International Journal of Molecular Sciences

Article Transcriptomic Analysis of Calcium Remodeling in Colorectal Cancer

Enrique Pérez-Riesgo 1,2, Lucía G. Gutiérrez 1,2, Daniel Ubierna 1, Alberto Acedo 3, Mary P. Moyer 4, Lucía Núñez 1,2 and Carlos Villalobos 1,*

1 Institute of Molecular Biology and Genetics (IBGM), National Research Council (CSIC), 47003 Valladolid, Spain; [email protected] (E.P.-R.); [email protected] (L.G.G.); [email protected] (D.U.); [email protected] (L.N.) 2 Department of Biochemistry and Molecular Biology and Physiology, University of Valladolid, 47005 Valladolid, Spain 3 AC-Gen Reading Life, 47011 Valladolid, Spain; [email protected] 4 INCELL Corporation, San Antonio, TX 78249, USA; [email protected] * Correspondence: [email protected]; Tel.: +34-983-184-821

Academic Editors: Peter J. K. Kuppen and Dario Marchetti Received: 8 March 2017; Accepted: 13 April 2017; Published: 27 April 2017

Abstract: Colorectal cancer (CRC) cells undergo the remodeling of intracellular Ca2+ homeostasis, which contributes to cancer hallmarks such as enhanced proliferation, invasion and survival. Ca2+ remodeling includes critical changes in store-operated Ca2+ entry (SOCE) and Ca2+ store content. Some changes have been investigated at the molecular level. However, since nearly 100 genes are involved in intracellular Ca2+ transport, a comprehensive view of Ca2+ remodeling in CRC is lacking. We have used Next Generation Sequencing (NGS) to investigate differences in expression of 77 selected gene transcripts involved in intracellular Ca2+ transport in CRC. To this end, mRNA from normal human colonic NCM460 cells and human colon cancer HT29 cells was isolated and used as a template for transcriptomic sequencing and expression analysis using Ion Torrent technology. After data transformation and filtering, exploratory analysis revealed that both cell types were well segregated. In addition, differential gene expression using R and bioconductor packages show significant differences in expression of selected voltage-operated Ca2+ channels and store-operated Ca2+ entry players, transient receptor potential (TRP) channels, Ca2+ release channels, Ca2+ pumps, Na+/Ca2+ exchanger isoforms and genes involved in mitochondrial Ca2+ transport. These data provide the first comprehensive transcriptomic analysis of Ca2+ remodeling in CRC.

Keywords: colorectal cancer; Ca2+ remodeling; transcriptomics; RNA-sequencing

1. Introduction Ca2+ signaling is involved in the control of a large series of cell and physiological functions in health and disease from exocytosis and muscle contraction to gene expression, cell proliferation, migration and cell death [1]. Over the last few years, evidence has been accumulating that dis-homeostasis of intracellular Ca2+ may be involved in many different disorders including cancer [2,3]. In support of this view, several authors have reported that remodeling of Ca2+ signaling in different forms of cancer contributes to cancer hallmarks, including exaggerated cell proliferation, acquisition of cell migration and invasion capabilities, and enhanced resistance to cell death [2,3]. We have recently compared intracellular Ca2+ homeostasis in normal human colonic cells and human colon carcinoma cells [4]. We found critical differences that may contribute to several of the above cancer hallmarks. The most salient characteristics of Ca2+ remodeling in colon cancer cells relative to their normal counterparts are the dramatic rise in store-operated Ca2+ entry (SOCE) and the partial

Int. J. Mol. Sci. 2017, 18, 922; doi:10.3390/ijms18050922 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2017, 18, 922 2 of 23 depletion of intracellular Ca2+ stores [4]. Enhanced entry of Ca2+ in colon cancer cells correlates with enhanced cell proliferation and all SOCE antagonists tested and/or targeting molecular players involved in this pathway result in inhibition of cell proliferation and cell migration [4–6]. In addition, the partial depletion of Ca2+ stores provides resistance to cell death, probably because of impaired mitochondrial Ca2+ overload and apoptosis [4–6]. SOCE, first reported by James W Putney in 1986 [7,8] is a ubiquitous Ca2+ entry pathway activated after agonist-induced release of Ca2+ from intracellular stores at the endoplasmic reticulum (ER). This is due to the opening of intracellular Ca2+ release channels gated by phospholipase C-dependent 2+ 2+ synthesis of inositol trisphosphate IP3 receptors (IP3Rs) and/or Ca -induced Ca release mediated by ryanodine receptors (RYRs). At the molecular level, it is well established that Stromal Interaction Molecules (STIM) sense ER Ca2+ levels. Emptying of Ca2+ stores promotes STIM oligomerization at the ER endomembranes [9] and their interaction with cation channels in the plasma membrane of the Orai1 [10] and transient receptor potential (TRP) families of Ca2+ channels [11]. Activation of SOCE not only permits the refilling of Ca2+ stores but also promotes a sustained increase in intracellular Ca2+ concentration that is critical for the sustained activation of effector proteins including, for instance, calcineurin and the ensuing activation of the nuclear factor of activated T cells [10]. 2+ 2+ After stimulation, the increased cytosolic free Ca concentrations ([Ca ]cyt) must return to resting levels in the low nM range. This is achieved by systems that transport Ca2+ back from cytosol to the extracellular space or into the ER against a large electrochemical gradient for Ca2+. This is carried out by different adenosine triphosphate (ATP)ase Ca2+ pumps including plasma membrane Ca2+ ATPases (PMCAs) [12], sarcoplasmic and/or endoplasmic reticulum Ca2+ ATPases (SERCAs) [13] and the secretory pathway Ca2+ ATPases (SPCAs) [14]. An additional cotransporter, the Na+/Ca2+ exchanger, uses the passive transport of Na+ into the cell to extrude Ca2+ out of the cell [15]. Finally, as has become increasingly evident in recent years, mitochondria contribute to clear Ca2+ loads by means of the mitochondrial Ca2+ uniporter (MCU) and regulatory proteins [16]. In this case, Ca2+ is transported down a huge electromotive force, the mitochondrial potential (∆Ψ) of about −180 mV, negative inside the mitochondrial matrix, that enables Ca2+ influx into mitochondria [17,18]. This potential is particularly high in cancer cells due to the Warburg effect, the metabolic signature of cancer cells [19]. Interestingly, mitochondria are also involved in control of SOCE in different cell types [20,21], including colorectal cancer cells [18,22] where, store-operated Ca2+ channels may undergo slow, Ca2+-dependent inactivation unless this process is prevented by Ca2+ removal by surrounding mitochondria [20,21]. Our recent analysis of Ca2+ remodeling in colon cancer cells revealed increased expression of several molecular players involved in SOCE [4–6]. However, Ca2+ remodeling is complex and may involve changes in expression and/or activity of hundreds of genes related to Ca2+ signaling [23], with nearly 80 of them directly involved in Ca2+ transport across biological membranes. By taking advantage of Next Generation Sequencing technologies, particularly RNA-sequencing (RNA-seq) by Ion Torrent methodologies, we were able to study in detail intracellular Ca2+ homeostasis in NCM460 cells reflecting normal human colonic cells [24,25] and HT29 cells [26] representing human colorectal cancer cells, to investigate, for the first time, the transcriptomics of Ca2+ remodeling in CRC. To this end, mRNA was isolated from four independent cultures of NCM460 and four independent cultures of HT29 cells to be used as templates for transcriptomic analysis of the selected 77 genes shown in Table1. The gene transcripts studied include all ten voltage-operated Ca2+ channels (VOCCs); all seven well-established players involved in SOCE; all 27 TRP channels; all six Ca2+ release 2+ channels—including IP3 receptors and ryanodine receptor isoforms—all nine Ca pumps including PMCAs, SERCAs and SPCAs; the three Na+/Ca2+ exchanger isoforms; all known genes involved in mitochondrial Ca2+ transport; and other related proteins. Clustering analysis segregated data in two different cell populations and genes were segregated into six independent gene families according to Int. J. Mol. Sci. 2017, 18, 922 3 of 23 expression behavior. Overall, we found that 30 genes are expressed differentially in CRC cells relative to normal cells, thus providing a first comprehensive view of Ca2+ remodeling in CRC.

Table 1. Genes involved in calcium transport analyzed in this study pooled by gene families.

Gene Group Protein Name (Gene Name) Cav1.1 (CACNA1S); Cav1.2 (CACNA1C); Cav1.3 (CACNA1D); Cav1.4 (CACNA1F); Voltage Operated Cav2.1 (CACNA1A); Cav2.2 (CACNA1B); Cav2.3 (CACNA1E); Cav3.1 (CACNA1G); Calcium Channels Cav3.2 (CACNA1H); Cav3.3 (CACNA1I) Store-Operated Orai1; Orai2; Orai3; STIM1; STIM2; CRACR2A (EFCAB4B); MS4A12 Calcium Entry Player TRPC1; TRPC3; TRPC4; TRPC5; TRPC6; TRPC7; TRPV1; TRPV2; TRPV3; TRPV4; TRPV5; TRPV6; TRPM1; TRPM2; TRPM3; TRPM4; TRPM5; TRPM6; TRPM7; TRPM8; TRP Channels TRPA1; TRPML1 (MCOLN1); TRPML2 (MCOLN2); TRPML3 (MCOLN3); TRPP1 (PKD2); TRPP2 (PKD2L1); TRPP3 (PKD2L2) Calcium Release IP R1 (ITPR1); IP R3 (ITPR2); IP R3 (ITPR3); RYR1; RYR2; RYR3 Channels 3 3 3 PMCA1 (ATP2B1); PMCA2 (ATP2B2); PMCA3 (ATP2B3); PMCA4 (ATP2B4); SERCA1 Calcium Pumps (ATP2A1); SERCA2 (ATP2A2); SERCA3 (ATP2A3); SPCA1 (ATP2C1); SPCA2 (ATP2C2) Sodium Calcium NCX1 (SLC8A1); NCX2 (SLC8A2); NCX3 (SLC8A3) Exchangers Mitochondrial Calcium MCU; MICU1; MICU2 (EFHA1); MICU3 (EFHA2); MCUR1 (CCDC90A); Transport Proteins EMRE (C22orf32); MCUb (CCDC109B); VDAC1; VDAC2; VDAC3 Bcl-2 (BCL2); Calsequestrin 1 (CASQ1); Calsequestrin 2 (CASQ2); PICALM; Other Proteins Phospholamban (PLN) Cav: Ca2+ channel; STIM: Stromal Interaction Molecules; TRPC: canonical TRP channels; TRPV: vaniloid family of transient receptor potential channels; TRPM: melastatin family of transient receptor potential channels; TRPP: polycystine family of transient receptor potential channels; IP3R: inositol trisphosphate receptors; RYR: ryanodine receptors; PMCA: plasma membrane Ca2+ ATPases; SERCA: sarcoplasmic and/or endoplasmic reticulum Ca2+ ATPases; SPCA: secretory pathway Ca2+ ATPases; NCX: sodium-calcium exchanger; MCU: mitochondrial Ca2+ uniporter; EMRE: MCU regulator; VDAC: voltage-dependent anion channels; PICALM: phosphatidylinositol binding clathrin assembly protein.

2. Results and Discussion

2.1. Data Set Transformation and Filtering mRNA from four independent cultures of normal (NCM460) human colonic cells and four independent cultures of human colorectal (HT29) cells were isolated and used as templates for transcriptomic analysis of 77 genes involved in intracellular Ca2+ transport (Table1) as detailed in the Methods section. RNA-seq data sets consist of aligned reads from gene fragmentations against a reference genome. Accordingly, the longer a gene is, the higher the amount of fragments that come from it. In addition, the longer the gene is, the more likely it is that one of the reads aligns against this gene. As we are concerned with analyzing not only differential expression between normal and tumor cells, but also with differential expression among genes of the same family, raw gene data was normalized for the corresponding gene length (l) and the size of the library samples (n), thus making expression values among different genes readily comparable. As expression values after normalization are very small, they were multiplied by 109 to obtain expression values as Reads per Kilobase per Millions of mapped reads (RPKM) that can be obtained using the following expression (1).

reads RPKM = ·109 (1) l·n Another important issue relative to RNA-seq data sets is that they contain usually a large amount of low and disparate values. Thus, it is very important to be careful with the scale employed. If we Int. J. Mol. Sci. 2017, 18, 922 4 of 23 Int. J. Mol. Sci. 2017, 18, 922 4 of 22 useoccluding a raw large data set,parts almost of data all (Figure data will 1A,B). plot This on pr lowoblem values could and affect only techniqu a few ones higherof Exploratory values, Data thus occludingAnalysis such large as parts Principal of data Components (Figure1A,B). Analysis This problem or Cluster could Hierarchical affect techniques Analysis, of Exploratorysince highly Dataexpressed Analysis genes such could as Principal dominate Components them. To Analysis solve this or Clusterissue, Hierarchicalraw data has Analysis, to be transformed since highly expressednumerically genes using, could for dominate example, them. Box-Cox To solve Transfor this issue,mations raw or data Logarithmic has to be transformedTransformations. numerically In the using,present for study, example, we Box-Coxused a data Transformations transformation or Logarithmicsimilar to logarithmic Transformations. transformation In the present in base study, 2. weTransformation used a data transformation shrinks data variation similar to across logarithmic sample transformations. However, low-expression in base 2. Transformation genes tend to shrinks over- datadominate variation when across this transformation samples. However, is used. low-expression This is due, on one genes hand, tend to toPoisson over-dominate noise, related when to thislow transformationvalues of reads isand, used. on Thisthe other is due, hand, on one to hand,the fact to that Poisson logarithm noise, relatedtransformation to low values amplifies of reads differences and, on theamong other low hand, values. to the factThus, that low-expr logarithmession transformation genes will amplifies show relati differencesvely larger among differences low values. among Thus, low-expressionsamples (Figure genes 1C,D). will To showsolve relativelythis problem, larger we differences have used amongRegularized samples Logarithm (Figure 1TransformationC,D). To solve this(rlog). problem, In this we case, have the used outcomes Regularized of highly Logarithm expressed Transformation genes are similar (rlog). Into thisoutcomes case, the obtained outcomes by oflogarithmic highly expressed transformation genes are in similar base totwo. outcomes In addition, obtained differences by logarithmic among transformation low values inessentially base two. Indisappear addition, (Figure differences 1E,F). among low values essentially disappear (Figure1E,F).

Figure 1. Gene expression level data transformation. Raw data set values (A,B). Log2 transformation Figure 1. Gene expression level data transformation. Raw data set values (A,B). Log2 transformation (C,D); rlog transformation (E,F). RNA-seq: RNA-sequencing. (C,D); rlog transformation (E,F). RNA-seq: RNA-sequencing.

Another key point of RNA-seq data analysis is filtering in order to minimize hypothesis testing. We haveAnother to take key into point account of RNA-seq that RNA-seq data analysis data sets is filtering contain in a orderlarge amount to minimize of values hypothesis equal to testing. zero, Wesince have not toall take genes into are account expressed that by RNA-seq every kind data of sets cell contain, and that a largeis very amount important of values relative equal to filtering. to zero, sinceThus, not those all genes that are expressedmay not play by everyany role kind or ofsh cell,ow no and relationship that is very to important phenotype relative may be to removed filtering. Thus,from the those data genes expression that may set. not Nevertheless, play any role we or need show tono be relationshipcareful in this to step phenotype to avoid may removing be removed genes fromwhich the marginally data expression do not set. show Nevertheless, any kind of we activity need to in be a careful cell but in may this stepact jointly to avoid with removing other genes. genes whichThus, we marginally have considered do not show taking any into kind account of activity the incriteria a cell set but up may by act Hackett jointly [27] with and other Seyednasrollah genes. Thus, we[28], have who considered suggested filtering taking into out accountthose genes the criteriathat are setnot up readily by Hackett expressed [27] in and any Seyednasrollah of the samples [and28], whowhose suggested median expression filtering out profile those is genes lower that than are 0.125 not RPKM readily [29]. expressed Data transformed in any of the in samples this manner and whosewere filtered median in expressionorder to remove profile those is lower genes than considered 0.125 RPKM as non-expressed, [29]. Data transformed whose median in this expression manner profile is lower than 0.125 RPKM (Figure 2). Interestingly, three types of gene populations according

Int. J. Mol. Sci. 2017, 18, 922 5 of 23 were filtered in order to remove those genes considered as non-expressed, whose median expression Int. J. Mol. Sci. 2017, 18, 922 5 of 22 profile is lower than 0.125 RPKM (Figure2). Interestingly, three types of gene populations according to expressionto expression values values emerge emerge after after filtering: filtering: genes genes expressed expressed at at low, low, intermediate intermediate andand highhigh expressionexpression levels (Figure2 2).).

Figure 2.2. Gene expression level filtering.filtering. Histograms Histograms of of data data before before ( (AA)) and after ( B) the data setset filtrationfiltration process. Filtering was carried out by removingremoving genes with a profileprofile expression lower than 0.125 Reads per Kilobase per Millions of mapped readsreads (RPKM). Data are shown as rlog transformed.

2.2. Exploratory Data Analysis: Hierarchical Clustering 2.2. Exploratory Data Analysis: Hierarchical Clustering After data transformation and filtration, two different exploratory data analyses were carried After data transformation and filtration, two different exploratory data analyses were carried out; although they are not hypothesis testing, they are useful for generating hypotheses. The first one, out; although they are not hypothesis testing, they are useful for generating hypotheses. The first known as Hierarchical Cluster Analysis, is carried out both in samples and genes. This analysis, using one, known as Hierarchical Cluster Analysis, is carried out both in samples and genes. This analysis, Euclidean distance as dissimilarity measurement, allows the breaking up of samples or genes into using Euclidean distance as dissimilarity measurement, allows the breaking up of samples or genes into groups of members sharing common characteristics. It also establishes which genes are co-regulated. groups of members sharing common characteristics. It also establishes which genes are co-regulated. The second exploratory data analysis is known as Principal Components Analysis, which is very The second exploratory data analysis is known as Principal Components Analysis, which is very useful useful in order to reduce the dimension of the data or to find patterns among genes or samples, thus in order to reduce the dimension of the data or to find patterns among genes or samples, thus getting getting new variables which are a linear combination of the original ones. new variables which are a linear combination of the original ones. Hierarchical Clustering Analysis forms observation clusters whose members share common Hierarchical Clustering Analysis forms observation clusters whose members share common characteristics. For example, it is possible to detect different populations, such as healthy and tumor characteristics. For example, it is possible to detect different populations, such as healthy and tumor phenotypes, thereby clustering each sample into a healthy or tumor group. Furthermore, if the phenotypes, thereby clustering each sample into a healthy or tumor group. Furthermore, if the existence existence of different groups is known, it could be possible to identify a rule to sort the observations. of different groups is known, it could be possible to identify a rule to sort the observations. For example, For example, we can sort samples with Discriminant Analysis [30]. In the present study, we clustered we can sort samples with Discriminant Analysis [30]. In the present study, we clustered both samples both samples and genes. Thus, if we classify samples, we can discover the different populations they and genes. Thus, if we classify samples, we can discover the different populations they came from came from and, if we classify genes, we can use the information to carry out, for example, an and, if we classify genes, we can use the information to carry out, for example, an Enrichment Gene Enrichment Gene Set Analysis that identifies which gene clusters are related to the tumoral Set Analysis that identifies which gene clusters are related to the tumoral phenotype. However, phenotype. However, this analysis was not undertaken here because of the low number of genes this analysis was not undertaken here because of the low number of genes studied and for the sake studied and for the sake of brevity. of brevity. We first carried out the Hierarchical Cluster Analysis considering the eight samples used as We first carried out the Hierarchical Cluster Analysis considering the eight samples used as observations and genes as variables. The dissimilarity measurement chosen is Euclidean distance, observations and genes as variables. The dissimilarity measurement chosen is Euclidean distance, and the analysis is shown as a heatmap with dendograms in both axes of Figure 3. According to the and the analysis is shown as a heatmap with dendograms in both axes of Figure3. According to map, it is clear that two groups have been formed, where the first one contains all four samples the map, it is clear that two groups have been formed, where the first one contains all four samples belonging to the normal, healthy phenotype (A, B, C, D), and the second one contains all four samples belonging to the normal, healthy phenotype (A, B, C, D), and the second one contains all four samples corresponding to the tumor phenotype (E, F ,G ,H) (Figure 3). The distance among samples belonging corresponding to the tumor phenotype (E, F ,G ,H) (Figure3). The distance among samples belonging to the same phenotype is much smaller than the distance among samples belonging to different to the same phenotype is much smaller than the distance among samples belonging to different phenotypes. Each value represented in the heatmap corresponds with the distance between each phenotypes. Each value represented in the heatmap corresponds with the distance between each sample couple. sample couple. A second Hierarchical Clustering Analysis was carried out in a similar way, except that now the A second Hierarchical Clustering Analysis was carried out in a similar way, except that now the 77 genes are considered as observations and the eight samples as variables. The result of the analysis 77 genes are considered as observations and the eight samples as variables. The result of the analysis is shown in Figure 4. The Euclidian distances map shows that the 77 genes in the pool break into six different groups. All the genes in each group behave similarly in the sense that, regardless of whether they are high or low expressed in normal and tumor cells, expression levels are similar for all genes within each group and different from expression levels of the other gene families according to the Euclidian distances shown in Figure 4.

Int. J. Mol. Sci. 2017, 18, 922 6 of 23 is shown in Figure4. The Euclidian distances map shows that the 77 genes in the pool break into six different groups. All the genes in each group behave similarly in the sense that, regardless of whether they are high or low expressed in normal and tumor cells, expression levels are similar for all genes within each group and different from expression levels of the other gene families according to the

EuclidianInt. J. Mol. distances Sci. 2017, shown 18, 922 in Figure4. 6 of 22

Int. J. Mol. Sci. 2017, 18, 922 6 of 22

Figure 3. Heatmap Hierarchical Cluster of samples with healthy phenotype (A–D) and tumor Figure 3. Heatmap Hierarchical Cluster of samples with healthy phenotype (A–D) and tumor Figurephenotype 3. Heatmap (E–H). PseudocolorHierarchical Clusterscale shows of samples hierarchical with healthydistance phenotype from minimum (A–D) (0,and blue) tumor to phenotype (E–H). Pseudocolor scale shows hierarchical distance from minimum (0, blue) to maximum phenotypemaximum (6,(E– red).H). Pseudocolor scale shows hierarchical distance from minimum (0, blue) to (6, red). maximum (6, red).

Figure 4. Hierarchical clustering of genes. Genes have been divided into six different groups Figureaccording 4. Hierarchical to their Euclidean clustering distances of genes. relative Genes to their have expression been divided values (ininto RPKM). six different groups Figure 4. Hierarchical clustering of genes. Genes have been divided into six different groups according according to their Euclidean distances relative to their expression values (in RPKM). to2.3. their Gene Euclidean Correlations distances relative to their expression values (in RPKM). 2.3. GeneNext, Correlations we questioned possible correlations between gene couples. In this way, correlation values 2.3. Gene Correlations for eachNext, gene we questionedcouple were possible plotted correlationsin the heatmap between shown ge inne Figure couples. 5. Thus, In this in way, the casecorrelation that two values genes Next,forare each positively we gene questioned couple and strongly were possible plotted correlated, correlations in the ifheatmap the expression between shown geneinof Figureone couples. of 5.them Thus, is In lowin this the in way, case tumor that correlation samples, two genes the values expression of the other one will also be low in the normal sample. Conversely, if the pair of genes is for eachare positively gene couple and strongly were plotted correlated, in the if the heatmap expression shown of one in of Figure them 5is. low Thus, in tumor in the samples, case that the two negatively and strongly correlated and the expression of one of them is low in tumor samples, the genesexpression are positively of the and other strongly one will correlated, also be low ifin the the expressionnormal sample. of one Conversely, of them isif lowthe pair in tumor of genes samples, is negativelyexpression and of the strongly other onecorrelated would beand very the highexpression in normal of one cells of (Figure them is5). low Thus, in tumorfor correlated samples, genes, the the expression of the other one will also be low in the normal sample. Conversely, if the pair of expressionexpression of of the one other of them one willwould allow be veryprediction high in of normal the behavior cells (Figure of the 5).corre Thus,lated for genes. correlated For instance, genes, genesexpressionMCU is negatively and MICU1of one and of are stronglythem very will positively correlated allow prediction co-regulated and the of expressionthe with behavior Orai2 ofand of onethe RYR1 corre of themandlated are is genes. lowthus inextremely For tumor instance,samples, and the expressionMCUnegatively and ofMICU1 co-regulated the other are very one with positively would RYR2 be. co-regulatedThus, very highwhen in thewith normal expression Orai2 cells and (Figure ofRYR1 MCU and5 ).and Thus,are MICU1 thus for extremely correlatedincreases, and the genes, expressionnegativelyexpression of one co-regulated of ofOrai2 them and willwith RYR1 allow RYR2 will prediction. Thus,be also when high, of the thewhereas expression behavior the expression of MCU the correlated and of RYR2MICU1genes. would increases, Forbe muchinstance, the expressionlower. Nevertheless, of Orai2 and not RYR1 all genes will beare also co-regulated high, whereas, either the positively expression or ofnegatively, RYR2 would and bethere much are lower.many Nevertheless,genes that are not not all co-regulated. genes are co-regulated For example,, either MCU positively and MICU1 or negatively, do not co-regulate and there withare manyATP2A3 genes, and that they are are notalmost co-regulated. uncorrelated For with example, TRPM3 MCU and TRPM4and MICU1. Therefore, do not while co-regulate the expression with ATP2A3 , and they are almost uncorrelated with TRPM3 and TRPM4. Therefore, while the expression

Int. J. Mol. Sci. 2017, 18, 922 7 of 23

MCU and MICU1 are very positively co-regulated with Orai2 and RYR1 and are thus extremely

Int.and J. Mol. negatively Sci. 2017, 18 co-regulated, 922 with RYR2. Thus, when the expression of MCU and MICU17 ofincreases, 22 the expression of Orai2 and RYR1 will be also high, whereas the expression of RYR2 would be much oflower. MICU1 Nevertheless, and MCU is not enhanced all genes in arethe co-regulated,tumor phenotype, either the positively expression ornegatively, of ATP2A3 and, TRPM3 there and are many TRPM4genes thatseems are not not to co-regulated.vary much. Therefore, For example, this analysisMCU enabledand MICU1 us to uncoverdo not co-regulate which genes with behaveATP2A3 , inand thethey same are way almost and which uncorrelated do not withwhenTRPM3 comparinandg healthyTRPM4 and. Therefore, tumor phenotypes. while the expression In addition, of itMICU1 canand predictMCU whetheris enhanced or not in the the behaviors tumor phenotype, are similar. the expression of ATP2A3, TRPM3 and TRPM4 seems not to vary much. Therefore, this analysis enabled us to uncover which genes behave in the same way and which do not when comparing healthy and tumor phenotypes. In addition, it can predict whether or not the behaviors are similar.

Figure 5. Figure 5. CorrelationCorrelation or orco-regulation co-regulation between between couples couples of ofgenes. genes. The The larger larger the the circle circle and and the the darker darker the thecolor, color, the the higher higher the the correlation correlation (either (either positive positive in in blue blue or or negative negative in in red) red) between between eacheach pairpair ofof genes. genes. 2.4. Principal Component Analysis 2.4. Principal Component Analysis The Principal Component Analysis describes the variation produced by a multivariate observation, suchThe that Principal new variables Component are made Analysis from describes linear combinations the variation of theproduced original by variables. a multivariate These new observation,variables are such known that asnew Principal variables Components are made from (PCs). linear Thus, combinations if the observation of the hasoriginal p original variables. variables, Theseup to new p PCs variables can be are made, known which as arePrincipal sorted Comp by theonents amount (PCs). of explainedThus, if the variance observation by each has ofp them, originalwhere PC1variables, explains up theto p largest PCs can amount be made, of variance, which are followed sorted by by the PC2, amount and so of on. explained Therefore, variance this analysis byis intendedeach of them, to reduce where the PC1 dimension explains ofthe the largest observations. amount Withof variance, the new followed dimensions by PC2, selected, and so PCs on. explain Therefore,the largest this possible analysis amount is intended of variance. to reduce Indeed, the a dimension criterion for of deciding the observations. how many With PCs the to keep new is that dimensions selected, PCs explain the largest possible amount of variance. Indeed, a criterion for the proportion of variance explained for all PCs selected is larger than 70%. Other criteria are taken deciding how many PCs to keep is that the proportion of variance explained for all PCs selected is from the Decay graph, which represents the explained variance by each PC against the corresponding larger than 70%. Other criteria are taken from the Decay graph, which represents the explained PC. Thus, the number of PCs located in the Decay graph before the slope of the graph changes variance by each PC against the corresponding PC. Thus, the number of PCs located in the Decay drastically (Figure6) shows that the variance explained does not increase much despite considering graph before the slope of the graph changes drastically (Figure 6) shows that the variance explained doesmore not PCs. increase much despite considering more PCs. As it is really difficult for multivariate data to verify the assumption that they fit a normal distribution, the Principal Component Analysis is considered as a kind of exploratory data analysis.

Int. J. Mol. Sci. 2017, 18, 922 8 of 22

This is why data have been filtered and transformed previously—to fit normal distribution as much Int. J.as Mol. possible. Sci. 2017 ,Furthermore,18, 922 data have been centered with their mean, and standardized with their8 of 23 variance.

FigureFigure 6. Principal 6. Principal Component Component Analysis Analysis (PCA), (PCA), where wh genesere aregenes variables are variables and samples and aresamples observations. are Theobservations. proportion of The variance proportion explained of variance by PC1 explained is equal by to PC1 59.82%, is equal and to 16.19% 59.82%, for and PC2. 16.19% (A) PC2 for PC2. vs. PC1; (B) Decay(A) PC2 Variance vs. PC1; Explained(B) Decay Variance graph; ( CExplained) Correlation graph; coefficients. (C) Correlation coefficients.

In the present study, a Principal Component Analysis between samples as a function of the expressionAs it is really profile difficult of p genes for has multivariate been carried data out and to verify the results the assumptionare shown in thatFigure they 6. We fit found a normal distribution,that PC1 clearly the Principal explains Component the difference Analysis between is ph consideredenotypes, since as a kindthe projections of exploratory of the datavalues analysis. for Thiseach is why sample data over have PC1 been show filtered how andthe samples transformed belong previously—to to a healthy phenotype fit normal and distribution are well separated as much as possible.from the Furthermore, samples belonging data have to the been tumor centered phenotype. with their Since mean, the variance and standardized explained by with PC1 their is 59.82%, variance. andIn thethe one present explained study, by athe Principal two first PCs Component is 76.79%, Analysistogether with between the fact samples that the slope as a functionof the Decay of the expressiongraph changes profile drastically of p genes from has PC2, been it carriedis a good out decision and the to keep results only are the shown first two in FigurePCs. 6. We found that PC1Given clearly that explains PC1 clearly the difference reproduces between the two phenotypes,different groups since related the projectionswith the phenotype, of the values it is for eachinteresting sample over to evaluate PC1 show the how influence the samples of each belong original to variable a healthy gene, phenotype which is and proportional are well separated to a fromcoefficient the samples associated belonging with tothe the linear tumor combination phenotype. for each Since gene. the varianceThe way explainedin which this by is PC1 evaluated is 59.82%, in the present study is by estimating the correlation between each gene and PC1. Genes with and the one explained by the two first PCs is 76.79%, together with the fact that the slope of the Decay correlations larger than |0.7| are shown in Table 2. graph changes drastically from PC2, it is a good decision to keep only the first two PCs. Accordingly, it is possible to obtain the following expression (2): Given that PC1 clearly reproduces the two different groups related with the phenotype, it is interesting to evaluate the influence of each original variable gene, which is proportional to a coefficient PC1 = . · (2) associated with the linear combination for each gene. The way in which. this is evaluated in the present study is by estimating the correlation between each gene and PC1. Genes with correlations larger than where PC1 is the value of the Principal Component 1, rlog(expression of gene.i) is the value of |0.7| are shown in Table2. expression for each gene after transformation (see supplementary data for raw expression data of individualAccordingly, genes) it is and possible βgene.i to isobtain the coefficient the following value for expression the same (2): gene (Table 2). This enables the sorting of a given sample to the normalp or tumor phenotype. It is important to take into account that the values of the original variables have been transformed, centered and standardized as reported. PC1 = ∑ rlog(expression of gene.i)·βgene.i (2) i = 1 where PC1 is the value of the Principal Component 1, rlog(expression of gene.i) is the value of expression for each gene after transformation (see supplementary data for raw expression data of individual genes) and βgene.i is the coefficient value for the same gene (Table2). This enables the sorting of a given sample to the normal or tumor phenotype. It is important to take into account that the values of the original variables have been transformed, centered and standardized as reported. Int. J. Mol. Sci. 2017, 18, 922 9 of 23

Table 2. Coefficient values from Principal Component (PC1) corresponding to each gene. Raw data were transformed, filtered, centered and standardized. To obtain PC1 value for a given sample, use coefficients in this table in expression 2.

Gene Expression Gene Expression Gene Expression Gene Expression Gene Expression ATP2A1 −0.166 CACNA1C −0.174 ITPR1 0.143 ORAI3 0.090 TRPA1 −0.147 ATP2A2 0.148 CACNA1D 0.162 ITPR2 −0.173 PICALM −0.089 TRPM3 −0.070 ATP2A3 −0.088 CACNA1G 0.072 ITPR3 0.152 PKD2 0.020 TRPM4 −0.113 ATP2B1 0.172 CACNA1H −0.173 MCOLN1 −0.175 PKD2L1 −0.169 TRPM5 −0.168 ATP2B4 −0.173 CACNA1I −0.084 MCOLN2 −0.174 RYR1 0.103 TRPM7 0.105 ATP2C1 −0.121 CASQ1 0.093 MCOLN3 −0.148 RYR2 −0.174 TRPV1 −0.141 ATP2C2 0.142 CCDC109B −0.167 MCU 0.149 SLC8A1 −0.010 TRPV2 0.096 BCL2 −0.141 CCDC90A 0.140 MICU1 0.131 SLC8A2 0.165 TRPV3 −0.060 C22orf32 0.131 EFCAB4B −0.169 ORAI1 0.061 STIM1 0.174 TRPV4 0.026 CACNA1B −0.141 EFHA1 −0.149 ORAI2 0.168 STIM2 0.057 TRPV6 0.133 VDAC1 −0.175 VDAC2 0.147 VDAC3 −0.170 TRPC1 0.041 Int. J. Mol. Sci. Sci. 20172017,, 1818,, 922 922 1110 of 22 23

2.5. Differential Expression Analysis There is no consensus on the best way to carry outout a Differential ExpressionExpression Analysis. It is also debatable which methods perform best as well as which probability distribution fits fits best to a given data setset [ 28[28,31].,31]. In In the the present present study, study, a data a setdata shows set ashows low sequencing a low sequencing depth, unbalanced depth, unbalanced sequencing depthsequencing among depth samples, among and samples, low number and lo ofw technicalnumber of replicates technical (nreplicates= 4). In order(n = 4). to In keep order a relativelyto keep a conservativerelatively conservative approach and,approach at the and, same at time, the same identify time, a goodidentify number a good of numb geneser differentially of genes differentially expressed, threeexpressed, different three methods different weremethods used: were EdgeR used: [ 32Edge], DESeq2R [32], DESeq2 [33] and [33] Limma and Limma [34]. EdgeR[34]. EdgeR is the is lessthe conservativeless conservative method method because because it detects it detects the largest the la numberrgest number of true of positives true positives (genes which(genes arewhich indeed are expressedindeed expressed differentially) differentially) at the expense at the ofexpense introducing of introducing more false more positives. false Inpositives. contrast, In DESeq2 contrast, is consideredDESeq2 is considered the most conservative the most conservative one [31]. Thus, one [3 Zhang1]. Thus, et al.Zhang [31] recommendet al. [31] recommend using EdgeR using when EdgeR the experimentalwhen the experimental design is unbalanceddesign is unbalanced and sequencing and se depthquencing and depth number and of number replicates of replicates are low. However, are low. SeyednasrollahHowever, Seyednasrollah [28] suggests [28] using suggests Limma using because Limma of because its good of documentation its good documentation and speed, and as wellspeed, as easeas well of useas ease and of robustness. use and robustness. Thus, we followedThus, we thefollowed suggestion the suggestion by Zhang by et al.Zhang [31] ofet takingal. [31] theof taking genes differentiallythe genes differentially expressed expressed which belong which to belong the intersection to the intersection from these from three thes methodse three methods in order toin avoidorder ato high avoid amount a high of amount false positives. of false Accordingly,positives. Accord we usedingly, an we R/Bioconductor used an R/Bioconductor package called package Enrichment called BrowserEnrichment [35 ],Browser and applied [35], and Limma, applied EdgeR Limma, and EdgeR DESeq2 and methods DESeq2 ofmethods differential of differential expression expression analysis. Onlyanalysis. those Only genes those whose genes differential whose differential expression wasexpression considered was significantconsidered by significant all three independentby all three methodsindependent were methods considered were to beconsidered expressed to differentially. be expressed Table differentially.3 shows fold Table changes 3 shows for all fold 30 changes considered for differentiallyall 30 considered expressed differentially genes outexpressed of the 77genes analyzed, out of the along 77 withanalyzed, the three alongp valueswith the obtained three p values by the threeobtained independent by the three methods. independent We found methods. that We 12 genesfound werethat 12 significantly genes were upregulatedsignificantly inupregulated CRC cells (positivein CRC cells fold (positive change) whilefold change) the remaining while the 18 genesremaining were 18 significantly genes were downregulated significantly downregulated (negative fold change).(negative The fold most change). robust The changes most correspondedrobust changes to thecorresponded transient receptor to the potentialtransient channel,receptor vaniloidpotential 6 (channel,TRPV6) andvaniloid the sodium-calcium 6 (TRPV6) and the exchanger sodium-calcium 2 (NCX2) exchanger for upregulated 2 (NCX2 genes) for in upregulated CRC, and ryanodine genes in 2+ receptorCRC, and 2 (RyR2ryanodine) and voltage-dependent receptor 2 (RyR2 Ca) 2+andchannel voltage-dependent 1.2 (Cav1.2) for Ca downregulated channel 1.2 genes. (Cav1.2 In order) for todownregulated appreciate best genes. the differential In order gene to expression,appreciate changesbest the corresponding differential gene to each expression, family of geneschanges are detailedcorresponding next. to each family of genes are detailed next.

Table 3.3. DifferentialDifferential gene gene expression fold change. Fold Fold changes changes are are expressed expressed as as 〖log〗_2 (E_Healthy/E_Tumoral)(E_Healthy⁄E_Tumoral) and and E E means means Expression, Expression, and and pp-values-values adjusted adjusted by by Be Benjamininjamini and Hochberg (BH) method for eacheach differentdifferent packagepackage use.use. p-Value Adjusted by BH Method p-Value Adjusted by BH Method ID Fold Change BH.DESeq2 BH.edgeR BH.limma ID Fold Change BH.DESeq2 BH.edgeR BH.limma ATP2A1 1.02810479 9.71 × 10−8 0.0027235 0.00160014 −8 ATP2A1ATP2A2 1.028104790.83492287 9.710.00142512× 10 0.000294520.0027235 0.02416078 0.00160014 ATP2A2 0.83492287 0.00142512 0.00029452 0.02416078 ATP2B1 1.66289207 1.32 × 10−29 1.69 × 10−13 1.58 × 10−5 ATP2B1 1.66289207 1.32 × 10−29 1.69 × 10−13 1.58 × 10−5 -18 −22 −6 ATP2B4ATP2B4 2.756283222.75628322 5.53 5.53× 10× -1810 7.867.86 × 1010−22 3.013.01 × 10× 10−6 BCL2BCL2 2.348493792.34849379 0.003073010.00307301 0.00070215 0.00070215 0.00754796 0.00754796 CACNA1BCACNA1B 2.191321222.19132122 0.001909510.00190951 0.00050421 0.00050421 0.00207432 0.00207432 −24 −53 −9 CACNA1CCACNA1C −11.408836−11.408836 1.05 1.05× 10 × 10−24 1.401.40 × 1010−53 1.241.24 × 10×−9 10 CACNA1D 1.55721384 1.97 × 10−08 9.80 × 10−06 0.00077412 CACNA1D 1.55721384 1.97 × 10−08 9.80 × 10−06 0.00077412 CACNA1H −4.7419621 2.25 × 10−29 1.04 × 10−17 3.37 × 10−6 −29 −17 −6 CCDC109BCACNA1H 2.08593613−4.7419621 4.67 2.25× 10× −108 1.042.73 × 1010−09 3.37 0.00018476× 10 EFCAB4BCCDC109B 2.031862132.08593613 4.66 4.67× 10 ×− 1011 −8 2.731.96 × 1010−0914 0.000184764.98 × 10 −5 EFHA1EFCAB4B 0.746758242.03186213 0.00147429 4.66 × 10−11 1.96 0.00014479 × 10−14 4.98 0.00278932× 10−5 −5 ITPR1EFHA1 0.838872830.74675824 3.720.00147429× 10 0.000144790.00927132 0.00278932 0.00956689 ITPR2 × −46 × −21 × −6 ITPR1 1.769535690.83887283 4.12 3.7210 × 10−5 0.009271325.79 10 0.009566893.01 10 ITPR3 0.52236077 3.25 × 10−5 0.0068986 0.01284295 −46 −21 −6 MCOLN1ITPR2 1.797374021.76953569 8.88 4.12× 10 × −1082 5.792.08 × 1010−17 3.018.54 × 10× 10−7 −5 MCUITPR3 0.735886160.52236077 6.87 3.25× 10 × −108 0.00689860.00151019 0.01284295 0.00278932 MICU1MCOLN1 0.501299431.79737402 0.00011815 8.88 × 10−82 2.08 0.02325149 × 10−17 8.54 0.01284295× 10−7 − − ORAI2MCU 2.153948040.73588616 4.74 6.87× 10 × 1015 −8 0.001510193.48 × 10 14 0.002789320.00018476 MICU1 0.50129943 0.00011815 0.02325149 0.01284295 ORAI2 2.15394804 4.74 × 10−15 3.48 × 10−14 0.00018476

Int. J. Mol. Sci. 2017, 18, 922 11 of 23

Table 3. Cont.

p-Value Adjusted by BH Method Int. J. Mol. Sci. 2017ID, 18, 922 Fold Change BH.DESeq2 BH.edgeR BH.limma 12 of 22 PKD2L1 5.34354792 1.49 × 10−8 2.43 × 10−10 3.01 × 10−6 PKD2L1 5.34354792 1.49 × 10−8 2.43 × 10−10 3.01 × 10−6 RYR2 12.5085599 1.17 × 10−30 1.39 × 10−77 2.95 × 10−10 −30 −77 −10 SLC8A2 RYR2 4.7610114412.5085599 2.08 1.17× 10 × −1015 1.391.17 ×× 1010− 14 2.95 × 100.00010438 −15 −14 STIM1SLC8A2 1.923405464.76101144 5.41 2.08× 10 × −1070 1.171.20 ×× 1010− 230.000104381.80 × 10−6 TRPA1 STIM1 −7.24450881.92340546 9.71 5.41× 10 × −108−70 1.204.32 ×× 1010−23− 10 1.80 × 3.5910−6 × 10−6 TRPM5TRPA1 1.57085974−7.2445088 7.99 9.71× 10 × −106 −8 4.320.00121578 × 10−10 3.59 × 10 0.00097882−6 TRPV6TRPM5 3.554563631.57085974 7.99 7.99× 10 × −106 −6 0.001215781.29 × 10− 6 0.000978820.02627047 − − − VDAC1TRPV6 1.705338483.55456363 1.03 7.99× 10 × 1075−6 1.291.02 ×× 1010−6 390.026270471.03 × 10 8 VDAC2VDAC1 0.327917121.70533848 0.00017495 1.03 × 10−75 1.02 0.035753 × 10−39 1.03 × 10 0.00617185−8 −10 −14 −6 VDAC3VDAC2 1.502374610.32791712 6.800.00017495× 10 1.070.035753× 10 0.006171855.86 × 10 VDAC3 1.50237461 6.80 × 10−10 1.07 × 10−14 5.86 × 10−6 Expression levels for all ten voltage-operated Ca2+ channels (VOCCs) are shown in Figure7. 2+ This familyExpression is formed levels by for three all subfamiliesten voltage-operated named Cav1,Ca channels Cav2 and (VOCCs) Cav3. are Although shown in epithelial Figure 7. This colonic family is formed by three subfamilies named Cav1, Cav2 and Cav3. Although epithelial colonic cells cells are not electrically excitable cells and lack functional voltage-operated Ca2+ entry, both normal are not electrically excitable cells and lack functional voltage-operated Ca2+ entry, both normal and and colon cancer cells express mRNAs for some VOCCs. Cav1.1 to Cav1.4 are L-type VOCCs sensitive colon cancer cells express mRNAs for some VOCCs. Cav1.1 to Cav1.4 are L-type VOCCs sensitive to to dihydropyridines.dihydropyridines. Only Only Cav1.3 Cav1.3 is is expressed expressed in in both both normal normal colonic colonic and and colon colon cancer cancer cells cells and and expressionexpression levels levels are are significantly significantly enhanced enhanced inin tumortumor cells (Figure (Figure 7A).7A). Cav1.2 Cav1.2 is isexpressed expressed only only in in normalnormal cells cells and and the the remaining remaining Cav1.1 Cav1.1 and and Cav1.4 Cav1.4 areare missing in in both both cell cell types types (Figure (Figure 7A).7A).

2+ FigureFigure 7. Expression 7. Expression of of voltage-gated voltage-gated Ca Ca2+ channelschannels in in normal normal and and colon colon cancer cancer cells. cells. Expression Expression levels of Cav1s (A), Cav2s (B) and Cav3s (C) in normal colonic cells (grey bars) and colon cancer cells levels of Cav1s (A), Cav2s (B) and Cav3s (C) in normal colonic cells (grey bars) and colon cancer cells (red bars). * p statistically significant by three independent methods. (red bars). * p statistically significant by three independent methods. The second subfamily of VOCCs, Cav2s, include P/Q and R type channels typically restricted to neurons. Only Cav2.2 is expressed in normal colonic cells and expression is essentially lost in CRC cells (Figure 7B).

Int. J. Mol. Sci. 2017, 18, 922 12 of 23

The second subfamily of VOCCs, Cav2s, include P/Q and R type channels typically restricted to neurons. Only Cav2.2 is expressed in normal colonic cells and expression is essentially lost in CRC cells (Figure7B). Finally, Cav3.1 to Cav3.3 are T-type Ca2+ channels activated only at high potentials and expressed Int. J. Mol. Sci. 2017, 18, 922 13 of 22 typically in cardiac and smooth muscles as well as in some neurons. Only Cav3.2 is expressed in normal colonicFinally, cells Cav3.1 and to expression Cav3.3 are is alsoT-type nearly Ca2+ lostchannels in CRC activated cells(Figure only at7 C).high Interestingly, potentials and it has been shownexpressed recently typically that in blockerscardiac and of T-typesmooth channels muscles as may well prevent as in some colon neurons. cancer growthOnly Cav3.2 [36]. is Thus, transcriptsexpressed of Cav1.2, in normal Ca1.3, colonic Cav2.2 cells and and Ca3.2 expression are differentially is also nearly expressed lost in in CRC normal cellscolonic (Figure and7C). CRC cells. InInterestingly, any case, weit has must been take shown into recently account that that bloc expressionkers of T-type of these channels channels may atprevent the protein colon cancer level may or maygrowth not correlate [36]. Thus, with transcripts expression of Cav1.2, at the messenger Ca1.3, Cav2.2 level. and Ca3.2 are differentially expressed in Genenormal expression colonic and levels CRC cells. for molecularIn any case, players we must involved take into inaccount SOCE that are expression shown inof Figurethese 8. channels at the protein level may or may not correlate with expression at the messenger level. Store-operated Ca2+ entry, also named capacitative Ca2+ entry, is activated after agonist-induced Gene expression levels2+ for molecular players involved in SOCE are shown in Figure 8. Store- emptyingoperated of intracellular Ca2+ entry, also Ca namedstores. capacitative This pathway Ca2+ entry, is mediated is activated by interactions after agonist-induced between STIMemptying sensors at theof ER intracellular and Orai channelsCa2+ stores. at This the pathway plasma is membrane. mediated by All inte threeractionsOrais betweenare expressed STIM sensors in bothat the normalER and colonand Orai cancer channels cells andat the expression plasma membrane. appears All to three be increased Orais are forexpressed all of them in both (Figure normal8A). and However, colon only differencescancer cells forand Orai2expressionmRNA appears are significantto be increa insed tumor for all cells of them (Figure (Figure8A). 8A).STIM However,and 2 only are also expresseddifferences in normal for Orai2 and mRNA colon cancerare significant cells (Figure in tumor8B). cells Interestingly, (Figure 8A). STIM1STIM andis significantly2 are also expressed enhanced in tumorin normal cells, whereas and colonSTIM2 cancerremains cells (Figure similar 8B). (Figure Interestingly,8). In addition STIM1 is tosignificantly these players, enhancedMS4A12 in tumorhas been reportedcells, to whereas be a colon-selective STIM2 remains store-operated similar (Figure Ca 8).2+ channelIn addition promoting to these players, malignant MS4A12 cell processes has been [37]. 2+ However,reported MS4A12 to be a mRNA colon-selective is barely store-operated or not expressed Ca channel either inpromoting normal cellsmalignant and CRC cell processes cells (Figure [37]. 8C). However, MS4A12 mRNA is barely or not expressed either in normal cells and CRC cells (Figure 8C). Finally, CRACR2A, a SOCE regulatory protein [38], is expressed in both normal and tumor cells and Finally, CRACR2A, a SOCE regulatory protein [38], is expressed in both normal and tumor cells and expressionexpression levels levels are diminishedare diminished in tumorin tumor cells cells (Figure (Figure8 8D).D).

FigureFigure 8. Expression 8. Expression of genes of genes involved involved in in store-operated store-operated CaCa2+ entryentry in in normal normal and and colon colon cancer cancer cells. cells. ExpressionExpression levels levels of Orais of Orais(A), (ASTIMs), STIMs(B (),BMSA412), MSA412( C(C)) andand CRACR2A (D (D) in) in normal normal colonic colonic cells cells (grey (grey bars) andbars) colon and colon cancer cancer cells cells (red (red bars). bars). * p * statisticallyp statistically significantsignificant by by three three independent independent methods. methods.

We have assessed differential gene expression of all 27 human TRP channels (Figure 9). TRP Wechannels have are assessed cationic differentialchannels that genemay permeate expression Ca2+ ofwith all variable 27 human selectivity TRP and channels are involved (Figure in 9). 2+ TRP channelsdisparate are cell cationicfunctions, channels some of thatthem may contributing permeate to SOCE, Ca with particularly variable the selectivity canonical andTRP arechannels involved in disparate(TRPCs). cell There functions, are seven somemembers of themof the contributingTRPC family of to cation SOCE, channels particularly TRPC1 to the TRPC7. canonical RNA- TRP channelsseq data (TRPCs). shows There that both are colonic seven membersand CRC cells of the express TRPC only family TRPC1 of cationand expression channels seems TRPC1 increased to TRPC7. RNA-seqin colon data cancer shows cells. that However, both colonic difference and is CRCnot significant cells express (Figure only 9A).TRPC1 These resultsand expressionare at variance seems increasedwith inour colon previous cancer results cells. using However, quantitative difference reverse transcription-polymerase is not significant (Figure chain9A). reaction These (qRT- results PCR) [4]. Differences could be explained by increased sensitivity and/or threshold for significance in our present analysis. In any case, we have confirmed that expression of TRPC1 protein is significantly

Int. J. Mol. Sci. 2017, 18, 922 13 of 23 are at variance with our previous results using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) [4]. Differences could be explained by increased sensitivity and/or threshold for Int. J. Mol. Sci. 2017, 18, 922 14 of 22 significance in our present analysis. In any case, we have confirmed that expression of TRPC1 protein is significantlyenhanced enhancedin CRC [4], in CRCstressing [4], stressingfurther the further need the for need protein for proteinand functional and functional confirmation confirmation of of transcriptomic/genetictranscriptomic/genetic analysis.analysis.

FigureFigure 9. Expression 9. Expression of transient of transient receptor receptor potential potential (TRP)(TRP) channels in in normal normal and and colon colon cancer cancer cells. cells. ExpressionExpression levels levels of canonical of canonical transient transient receptor receptor potentialpotential channels TRPCsTRPCs (A(),A canonical), canonical Transient Transient ReceptorReceptor Potential Potential channels channels(TRPVs (TRPVs)(B)), ( MelastatinB), Melastatin Transient Transient Receptor Receptor Potential Potential channels channels ((TRPMsTRPMs))( C), (C), Mucolipin Transient Receptor Potential channels (TRPMLs) (D), Polycystin Transient Receptor Mucolipin Transient Receptor Potential channels (TRPMLs)(D), Polycystin Transient Receptor Potential Potential channels (TRPPs) (E) and Transient Receptor Potential channel member A, subfamily 1 channels (TRPPs)(E) and Transient Receptor Potential channel member A, subfamily 1 (TRPA1)(F) in (TRPA1) (F) in normal colonic cells (grey bars) and colon cancer cells (red bars). * p statistically p normalsignificant colonic cellsby three (grey independent bars) and methods. colon cancer cells (red bars). * statistically significant by three independent methods. The vaniloid family of transient receptor potential channels (TRPVs) are a very heterogeneous Thefamily vaniloid of six cationic family channels of transient from receptorTRPV1 to potentialTRPV6. Only channels TRPV1, (TRPVs) TRPV3 and are TRPV6 a very are heterogeneous expressed familyin of colonic six cationic cells. Interestingly, channels from while TRPV1 expression to TRPV6. levels of Only TRPV1TRPV1 and ,TRPV3TRPV3 areand similarTRPV6 in normalare expressed and CRC cells, TRPV6 is significantly enhanced in tumor cells (Figure 9B). This channel is involved in in colonic cells. Interestingly, while expression levels of TRPV1 and TRPV3 are similar in normal vitamin D-dependent absorption of extracellular Ca2+ in the intestines. Interestingly, it has been and CRC cells, TRPV6 is significantly enhanced in tumor cells (Figure9B). This channel is involved previously reported that TRPV6 is overexpressed in prostate cancer [2,3]. 2+ in vitaminThe D-dependent melastatin family absorption of transient of extracellular receptor potential Ca channelsin the intestines. (TRPMs) are Interestingly, a big family of it haseight been previouslycation reportedchannels from that TRPV6TRPM1 to is overexpressedTRPM8. Only three in prostateof them, TRPM4 cancer,[ TRPM52,3]. and TRPM7, appear to Thebe expressed melastatin in familycolonic ofcells. transient Levels of receptor TRPM4 potential and TRPM7 channels are similar (TRPMs) in normal are acolonic big family and CRC of eight cationcells. channels However, from expression TRPM1 to of TRPM8.TRPM5 decreases Only three significantly of them, inTRPM4 CRC cells, TRPM5 (Figureand 9C).TRPM7 , appear to be expressedThe inmucolipin colonic cells.family Levelsof transient of TRPM4 receptorand potentialTRPM7 channelare similar (TRPMLs) in normal are a family colonic of andthree CRC cells. However,channels, TRPML1 expression to 3, of alsoTRPM5 nameddecreases mucolipins. significantly Only the two in CRC first cellsmucolipines (Figure 9areC). expressed in Thenormal mucolipin and tumor family cells and of transientboth are significantly receptor potential decreased channel in CRC cells. (TRPMLs) Particularly are anoteworthy family of is three the case of TRPML2 that is dramatically downregulated in colon cancer cells (Figure 9D). channels, TRPML1 to 3, also named mucolipins. Only the two first mucolipines are expressed in

Int. J. Mol. Sci. 2017, 18, 922 14 of 23 normal and tumor cells and both are significantly decreased in CRC cells. Particularly noteworthy is the case of TRPML2 that is dramatically downregulated in colon cancer cells (Figure9D). The polycystine family of transient receptor potential channels (TRPPs) are also a family of three channels,Int. J. Mol. TRPP1 Sci. 2017 to, 18 TRPP3,, 922 TRPP1 being the best known for its role in polycystic kidney15 of disease. 22 TRPP1 is expressed in both normal and colon cancer cells to the same extent (Figure9E). TRPP2 is The polycystine family of transient receptor potential channels (TRPPs) are also a family of three expressedchannels, only TRPP1 in normal to TRPP3, cells (FigureTRPP1 9beingE) and theTRPP3 best knownis absent for its in role both in normal polycystic and kidney colon disease. cancer cells (FigureTRPP19E). is expressed in both normal and colon cancer cells to the same extent (Figure 9E). TRPP2 is Finally,expressed the only transient in normal receptor cells (Figure potential 9E) and channel, TRPP3 member is absent A, in subfamilyboth normal 1 and (TRPA1), colon cancer a stress cells sensor and only(Figure member 9E). of the TRPA family of channels, is expressed in normal colonic cells but not in colon cancer cellsFinally, (Figure the9 transientF). receptor potential channel, member A, subfamily 1 (TRPA1), a stress sensor Inand summary, only member only of 11the out TRPA of family the 27 of TRPs channels, are expressedis expressed in in colonic normal colonic cells, either cells but normal not in colon or tumor. In addition,cancer cells only (Figure six show9F). differential expression. In all the cases but one, TRP channels are downregulatedIn summary, in CRC only cells 11 relativeout of the to 27 normal TRPs are cells, expressed TRPV6 in being colonic the cells, only either TRP channelnormal or significantly tumor. In addition, only six show differential expression. In all the cases but one, TRP channels are increased in CRC cells. Functional consequences of changes in expression of TRP channels in CRC downregulated in CRC cells relative to normal cells, TRPV6 being the only TRP channel significantly remainincreased to be established. in CRC cells. Functional consequences of changes in expression of TRP channels in CRC 2+ 2+ Expressionremain to be of established. Ca release channels is shown in Figure 10. IP3 and ryanodine receptors are Ca 2+ release channelsExpression at theof Ca ER,2+ release the most channels important is shown intracellular in Figure 10. Ca IP3 andstore. ryanodine There are receptors three IPare3 receptorCa2+ (IP3R)release subtypes, channels IP3R1 at tothe IP ER,3R3, the and most all important of them are intracellular expressed Ca in2+ both store. normal There colonicare three and IP3 colonreceptor cancer cells (Figure(IP3R) subtypes, 10A). Interestingly, IP3R1 to IP3R3, all and three all isoformsof them are are expressed also differentially in both normal expressed colonic and in colon colon cancer cancer cells cells (Figure 10A). Interestingly, all three isoforms are also differentially expressed in colon cancer relative to normal cells. While genes corresponding to IP3R1 and IP3R3 are overexpressed in colon cells relative to normal cells. While genes corresponding to IP3R1 and IP3R3 are overexpressed in cancer cells, gene coding for IP3R2 is downregulated (Figure 10A). Interestingly, consistent with these colon cancer cells, gene coding for IP3R2 is downregulated (Figure 10A). Interestingly, consistent with results, it has been reported that the type III IP3R is absent in normal colonic cells but is expressed these results, it has been reported that the type III IP3R is absent in normal colonic cells but is in colon cancer. Moreover, expression levels are directly related to aggressiveness of the tumor [39]. expressed in colon cancer. Moreover, expression levels are directly related to aggressiveness of the In addition,tumor [39]. it has In addition, been also it has reported been also that reported depletion that depletion of mutated of mutated K-Ras inK-Ras a colon in a colon cancer cancer cell line 2+ changescell Caline storechanges content Ca2+ andstore susceptibility content and tosusceptibility apoptosisby to promotingapoptosis changesby promoting in expression changes ofin IP3R 2+ isoformsexpression [40]. Therefore, of IP3R isoforms IP3Rs [40]. are criticalTherefore, players IP3Rs in are Ca criticalremodeling players in in Ca colon2+ remodeling cancer, contributingin colon likelycancer, to changes contributing in Ca2+ likelystore to content changes and in Ca susceptibility2+ store content to and apoptosis. susceptibility to apoptosis.

2+ FigureFigure 10. Expression 10. Expression ofCa of 2+Carelease release channels channels inin normalnormal and and colon colon cancer cancer cells. cells. Expression Expression levels levels of of inositol trisphosphate receptors (IP3Rs) (A) and ryanodine receptors (RyRs) (B) in normal colonic cells inositol trisphosphate receptors (IP3Rs) (A) and ryanodine receptors (RyRs) (B) in normal colonic cells (grey bars) and colon cancer cells (red bars). * p statistically significant by three independent methods. (grey bars) and colon cancer cells (red bars). * p statistically significant by three independent methods.

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Regarding RyRs, only genes coding for RyR1 and 2 are expressed in colonic cells, although RyR2 is byby farfar thethe mostmost abundantabundant isoform isoform (Figure (Figure 10 10B).B). In In addition, addition, RyR2 RyR2 is is dramatically dramatically downregulated downregulated in colonin colon cancer cancer cells cells (Figure (Figure 10 10B).B). Together, Together, these these data data indicate indicate that that Ca Ca2+2+release release couldcould bebe substantially influencedinfluenced inin CRC cellscells relativerelative toto normalnormal cellscells asas reportedreported recentlyrecently [[4].4]. Gene expression levels for CaCa2+2+ pumpspumps including PMCAs, SERCAs and SPCAs are shown in Figure 1111.. RegardingRegarding genes genes coding coding for PMCAs,for PMCAs, we found we found that only that PMCA1 only PMCA1 and PMCA4 and arePMCA4 expressed are inexpressed both normal in both and normal colon and cancer colon cells, cancer while cells, PMCA2 while PMCA2 and PMCA3 and PMCA3 are absent are absent in both in cell both types cell (Figuretypes (Figure 11A). Interestingly,11A). Interestingly, PMCA1 PMCA1 expression expression increases increases in cancer in cellscancer while cells expression while expression of PMCA4 of isPMCA4 downregulated is downregulated in tumor in cells tumor (Figure cells 11 (FigureA). Thus, 11A). data Thus, suggest data asuggest molecular a molecular switch from switch PMCA4 from toPMCA4 PMCA1 to inPMCA1 CRC. These in CRC. results These are consistentresults are with consistent a previous with report a previous showing report that undifferentiated showing that colonicundifferentiated cell lines expresscolonic PMCA1cell lines and express differentiation PMCA1 downregulates and differentiation it while downregulates promoting expression it while of PMCA4promoting [41 expression]. of PMCA4 [41].

2+ Figure 11. Expression of CaCa2+ pumps in normal and colon cancer cells. Expression levels of plasma membrane CaCa2+2+ ATPasesATPases (PMCAs)(PMCAs) ((AA),), sarcoplasmicsarcoplasmic and/orand/or endo endoplasmicplasmic reticulum Ca2+ Ca2+ ATPases (SERCAs) ((BB)) andand secretory secretory pathway pathway Ca Ca2+2+ ATPases ATPases (SPCAs) (SPCAs) (C )(C in) normalin normal colonic colonic cells cells (grey (grey bars) bars) and colonand colon cancer cancer cells cells (red (red bars). bars). * p statistically *p statistically significant significant by three by three independent independent methods. methods.

All three SERCAs are also expressed in both normal and tumor tumor cells cells (Figure (Figure 11B).11B). Interestingly, Interestingly, SERCA1 expressionexpression decreasesdecreases significantly significantly in in tumor tumor cells cells while while SERCA2 SERCA2 is upregulated is upregulated in tumor in tumor cells. Nocells. difference No difference is observed is observed in SERCA3 in SERCA3 (Figure (Fig11B).ure Thus, 11B). a Thus, molecular a molecular switch from switch SERCA1 from SERCA1 to SERCA2 to maySERCA2 be also may involved be also involved in CRC. Finally,in CRC. both Finally, SPCAs both are SPCAs expressed are expressed in normal in and normal colon and cancer colon cells cancer but expressioncells but expression levels are levels similar are (Figure similar 11 C).(Figure Although 11C). SPCA2Although seems SPCA2 to be seems overexpressed to be overexpressed in cancer cells, in differencescancer cells, were differences significant were by significant only two by of only the threetwo of methods the three used methods to assess used significance. to assess significance. Changes Changes in SERCA activity could lead to possible differences in Ca2+ store content between normal and CRC cells. In fact, we have reported recently that CRC cells display the partial depletion of Ca2+

Int. J. Mol. Sci. 2017, 18, 922 16 of 23

2+ inInt. SERCA J. Mol. Sci. activity 2017, 18,could 922 lead to possible differences in Ca store content between normal and17 CRC of 22 cells. In fact, we have reported recently that CRC cells display the partial depletion of Ca2+ stores [4]. Thisstores effect [4]. hasThis beeneffect partially has been attributed partially toattributed loss of ER toCa loss2+ sensorof ER Ca STIM22+ sensor [4]. WhetherSTIM2 [4]. changes Whether in SERCAchanges isoform in SERCA contribute isoform tocontribute this remodeling to this remodeling as well remains as well to beremains established. to be established. ExpressionExpression ofof Na Na+/Ca+/Ca2+2+exchanger exchanger isoforms isoforms is shown is shown in Figure in Figure12. These 12. co-transporters These co-transporters normally extrudenormally Ca extrude2+ back Ca to2+ the back external to the medium external in medium exchange in forexchange Na+ using for Na the+ electrochemicallyusing the electrochemically favorable gradientfavorable for gradient Na+. There for Na are+. threeThere isoforms are three named isoforms NCX1, named NCX2 NCX1, and NCX2 NCX3, and but NCX3, only NCX1 but only and NCX2NCX1 areand expressed NCX2 are inexpressed normal and in normal colon cancerand colon cells. cancer Interestingly, cells. Interestingly, while expression while expression of NCX1 is of similar NCX1in is normalsimilar andin normal CRC cells, and expression CRC cells, of expression NCX2 is dramatically of NCX2 is enhanced dramatically in CRC enhanced cells, actually in CRC showing cells, theactually largest showing fold increase the largest in expression fold increase in tumor in expression cells of all in upregulatedtumor cells of genes all upregulated tested (Figure genes 12). tested (Figure 12).

FigureFigure 12.12. ExpressionExpression ofofNa Na++/Ca/Ca2+2+ exchangerexchanger isoforms isoforms in in normal normal and colon cancer cells.cells. ExpressionExpression levelslevels ofof NCXsNCXs inin normalnormal coloniccolonic cellscells (grey(grey bars)bars) andand coloncolon cancercancer cellscells (red(red bars).bars). ** pp statisticallystatistically significantsignificant byby threethree independentindependent methods.methods.

2+ GeneGene expressionexpression levels levels for for molecular molecular players players involved involved in mitochondrial in mitochondrial Ca2+ transport Ca transport are shown are 2+ inshown Figure in 13 Figure. Ca2+ enters13. Ca mitochondria enters mitochondria down its electrochemical down its electrochemical gradient through gradient the mitochondrial through the 2+ Camitochondrial2+ uniporter (MCU)Ca uniporter that is modulated (MCU) that by severalis modulated regulatory by several proteins regulatory including mitochondrialproteins including Ca2+ 2+ uptakemitochondrial (MICU) Ca isoforms uptake 1 to (MICU) 3, mitochondrial isoforms calcium 1 to 3, uniportermitochondrial regulator calcium 1 (MICUR1), uniporter mitochondrial regulator 1 calcium(MICUR1), uniporter mitochondrial dominant calcium negative uniporter beta (MCUb) domina andnt MCUnegative regulator beta (MCUb) (EMRE). and Voltage-dependent MCU regulator anion(EMRE). channels Voltage-dependent (VDAC) 1 to 3 anion in the channels outer mitochondrial (VDAC) 1 membraneto 3 in the haveouter been mitochondrial involved in membrane apoptosis. 2+ Interestingly,have been involved messengers in apoptosis. involved Interestin in mitochondrialgly, messengers Ca2+ transport involved are in very mitochondrial highly expressed Ca transport in both normalare very and highly CRC expressed cells (Figure in both 13). normal In addition, and CRC all genes cells but(Figure MICU3 13). are In addition, expressed all in genes both normalbut MICU3 and tumorare expressed cells (Figure in both 13). normal Expression and of tumor the channel cells (F MCUigure and 13). its Expression positive modulator of the channel MICU1 MCU is enhanced and its inpositive tumor modulator cells (Figure MICU1 13A,B), is while enhanced expression in tumor of negative cells (Figure modulator 13A,B), MICU2 while is expression decreased. of Expression negative ofmodulator other MCU MICU2 modulators is decreased. including Expression MICUR1 of andother EMRE MCU ismodulators similar (Figure including 13B,C,E), MICUR1 while and MCUb EMRE is significantlyis similar (Figure downregulated 13B,C,E), while in colon MCUb cancer is significantly cells (Figure downregulated13D). Finally, all in three colon VDACs cancer are cells expressed (Figure at13D). very Finally, high levels all three in both VDACs normal are and expressed tumor cells at (Figurevery high 13F). levels Interestingly, in both expressionnormal and of tumor all of themcells is(Figure significantly 13F). Interestingly, different in colon expression cancer of cells. all of VDAC1 them andis significantly 3 are downregulated different in in colon colon cancer cancer cells. cells whileVDAC1 VDAC2 and 3 isare significantly downregulated overexpressed in colon cancer in cancer cells cells. while VDAC2 is significantly overexpressed in cancerFinally, cells. expression of other related proteins is shown in Figure 14. Bcl-2 modulates Ca2+ release 2+ and apoptosis.Finally, expression This gene of is other significantly related proteins downregulated is shown in in cancer Figure cells 14. (FigureBcl-2 modulates 14) while Ca expression release ofand two apoptosis. other proteins This gene including is significantly calsequestrin downregulated 1 and phosphatidylinositol in cancer cells (Figure binding 14) clathrin while expression assembly proteinof two other (PICALM), proteins is similarincluding (Figure calsequestrin 14B,C). No 1 and expression phosphatidylinositol of calsequestrin2 binding and phospholamban clathrin assembly is observedprotein (PICALM), in normal is and similar colon (Figure cancer cells.14B,C). No expression of calsequestrin2 and phospholamban is observed in normal and colon cancer cells.

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Figure 13. Expression of genes involved in mitochondrial Ca2+ transport in normal and colon cancer FigureFigure 13. 13.Expression Expression of of genes genes involved involved in in mitochondrial mitochondrial CaCa2+ transporttransport in in normal normal and and colon colon cancer cancer cells. Expression levels of Mitochondrial Calcium Uniporter (MCU) (A), Mitochondrial Calcium cells.cells. Expression Expression levels levels of of Mitochondrial Mitochondrial CalciumCalcium UniporterUniporter (MCU) ( (AA),), Mitochondrial Mitochondrial Calcium Calcium Uptake (MICU) isoforms (B), Mitochondrial Calcium Uptake Regulator 1 (MICUR1) (C), UptakeUptake (MICU) (MICU) isoforms isoforms (B), Mitochondrial (B), Mitochondrial Calcium Calcium Uptake RegulatorUptake Regulator 1 (MICUR1) 1 (C(MICUR1)), Mitochondrial (C), Mitochondrial Calcium Uniporter Dominant Negative Beta Subunit (MCUb) (D), Essential MCU CalciumMitochondrial Uniporter Calcium Dominant Uniporter Negative Dominant Beta Subunit Negative (MCUb) Beta Subunit (D), Essential (MCUb) MCU (D), regulator Essential (EMRE)MCU regulator (EMRE) (E) and voltage-dependent anion channels (VDACs) (F) in normal colonic cells (E)regulator and voltage-dependent (EMRE) (E) and anion voltage-dependent channels (VDACs) anion ( Fchannels) in normal (VDACs) colonic (F cells) in normal (grey bars) colonic and cells colon (grey bars) and colon cancer cells (red bars). * p statistically significant by three independent methods. cancer(grey cells bars) (red and bars). colon * cancerp statistically cells (red significant bars). * p statistically by three independent significant by methods. three independent methods.

Figure 14. Expression of selected genes coding for other selected proteins. Expression levels of BCL2, Figure 14. Expression of selected genes coding for other selected proteins. Expression levels of BCL2, Figure(A) calsequestrins 14. Expression CASQ1 of selected and CASQ2 genes (B coding), PICALM for ( otherC) and selected PLN (Phospholamban) proteins. Expression (D) in normal levels of (A) calsequestrins CASQ1 and CASQ2 (B), PICALM (C) and PLN (Phospholamban) (D) in normal BCL2colonic,(A) cells calsequestrins (grey bars)CASQ1 and colonand cancerCASQ2 cells(B), (redPICALM bars).( C* )p and statisticallyPLN (Phospholamban) significant by three (D) in colonic cells (grey bars) and colon cancer cells (red bars). * p statistically significant by three normalindependent colonic methods. cells (grey bars) and colon cancer cells (red bars). * p statistically significant by three independentindependent methods. methods.

Int. J. Mol. Sci. 2017, 18, 922 18 of 23

Int. J. Mol. Sci. 2017, 18, 922 19 of 22 Changes in expression of all significantly expressed transcripts are shown in Figure 15A. To best Changes in expression of all significantly expressed transcripts are shown in Figure 15A. To best appreciate the differences, all genes that are significantly downregulated in tumor cells are removed appreciate the differences, all genes that are significantly downregulated in tumor cells are removed for the genes shown in normal cells (Figure 15B) but show up in the tumor cell (Figure 15C). for the genes shown in normal cells (Figure 15B) but show up in the tumor cell (Figure 15C). In summary, we report here a comprehensive transcriptomic analysis of Ca2+ remodeling in In summary, we report here a comprehensive transcriptomic analysis of Ca2+ remodeling in colorectalcolorectal cancer cancer cells. cells. A A similarsimilar analysisanalysis has been recently recently carried carried out out in in glioblastoma glioblastoma using using data data gatheredgathered from from repository repository datadata setssets ofof aa largelarge seriesseries of glioblastomas [23] [23.]. Data Data shown shown here here were were generatedgenerated using using Ion Ion Torrent Torrent RNA-sequencingRNA-sequencing from mRNA mRNA isolated isolated from from normal normal human human colonic colonic NCM460NCM460 cells cells and and colon colon cancer cancer HT29 HT29 cells. cells. Therefore, Therefore, while while the the study study has has the the limitation limitation that that both both cell linescell may lines not may reflect not reflect entirely entirely normal normal human human colonic colonic and colonand colon cancer cancer cells, cells, respectively, respectively, it does it does profit fromprofit the from fact thatthe fact changes that changes in intracellular in intracellular Ca2+ handling Ca2+ handling between between the two the cell two lines cell have lines been have analyzed been inanalyzed detail [4]. in The detail results [4]. The provide results a strongprovide molecular a strong molecular basis for Cabasis2+ remodelingfor Ca2+ remodeling in CRC. in CRC.

FigureFigure 15. 15.Molecular Molecular players playersdifferentially differentially expressed in in NCM460 NCM460 normal normal colonic colonic and and HT29 HT29 colorectal colorectal cancer cells. Genes significantly upregulated (↑) and downregulated (↓) in colorectal cancer cells vs. cancer cells. Genes significantly upregulated (↑) and downregulated (↓) in colorectal cancer cells vs. normal colonic cells are shown in specific locations in the plasma membrane, the endoplasmic normal colonic cells are shown in specific locations in the plasma membrane, the endoplasmic reticulum reticulum (ER) and mitochondria (A). To best appreciate the differences, molecular players (ER) and mitochondria (A). To best appreciate the differences, molecular players downregulated in downregulated in cancer cells have been removed in the prototypical, normal colonic cell (B), whereas cancer cells have been removed in the prototypical, normal colonic cell (B), whereas only genes only genes upregulated in CRC are shown in the prototypic CRC cell (C). Molecular players in red upregulated in CRC are shown in the prototypic CRC cell (C). Molecular players in red are those are those with the largest fold changes in expression from the normal to the tumor phenotype. with the largest fold changes in expression from the normal to the tumor phenotype. PMCAs, PMCAs, plasma membrane calcium ATPases; VOCCS, voltage-operated calcium channels; SOCE, plasma membrane calcium ATPases; VOCCS, voltage-operated calcium channels; SOCE, store-operated store-operated calcium entry; TRPs, Transient Receptor Potential channels. calcium entry; TRPs, Transient Receptor Potential channels.

Int. J. Mol. Sci. 2017, 18, 922 19 of 23

3. Materials and Methods

3.1. Materials HT29 cells were donated by José Carlos Fernández-Checa (CSIC, Barcelona, Spain). NCM460 cells were obtained after a material transfer agreement with INCELL Corporation (San Antonio, TX, USA). Dulbecco’s Modified Eagle’s Medium (DMEM), penicillin, streptomycin, L-glutamine and fetal bovine serum came from Lonza (Basel, Switzerland). M3:10TM medium is from INCELL Corporation, San Antonio, TX, USA).

3.2. Cell Culture Cells are cultured in DMEM 1 g/L glucose or in M3:10TM medium as reported previously [4] and supplemented with 1% penicillin-streptomycin, 1% L-glutamine and 10% fetal bovine serum. Cells are ◦ maintained in standard conditions (37 C, 10% CO2) and cultured once a week. All cells were used at passages 3 to 10.

3.3. mRNA Isolation and Ion Torrent Reading Total cellular RNA was isolated from NCM460 and HT29 cells using Trizol reagent (Invitrogen, Carlsbad, CA, USA). Four totally independent samples from each cell line were used for molecular analysis. Extracted RNA integrity was tested by electrophoresis on agarose gels and the purity and concentration were determined by spectrophotometry. RNA was reverse transcribed using a High Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA) and the cDNA diluted prior to PCR amplification. A targeted, multiplex PCR primer panel was designed using the custom Ion Ampliseq Designer v1.2 (Thermo Fisher Scientific), where target regions were selected for 77 genes directly or indirectly involved in Ca2+ transport and listed in Table1. The panel was designed to amplify PCR products appropriate for use with RNA from eight different cellular cultures, where four of them belonged to one cellular line and the other four to another. Sequencing libraries were prepared with Ion AmpliSeq RNA Library kit (Thermo Fisher Scientific) using the custom primers. After primer digestion, adapters and Ion Xpress Barcode Adapters (Thermo Fisher Scientific) were ligated to the amplicons followed by magnetic bead purification. Amplicon size and DNA concentration were measured using an Agilent High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer’s recommendation. A total of eight picomols of each of the eight samples were pooled for emulsion PCR (ePCR) on Ion Sphere Particles (ISPs) using the Ion PGM Hi-Q OT2 Kit (Thermo Fisher Scientific) using the Ion OneTouch 2 Instrument. Following automated Ion OneTouch, enrichment of template-positive ISP samples were loaded on an Ion 316 V2 Chip and sequenced on a Personal Genome Machine (PGM-Thermo Fisher Scientific) System with Ion PGM Hi-Q Sequencing Kit (Thermo Fisher Scientific). Raw sequencing reads were initially filtered for high quality reads, and the adaptors were removed using the Ion Torrent Suite 4.4.3; reads were then aligned with the hg19_rna reference sequence by TMAP [42] using default parameters. Resulting binary format files (BAM) storing sequence data were processed through an in-house quality control (QC) filter in order to keep those reads whose quality is larger or equal than Q20 and remove polyclonal and primer dimers. After that, outcomes show that 253 Mbp were amplified: the length read median is 108 bp. Reads were aligned against hg19_rna reference genome using GATK LeftAlignIndels module, for 77 genes of interest, where average coverage was 93%, and mean raw accuracy was 99.3%. BAM files were aligned. Amplicon primers were trimmed from aligned reads by Torrent Suite 4.4.3.

3.4. Transformation and Filtration of Raw Data Set Raw data were transformed with an R/Bioconductor package called DESeq2 [33], applying rlog function on data, and normalized against their gene length, obtained from R package EDASeq [43]. The R code used and the raw data employed in this study is provided as a supplementary file data (esetCalcium file) so that anyone can reproduce the results. Subsequently, using R software, Int. J. Mol. Sci. 2017, 18, 922 20 of 23 the transformed data set was filtered in order to remove those genes that are not expressed by samples; thus genes whose median of expression profile was lower than 0.125 RPKMs were removed [29].

3.5. Exploratory Data Analysis A transformed and filtered data set was analyzed through Exploratory Data Analysis. At first, for both samples and genes, a Hierarchical Clustering Analysis using the Euclidean distance was carried out as well as a correlation between different genes, using R software and stats, heatmap [44] and Corrplot [45] R packages. A second method, the Principal Components Analysis, was applied in order to reveal patterns and reduce the dimensionality of the data, using R software and stats, EDASeq [43] and IRanges R packages.

3.6. Differential Expression Analysis In order to carry out a differential expression analysis, three different methods were used to assess significance. These methods are EdgeR [32], DESeq2 [33] and Limma [34], and they were implemented using R software and EnrichmentBrowser R package [35] because this package allows the performing of differential expression analyses either based on Limma, EdgeR or DESeq2, and adjusted p value using the Benjamini-Hochberg method [46]. A critical issue is normalization of data. We have used the default method of normalization in each package as suggested by Seyednasrollah [27] which has a low influence on the analysis outcomes. In addition, we have controlled False Discovery Rate (FDR), proposed by Benjamini-Hochberg [46] where the percent of false positives among whole test where null hypothesis has been rejected is controlled.

Supplementary Materials: Supplementary materials can be found at www.mdpi.com/1422-0067/18/5/922/s1. Acknowledgments: We thank David del Bosque for technical assistance. This work was supported by grants BFU2015-70131-R from Ministry of Economy and Competitivity of Spain to Carlos Villalobos and VA145U13. from Regional Government of Castilla y León, Spain to Lucía Núñez. Enrique Pérez-Riesgo is supported by a fellowship from the Spanish Association Against Cancer (AECC). Daniel Ubierna is supported by predoctoral fellowships from Junta de Castilla y León, Spain and the EU Social Fund. Lucía G. Gutiérrez is supported by a predoctoral fellowship from the University of Valladolid, Spain. Author Contributions: Enrique Pérez-Riesgo carried out all the statistical analyses. Lucía G. Gutiérrez and Daniel Ubierna cultured the cells, isolated quality mRNA for analysis and carried out preliminary analysis. Alberto Acedo carried out the Ion Torrent transcriptomic sequencing. Mary P Moyer developed and provided NCM460 cells. Lucía Núñez and Carlos Villalobos the idea, directed the study and developed the cartoon model. Carlos Villalobos and Enrique Pérez-Riesgo wrote the manuscript. Conflicts of Interest: The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Abbreviations

CRC Colorectal cancer VOCC Voltage-operated Ca2+ channels SOCE Store-operated Ca2+ entry TRP Transient receptor potential channels IP3R Inositol trisphosphate receptor RyR Ryanodine receptor PMCA Plasma membrane Ca2+/ATPase SERCA Sarcoplasmic and endoplasmic reticulum Ca2+/ATPase SPCA Secretory pathway Ca2+/ATPase MCU Mitochondrial calcium uniporter Int. J. Mol. Sci. 2017, 18, 922 21 of 23

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