NAP1L1, NUCL, CRMP1, ACTB, and VIM for differentiation between glioblastoma and lower grade gliomas, with DPYSL2 as a promising “glioma versus reference” biomarker. A small scale validation study confirmed significant changes in mRNA expression levels of VIM, DPYSL2, ACTB and TRIM28. This work helps to fill the information gap in this field by defining novel differences in biochemical profiles between gliomas and reference samples. Thus, selected genes can be used to distinguish glioblastoma from lower grade gliomas, and from reference samples. These findings should be valuable for glioblastoma patients once they are validated on a larger sample size.Glioblastoma multiforme is the most frequent primary malignancy of the central nervous system. Despite remarkable progress towards an understanding of tumor biology, there is no efficient treatment and patient outcome remains poor. Here, we present a unique anti-proteomic approach for selection of nanobodies specific for overexpressed glioblastoma proteins. A phage-displayed nanobody library was enriched in protein extracts from NCH644 and NCH421K glioblastoma cell lines. Differential ELISA screenings revealed seven nanobodies that target the following antigens: the ACTB/NUCL complex, VIM, NAP1L1, TUFM, DPYSL2, CRMP1, and ALYREF. Western blots showed highest protein up-regulation for ALYREF, CRMP1, and VIM. Moreover, bioinformatic analysis with the OncoFinder software against the complete “Cancer Genome Atlas” brain tumor gene expression dataset suggests the involvement of different proteins in the WNT and ATM pathways, and in Aurora B, Sem3A, and E-cadherin signaling. We demonstrate the potential use of  Glioblastoma multiforme (GBM) is the most fre-quent and lethal form of primary brain tumor, with an annual incidence of 5.26 per 100,000 people, or 17,000 new cases diagnosed yearly worldwide . As a reAmong the major issues in GBM management are rapid tumor growth, tumor location, and late diagnosis. The general late diagnosis of GBM is a consequence of the asymptomatic nature of the early disease stages, which present with nausea, headache, and cognition changes. To better define GBM, a number of proteins have been proposed as possible biomarkers, including the EGFR/EGFRvIII pair, and the glycoprotein CD133 –22. FurtAn alternative approach for discovering proteins with specificity for GBM is offered by using nanobodies, which are antigen-binding fragments that are derived from naturally occurring heavy-chain-only antibodies in camelids , 28. ImpDPYSL2 as a “glioma versus reference” tissue biomarker, and the roles of some of the suggested biomarkers for class differentiation have been confirmed with qPCR on a small scale study, but need to be validated with a larger sample number.This straightforward anti-proteomic approach led to the identification of seven candidate biomarkers that conventional techniques have failed to uncover. This finding illustrates that the nanobody technology is a suitable alternative approach for identification of GBM-specific proteins and it has provided a good starting point for further investigation of their roles. Potential use of 8 individual transformants, which is consistent with the average size of a high quality immune nanobody library  molecular interactions databases. The results from our previous study for TRIM28 and ACTB [CRMP1, DPYSL2, NAP1L1, NUCL, TRIM28, VIM, and ACTB), with the sole exception of ALYREF. The genes were checked for specific overexpression or underexpression among the data available from TCGA, using “cancer-to-normal” ratios calculated with OncoFinder. The AUC values for all of these genes were calculated as “GBM versus LGG”, “GBM versus LGG versus reference” and “glioma versus reference”. The RNA sequencing data were normalized using the DeSeq2 package, and the Firehose software was used to retrieve data.Possible interactions between the identified proteins were analyzed using the OncoFinder software  and the and ACTB  were als260/A280 and A260/A230 ratios. The RNA integrity numbers (RINs) were determined using a bioanalyzer . Twelve samples with insufficient RNA quality  were excluded from further analysis. Then, 2 μg of each mRNA sample was used for cDNA reverse transcription. The samples were treated with recombinant RNase-free DNase I (Roche) to remove possible DNA traces  and transcribed with Transcriptor Universal cDNA Master (Roche), as follows: 5 min at 25 °C, 10 min at 55 °C, and 5 min at 85 °C.qPCR was used to determine changes in mRNA expression in the samples. mRNA was extracted from 13 GBM, 10 LGG and 22 paired reference samples. Extractions were performed with TRI Reagent (Sigma Aldrich), according to the manufacturer instructions. mRNA concentrations were estimated using a NanoDrop ND-1000 , and mRNA purities were determined using A2O. The reactions were performed in triplicate using the following thermal cycling: pre-incubation 10 s at 95 °C; cycling, 20 s at 60 °C, 20 s at 72 °C, for 45 cycles; melting curve, 5 s at 95 °C, 1 min at 65 °C; continuous at 97 °C; and cooling, 30 s at 4 °C. From the five candidate reference genes  chosen from the literature [RPL13A and CYC1 were selected for gene normalization using the NormFinder algorithm [https://pga.mgh.harvard.edu/primerbank/) [versus LGG versus reference” was calculated using one-way ANOVA and Holm-Sidak's corrections for multiple comparisons. For samples not following Gaussian distribution differential expression was calculated using Kruskal-Wallis test. In both cases P ≤0.05 was considered statistically significant .The qPCR was performed using a Roche LightCycler 480 platform. The total reaction volume was 5 μL, and consisted of: 0.75 μL cDNA, 2.5 μL 2× LightCycler 480 SYBR Green I Master (Roche), 0.3 μL of each of the 2.5 mM primers, and 1.15 μL distilled Hterature –64, RPL1erbank/) . Relativerbank/) . For samversus reference” and “GBM versus LGG” in GraphPad Prism 6 . For samples following Gaussian distribution analysis was performed using unpaired two-tailed Student's t-test. Mann-Whitney test was used where samples did not follow Gaussian distribution. In both cases P ≤0.05 was considered statistically significant .To confirm the bioinformatic findings, relative mRNA expression levels of the genes of interest were analyzed as “glioma


Particularly, anaerobic acetogenic bacteria are naturally capable of reducing CO2 or CO to various metabolites. To fully utilize their biosynthetic potential, an understanding of acetogenesis-related genes and their regulatory elements is required. Here, we completed the genome sequence of the syngas fermenting Eubacterium limosum ATCC 8486 and determined its transcription start sites (TSS). We constructed a 4.4 Mb long circular genome with a GC content of 47.2% and 4,090 protein encoding genes. To understand the transcriptional and translational regulation, the primary transcriptome was augmented, identifying 1,458 TSSs containing a high pyrimidine (T/C) and purine nucleotide (A/G) content at the −1 and +1 position, respectively, along with 1,253 5′-untranslated regions, and principal promoter elements such as −10 (TATAAT) and −35 (TTGACA), and Shine-Dalgarno motifs (GGAGR). Further analysis revealed 93 non-coding RNAs, including one for potential transcriptional regulation of the hydrogenase complex via interaction with molybdenum or tungsten cofactors, which in turn controls formate dehydrogenase activity of the initial step of Wood-Ljungdahl pathway. Our results provide comprehensive genomic information for strain engineering to enhance the syngas fermenting capacity of acetogenic bacteria.Autotrophic conversion of CO An alternative approach is to utilize the metabolism of acetogenic bacteria to convert CO2 into biomass and commodity chemicals; this is considered a promising approach to reduce and potentially replace fossil resources2. Representing 23 different genera, acetogenic bacteria robustly synthesize acetate and biomass from chemolithoautotrophic substrates3. In particular, Eubacterium limosum ATCC 8486, which is a gram-positive obligate anaerobic bacterium isolated from the rumen fluid of sheep and a commercially available type strain, is reported to convert syngas, such as CO, CO2, and H2, to butyrate, caproate, and acetate via the reductive acetyl-CoA pathway, also called the Wood-Ljungdahl pathway (WLP)6. In addition, syngas fermentation using this strain has advantages over the traditional thermochemical process of the Fischer-Tropsch synthesis, including higher catalyst specificity, lower operational temperature, higher tolerance of impurities, and lower energy costs9. Furthermore, the strain is capable of biotransformation of biochanin A, formononetin, and glycitein to multiple estrogenic metabolites with anticancer, antioxidant, anti-inflammatory, and enzyme inhibitory properties10.Demands for renewable and sustainable alternatives for fossil resources have rapidly increased along with concerns about environmental issues. A number of methods have been suggested for replacing fossil resources, but most of them demonstrate limitations in terms of lack of environmental, social, and economic sustainabilitycis-encoded determinants for transcriptional and translational regulation embedded in the genome11. The precise locations of TSSs determined by experimental methods provide the sequence and structure of the 5′ end of mRNA for investigating transcriptional regulation, mRNA stability, and translational efficiency. To understand the regulation of syngas fermentation by acetogenic bacteria, it is important to comparatively analyse the genomic features among acetogenic bacteria and determine the genome embedded non-coding regulatory elements, such as promoters, 5′UTRs, and non-coding RNAs (ncRNA), which play key roles in transcriptional and post-transcriptional regulation.Despite several reports on the genomic information and understanding of biochemical mechanism of WLP, studies on acetogenic bacteria have mostly focused on discovery of gene contents. Along with gene content information, DNA sequences such as transcription start sites (TSSs), principal promoter elements, such as −10 and −35 motifs, 5′-untranslated regions (UTRs), and Shine-Dalgarno (SD) sequences are E. limosum ATCC 8486. To this end, we integrated a long-read sequencing and short-read sequencing platform to obtain an accurate genomic sequence, and then identified the metabolic pathways responsible for syngas fermentation. In addition, we determined, for the first time in acetogenic bacteria, the primary transcriptome to unravel the transcriptional landscape using differential RNA sequencing (dRNA-Seq). Based on the integrated analysis with the complete genomic sequence, we suggest that the regulatory features such as principal promoter elements and 5′UTRs orchestrate the WLP and energy conservation system of E. limosum.In this study, we analysed the complete genomic sequence and determined the genome-wide TSSs of E. limosum ATCC 8486, we exploited SMRT sequencing and a hierarchical genome assembly process pipeline12. In total, 8,986 reads with 994,287,089 bases were obtained, which were assembled into one circular contig comprising 4,423,093 bases. Compared to the previously assembled draft genome that consisted of 31 contigs with 4,370,113 bases, the newly completed genome sequence is 52,980 bases longer, but with similar GC content  obtained from the Illumina sequencing onto the assembled circular contig, resulting in 121 nucleotide conflicts with 106 deletions and 15 mismatches  included 54,268 genes with 15,436 orthologous groups, composed of 471 core gene groups with 13,513 genes, 5,596 dispensable gene groups with 31,387 genes, and 9,368 unique genes ; however, 49.2% unique genes were identified with hypothetical functions . An FDH-encoding gene (ELIM_c2470) was identified in the core gene group, which is clustered with genes encoding the molybdopterin-guanine dinucleotide biosynthesis protein (ELIM_c2471) and an FDH-accessory protein (ELIM_c2472)15. Similar FDH cluster organization, which contains Fdh, Mob, and FdhD, in that order, was observed in Clostridium ljungdahlii (CLJU_c06990-CLJU_c07020), C. autoethanogenum (CAETHG_2790-CAETHG_2793), and E. limosum KIST 612 (ELI_0994-ELI0997). In contrast to the direct association of FDH with an additional hydrogenase subunit for the generation of reducing equivalents from H2 for CO2 reduction in A. woodii, the gene clusters encoding hydrogenases in E. limosum ATCC 8486 were located in at different genomic regions. Despite the different FDH complex identified, the putative electron-bifurcating hydrogenase (ELIM_c2347-ELIM_c2351) was similar to the one in A. woodii (Awo_c27010-Awo_c26790). In addition, two gene clusters encoding putative hydrogenases were predicted (ELIM_c3789 and ELIM_c3129-ELIM_c3132). Interestingly, most genes (six out of eight genes) in the putative hydrogenase gene clusters were included in the core gene groups  as core gene groups, encoding the formyl-THS synthetase (fhs1), methenyl-THF cyclohydrolase (fchA), methylene-THF dehydrogenase (folD), methylene-THF reductase (metV and metF), and dihydrolipoyl dehydrogenase (lpdA). Despite a similar gene arrangement of methyl branch coding genes, the only difference with the gene cluster of the methyl branch in A. woodii is the absence of an additional rnfC2 encoding electron transport complex subunit  for the carbonyl branch, required for acetyl-CoA formation from methyl-THF and a second CO2, was located at genomic regions different from the gene cluster for the methyl branch  and acsB (ELIM_c1655), together with the genes encoding the corrinoid/iron sulphur protein (acsCD), the methyltransferase (acsE), the CODH nickel-insertion accessory proteins (cooC1 and cooC2), the corrinoid activation and regeneration protein (acsV), and a hypothetical protein 24. In the E. limosum genome, similar to the electron transfer system found in A. woodii, all the genes encoding the Rnf complex were identified (ELIM_c3879-ELIM_c3884) and the cytochrome coding genes were not, which were classified as dispensable gene groups  and autotrophic (H2/CO2 (80:20) at a pressure of 200 kPa) conditions with generation times of 0.96 h and 9.38 h, respectively. Under heterotrophic conditions, glucose consumption was completed to reach an optical density of 1.63 ± 0.11 at wavelength 600 nm (OD600nm) and to produce acetate (11.62 ± 1.33 mM) as well as lactate (12.00 ± 3.43 mM), with a maximal optical density (OD600nm) of 2.00 ± 0.28 after 30 h  of 0.26 ± 0.01 was reached after 118 h and only acetate (20.46 ± 1.02 mM) was detected as a metabolic end product at higher levels than those produced under the heterotrophic conditions  for adapter ligation, we prepared two cDNA sequencing libraries, which were generated from TEX-treated (TEX+) and non-treated (TEX−) RNA, respectively. Approximately, 1.4 million sequence reads were uniquely mapped to the genome sequence with an average read length of 90.6 bp corresponding to a 33-fold genomic coverage (Supplementary Table ). This was followed by data integration of the TEX+ and TEX− libraries with the criteria that selectively identified the 5′ ends of primary transcripts (see the Methods for TSS mapping criteria). As illustrated in Fig. The isolated total RNA samples were treated with terminator exonuclease (TEX) . TEX treatment selectively enriches the 5′ triphosphorylated RNAs  by degrading the processed or degraded transcripts29. Taken together, we have identified an average of one TSS for every three currently annotated genes, which is further used to identify the promoter sequences within the upstream regions of individual genes.The obtained TSSs were further classified by their positions relative to corresponding coding sequences, yielding 1,253 primary TSSs with 30.6% of the currently annotated genes (note that monocistronic and operonic structures have not been considered) Fig. . Transcr31. Thus, determination of σ factor-specific promoter sequences is important to understand the transcription initiation and transcriptional regulation in response to environmental conditions. Genome annotation accounts for seven σ factors (rpoD (ELIM_c3331), rpoN (ELIM_c0109), sigH (ELIM_c1247), sigB (ELIM_c2321), sigR (ELIM_c3151), sigE (ELIM_c3281), and sigV (ELIM_c3405)). To determine the promoter elements in the genome, the consensus sequence motifs were searched from 50 bp upstream of the identified TSSs using MEME software32. The consensus sequence motifs were composed of three boxes  often found in the promoter regions of B. subtilis33. The −10 motif (TATAAT) and −35 motif (TTGACA) for the housekeeping σ factor were conserved in 90.3%  and 47.5%  of the identified TSSs, respectively, with an internal length between 16 and 18 bp  with 86.3% pyrimidines whereas, the +1 site was rich in purine nucleotide content (A/G) with 93.8% purines, consistent with the common observation from promoters in archaea, bacteria, and mammalian cells , corrinoid activation/regeneration protein (acsV), and a hypothetical protein (ELIM_c1649). Interestingly, the first two genes, cooC1 and acsV, in the first cluster of E. limosum, are located in both flanks of the carbonyl branch gene  of acetogenic Clostridium species, indicating that transcriptional regulation of the WLP genes may vary throughout acetogenic bacteria.The bacterial RNA polymerase (RNAP) is sufficient to synthesize RNA transcripts in bacteria: however, the formation of RNAP holoenzyme with sigma factor (σ factor) is essential to recognize the specific promoter sequence bp Fig. . This inlls Fig. . With thlls Fig. . Based o36. Interestingly, the methyl cluster, first carbonyl cluster, hydrogenase cluster (ELIM_c2347-ELIM_c2351), both ATP synthases, and Rnf complex contain relatively longer 5′UTRs (41-64 nt). We searched for ribosome binding sites (RBS) in 5′UTRs from −17 to +3 nt of the corresponding start codons. The alignment yielded a highly conserved motif (P < 0.05), identifying the GGAGR sequence similar to motifs in many different bacterial genomes , which was previously reported to be an optimal space for translation in bacterial cells39. The 5′UTRs responsible for WLP and FDH gene clusters are composed of RBS sequences of AGGAGG, a spacer 7-9 nt long, and ATG as the starting codon , putative ABC transporter (ELIM_c0563-ELIM_c0565), methionyl-tRNA synthetase (ELIM_c0596), 2-isopropylmalate synthase (ELIM_c1326), alanyl-tRNA synthetase (ELIM_c1518), threonyl-tRNA synthetase (ELIM_c3489), tryptophan synthase (ELIM_c3008-ELIM_c3014), and precursors for branched-chain amino acids (ELIM_c3959-ELIM_c3965). In addition, Moco-RNA motif was located upstream of the hydrogenase complex cluster (ELIM_c3126-ELIM_c3132), which is predicted to reduce FDH that is responsible for the initial step of WLP by converting CO2 into formate , was positioned upstream of the cobalamin biosynthesis encoding genes (ELIM_c2254-ELIM_c2264) responsible for synthesizing cobyrinic acid a,c-diamide from precorin-2. The other cobalamin RNA, confirmed by intergenic TSS (ELIM_n0060), was located upstream of the other cobalamin encoding genes (ELIM_c2270-ELIM_c2283) that convert glutamate to precorrin-2 and threonine to adenosyl cobinamide phosphate. Although the cobalamin encoding operons were nonadjacent, they were proximal and separated by 6,009 bp, containing their own cobalamin regulating RNAs, which suggests that the cell closely orchestrates the expression of the pathwayate Fig. . Moco-RNE. limosum ATCC 8486 was completed, and was found phylogenetically close to A. woodii. Genes encoding the methyl and carbonyl branches of the WLP were located in two different genomic regions, whereas a single gene cluster encodes both methyl and carbonyl branch in other acetogenic Clostridia21. The gene arrangement of clusters was almost identical to that of gene clusters in A. woodii, except an additional copy of a hypothetical gene in the carbonyl branch and an absence of the rnfC gene in the methyl branch. In addition, the energy conservation system was similar to the system in A. woodii, containing two gene clusters encoding ATP synthase complexes and utilizing the Rnf complex as the electron transfer system for reduction of the electron carrier20. It is unclear which ion in the Rnf complex translocates but based on the presence of an Na+ binding motif, the complex probably generates sodium chemiosmosis. Despite being phylogenetically related to A. woodii, based on its FDH and hydrogenase composition, E. limosum is incapable of hydrogen-dependent CO2 reduction; it instead accepts electrons from ferredoxin similar to C. ljungdahlii. Phylogenetically related acetogenic bacteria demonstrate different acetogenesis characteristics, indicating that the WLP and energy conservation system compartments should be considered as modules and acetogenic bacteria probably adopted parts that fit in their own environments.Acetogenic bacteria inhabit diverse environments and hence culture conditions vary, indicating that the regulatory elements embedded in their genomes may differ across strains. Among them, the genome sequence of E. limosum show multiple usage in an ORF, whereas bacteria inhabiting a wide range of environments are reported to utilize significantly more TSSs, which indicates less complexity of transcriptional regulation in the strain compared to other generalist bacteria47. However, a wide range of ncRNAs were identified in the antisense and non-coding regions, which are involved in post-transcriptional regulation48. Interestingly, one of the ncRNAs identified upstream of the hydrogenase complex (ELIM_c3126 – ELIM_c3132) was predicted to play a key role in reducing the electron carriers in autotrophic conditions. The function of Moco-RNA motif that regulates transcriptional expression of the downstream gene depends on the concentration of molybdenum or tungsten cofactor, which is involved in increasing the efficiency of FDH49. Thus, E. limosum may balance the expression of FDH and the hydrogenase complex by using the availability of cofactors to minimize energy wastage during transcription in the autotrophic conditions.The primary transcriptome landscape provides an understanding of the transcriptional and translational regulation of acetogenesis in autotrophic conditions. Although the complexity of TSSs varies in organisms, less than 10% of the total TSSs in 51. Interestingly, the lengths of 5′UTRs that are responsible for regulating WLP were longer than the median 5′UTR length (36 nt), suggesting that transcriptional expression might be regulated by RNA structure in the 5′UTR52. This questions the post-transcriptional regulation of WLP and energy conservation system in autotrophic conditions, which must be approached with the integration of transcriptome and translatome analysis53. In this study, the integration of genome and primary transcriptome contributes to an insight on the genomic features between acetogenic bacteria and E. limosum metabolism. Furthermore, determination of 1,458 TSSs provides a list of promoter sequences, RBS, 5′UTRs, and ncRNAs that predict the transcriptional regulatory system, along with potential post-transcriptional regulation. Together with the results, this information will serve as an important resource for understanding syngas fermentation and strain designing for obtaining better productivity.5′UTRs are reported to play an important role by forming structures during post-transcriptional regulationE. limosum ATCC 8486 was obtained from the Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures  and cultivated anaerobically at 37 °C in DSM 135 media supplemented with 5 g L−1 glucose or H2/CO2 (80:20) at a pressure of 200 kPa for heterotrophic and autotrophic growth conditions, respectively. After harvesting by anaerobic centrifugation, the collected cells were washed with basal DSM 135 media and inoculated in 100 mL of fresh medium.De novo assembly of the filtered reads was performed using HGAP.2 SMRT Analysis v2.3.0, giving rise to one contig in a circular form. The highly accurate sequence reads obtained from previous Illumina genome sequencing were then used to correct the random errors in the assembled contig13. In total, 5,826,333,852 bases were mapped onto the de novo assembled genome with default parameters . Using the Extract Consensus Sequences program in CLC Genome Workbench, conflict sites  were detected in the assembled contig. The conflict sites were further confirmed using Sanger Sequencing.The sequencing library with an average size of 20 kb was sequenced using the PacBio system with Polymerase version P6, and C4 chemistry. Using SMRT Analysis v2.3.0, raw reads with a read quality lower than 0.75 were filtered out, resulting in 1,101,693,824 bases and 105,034 reads with an average length of 10,488 bp. http://genskew.csb.univie.ac.at). tRNA and rRNA were predicted using tRNAscan-SE1.3154 and RNAmmer 1.255, respectively. Genome prediction and annotation were performed using Prodigal56 and the Swiss-Prot (from Uniprot) database, respectively. The other unknown predicted genes were annotated using the non-redundant protein database of GenBank. Subsequently, the amino acid sequences of annotated genes were aligned against the protein database to identify Gene Ontology (GO), KO ID, and Clusters of Orthologous Groups (COG)58.Using the error corrected genome, the origin and terminus of replication were determined using GenSkew ; the core genome, dispensable genomes, and unique genomes were then identified16. The translated protein sequences were compared using BLASTP with a minimum score of 50 and E-value of 10−10. The results were then clustered using the Markov cluster algorithm59. The phylogenetic tree of the acetogens was constructed based on the pan-genome pool using neighbour-joining method.In total, 15 complete acetogen genomes, including 14 acetogen genomes downloaded from the National Centre for Biotechnology Information database (ftp://ftp.ncbi.nih.gov/genomes/genbank/bacteria) and the completed genome of TM 5′-Phosphate-Dependent Exonuclease  was treated with 2 µL of 10× TerminatorTM Reaction Buffer A  and 20 U of SUPERase In RNase Inhibitor . For the preparation of TEX− and TAP− libraries, TEX treatment was omitted but 2 µL of 10× TerminatorTM Reaction Buffer A and 20 U of SUPERase In RNase Inhibitor were added. All the samples were incubated at 30 °C for 1 h, and the reaction was then terminated by adding 1 µL of 100 mM EDTA (pH 8.0). To ligate the 5′ RNA adaptor, 20 U of RNA 5′-polyphosphatase  was added to the TEX+ and TEX− samples. For the TAP− sample, the RNA 5′-polyphosphatase treatment was omitted. Two microliters of 10× RNA 5′-phosphatase Reaction buffer  was added to all samples with 20 U of SUPERase In RNase Inhibitor and incubated at 37 °C for 1 h. The samples were purified by phenol-chloroform extraction and ethanol precipitation. The 5′ RNA adaptor (5′-GUUCAGAGUUCUACAGUCCGACGAUC-3′) (10 pM) was added to the purified mRNA with 2 µL of T4 RNA ligase buffer , 2 µL of BSA (1 mg mL−1) , 20 U T4 RNA ligase , and 20 U of SUPERase In RNase Inhibitor . The samples were then incubated at 37 °C for 90 min, followed by incubation at 70 °C for 10 min. For cDNA synthesis, 10 pM of the random hexamer 3′ overhanging primer (5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNN-3′) was added to the purified sample along with 1 µL of 10 mM dNTPs  and incubated at 65 °C for 10 min. Subsequently, 2 µL of 10× RT buffer, 2 µL of 100 mM DTT, 4 µL of 25 mM MgCl2, 20 U of SUPERase In RNase Inhibitor , and 200 U of SuperScript III Reverse Transcriptase  were added. The reaction was incubated at 25 °C for 10 min, at 50 °C for 60 min, 85 °C for 5 min, and then cooled down to 4 °C. To remove the remaining RNA, 2 U of RNase H  was added and incubated at 37 °C for 20 min. The cDNA fraction at a size range between 120 bp and 400 bp was purified on 2% agarose gel using MinElute Gel Extraction kit . The purified library was amplified with the indexed primer for Illumina sequencing platform and monitored on the CFX96TM Real-Time PCR Detection System . Amplification was stopped at the beginning of the saturation point. The amplified cDNA was size-selected at a range between 150 bp and 400 bp, which was then sequenced using the 100 bp read recipe on Illumina Hiseq. 2500.The rRNA-depleted RNA was split into three samples for constructing three different libraries . For preparing the TEX+ library, TerminatorE. limosum ATCC 8486 genome with the following parameters , and the uniquely mapped reads were retained.The sequence reads obtained from dRNA-Seq experiments were trimmed to remove the adapter sequences. The dRNA-Seq reads with shorter than 25 bp and random 3′-overhanging (N9) sequences were removed. Using the CLC Genomics Workbench, the trimmed reads were aligned to the assembled 60, and then manually inspected using SignalMapTM  for visualization. The obtained TSSs were combined with TEX + reads to determine TSSs using a previously published method61. Among the TSSs located from 300 bp upstream to 100 bp downstream of the respective annotated start codon of each ORF, higher TSS score was classified as primary (P) and the others as secondary (S).Initially, TSSs identified using TEX- and TAP- reads were obtained as described previously52. Conserved sequences were determined by MEME software32. The 20 bp upstream sequences of each TSS were extracted and the −10 motif was identified using MEME software. Following the extraction, 8 bp upstream and 7 bp downstream sequences of the conserved sequence were obtained and illustrated using Weblogo. Sequences 50 bp and 20 bp upstream of TSS were extracted to obtain the −35 motif, and the 4 bp upstream and 6 bp downstream sequences were then obtained. The obtained sequence was illustrated as mentioned above. The space between the −10 motif and −35 motif was calculated.Motif discovery was achieved using a previously published method52. The RNA samples used for dRNA-Seq library preparation were used to amplify the downstream regions of selected TSSs. The short RNA adapter (5′-ACGGACUAGAAGAAA-3′) (50 µM) was added to 1 µg of the RNA sample with 10 U of T4 RNA ligase (Thermo Scientific), 2 µL of T4 RNA ligase buffer, 2 µL of BSA (1 mg mL−1), and 20 U of SUPERase In RNase Inhibitor. The reaction was incubated at 37 °C for 90 min and then at 70 °C for 10 min. After purification, half of the purified sample was treated with 20 U of TAP, which was omitted for the other half as a negative control. The samples were incubated at 37 °C for 1 h. Following purification, 10 µM of RNA adapter (5′-AUAUGCGCGAAUUCCUGUAGAACGAACACUAGAAGAAA-3′), 10 U of T4 RNA ligase (Thermo Scientific), 2 µL of T4 RNA ligase buffer, 2 μL of BSA (1 mg mL−1), and 20 U of SUPERase In RNase Inhibitor was added to the samples. The ligated RNAs were purified by ethanol precipitation, and then converted to cDNAs using SuperScript III Reverse Transcriptase. The cDNAs were amplified using 10 µM of cDNA-specific primers and 25 µM of the primer complementary to the latter ligated RNA (5′-GCGCGAATTCCTGTAGAACG-3′). The primer sequences used are listed in Supplementary Table 5′-Tag-cDNA library was constructed as described previouslySupplementary Table 3Supplementary Table 5Supplementary Table 7Supplementary Table 8Supplementary Table 9Supplementary Information


Crystalline nanoparticles or nanoprecipitates with a cubic structure often have near polyhedral shapes composed of low-index planes with {100}, {111} and {110}. To consider such near polyhedral shapes, algebraic formulas of extended superspheres that can express intermediate shapes between spheres and various polyhedra have been presented. Four extended superspheres, (i) {100} regular-hexahedral; (ii) {111} regular-octahedral (iii) {110} rhombic-dodecahedral and (iv) {100}-{111}-{110} rhombicuboctahedral superspheres are treated in this study. A measure ∏ to indicate the degree of polyhedrality is presented to discuss shape transitions of the extended superspheres. As an application of ∏ superspherical coherent precipitate is shown. R when p = 2 and a cube with edges 2R as p. To consider intermediate shapes of nanoparticles or nanoprecipitates between a sphere and cube, a solid figure called a supersphere was discussed in previous studies ,2. An eqorted in  that Eqund |z|=R . Shape tIn the case of crystals having cubic structures, nanoparticles or nanoprecipitates often have near polyhedral shapes composed of low-index planes with {100}, {111} and {110} ,6. TheseOnaka recently extended Equation (1) and derived algebraic formulas to describe intermediate shapes between spheres and various convex polyhedra ,11. Now p in Equation (1), p has been hence used as the measure of the degree of polyhedrality1/p=bR . The par a and b . In the Sphere is the solid figure having the minimum surface area under constant volume. The shape transitions of the extended superspheres from spheres to polyhedra cause the increase of surface area when the volume is kept constant. When t volume ,12. The lyhedral ,23.N with increasing The shape transitions of the extended superspheres from spheres to various polyhedra represented by the relationship between To compare the changes in For a sphere and polyhedron, we have The parameter We have considered relation  which isrelation .It is well known that the total surface or interface energy changes when shapes of particles or precipitates change. Similarly, mechanical energy such as elastic strain energy is also changed by shape changes of misfit precipitates in alloys ,10,14. AThe situation to consider the elastic-strain energy is summarized in cipitate , where δ, η or Π . Figure polation  as shownAs shown in Crystalline nanoparticles or nanoprecipitates often have near polyhedral shapes composed of low-index planes. Intermediate shapes between spheres and various convex polyhedra can be approximated with the concept of the extended superspheres. Four extended superspheres, (i) {100} regular-hexahedral; (ii) {111} regular-octahedral, (iii) {110} rhombic-dodecahedral and (iv) {100}-{111}-{110} rhombicuboctahedral superspheres have been treated in this study. A measure


This report describes the novel sampling of autosomal dominant polycystic kidney disease (ADPKD) combined with hypertrophic cardiomyopathy (HCM).A 48-year-old Chinese man presented with anasarca, hypourocrinia, gross hematuria, and weight gain by 10 kg subsequently developed acute kidney injury after struck by acute respiratory distress syndrome, really a threat to his heart.Abdominal ultrasound revealed multiple small cysts in both kidneys, with the right kidney measuring 11.6 cm in length, and the left kidney measuring 11.5 cm in length, which supported ADPKD. Echocardiography showed left ventricular posterior wall thickness measuring 15.2 mm, interventricular septum measuring 17.2 mm, left atrial size 31.9 mm, ejection fraction measuring 69%, approving the diagnose of HCM.Because of the failure treatment with tripterygium wilfordii and valsartan, the patient was administered with prednisone 1 mg/kg/day. Continuous renal replacement therapy was required to prevent heart and kidney from failure.The patient responded well and his renal function improved.This is the first reported case of ADPKD with HCM, with complete remission of acute kidney injury and preservation of cardiac function. Serial checks and measures should be considered for appropriate treatment of ADPKD patient who present with rapid decline of renal function. We present detailed analysis of the patient's disease course and review literature. Written informed consent was obtained from the patient for publication of this case report. It has been permitted by Committee on Ethics of Biomedicine, Second Military Medical University. Hypertrophic cardiomyopathy (HCM) is another genetic disorder involving heart muscle.2A 48-year-old man with a history of polycystic kidney disease was admitted to Third Affiliated Hospital of Second Military Medical University on May 24, 2017. At the beginning of 2017, he sought for medical advice because of abdominal pain, and was diagnosed with intestinal obstruction. Computed tomography (CT) had scanned multiple cysts in both kidneys, and serum creatinine increased to 115 μmol/L. In April, though he had no sign of smoky urine, periorbital and extremities edema, ascites, or dyspnea, laboratory investigations showed increased serum creatinine (141 μmol/L), proteinuria (6929 mg/day), decreased serum albumin (19 g/L), and increased serum cholesterol (6.02 mmol/L). Abdominal ultrasound revealed multiple small cysts in both kidneys, with the right kidney measuring 11.6 cm in length, and the left kidney measuring 11.5 cm in length, no cyst in liver and spleen. Echocardiography showed left ventricular (LV) posterior wall thickness measuring 15.2 mm, interventricular septum measuring 17.2 mm, left atrial size 31.9 mm, ejection fraction measuring 69%.Based on the typical CT and ultrasound findings, we diagnosed him as ADPKD, and prescribed tripterygium wilfordii (20 mg per day) and valsartan (80 mg per day). In addition, he has a history of HCM, and used to be treated by cardiac interventional therapy for recurrent arrhythmia even sudden cardiac arrest. The patient had no family history in either polycystic kidney or HCM.Nevertheless, tripterygium wilfordii treatment proved to be failed 2 weeks later for anasarca, hypourocrinia, gross hematuria, and weight gain by 10 kg. On May 18, the patient was started on daily prednisone (60 mg/day) instead. But what's worse was that he was attacked by streptococcus pneumonia on May 29, which led to septic shock, type I respiratory failure, even acute renal injury. At that moment, his pulse oxygen was 95% (oxygen flow rate = 10 L/minute), pulse rate was 125 beats per minute and blood pressure was 90/61 mm Hg. A chest radiograph showed consolidation of right upper lung lobe. Serum biochemistry revealed a creatinine of 300 μmol/L, albumin16.4 g/L, brain natriuretic peptides 1070 pg/mL, C-reactive protein > 200 mg/L, procalcitonin 100 ng/mL, endotoxin 72.58 pg/mL, arterial partial pressure of oxygen 78.0 mm Hg (oxygenation index = 128). Then he turned to respiratory distress, cyanosis, oliguria, gross edema, and renal deterioration.In consideration of severe state, ceftazidime combined with moxifloxacin was prescribed, oral prednisone was replaced by intravenous methylprednisolone, and low molecular heparin was injected subcutaneously for anticoagulation. On May 30, though vital signs were stable, urine volume was still 310 mL in day, creatinine zoomed up to 579 μmol/L, far worse than expectation. Continuous renal replacement therapy was started immediately through right femoral vein catheter. Two hundred fifty milliliters of water was removed every hour, and total ultrafiltration volume reached to 3913 mL in 3 days. In the following days, the patient was on the mend with increasing urine, releasing abdominal distension, relieved dyspnea, just ankle edema left. On June 12, he was found to have lowering creatinine (372 μmol/L), with serum albumin of 22.7 g/L, brain natriuretic peptides of 570 pg/mL, procalcitonin of 0.63 ng/mL.33.1 ADPKD is a congenital disorder primarily affecting kidneys, characterized by the development and growth of cysts, though other organs may be involved, such as liver, gastrointestinal tract, and arterial blood vessels. In most cases, almost 85%, is caused by a mutation localized on chromosome 16 (ADPKD1), and nearly 15% by a mutation in chromosome 4 (ADPKD2), with a little left unknown.,4With an estimated incidence between 1:400 and 1:1000 live birth, The histopathological lesions reported are variously covering focal segmental glomerulosclerosis, membranous nephropathy, minimal change disease, crescentic glomerulonephritis, immunoglobulin A nephropathy, amyloidosis, and mesengioproliferative glomerulonephritis. It proved to be invalid of hormone empirical therapy for the patient with no sign of proteinuria reducing. Though a kidney biopsy is an invasive but meaningful procedure, a new approach combining with computed tomography or laparoscopy is not available.Usually in this disorder, urinary protein excretion is less than 1 g/24 hour, while nephrotic syndrome is considered to be rare.,14 Severe hypertension, gross hematuria, repeated infection, especially in men, and massive kidney size would accelerate progression to end-stage renal disease.,14 ADPKD1 appears to also have a worse prognosis than ADPKD2. This patient was attacked by acute renal failure, acute respiratory distress syndrome, septicemia, which aggravated the state of illness. Acute renal failure due to nephritic syndrome generally is reversible with a favorable prognosis. There may be following mechanisms: Nephrotic-range proteinuria and hypoalbuminemia leads to sodium retention, causing hypovolemia and insufficient renal blood flow; Renal interstitial edema raises the compression of renal tubule, resulting in obstruction; The protein in urine will block the renal tubules.The most common causes of an acute exacerbation on chronic renal failure in ADPKD patients embrace volume depletion, obstruction, and infection.3.2 The generally accepted definition of HCM is a disease state characterized by LV hypertrophy without any obvious cause. Diagnosis is usually by echocardiogram by maximal LV wall thickness 15 mm, particularly in the presence of other compelling information. In fact, fully one-third of patients have no obstruction. The clinical presentation of HCM may also be frequently asymptomatic, until featured by disabling cardiac symptoms, tachyarrhythmia, and electrocardiographic abnormalities. HCM is most commonly due to a mutation in 1 of 8 genes that results in a mutated protein in the sarcoma, for beta-myosin heavy chain, myosin binding protein C, troponin T, troponin I, alpha tropomyosin, actin, regulatory light chain, and essential light chain.HCM, another inherited disorder, is a global disease reporting a prevalence of 0.2% of the general population, or 1 in 500. It showed that cardiac arrest or sustained ventricular tachycardia, early onset of family history of sudden cardiac arrest, unexplained syncope, nonsustained tachycardia, exercise-induced low blood pressure under the age of 50, severe LV hypertrophy (>30 mm), cardiac insufficiency (<50%) are main risk factors of SCD. This patient had a history of unexplained syncope without atrial fibrillation, LV wall thickness less than 30 mm, the ejection fraction greater than 50%, but the genetic test was not done. In according to O’Mahony et al the ESC-HC prediction formula for SCD is as follows:There are 3 common complications in clinical progression, sudden cardiac death (SCD), heart failure, and atrial fibrillation.exp(Prognostic index);Probability SCD at 5 years = 1–0.998where prognostic index = [0.15939858 × maximal LV wall thickness (mm)]2 (mm2)] + [0.0259082 × left atrial diameter (mm)]−[0.00294271 × LVmaximal wall thickness+[0.00446131 × maximal  LV outflow tract gradient (mm Hg)] + [0.4583082 × family history SCD]+[0.82639195 × nonsustained ventricular tachycardia] + [0.71650361 × unexplained syncope]−[0.01799934 × age at clinical evaluation (years)].On the basis of the score, our patient got score 5.74, implantable cardioverter defibrillator may be considered. Aimed to a heart rate of less than 60 to 65 beats per minute, beta-blocking drugs are highly recommended for treatment of symptoms as angina and dyspnea. With the heart rhythm slowing down, myocardial oxygen supply is improved to meet the demand, so that to reduce myocardial ischemia, prolong the diastolic filling period. In consideration of the patient's condition, type I respiratory failure was unable to provide oxygen to the heart muscle, oliguria increased the load on the heart. To be on the safe side, tartaric metolol was increased to maximum tolerable dose, and hemodialysis was used to reduce cardiac capacity.Patients with HCM have raised oxygen demand due to the hypertrophy and adverse loading conditions.4In summary, polycystic kidney disease and HCM are both relatively rare diseases. There might have been a chance association, but nephrologists should be aware of this potential association. To our knowledge, this is the first reported case of ADPKD with HCM, with complete remission of acute kidney injury and preservation of cardiac function.


A reference is omitted from the first sentence of the last paragraph in the “Diversification Rate Calculations” section of the Methods. The sentence should read: “Last, we identified major shifts in diversification rate across the tree using MEDUSA [54] in the geiger  package in R [55], with the phylogeny and species richness estimates for each tip as inputs.”The reference is: Pennell MW, Eastman JM, Slater GJ, Brown JW, Uyeda JC, FitzJohn RG, Alfaro ME, Harmon LJ. geiger v2. 0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics. 2014 10:btu181.The number of independent calibration points in the phylogenetic dating, as detailed in S2 Table, appears incorrectly throughout the article as 26 calibrations. The correct number of fossil calibrations is 29.The prior type appears incorrectly in the fifth paragraph in the “Phylogeny Reconstruction” section of the Methods. “Log-normal” should be listed as “exponential”.


This suggested that dihydrotestosterone (DHT) production in the testes may prevent cell death due to plumbagin treatment, but the underlying mechanism is not understood. We performed RNA-seq analysis on cells treated with combinations of plumbagin and DHT, and analyzed differential gene expression, to gain insight into the interactions between androgen and plumbgin. DHT and plumbagin synergize to alter the expression of many genes that are not differentially regulated by either single agent when used alone. These experiments revealed that, for many genes, increases in mRNAs caused by DHT are sharply down-regulated by plumbagin, and that many transcripts change in response to plumbagin in a DHT-dependent manner. This suggests that androgen receptor mediates some of the effects of plumbagin on gene expression.Treatment of mice harboring PTEN-P2 tumors in the prostate or on prostate tissue  It has been used in traditional medicine for various purposes, and has been studied more recently as a potential cancer therapeutic13. Plumbagin prevents growth of androgen-independent prostate cancer cells in xenografts, and invasion in cell culture experiments8.Plumbagin  is a small molecule derived from the Plumbaginaceae family, including Plumbago indica and Plumbago zelanica14. Other quinones are known to induce endoplasmic reticulum stress via their Michael addition property, with attendant changes in gene expression15. Plumbagin also engages in redox recycling that generates reactive oxygen species (ROS). Plumbagin can exchange electrons with NADH and NADPH using enzyme catalysis, possibly using POR (cytochrome p450 oxidoreductase) or NQO1 (NAD(P)H-quinone dehydrogenase 1)16. In its semiquinone state, plumbagin can reduce oxygen to produce superoxide, which can be converted to hydrogen peroxide in a reaction catalyzed by superoxide dismutase. Both superoxide and peroxides can damage proteins, nucleic acids, and other molecular components in the cell17, suggesting a complex initial cellular response to plumbagin. A third mechanism, wherein plumbagin binds directly to active sites of enzymes, has also been explored18.Plumbagin has three properties that may influence gene expression. Plumbagin can engage in Michael addition, which results in arylation of nucleophiles, including thiols and amines, in circulation and in the cell. This activity explains the rapid clearing of free plumbagin from the circulation20. Pten−/+ mice develop prostatic interstitial neoplasia (PIN) after long latency, which do not progress to carcinoma20. However, loss of both Pten alleles in conditional knockout mice leads to PIN lesions that progress to invasive carcinoma20. Also, when Pten−/+ was coupled with Cdkn1b−/− (aka p27Kip1−/−), prostate carcinoma developed within 3 months of birth, with complete penetrance21. Whereas PTEN-P2 cells do not grow in subcutaneous xenografts, PTEN-P2 cells form tumors when implanted orthotopically in nude mice22. These PTEN-P2 tumors are androgen sensitive, in that growth slows dramatically upon castration6. Therefore, we used PTEN-P2 cells implanted in the prostate and cultured in vivo in dorsal skin fold chambers as a model of pre-invasive prostate cancer.Androgen dependent PTEN-P2 cells were derived from a heterozygous null mutant for the Pten gene18, and with largely indiscriminate effects due to free radical damage, RNA-seq data show that diverse subsystems of genes respond to plumbagin. We found that many genes that were up-regulated by DHT were down-regulated by plumbagin. Interestingly, many other genes that were not up-regulated by DHT were down-regulated by plumbagin, but only in combination with DHT, suggesting activation of a repressive mode of Ar. The suppression of normal androgen functions by plumbagin may be a component of its anti-prostate cancer properties in vivo. RNA-seq data also suggested that RNA metabolism is dramatically altered by plumbagin treatment. Therefore, we examined RNA integrity directly, and found that plumbagin treatment causes extensive RNA damage that increases with increasing plumbagin concentration.In this study, we develop an overview of the genetic subsystems affected by plumbagin and DHT in PTEN-P2 cells using RNA-seq and bioinformatics. Consistent with other broadly focused studiesPlumbago indica were purchased from Sigma-Aldrich . Both were dissolved in DMSO (Dimethyl Sulfoxide). PTEN-P2 mouse prostate cancer cells were provided by the Wu laboratory20, and were modified to express H2B-GFP as described previously6.Dihydrotestosterone (DHT) and plumbagin from PTEN-P2 cells were maintained in DMEM with phenol red (Dulbecco’s Modified Eagle’s Medium) containing 4.5 g/L glucose, 10% FBS , 100 U/ml penicillin, 100 μg/ml streptomycin, insulin-selenium-transferrin (5 μg/ml insulin). G418 (100 μg/ml) was included to maintain stable expression of H2B-GFP. For experiments, PTEN-P2 cells were grown in DMEM free of phenol red (Dulbecco’s Modified Eagle’s Medium) containing 4.5 g/L glucose, 10% FBS which was treated with charcoal dextran to remove hormones , 2 mM L-glutamine, 100 U/ml penicillin, 100 μg/ml streptomycin, insulin-selenium-transferrin (5 μg/ml insulin), without G418.In studies of the effects of plumbagin and DHT on cell growth Fig. , treatme−8 M DHT in DMSO, or mock treatment with DMSO. Treatments were in duplicate. No-treatment controls were in triplicate. The DMSO concentration was constant between treatment conditions. After treatment, cells were harvested and RNA was purified using RNeasy . NGS sequencing libraries were prepared from using NEBNext for Illumina® using index primers . These libraries were sequenced, 6 index primers per lane, using the Illumina HiSeq platform at the UCSD sequencing facility (http://igm.ucsd.edu/genomics). Reads were from 252–269 reads per lane.For gene expression analysis in response to plumbagin and DHT, cells in culture were treated for 1 hour with 0, 0.5, or 4 μM plumbagin dissolved in DMSO, with or without overnight and continued treatment with 1023 and cufflinks24 to count sequence reads, associate them with mRNAs, and report fragments per kilobase of transcript per million mapped reads (FPKM). We removed all genes for which the average count across all samples was <1 FPKM, and all genes that produce transcripts lacking a poly-A tail, because we used oligo-dT priming to produce first strand cDNA. We used quantile normalization to map the distribution from each experimental variable to a common distribution. These data present as a two dimensional table in which each row represents an mRNA from a gene and each column represents one of the 6 combinations of +/− plumbagin (at 0.5 μM and 4 μM) and +/− DHT. Treatments were in duplicate, and no-treatment controls were in triplicate, as mentioned above. We then generated ratios between the average of the three no treatment controls and the averages of the duplicates of each treatment. We calculated false discovery rates (fdr) by determining the number of control sample differential expression ratios  that exceeded an experimental sample/control sample ratio, and dividing by the rank order of the experimental sample/control sample ratio. This is the fdr, i.e. fraction of genes that have higher apparent differential expression ratios by chance. 5% of genes with an fdr ≤ 0.05 are expected to be false discoveries. Only genes for which at least one condition had a differential expression ratio with a fdr ≤ 0.05 were included in further analysis. These were converted to log2 values. We then computed 36 k-means groups using the statistical computation programming environment, R25, with 10,000 starts and 1000 iterations, for mRNA frequencies averaged over all isoforms. We chose to use 36 groups subjectively, based on most of the k-means groups reflecting obvious trends . These data after processing can be viewed by going to www.voxvill.org/relnet.plumbagin and selecting DHT_Plumbagin under “Select a pre-loaded experiment”. Pre-processed data has been deposited with NCBI GEO (GSE104305).We used bowtie2www.voxvill.org/relnet.plumbagin), that uses standard methods to identify proteins whose connections to proteins in a list are enriched, and is described in detail in (http://biorxiv.org/content/early/2016/10/07/079590). In GIRN, genes that are locally enriched are identified using a z-score calculated using a binomial proportions test, as in26. We used GIRN DB version 0, in which the protein-protein interaction network consists of 1,145,437 non-redundant interactions from Kamburov et al.30 in Consensus Path Database (CPDB) (http://consensuspathdb.org), from Cerami et al.31 in Pathway Commons (http://www.pathwaycommons.org), and from HTRIdb  for the Transcription Factor-Transcribed Gene component from Bovolenta et al.32 (http://www.lbbc.ibb.unesp.br/htri). CPDB includes BioCarta (http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways), EHMN, HumanCyc (http://humancyc.org), INOH, KEGG (http://www.genome.jp/kegg), NetPath (http://www.netpath.org), PharmGKB (http://www.pharmgkb.org), PID (http://pid.nci.nih.gov), Reactome (http://reactome.org), Signalink , SMPDB (http://www.smpdb.ca), and Wikipathways (http://www.wikipathways.org).For identification of protein-protein interactions, we developed a web-based software platform, Gene Information Relationship Network (GIRN) (www.geneontology.org)34. Specifically, we used the gene_association.goa_ref_human subset (9.7.2016 release) of the UniProt-GOA database. Drug-gene interactions are from DGIdb35 (http://dgidb.genome.wustl.edu). Genes associated with diseases are from DisGeNET (http://www.disgenet.org)37. Pathways are from Reactome39 (http://reactome.org). Enrichment analysis used Fisher’s exact test (two-sided), and p-values were modified post-hoc for multiple testing using Bonferroni correction, yielding an estimate of the family-wise error rate (FWER). Fisher’s exact test uses a 2 × 2 contingency table that contains the number of genes in a group associated with an annotation term, the number of genes in the group not associated with the term, the total number of genes associated with the term, and the total number of genes. For Bonferroni correction, we used α/N, where N is the total number of genes.The gene ontology database used by GIRN consists of the Gene Ontology Annotation (UniProt-GOA) Database and the Gene Ontology Consortium  after a long latency period20. These PIN lesions do not progress to metastatic cancer, whereas mice with both Pten+/− and Cdkn1b−/− develop prostate carcinoma with high penetrance21. In this context, the PTEN-P2 cell line, which is hemizygous null for Pten, represents a model for an early event in the development of prostate cancer. In intravital microscopy (IVM) experiments, PTEN-P2 tumors stop growing upon androgen deprivation therapy (ADT) but do not regress, grow at a slower rate when treated with plumbagin alone, and regress when treated with plumbagin and ADT in combination6. In animals with prostate tumors, plumbagin treatment alone and ADT alone ultimately failed, but the combination therapy resulted in complete tumor regression22. IVM studies reveal many pyknotic structures in experimental tumors when the animals are castrated and treated with plumbagin, consistent with previous observations that cell cycle arrest due to plumbagin occurs primarily in G2/M6.Ptenin vivo, it has only minor effects on growth of PTEN-P2 cells in cell culture.Figure −8 M DHT, or both, and performed RNA-seq expression profiling. DHT treatment was for ~24 hr, and plumbagin treatment was for 1 hr. This short time frame and the lower concentration of plumbagin (0.5 μM) were chosen to allow us to examine immediate responses to plumbagin, and to minimize confounding effects associated with profound damage and cell death. Under these conditions, plumbagin causes hundreds of changes in gene expression within one hour of treatment, and this response often depends on prior treatment with 10−8 M DHT. Interaction between plumbagin and DHT is clear when RNA-seq data is resolved into different response classes using k-means. Figure We treated PTEN-P2 cells with plumbagin (0.5 μM or 4 μM), 10www.voxvill.org/relnet.plumbagin). GIRN uses lists of genes, such as those contained within one or more of the k-means groups of Fig. 26. Genes in the subgraph were then explored for enrichment of associated functional annotation terms. We iterated through the various annotation capabilities of GIRN using a set of common parameters (z-score = 50) to generate networks that are annotated with enriched GO, Pathway, GSEA, Diseases, and Drugs for most of the 36 k-means groups. Three versions of these systematic analyses, Annotations, which contains all genes and annotation terms for each group with p-values or false discovery rates, Summary Annotations, which contains only the top annotations and statistics, and Short Summary Annotations, which contains the top 5 annotations and statistics, can be accessed at www.voxvill.org/Annotations.The K-means algorithm groups genes whose similar expression patterns suggest coordinated participation in one or more biological processes. To analyze plumbagin effects on gene expression, we applied enrichment analysis to each k-means group for terms describing gene function. These analyses were performed using Genetic Information Relationship Network (GIRN), which facilitates enrichment analysis based on data consolidated from many other sources, described in (10.1101/079590). The data discussed herein can be explored using a version of GIRN customized to this study , which is rate limiting for fatty acid synthesis42; arrestin-1 (Arrb1), which is interesting because it may promote tumor survival by metabolic reprogramming to rely on glycolysis, rather than on oxidative phosphorylation, the former favoring the frequently encountered hypoxic tumor environment43; and annexin-6 (Anx6), which is over-expressed in aggressive pancreatic cancer. KM4 also contains the androgen receptor (Ar), itself, suggesting that plumbagin may affect prostate cancer by directly down-regulating Ar, which is consistent with our previous observation that plumbagin causes reduction of Ar protein22. Down-regulation of these and other functions associated with genes in KM4 by plumbagin may account for some of its anti-tumor properties in a manner similar to ADT. KM4 is enriched for GO biological processes “regulation of endothelial cell migration” and “circulatory system development” (FWER < 0.05) (www.geneontology.org). When KM4 is used as a seed for an enriched network in GIRN, using z-score = 50, the resulting network, which contains a larger number of genes than in KM4, is not enriched for any GO, Pathway, or GSEA term. GIRN generates networks beginning with statistical inferences of nearest neighbor genes. Often, a network including only nearest neighbors does not penetrate sufficiently deeply into the global network to include a significant number of genes associated with any annotation term. To remedy this, the seed list augmented with genes that are inferred as nearest neighbors can be reintroduced to GIRN to extend to the next-nearest neighbors. An augmented seed list for KM4 was produced in this manner, and found to be enriched for GO terms relating to regulation of the extracellular microenvironment, with good false discovery rates (FWER < 0.05)  not only inhibits the growth of CRPC in xenografts, but also down-regulates the expression of Ar; down-regulation of Plk1, which is in KM30, may account for down-regulation of Ar and other genes in KM4.KM4 see Fig.  containsIn vivo experiments indicate that regression of PTEN-P2 tumors during plumbagin treatment requires both plumbagin and ADT6. KM10, 15, and 36 show responses to plumbagin that are dampened by DHT, and KM31 contains genes that are up-regulated by plumbagin, and down-regulated when both plumbagin and DHT are present. KM31 genes are enriched for GO terms “cell division”, “mitotic cell cycle”, “DNA replication”, and “DNA repair”, and includes cyclin B1, the expression of which decreases in response to plumbagin in several cancers50. KM15 is enriched for “response to ethanol”, “gene expression” and “membrane raft”, none of which is particularly informative. However, KM36 is enriched for “exosome (RNase complex)”, “RNA phosphodiester bond hydrolysis, exonucleolytic”, “rRNA processing”, “3′−5′-exoribonuclease activity”, and “nuclear exosome (RNase complex)”, suggesting altered RNA metabolism, discussed later.Genes in KM10 are largely transcriptionally regulated by Ar, and may mediate events that are induced by plumbagin but suppressed by DHT , the DNA replication and repair role of this small module is evident in the terms “meiotic DNA recombinase assembly”, “Rad51B-Rad51C-Rad51D-XRCC2 complex”, “strand invasion”, “recombinase activity”, “double-strand break repair via homologous recombination”, “mitotic recombination”, “four-way junction DNA binding”, and “replication fork” , Eif4a2 (eukaryotic translation initiation factor 4a2), Fcf1 (rRNA-processing protein), Fxr1 , Hnrnph1 (heterogeneous nuclear ribonucleoprotein H1), Magohb  and 40 others (out of 136 in the z-score = 50 network) annotated as RNA binding proteins by the Gene Ontology Consortium. These terms, together with the known ability of plumbagin to generate reactive oxygen, suggested that genes in KM16 may respond to RNA damage, or disruption of some step in RNA metabolism. The trajectory of KM26 is similar to that of KM16, and like KM16, KM26 is concerned with nucleic acids, including the following top 7 GO terms under Bonferroni correction: “5′-nucleotidase activity”, “purine nucleotide catabolic process”, “pyrimidine nucleoside catabolic process”, “nucleobase-containing small molecule metabolic process”, “pyrimidine nucleobase metabolic process”, “purine nucleobase metabolic process”, and “adenosine metabolic process”. Therefore we examined ribosomal RNA by gel electrophoresis and found that 24 hr treatment with plumbagin causes damage to ribosomal RNA. Although >20% of the ribosomal RNA in PTEN-P2 cells is damaged after 24 hr treatment with 0.5–1.5 μM plumbagin Fig. . Additio53. Bub1 and Ttk(Mps1) are essential SAC kinases. Ercc6l is Ercc excision repair 6 like, spindle assembly checkpoint helicase54; the kinesins Kifll (Eg5), Kif15, Kif23, and Kifc1 function to regulate microtubule dynamics during cell division56. Pathway enrichment includes several that overtly related to cell division, as well as “plk1 signaling events” (from PID and NDEx), which is a prominent regulator of mitosis and cytokinesis57. Plk1, itself, is a member of KM30, which is almost identical in trajectory to KM5. Plk1 is down-regulated by plumbagin without DHT, but more strongly down-regulated by the combination of plumbagin and DHT. BIRC5 (survivin) is also in KM30; the genes Aurkb, Foxm1 and Incenp are in KM28, which has a similar profile; and Gsg2 (haspin) is in KM29, which contains genes that are down-regulated by plumbagin regardless of DHT. All of these genes, Birc5, Aurkb, Foxm1, Incenp, and Gsg2 are important in centromere structure and dynamics57. Prkce and Stat3 been reported to be down-regulated in PTEN-KO (i.e. knock-out) mice treated with plumbagin58. Neither Prkce nor Stat3 are expressed in PTEN-P2 cells, but Prkca is in KM5, while several Stat family members, Stat1, 6, and 5a, are in KM15, which contains genes down-regulated by plumbagin, Stat5b is in KM28, which has a similar profile to KM5, and Stat2 (KM36) is down-regulated by plumbagin in the absence of DHT. Also, similar to PTEN-KO mice58, Cox-2  is down-regulated in PTEN-P2 cells when treated with plumbagin. Plumbagin and COX-2 inhibitor celecoxib act synergistically in melanoma to induce apoptosis59.KM5 is one of several groups in which cells respond to plumbagin through an androgen-mediated pathway. A force-directed graph from GIRN for KM5 is shown in Fig. in vitro , including “extracellular matrix organization”, “caveola”, “membrane raft”, and others. KM28 is similar to Groups 5 and 30, and is enriched for the GO terms “DNA replication” and “mitotic cell cycle” (as well as “liver development” and “response to estradiol”) under Bonferroni correction (FWER ≤ 0.05), suggesting that down-regulation of KM28 genes favors cell cycle arrest. However, treatment with 0.5 μM plumbagin in the presence of DHT is quite effective in reducing the transcripts of these genes without causing cell cycle arrest −16, but there are 1107568 possible combinations of 33 KM groups  taken 6 at a time, so the Bonferroni-corrected p-value is p = 2.4 × 10−10. Determination of the p-values for five thousand (5000) randomly selected groups of 6 KM groups, i.e. permutation analysis, yielded only one smaller p-value (i.e. 0.02%). This means that genes that are down-regulated by plumbagin in the presence of DHT are enriched for genes for which Ar is a transcription factor. Interestingly, Esr1, a member of KM28, is not a known Ar target gene, but does engage in protein-protein interactions with Ar. This may indicate that Ar can regulate genes that are not direct transcription factor targets of Ar60.One frequently seen gene expression response is down-regulation in the presence of both plumbagin and DHT, but no decrease in the presence of either agent alone . This suggests that the cellular response to plumbagin is mediated by Ar. If this were true, Ar transcriptional target genes may be enriched in these groups. However, this is not certain. Alternatively, the response could be due to one or more regulators that are indirectly affected by DHT, for example, a transcription factor or signal transduction molecule that is regulated by androgen receptor, or by DHT independently of Ar. Therefore, we determined whether Ar target genes are enriched in KM5, 20, 21, 28, 30, and 31. GIRN allows one to determine the number of genes in a seed list that are regulated by several well-studied transcription factors, including Ar, Ets1, Gata1, Gata2, Ybx1, Foxp3, E2f4, and others. Indeed, 5 out of 6 of these KM groups taken individually are enriched for androgen receptor target genes, with p ≤ 0.05 for KM20, 21, 28, 30, and 31, but not KM5. Collectively, KM5, 20, 21, 28, 30, and 31 contain 627 Ar target genes out of 1429 genes (i.e. 44%), whereas, globally, Ar binds to the promoters of 6443 of 23192 genes (28%). The Fisher’s exact test p-value indicating enrichment is p = 2.2 × 1061. In the composite group consisting of KM5, 20, 21, 28, 30, 31, Ets1 is enriched relative to the ensemble of all genes with a p-value of p = 6.8 × 10−40, with Bonferroni correction. However, permutation analysis indicated that 1932 out of 5000 (39%) of randomly selected groups of six KM groups resulted in smaller corrected p-values. This means that Ets1 target genes were enriched by the original selection criteria used to include genes during k-means analysis. Those criteria included (1) responsiveness to DHT, or (2) responsiveness to plumbagin, or both, and (3) an average of at least one FPKM when all FPKM per condition were averaged. Therefore, these data provide evidence that Ar plays a role in the down-regulation due to plumbagin observed in groups KM5, 20, 21, 28, 30, and 31, but that the role of ETS1, if any, is not clear.Another transcription factor that appears frequently with Ar is the Ets1 transcription factor, which interacts with the promoters of ~70% of Ar target genes, and may interact physically with ArIn KM16 and 26, in which plumbagin causes up-regulation of 360 genes without any apparent effect of DHT, 89 out of 314 genes (28%) are targets of plumbagin, approximately the global average for Ar targets. Therefore, the expression behavior of these genes is not expected to be attributable to Ar.in vivo synergism between plumbagin and DHT. KM15 is concerned with “neutrophil degranulation”, “secretory granule lumen”, “aging”, and “ficolin-1-rich granule lumen”. “Extracellular space” and “extracellular matrix organization” are invoked by the genes in KM24. Ar targets 29%, 30% and 33% of the genes in KM15, 24, and 29, which are unremarkable relative to the global average of 28%.KM15, 24, and 29 comprise genes that are down-regulated by plumbagin, without small or no DHT affects. Theoretically, genes that display this behavior could synergize with genes that are responsive to DHT to explain the 22. In those studies, we observed that plumbagin synergizes with androgen deprivation to cause tumor regression. Here, we used RNA-seq and bioinformatic methods to gain a better understand the cellular response to plumbagin, and possibly gain insight into the molecular mechanisms that might account for the synergistic interaction between plumbagin and DHT.Plumbagin is a potential chemotherapeutic for treatment of various cancers, including prostate cancer. In the studies described herein, we examined the effects of plumbagin on gene expression in a murine prostate cancer cell line that we used previously to generate experimental tumor models for prostate cancerin vitro. However, we found substantial changes in gene expression that differed with respect to treatment with plumbagin, DHT, or both. In these experiments, RNA was purified after only one hour of treatment with plumbagin, in an attempt to minimize secondary responses. A surprising result was that plumbagin treatment in combination with DHT appears to activate a repressive phenomenon that affects genes that are regulated by Ar disproportionately. The molecular basis for this phenomenon is unknown, but the enrichment of Ar target genes in this response class suggests possible modification of the Ar itself, or modification of a repressive complex in which it plays a role. This repressive phenomenon cannot easily explain our previous observation that plumbagin and ADT synergize to cause regression of experimental tumors. In the absence of DHT, these repressive phenomena are not observed.We found mild, but reproducible, synergy between plumbagin and DHT with regard to cell growth in vivo, but the combination was required to cause tumor regression6. Differential regulation of genes that depend on both plumbagin and DHT would seem not to pertain to the requirement that ADT and plumbagin treatment be combined to cause tumor regression. The observation that some metastatic prostate tumors produce androgens intratumorally may be relevant65. In this scenario, androgen production within the tumor could support gene expression dependent on both plumbagin and androgen, while the tumor microenvironment experiences androgen deprivation. In PTEN-P2 cells growing in culture, steroidogenic enzymes Cyp17a1 and Hsd3b1 were undetectable by RNA-seq, but their status in vivo is unknown.Plumbagin alone can partially arrest prostate tumor growth, and ADT can arrest the growth of androgen-dependent PTEN-P2 tumors in vivo synergy of plumbagin and ADT. In some, plumbagin up-regulates or down-regulates genes independently of DHT, and it is possible that the cytostatic effect of ADT combines with a cellular response to plumbagin to lead to cell death, whereas neither the ADT nor plumbagin treatment alone is sufficient. One such affect may be that of plumbagin on RNA integrity. This possibility was suggested by the enrichment of GO terms concerning RNA metabolism in KM16 and 26. Plumbagin, even at concentrations well below the IC50 for PTEN-P2 cells in culture, causes extensive RNA damage independently of DHT. Surprisingly, despite damage to as much as 50% of ribosomal RNA, the cells continue to divide. It is not yet clear how much RNA damage occurs in vivo or whether in combination with ADT, it would lead to cell death, or preferentially to the death of tumor cells.There are several response classes consistent with the in vitro and in vivo effects of DHT on plumbagin treatment also suggest that ADT may synergize with plumbagin via tumor microenvironment factors that were not present in the in vitro experiments. Detailed examination of gene expression in resected experimental tumors from animals exposed to combinations of plumbagin and ADT might further shed light on the pathways involved.The difference noted between Supplementary Data


Abnormal wound healing is likely to induce scar formation, leading to dysfunction, deformity, and psychological trauma in burn patients. Despite the advancement of medical care treatment, scar contracture in burn patients remains a challenge. Myofibroblasts play a key role in scar contracture. It has been demonstrated that myofibroblasts, as well as inflammatory cells, fibroblasts, endothelial cells, and epithelial cells, secrete transforming growth factor-β1 (TGF-β1) and other cytokines, which can promote persistent myofibroblast activation via a positive regulation loop. In addition to the cellular contribution, the microenvironments, including the mechanical tension and integrin family, are also involved in scar contracture. Most recently, eukaryotic initiation factor 6 (eIF6), an upstream regulator of TGF-β1, has been demonstrated to be involved in myofibroblast differentiation and contraction in both in vitro fibroblast-populated collagen lattice (FPCL) and in vivo external mechanical stretch models. Moreover, the data showed that P311 could induce the transdifferentiation of epidermal stem cells to myofibroblasts by upregulating TGF-β1 expression, which mediated myofibroblast contraction. In this review, we briefly described the most current progress on the biological function of myofibroblasts in scar contracture and subsequently summarized the molecular events that initiated contracture. This would help us better understand the molecular basis of scar contracture as well as to find a comprehensive strategy for preventing/managing scar contracture. It is commonly accepted that scars are a pathologic wound healing response to burn injuries, traumatic injuries, and surgeries. Hypertrophic scars and keloids, which only occur in humans, present with exuberant scar formation . AlthougWound healing proceeds through three overlapping stages. The inflammatory stage is triggered by injury, wherein platelets, neutrophils, and macrophages release inflammatory mediators and cytokines that participate in the recruitment of inflammatory cells, fibroblasts, endothelial cells, and epithelial cells. The proliferative stage involves fibroblast activation, myofibroblast differentiation, and extracellular matrix (ECM) deposition. In this phase, the myofibroblasts have acquired contractile properties that can contract the wound and promote re-epithelialization. The third healing stage is matrix remodeling, including scar tissue remodeling. During this stage, the persistent activation of myofibroblasts, imbalance of deposition and degradation of ECM, and poor arrangement of newly formed fibers can lead to scar formation.Myofibroblasts, a type of cell differentiated from quiescent fibroblasts and other cells, have been demonstrated to play an essential role in the induction and maintenance of scar contracture. In normal acute wound healing, the myofibroblasts are temporally limited and cleared by apoptosis in the third healing phase when the tissues are repaired. However, in hypertrophic scars and keloids, these myofibroblasts persist at a high number for long periods and promote the synthesis of α-smooth muscle actin (α-SMA), transforming growth factor-β1 (TGF-β1), and other growth factors, and they have sustained contractile ability via the TGF-β1 positive loop .In the inflammatory stage, fibrocytes and fibroblasts are believed to be activated in response to inflammatory factors; they then migrate to the location of injury based on a chemoattractant gradient and differentiate into myofibroblasts. In general, most myofibroblasts are derived from fibroblast differentiation around the local wound area . In addiThe myofibroblast cell is a phenotypically intermediate cell type between fibroblasts and smooth muscle cells. The myofibroblasts exhibit the characteristics of smooth muscle cells, containing high-contractile stress fibers. The stress fibers consist of α-SMA protein, which can be used to differentiate between myofibroblasts and fibroblasts in tissues. However, it remains difficult and complicated to discriminate between myofibroblasts and other contractile cells, such as smooth muscle cells, pericytes, and myoepithelial cells, even if the smooth muscle cells express smooth muscle myosin heavy chain, h-caldesmon (H-CAD), smoothelin, and the muscle intermediate filament protein, desmin, which are absent from myofibroblasts . There aIt has been demonstrated that TGF-β1 is one of the most important factors controlling myofibroblast differentiation and function. TGF-β is found in all tissues, and it consists of three isoforms, β1, β2, and β3. Binding of active TGF-β1 to the TGF-β receptor type II leads to the phosphorylation and recruitment of TGF-β receptor type I. The heteromeric receptor complex induces the phosphorylation of Smad2/3, followed by association with Smad4. Subsequently, the Smad complex translocates into the nucleus to enhance gene transcription via cooperation with DNA transcription factors. Additionally, it induces overproduction of α-SMA, collagen I, collagen III, fibronectin (FN), and other cytokines . TGF-β1 Importantly, TGF-β1 is an inducer of myofibroblast differentiation, which is considered a potential therapeutic target for hypertrophic scars and keloids. It has been reported that peroxisome proliferator-activated receptor-γ (PPARγ) ligands, 15d-PGJ2 and GW7845, could inhibit the expression and phosphorylation of TGF-β1/Smads . Either In addition, many other growth factors show positive roles in myofibroblast differentiation, such as connective tissue growth factor (CTGF), platelet-derived growth factor (PDGF), insulin growth factor (IGF), and vascular endothelial growth factor (VEGF) , 28. CTGMoreover, interleukin-6 (IL-6) and interleukin-8 (IL-8) increase α-SMA transcription in fibroblast cultures , 30. SimIn contrast, some negative factors act against myofibroblast differentiation. Basic fibroblast growth factor , epidermal growth factor (EGF), interferon-γ (IFN-γ), interleukin-10 (IL-10), prostaglandin E2 (PGE2), eIF6, and TGF-β3 have been shown to suppress the expression of α-SMA and ECM synthesis , 38, 39.Hypertrophic scars frequently occur at particular sites, including the anterior chest wall, auricle, scapula, and suprapubic regions, which are frequently subjected to the high stretch tension from the natural daily movements of the body . The actα-SMA has been demonstrated as a mechano-sensitive protein that induces a rapid mechanism to control the myofibroblast contractile function. There is positive regulation between stress and α-SMA expression level. The exogenous mechanical force, when applied through integrins, activates the Rho or mitogen-activated protein kinase (MAPK)/p38 signaling pathway, which then enhances the activation of serum response factor (SRF) and increases α-SMA transcription and incorporation into actin filaments. The persistent upregulation of α-SMA increases the intracellular tension and induces a higher force compared to the cytoplasmic actin stress fiber, which stimulates ECM contraction . A decre1 knockout are less able to adhere to and contract the ECM [1β1, α2β1, α3β1, αvβ5, α5β1, αvβ3, αvβ6, αvβ8, and α11β1 were demonstrated to partially participate in fibroblast proliferation, collagen contraction, and myofibroblast differentiation [3β1 decreased the accumulation of myofibroblasts and collagen I, which decreased the fibrosis [vβ5, αvβ3, and α8β1, were shown to bind to LAP-TGF-β1 and are involved in TGF-β1 activation [Integrin is an essential mechano transducer that is connected with stress fibers in cells and the ECM surrounding the cells . These s the ECM . Integrintiation . Deletiofibrosis . Moreovetivation , 59.After injury, the quiescent fibroblasts and other original cells are activated in response to inflammatory signals, such as TGF-β1. Following the TGF-β/Smad signaling cascade, TGF-β1 enhances gene transcription, as demonstrated by the upregulation of α-SMA, collagen I/III, and other fibrotic genes. Meanwhile, the expression of TGF-β1 is also increased by a positive feedback loop. Furthermore, eIF6 and P311 were involved in myofibroblast differentiation and contraction via regulating the TGF-β1 expression. This indicated that eIF6 and P311 may be new potential target genes for treating scar contracture. In addition, there are other cytokines, such as growth factors and inflammatory relative factors, which can up/downregulate myofibroblast contracture. Continuing ECM alignment creates larger surfaces for adhesion formation, which connects TGF-β-LAP-LTBP and integrins. The larger adhesions permit the development of stronger stress fibers and generation of a higher contractile force.In the wound repair phase, contraction can close the original wound and reduce the surface area. However, the continuation of contraction after wound healing can have high clinical morbidity of the joint contractures, functional loss, delayed return to work, and poor cosmetic results. The molecular basis/pathogenesis of scar contracture is complicated and includes cellular factors and environmental contributions. No single treatment method has been demonstrated to be effective . To find


The original article was corrected.The article “A comparison of isomaltulose versus maltodextrin ingestion during soccer-specific exercise”, written by “Emma J. Stevenson, Anthony Watson, Stephan Theis, Anja Holz, Liam D. Harper, Mark Russell”, was originally published Online First without open access. After publication in volume [117], issue [11], page [2321–2333] the author decided to opt for Open Choice and to make the article an open access publication. Therefore, the copyright of the article has been changed to © The Author(s) [2017] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (