Arabidopsis pRb homolog RETINOBLASTOMA RELATED 1 (RBR1), similar to its animal counterparts, controls not only cell proliferation but is also implicated in developmental decisions, stress responses and maintenance of genome integrity. Although most functions of pRb-type proteins involve chromatin association, a genome-wide understanding of RBR1 binding sites in Arabidopsis is still missing. Here, we present a plant chromatin immunoprecipitation protocol optimized for genome-wide studies of indirectly DNA-bound proteins like RBR1. Our analysis revealed binding of Arabidopsis RBR1 to approximately 1000 genes and roughly 500 transposable elements, preferentially MITES. The RBR1-decorated genes broadly overlap with previously identified targets of two major transcription factors controlling the cell cycle, i.e. E2F and MYB3R3 and represent a robust inventory of RBR1-targets in dividing cells. Consistently, enriched motifs in the RBR1-marked domains include sequences related to the E2F consensus site and the MSA-core element bound by MYB3R transcription factors. Following up a key role of RBR1 in DNA damage response, we performed a meta-analysis combining the information about the RBR1-binding sites with genome-wide expression studies under DNA stress. As a result, we present the identification and mutant characterization of three novel genes required for growth upon genotoxic stress.Retinoblastoma (pRb) is a multifunctional regulator, which was likely present in the last common ancestor of all eukaryotes. The  Arabidopsis thaliana. We see prominent binding of RBR1 to the 5’ region of genes involved in cell cycle regulation, chromatin organization and DNA repair. Moreover, we also reveal extensive binding of RBR1 to specific classes of DNA transposons. Since RBR1 is involved in a plethora of processes, our dataset provides a valuable resource for researches from different fields. As an example, we used our dataset to successfully identify new genes necessary for growth upon DNA damage exerted by drugs such as cisplatin or the environmentally prevalent metal aluminum.The Retinoblastoma (pRb) tumor suppressor is a master regulator of the cell cycle and its inactivation is associated with many types of cancer. Since pRb’s first description as a transcriptional repressor of genes important for cell cycle progression, many more functions have been elucidated, e.g. in developmental decisions and genome integrity. Homologs of human pRb have been identified in most eukaryotes, including plants, indicating an ancient evolutionary origin of pRb-type proteins. We describe here the first genome-wide DNA-binding study for a plant pRb protein, i.e. RBR1, the only pRb homolog in  Retinoblastoma (pRb) was that of a transcriptional repressor controlling entry into S-phase. It was shown that pRb binds and inhibits the function of E2F-DP transcription factors and that phosphorylation of pRb by CDK-cyclin complexes disrupts this interaction, releasing E2F-controlled genes from repression [The first molecular function assigned to the human tumor suppressor pression .Since then, a wealth of additional functions in cell proliferation, differentiation, environmental response and genome stability have been discovered for the family of pRb related proteins in various organisms –5. To daC. elegans, i.e. lin-35, there are two homologs, Rbf1 and Rbf2, in Drosophila and in humans, pRb is member of a small gene family comprising pRb (p105), p107 and p130 [Arabidopsis thaliana, there is only one homolog, termed RBR1 (RETINOBLASTOMA RELATED 1) and its loss is female gametophytic lethal [The pRb–E2F module originated before the divergence of the plant and animal lineages, likely in the last common ancestor of all eukaryotes . While ting pRb p5, p107 ac lethal .In their function as transcriptional regulators of the cell cycle, pRb-type proteins not only control G1-S transition in proliferating cells but also operate at other phases, i.e. as repressors of G2 and M phase genes in response to DNA damage  as well DP, pRb-like (p130 or p107), E2F and the Multivulval class B (MuvB) core-complex, comprising five additional proteins, LIN9, LIN37, LIN52, LIN54, and RBAP48. When cells exit G0 and reenter the cell cycle, the DREAM-complex is disassembled upon phosphorylation of p130/p107 and the MuvB-core-complex sequentially associates with other transcription factors, like B-Myb and Fox-M1 to activate different sets of genes important for subsequent phases of the cell cycle [Drosophila, a homologous complex, termed dREAM , has been characterized but, in contrast to mammals, the fly pRb-type proteins and the only Myb transcription factor have been shown to concomitantly bind the MuvB-core complex [The human DREAM complex consists of Rb-like p0 or p107ll cycle . In Dros complex ,16.Arabidopsis DREAM-like complex were described by Kobayashi and co-workers and have been implicated in the control of mitotic genes [Arabidopsis contains five three-repeat MYBs (MYB3R), that function as transcriptional activators and/or repressors of mitosis [Recently, different versions of an ic genes . Arabido mitosis –19. Sinc mitosis .WUSCHEL [In addition to its role in controlling progression through the cell cycle, RBR1, like its animal counterparts , is invoWUSCHEL .Arabidopsis RBR1 has also been linked to chromatin modification [Like human pRb, which interacts with different chromatin modifiers, such as histone deacetylases and methyltransferases , the SWIfication . For exafication  as well fication , and a Rfication .Arabidopsis RBR1 is its role in DNA damage response (DDR) [Another facet of se (DDR) ,35, whicse (DDR) ,35. On tse (DDR) ,35, and se (DDR) . In addise (DDR) .Arabidopsis RBR1 is a multi-functional protein. However, despite several studies on specific aspects, a comprehensive view of RBR1 action has been precluded by the lack of genome-wide DNA binding data. Using an optimized ChIP protocol on Arabidopsis cell culture material, we now generated the first comprehensive collection of direct RBR1 targets. These data allowed us to obtain insights into the properties of RBR1 binding in mitotically active cells revealing the localization of RBR1 to genes and transposable elements (TEs). Furthermore, we then used this dataset to identify three new genes to be required for growth under genotoxic stress providing functional evidence for the power of our genome-wide study.Thus, like its animal homologs, Arabidopsis [One likely reason for the lack of genome-wide RBR1-ChIP data is the indirect binding of RBR1 to DNA, i.e. via E2F and other transcription factors/chromatin modifiers. To close this gap, we introduced several changes to our standard plant ChIP protocol . Startinbidopsis , we founArabidopsis cell culture  in the second intron, nor AT1G60020, where a Simplehat2 transposon (AT1TE72980) is inserted into the upstream-region, showed upregulation in hypomorphic mutants of RBR1 VANDAL21.wildtype . So far,Arabidopsis E2F-transcription factor family, i.e. WTTSSCSS, where W stands for A or T and S stands for C or G [A motif analysis of the RBR1 bound domains associated with TEs using the MEME-Chip software  detectedr C or G   including all three overrepresented motifs. A total of 218 clusters were identified in the RBR1-marked TE associated domains 22 of which contained only TE-motif 1, while pure TE-motif 2 or TE-motif 3 clusters occurred merely once. In most clusters however all 3 motifs were present in a variety of different layouts .PCNA1, ORC3, ORC1A, MCM5 and MCM2, the DNA repair genes RAD51 and BRCA1 as well as the cyclin-dependent kinases CDKB1;1 and CDKB1;2 have been shown to be RBR1 targets by gene specific RBR1-ChIP-qPCR experiments [The S-Phase genes eriments –35,50,51cell cycle, DNA repair, DNA replication and chromatin organization , by RBR1-only and by MYB3R3-only . This rely class . Furthere mutant , we saw  [KNOLLE  , we performed a search for overrepresented motifs in our RBR1-ChIP data. A MEME-ChIP analysis with standard settings identified six motif-clusters. The most significantly enriched motif of each cluster is shown in To estimate the genome-wide frequency of these motifs as well as the distribution among the MYB3R3- and/or RBR1-marked protein coding genes, we performed a FIMO-analysis   using 40Arabidopsis telomere repeat, are enriched in promoters of components of the translational machinery and can be bound by AtPurα [Arabidopsis [This analysis showed that sequences matching the repetitive motifs 1 and 3 occur at high frequency within 400 bp upstream of the translational start of genes on a genome-wide level. Gene-motif 1 strongly resembles the so-called GAGA-motif ..rbr1-2 mAT2G45460 grew like the wildtype on bleomycin, cisplatin as well as HU containing media, the loss of any of the other genes resulted in different patterns of hypersensitivity to these three DNA damaging drugs (AT1G04650 was shown to participate in meiotic recombination and designated MEICA1 (Meiotic Chromosome Association1) [Arabidopsis and therefore named FLIP (FIDGETIN-LIKE-1 INTERACTING PROTEIN) [AT5G46740 will be referred to as UBP21 (Ubiquitin-specific protease 21), according to the nomenclature introduced by Yan et al. [AT3G20490 will be called KNOTEN1  since the mutant shows an accumulation of DNA lesions upon genotoxic stress (see below).Whereas plants mutant for ng drugs , S11 Figiation1) . More rePROTEIN) . While Fn et al.  and AT3GKNO1 showed a strong growth inhibition on cisplatin, were only mildly affected by HU and displayed no significant growth reduction on bleomycin at the concentrations used in our assay  is a toxin plants are frequently exposed to on acidic soils [kno1 mutants from day three after treatment onwards. Also the flip mutant lines showed a clear trend towards growth reduction on aluminum. However, the result was statistically significant only for line flip-3 after 4 days. In contrast, the ubp21 mutants did not show any obvious reduction in recovery growth after aluminum treatment at the conditions tested.We further tested all hypersensitive lines for recovery growth after treatment with 1.5 mM aluminum . Bioavaiic soils  and previc soils . Signifikno1, flip and ubp21 plants to genotoxic agents is indeed due to enhanced DNA damage, we monitored γH2AX foci as a marker for double strand breaks after short term incubation in media with and without cisplatin or bleomycin [kno1-1 and flip-2 on cisplatin was slightly more severe than the damage on bleomycin at the conditions tested, the opposite was true for ubp21-1, which showed a stronger accumulation of γH2AX-foci on bleomycin than cisplatin in agreement with its slightly higher sensitivity towards bleomycin in the root growth assay.To analyze if the observed hypersensitivity of leomycin . While wSOG1 targets CYCB1;1 and RAD51, known to be transcriptionally upregulated upon DNA lesions , and on Ku70/Ku80, a key player in non-homologous end-joining (NHEJ), but also that Sleeping Beauty transposase directly interacts with the Ku70/Ku80 hetero-dimer [Arabidopsis, we and others have seen that RBR1 is involved in DNA repair at the site of the lesion [Our ChIP data indicates that RBR1 is recruited to E2F-sites that have been picked up and amplified by TEs in  species . Interes species , one posro-dimer . Conversro-dimer ,103. Alse lesion ,35, parte lesion . It is ke lesion ,105. ThuHiun, which belongs to the VANDAL21 family, we see a different example of interplay between the host machinery and the transposon, since one of the Hiun-localized ORFs is transcriptionally controlled by RBR1 and therefore potentially activated during G1/S phase, the moment when DNA transposons excise and mobilize [Additionally, in case of the non-MITE transposon mobilize .KNO1 (AT3G20490) and FLIP (AT1G04650) were described as genes of unknown function. With the new set of mass annotation provided by Araport11 [KNO1 became annotated as putative Rho GTPase-activating protein and FLIP as Holliday junction resolvase, but both annotations are still lacking experimental support in Arabidopsis. Holliday junction resolvases function in meiotic as well as somatic HR in different organisms [To make further use of the information derived from our genome-wide study, we combined the core-set of RBR1-bound genes with transcriptional data from DNA stress experiments. This led to the identification of three genes with so far unknown function in protection against DNA damage. At the beginning of this study, only two of the four genes analyzed had a functional annotation based on homology, i.e. AT5G46740 as ubiquitin-specific protease (UBP21) and AT2G45460 as SMAD/FHA domain-containing protein, while raport11 , KNO1 berganisms  and FLIPrganisms . A role KNO1 as well as UBP21 are among 146 recently identified direct targets of the major DNA damage related transcription factor SOG1 [Arabidopsis is a ubiquitin carboxyl-terminal hydrolase, has been shown to de-ubiquitinate and stabilize BRCA2 to promote efficient RAD51 loading at DNA double-strand breaks [ubp21 mutants. Further studies are needed to unravel the exact molecular function of Arabidopsis KNO1, FLIP and UBP21 in somatic cells to fully understand their here discovered requirement under DNA damaging conditions.Our results show that KNO1 is needed after DNA damage to efficiently up-regulate and probably also localize components of the HR repair machinery like RAD51. In humans genotoxin-induced DNA damage stimulates nuclear Rac1, a Rho GTPase required for the activation of stress kinases . Howevertor SOG1 , adding d breaks  and it icell cycle, replication, chromatin and DNA repair in actively dividing cells. However, previously described developmental RBR1 targets remain unmarked. To investigate RBR1's proposed role as a potential integrator of cell cycle regulation with developmental processes, genome-wide RBR1 distribution at specific developmental time points and in defined cell types will be beneficial and can be achieved by applying our optimized ChIP protocol in combination with FACS or INTACT methods [Here we have presented the first genome wide RBR1-binding study in plants. We show, that RBR1 associates with TEs, especially MITEs, and with genes highly enriched for GO-terms like  methods ,114.Further, our results demonstrate a vast commonality of genes bound by E2Fa, MYB3R3 and RBR1. In this respect, it will be valuable to gain and integrate information on genome wide binding of the different MYB3R and E2F transcription factors as well as RBR1 in distinct cell types as well as upon short and long term DNA damage. Recently, an involvement in DDR has been shown for repressive MYB3R proteins, but so far only the impact on G2/M genes has been analyzed in detail . A comprFinally, the here presented set of RBR1-controlled genes is a valuable resource that can be exploited to identify new genes involved e.g. in cell cycle control, chromatin remodeling and DNA repair as exemplified by our successful approach to reveal new DNA damage regulators.Landsberg erecta [Triplicates of the Arabidopsis MM2d cell culture, ecotype g erecta , were cog erecta  and antig erecta , respectArabidopsis thaliana genome assembly using Bowtie  and genomic (INPUT) DNA were used to prepare libraries with the MicroPlex Library Preparation kit (Diagenode). Quality of libraries was validated using 2100 Bioanalyzer (Agilent). Multiplexed libraries were sequenced using a HiSeq 2000 system (Illumina) with single-end 50-bp reads. Following a FASTQC (version 0.11.5) quality control, reads were mapped onto the TAIR10 sion 0.3 ) run in sion 0.3 ) was usemakeMetaGeneProfile.pl of the Homer Software [VENN diagram generator designed by Tim Hulsen at http://www.biovenn.nl [phyper function in R [The MetaGene Profile was generated using the tool Software . Venn diovenn.nl . The tesion in R . Data soion in R , Fisher’ion in R  for Motiion in R , includiion in R  and DREMion in R  for motiion in R  for calcion in R  to countion in R  to perfoion in R  was usedt-test: *, p < 0.05.Plants were germinated and grown on vertical plates containing half Murashige and Skoog (1/2 MS) medium under long day light conditions (16h) at 22°C for 6 days. Chemicals used in this study are bleomycin , cisplatin  and hydroxyurea (Sigma-Aldrich). Seedlings were transferred to medium with or without 0.3 μg/ml bleomycin, 20 μM cisplatin, 2 mM hydroxyurea or 2.5 mM hydroxyurea and grown for 6 days further. The position of the primary root tip was marked daily for each plant. After 6 days, plates were photographed and root length was measured using ImageJ software. Data are presented as mean ± SD (n > 30). Significant differences from wildtype were determined by Student’s 3, 0.2 mM KH2PO4, 2 mM MgSO4, 0.25 mM (NH4) 2SO4, 1 mM Ca(NO3)2, 1 mM CaSO4, 1 mM K2SO4, 1 μM MnSO4, 5 μM H3BO3, 0.05 μM CuSO4, 0.2 μM ZnSO4, 0.02 μM NaMoO4, 0.1 μM CaCl2, 0.001 μM CoCl2) prepared as previous described [t-test: *, p < 0.05.For the aluminum recovery growth assay, plants were germinated and grown on vertical plates containing ½ MS medium for 6 days. Seedling were then transferred to 1.5 mM Al-containing hydroponics (water solution (pH 4.2) consisting of 1 mM KNOescribed ,129 and 10-day-old seedlings were transferred to ½ MS liquid medium containing 3μg/ml bleomycin or 50μM cisplatin. Incubation time was 3 hrs. Root tip spreads and immunostaining was subsequently performed as described earlier in Friesner et al . γH2AX iArabidopsis seedlings or inflorescence material using the RNeasy Plant Mini Kit from Qiagene according to the instructions of the manufacturer. cDNA synthesis was performed using a Transcriptor First-Strand cDNA Synthesis kit for RT-PCR according to the manufacturer’s instructions (Roche). The cDNA produced was used in semi-quantitative PCR experiments to test for presence of mRNA. Quantitative PCR was performed with a Roche LightCycler 480 SYBR Green I Master with 0.5 μM specific primers and 0.1 μg of first-strand cDNAs. PCR reactions were conducted with the LightCycler 480 Real-Time PCR System (Roche) under the following conditions: 95°C for 5 min; 45 cycles of 95°C for 10 sec, 60°C for 10 sec and 72°C for 15 sec. Cq calling was done using the second derivative maximum method. Target-specific efficiencies were calculated as the mean of all reaction-specific efficiencies for a given target. Reaction-specific efficiencies were deduced using LinRegPCR 2015.2 [RNA was extracted from 10-day-old R 2015.2 ,132. DatR 2015.2 . PrimersSequence data for genes characterized in this article can be found in the EMBL/GenBank data libraries under accession numbers AT3G12280 (RBR1), AT3G20490 (KNO1), AT1G04650 (FLIP), AT5G46740 (UBP21) and AT2G45460.The RBR1-ChIP-seq data generated in this publication have been deposited in NCBI's Gene Expression Omnibus  and are http://publiclines.versailles.inra.fr). They are part of the SALK line collection [http://publiclines.versailles.inra.fr), respectively.The mutant lines used in this study were provided by the Nottingham Arabidopsis Stock Centre (NASC ) and thellection  or the Fkno1-1, SALK_023527 is kno1-2)AT3G20490 –note, that we renamed our mutant lines to be congruent with the numbering used by Fernandes et al. [AT1G04650 AT5G46740 .AT2G45460  Flow cytometry profiles of propidium iodide-stained nuclei from MMd2 culture cells 3 (3D) and 7 days (7D) after inoculation. 3D cultures were used for the RBR1-ChIP experiment since flow cytometry showed a higher number of cells in 4n, i.e. more cells progressing through S-phase, indicative of proliferation. (C) ChIP-qPCR results for the previously confirmed E2Fa and RBR1 targets MCM5, ORC3 as well as RBR1 (E2F-consensus site) and negative controls, i.e. CDKA;1 as well as 5’- and 3’ locations of the RBR1_E2F-consensus site. ChIP was performed using an anti-RBR1 or an anti-E2Fa antibody, respectively. Bars show the average of enrichment of IP vs. input of three biological replicates and its standard deviation. (D) Genomic representation of the RBR1 locus and the corresponding qPCR fragments analyzed in (C). One bar equals 100nt length, transcripts are shown as boxes and exons as blue arrows, marking the direction of the transcript. The E2F-consensus site is indicated by an orange circle.(A) Workflow of an (PDF)Click here for additional data file.S2 FigMCM5, PCNA1, RBR1, KNO1). (B) Examples for RBR1-ChIP signals over transposable elements (AT3TE91990 (Simpleguy 1), AT2TE23725 (Simplehat 2), AT5TE47555 (Simplehat 1)). (C) No significant RBR1 signal was detected upstream of the mitotic gene KNOLLE (KN). WTTSSCSS sites are indicated.(A) Examples for RBR1-ChIP signals associated with genes ((PDF)Click here for additional data file.S3 Fig(A) Domains identified as origins of replication by Costas et al.  were com(PDF)Click here for additional data file.S4 FigMotif alignments using the TOMTOM software . The P v(A) TE-motif 1 aligned to the E2F consensus WTTSSCSS, P value = 1.11e-02. (B) Gene-motif 1 aligned to the GAGA-element , P value(PDF)Click here for additional data file.S5 FigVenn diagrams showing overlap of RBR1-marked genes with genes involved in (A) replication, (B) cell cycle, (C) DNA repair and (D) chromatin. In each case, the overlap is highly significant.Overlap of RBR1-ChIP withGO:0006260 (Replication): P(X> = 61) = 1.37E-56Core replication (Shultz): P(X> = 45) = 5.16E-57GO:0007049 (Cell cycle): P(X> = 125) = 4.15E-79Core cell cycle (Vandepoele): P(X> = 24) = 8.18E-22GO:0006281 (DNA repair): P(X> = 70) = 6.00E-47DNA repair (Dohmann/Britt/Girard): P(X> = 51) = 1.27E-35GO:0006325 (Chromatin organization): P(X> = 48) = 1.23E-20ChromDB/manual collection: P(X> = 65) = 5.49E-24See (PDF)Click here for additional data file.S6 FigRBR1-bound genes were compared with E2F targets identified by different techniques. In addition, the number of genes having an E2F-binding site within 400 bp upstream of the START codon (upstream of the gene for non-protein coding genes) is indicated in each comparison. Overlap of RBR1-bound genes with (A) E2Fa-bound genes identified by ChIP, ChAP and TChAP  or the tiling GeneChip (C). For comparison, RBR1-ChIP data were therefore reduced to genes present on the Ath1-Chip or GeneChip, respectively.Transcriptional upregulation was monitored using the *P values marked by an asterisk (*) were below the calculation limits of the software (highly significant).(PDF)Click here for additional data file.S7 Fig(A) RBR1-ChIP fragments were mapped with respect to the center of E2Fa-binding sites identified by ChIP, ChAP and TChAP . (B) RBR(PDF)Click here for additional data file.S8 Figmyb3r1/3/5 triple mutants with MYB3R3-bound genes  Overlap of genes upregulated in leaves of 15 day old i et al. , P(X> = (B) GO-term enrichment analysis of genes that show binding by MYB3R3 and RBR1 together (overlap), by RBR1 only and by MYB3R3 only.(PDF)Click here for additional data file.S9 FigBRCA1, RAD51) and candidate genes (AT3G20490 (KNO1), AT1G04650 (FLIP), AT5G46740 (UBP21) and AT2G45460) in wildtype and rbr1-2 mutant seedlings. Significant differences to the wildtype are marked by an asterisk (p<0.05).qRT-PCR results showing relative expression of known DDR genes ((PDF)Click here for additional data file.S10 FigAT2G45460 (B) AT3G20490 (KNO1), (C) AT1G04650 (FLIP) and (D) AT5G46740 (UBP21). The location of the T-DNA insertions and the primers to test for mRNA expression are indicated. We tested for full-length as well as transcript before and after the insertion site. While transcript 5’ of the insertion site was present in all of the cases, in none of the insertion lines, full length mRNA or transcript 3’ of the insertion site could be detected by RT-PCR. Two independent insertion lines were used per gene. Note that the exact position of the T-DNA in kno1-2 was not determined due to masking of the T-DNA borders by vector backbone and inverted T-DNA fragments. However, we provide PCR-results from genomic DNA of kno1-2 showing that amplification the UP and DOWN fragments was possible while only the UP-fragment could be amplified from kno1-2 mutant cDNA. Primers used in this study are listed in Overview of the genomic region of (A) (PDF)Click here for additional data file.S11 Figkno1-1, flip-2, ubp21-1 and at2g45460-1 mutant lines on hydroxyurea-containing media (A) Pictures were taken after 6 days of growth. (B) Root length per day. Statistically significant differences to the wildtype are indicated by asterisks (p < 0.05).Root growth of the wildtype and (PDF)Click here for additional data file.S12 Figkno1 (at3g20490), flip (at1g04650) and ubp21 (at5g46740), represented by 2 mutant alleles each, on MS medium after treatment with 1.5mM aluminum for 12 hours. (A) Pictures after 5 days of recovery growth. (B) Root length per day. Statistically significant differences to the wildtype are indicated by asterisks (p < 0.05).Recovery root growth of the wildtype and (PDF)Click here for additional data file.S13 FigVenn diagrams showing comparison of the RBR1-ChIP data with H3K27me3 and FIE ChIP data from different publications  Overlap of RBR1-ChIP with H3K27me3, 14 d seedlings (Bouyer): P(X< = 79) = 4.58E-14.(B) Overlap of RBR1-ChIP with H3K27me3, 12 d seedlings (Deng): P(X< = 30) = 2.26E-18 and with FIE, 12 d seedlings (Deng): P(X< = 28) = 0.1122245, P(X> = 28) = 0.9194621.(C) Overlap of RBR1-ChIP with H3K27me3 in germinating embryos (Xiao): P(X< = 74) = 2.32E-11 and with FIE in germinating embryos (Xiao): P(X< = 40) = 0.04813777, P(X> = 40) = 0.9656176.(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(HTML)Click here for additional data file.


Treatment of patients with Sjögren’s syndrome (SjS) is a clinical challenge with high unmet needs. Gene expression profiling and integrative network-based approaches to complex disease can offer an insight on molecular characteristics in the context of clinical setting.An integrated dataset was created from salivary gland samples of 30 SjS patients. Pathway-driven enrichment profiles made by gene set enrichment analysis were categorized using hierarchical clustering. Differentially expressed genes (DEGs) were subjected to functional network analysis, where the elements of the core subnetwork were used for key driver analysis.We identified 310 upregulated DEGs, including nine known genetic risk factors and two potential biomarkers. The core subnetwork was enriched with the processes associated with B cell hyperactivity. Pathway-based subgrouping revealed two clusters with distinct molecular signatures for the relevant pathways and cell subsets. Cluster 2, with low-grade inflammation, showed a better response to rituximab therapy than cluster 1, with high-grade inflammation. Fourteen key driver genes appeared to be essential signaling mediators downstream of the B cell receptor (BCR) signaling pathway and to have a positive relationship with histopathology scores.Integrative network-based approaches provide deep insights into the modules and pathways causally related to SjS and allow identification of key targets for disease. Intervention adjusted to the molecular traits of the disease would allow the achievement of better outcomes, and the BCR signaling pathway and its leading players are promising therapeutic targets. Sjögren’s syndrome (SjS) is a systemic autoimmune disease with a specific predisposition for causing inflammation of the exocrine glands, predominantly the salivary and lacrimal glands , 2. This+ T cells differentiate into follicular helper T(TFH) cells, which are involved in the ectopic formation of germinal centers in salivary glands and contribute to the survival and autoantibody production of B cells. Interaction between SGECs and B cells promotes B cell differentiation. Chemokines and cytokines such as interferon (IFN)-γ, interleukin (IL)-6, IL-12, IL-17, and BAFF  have key regulatory roles in these responses. The initiation and progression of SjS is not the sum of fragmentary states but a chain reaction mediated by multiple coordinated molecular pathways and cellular activities. In the light of this complexity, new approaches are needed to boost understanding of the molecular evolution and cellular networks of clinical trajectories of SjS.The pathological hallmarks of SjS are the extensive infiltration of mononuclear cells into salivary glands and the activation of salivary gland epithelial cells (SGECs) , 4, 5. ASystems biology approaches provide powerful means to elucidate the coordinated molecular processes underlying the pathophysiology of complex diseases –9, and wWe used the keywords “Sjögren’s syndrome,” “salivary gland,” “transcriptomics or microarray,” and “dataset” in PubMed, Google Scholar, and public data repositories  to find relevant publications to the topic of salivary gland gene signatures of patients with SjS Fig. . We retrAll datasets were profiled for gene expression using the Affymetrix array and the Robust Multi-array Average method was applied on the image data for a set of replicates for background correction, normalization, and probe-set summarization. Residual technical batch effects arising due to heterogeneous data integration were corrected using the ComBat function , 15. QuaP values were adjusted by the Benjamin Hochberg method, and (4) the adjusted P value cutoff of 0.01 was then applied.In order to identify the differentially expressed genes (DEGs), we used limma R package, a software designed for the analysis of gene expression involving comparisons between many gene targets simultaneously . limma bTo assess the interconnectivity of DEGs in the SjS salivary gland samples, we constructed a protein-protein network based on the human interactome database . In the P value is lower than 0.01. GO terms irrelevant to salivary gland were filtered out. Gene set enrichment analysis (GSEA) analysis was carried out using the GSEA software from the Broad Institute to assess the overrepresentation of SjS-related gene sets [We performed functional enrichment analysis focusing on the list of upregulated DEGs using the Enrichr software . Gene onene sets . The enrene sets . To intuTo test for gene enrichment in individual samples, we used a single sample version of gene set enrichment analysis (ssGSEA), which defines an enrichment score as the degree of absolute enrichment of a gene set in each sample within a given data set . The genIn order to deconvolute the cellular composition of the two clusters in our data, we used an algorithm called xCell , a powert-distributed stochastic neighborhood embedding (t-SNE) [t-SNE method captures the variance in the data by attempting to preserve the distances between data points from high to low dimensions without any prior assumptions about the data distribution.To classify the SjS patients into subgroups based on their molecular signatures, we used the agglomerative hierarchical clustering method, a commonly used unsupervised learning tool . An aggl (t-SNE) , a powerWe constructed a classifier, where a set of predictors consists of 26 pathways, using a naive Bayes machine learning algorithm . For traG) and a directed gene network . The objective is to identify the key regulators for the gene sets with respect to the given network. KDA first generates a subnetwork NG, defined as the set of nodes in N that are no more than h layers away from the nodes in G, and then searches the h-layer neighborhood  for each gene in NG  for the optimal h*, such thatTo predict genes that modulate the regulatory state of the disease module, we employed key driver analysis (KDA), an algorithm that mathematically identifies causal modulators of the regulatory state of functionally relevant gene groups , 28, 29.h,g is the computed enrichment statistic for HLNg,h. A node becomes a candidate driver if its HLN is significantly enriched for the nodes in G. Candidate drivers without any parent node  are designated as global drivers and the rest are local drivers. The statistical significance of a key driver for a given gene set in a particular Bayesian network is determined by Fisher’s exact test which assesses the enrichment of the genes in the candidate key driver’s network neighborhood. Bonferroni-corrected P < 0.01 was used to determine key drivers. Functional classification of the genes was detected by DAVID Bioinformatics resources [where ESesources .t test or Mann-Whitney U test. Categorical or dichotomous variables were compared using the chi-squared test or Fisher’s exact test. Correlation analysis between two variables was carried out using Pearson’s correlation coefficient. All analyses were conducted in R .For continuous distributed data, between-group comparisons were performed using the unpaired FCGR2B, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, IRF8, OAS1, PRDM1, and VCAM1) [CXCL13 and GNA13) [FYN, LYN, LCK, SYN, and YWHAG.An overview of our study design, including sample processing and the integrative network-based approach, is shown in Fig. d VCAM1) –33 and td GNA13) , 35. Thed GNA13) , 37. Thed GNA13) . LCC of P = 6.29 × 10−13), BCR signaling pathway (P = 1.33 × 10−8), regulation of BCR signaling pathway (P = 2.90 × 10−7), Fc-γ receptor signaling pathway (P = 8.67 × 10−8), and the antigen receptor-mediated signaling pathway (P = 1.46 × 10−14).We performed functional enrichment analysis for the DEGs using the Enrichr tool , from whP < 0.05) and segregation of the SjS subgroups was reproduced by t-SNE analysis  cells, Th1 cells, regulatory T(Treg) cells, natural killer(NK) cells, and macrophages, while cluster 2 was enriched with epithelial cells (P < 0.05) .To characterize the cell types responsible for gene expression differences among the salivary gland samples, we applied xCell software, the machine learning framework to estimate cell type enrichment . ClusterAec1Aec2 mouse [FH cells, and NK cells) peaked at week 16 and weakened by week 20. When compared with the patient’ samples by hierarchical clustering, the earlier phase  was similar to cluster 2, while the later phase (week 16 and 20) bore a close parallel to cluster 1 , and the key cellular components  also diminished . In contrast, the non-responders had much higher signals for the pathways or cell subsets at baseline compared with the responders or showed a worsening tendency. Taken together, it is supposed that cluster 1 represents the advanced status of high-grade inflammation, while cluster 2 is the early or regressed status of low-grade inflammation.The microarray dataset E-MEXP-1883 includes 11 paired samples of baseline and 12-week follow-up patients from an open-label trial of rituximab therapy in patients with SjS . PatientBTK, CR2, BLINK, PRKCB, PIK3CD, and PLCG2 were the leading-edge genes shared by both the BCR signaling pathway and B cell activation  that are predicted to modulate the regulatory state of the module, and will be of high interest to prioritize as causal to disease development and progression. We constructed a Bayesian network by projecting the genes from the LCC onto the human interactome and employed key driver analysis (KDA), an algorithm that mathematically identifies causal modulators of the regulatory state of functionally relevant gene groups to predict gene that modulate the regulatory state of the SjS core module , 28, 29.In the present study, we collected salivary gland transcriptomic profiles from patients with SjS and an SjS-like mouse model. We carried out an integrative analysis to understand differential expression patterns by histopathologic index or treatment response in terms of the pathways and cell subsets and to identify key drivers and molecules that may serve as effective targets for therapeutic intervention. The core processes of the DEG network in SjS were the BCR signaling pathway and B cell activation, supported by activated T cells and various kinds of cytokines. Unsupervised cluster analysis of the SjS transcriptomic profiles resulted in two subgroups of SjS patients with distinct activities of the relevant pathways, which had a positive relationship with histopathology scores and showed differing responses to rituximab therapy. To pinpoint key regulators, we projected the SjS core gene set onto the human interactome and identified KDGs. These KDGs appear to be essential linkers or signaling mediators downstream of the SjS core biological processes.Although many immunomodulatory therapies or biologics have been trialed in SjS, the primary efficacy endpoint has not been met and these treatments have not been proven effective . This coSYK, LYN, NCK1, and PLCG2 are the key mediators regulating signal transduction of the BCR signaling pathway [CD19, FYN, and LCK are also linked to this pathway through the PI3K–Akt pathway [BTK, BLINK, and PIK3CD, the differentially expressed leading-edge genes of the BCR signaling pathway, were situated in the middle of the KDG network to effectively perturb the KDGs. These results are reminiscent of B cell malignancies such as chronic lymphocytic leukemia [B cell hyperactivity was the key process in the core subnetwork of SjS and cluster 1 showed much stronger signatures for B cells and the associated pathological pathways compared with cluster 2. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules , 55; mor pathway , 57 and  pathway . In addileukemia , 60 and leukemia . Taken tThere are some limitations to address in this study. First, the number of patient’ samples was not large enough, although we gathered all the available datasets. The accumulation of more data in the future could facilitate more precise subgrouping and analysis. Second, we did not address the association of each SjS subgroup with other clinical factors, such as autoantibodies and disease activity indices, because of a lack of complete annotation for those parameters. Third, minority signatures by specific processes or cell subsets might have been diluted because the gene expression signature was at the tissue level and on a wide spectrum across the patients.SjS is a major medical challenge with a high unmet need. In this study, we comprehensively profiled salivary gland transcriptomic changes in SjS individuals. By adopting an integrative, data-driven approach, we demonstrated the breadth of cellular and mechanistic signatures in SjS, separated the patients into two subtypes with distinct molecular traits and treatment responses, and suggested the promising molecular targets based on these subtypes. This combination of findings is useful for ensuring better targeting of B cell hyperactivity and concomitantly better selection of patients most likely to benefit from investigational drugs, potentially enabling more personalized therapy in the future.Additional file 1. Figure S1. Principal component analysis on the merged gene expression profiles of salivary gland before and after normalization and batch correction. Figure S2. Functional enrichment map for up-regulated DEG. Figure S3. Identification of the optimal number of clusters. Figure S4. Hierarchical clustering of pathway enrichment profiles from patients with SjS and SjS-like mouse models. Figure S5. Correlation between two key pathways enrichment score and KDGs expression values. Figure S6. Enrichment and leading-edge genes of the B cell receptor signaling pathway and B cell activation. Figure S7. Details on the KDGs and the leading edge genes from the B cell receptor signaling pathway and B cell activation.Additional file 2. A full list of differentially expressed genes (up-regulated and down-regulated).Additional file 3. A matrix of enrichment scores by single-sample gene-set enrichment analysis Sheet #1: Data from patients with SjS . Sheet #2: Data from SjS patients treated with rituximab (E-MEXP-1883).


Progress in genome sequencing and bioinformatics opens up new possibilities, including that of correlating genome annotations with functional information such as metabolic pathways. Thanks to the development of functional annotation databases, scientists are able to link genome annotations with functional annotations. We present MetAboliC pAthways DAtabase for Microbial taxonomic groups (MACADAM) here, a user-friendly database that makes it possible to find presence/absence/completeness statistics for metabolic pathways at a given microbial taxonomic position. For each prokaryotic ‘RefSeq complete genome’, MACADAM builds a pathway genome database (PGDB) using Pathway Tools software based on MetaCyc data that includes metabolic pathways as well as associated metabolites, reactions and enzymes. To ensure the highest quality of the genome functional annotation data, MACADAM also contains MicroCyc, a manually curated collection of PGDBs; Functional Annotation of Prokaryotic Taxa (FAPROTAX), a manually curated functional annotation database; and the IJSEM phenotypic database. The MACADAM database contains 13 509 PGDBs , 1260 unique metabolic pathways, completed with 82 functional annotations from FAPROTAX and 16 from the IJSEM phenotypic database. MACADAM contains a total of 7921 metabolites, 592 enzymatic reactions, 2134 EC numbers and 7440 enzymes. MACADAM can be queried at any rank of the NCBI taxonomy (from phyla to species). It provides the possibility to explore functional information completed with metabolites, enzymes, enzymatic reactions and EC numbers. MACADAM returns a tabulated file containing a list of pathways with two scores (pathway score and pathway frequency score) that are present in the queried taxa. The file also contains the names of the organisms in which the pathways are found and the metabolic hierarchy associated with the pathways. Finally, MACADAM can be downloaded as a single file and queried with SQLite or python command lines or explored through a web interface. Bergey’s Manual of Determinative Bacteriology , respectively]. KEGG, despite having >5299 prokaryotic organisms, has moved to a subscription mode and cannot be accessed offline. PATRIC depends on the KEGG pathway data and cannot be downloaded . The BioArchaea and Bacteria kingdoms. The user can request data about several taxonomies or organisms at the same time and can do it using partial names. Second, MACADAM relies on up-to-date NCBI taxonomy . This taxID is a unique time-stable identifier because of its interoperability with other taxonomic databases . BrieflyIn conclusion, to create MACADAM, we need (i) RefSeq ‘complete genomes’, (ii) Pathway Tools with MetaCyc, (iii) MicroCyc PGDBs, (iv) NCBI taxonomy files, (v) the FAPROTAX file and (vi) the IJSEM phenotypic database file.The MACADAM database is a SQLite database  that optProteobacteria—taxID = 1224, parentTaxID = 2, taxonomicRank = phylum—lineage = 2.1224.As shown in E. coli species. This means that these 667 PGDBs are linked to 667 strains of E. coli; thus, the numberOfPGDBInSpecies for E. coli is equal to 667. The fifth key is the number of pathways in a PGDB (numberOfpathway). The sixth key is the pathwayFrameID key that is the official unique MetaCyc identifier of each pathway. The seventh and eighth keys are PS and the pathway frequency score (PFS), as described in An important table is the pathway table . This tade novo biosynthesis pathway is composed, among other things, of ADPREDUCT-RXN, known as ADP reductase (EC number = 1.17.4.1), which reduces the ADP metabolite thanks to the ribonucleoside-diphosphate reductase enzyme.In the hierarchy table, the pathwayHierarchy corresponds to the pathway functional hierarchy found in MetaCyc. For example, for the nitrogen fixation I (pathwayName) pathway, its hierarchy is Degradation/Utilization/Assimilation—Inorganic Nutrient Metabolism—Nitrogen Compound Metabolism—Nitrogen fixation, and its pathwayFrameID key is N2FIX-PWY. The PWYRXN table binds each pathwayFrameID to all MetaCyc official identifiers of reactions that compose the pathways. Each reaction can be described by its name, its metabolites, its enzymes and its EC numbers. These data are stored in RXNName, RXNMTB, RXNENZ and RXNECNumber tables, respectively. For example, the adenosine deoxyribonucleotide For each organism encoded with its taxID key, the FAPROTAX table and the IJSEMPhenoDB table contain its numeric lineage (taxonomy key) and complementary information such as functional features (pathwayName or pathway keys), environmental habitat or substrate to culture the organism.How: The MACADAM database is built from a pipeline of python scripts. The update process takes around 2 days, depending on the parallelization capacities. In terms of dependencies, MACADAM requires Python 3, the Pandas package and a valid license of Pathway Tools that have to be installed. All other dependencies are included by default in the python 3 setup. MACADAM can be updated at each RefSeq release . MACADAM automatically downloads the new index summary from RefSeq. MACADAM then builds script downloads and processes all the genomes that matched our quality standards and launches Pathway Tools on each one of them. This is the crucial part in terms of computing power. In order to save time, the process is parallelized on the cluster of the GenoToul Bioinformatics Platform (http://bioinfo.genotoul.fr/) that provides access to high-performance computing resources. To do this, we process genomes by batches of 50 genomes and the whole step takes ~1 day. This step needs at least 8 GB of RAM for each batch. The generation of the unique ID and the calculation of the PS take around 6 h each due to file movements and extraction of archives. MACADAM can take up to 600 GB of disk space during the construction of the PGDBs, which is the most critical part. After compression, this file takes up 150 GB of disk space.Who: MACADAM database benefits from the GenoToul Bioinformatics Platform facilities, including a permanent staff and technical support. The database will be available for downloading and querying as long as it demonstrates its utility for the research community. Moreover, the python script pipeline is available on the GitHub repository .How often: We intend to update MACADAM every 6 months so as to benefit from the latest information on genome annotation from RefSeq and prokaryotic classification from NCBI taxonomy.E. coli will not be a match. One specificity of MACADAM is that if the queried organism/taxonomic name has no link to functional information, the user still obtains functional information about the upper rank of the target. To limit this, the ‘Strict taxonomy’ option allows the user to query only on target organism/taxonomic names and not beyond the specified taxonomic ranks. The search can also be refined by specifying (i) the full name (or part of the name) of a metabolic pathway or a functional feature , (ii) a MetaCyc class hierarchy ID , (iii) a specific PS (min score = 1 means that MACADAM returns only complete metabolic pathways), (iv) metabolite names (n = 7921), (v) reaction names (n = 592), (vi) enzyme names (n = 7440) and/or (vi) EC numbers (n = 2134). All search fields can be filled with complete or incomplete strings.MACADAM provides users with metabolic pathways and functional annotations about taxonomic names. Thus, the user has to query MACADAM with one or several species and/or one or several taxonomic names . For more precise queries on taxonomic names, the taxonomic rank can be specified thanks to a drop-down menu . Otherwise, the user has to query on ‘all ranks’. Thus, if the user specifies ‘coli’ and ‘genus’, http://macadam.toulouse.inra.fr.We have created a web interface called MACADAM Explore  to facilStaphylococcus aureus and Kitasatospora aureofaciens, although the ‘Urea Degradation I’ pathway is present in only nine genomes of S. aureus, and the ‘allantoin degradation to ureidoglycolate’ is only present in one strain of K. aureofaciens. The output also provides information about the median value of the PS and PFS. There are also columns corresponding to metabolite names, reaction names, enzyme names and EC numbers entered as filtered input by the user. The last columns contain targeted taxonomy names, the list of strains that have the pathways and the MetaCyc metabolic pathway hierarchy with the corresponding URL.MACADAM returns a Tabular Separated Value (TSV) file as output  that is Reliable data are needed to infer the functional potential of complex prokaryotic communities. Indeed, obsolete functional information can lead to inaccurate insights . As far http://www.bacterio.net/) and the 10 other newly proposed phyla from the NCBI taxonomy . Interestingly, according to Acidobacteria phylum is weakly represented in MACADAM . But these PGDBs include the highest number of metabolic pathways , MACADAM can be included in any functional inference tool able to integrate the abundance tables of complete microbial communities generated, among others, we plan to include MACADAM in the FROGS software . The database is also based on the complete and interoperable NCBI taxonomy. MACADAM covers all known bacterial and archaeal phyla, as of February 2019. A standardized score enables quick comparison and comprehension of the potential presence of a pathway. If there is no functional information on the taxonomy entered, MACADAM automatically checks the upper taxonomic rank in order to provide functional information associated with related organisms to users. MACADAM can be explored via metabolites, reactions, enzymes, EC numbers or specific pathways. A user-friendly web interface makes querying easy. MACADAM will be useful to all biologists who need to determine the functional potential of a prokaryotic species or any other taxonomic rank. Since the source code to build MACADAM is available to everyone (GitHub URL: software  to analy


We aimed to explore potential molecular mechanisms of clear cell renal cell carcinoma (ccRCC) and providecandidate target genes for ccRCC gene therapy.  This is a bioinformatics-based study. Microarray datasets of GSE6344, GSE781 and GSE53000were downloaded from Gene Expression Omnibus database. Using meta-analysis, differentially expressed genes(DEGs) were identified between ccRCC and normal samples, followed by Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway and Gene Ontology (GO) function analyses. Then, protein-protein interaction (PPI) networks andmodules were investigated. Furthermore, miRNAs-target gene regulatory network was constructed.TYROBP), interferon regulatory factor 7 (IRF7) andPPARG co-activator 1 alpha (PPARGC1A) were detected in PPI network. Furthermore, the miRNAs-target generegulation analyses showed that miR-412 and miR-199b respectively targeted IRF7 and PPARGC1A to regulate theimmune response in ccRCC. Total of 511 up-regulated and 444 down-regulated DEGs were determined in the present gene expressionmicroarray data meta-analysis. These DEGs were enriched in functions like immune system process and pathways likeToll-like receptor signaling pathway. PPI network and eight modules were further constructed. A total of 10 outstandingDEGs including TYRO protein tyrosine kinase binding protein (TYROBP, IRF7 and PPARGC1A might play important roles in ccRCC via taking part in the immunesystem process. Clear cell renal cell carcinoma (ccRCC) is a type ofRCC developed in adults . It has SETD2) is a novel tumor suppressor gene in theprocess of ccRCC. A chromatin-remodeling gene ATrich interaction domain 1A (ARID1A) is considered to bea new prognostic marker in ccRCC (KEAP1) gene promoter was vitalfor ccRCC development via KEAP1/ nuclear factorerythroid-2 related factor (NRF2) pathway  are closely relatedto disease progression. Duns et al.  showed tin ccRCC . Actuall pathway . Moreove pathway . Yang et pathway  showed tIn this study, meta-analysis was used to detect potentialDEGs between ccRCC and normal samples based onthree microarray datasets. Moreover, functional andpathway enrichment analyses were carried out for theseDEGs. Then, protein-protein interaction (PPI) networkwas investigated. Furthermore, miRNA-target geneinteraction network was constructed. We hoped to explore the underlying molecular mechanisms of ccRCC andprovide candidate target genes for ccRCC gene therapy.www.celljournal.org), two samplesfrom lymph node metastasis and one sample from venousthrombus metastasis were obviously clustered together,while separated from normal samples. So they could beunified as tumor group for the following analysis. Sampleinformation of the three datasets was shown in TablesS1-S3 . In order to eliminate the expression valueheterogeneity of each gene in different platforms, CELformat of files of three datasets were pooled together andpreprocessed using Affy software, including backgroundadjustment, quantiles normalization, summarizationand log2 fold change (FC) transformation using RobustMulti-array Average (RMA) algorithm in Affy package and numberof citation. Finally, three datasets were selected: GSE6344,GSE781 and GSE53000. The reasons for selection are asfollows: i. Large data quantities, ii. Clear grouping of theexperiment , iii. Common microarrayplatforms (Affymetrix), iv. Consistent sample types(tissue samples). In details, 10 ccRCC and 10 normaltissue samples sequencing on the platform of GPL96 HGU133A] Affymetrix Human Genome U133A Array wereselected for analysis in microarray dataset GSE6344 , 12 ccRC3A AffymeDEGs of ccRCC and normal samples were separatelyscreened based on multiple experimental datasets usingMetaDE package in R software . The hetThe clusterProfiler is an online tool applied forenrichment analysis . Gene OnPPI plays a key role in the completion of cellular functions,while they are usually correlated to each other in the formof a PPI network. The Search Tool for the Retrieval ofInteracting Genes/Proteins (STRING) is a biologicaldatabase of predicted and known PPIs. According to thisdatabase, the PPI network of DEG-encoding proteins ineach group was constructed with the criterion of combinedscore (medium confidence) >0.4, and it was then visualizedby the Cytoscape (version 3.2.0) software . Score of nodes in thecurrent network was analyzed using degree centrality, atopology property index. Higher node score presented moreimportant node in the network, suggesting that is more likelythe hub node in this network. Furthermore, the MCODEtool  in CytosThe potential ccRCC related miRNAs were exploredbased on Enrichr database. The miRNA-target generegulatory network was constructed with the miRNAassociating with up- and down-regulated gene basedon Cytoscape software .With fdr <0.05, tau2=0 and Qpval >0.05, 955 DEGs were identified in ccRCC group compared to that of thenormal controls, including 511 up-regulated and 444down-regulated genes.-18),intracellular  and protein binding. Meanwhile, the down-regulatedgenes were mainly enriched in functions like smallmolecule metabolic process ,cytoplasm  and binding   .-05), while the downregulated genes were enriched in pathways like metabolicpathways (P=1.27E-28). The top five enriched pathwaysare listed in Table 1.Pathway enrichment analysis showed that the upregulated genes were enriched in pathways like Toll-likereceptor signaling pathway , cathepsin S , colony stimulating factor 1 receptor, Fc fragment ofIgE receptor Ig ,protein tyrosine phosphatase, receptor type C , mitogen-activated proteinkinase 1 , CD53molecule , Rasrelated C3 botulinum toxin substrate 2 , cluster of differentiation14 , cytochrome C,and somatic were the top 10 proteins encoded by DEGs.-05), Parkinson’s disease (P=3.17E-24), proteindigestion and absorption (P=3.31E-08) and ribosome(P=5.00E-08) were the most significant pathwaysenriched by DEG-encoded proteins in the respectivelymodules a, b, c and d. Meanwhile, NOTCH signalingpathway (P=3.99E-02), oxidative phosphorylation(P=7.89E-10), oxidative phosphorylation (P=5.74E-08)and adipocytokine signaling pathway (P=2.18E-02)were the most significant pathways enrichedrespectively by modules e, f, g and h. The top twoKEGG pathways in each module are listed in Table 2.The MOCDE software showed eight modules fromPPI network. The detail information was showedin Figure 2. To further investigate crucial pathwaysinvolved in the process of ccRCC, the KEGG pathwayanalysis was performed on DEGs in these modules.Results showed that chemokine signaling pathway(P=4.88EIRF7), TYRO proteintyrosine kinase binding protein (TYROBP) and CD14)],as well as 45 down-regulated genes  in the present network, 94 up-regulated genes [such asinterferon regulatory factor 7 ( network.TYROBP and IRF7, and down-regulated PPARGC1A,were mainly enriched in functions like immune system andsmall molecule metabolic processes. KEGG enrichmentanalysis demonstrated that these DEGs were significantlyenriched in the pathways like Toll-like receptor signalingpathway as well as metabolic pathways. Furthermore,IRF7 was predicted to be targeted by miR-412 andPPARGCA1 could be targeted by miR-199B.ccRCC is one of the most common RCC identified inadults presenting the worst prognosis among the commonepithelial tumors of kidney. In this study, total 955 DEGswere identified in ccRCC from normal samples. GOanalysis showed that these DEGs, including up-regulatedCD38 and CD204 involved in the tumormicroenvironment modulate cancer progression. Previousstudy has presented that orchestration of immunecheckpoints has prognostic value in ccRCC (Immune system protects host body against disease.Aberration of immune system could lead to inflammatorydiseases, autoimmune diseases and cancer .ImportaTYROBP is the encoding gene of a transmembranesignaling polypeptide, which possesses an immunoreceptortyrosine-based activation motif in its cytoplasmic domain(TYROBP is reported to be relatedto Alzheimer’s pathology (TYROBP isneuroprotective in a mouse model with early Alzheimer’spathology. Meanwhile, the bone remodeling and brainfunction also depend on the integrity of TYROBP signal(TYROBP correlation with ccRCC. In the current study,functional enrichment analysis showed that TYROBPwas significantly enriched in the BP of immune system,indicating that TYROBP might also play critical role in thepathogenic inflammatory response of ccRCC. However,further analysis of TYROBP is still required to reveal theexact mechanism involved in pathogenesis of ccRCC.c domain. Previouc domain. TYROBP athology . DeficieP signal. In addiP signal. HoweverIRF7, encoded interferon regulatory factor, is a memberof type I interferon regulatory transcription factor family,which plays a critical role in the innate immunity (IRF7 silencing promotes bone metastasis of breastcancer via immune escape way (IRF7 plays critical role in renal involvedin lupus via regulating demethylation of DNA in naiveCD4+ T cells. It was also reported that IRF7 can serve asa therapeutic target against renal tissue damage caused bybacterial infection (IRF7 was significantlyenriched in the Toll-like receptor signaling pathway andimmune response biological process. Toll-like receptorsignaling pathway has been reported to play crucial rolesin the inflammation, infection and cancer (immunity .IRF7 sicape way . Coit etcape way  havedocnfection . In the d cancer . Moreoved cancer . Taken tPPARGCA1 is the encoding gene of PPARG coactivator 1α, which is a co-regulator of mitochondrialbiogenesis and oxidative phosphorylation. Cho et al. (PPARGCA1regulates diet-associated inflammation in colorectalcancer. Moreover, polymorphisms of PPARGCA1 andlower expression of PPARGCA1 are risk factors inthe pathogenesis of non-alcoholic fatty liver disease,characterized by abnormal inflammatory response duringetiology (PPARGCA1 was identified in ccRCC samples andfunctional enrichment showed that PPARGCA1 wassignificantly enriched in the immune response biologicalprocess. Further analysis showed that RRARGCA1 wasregulated by miR-199b. miR-199b is down-regulated inseveral solid tumors (PPARGCA1 expression.o et al. have docetiology . In the d tumors . Hou et d tumors  have docd tumors . These eTYROBP, IRF7 and PPARGCA1might play important roles in ccRCC via taking partin the immune system. Furthermore, miR-199B andmiR-412 might serve critical roles in the regulation ofimmune response via targeting PPRARGCA1 and IRF7,respectively. Considering these findings, miR-199band miR-412 might be used as the prognostic target forccRCC gene therapy.Abnormal immune response might be an importantmechanism of ccRCC.


This preface introduces the content of the BioMed Central Bioinformatics journal Supplement related to the 15th annual meeting of the Bioinformatics Italian Society, BITS2018. The Conference was held in Torino, Italy, from June 27th to 29th, 2018. BITS, the Italian Society of Bioinformatics , is the The environment created during the BITS2018 meeting provided a unique opportunity for all participants to meet, debate with, and get to know the latest activities of the Italian Bioinformatics community.Since 2004, the BITS society has organized the annual meeting to gather not only the BITS members but all the researchers interested in the bioinformatics field. These events soon became the most important annual meeting for the Italian bioinformatics community, and every year the local organizing committee complements the BITS meeting with workshops and activities centered on the main current hot research topics.Algorithms for Bioinformatics to Gene regulation, Transcriptomics and epigenomics, Protein structure and function, Systems Biology, Bioimaging and more. In addition, a special session was devoted to the Bioinformatic challenge in microbiome research.The BITS2018 meeting was held in Torino, from June 27th to 29th in the Aula Magna of the Cavellerizza Reale of the University of Turin. BITS2018 highlights keynotes talks by excellent scientists in bioinformatics and its applications, oral presentations of the state-of-the-art research in computational biology, and poster session on the latest research progress. The meeting was organized by Francesca Cordero , Raffaele A. Calogero  and Michele Caselle , with the support of the BITS steering committee. About 180 participants attended the meeting and most of them submitted contributions as oral presentation or poster. After the abstracts evaluation, the BITS2018 Program Committee  were accepted and included in this supplement. A brief summary of each contribution is reported in the following.In this paper, the authors introduced TACITuS, a web-based system supporting rapid query access to high-throughput microarray and NGS repositories. The system is equipped with modules capable of managing large files, storing them in a cloud environment and extracting subsets of data in a efficient way. The system also supports the import of data into Galaxy for further analysis.The authors proposed a comprehensive study which updated the number of human-specific genes following a critical bibliographic survey. Human-specific genes were functionally assessed providing unique information. The results presented are consistent with environmental changes, such as immune challenges and alterations in diet, as well as neural sophistication, as significant contributors to recent human evolution.The authors present STRAIN (ST Reduced Assembly IdentificatioN), an R package implementing a hybrid strategy between assembly and mapping of the reads to assign the ST to an isolate starting from its read-sets. STRAIN is designed for single allele typing as well as MLST. Its implementation in R makes allele and ST assignment simple, direct and prompt to be integrated in a wider pipeline of downstream bioinformatics analyses.The authors present MetaCon a novel tool for unsupervised metagenomic contig binning based on probabilistic k-mers statistics and coverage. MetaCon uses a signature based on k-mers statistics that accounts for the different probability of appearance of a k-mer in different species, also contigs of different length are clustered in two separate phases.The authors present YALFF (Yet Another Lossy Fastq Filter), a tool for quality scores compression by smoothing leading to improve compressibility of FASTQ files. Authors used FM-Index to reduce the storage requirements of a dictionary of k-mers and an effective smoothing algorithm to maintain high precision for SNP calling pipelines, while reducing quality scores entropy.The authors present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution to effectively simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity.The authors explored the use of deep learning in evolutionary biology and implemented a program, called ImaGene, applying convolutional neural networks on population genomic data for the detection and quantification of natural selection. To detect and quantify signatures of positive selection, ImaGene implements a convolutional neural network which is trained using simulations.The authors address cancer related signaling pathways to investigate miRNA therapeutics. Their approach is based on drug discovery and miRNA therapeutics and uses a digital circuit simulation for cancer pathways. The most effective combination of drugs and miRNAs are then validated by the literature. Two different case studies on non-small cell lung cancer and melanoma are described.The authors investigate the performance of the Logic Learning Machine (LLM) - an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules - in classifying patients with cancer. Performance was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. The set of simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.Caenorhabditis elegans early embryo asking if, where and when TE are expressed and whether their expression is correlated with genes playing a role in early embryo development.The authors focus their research on Transposable Elements (TE) which are mobile sequences elements that make up large portions of eukaryote genomes. They analyzed TE expression among different cell types of the In this work, the authors addressed another aspect of TEs: their presence in cis on the expression of flanking genes by producing associations between polymorphic TEs and flanking gene expression levels in human lymphoblastoid cell lines. They exploit an expression quantitative trait loci approach integrated with additional bioinformatics data mining analyses.


Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research. Owing to technological advances allowing rapid and low-cost profiling of biological systems, multiple types of omics data are now routinely collected from patient cohorts in studies of human diseases. These data can lead to a new taxonomy of disease . DiseaseTogether with data warehousing and data analytics, the contextualization of data emerging from high-throughput experiments is an important component of a translational informatics pipeline. The contextualization of experimental data is facilitated by mapping the data to background knowledge, which can include information at multiple levels of granularity. An effective representation of the disease needs to relate disease-specific information to background knowledge so as to help researchers identify how dysfunctional proteins, pathways or other molecular processes lead to the cellular or physiological changes contributing to its aetiology.Here, we review efforts to represent the context of disease-implicated genes, and we suggest that they can be divided into three broad themes, namely, pathway-centric, molecular network centric and approaches that represent biological statements as a knowledge graph . We descSTAT6 activation is linked to mucous metaplasia, or Fluticasone up-regulates expression of FKBP51 in asthmatics  or genertension .M, which maps an input x to an output y, y: M: f^(x)(ax+ε) => y where the real mechanism f is approximated by f^. An important part of the goal of the BMP is not only to develop a collection of such models but also to automate the process of their construction. Therefore, a large part of the project’s efforts is devoted to development of sophisticated TM systems that can automatically capture such mechanistic models directly from the literature [Hofmann-Apitius  describeterature . The conterature  implies There is much information that may help to suggest disease mechanisms to be included in an integrated representation of disease. For example, information from disease-associated single-nucleotide polymorphisms (SNPs), information from protein–protein interactions and sequence similarity, information on the effects profiled in mutational studies in model organisms and the relationships of molecular variations with physiological and cellular changes. The suitable representation of this information in a manner that can be efficiently stored, envisioned and queried is important for obtaining a mechanistic understanding of disease.What is the most appropriate way to represent disease-related information in a computable format that can be useful for hypothesis generation and for obtaining mechanistic insight? To some extent, this depends on what questions are being asked of the data. Here, we describe three approaches to representation of contextual information, namely, pathway-centric approaches, molecular network centric approaches and information graphs . We contNetwork and graph-based representations are prodigiously effective representations for integration, query, exchange and visualization of numerous possible relationships and interactions between biological entities. Owing to well-studied formal mathematics of graphs, they also often serve as a key data structure for algorithmic, machine learning and statistical approaches for analysis of these data. Construction of biological networks from high-throughput experimental data is an essential first step that enables many types of analysis discussed below. While we recognize importance of this research, in this review, our focus will be primarily on downstream applications of biological and knowledge networks for molecular signature contextualization, but for completeness, we would like to recommend the following works, which cover this closely related topic in detail . For conTo provide a practical illustration of how different types of contextualization approaches can be used, we have included examples based on a severe asthma differential expression gene signature and a manually curated core asthma gene signature acquired from DisGeNET database . The difNetworks or graphs are sets of nodes and edges where a node represents a concept and an edge represents a relationship. Biological concepts can correspond to real, physical entities like molecules, genes and proteins or represent more abstract entities, like typed groups  and diseases. The relationships represent a meaningful association between these concepts, such as ‘protein A interacts with protein B’, ‘enzyme participates in pathway’ or ‘protein is a transcription factor’. In many network studies in systems medicine, the nodes are restricted to particular single type (such as protein), and such graphs are termed homogeneous. In this interpretation, an edge can represent a single relationship type such as a protein–protein interaction, or it can represent multiple types of evidence that associates the two protein nodes. For example, in the STRING protein–protein interaction (PPI) database , evidencNetwork representation can be leveraged to discover high-level patterns that underlie organization of complex biological systems, and more specifically identify patterns relating to development of disease. One prominent early example of this was from the work of Barabasi and co-workers , which rThe network can also be analysed to identify notable topological features, many of which have been associated with biologically meaningful properties. One of such features is the modular organization of the network. Modules are groups of nodes that are more interconnected to other nodes within the region, than to other nodes outside the region . In PPI etal. [GWAS information enabled a ranking of pathways and subsequent identification of pathways, not traditionally associated with asthma. The plausibility of one of these  was shown experimentally. The authors also explored the differential expression of the GAB1 gene in other immune-related diseases. This study illustrates the use of both pathways and networks with other supporting evidence  to contextualize disease genes and provide new avenues for understanding disease mechanisms. It highlights the need for knowledge representations that can capture multiple levels of information such as the conditions in which a gene may be up-regulated.A powerful example of exploiting the network neighbourhood of known disease genes to get mechanistic insight is given in . Using aetal.  identifiet al. [One approach to representation of the complex networks that are associated with disease is to decompose the information into layers, with each layer representing information at different levels of biological organization. Radonjic and co-workers , 22 use et al.  discusseet al. , 24, andThe network-based integration of different types of information can suggest underlying mechanisms of disease. Huan and co-workers  perform HIF [TGF-beta [FYN is involved in inflammation [IL1B shown to be genetically associated with asthma susceptibility [SMAD4) is involved in airway smooth muscle hyperplasia [To illustrate these contextualization strategies, 22 genes from the differential expression signature . Severaammation , IL1B shtibility  and the erplasia .PTEN gene, known to be prominently involved in airway hyperresponsiveness and inflammation, [PI3K/Akt pathway that promotes asthma-related airway remodelling [Given that the number of identified human PPIs is now in the millions, visual exploration is typically limited by the sheer amounts of relevant information that can be usefully displayed. Approaches like network diffusion and graph random walks offer a mathematically robust way to select most relevant nodes even in large graphs. These methods are effective for disease analysis, as disease-related genes are commonly located close to each other in the network. In this example, we have demonstrated the applicability of this key principle and also identified 10 most relevant (closest to the signature) genes by the diffusion state distance metric . This anmmation,  as well odelling .Understanding the mechanism of disease often starts with identification of the aberrant molecular pathways that may be associated with the disease. Molecular pathway models aim to capture sequences of actions or events of molecular ‘actors’. Some examples of commonly modelled events include biochemical reactions, state changes and movements between different cellular compartments. Mapping known disease genes to known pathways is a well-established methodology for contextualization, as pathways capture an intermediate level of organization between molecular entities and phenotype. The identification of hallmark pathways believed to be associated with disease conditions has been used to create pathway centric disease maps , which aIdeally, a computational framework for contextualizing disease-implicated genes should allow annotation by members of the research community, and some selected frameworks offer this important functionality. WikiPathways  containsAt present, there are two leading exchange standards adopted for representing biochemical pathway networks. The first one is Systems Biology Markup Language (SBML) , which wThe SBGN PD language  was desiAnother prominent data exchange standard is BioPAX , which wBoth SMBL and BioPAX are predicated on the developments of the past decade, when several key technologies and design principles were introduced that greatly influenced how biomedical data-sharing standards are implemented. One of these developments was increasing adoption of formal ontologies that allowed development of well-structured controlled vocabularies for different domains and definition of cross-mappings between them. For example, the SBML standard allows annotation of models with System Biology Ontology (SBO) terms, which are also used in SBGN and can be mapped on to BioPax via SBPAX . TherefoIn recent years, the community efforts have been increasingly devoted to increasing inter-compatibility across all main biomedical standards. These efforts include development of converters, controlled vocabulary mappings and better support of different standards both by data providers and analytical tool developers. Greater compatibly also means that smaller models  can now be meaningfully shared and combined. One prominent resource that aims to facilitate this process is NDex . NDex alPathway-based representations enable relevant methods to incorporate the directionality and interaction type (e.g. up- or down-regulation), and in this way, they can capture causal associations underlying different disease mechanisms. To illustrate this application, we have used ‘Tied Diffusion through Interacting Events’ method , which cThere are many specialist sources of disease-related information such as databases of mutations and their consequences. In addition, there is vast information in unstructured formats in the scientific literature and much of it is contextual, for example statements that describe correlations between gene products or between small molecules and gene products that are observed in particular cell types. Information in the literature may also describe weak associations only seen in certain conditions, or for which there may be limited evidence that could nevertheless still be useful for hypothesis generation.The Biological Expression Language (BEL)  represenThe knowledge captured by BEL statements can be represented as a graph. The object can be a BEL term or a BEL statement. Each statement can be associated with metadata that contextualizes it, for example qualifying it to be true only in specific tissues. The BEL knowledge network can facilitate data interpretation using a reverse causal reasoning approach . For exaHofmann-Apitius  posits tConstructing such information-rich representations can involve considerable manual curation efforts. For this reason, TM will be increasingly important for supporting this process . AlreadyAnother example of a specialized curation data modelling format is the Nanopublication standard . Nanopub®). IPA® suite aims to facilitate interpretation of experimental data from high-throughput omics experiments and includes methods for identification of causal networks and visual exploration of biological graphs [Knowledge network representations of biological data are increasingly being leveraged in in integrative analysis platforms, which aim to combine information resources with common analysis and visualization tasks. One example of such platforms is Ingenuity Pathway Analysis  and TM methods, which can handle tasks like identification of relevant documents, sections and sentences, recognition of concepts and relationships between them or even generation of novel hypotheses . IntroduAs well as assisting with curation, both TM and IR can be applied in a high-throughput and automated manner; however, the utility of these applications is still limited by accuracy and generalizability . In its Extraction of information from text is contingent on unambiguous identification of entities and relationship types from which model statements can be constructed. Therefore, TM technologies have a natural synergy with ontology-based data modelling, and to a lesser extent Semantic Web formats, like RDF , 107. ThTo handle more advanced tasks, it is usually necessary to combine specialized tools into TM workflows, e.g. a hypothesis generation tool might rely on output of a relationships identification tool, which in turn relies on named entity recognition. Inevitably, this raises questions of interoperability of different analysis methods. One way to address this is has been through increasing use of Web services to link up different tools . AdditioF-measure of 20% for this task. After the challenge, BELSmile system was developed that reached an even better score of 28% on the same dataset [Recent efforts have been exploring the possibility of extracting more complex statements from text , and in  dataset . BELSmilIt is clear that at present a substantial proportion of valuable biomedical knowledge is ‘trapped’ in scientific text, in a representation not readily suitable for automated computer-driven interpretation. Appropriate data formats and ontologies are an essential prerequisite for enabling sophisticated TM methods to begin addressing this challenge. The most recent developments indicate that current TM approaches are becoming powerful enough to leverage such rich representations and are already useful to curators working on complex models of human diseases. To explore further developments in this important area, we direct reader to the following recent reviews , 123.Many translational and systems medicine projects involve collection of molecular and clinical data with a view to the identification of disease subtypes. The data are typically warehoused and analysed, for example, to extract molecular fingerprints that characterize the subtypes, and this data interpretation step of the translational informatics pipeline remains highly challenging. Moving from diagnostic molecular patterns to being able to suggest individualized healthcare pathways is complex. Data integration links the molecular patterns to background knowledge, which can then be reviewed, explored and analysed to develop a more detailed understanding of the underlying biology that distinguishes disease subtypes.To support this process, several standards have emerged to capture, integrate and facilitate analysis of these data. This proliferation of standards suggests that different formalisms will continue to be necessary to model different aspects of biomedical data and that we are unlikely to converge on one ultimate modelling solution in the foreseeable future. The open question therefore becomes how to effectively manage the interoperability between these different representations? In this respect, several common trends and best practices are beginning to emerge. In particular, it is now evident that development of common controlled vocabularies and specialized domain ontologies is essential for effective management of increasingly complex biomedical data. Use of ontologies facilitates identification of equivalent entities and concepts across different modelling formats and facilitates conversion between them, allowing efficient data integration.As outlined by Cohen in , modern In our view, accurate handling of the context of biological observations is critical for construction of correct mechanistic models of disease. Traditionally, information has been compiled into largely homogenized collections like pathway maps or global interactome networks that aim to show all possible processes of a certain type for a given organism. However, in practice, only a small subset of all these processes will be occurring in a real cell at a given time. Furthermore, such events may be transient (enzymatic reactions), have stable outcome (protein complex formation) or be mutually exclusive . An additional level of complexity is introduced from the imperfection of experimental techniques. For example, transcriptomics profiling is often done at a tissue level, which may mask important differences at a level of individual cells. As such, mechanistic models built from these data may be subject to considerable uncertainty and abstraction. The next generation of formats and integration solutions will therefore need to be more context aware. Future progress on one hand requires better means of expressing this highly granular context of biological processes and on the other necessitates solutions to model vast volumes of data and knowledge in such form. Once efficient and accurate approaches for these tasks are established, cutting-edge mathematical and machine learning methods could be leveraged to their full potential to give new insight into disease mechanisms and suggest improved avenues for therapeutic intervention.Precision medicine relies on identification of disease subtypes at a molecular level and linking them to diagnostic models to determine optimal treatment strategies.In practice, establishing a contextual link between molecular signatures and disease processes requires integration of qualitatively diverse types of relevant data and knowledge.At present, several alternative philosophies guide the process of transformation of experimental data into mechanistically informative and ultimately, clinically actionable insights.The spectrum of possible approaches ranges from purely associative knowledge link discovery to fully quantitative mathematical models.The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking eTRIKS Project (Grant Number: IMI 115446) resources of which are composed of a financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in kind contributions. The work was also partially supported by Core Research for Evolutional Science and Technology (CREST) Grant from the Japan Science and Technology Agency (Grant Number: JPMJCR1412), and Japan Society for the Promotion of Science KAKENHI (Grant Numbers: 17H06307 and 17H06299).bby025_SuppClick here for additional data file.


Gene set analysis (GSA) has become the common methodology for analyzing transcriptomics data. However, self-contained GSA techniques are rarely, if ever, used for proteomics data analysis. Here we present a self-contained proteome level GSA of four consensus molecular subtypes (CMSs) previously established by transcriptome dissection of colon carcinoma specimens. Despite notable difference in structure of proteomics and transcriptomics data, many pathway-wide characteristic features of CMSs found at the mRNA level were reproduced at the protein level. In particular, CMS1 features show heavy involvement of immune system as well as the pathways related to mismatch repair, DNA replication and functioning of proteasome, while CMS4 tumors upregulate complement pathway and proteins participating in epithelial-to-mesenchymal transition (EMT). In addition, protein level GSA yielded a set of novel observations visible at the proteome, but not at the transcriptome level, including possible involvement of major histocompatibility complex II (MHC-II) antigens in the known immunogenicity of CMS1 and a connection between cholesterol trafficking and the regulation of Integrin-linked kinase (ILK) in CMS3. Overall, this study proves utility of self-contained GSA approaches as a critical tool for analyzing proteomics data in general and dissecting protein-level molecular portraits of human tumors in particular. They do not offer immediate insight into the difference between phenotypes or mechanism of disease. These profiles require further analysis and interpretation, and to understand the underlying biological processes behind phenotypic differences, the data typically are integrated with pre-existing biological knowledge, including e.g. biological pathways, protein-protein and drug-protein interaction data, disease-specific databases and other relevant information. Here we consider the very first and most popular integration step of molecular profiles with biological pathways, a priori selected, significantly differentially expressed (DE) genes, is tested for overrepresentation in annotated gene sets such as Gene Ontology (GO) categories or Kyoto Encyclopedia of genes and genomes (KEGG), or Molecular Signature Database (MSigDB), or any other pathway database using standard statistical tests for enrichment [n1 = 72 patients with Parkinson disease and n2 = 72 healthy controls [p-values were used, leading to high risk of false positives.Approaches that incorporate existing biological knowledge, in a form of functionally related gene sets or known biological pathways, into the analysis became a routine for transcriptomics data more than a decade ago  with no requirement for proteins to be selected a priori. These methodologies were developed for transcriptomics data and are collectively called Gene Set Analysis (GSA) approaches. In GSA, a gene set is treated as a unit of expression [self-contained or competitive [Self-contained approaches compare whether a gene set is differentially expressed between two phenotypes, while competitive approaches compare a gene set against its complement that contains all genes except genes in the set [self-contained GSA approaches in analyzing consensus colon cancer subtypes on proteomics data. For the sake of comparison we also present the results of competitive GSA tests, developed for transcriptomics data, for the same proteomics data set.Our aim here is to present, in the context of proteomics data, an alternative technique that integrates proteomics data with biological pathways . Howeve the set . To bridTNM  stage [T describes the size of the original (primary) tumor and whether it has invaded nearby tissue, N describes nearby lymph nodes that are involved, and M describes distant metastasis [Colorectal cancer (CRC) is a heterogeneous disease with distinct molecular properties resulting in different clinical outcomes and 5-year survival below 60% . Large vs) stage , a notattastasis . Despitetastasis ,27. Gradtastasis , chromostastasis  and the tastasis .+ cytotoxic T lymphocytes (CTL), CD4+ T helper (TH1) cells and natural killer (NK) cells (MSI immune) [In the last two decades whole transcriptome analysis became routinely used to dissect cancer molecular subtypes correlating with clinical outcomes. Starting with the seminal paper of Golub et al , definin immune) . CMS2-CM immune) ,41 for mAs proteins link genotype to phenotype, for more detailed characterization of CRC subtypes, respective proteomes were also analyzed . Using aGSAR [The multivariate generalization of Kolmogorov-Smirnov (KS) statistic tests the null hypothesis of mean vectors equality between two phenotypes, while ‘radial’ Kolmogorov-Smirnov (RKS) statistic tests the variance vectors equality between two phenotypes and is sensitive to alternatives having similar mean vectors but differences in scale ,46. KS aup, down or mixed (up or down) [limma Bioconductor package.Rotation gene set tests (ROAST)  uses theor down) . Insteador down) . ROAST tGSAR [The Gene Sets Net Correlations Analysis (GSNCA) method detects the differences in net correlation structure for a gene set between two conditions  and was GSAR .We applied KS, ROAST, RKS and GSNCA tests to find (1) differential expression (DE) (2) differential variability (DV) and (3) differential co-expression (DC) of gene sets between subtypes. The results of ROAST and KS tests were similar, and only ROAST results were included. RKS test did not find any statistically significant pathways and its results were not included.padj<0.01 after correction for multiple testing. Specifically for ROAST test a pathway was also required to include 1) more than 60% of up-regulated or more than 60% of down-regulated proteins and 2) at least 50% of the original pathway members. In order to be included in a list of DC pathways, a pathway was required to have an adjusted padj<0.1 after correction for multiple testing.In order to be included in the list of DE or DV pathways, a pathway was required to have a Benjamini-Hochberg adjusted http://software.broadinstitute.org/gsea/index.jsp).The first competitive GSA test for microarray data analysis was Gene Set Enrichment Analysis (GSEA) method ,50. As alimma Bioconductor package.Rotation testing using MEan Ranks (ROMER) tests the same hypothesis as GSEA, the only difference is that, similar to ROAST, instead of permutations it uses rotations to obtain p-values . ROMER thttp://www.synapse.org/#!Synapse:syn2623706/wiki/) [The CRC proteomes, 95 samples and 7210 proteins were downloaded from ; the dat6/wiki/) . Out of When the samples for all four CMSs (N = 86) were analyzed by PCA based on their proteome features, the separation of subtypes was rather poor, with only CMS1 visibly separated from the rest . We therpadj<0.01). For GSEA, nothing was significant at padj<0.01 for all comparisons . For ROMER, nothing was significant at padj<0.01 as well. Minimum padj value was 0.027 for CMS1-CMS2 comparison and minimum padj value was 0.020 for CMS1-CMS234 comparison.Neither ROMER nor GSEA detected significantly differentially expressed pathways at the level that was used for KS and ROAST -deficient MSI tumors are much higher than that in microsatellite stable (MSS) colorectal cancers. Presumably, MMR defect results in large amounts of neoantigens leading to higher immunogenicity of MSI tumors and their infiltration with CD8+ T cells and CD4+ T helper (TH1) cells [MHC class II binds antigenic peptides and ‘presents’ them to antigen-specific CD4 subsets , while t1) cells ,53. In t1) cells . As a co1) cells .+ T cells [The finding of eleven protein components of MHC class II at the intersection of seven pathways, involved in adaptive immune response  suggest  T cells . Recentl T cells . SimilarThree other pathways, up-regulated in CMS1 as compared to CMS2, CMS3 and CMS4 subtypes, were ‘KEGG mismatch repair’, ‘KEGG proteasome’ and ‘KEGG DNA replication’. These findings are unexpected ones, as CMS1 is characterized by MSI with defects in MMR genes  , and oneThe only pathway down-regulated in CMS1 as compared to other subtypes was ‘KEGG steroid biosynthesis’, with expression of almost all proteins being lower in CMS1 as compared to other subtypes .According to analysis performing in a ROAST framework, the descriptions of CMS1 subtype at the transcriptome level ,41 and aPathways, differentially expressed between CMS2 and other subtypes. CMS2 is considered to be canonical CRC subtype. Interestingly, both the synthesis and the degradation pathways for branched-chain amino acid  were up-regulated in CMS2 as compared to other subtypes of CRC . While iAnother interesting pathway up-regulated in CMS2 was ‘KEGG propionate metabolism’. As one of microbiome produced short chain fatty acids (SCFAs), propionate considered to have beneficial effect on colon physiology. It has been shown that microbial production of propionate may be stimulated by lactate which is secreted by CRC cells in course of glycolysis . FindingThe analysis of the CMS2 CRC proteome subtype with ROAST did not point toward up-regulation of WNT and MYC downstream targets, or increased levels of cyclins observed at the transcriptome level ,41. InstPathways, differentially expressed between CMS3 and other subtypes. At the given level of significance, no pathways were differentially expressed between CMS3 and other subtypes of CRC .Pathways, differentially expressed between CMS4 and other subtypes. In ROAST analysis, a total of five pathways, were differentially expressed between CMS4 and other subtypes of CRC  ,41. ThesFor CMS2, proteome-level descriptors were different from mRNA signatures. Proteome portrait of CMS2 was reminiscent of ‘garden-variety’ pan-tumor signature augmented by notable upregulation of the metabolism of branched chain amino acids, propionate and amino acyl t-RNA synthesis. Given the aforementioned incongruence of transcriptome and proteome data some pathways are expected to be identified at proteome level only. These additional pathways may serve as a source of CMS2-specific metabolic biomarkers, capable of tracing the tumor burden.For CMS3, also known as ‘metabolic’ subtype, an analysis with ROAST framework failed to detect any differentiating protein-level pathways. However, when the same dataset was analyzed with GSNCA, two differentially co-expressed CMS3 specific pathways were detected. One of them, PPAR signaling, was critically rearranged to emphasize on a connection between cholesterol trafficking and the regulation of kinase ILK. This observation may pave the way to CMS3-specific therapies aimed at the metabolism of cholesterol, for example, with lipid-lowering drugs.Overall, the use of self-contained GSA approaches allow reconciliation of the insights derived from the molecular portraits of tumor subtypes independently built on transcriptomics and proteomics levels for colon cancer data. Moreover, protein level self-contained GSA approaches are capable of highlighting additional molecular pathways and actionable targets, which are visible at the protein level only. In the future, it would be beneficial to complement proteomics data analysis with self-contained GSA tests, in addition to competitive tests specifically developed for proteomics data.S1 Fig(PDF)Click here for additional data file.