characterized the fiber morphology of the samples, and wrote sections of the manuscript. RD developed the microindentation system and mechanically characterized the samples. JS guided the efforts related to the development of the hybrid manufacturing process and the subsequent fiber characterization. KM conceived and contributed to the design of the study, interpreted the results, and wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

#### FUNDING

JN acknowledges support from the U.S. National Science Foundation Graduate Research Fellowship Program (NSF GRFP) under Grant No. #DGE-1247271. JS acknowledges support from the U.S. National Science Foundation Grant No. #CMMI-1462648.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys. 2018.00966/full#supplementary-material

Coburn, J., Gibson, M., Bandalini, P. A., Laird, C., Mao, H. -Q., Moroni, L., et al. (2011). Biomimetics of the Extracellular matrix: an integrated threedimensional fiber-hydrogel composite for cartilage tissue engineering. Smart Struct. Syst. 7, 213–222. doi: 10.12989/sss.2<IP_ADDRESS>

Condeelis, J., and Segall, J. E. (2003). Intravital imaging of cell movement in tumours. Nat. Rev. Cancer 3, 921–930. doi: 10.1038/nr c1231

**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.

The reviewer PM and handling Editor declared their shared affiliation.

Copyright © 2018 Williams, Nowak, Dass, Samuel and Mills. 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.

# Metabolic Changes Associated With Muscle Expression of SOD1G93A

Gabriella Dobrowolny1,2 \* † , Elisa Lepore1,2† , Martina Martini1 , Laura Barberi1 , Abigail Nunn2 , Bianca Maria Scicchitano3 and Antonio Musarò1,2 \*

1 Laboratory affiliated to Istituto Pasteur – Fondazione Cenci Bolognetti, DAHFMO – Unit of Histology and Medical Embryology, Sapienza University of Rome, Rome, Italy, 2 Center for Life Nano Science, Istituto Italiano di Tecnologia, Rome, Italy, 3 Istituto di Istologia ed Embriologia, Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy

#### Edited by:

Cesare Gargioli, Università degli Studi di Roma "Tor Vergata", Italy

#### Reviewed by:

Chiara Donati, Università degli Studi di Firenze, Italy Renée Morris, University of New South Wales, Australia

#### \*Correspondence:

Gabriella Dobrowolny<EMAIL_ADDRESS>Antonio Musarò<EMAIL_ADDRESS>
†These authors have contributed equally to this work.

#### Specialty section:

This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

Received: 30 March 2018 Accepted: 13 June 2018 Published: 10 July 2018

#### Citation:

Dobrowolny G, Lepore E, Martini M, Barberi L, Nunn A, Scicchitano BM and Musarò A (2018) Metabolic Changes Associated With Muscle Expression of SOD1G93A . Front. Physiol. 9:831. doi: 10.3389/fphys.2018.00831 Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disorder, classified into sporadic or familial forms and characterized by motor neurons death, muscle atrophy, weakness, and paralysis. Among the familial cases of ALS, approximately 20% are caused by dominant mutations in the gene coding for superoxide dismutase (SOD1) protein. Of note, mutant SOD1 toxicity is not necessarily limited to the central nervous system. ALS is indeed a multi-systemic and multifactorial disease that affects whole body physiology and induces severe metabolic changes in several tissues, including skeletal muscle. Nevertheless, whether alterations in the plasticity, heterogeneity, and metabolism of muscle fibers are the result of motor neuron degeneration or alternatively occur independently of it remain to be elucidated. To address this issue, we made use of a mouse model (MLC/SOD1G93A) that overexpresses the SOD1 mutant gene selectively in skeletal muscle. We found an alteration in the metabolic properties of skeletal muscle characterized by alteration in fiber type composition and metabolism. Indeed, we observed an alteration of muscle glucose metabolism associated with the induction of Phosphofructokinases and Pyruvate dehydrogenase kinase 4 expression. The upregulation of Pyruvate dehydrogenase kinase 4 led to the inhibition of Pyruvate conversion into Acetyl-CoA. Moreover, we demonstrated that the MLC/SOD1G93A transgene was associated with an increase of lipid catabolism and with the inhibition of fat deposition inside muscle fibers. All together these data demonstrate that muscle expression of the SOD1G93A gene induces metabolic changes, along with a preferential use of lipid energy fuel by muscle fibers. We provided evidences that muscle metabolic alterations occurred before disease symptoms and independently of motor neuron degeneration, indicating that skeletal muscle is likely an important therapeutic target in ALS.

Keywords: skeletal muscle, SOD1G93A, muscle fiber types, metabolic alterations, ALS, oxidative stress

## INTRODUCTION

Amyotrophic Lateral Sclerosis (ALS) is a multi-factorial and multi-systemic disease due to severe alterations in several tissues and cell compartments, such as motor neurons, glia, and muscle (Wijesekera and Leigh, 2009). In addition, energy balance is severely compromised in ALS patients, owing to higher consumption than intake with increased resting energy use, along with abnormal

**190**

lipid metabolism (Kasarskis et al., 1996; Desport et al., 2005; Dupuis et al., 2008; Bouteloup et al., 2009; Funalot et al., 2009).

Interestingly, while lower body fat represents an ALS premorbidity factor (Gallo et al., 2013), overweight or obese people have a lower risk to develop ALS (O'Reilly et al., 2013). A recent study has pointed out that ALS patients lose weight about 10 years before motor symptoms (Peter et al., 2017) and have higher daily energy intake to compensate the higher energy consumption that has been documented before clinical onset of the disease (Huisman et al., 2015).

Similar energetic alterations were found in transgenic animal models of ALS that ubiquitously express the mutant isoform of the gene coding for superoxide dismutase 1 (SOD1G93A) (Gurney et al., 1994). Indeed, the SOD1G93A mice are characterized by increased energy expenditure and by a concomitant skeletal muscle hypermetabolism (Dupuis et al., 2004). It has been demonstrated that a diet rich in lipids can delay disease onset and motor neuron degeneration and can extend life expectancy of mutant SOD1 mice (Dupuis et al., 2004).

During disease progression and muscle denervation, the SOD1G93A mice exhibit a loss of fast-twitch glycolytic fibers and show a transition of skeletal myosin from fast glycolytic type toward slower oxidative one (Peggion et al., 2017). The loss of fast glycolytic fibers is considered a consequence of the degeneration of fast fatigable motor neuron synapsis that leads to a shift in muscle fiber type. In addition, skeletal muscle of ALS patients and transgenic ALS mouse models show mitochondrial functional impairment (Carri and Cozzolino, 2011); this mitochondria alteration together with increased energy expenditure might represent one of the first targets of ALS pathology, retrogradely affecting the nerve in a sort of dying back phenomenon (Shi et al., 2010).

It has been recently demonstrated, in the muscles of SOD1G93A mouse model, that the metabolic switch toward lipid use represents an early pathological event, suggesting that the metabolic defect is distinct from denervation (Palamiuc et al., 2015). However, the animal model chosen to conduct the study did not help to definitely disclose whether muscle metabolic changes and neuromuscular junction alterations, two pathogenic events associated with ALS, are a consequence of changes in synapsis functionality or are independent of it.

In previous works we have demonstrated that muscleselective expression of SOD1 mutation (MLC/SOD1G93A), induces alterations in the contractile apparatus, and causes mitochondrial dysfunction without affecting motor neuron survival (Dobrowolny et al., 2008). Recently, we found a significant reduced stability of muscles mitochondrial membrane potential, and we observed a reduced integrity of mitochondrial network in the region of the Neuromuscular Junctions (NMJ) of MLC/SOD1G93A transgenic mice, suggesting that mitochondrial alterations and accumulation of oxidative stress negatively impact on NMJ stability (Dobrowolny et al., 2018).

Here, taking advantage of MLC/SOD1G93A mice, we investigated whether muscle specific accumulation of SOD1G93A can induce metabolic changes that occur independently from motor neuron degeneration and precede muscle denervation.

### MATERIALS AND METHODS

#### Mice

Four-month-old MLC/SOD1G93A mice overexpressing the mutant SOD1 gene (SOD1G93A) under the control of the Myosin Light Chain (MLC) muscle specific promoter (Dobrowolny et al., 2008) and 4-month-old Friend leukemia virus B (FVB) (Jackson Laboratories) have been used. Male and female mice were used indiscriminately. The animals were housed in a temperature controlled (22◦C) room with a 12:12 h light–dark cycle and housed in a number of three to five per cage. All animal experiments were approved by the ethics committee of Sapienza University of Rome-Unit of Histology and Medical Embryology and were performed in accordance with the current version of the Italian Law on the Protection of Animals.

### Histological Analysis

Segments of tibialis anterior (TA) muscles isolated from both wild type (Wt) and MLC/SOD1G93A transgenic mice were embedded in tissue freezing medium and snap frozen in nitrogen-cooled isopentane. Ten µm sections were prepared for either NADH-transferase or PAS staining. Images were collected using a Zeiss AX10-Imager A2 connected to the Axiocam 503 color.

### RNA Preparation and Real-Time Analysis

Total RNA from Wt and MLC/SOD1G93A transgenic muscles was isolated from tibialis anterior muscles (TA) by TRIzolTM reagent (Thermo Fisher Scientific). The yield, quality, and integrity of RNA were determined using NanoDrop ND-2000 (Thermo Fisher Scientific).

Total RNA (1 µg) reverse-transcription was performed using Qiagen Reverse Trascription Kit (Qiagen) whereas 10 ng of RNA was reverse transcribed using the TaqMan micro-RNA Reverse Transcription Kit (Thermo Fisher Scientific). Quantitative PCR was performed using the ABI PRISM 7500 SDS (Thermo Fisher Scientific), TaqMan universal MMIX II (Thermo Fisher Scientific), and TaqMan probe (Thermo Fisher Scientific). Quantitative RT-PCR sample value was normalized for the expression of β-actin and U6 snRNA for mRNA and microRNA, respectively. The relative expression was calculated using the 2−11Ct method (Livak and Schmittgen, 2001) and reported as fold change.

### Protein Extraction and Western Blot Analysis

Protein extraction from both Wt and MLC/SOD1G93A transgenic tibialis anterior muscles was performed in Sodium Chloride, 1 mM Phenylmethylsulfonyl fluoride, 1 µg/ml Aprotinin, 1 µg/ml Leupeptin, 1 µg/ml Pepstatin, 1 mM Sodium orthovanadate, 1 mM Sodium fluoride. Equal amounts of protein from each muscle lysate were separated in SDS polyacrylamide gel and transferred onto a nitrocellulose membrane. Filters were saturated with 5% milk and then blotted

with antibodies against Slow myosin (1:3000) (Sigma Aldrich, United States), Glut4 (1:250) (Cell Signaling Technology, United States), PDH-E1α (pSer300) (1:400) (Calbiochem, United States and Canada), Pyruvate Dehydrogenase complex (PDH; 1:600) (Cell Signaling, United States), Phospho-GSk3β (pSer9) and total GSk-3β (1:1000) (Cell Signaling, United States), ATGL (1:100) (Cell Signaling, United States), pACC(1:600) (Cell Signaling, United States), ACC(1:250) (Cell Signaling, United States), Plin2 (2µg/ml) (Life Span Biosciences, United States), and α-tubulin (1:2000) (Sigma Aldrich, United States).

Then, filter was incubated with secondary antibodies Goat anti-mouse IgG HRP-conjugated (1:7000) (Bethyl, Montgomery, TX, United States) or Goat anti-rabbit IgG HRP-conjugated (1:7000) (Bethyl, Montgomery, TX, United States) in 1% milk for 1 h. All the antibodies were chosen as validated for western blot by manufactures.

#### Glucose Tolerance Test

Four-month-old Wt and MLC/SOD1G93A transgenic mice were fasted for 18 h and blood was drawn from a small incision at the tip of the tail. Blood glucose levels were evaluated using a commercial Glucose multicare (BSI) kit. Glucose tolerance test (GTT) was performed measuring glucose before and after IP injection of 2 g/kg body mass of glucose. Changes in blood glucose were followed for 120 min with measurements taken every 30 min.

#### CARS Microscopy

A multi modal non-linear microscope was used to record 3D stacks of images in cryosections of muscle tissue, using the strong CH vibration at 2840 cm−1 of lipids as image contrast. In brief, a picosecond laser source (Levante Emerald OPO, APE Angewandte Physik & Elektronik GmbH, Germany, pumped by an Nd:Vanadate laser at 532 nm, High Q Laser GmbH, Austria) generated two pulses at 76 MHz repetition rate, with powers of 50 mW and 120 mW for the pump (817 nm) and Stokes (1064 nm) beams, respectively, that were spatially and temporally overlapped and then coupled to a modified inverted laser scanning confocal microscope (Nunn et al., 2016).

#### Statistical Analysis

All details related to statistical tests, statistical parameters, including sample sizes (n = number of animal subjects per group), and significance are reported in Figure Legends.

For real-time PCR we considered sample size adequate when the two groups were significantly different (P < 0.05). Unless otherwise indicated, P-values for simple pair-wise comparisons were performed using a two-tailed unpaired and non-parametric Mann–Whitney test and graph values are reported as mean ± SEM. (error bars).

Data is considered statistically significant when p < 0.05. Asterisks in figures, indicate statistical significance ∗p < 0.05, ∗∗p < 0.05. All statistical analysis was performed using GraphPad PRISM 6 software.

#### RESULTS

### MLC/SOD1G93A Mice Exhibit a Fast-to-Slow Shift in the Fiber Type Composition

In a previous work we have demonstrated that localized expression of SOD1G93A promoted the fiber-type switching from glycolytic toward more oxidative fibers in EDL muscle of MLC/SOD1G93A mice (St. Pierre et al., 2006). To further support this evidence, we analyzed NADH levels in the tibialis anterior (TA) muscle, which contains high proportion of the fastest glycolytic fibers. **Figure 1A** shows higher content of NADH in the TA muscle of MLC/SOD1G93A mice compared to that observed in the TA of Wt littermates.

To further investigate the metabolic profile of the MLC/SOD1G93A muscles we assayed the expression of Myosin Slow type I levels, and we observed a slight increase of Myosin Slow type I in the transgenic muscles compared to the Wt ones (**Figure 1B** and Supplementary Figure S1).

Recent evidences have demonstrated that the nuclear receptors PPARβ/δ and PPARα play opposing roles upon the type I fiber specification program, regulating the expression of two non-coding RNA, MiR-208b, and MiR-499, both activating a signaling cascade for the expression of muscle slow twitch contractile proteins (van Rooij et al., 2009). In particular, it has been demonstrated that genetic ablation of PPARα or the upregulation of PPARβ/δ mediated by PGC-1α, lead to significant accumulation of MiR-208b and MiR-499 and specify for a slow muscle fiber program (Gan et al., 2013).

We analyzed the expression level of PPARα and MiR-499 in both Wt and MLC/SOD1G93A (Tg) TA muscles and we observed a significant down-modulation of PPARα expression levels and a concomitant up-regulation of MiR-499 (**Figures 1C,D**) in MLC/SOD1G93A mice compared to Wt littermates, suggesting that muscle specific expression of SOD1 mutant gene triggers a slow-oxidative program mediated by a PGC1α/PPAR dependent regulatory circuit.

A key regulator of PGC-1α expression is Mef2c, a transcriptional factor that is responsible for fast-to-slow switch of muscle fibers (Czubryt et al., 2003). We revealed a significant increased expression of Mef2C transcript in the muscle of MLC/SOD1G93A mice compared to that of the Wt littermates (**Figure 1E**), confirming the evidences of a metabolic transition of muscle fibers from glycolytic toward more oxidative type.

Overall these data indicate that perturbation in redox signaling cascades, induced by muscle specific expression of SOD1G93A, promoted a muscle metabolic adaptation in line with what observed in the skeletal muscle of SOD1G93A mice that ubiquitously express the mutant SOD1 gene (Palamiuc et al., 2015).

#### Muscle Glucose Metabolism Is Altered in the MLC/SOD1G93A Mice

Glycolysis is a metabolic pathway that converts glucose into pyruvate, providing high energy substrate. Glycolysis is

dysregulated in the animal model of ALS, the SOD1G93A mice that ubiquitously overexpress the mutant form of the SOD1 gene (Palamiuc et al., 2015). To verify whether glucose metabolism was also altered in the MLC/SOD1G93A mice, we performed the glucose tolerance test, demonstrating that transgenic mice, compared with Wt littermates, presented higher blood glucose 60 min after glucose supplementation (**Figure 2A**). This suggests that muscle specific expression of SOD1G93A determines a delay in glucose clearance. To substantiate this hypothesis, we analyzed the expression levels of Glut-4, a glucose receptor in muscle fibers, responsible for muscle glucose uptake. As shown in **Figure 2** protein levels of Glut4 was significantly down-modulated in the MLC/SOD1G93A muscles (**Figure 2B** and Supplementary Figure S2), suggesting that the clearance delay of glucose could mainly be due to a decreased capacity of transgenic muscle to uptake glucose from circulation.

Glycolysis is regulated by several enzymes such as the Hexokinase 2 (HK2) and Phosphofructokinase 1/2, (PFK1, PFK2) that catalyze, respectively, the first and the second steps of glycolysis. In the asymptomatic animal model of ALS, the PFK activity is significantly down-modulated and muscle glucose metabolism is inhibited (Palamiuc et al., 2015).

To verify whether glycolysis was also altered in the MLC/SOD1G93A mice, we analyzed the transcription levels of the Hexokinase 2 (HK2) and Phosphofructokinase 1/2, (PFK1, PFK2) in TA muscles of transgenic mice. As shown in **Figure 2**, differently from that observed in ALS mouse model (Shi et al., 2010), the glycolytic enzyme was significantly up-regulated in the muscle of MLC/SOD1G93A mice compared to the Wt littermates (**Figures 2C–E**).

Pyruvate is the end product of Glycolysis and its levels are governed by the PDH that transforms Pyruvate into Acetyl-CoA. Acetyl-CoA enters the Krebs cycle and produces ATP and energy fuel for cells through oxidative phosphorylation. The Pyruvate kinase 4 (PDK4) is a negative regulator of PDH complex and inhibits PDH activity by phosphorylation (Denton et al., 1975). To investigate whether Pyruvate is efficiently converted into Acetyl-CoA we assessed the level of total and the phosphorylated isoform of PDH and the levels of PDK4. Interestingly we observed a significant accumulation of the phosphorylated isoform of PDH and a

same gel but were noncontiguous.

blot analysis of PGSK3β and GSK3β protein expression in both Wt and Tg mice. Lower panel shows densitometric analysis for PGSK3β expression relative to total GSK3β content normalized for protein loading in Wt and Tg mice (t-test ∗p = 0.0476 Wt, Tg n = 10,10); (I) representative microphotographs of PAS staining from Wt and Tg TA muscles. Bar, 100 µm. (J) Real-time PCR analysis of CS (Wt, Tg n = 4,4). Data are represented as mean ± SEM. In (B,G,H) the lanes were run on the

concomitant up-regulation of PDK4, indicating the inhibition of Acetyl-CoA synthesis (**Figures 2F,G** and Supplementary Figure S3). Moreover, as pyruvate accumulation stimulates the conversion of glucose to glycogen, we assessed the glycogen synthesis in the MLC/SOD1G93A muscles. We observed a significant down-modulation of the inhibitory phosphorylated isoform of the Glycogen Synthase Kinase 3β (GSK3β) and a concomitant increase in intramuscular glycogen deposition, assayed by Periodic acid-Schiff (PAS) staining, in the TA muscle of MLC/SOD1G93A mice compared to that of Wt littermates (**Figures 2H,I** and Supplementary Figure S4). Conversely, the mRNA level of Citrate Synthase (CS), the first enzyme of Krebs cycle, was comparable between Wt and MLC/SOD1G93A muscles (**Figure 2J**), suggesting that alternative catabolic pathway, such as proteolysis or lipolysis, can guarantee sufficient Acetyl-CoA synthesis.

Overall these data suggest that muscle restricted expression of SOD1 mutant gene triggers the activation of the glycolysis process, which cannot efficiently contribute to oxidative phosphorylation and energy production.

### MLC/SOD1G93A Transgene Is Associated With the Increase of the Lipid Handling Pathway

Various factors, including increased fatty acid (FA) use by β-oxidation, stimulate PDK4 expression (Jeong et al., 2012). The β-oxidation process is strictly regulated by the levels of its inhibitor MalonylCoA that is in turn induced by the Acetyl-CoA carboxylase (ACC). Acetyl-CoA carboxylase strongly controls lipid synthesis/degradation flux, and its activity is inhibited by phosphorylation.

To investigate muscle fibers oxidative metabolism, we studied the expression levels of key proteins of the FA regulation: CD36, a membrane translocase that promotes FA entry into the cells, ATGL, a lipase involved in lipid droplet degradation, and CPT-1B, responsible for the FA transfer into mitochondria. When compared to Wt littermates, the MLC/SOD1G93A mice had a significant increase in the expression of all genes studied (**Figures 3A–C** and Supplementary Figure S5), including the pACC/ACC ratio (**Figure 3D** and Supplementary Figure S6), suggesting that lipid catabolism is stimulated and lipid synthesis is inhibited.

Qualitative analysis on lipid droplet accumulation inside muscle fibers confirmed a preferential use of lipid of the transgenic muscle. As described in **Figure 4** no droplets were found inside muscle fibers; we observed a significant accumulation of fat residues just outside muscle fibers (**Figure 4A**). Quantitative analysis revealed a significant up-regulation of lipid droplet volume in the MLC/SOD1G93A muscle compared to Wt littermates (**Figure 4B**). This data was further supported by western blot analysis for Perilipin 2 (PLIN2) expression, a coat protein of lipid droplet. As shown in **Figure 4C** we observed a significant up-regulation of Perilipin-2 in MLC/SOD1G93A muscles compared to Wt ones (**Figure 4C** and Supplementary Figure S7).

FIGURE 3 | MLC/SOD1G93A mice exhibit an increase of the lipid handling pathway. (A) Real-time PCR analysis of CD36 transcript in Wt and Tg mice (Wt, Tg n = 4,4); (B) Representative Western blot analysis of ATGL protein expression in both Wt and Tg mice. Lower panel shows densitometric analysis for ATGL expression relative to α-tubulin in Wt and Tg mice ( ∗p = 0.0336 Wt, Tg n = 9,12); (C) Real-time PCR analysis of CPT1 transcript in Wt and Tg mice (∗p = 0.0159 Wt, Tg n = 4,5.) (D) Representative Western blot analysis of pACC/ACC. Lower panel shows densitometric analysis for pACC expression relative to ACC in Wt and Tg mice (∗p = 0.0117 Wt, Tg n = 9,12). Data are represented as mean ± SEM. In (B,D) the lanes were run on the same gel but were noncontiguous.

These data suggest that muscle specific expression of mutant SOD1 induces a preferential recruitment of FAs to skeletal muscle tissue to sustain lipid flux into the skeletal muscle fibers and to guarantee FAs muscle availability and β-oxidation (Morales et al., 2017).

### DISCUSSION

Our study demonstrates that perturbation in redox signaling cascades, induced by muscle specific expression of SOD1G93A, determines a muscle metabolic adaptation towards a more oxidative metabolism, demonstrating the activation of the slowoxidative muscle fiber program and the preferential use of lipid fuel by the transgenic mice MLC/SOD1G93A.

Interestingly, we observed that muscle metabolic adaptation to oxidative stress does not involve significant changes in MyHC expression, in line with previous published works that have suggested a dissociation between metabolic and contractile

adaptive response to different stimuli (Schiaffino and Reggiani, 2011). Indeed, it has been demonstrated that moderate endurance training can induce metabolic changes without concomitant changes in MyHC composition. Moreover, slow-type electrical stimulation in rat fast muscles can early abrupt MyHC-2B to MyHC-2X transcript switch (Kirschbaum et al., 1990), in response to external stimuli of muscle activity or inactivity (Ausoni et al., 1990).

Our data support the evidences that metabolic and contractile properties are controlled by distinct signaling pathways and provide insight into the mechanism controlling the regulation of fast-glycolytic and slow-oxidative gene program, induced by muscle specific expression of SOD1G93A. In particular, among the possible mechanisms that account for the modulation of different fast and slow muscle genes, our data support the involvement of miRNAs hosted in β/slow and slow-tonic Myosin locus.

Recent works have demonstrated that some muscle-specific microRNAs are located within Myosin genes and thus are coexpressed with these genes (van Rooij et al., 2009). Among these microRNAs, miR-499 is located in an intron of Myh7b and targets a transcriptional repressor of β/slow Myosin, namely Sox6, during muscle development (van Rooij et al., 2008). Nevertheless, it remains to elucidate whether muscle-specific miRNAs and Sox6, controls of fiber type diversification in developing muscle, is also involved in the maintenance of fiber type properties in adult skeletal muscle (van Rooij et al., 2009; Gan et al., 2013). Here we provide evidences that the oxidative insult induced by SOD1 mutant gene expression can determine the modulation of miR-499 regulative circuit toward a slow muscle fiber program, in muscle tissue of adult mice.

The shift of muscle fibers, observed in MLC/SOD1G93A mice, toward the oxidative metabolism might represent a

compensatory mechanism activated to cope the toxic effects of mutant SOD1 protein.

In the SOD1G93A mice, which ubiquitously express the mutant gene (Gurney et al., 1994), the switch toward lipid use is considered an early sign of disease and occur early before symptomatic denervation (Palamiuc et al., 2015). It has been suggested that the early switch of glycolytic fibers toward oxidative phenotype is due to loss of connections of muscle fibers to large motoneurons, and to their subsequent reinnervation by slow motoneurons. Thus, these studies suggest that the oxidative switch of muscle fibers is a consequence of motor unit alteration, rather than an intrinsic property of muscle fibers plasticity that can change the metabolic properties independently from neuron physiology.

Here, we demonstrate that in the MLC/SOD1G93A fibers, the metabolic oxidative switch occurs independently from motor neuron degeneration (Dobrowolny et al., 2018) and along with neuromuscular instability and deficiency in mitochondrial chain function (Dobrowolny et al., 2018). Considering the mitochondria defects and the oxidative damage induced by muscle specific SOD1 mutant gene expression (Dobrowolny et al., 2008, 2018), we can speculate that the oxidative change in energy fuel of MLC/SOD1G93A fibers might reflect an adaptation to preserve muscle functionality (Blaauw et al., 2013). In addition, fiber type diversification may also reflect an adaptation to whole body metabolism and different pattern of activity. These observations are also in line of recent evidences that demonstrated how the up-regulation of Mir-499 and Mir-208b is associated with a reduced atrophy of type I fibers in muscle biopsies of ALS patients with slow progressive disease (Di Pietro et al., 2017).

The metabolic shift towards lipid use in the TA muscles in the ALS mouse model that ubiquitously express the SOD1 mutant gene (Gurney et al., 1994) is accompanied by lower levels of PFK activity in both presymptomatic and symptomatic stages of the disease (Palamiuc et al., 2015). These data are in apparent contrast with the up-regulation of both PFK1 and PFK2 observed in the MLC/SOD1G93A mice. In the ALS mouse model at the presymptomatic stage of the disease, higher levels of Pyruvates correlate with a decrease of PFK activity; conversely at the end stage of the disease low levels of Pyruvate correlates with low levels of PFK activity, due to the inhibitory feed-back of Pyruvate. Therefore, although the glycolysis is enhanced in the MLC/SOD1G93A mice, Pyruvate levels could

#### REFERENCES

be still not sufficient to induce a Pyruvate dependent inhibitory signal of glucose catabolism. Moreover, Pyruvate can be used in other compensative pathways that do not involve mitochondria machinery, such as glycogen synthesis or anaerobic glycolysis.

In conclusion, our data reveal that localized expression of SOD1G93A induces metabolic changes in skeletal muscle independently of motor neuron degeneration and further indicate that skeletal muscle is likely an important target for therapeutic intervention in ALS.

#### ETHICS STATEMENT