Bacteroides, Ruminococcus, and Streptococcus. All phages were classified within the viral order Caudovirales and include two phage morphotypes, representative of the Siphoviridae and Podoviridae families. The phage genomes displayed modular organization and conserved viral genes were identified which enabled further classification and determination of closest phage relatives. Co-examination of bacterial host genomes led to the identification of several genes responsible for modulating phage:host interactions, including CRISPR/Cas elements and restriction-modification phage defense systems. These findings provide new genetic information and insights into how lytic phages may interact with bacteria of the rumen microbiome.The rumen is known to harbor dense populations of bacteriophages (phages) predicted to be capable of infecting a diverse range of rumen bacteria. While bacterial genome sequencing projects are revealing the presence of phages which can integrate their DNA into the genome of their host to form stable, lysogenic associations, little is known of the genetics of phages which utilize lytic replication. These phages infect and replicate within the host, culminating in host lysis, and the release of progeny phage particles. While lytic phages for rumen bacteria have been previously isolated, their genomes have remained largely uncharacterized. Here we report the first complete genome sequences of lytic phage isolates specifically infecting three genera of rumen bacteria:  The rumen contains dense and highly diverse populations of bacteriophages (phages). Rumen phage populations have been examined using culture-independent techniques such as morphological surveys (electron microscopy), genome length profiling (pulsed field gel electrophoresis) and more recently, viral metagenomics    was used as the host strain for cultivation of ϕSb01 and Bacteroides sp. AR20 was used as the host strain for ϕBrb01 and ϕBrb02 and R. albus AR67 was used as the host strain for ϕRa02 and ϕRa04. Rumen bacterial host strains were cultivated using anaerobic techniques . Phage particles were harvested 24 h post-infection by differential centrifugation, filtration through 0.45 and 0.22 μm low protein binding filter units (Millipore)  and phage DNA extracted ; (3) reads were assembled with simulated read pairs using Allpaths–LG (version r46652) . An Illumina standard shotgun library was constructed and sequenced using the Illumina HiSeq 2000 platform . All raw Illumina sequence data was quality filtered with DUK, a sequence filtering program developed at JGI, which removes Illumina sequencing and library preparation artifacts . Following the JGI standard protocol, genome assembly involved the following steps (1) quality filtered reads were assembled using Velvet (version 1.2.07)      of the phages classified within the family Siphoviridae  and head-tail connector proteins for phages classified within the family Podoviridae (ϕRa01 and ϕRa02). Phylogenetic trees for respective phage genes were generated according to host genera  using MEGA7 . Phylogenetic analysis of the ϕRa02 and ϕRa04 head-tail connector genes included 33 head-tail connector protein sequences obtained from the NCBI reference sequence protein database using the search terms head-tail connector and head-tail joining and an additional seven representative head-tail connector protein sequences from Podoviridae phages infecting hosts classified within the Firmicute genera Staphylococcus, Streptococcus, and Clostridium. Genomic plots were generated using Geneious R9 , R. albus AR67 , and S. equinus strains 2B  Sb04  and Sb17 . The genome annotations from IMG  and IMG ID 2593339260  respectively.Bacteroides sp. AR20, ϕBrb01, and ϕBrb02, having higher GC content than the other phages. All phages had non-coding sequences associated with the complete genomes, and encoded for overlapping reading frames . Both ϕBrb01 and ϕBrb02 were isolated on the basis of their lytic ability, as evidenced by the formation of viral plaques within confluent lawns of the AR20 host strain, and no genes for phage integration and maintenance of lysogeny were identified within these phage genomes.Bacteroides strains  was used in the current study . Alignment of this scaffold to the lytic Bacteroides phage genomes showed extensive homology to 13 genes including those involved in DNA replication and phage particle packaging . There were also phage proteins in the ϕBrb01 and ϕBrb02 genomes related to prophage elements within other species within the genus Bacteroides  and Clostridium (C. botulinum).The ϕBrb01 and ϕBrb02 genomes showed some sequence homology to prophage-related gene sequences found within other ns Table , includies Table  as well TerL, a hallmark gene for phages the order Caudovirales , compared to the Podoviruses infecting bacterial genera classified within the Gram negative phylum Proteobacteria . The head-tail connector genes of phages ϕRa02 and ϕRa04 were distinctly different to those found in most other previously sequenced Podoviruses, being most like the head-tail connector protein of the phages such as GA1, infecting B. subtilis and classified with the Podovirus subfamily, Picovirinae, and Phi29 genus  is also characteristic of the Phi29-like viruses  and a single contig  in the genome assembly of Terriglobus sp. TAA 43 (NCBI Reference sequence NZ_JUGR01000007.1). The lack of any genes relating to phage integration within these scaffolds and the very high nucleotide sequence homology observed, suggests that these findings be treated with caution. The differences in G+C content of these phage-related scaffolds compared to the remainder of the bacterial genome assembly also suggests possible sequence contamination. For example, the Terriglobus genome has seven scaffolds, six of which have G+C of 54–58% whereas the phage-related scaffold has G+C of 35%.The ϕRa02 and ϕRa04 genomes were also unlike most prophage sequences associated with bacterial genomes Table , specifiStreptococcus phage ϕSb01 was the rumen phage most highly related to previously reported phages, with 34 of the 35 ORFs for which gene function could be assigned, having protein homology to previously characterized Streptococcus phage and prophage-associated genes  strains , Bacteroidetes phylum; (2) R. albus AR67, the Gram positive, anaerobic bacterial host of ϕRa02 and ϕRa04 and classified within the Phylum Firmicutes, order Clostridiales; and (3) three known bacterial hosts of ϕSb01, S. equinus strains 2B, Sb04 and Sb17 which are all Gram positive, facultative anaerobes, classified within the Phylum Firmicutes, order Lactobacillales. All bacterial sequences were interrogated to identify genetic factors which may contribute to phage resistance including prophage elements, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and restriction modification (RM) systems. The five lytic phages reported in this study however, must be unaffected by, or be able to counteract, these resistance mechanisms as they can all successfully infect and replicate within these bacteria.Five whole genome sequences representing at least one bacterial host strain for the five lytic phages, were examined. Host strains included (1) Bacteroides sp. AR20, the S. equinus strains Sb04 and Sb17 and R. albus AR67 incorporated phage integrase and DNA replication genes and were of variable lengths (predicted sequence length ranging between 4.7 and 32 kb). Genes for major structural proteins or phage particle morphogenesis were usually not associated with these prophage regions and given the relatively low number of ORFs identified, these regions most likely represent incomplete remnants of phage or horizontally-transferred genetic elements. Moreover, sequence alignment of these regions with each of the respective lytic phage genomes indicated that the majority of genes found in these incomplete phage-like regions, were not homologous to the genes found in the lytic phages.Phage-related genes were found to be present in all the bacterial host genomes examined Table  and wereTerL gene of ϕSb01 which was similar (69% pairwise nt identity) to the TerL gene present within the relatively long (42.3 kb) prophage element identified within S. equinus 2B. This prophage element, designated ϕSb2Bpro1 was the only host-related element identified in the study which contained a potentially complete complement of phage genes. Prophage ϕSb2Bpro1 encoded 52 ORFs and included genes with homology to both structural and non-structural phage proteins arranged in a modular sequence, interspaced by regulatory control genes  were found to be present in all the bacterial strains examined  regions present in multiple scaffolds of the AR67 genome assembly. This may be attributed to the relatively incomplete nature of this genome assembly (103 scaffolds), which may have hampered the detection of CRISPR-Cas regions with a full complement of genes. Interestingly, of all the CRISPR regions identified, none showed any spacer sequence homology to the lytic phage genome sequences reported in this study.CRISPR associated proteins  were either associated with these genes or detected elsewhere within the genome assemblies. The Bacteroides sp. AR20 genome contained a CRISPR incorporating three spacer regions however there were no Cas proteins identified within this genome. Two of the AR20 CRISPR spacer regions were homologous (100% homology over the complete 32 nt spacer region) to a plasmid replication protein previously identified in R. albus AR67  subunits, Type 1 methyltransferase (M) and Type I restriction (R) enzymes. These enzymes can act in combination to recognize bi-partite motifs and cleave at large distances from their binding site . S. equinus 2B also encoded a second putative Type IIG restriction enzyme/methyltransferase, Sin18ORF1583, from S. infantarius subsp. infantarius CJ18 (AEZ62887.1). In contrast R. albus AR67 encoded multiple copies of seven different putative Type IIG restriction enzyme/methyltransferases  and Bacteroides sp. AR20 encoded multiple copies of 11 different putative Type IIG restriction enzyme/methyltransferases . The Type IV RM systems, distinguished by their ability to cut modified DNA without relying on an associated methyltransferase component  and Bacteroidetes (Bacteroides). The genome sequences were novel in their genetic composition and while some more conserved phage genes could be identified, for example, DNA polymerase, head-tail connector proteins, and phage DNA packaging genes (terminase large subunit genes), the majority of phage genes encoded by these lytic phages could not be assigned functional roles on the basis of sequence homology. This was most apparent for the phages ϕRa02 and ϕRa04 infecting R. albus AR67, where only three genes could be annotated with confidence. Drawing on these few genes for phylogenetic analysis and the prediction of proteins with transmembrane potential, enabled these tailed phages to be further classified within the Podovirus subfamily, Picovirinae and the Phi29 genus.This study reports the first full genome sequences obtained for lytic phages that infect bacteria isolated from the rumen. These phages were sourced directly from the rumen and ruminant-housing and waste-water environments and infect Firmicutes , particle packaging and translocation  and host cell lysis (phage endolysin) could be identified. Phylogenetic analysis on the basis of the terminase large subunit gene and comparative analysis on the basis of predicted protein sequences, indicated that the ϕBrb01 and ϕBrb02 genomes were most closely related to prophage elements associated with bacterial isolates of Bacteroides and an intact prophage present in the genome of an ovine rumen Bacteroides isolate, strain AR29. The ϕBrb01 and ϕBrb02 genomes however lacked genes for phage integration and replication control, indicating that they could not establish a stable, lysogenic association with their bacterial host. These genomes however were also quite unique in their genetic composition, containing many genes of unknown function. Anticipated structural phage genes such as those encoding for tail and capsid proteins, homologous to any previously characterized phage structural proteins, were not found and further protein expression studies would be required to fully elucidate the functional roles of many of the ϕBrb01 and ϕBrb02 genes.The phages infecting Streptococcus genus. This greatly facilitated annotation of the phage genome and the functional, modular organization of the genome could be determined. Genes for phage integration and maintenance of lysogeny were absent from the genome indicating that this phage relied on the lytic cycle of phage replication to persist in the rumen environment. This lytic phage could also infect and successfully replicate in multiple hosts, being originally isolated with the ovine rumen isolate S. equinus 2B as host strain and later shown to also infect two additional bovine rumen isolates of S. equinus (strains Sb04 and Sb17) . Genomes of the rumen Bacteroides and Ruminococcus host strains AR20 and AR67 did not contain intact prophage elements and although short CRISPR regions were detected, these strains appeared to lack Cas proteins. The genomes of these bacteria however, did contain an extensive diversity and numbers of genes relating to RM systems, suggesting that these bacteria rely more on these mechanisms for specific and non-specific methylation and/or cleavage of incoming, foreign DNA and consequently protect against phage infection. In contrast the three Streptococcus genomes  contained fewer genes relating to Type II, Type IIG, and Type IV RM systems than the Bacteroides and Ruminococcus genomes. The complex Type I RM systems identified in the strains Sb04 and Sb17 were absent from the genome of strain 2B. In addition, the genome for strain 2B was the only bacterial host found to contain a possibly intact prophage element (designated ϕSb2Bpro1). This prophage element did not appear to convey super-infection immunity to the lytic phage ϕSb01 and the lack of CRISPR elements and relatively low numbers of genes relating to RM systems, may explain the previous observation that this Streptococcus strain may be more receptive to lytic and lysogenic phage infection than other rumen-derived Streptococcus isolates (Klieve et al., While the phages ϕBrb01 and ϕBrb02 were previously shown to infect three and four rumen-derived With improved methods for DNA sequencing and the advent of metagenomic studies to comprehensively sequence phage particle fractions obtained from rumen fluid, the full extent of viral diversity within the rumen is gradually being revealed. Exploring metagenomic datasets relies on sequence homology to classify the large volumes of sequence information generated and explorations of the rumen viral metagenome have indicated extensive sequence homology to Caudovirales-like lytic phages and prophage elements originating from rumen bacteria and bacteria from other environments (Berg Miller et al., RG, SL, WK, EA, DO, and AK: Contributed to the conception and design of the work; RG, CM, and DO: Contributed to the preparation of DNA; WK and SL: Contributed to aquisition of data; RG, WK, and EA: Contributed to data analysis. All authors contributed to the interpretation of data, critical revision and final approval of the version to be published. All authors are in agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Tissue-specific driver mutations in non-coding genomic regions remain undefined for most cancer types. Here, we unbiasedly analyze 212 gastric cancer (GC) whole genomes to identify recurrently mutated non-coding regions in GC. Applying comprehensive statistical approaches to accurately model background mutational processes, we observe significant enrichment of non-coding indels (insertions/deletions) in three gastric lineage-specific genes. We further identify 34 mutation hotspots, of which 11 overlap CTCF binding sites (CBSs). These CBS hotspots remain significant even after controlling for a genome-wide elevated mutation rate at CBSs. In 3 out of 4 tested CBS hotspots, mutations are nominally associated with expression change of neighboring genes. CBS hotspot mutations are enriched in tumors showing chromosomal instability, co-occur with neighboring chromosomal aberrations, and are common in gastric (25%) and colorectal (19%) tumors but rare in other cancer types. Mutational disruption of specific CBSs may thus represent a tissue-specific mechanism of tumorigenesis conserved across gastrointestinal cancers. The impact of non-coding somatic mutations in gastric cancer is unknown. Here, using whole genome sequencing data from 212 gastric tumors, the authors identify recurring mutations at specific CTCF binding sites that are common across gastrointestinal cancers and associated with chromosomal instability. Mutations at non-coding regions may drive cancer by dysregulating proto-oncogenes and tumor suppressor genes, as exemplified by recent studies demonstrating recurrent point mutations at the TERT promoter in multiple cancer types3 and TAL1 enhancer insertions in T-cell acute lymphoblastic leukemia4. While previous pan-cancer analyses of tumor genomes have nominated regulatory driver mutations6, these studies have typically not been sufficiently powered to identify tissue-specific non-coding driver mutations, as hundreds of samples are usually needed to reliably identify driver mutations in individual cancer types7. Recently, the whole-genome mutational landscapes of breast8, liver9, and pancreatic10 cancer tumors have been studied to identify cancer-specific non-coding drivers. However, the prevalence and impact of non-coding driver mutations is still unknown for most cancer types.Non-coding DNA constitutes over 98% of the human genome and harbors numerous functional elements essential for regulating gene expression and maintaining chromosomal architecture12. Together with the cohesin complex, CTCF organizes chromatin into large topologically associating domains (TADs), insulating the local chromosomal neighborhoods from adjacent regions. Disruption of CTCF binding can therefore lead to dysregulation of gene expression12. In cancer, CTCF binding is disrupted through various mechanisms, such as DNA copy number alterations spanning domain boundaries13, microdeletions within CBSs13, and hypermethylation of CBSs14. These alterations at CBSs may drive cancer progression by allowing ectopic expression of oncogenes. Notably, recent studies have reported a genome-wide elevated somatic mutation rate across CBSs in several cancer types18. This suggests that mutational and DNA repair processes may act differently at CBSs relative to other genomic regions, thereby resulting in an overall elevated mutational burden at such sites in cancer. However, no study to date has rigorously tested the hypothesis that even amidst this elevated mutational burden, positive selection may still act on specific CBSs to drive cancer in individual tumor types. To accurately identify such genomic sites under positive selection, statistical tests must take into account regional biases in the mutation burden.CTCF is a DNA-binding protein essential for the maintenance of genome architecture by mediating both short and long-range chromosomal contacts21. Studies have also investigated the extent and impact of mutational signatures23 and epigenetic dysregulation in gastric cancer (GC) genomes25. Yet, it is unknown to what extent mutations in specific non-coding elements may drive GC, a leading cause of global cancer mortality. Here, we performed uniform and accurate identification of somatic single nucleotide variants (SNVs) and insertions/deletions (indels) in 212 GC genomes using an ensemble mutation calling approach. We present a comprehensive statistical approach, incorporating both epigenetic and sequence covariates, to identify non-coding regions with significantly higher mutation burdens over background, indicating positive selection and a role in gastric tumorigenesis. Performing an unbiased genome-wide scan of focal mutation hotspots , we detect 34 significant recurring non-coding hotspots—of these, 11 overlapped CBSs. We further characterize these sites by analyzing CBS specific mutation biases, gene expression of neighboring genes, chromosomal instability, and incidence of these mutations in other cancer types. Overall, our analyses nominate these CBS hotspots as candidate drivers of GC. Furthermore, our analysis suggests a general link between CBS mutations and chromosomal instability in gastrointestinal cancers.Comprehensive genetic and molecular profiling have previously identified new molecular subtypes and genetic drivers of gastric adenocarcinoma26. We excluded 20 low-quality samples with less than 400 mutation calls from the discovery cohort , tumors with high levels of microsatellite instability (MSI), tumors that exhibit copy number instability (CIN), and tumor that are genomically stable (GS). We investigated the correlations between somatic mutation rates of the four cancer subtypes and epigenetic profiles of gastric tissue obtained from the Roadmap Epigenomics project29. In general, somatic mutation rates were negatively correlated with regions of open chromatin (DNaseI hypersensitivity) and histone marks of active promoters (H3K4me3) and enhancers (H3K27ac)  Fig. . The dep31. In addition, we found that MSI mutation profiles showed a strong positive association with heterochromatin (H3K9me3) and repressive domains (H3K27me3)  on the correlation matrix between the mutation profiles of individual tumors and the epigenetic covariates also revealed MSI tumors as a distinct cluster  from the discovery cohort to ensure all tumors had similar mutational biases.Tumors belonging to the MSI subtype displayed strikingly different associations between epigenetic features and mutation patterns. We observed little association between mutation rate and open chromatin marks or replication timing in MSI tumors. This is likely because mismatch repair (MMR) deficient MSI tumors have lost MMR-coupled enhanced repair efficiency at early replicating open-chromatin regionse3) Fig. . This ister Fig. . Accordi9. However, as mutation rates may also be influenced by wider sequence contexts33, we thus used an expanded sequence context model that considers the effects of tri-nucleotide (1 bp flanks) and penta-nucleotide (2 bp flanks) contexts on the mutation probability of each base. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to identify the most predictive epigenetic and sequence context features  that impact regional mutation rate variation, such as local sequence context and chromatin profiles. We considered a range of genetic and epigenetic features that could be correlated with GC somatic mutation rates. The features included 33 general and 36 gastric-specific chromatin features, 133 transcription factor binding profiles, and DNA replication timing profiles  project38. Interestingly, a recent study reported that LIPF, a lineage-specific gastric lipase, has broad enrichment of indels in GC39. Hypothesizing that other lineage-specific genes could show similar patterns of indel enrichment, we performed a gene-based recurrence analysis to identify all genes with broad enrichment of indels in their non-coding regions . Interestingly, the top three genes in this analysis were all lineage-specific genes highly expressed in stomach tissue: LIPF, PGC, and MUC6  across the non-coding genome Fig. . The topnge Fig. .Fig. 3GeP-value < 0.01; Fig. P = 0.012 by two-sided Fisher’s exact test), which is the most common GC subtype, accounting for ~50% of all GC cases .To test if the 21 bp window size was adequate to capture most mutation hotspots, we repeated the hotspot analysis using larger 41 bp windows. In general, the rankings of the hotspots remained stable  compared to their 1 Kb flanking regions (3.6 mutations/Mb). In addition, the mutation frequencies at CBSs were very different among tumors of different molecular subtypes. The somatic mutation rate was 7.1 and 4.7-fold higher at CBSs compared to flanking regions in CIN and GS tumors, respectively . These differences could indicate a functional difference between CBS hotspot and non-hotspot mutations.Consistent with findings by Katainen et al. in colorectal cancerons Fig. , suggest18, the CBS-specific background model differentiated between CBSs inside and outside chromatin loop boundaries. CTCF loop domains have not been profiled in gastric tissue but tend to be cell-type invariant42. We therefore used a constitutive set of CTCF domains shared across three cell lines  to define CTCF loop boundaries43. In addition, since the mutation spectrum at CBSs is distinct from the overall genomic mutation spectrum, we performed LASSO logistic regression to identify sequence context features correlated with the somatic mutation rate at CBSs. To identify other mutational processes that might be associated with the occurrence of CBS mutations, we calculated the correlation between the proportion of CBS mutations in each tumor and the percentage contribution of each COSMIC mutation signature to each tumor44. While CBS mutations are known to be positively associated with signature 1715, we found that CBS mutations were also strongly negatively associated with COSMIC mutation signature 1, an age related signature . We validated the results using expression data from the SG cohort (N = 14 samples). Since the chromatin structure is generally cell-type invariant42 and there is no published Hi–C data from gastric tissue, we used the Hi–C data from IMR90 cells published by Dixon et al.41 to examine the 3D chromatin structure around each hotspot , located ~200 kb downstream of the hotspot . To study if these hotspots could also play a role in other cancer types, we examined the recurrence of these 11 hotspots in 826 non-hypermutated tumors of 18 other cancer types5 . The median distance between CBS hotspot mutations and its nearest SCNA breakpoint in the same sample was ~1 Mbp, notably shorter than the ~2 Mbp distance for non-CBS hotspots  breakpoints. Strikingly, the distance between a CBS hotspot and its nearest SCNA breakpoint was significantly shorter in mutated than non-mutated tumors . Our results show that the accumulation of indels occur in multiple lineage specific genes in GC. Yet, indels at these three genes were not associated with change in gene expression. The functional consequences of these indels are therefore still unclear. Strikingly, genome-wide analysis of somatic SNVs revealed 34 significant mutation hotspots  that were disproportionately enriched in CBSs. Katainen et al. previously reported an increased mutation load at CBSs in colorectal cancer15, and a subsequent study by Kaiser et al. confirmed the general hypermutation at CBSs in 11 cancer types18. Both studies generally discounted CBS mutations as passengers, yet they did not rigorously explore the hypothesis that a subset of these mutated CBSs may be undergoing positive selection within individual cancer types. Indeed, a recent study on motif-breaking mutations identified a recurrent CBS mutation that disrupts CTCF binding40, confirming the motif-breaking potential of CBS mutations. Here, we used a large cohort of GC genomes in combination with rigorous statistics, to show that mutation rates at 11 specific CBSs are unexpectedly high and cannot alone be explained by a genome-wide elevated mutation burden at CBS, indicating positive selection at these sites. Out of the four CBS hotspots we examined, three of them were associated with nominally significant expression changes of neighboring genes , and these associations were validated in an independent tumor cohort. Furthermore, it is possible that mutations at these CBS hotspots also have long-range or spatio-temporal regulatory effects on gene expression that are not captured by bulk tumor transcriptome profiling. Overall, our analyses nominate these CBS hotspots as potential drivers in GC, and support the hypothesis that driver mutations may arise as a by-product of the increased mutation load at CBSs followed by positive selection at specific CBSs. This is comparable to a model of genomic rearrangement hotspots in breast cancer, where rearrangements initially arise from defective homologous-recombination-repair and those affecting cancer risk loci are subsequently positively selected, forming rearrangement hotspots58.In this study, we performed a comprehensive and unbiased analysis of non-coding SNVs and indels in 212 GC genomes, the largest studied cohort thus far. In addition to a previously identified indel enrichment at 59. One important open question is whether these processes are coupled, and if so, what is the temporal order of CBS mutations and chromosomal breaks. Interestingly, somatic variant allele frequencies (VAFs) of the CBS hotspot mutations supported that these were generally clonal and likely early events in tumor evolution  exhibited the highest mutation rate at CBSs compared to tumors of the other GC subtypes. Furthermore, CBS mutations were associated with the occurrence of nearby chromosomal breakpoints, suggesting a general link between CBS mutations and genomic instability. A previous study has suggested a model where genome higher-order interactions are directly poised for chromosomal breaksion Fig. . Further60. Importantly, our data suggest that the mutational mechanism underlying the association between CBS mutations and DNA breakpoints is distinct from that of kataegis. While kataegis is characterized by C>T and C>G substitutions, CBS mutations are mostly T>G and T>C substitutions. In addition, kataegis is defined by mutation clusters with inter-mutation distance <1 kb. CBS hotspots are confined focal regions of <30 bps including the CTCF motif and its 5′ flanking sequence.Previous studies found kilobase sized regions of hypermutation, termed “kataegis”, that tend to co-occur with genomic rearrangements in cancerTERT, confirming that the reactivation of TERT is very rare in GC3. For the non-CBS hotspots that overlapped gene regions, focused functional validation experiments could be performed on a case-by-case basis.Only a subset of tumor samples in our cohort had paired gene expression data (49/187 samples). This limited our ability to test for functional consequences of CBS hotspot mutations. Additional focused experiments involving transcriptome, copy number, and chromatin structure data should further clarify the regulatory and functional effects of the CBS mutations. We did not uncover a shared theme for the 23 significant non-CBS hotspots. Among the non-CBS hotspots, seven of them are intronic, one is downstream of a gene and the rest are intergenic. None of the genes associated with the hotspots are known cancer drivers. We did not observe any mutation hotspot near The statistics of cancer driver identification is still limited by our knowledge of the somatic mutation and repair processes. Although our background model corrected for many covariates of the somatic mutation rate, such as epigenetic and sequence context features, false positives and false negatives could still arise from the current model not considering as yet unknown mutational biases.Taken collectively, 25% of GC tumors and 19% of colorectal cancer tumors are mutated in at least one of the 11 CBS hotspots. Overall, our analyses nominate these CBS hotspots as potentially common drivers of gastrointestinal cancers. Furthermore, the data supports a general link between CBS mutations and chromosomal instability. This suggests that non-coding regulatory mutations could potentially drive tumor evolution through interfacing with cancer genome and epigenome plasticity.19.We performed whole-genome sequencing of 40 gastric GC tumors and matched normal samples from patients from Singapore . Informed consent was obtained from all participating patients. Genomic DNA of tumors and matched normal gastric tissues was extracted (QIAGEN). Libraries were constructed with 300–400 bp insert length, and 101 bp or 151 bp paired-end sequencing was performed on Illumina Hiseq instruments. The tumors were classified into four molecular subtypes as described previously by TCGAhttps://gdc.cancer.gov), 32 tumors from ICGC (https://ega-archive.org/datasets/EGAD00001003132), and 100 tumors from Wang et al. (HK)20. The molecular subtypes of tumors from the TCGA cohort were defined by TCGA. For the HK cohort, only EBV and MSI subtype status was available. The molecular subtypes of tumors from the ICGC cohort were unavailable, but we identified one MSI sample from the ICGC cohort using MSIseq61.We obtained WGS data of 40 GC tumors from TCGA . Briefly, sequencing reads were aligned to the human reference genome (hg19) using BWA69 Are found at >1% allele frequency in the 1000 Genomes ProjectAre found in more than 10% of the matched normal samples Are found in more than 1% of the matched normal samples and are within 20 bp to a common indel in the 1000 Genomes Project .False positive somatic calls could arise from sequencing and mapping errors. More false positives tend to be called in the non-coding regions of the genome because these regions are enriched for repeats and low sequence complexity regions. As the downstream mutation recurrence analysis is extremely sensitive to recurrent artefacts in somatic mutation calling, we applied additional post-processing filters to eliminate potential false positive calls. We removed candidate somatic mutation calls that:In addition, we removed indel calls that overlap mono-nucleotide repeats of 8 bp or longer. The final set of somatic SNVs and indels are provided in Supplementary Data 70. The normalized counts of RNA sequencing data of 35 tumors from the TCGA cohort were obtained from the Genomic Data Commons Portal.We performed RNA-sequencing on 19 matched tumor-normal pairs. Total RNA was extracted using the Qiagen RNeasy Mini kit. RNA-seq libraries were constructed according to manufacturer’s instructions using Illumina Stranded Total RNA Sample Prep Kit v2 , Ribo-Zero Gold option , and 1 μg total RNA. We validated the completed libraries with Agilent Bioanalyzer , and applied the libraries to an Illumina flow cell via the Illumina Cluster Station. RNA-seq reads (2 × 101 bp) were aligned to the human genome (hg19) using TopHat2-2.0.12 (default parameter and—library-type fr-firststrand). Transcript abundances at the gene level were estimated by Cufflinks30, especially those derived from the cell type of origin of the cancer32. We compiled 36 gastric specific and 24 general chromatin features that potentially affect mutation rate in GC. These 66 histone modification profiles and chromatin accessibility profiles were obtained from Roadmap Epigenomics29 and in-house data. We obtained P-value signal tracks of 853 DNaseI and histone modification profiles of 111 primary tissues and cell types from the Roadmap Epigenomics project. Among them, 27 epigenetic profiles were derived from gastric related tissues. For the 24 histone marks that were not assayed in gastric-related tissues, we created meta histone modifications profiles by taking the median profile of each mark across all tissues and cell-types assayed. In addition, we included histone modifications profiles of H3K4Me1, H3K4me3, and H3K27Ac of 19 GC tumor/normal samples and 13 GC cell lines 25. We used the median signal of each histone mark over all tumor samples, all normal samples, and all cell lines, respectively.The somatic mutation rate is correlated with epigenetic features such as histone modification and chromatin accessibility71.Replication timing profiles were not available for gastric tissue. We therefore used the mean replication timing profile of 13 cell lines  generated by ENCODE72. We used generic TF binding profiles as there is no comprehensive TF binding assay done in gastric tissue. In total, we considered 194 candidate epigenetic covariates potentially informative of somatic mutation rates in GC .The genome was divided into 1 Mb non-overlapping windows. CDS regions, immunoglobulin loci, and poorly mappable regions were masked from the genomic windows. Windows smaller than 250 kb after masking were removed. We calculated the mean signal of each epigenetic feature in Fig.  and the l is the negative log-likelihood function and λ is the regularization parameter.The LASSO is a regularized regression approach commonly used for automated feature selection. LASSO penalizes the sum of the absolute size of the regression coefficients, forcing some of the regression coefficients to shrink to zero, thereby selecting a simpler and more interpretable model. The LASSO objective function can be written as:λ was chosen by 10-fold cross-validation such that the error of the selected model was within one standard deviation from the minimum error. LASSO regression and cross validation were performed using the “glmnet” package in R.We used LASSO logistic regression to identify the most informative features for modeling the somatic mutation rate in GC. As it is computationally expensive to run a logistic regression on all positions in the non-coding genome with a large number of predictor variables, we used all mutated sites and an equal number of randomly sampled non-mutated sites as the input for feature selection in the LASSO logistic regression model. We regressed the binary mutation status of each site against the mean signal of each feature over an 11 bp region centered at the site. The regularization parameter We bootstrapped 100 samples with 50% of the data at each bootstrap, and performed LASSO regression using the bootstrap samples. Assuming that the most informative features would be robustly selected, we used features selected in more than 95% of the bootstrap samples for the final regression model.P-value signal tracks of the histone modification profiles from the Roadmap Epigenomics were binarized using a cutoff of 10−4. ENCODE TF-binding profiles were binarized according to the presence of a peak in any cell line assayed. We performed logistic regression using the frequency table of the counts of mutated and non-mutated sites for each combination of the covariates. Separate logistic regression models were fit to estimate the background mutation probabilities of SNVs and indels. This is to account for the different mutational processes from which SNVs and indels arise, as well as the different uncertainties associated with SNV and indel calls.The patient specific background mutation probabilities were estimated by fitting a logistic regression model on all genomic sites after masking CDS regions, immunoglobin loci, and poorly mappable regions. Replication timing was discretized into eight equally sized bins, the local mutation rate was discretized into ten equally sized bins, and the chromatin features and TF-binding profiles were binarized. Here rep is the Repli-seq profile, epi represents the epigenomic features, sequence represents the sequence context features, and pid is the patient ID. Features used in each model are shown in Supplementary Fig. pi, of mutation in tumor i is a function of the length of that region and the expected mutation rates of individual nucleotides in that region under the null hypothesis. Assuming qi, j is the mutation probability of nucleotide j in tumor i, and l is the length of the region of interest:For a specific region of interest, the probability, k or more individuals is given by:Mutation recurrence is then modeled using the Poisson binomial distribution, which accounts for variation in mutation rate across tumors. For a specific region of interest, the probability of having mutations in n is the total number of tumors sequenced, k is the number of tumors with mutations in the region of interest, Fm is the set of all subsets of k integers selected from {1,2,…,n}, A is a subset of Fm, Ac is the complement of set A, pi is the probability of mutation in tumor i, and pj is the probability of mutation in tumor j. The Poisson binomial probability is calculated using an efficient and accurate normal approximation in the “poibin” R package.Here P value of mutation recurrence of each hotspot was calculated using the Poisson binomial model described in the previous section. The total number of hypothesis tested is equal to the number of bases in the masked non-coding genome. We used the Bonferroni correction to adjust for multiple testing of 2,533,374,732 hypotheses, to maintain the overall α at 0.01.The hotspot analysis aims to identify small focal regions with high mutation rates. We first considered all mutated 21 bp regions by taking 10 bp flanks on each side of each mutation. Then we calculated the mutation recurrence scores for all 21 bp regions with three or more mutated samples (two or more for indels). The α at 0.01.We scanned for non-coding regions of genes with recurrence of indels. Gene regions were defined by Ensembl v75 annotations. We considered the merged non-coding regions of each gene by masking all coding regions of each gene, and extending the gene boundaries by 1 kb to take into account its promoter region. We calculated the mutation recurrence scores for all protein-coding genes, and their individual merged non-coding regions, using the Poisson binomial model described in the previous section. The Bonferroni correction was used to maintain the overall Ptfbs > 0.75, where Ptfbs is the probability that the TFBS is bound by a TF for any given ENCODE cell line. Ptfbs for all TFBSs were obtained from the ENSEMBL regulatory build. Conserved elements generated by GERP73 from the alignment of hg19 to 36 mammals were downloaded from the UCSC genome browser.We calculated the log odds ratio of the enrichment of hotspot mutations in TF binding regions and conserved DNA elements. Gastric-specific TFBSs were defined as a ChIP-seq peak of a TF in any of the ENCODE cell lines that overlaps a gastric tissue DNaseI hypersensitivity site (data from Roadmap Epigenomics). Constitutive TFBSs are defined as TFBSs with p2) is the fraction of the genome that constitutes the functional region. The observed fraction of hotspot (or non-hotspot) mutations in the functional region is calculated by adding all mutations in the functional region type and dividing by the total number of mutations genome-wide (p1). The log odds ratio of the enrichment of hotspot (or non-hotspot) mutations in a functional region type is given by,Z-test.The expected fraction of hotspot (or non-hotspot) mutations in the functional region type  obtained from the supplementary information of Hnisz et al.13 CBSs that overlap the boundaries of these constitutive CTCF loops were defined as boundary CBSs.The position weight matrix of the CTCF binding motif was obtained from JASPARFor the CBS-specific background model, we limited the model and search space to CBS regions and their 5 bp flanking DNA.77 to quantify the prevalence of each of the 30 COSMIC consensus mutation signatures in each tumor.Here subtype is the tumor subtype, boundary indicates if the CBS is located at a CTCF loop boundary, and mutsig1 and mutsig17 represent the percentage contributions of signature 1 and signature 17 of the tumor. We used deconstructSigsP value of mutation recurrence of each CBS was calculated using the Poisson binomial model described in the previous section. The Bonferroni correction was applied to maintain the overall α at 0.01.The We extracted the ±40 bp sequence context around each mutation, and used DeepBind to predict the binding scores of 472 TFs for the reference (ref score) and mutated sequences  of each mutation. Since the binding scores output by DeepBind are on an arbitrary scale and vary between different TF models, we estimated the background distributions of the binding scores of each TF by applying DeepBind to 10,000 randomly sampled non-hotspot mutations. For a particular TF, a mutation is predicted to be motif-disrupting if its reference sequence scores higher than 99.9% of the random mutations, and the score difference between its alternate and reference sequences  is smaller than 99.9% of the random mutations for that TF. A mutation is predicted to create a motif for a specific TF if its alternate sequence scores higher than 99.9% of the random mutations, and the score difference between its alternate and reference sequences  is greater than 99.9% of the random mutations for that TF.5 Hypermutated tumors with more than 200,000 mutations were excluded from the analysis. Cancer types with less than ten samples were excluded from the analysis. For CBS mutation rate calculation in Fig. Somatic mutations of 858 tumors from 22 cancer types were downloaded from the supplementary information of Weinhold et al.78 using default settings (bcbio-nextgen v0.9.3). SCNA breakpoints were defined as the ends of non-diploid segments. Assuming tumor purity of 50%, the estimated mean purity of these tumors, non-diploid segments were defined as segments with log2 < log2(1.5/2) or log2 > log2(2.5/2).Copy number segmentations were generated by CNVkit79 and the driver genes identified by TCGA19 and Wang et al.20 We excluded TP53 from the analysis as TP53 frequently undergo deletions and loss of heterozyosity. We identified non-synonymous and truncating mutations on known GC driver genes, and compared their VAFs to the VAFs of CBS hotspot mutations from the same samples using a matched Wilcoxon rank-sum test. Only mutations in diploid regions in each sample were included in the analysis.The list of known GC driver genes was collated from the Cancer Gene Census68, can be found at https://github.com/skandlab/SMuRF. Source code for estimating background mutation rate from genomic covariates and identification of non-coding mutation hotspots is available at: https://github.com/skandlab/MutSpot.All R code used to generate the figures and statistics of the paper is included in Supplementary Data SG tumor data: Sequence data has been deposited at the European Genome–phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001002872.https://portal.gdc.cancer.gov/projects/TCGA-STADTCGA tumor data: https://ega-archive.org/datasets/EGAD00001003132ICGC tumor data: https://ega-archive.org/datasets/EGAD00001000782HK tumor data: http://www.roadmapepigenomics.org/data/Roadmap Epigenomics data: Encode data: ftp://ftp.ensembl.org/pub/release-85/regulation/homo_sapiens/.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5


Irisin is an exercise induced myokine that is shown to promote browning of adipose tissue and hence, increase energy expenditure. Furthermore, our unpublished results indicate that Irisin improves myogenic differentiation and induces skeletal muscle hypertrophy. Since exercise induced skeletal muscle hypertrophy improves muscle strength, we wanted to investigate if ectopic injection of Irisin peptide improves skeletal muscle function in a mouse model of muscular dystrophy. This utility of Irisin peptide is yet to be studied in animal models.E. coli. Three- to six-week-old male mdx mice were injected IP with either vehicle  or Irisin recombinant peptide for two or four weeks, three times-a-week.In order to test this hypothesis, we expressed and purified recombinant murine Irisin peptide from mdx mice. Improved muscle strength can be attributed to the significant hypertrophy observed in the Irisin injected mdx mice. Moreover, Irisin treatment resulted in reduced accumulation of fibrotic tissue and myofiber necrosis in mdx mice. In addition, Irisin improved sarcolemmal stability, which is severely compromised in mdx mice.Irisin injection increased muscle weights and enhanced grip strength in Irisin injection induced skeletal muscle hypertrophy, improved muscle strength and reduced necrosis and fibrotic tissue in a murine dystrophy model. These results demonstrate the potential therapeutic value of Irisin in muscular dystrophy. Mutationecrosis . The funnecrosis . Moreovenecrosis . In the necrosis . Currentmdx mouse model of DMD [mdx mice undergo a sudden onset of muscular necrosis [Mdx muscle growth is slower than that of the wild type controls and hence, they have smaller fibers, myonuclear domains and less myonuclei [mdx mice undergo severe muscle fiber destruction and subsequent complete regeneration, which leads to muscle fiber necrosis [mdx mice attain the maximum size of muscle fibers, which are hyper-nucleated with more centrally placed nuclei [The naturally occurring l of DMD  is one onecrosis . This acyonuclei . Three wnecrosis . After 8d nuclei .et al., (2012) demonstrated that exercise resulted in increased serum Irisin levels in humans, more recent work from Raschke et al., (2013) revealed the absence of a canonical translational start codon in human Irisin, which indicated the production of a non-functional Irisin protein in humans [et al., (2015) provided evidence to suggest that all commercial antibodies, available at the time, were unable to detect circulating Irisin protein in humans [et al, (2015) revealed that although human Irisin protein contains a non-canonical start codon, the full length Irisin precursor protein FNDC5 is translated, indicating complex regulation of translation. FNDC5 protein is present in human skeletal muscle and is cleaved to give rise to detectable levels of Irisin in circulation. In fact, the level of Irisin in circulation is more abundant than other commonly known circulating proteins, such as Insulin and Interleukin 6 (IL6). In addition, the authors showed that Irisin levels are elevated in response to exercise in humans and is indeed an exercise induced myokine as previously reported by Boström et al., (2012) [Irisin is a newly discovered myokine that is induced in response to exercise . The 112n humans . Moreovemyostatin-null mice, which display a dramatic double muscled phenotype [In humans, circulating Irisin levels are positively correlated with biceps circumference and expression of IGF-1 . This suhenotype . Recentlhenotype . In humahenotype . Consistmdx mouse model of DMD. Our results strongly suggest that Irisin is able to improve skeletal muscle mass and strength of dystrophic mdx mice, reduce fibrotic tissue accumulation as well as reduce necrosis of muscle fibers. Moreover, Irisin is able to protect dystrophic muscle from sarcolemmal instability, which could be indicative of enhanced muscle function in mdx mice. These data underscore the therapeutic potential of Irisin in improving skeletal muscle mass and protecting against dystrophic skeletal muscle loss.To study the potential therapeutic role of Irisin in increasing skeletal muscle growth, we investigated the effect of recombinant Irisin protein on skeletal muscle mass, quality and function in the E.coli is a homogenous protein  or Irisin. Irisin injected mice showed a significant increase in the percentage change in body weight just after one week of Irisin injection (Injection 4). The significant increase in body weight was further maintained throughout the trial  muscle, the increase was not statistically significant  muscle revealed hypertrophy of muscle fibers  and a reduced proportion of fibers with smaller cross sectional area (<2500μm2)  Figure  and 1h.mdx mice undergoes constant cycles of degeneration and regeneration. One of the hallmark features of newly formed (regenerated) muscle fibers is centrally placed nuclei. As such, we next quantified the number of myofibers with centrally placed nuclei in DB and Irisin injected mice, to assess if Irisin treatment improves postnatal myogenesis in mdx mice. Our analysis revealed a ~20% increase  in the percentage of myofibers with centrally placed nuclei in mice injected with Irisin, when compared to DB controls  muscle following two weeks of Irisin injection. Utrophin expression was normalized to the housekeeping protein, Vinculin. Results revealed a slight reduction, in the levels of Utrophin in mdx mice injected with Irisin, when compared to DB injected controls, albeit not statistically significant  mdx mice were injected with either DB or Irisin three times-a-week for two weeks. No significant difference in body weight , Sol, M. quadriceps (Quad) and M. biceps femoris (BF) muscles was seen in adult mice injected with Irisin, although the increase in muscle weights was not statistically significant  and a reduced proportion of fibers with smaller cross sectional area (<2000μm2) was observed in Irisin injected adult mdx mice, when compared to DB injected controls , when compared to DB injected controls  and Fast green (non-collagenous proteins) staining of skeletal muscle sections from TA muscle of DB and Irisin injected mdx mice  injection of Irisin into t Figure  or food t Figure  was notes Figure  and 5d. s Figure  and 5d. e Figure . These dmdx mice. Taken together, these data exemplify the therapeutic benefit of Irisin in improving muscle mass, muscle healing and muscle function in a dystrophic mouse model.Irisin is a novel exercise-induced myokine and to date the role of Irisin on adipose tissue  and in tmdx mice. We have utilized mdx mice in the current study, as this model has been extensively used to study the pathophysiology of DMD [mdx mice at 3 weeks of age, with a gradual decrease in the intensity of the phenotype observed from 8 weeks of age onwards [mdx mice throughout their life, with a further reduction in severity noted after one year [To assess the effect of Irisin on dystrophic muscle loss we have undertaken studies in both young and adult y of DMD . Acute oone year .mdx mice undergoing acute dystrophic muscle loss. It is important to mention that we did not observe any adverse effects on the mice in response to Irisin injection. Parameters such as movement, behavior, or food consumption did not show significant differences after Irisin injection. The Irisin injected mice did not show inflammation, organ toxicity or fluid retention upon gross pathological examination. This indicated that the Irisin protein used for the experiments was safe for injection into the mice. Interestingly, while no apparent difference in food intake was noted between DB and Irisin injected mice  e Figure , a signie Figure . We prope Figure  and 1e  reported that blockade of Myostatin signaling in mdx mice, using a soluble form of the Activin Type II B receptor (sActRIIB), resulted in increased myofiber size in Soleus muscle without altering the overall levels of CK in serum [mdx mice. However, it is quite possible that the protective role of Irisin was not sufficient to reduce the levels of serum CK in young mdx mice and perhaps prolonged Irisin treatment may have been required.CK is an enzyme predominantly present in skeletal muscle, heart and the brain . During s Figure . We propin serum . Therefomdx mice, injection of Irisin into adult mdx mice resulted in increased muscle weights, although the increase was not statistically significant    Figure  to a gre) Figure , suggestmdx mice injected with Irisin . The recombinant murine Irisin protein was induced and purified to homogeneity using Ni-Agarose resin under native conditions. Purified Irisin protein was dialyzed for three hours against buffer containing 50mM Tris-HCl (pH 8.0), 500 mM NaCl and 10% Glycerol  with three changes. Endotoxin in the purified Irisin protein was estimated using the ToxinSensor Chromogenic LAL kit, according to the protocol supplied with the kit.cDNA corresponding to murine Irisin (amino acids 29-140 of FNDC5) was PCR amplified and cloned in frame into the pET-16b T7 expression vector, as per standard molecular biological techniques, and the resulting plasmid was transformed into the BL21 strain of In order to extract proteins, muscle tissue (thirty milligrams) homogenates were prepared in RIPA buffer , 0.1% Sodium dodecyl sulfate (w/v), 0.5% Sodium deoxycholate (w/v)), 50mM Sodium fluoride) using the Tissue Lyser II disruptor  at 30Hz (3 × 1 minute). After centrifugation of homogenized muscle tissue for 10 minutes at 12,000 rpm, supernatant was used for protein estimation and western blot analysis. 50μg of total protein was separated by SDS-PAGE (8% Tris-Glycine acrylamide gel) and transferred to nitrocellulose membrane by electroblotting. Membranes were stained with Ponceau S  to confirm equal loading of protein samples. The membranes were blocked for 1 hour at room temperature with 5% milk  in TBST and subsequently incubated with either anti-Utrophin  or anti-Vinculin  primary antibodies overnight at 4°C. The membranes were washed with TBST (3 × 10 minutes), followed by secondary antibody incubation for 1 hour at room temperature (RT). Secondary antibodies used in this paper were anti-mouse HRP . Following secondary antibody incubations, membranes were washed as stated above and HRP activity was detected using Western Lighting Chemiluminescence Reagent Plus . Bands were detected using the ChemiDoc Touch Imaging System  and were analyzed using Image Lab software .mdx mice were bred in the Nanyang Technological University (NTU) animal house, Singapore. Animal experiments were performed according to the protocols approved by the NTU Institutional Animal Care & Use Committee (IACUC), Singapore. Mice were housed at a constant temperature of 20°C under an artificial 12h light and 12h dark cycle with ad libitum access to standard chow diet and water at NTU animal house. To monitor the effect of Irisin on dystrophic muscle growth, three trials were performed. In the first trial, 3-week-old mdx mice were injected with either recombinant Irisin protein (2.5μg of Irisin per gram of body weight) or DB for two weeks, three times-a-week and all hind limb muscles were collected for extensive characterization of the mdx phenotype and the role of Irisin in mdx mice. Cross sectional area (CSA) and extent of muscle fiber damage was analyzed. A second trial was performed in order to assess the CSA and fibrotic tissue accumulation by injecting 6-week-old mdx mice with either recombinant Irisin protein (2.5μg of Irisin per gram of body weight) or DB three times-a-week for a total of 2 weeks. In the final trial, 4-week-old mdx mice were injected intraperitoneally (IP) with either recombinant Irisin protein (2.5μg of Irisin per gram of body weight) or DB three times-a-week for a total of 4 weeks to analyze if a longer period of treatment enhances muscle weight and grip strength. Grip strength was measured just prior to the dissection in the final trial. Six readings were obtained for each mouse with a 30 seconds rest between each reading. The highest three values were used to calculate the average grip strength for each mouse in both the DB and Irisin injected groups. Measurements were recorded in Newtons (N). Food intake was measured before every injection. At the end of each trial, mice were euthanized by CO2 asphyxiation and various hind limb muscles were harvested, weighed and stored for further experiments. The weights of the hind limb muscles were normalized to the tibia lengths of respective mice. TA muscle tissue was embedded in O.C.T , and stored at -80°C.Male For performing muscle histology, 10μm TA muscle transverse sections were generated from the midbelly region of O.C.T embedded muscle tissue using a Cryostat . The muscle sections were stained with Gill's Haematoxylin followed by 1% Eosin (Merck) (H&E). The cross sectional area of 200 muscle fibers from 5 random fields (1000 fibers) were analyzed per section. Myofibers that contained centrally placed nuclei were counted and normalized as a percentage of the total number of myofibers in the muscle section using ImageJ software . Necrotic muscle fibers were assessed based on the areas of fragmented sarcoplasm, influx of mononucleated cells and hyper-contracted muscle fibers. Muscle sections were tiled using the Leica CTR 6500 microscope, equipped with the Leica DFC 420 camera and Image Pro Plus software  and a 10x objective. Representative images were taken at a higher magnification (20x objective).Muscle sections were stained using the Sirius Red/Fast Green collagen staining kit, as per the manufacturer's protocol . Muscle sections were immersed in dye solution for 30 minutes at room temperature and excess dye was washed off with distilled water. Stained sections were then dried and mounted using DPX mounting medium . The tissue sections were photographed and tiled using the Leica CTR 6500 microscope, equipped with the Leica DFC 420 camera and Image Pro Plus software  and a 10x objective. Representative images were taken at a higher magnification (40x objective). The extent of collagen deposition (red stain) was quantified by determining the total area that was stained red across the entire muscle cross section. The total area stained red was normalized to the total TA muscle section area and expressed as a percentage, to represent percentage fibrosis.To detect IgM accumulation in muscle fibers, muscle sections were fixed with ice-cold acetone for 2 minutes and washed 3 times with 1xPBS for 5 minutes each. Blocking was performed for 1 hour at room temperature with 1% BSA and 5% horse serum in 1x PBS. Sections were subsequently incubated with Goat anti-mouse IgM-FITC conjugated antibody  for 1 hour and washed again 3 times with 1xPBS for 5 minutes each. Sections were mounted with Slowfade Gold anti fade reagent (Molecular Probes) and visualized and tiled using the Leica CTR 6500 microscope, equipped with the Leica DFC 420 camera and Image Pro Plus software  and a 10x objective. Representative images were taken at a higher magnification (20x objective). Stained samples were analyzed and IgM positive fibers were counted, with the number of IgM positive myofibers expressed as a percentage of total muscle fiber area.mdx mice were injected intraperitoneally with a solution of 1% Evans Blue Dye (EBD) at a volume of 10μl per gram body weight 24h prior to hind limb muscle collection. The extent of EBD accumulation was assessed based on the area of the muscle section that displayed red fluorescence, expressed as a percentage of the total area of the muscle section. Sections were visualized and tiled using the Leica CTR 6500 microscope equipped with the Leica DFC 420 camera and Image Pro Plus software  and a 10x objective. Representative images were taken at a higher magnification (20x objective).5-week-old male mdx mice injected with either DB or Irisin. CK is usually present in skeletal muscle, but leaks into the serum upon muscle fiber damage. CK assay was performed using a Creatine Kinase Activity Assay Kit (KA3766), according to the manufacturer's protocol. CK activity was measured in a clear bottom 96-well plate at 450 nm using a Microplate Spectrophotometer  and the MPM 6 software. Calculations were performed according to the manufacturer's instructions and creatine kinase values were represented as mU/mL.Creatine kinase (CK) assay was performed to assess the extent of muscle damage in the *), p< 0.01 (**) and p< 0.001(***). Data is presented as mean ± SEM.Two-tailed Student's t-test was used to compare differences between two groups. Equal variance between the two groups was determined by performing an F test on data prior to performing the Student's t-test. If the F-value was smaller than F-critical (Type 2 error), the variance between the two groups was deemed equal. However, when the F-value was greater than F-critical values (Type 3 error) then the variance was deemed to be unequal. Results were considered significant at p< 0.05 (


Oral drugs stimulating insulin production may impact growth factor levels. The data presented shows the relationship between pre-existing insulin secretagogues use, growth factor profiles at the time of breast cancer diagnosis and subsequent cancer outcomes in women diagnosed with breast cancer and type 2 diabetes mellitus. A Pearson correlation analysis evaluating the relationship between growth factors stratified by diabetes pharmacotherapy and controls is also provided. Specifications TableValue of the data•This dataset shows the observed relationship between baseline insulin secretagogues use, circulating growth factor levels at the time of cancer diagnosis and breast cancer outcomes.•Reported data may guide future studies evaluating pharmacotherapy-induced growth factor modulation in breast cancer.•These observations can assist future study design in evaluating the relationship between diabetes pharmacotherapy safety and circulating growth factors levels at the time of cancer diagnosis.1Reported data represents the observed association between use of insulin secretagogues preceding breast cancer and the growth factor profiles at the time of cancer diagnosis in women with diabetes mellitus . Data in2Evaluation of growth factor profile association with insulin secretagogue use and BC outcomes was carried out under two protocols approved by both Roswell Park Cancer Institute (EDR154409 and NHR009010) and the State University of New York at Buffalo (PHP0840409E). Demographic and clinical patient information was linked with cancer outcomes and growth factor profiles of corresponding plasma specimen harvested at BC diagnosis and banked in the Roswell Park Cancer Institute Data Bank and Bio-Repository.2.1n=2194). Medical and pharmacotherapy history were used to determine the baseline presence of diabetes.As described in the original research article by Wintrob et al. 2.2Inclusion criteria were as follows: minimum 18 years of age at diagnosis, presence of pre-existing diabetes at breast cancer diagnosis, and having available banked treatment-naïve plasma specimens in the Institute׳s Data Bank and Bio-Repository. That is, the blood had to be collected prior to the initiation of any cancer-related therapy .Subjects were excluded if they were male, had prior cancer history or unclear date of diagnosis, incomplete clinical records, type 1 or unclear diabetes status. For a specific breakdown of excluded subjects, please see the original research article by Wintrob et al. A total of 97 female subjects with breast cancer and baseline diabetes mellitus were eligible for inclusion in this analysis.2.3Each of the 97 adult female subjects with breast cancer and diabetes mellitus (defined as “cases”) was matched with two other female subjects diagnosed with breast cancer, but without baseline diabetes mellitus (defined as “controls”). The following matching criteria were used: age at diagnosis, body mass index category, ethnicity, menopausal status and tumor stage (as per the American Joint Committee on Cancer). Some matching limitations applied 2.4Clinical and treatment history was documented by medical chart review. Vital status was obtained from the Institute׳s Tumor Registry, a local database updated biannually with data obtained from the National Comprehensive Cancer Networks’ Oncology Outcomes Database. Outcomes of interest were overall survival (death from breast cancer) and disease-free survival (breast cancer recurrence and/or death). Mean overall and disease-free survival were 113.3 and 107.3 months respectively, both with a minimum follow-up of 25.6 months. For additional details concerning data collection, specific definitions regarding censoring and drug use , and a comprehensive demographic report, please see the original article by Wintrob et al. 2.5All the plasma specimens retrieved from long-term storage were individually aliquoted in color coded vials labeled with unique, subject specific barcodes. Overall duration of freezing time was accounted for all matched controls ensuring that the case and matched control specimens had similar overall storage conditions. Only two instances of freeze-thaw were allowed between biobank retrieval and biomarker analyses: aliquoting procedure step and actual assay.2.6A total of 6 biomarkers  were quantified according to the manufacturer protocol. The following Luminex® biomarker panels were utilized in this study: TGFB-64K (tumor growth factor-β), HCYTOMAG-60K (platelet-derived growth factor BB), and HAGP1MAG-12K  produced by Millipore Corporation, Billerica, MA. C-peptide determinations were done according to the manufacturer protocol as previously reported 2.7Biomarker cut-point optimization was performed for each analyzed biomarker. Biomarker levels constituted the continuous independent variable that was subdivided into two groups. Cut-point selection was determined by p-value optimization using the log rank method with respect to survival  as the dependent variable with the condition of a minimum biomarker group size of 10 patients. The results of this analysis yielded the cut-point for each biomarker that would provide the most significant separation of a Kaplan-Meier survival probability curve by assigning the subject to their respective biomarker category, specifically above or below the identified cut-point. Thus identifying potential biomarker ranges associated with poorer outcomes, specifically, ranges associated with a lower survival probability. Quartiles were also constructed. The resultant biomarker categories were then ztested for association with type 2 diabetes mellitus therapy and controls by Fisher׳s exact test. The continuous biomarker levels were also tested for association with diabetes therapy and controls across groups by the Kruskal–Wallis test and pairwise by the Wilcoxon rank sum. Multivariate adjustments were performed accounting for age, tumor stage, body mass index, estrogen receptor status, and cumulative comorbidity. The biomarker analysis was performed using R Version 2.15.3. Please see the original article for an illustration of the analysis workflow Correlations between biomarkers stratified by type 2 diabetes mellitus pharmacotherapy and controls were assessed by the Pearson method. Correlation models were constructed both with and without adjustment for age, body mass index, and the combined comorbidity index. Correlation analyses were performed using SAS Version 9.4.Wadsworth Foundation Peter Rowley Breast Cancer Grant awarded to A.C.C. .This research was funded by the following grant awards:


Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease, accounting for 1% of the population over 60 years of age [In our recent work, we investigated mitochondrial function across multiple neuronal cell types derived from human induced pluripotent stem cells (iPSCs) carrying the G2019S mutation in leucine-rich repeat kinase 2 (LRRK2), a gene that has been associated with both familial and sporadic PD . Thus, wNAD+ is a key regulator of many important cellular processes including oxidative metabolism mediated by sirtuins. With low levels of NAD+ in the LRRK2 G2019S iPSC-derived dopaminergic neurons, increased levels of acetylation of known sirtuin targets, and robust mitochondrial defects, an important question arises: can NAD+ be used as a therapeutic target to help prevent dopaminergic degeneration in PD? Not surprisingly, there are many studies providing evidence for using NAD+ as a therapeutic target for a variety of applications from general aging to neurodegenerative disorders . However