where applicable , please follow the ontology for biomedical investigations : http://obi-ontology.org/page/main_pageimage 1organism / cell line / tissuenih3t3 mouse fibroblasts and transformed nih3t3-k - rassexnasequencer or array typemouse genechip mogene-1_0 -st - v1 arrays ( affymetrix)data formatraw data : cel files , normalized data : soft , miniml , txtexperimental factorsimmortalized nih3t3 mouse fibroblasts vs. transformed nih3t3-k - ras mouse fibroblasts cultured at 1  mm glucose and daily treated with 10  m forskolinexperimental featurestranscriptome profiling of genes modulated by pka in immortalized and transformed mouse fibroblasts .
cells were cultured in medium with 1  mm glucose and then treated with fsk .
the samples for the microarray analysis were collected at 72  h of culture in low glucose after daily treatments with fsk.consentdata are publicly available
the immortalized nih3t3 and the transformed nih3t3-k - ras cell lines were routinely maintained in dmem medium ( life technologies ) with 10% new born calf serum ( life technologies ) , 25  mm glucose and other supplements as described previously , .
all cells were cultured at 37  c in 5% co2 . to perform the array experiments
after 16  h , the cells were washed twice with phosphate - buffered saline and incubated in medium ( time 0 ) supplemented with 1  mm glucose instead of 25  mm ( fig .
hence pka activation , the cells were daily treated with 10  m forskolin ( fsk ) ( sigma - aldrich ) starting from 24  h after medium replacement .
fsk - treated and untreated ( dmso ) cells were then collected for array analyses at 72  h of culture in low glucose medium , which corresponds to 48  h of fsk - treatment ( fig .
the basal and the fsk - induced camp levels as well as the pka activation in both cell lines are described in , .
cells were lysed in trizol ( invitrogen , carlsbad , ca ) and total rna was further purified with the rneasy kit ( qiagen , hilden , germany ) following manufacturer instructions .
rna quality and integrity was determined by an agilent 2100 bioanalyzer ( agilent technologies , santa clara , ca ) on rna nano chips .
single strand biotinylated cdna was generated as follows : 100  ng of total rna were subjected to 2  cycles of cdna synthesis with the ambion wt expression kit ( life technologies ) . the first cycle
- first strand synthesis is performed using an engineered set of random primers that exclude rrna - matching sequences and include the t7 promoter sequences .
after second - strand synthesis , the resulting cdna is in vitro transcribed with the t7 rna polymerase to generate a crna .
this crna is subjected to a second cycle - first strand synthesis in the presence of dutp in a fixed ratio relative to dttp .
single strand cdna is then purified and fragmented with a mixture of uracil dna glycosylase and apurinic / apirimidinic endonuclease 1 ( affymetrix ) in correspondence of incorporated dutps .
dna fragments are then terminally labeled by terminal deoxynucleotidyl transferase ( affymetrix ) with biotin .
the biotinylated dna was hybridized to the mouse genechip mogene-1_0 -st - v1 arrays ( affymetrix ) , containing almost 23000 genes selected from mus musculus genome databases refseq , ensembl and genbank .
chips were washed and scanned on the affymetrix complete genechip instrument system , generating digitized image data ( dat ) files .
data where then normalized using a global normalization approach with the affymetrix gene expression console software , generating the .cel files microarray analysis was performed on biological triplicate samples .
labeling of cdna and hybridization to mouse mogene-1_0 -st - v1 arrays were performed at the irgs , biogem , ariano irpino ( av ) , italy .
the samples included ( see table 1 ) : 1 ) gsm1666885 , immortalized nih3t3 mouse fibroblasts replicate 1 , 2 ) gsm1666886 , immortalized nih3t3 mouse fibroblasts replicate 2 , 3 ) gsm1666887 , immortalized nih3t3 mouse fibroblasts replicate 3 , 4 ) gsm1666888 , nih3t3-k - ras transformed mouse fibroblasts replicate 1 , 5 ) gsm1666889 , nih3t3-k - ras transformed mouse fibroblasts replicate 2 , 6 ) gsm1666890 , nih3t3-k - ras transformed mouse fibroblasts replicate 3 , 7 ) gsm1666891 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 1 , and 8) gsm1666892 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 2 , 9 ) gsm1666893 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 3 , 10 ) gsm1666894 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 1 , 11 ) gsm1666895 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 2 , 12 ) gsm1666896 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 3 .
all samples were normalized through the robust multi - array average ( rma ) method   with background correction and quantile normalization at core genes level as implemented by the r / bioconductor package
differential expression analysis was performed using a linear model approach with least - square fitting and false discovery rate correction as provided by the r / bioconductor package 
two differential expression tests ( contrasts ) were performed : untreated normal cells vs. fsk - treated normal cells - denoted as nf / n - and untreated transformed cells vs. fsk - treated transformed cells - denoted as tf / t .
only the probe of each gene having the minimum average p - value in the two contrasts has been kept .
there were 496 genes ( probes ) with absolute log fold - change at least 1 for contrast nf / n and 208 genes for contrast tf / t . to gain a better understanding of the biological implications of these expression profiles
, we performed a comprehensive gene set analysis of the differential expression results using the implementation provided by the r / bioconductor package 
the analysis used as gene sets the 202 kegg pathways classified as metabolic or signaling as downloaded on jan .
the pathways contain 6885 unique genes of which 6010 are included in the analysis dataset ( approx .
piano  provides the implementation of several gene set analysis tests and of a consensus methodology to summarize the results obtained with the different tests .
this approach should be less sensitive to possible hidden biases of the single tests and allows , for example , the integration of sources of gene expression data with other types of high throughput data like proteomic data obtained by 2-dige .
the following tests have been performed : a)meanb)medianc)sumd)maxmeane)gene set enrichment analysis ( gsea)f)parametric analysis of gene set enrichment ( page)g)reporter featuresh)wilcoxon rank - sum testi)tail strength .
gene set enrichment analysis ( gsea ) parametric analysis of gene set enrichment ( page ) wilcoxon rank - sum test tests ( a)(f ) used t - values computed in the differential expression analysis while tests ( g)(i ) used the corresponding p - values ( and log fold changes , for devising the directionality ) .
tests ( f ) , ( g ) , ( h ) computed gene set statistics significance via the theoretical null distribution , while the remaining ones via gene ( re)sampling . each test may return distinct p - values for each gene set ( i.e. , pathway ) in the following directionality classes : distinct - directional ( up or down ) , indicated as dist(up ) and dist(down ) , which tests if the expression of the gene set as a whole is regulated in a distinct direction ( either up or down ) . by definition , a gene set can not be significant in both classes as regulations in opposite directions will cancel out the effect.mixed-directional ( up or down ) , indicated as mix(up ) and mix(down ) , which tests if a significant subset of its genes are coordinately up- or down - regulated .
a gene set can be significant in both classes.non-directional , indicated as nondir , which tests if the expression of the gene set as a whole is significantly perturbed , discarding any information about directionality .
distinct - directional ( up or down ) , indicated as dist(up ) and dist(down ) , which tests if the expression of the gene set as a whole is regulated in a distinct direction ( either up or down ) . by definition
, a gene set can not be significant in both classes as regulations in opposite directions will cancel out the effect .
mixed - directional ( up or down ) , indicated as mix(up ) and mix(down ) , which tests if a significant subset of its genes are coordinately up- or down - regulated .
non - directional , indicated as nondir , which tests if the expression of the gene set as a whole is significantly perturbed , discarding any information about directionality .
a total of 35 p - value vectors over the 45 possible combinations for each contrast have been obtained since not all tests returned results for all the directionality classes .
the combined analysis of the p - values obtained by each gene set in the various directionality classes provides a clear picture of the ( possible ) regulation of that gene set .
consistency of the results has been empirically verified by computing the pairwise correlation between the p - value vectors .
2 depicts the correlation matrices of the p - value vectors of contrast nf / n on the left and of contrast
directionality classes of the p - value vectors are indicated by the annotation bars on the top and on the left of the two plots , while letters refer to the different gene set analysis tests as per the previous list . according to the plots , p - value vectors of the same directionality class ( obtained with different tests ) have strong correlation ( see , for example , the class dist(dn ) ) , p - value vectors of similar classes have a non - negligible correlation ( see , for example , mix(dn ) and dist(dn ) ) and
p - value vectors of opposite classes are inversely correlated ( see , for example , dist(dn ) and dist(up ) ) .
further confirmation is provided by the dendograms depicted on the sides , showing that tests of the same directionality class cluster together .
the only exception is test ( i )  tail strength  for the distinct down - directional class on both contrasts . despite that
, the p - values of the gene sets on these classes still appear to be in accordance ( albeit less correlated ) with the rest of the results and represent only a small perturbation that the final consensus analysis should easily accommodate .
principal component analysis ( pca ) of the same set of vectors provides a second empirical support to the consistency of the results computed by the different tests .
3 depicts each p - value vector ( represented as the corresponding letter , see the list above ) for contrast
nf / n ( left ) and contrast tf / t ( right ) in the space of its first three principal components ( x- and y - axes and size ) .
ideally , each directionality class ( represented by colors ) should cluster together in the resulting space .
the most evident outliers are those computed by test ( i ) for the distinct down - directional class , that are placed near the vectors of the mixed down - directional class .
piano  computes the overall consensus rank by first ranking each gene set in each directionality class for each method according to the predicted p - value .
these ranks are then aggregated according to the borda rule ( a single - winner election method ) .
4 depicts the aggregated ranks obtained by the gene sets that ranked in the first 5 positions in at least one directionality class .
directionality classes are represented as columns while numbers and colors represent the position of each gene set ( row ) in the aggregated rank of that column for both contrast nf / n ( left block ) and contrast tf / t ( right block ) . in general , a pathway can be considered significantly regulated in a particular directionality class if it ranks in the first positions ( albeit a general cut - off does not exist ) .
as expected , pathways ranking high ( i.e. , small numbers ) in a directionality class ( see , for example ,  ribosome biogenesis in eukaryotes  ) are ranked low ( large numbers ) in the opposite directionality class .
such a representation provides a clear and concise comparison of the regulation of pathways in the two contrasts , highlighting common regulation of pathways in normal and transformed cells , like  ribosome biogenesis in eukaryotes  , down - regulated in both nf / n and tf / t , and specific pathway regulations , different for the two cell lines , like the up - regulation of pathway  steroid biosynthesis  detected in tf / t but not in nf / n .
the immortalized nih3t3 and the transformed nih3t3-k - ras cell lines were routinely maintained in dmem medium ( life technologies ) with 10% new born calf serum ( life technologies ) , 25  mm glucose and other supplements as described previously , .
all cells were cultured at 37  c in 5% co2 . to perform the array experiments
after 16  h , the cells were washed twice with phosphate - buffered saline and incubated in medium ( time 0 ) supplemented with 1  mm glucose instead of 25  mm ( fig .
hence pka activation , the cells were daily treated with 10  m forskolin ( fsk ) ( sigma - aldrich ) starting from 24  h after medium replacement .
fsk - treated and untreated ( dmso ) cells were then collected for array analyses at 72  h of culture in low glucose medium , which corresponds to 48  h of fsk - treatment ( fig .
the basal and the fsk - induced camp levels as well as the pka activation in both cell lines are described in , .
cells were lysed in trizol ( invitrogen , carlsbad , ca ) and total rna was further purified with the rneasy kit ( qiagen , hilden , germany ) following manufacturer instructions .
rna quality and integrity was determined by an agilent 2100 bioanalyzer ( agilent technologies , santa clara , ca ) on rna nano chips .
single strand biotinylated cdna was generated as follows : 100  ng of total rna were subjected to 2  cycles of cdna synthesis with the ambion wt expression kit ( life technologies ) . the first cycle
- first strand synthesis is performed using an engineered set of random primers that exclude rrna - matching sequences and include the t7 promoter sequences .
after second - strand synthesis , the resulting cdna is in vitro transcribed with the t7 rna polymerase to generate a crna .
this crna is subjected to a second cycle - first strand synthesis in the presence of dutp in a fixed ratio relative to dttp .
single strand cdna is then purified and fragmented with a mixture of uracil dna glycosylase and apurinic / apirimidinic endonuclease 1 ( affymetrix ) in correspondence of incorporated dutps .
dna fragments are then terminally labeled by terminal deoxynucleotidyl transferase ( affymetrix ) with biotin .
the biotinylated dna was hybridized to the mouse genechip mogene-1_0 -st - v1 arrays ( affymetrix ) , containing almost 23000 genes selected from mus musculus genome databases refseq , ensembl and genbank .
chips were washed and scanned on the affymetrix complete genechip instrument system , generating digitized image data ( dat ) files .
data where then normalized using a global normalization approach with the affymetrix gene expression console software , generating the .cel files
labeling of cdna and hybridization to mouse mogene-1_0 -st - v1 arrays were performed at the irgs , biogem , ariano irpino ( av ) , italy .
the samples included ( see table 1 ) : 1 ) gsm1666885 , immortalized nih3t3 mouse fibroblasts replicate 1 , 2 ) gsm1666886 , immortalized nih3t3 mouse fibroblasts replicate 2 , 3 ) gsm1666887 , immortalized nih3t3 mouse fibroblasts replicate 3 , 4 ) gsm1666888 , nih3t3-k - ras transformed mouse fibroblasts replicate 1 , 5 ) gsm1666889 , nih3t3-k - ras transformed mouse fibroblasts replicate 2 , 6 ) gsm1666890 , nih3t3-k - ras transformed mouse fibroblasts replicate 3 , 7 ) gsm1666891 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 1 , and 8) gsm1666892 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 2 , 9 ) gsm1666893 , immortalized nih3t3 mouse fibroblasts treated with fsk replicate 3 , 10 ) gsm1666894 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 1 , 11 ) gsm1666895 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 2 , 12 ) gsm1666896 , nih3t3-k - ras transformed mouse fibroblasts treated with fsk replicate 3 .
all samples were normalized through the robust multi - array average ( rma ) method   with background correction and quantile normalization at core genes level as implemented by the r / bioconductor package
differential expression analysis was performed using a linear model approach with least - square fitting and false discovery rate correction as provided by the r / bioconductor package 
two differential expression tests ( contrasts ) were performed : untreated normal cells vs. fsk - treated normal cells - denoted as nf / n - and untreated transformed cells vs. fsk - treated transformed cells - denoted as tf / t . only the probe of each gene having the minimum average p - value in the two contrasts has been kept .
there were 496 genes ( probes ) with absolute log fold - change at least 1 for contrast nf / n and 208 genes for contrast tf / t . to gain a better understanding of the biological implications of these expression profiles
, we performed a comprehensive gene set analysis of the differential expression results using the implementation provided by the r / bioconductor package 
the analysis used as gene sets the 202 kegg pathways classified as metabolic or signaling as downloaded on jan .
the pathways contain 6885 unique genes of which 6010 are included in the analysis dataset ( approx .
package  piano  provides the implementation of several gene set analysis tests and of a consensus methodology to summarize the results obtained with the different tests .
this approach should be less sensitive to possible hidden biases of the single tests and allows , for example , the integration of sources of gene expression data with other types of high throughput data like proteomic data obtained by 2-dige .
the following tests have been performed : a)meanb)medianc)sumd)maxmeane)gene set enrichment analysis ( gsea)f)parametric analysis of gene set enrichment ( page)g)reporter featuresh)wilcoxon rank - sum testi)tail strength .
gene set enrichment analysis ( gsea ) parametric analysis of gene set enrichment ( page ) wilcoxon rank - sum test tests ( a)(f ) used t - values computed in the differential expression analysis while tests ( g)(i ) used the corresponding p - values ( and log fold changes , for devising the directionality ) . tests ( f ) , ( g ) , ( h ) computed gene set statistics significance via the theoretical null distribution , while the remaining ones via gene ( re)sampling . each test may return distinct p - values for each gene set ( i.e. , pathway ) in the following directionality classes : distinct - directional ( up or down ) , indicated as dist(up ) and dist(down ) , which tests if the expression of the gene set as a whole is regulated in a distinct direction ( either up or down ) . by definition , a gene set can not be significant in both classes as regulations in opposite directions will cancel out the effect.mixed-directional ( up or down ) , indicated as mix(up ) and mix(down ) , which tests if a significant subset of its genes are coordinately up- or down - regulated .
a gene set can be significant in both classes.non-directional , indicated as nondir , which tests if the expression of the gene set as a whole is significantly perturbed , discarding any information about directionality .
distinct - directional ( up or down ) , indicated as dist(up ) and dist(down ) , which tests if the expression of the gene set as a whole is regulated in a distinct direction ( either up or down ) . by definition , a gene set can not be significant in both classes as regulations in opposite directions will cancel out the effect .
mixed - directional ( up or down ) , indicated as mix(up ) and mix(down ) , which tests if a significant subset of its genes are coordinately up- or down - regulated .
non - directional , indicated as nondir , which tests if the expression of the gene set as a whole is significantly perturbed , discarding any information about directionality .
a total of 35 p - value vectors over the 45 possible combinations for each contrast have been obtained since not all tests returned results for all the directionality classes .
the combined analysis of the p - values obtained by each gene set in the various directionality classes provides a clear picture of the ( possible ) regulation of that gene set .
consistency of the results has been empirically verified by computing the pairwise correlation between the p - value vectors .
2 depicts the correlation matrices of the p - value vectors of contrast nf / n on the left and of contrast tf / t on the right .
directionality classes of the p - value vectors are indicated by the annotation bars on the top and on the left of the two plots , while letters refer to the different gene set analysis tests as per the previous list . according to the plots , p - value vectors of the same directionality class ( obtained with different tests )
have strong correlation ( see , for example , the class dist(dn ) ) , p - value vectors of similar classes have a non - negligible correlation ( see , for example , mix(dn ) and dist(dn ) ) and p - value vectors of opposite classes are inversely correlated ( see , for example , dist(dn ) and dist(up ) ) .
further confirmation is provided by the dendograms depicted on the sides , showing that tests of the same directionality class cluster together .
the only exception is test ( i )  tail strength  for the distinct down - directional class on both contrasts . despite that
, the p - values of the gene sets on these classes still appear to be in accordance ( albeit less correlated ) with the rest of the results and represent only a small perturbation that the final consensus analysis should easily accommodate .
principal component analysis ( pca ) of the same set of vectors provides a second empirical support to the consistency of the results computed by the different tests .
3 depicts each p - value vector ( represented as the corresponding letter , see the list above ) for contrast nf / n ( left ) and contrast tf / t ( right ) in the space of its first three principal components ( x- and y - axes and size ) .
ideally , each directionality class ( represented by colors ) should cluster together in the resulting space .
the most evident outliers are those computed by test ( i ) for the distinct down - directional class , that are placed near the vectors of the mixed down - directional class .
this confirms the observation made before for fig . 2 and , again , this should not represent an issue for the consensus analysis .
piano  computes the overall consensus rank by first ranking each gene set in each directionality class for each method according to the predicted p - value .
these ranks are then aggregated according to the borda rule ( a single - winner election method ) .
4 depicts the aggregated ranks obtained by the gene sets that ranked in the first 5 positions in at least one directionality class .
directionality classes are represented as columns while numbers and colors represent the position of each gene set ( row ) in the aggregated rank of that column for both contrast nf / n ( left block ) and contrast tf / t ( right block ) . in general , a pathway can be considered significantly regulated in a particular directionality class if it ranks in the first positions ( albeit a general cut - off does not exist ) .
as expected , pathways ranking high ( i.e. , small numbers ) in a directionality class ( see , for example , 
ribosome biogenesis in eukaryotes  ) are ranked low ( large numbers ) in the opposite directionality class .
such a representation provides a clear and concise comparison of the regulation of pathways in the two contrasts , highlighting common regulation of pathways in normal and transformed cells , like  ribosome biogenesis in eukaryotes 
, down - regulated in both nf / n and tf / t , and specific pathway regulations , different for the two cell lines , like the up - regulation of pathway  steroid biosynthesis  detected in tf / t but not in nf / n .
the microarray data and the  piano  computational approach here presented , have allowed to the identification of significant pro - survival processes , otherwise hidden , regulated by pka in glucose deprivation . a part from our approach , these data could be further used to uncover other mechanisms of cancer resistance mediated by pka , whose role in cancer progression has assumed great relevance in recent years , , .
 forskolin ( fsk ) induces activation of protein kinase a ( pka ) . 
 this activation protects specifically some cancer cells from death induced by glucose starvation . 
 cell effects upon fsk treatment prompted us to investigate in detail the physiological role of pka in the activation of pro - survival mechanisms in glucose starvation . in this regard 
 we performed a microarray analysis of normal nih3t3 and transformed nih3t3-k - ras mouse fibroblasts cultured at 1  mm glucose and daily treated or not with 10  m fsk until 72  h of growth , when the samples were collected . 
 the microarray is deposited into gene expression omnibus under series gse68266 . 
 the microarray data revealed that the activation of pka regulates the expression of genes involved in metabolic , stress - response and pro - survival processes , like glutamine metabolism , autophagy and unfolded protein response , preventing cancer cell death in glucose starvation . 
 altogether these findings suggest that pka activation , by inducing a complex transcriptional program , leads to cancer survival in nutrient stress , a typical feature of developing tumor 
 . these transcriptional data , identifying this important role of pka , will be useful to identify novel target in cancer therapy . 