The efficient biological production of industrially and economically important compounds is a challenging problem. Brute-force determination of the optimal pathways to efficient production of a target chemical in a chassis organism is computationally intractable. Many current methods provide a single solution to this problem, but fail to provide all optimal pathways, optional sub-optimal solutions or hybrid biological/non-biological solutions.Here we present RetSynth, software with a novel algorithm for determining all optimal biological pathways given a starting biological chassis and target chemical. By dynamically selecting constraints, the number of potential pathways scales by the number of fully independent pathways and not by the number of overall reactions or size of the metabolic network. This feature allows all optimal pathways to be determined for a large number of chemicals and for a large corpus of potential chassis organisms. Additionally, this software contains other features including the ability to collect data from metabolic repositories, perform flux balance analysis, and to view optimal pathways identified by our algorithm using a built-in visualization module. This software also identifies sub-optimal pathways and allows incorporation of non-biological chemical reactions, which may be performed after metabolic production of precursor molecules.The novel algorithm designed for RetSynth streamlines an arduous and complex process in metabolic engineering. Our stand-alone software allows the identification of candidate optimal and additional sub-optimal pathways, and provides the user with necessary ranking criteria such as target yield to decide which route to select for target production. Furthermore, the ability to incorporate non-biological reactions into the final steps allows determination of pathways to production for targets that cannot be solely produced biologically. With this comprehensive suite of features RetSynth exceeds any open-source software or webservice currently available for identifying optimal pathways for target production.The online version of this article (10.1186/s12859-019-3025-9) contains supplementary material, which is available to authorized users. Without complex routing algorithms the number of possible optimal pathways grows exponentially relative to the pathway length. As new biological reactions enter the literature and are available for synthetic addition, the optimal pathways may fork down completely different routes. Furthermore, there may be scenarios where the yield of a given compound is optimized, but the number of gene additions are sub-optimal . These all represent the distinct challenges in determination of pathways to production.The biological production of compounds for industrial applications is an interesting and complex problem. From the perspective of biological retrosynthesis, there are essentially two challenges 1) identifying new enzymes to perform difficult and/or important chemical reactions and 2) determining the optimal  number of gene additions that is required to convert an industrial organism into one capable of successfully producing a compound of interest. There is a growing body of literature for solving the first problem and recent work on polyketide design has demonstrated considerable success [Reaction additions and subsequent optimal pathways can inefficiently be determined computationally by one-by-one addition of non-native reactions to a stoichiometric matrix for a chassis organism, and then performing flux balance analysis (FBA) to determine if there is compound production without interfering biomass production. FBA is a tool widely used in predicting genome-scale metabolic behavior . FBA is k reactions to produce x, the naive approach to adding new reactions is to search each of the k reactions in the database to see if x is produced given the available compounds from FBA. This requires query of each of the k reactions. If there is a single step solution, it solves in FBA(k) time. Where there are no single step solutions, the problem explodes exponentially. A two-step solution requires not just k reactions, but all reactions that produce precursors to the k reactions. If the average number of reactions producing a given compound is y step solution in the worst case is Given RetSynth overcomes the naive and inefficient method of identifying solutions, particularly the worst-case, using constraint based mixed-integer linear programming (MILP). Given a database of known biological and chemical reactions and a genome-scale metabolic model, which can be constructed using RetSynth from numerous metabolic repositories with known enzymatic and chemical transformations, all optimal genetic additions required to produce a given compound of interest can be determined. The manner in which MILP is implemented is to minimize the objective value which represents the number of steps in the pathway. While selecting pathways based on number of reaction steps does not account for other issues in synthetic pathways  this is an ideal starting method for identifying synthetic pathways as minimizing the alterations made to a chassis organism is likely to lessen the above-mentioned issues as well as be more cost effective. Additionally, by resetting weights for reactions in the optimal pathway, RetSynth will automatically find novel sub-optimal pathways thereby providing alternative pathways that may have better target yield or fewer toxicity problems. This can be performed iteratively to determine all sub-optimal pathways for a specific path length.Herein we describe the algorithm developed as part of RetSynth to efficiently provide solutions targeted compound production. Subsequently, RetSynth can determine which pathway will produce the highest yields of a target compound using FBA. With this comprehensive suite of features, RetSynth is an efficient tool for identifying optimal solutions to target compound synthesis. Additionally, we compare RetSynth performance to other tools that can find optimal pathways to target compound production, such as OptStrain , MetaRouRetSynth includes a comprehensive suite of features necessary for complete implementation of the software. To find pathways RetSynth requires a metabolic database of reaction  and compound information. RetSynth can construct a database of metabolic information from number of metabolic repositories, including PATRIC , 11, KBaThe algorithm described below was developed for the RetSynth software to rapidly and efficiently identify all optimal pathways to target compound production in a specified chassis organism. Optimal pathways can then be ranked based on their ability to produce the highest yields of a compound by evaluating flux through each candidate pathway.To identify optimal pathways, we constructed a MILP: m molecules ×n reactions which are in the database. x is a vector of variables the length of n which represent the presence or absence (1 or 0) of each reaction in an optimal path. Cx=d where d is a vector of the length m which sets bounds on metabolite availability depending on whether the molecule is a native metabolite to the chassis organism (n) which is not constrained, a non-native metabolite (w) which constrains the molecule to ensure if the molecule is consumed in the optimal path it has to also be produced by a reaction in the optimal path or the target molecule (g) which has to be produced by a variable (2). where the entire RetSynth metabolic database is represented by a stoichiometric matrix C, with dimensions I, vector of weights for native variables) or not native  (3). The objective function is set to minimize the number of variables (reactions) needed to produce the target compound. The objective function weights are distributed based on whether the variables (reactions) are native <β∗+1, where β∗ is the number of reaction steps in the optimal pathway.To identify all the optimal pathways, a penalty function is added to variables that are already identified as part of an optimal pathway, forcing the algorithm to seek an alternative optimal pathway. To implement this algorithm, jth variable is a part of an optimal pathway but is not included in tj=1. The weights in t for the other β∗−1 variables that are part of the optimal pathway are 1+1/(2β∗). All together the optimal pathway value to the modified problem will be β∗+1/2−1/(2β∗). The algorithm terminates only after the objective function value to the modified problem reaches β∗(1+1/(2β∗)), which is higher than the pathway that includes the jth variable (Algorithm 1). This leads to a contradiction and proves that our algorithm includes all variables that are part of an optimal pathway. Here we illustrate how variables are weighted given that they are in an identified optimal pathway β∗+k, where k is a parameter set by the user and indicates the level of sub-optimal pathways to be identified. This involves adding additional constraints to (1) which prevents any of the initial optimal pathways from being discovered, forcing the algorithm to seek the next best pathway. For each initial optimal pathway, a constraint is added: RetSynth is able to find pathways that are not only optimal, but pathways up to Y are variables that are not part of a given optimal pathway and O are variables in an optimal pathway Y and O results in vector P (4). Constraints are set so that the combination of reactions in the optimal pathway cannot be identified as a solution. With the new constraints the metabolic system is: where k constraints are added to prevent the algorithm from finding previous levels of optimal or sub-optimal pathways. For each level of k algorithm (1) is implemented to identify all sub-optimal pathways at that level, with the exception that instead of resolving algorithm (1) it is resolving (5).Adding these constraints forces the algorithm to seek the next best sub-optimal pathway (5). At each level, After all optimal and sub-optimal solutions are identified, pathways are integrated into an FBA model for the chassis organism and FBA is run optimizing growth (the biomass reaction) and production of the target compound , 18.Sv, and drastically reduces the search space for enumerating all optimal solutions. To track optimal paths, define a directed graph G= with two types of nodes: V=Vc∪Vp and Vc∩Vp=∅. The process nodes Vp represent the enzymes selected in the previous section, whereas the compound nodes Vc represent all compounds that are inputs to the processes. Directed edges represent the input/output relationships between compounds and processes. The backtracking proceeds by starting with target compound x. Step 1 is to determine processes in Vp that produce x. A directed edge is connected between nodes in Vp and x. These nodes are then removed from Vp. Step 2 is to determine compounds that serve as inputs for these removed nodes and to add them from Vc. If Vp is not empty, step 1 will be repeated for each added node from Vc. This process will be repeated until Vp is empty, resulting in a directed dependency graph G of all pathways to production by native metabolism to x.The new set Given a compound of interest and a dependency graph G, a connected subgraph that includes the node for the compound of interest and at least one predecessor node for each compound node describes a feasible solution to the problem. Symmetrically, any feasible solution is a subgraph that satisfies these conditions. Subsequently, such a subgraph with minimum number of process nodes defines an optimal solution.Escherichia coli. Comparing model results to experimentally validated pathways demonstrates that RetSynth can generate practical candidate pathways for compound synthesis.Using metabolic networks from KBase and data from the MetaCyc metabolic repository, RetSynth was used to identify optimal pathways for compounds which already have experimentally tested synthetic pathways in Escherichia coli JM109 grown on LB media. Enzymes were added into E. coli in order to convert the native precursor acetyl-CoA into 2-propanol [Escherichia coli JM109 to facilitate these reactions. For RetSynth, the chassis organism Escherichia coli strain K-12 M1655 was used because a metabolic model for strain JM109 was not freely available. The optimal pathway identified by RetSynth consisted of the catalytic conversions acetoacetate to acetone and acetone to 2-propanol  . Though shorter because the Escherichia coli K-12 M1655 strain has acetoacetate  RetSynth’s optimal pathway uses the overall production pathway shown by Jojima et al. to be effective in producing 2-propanol [2-propanol has previously been produced in propanol . These cpropanol .Escherichia coli BW25113 on an M9 media, Atsumi et al. added a synthetic pathway consisting of 3 enzymatic conversions starting with the conversion 2-ketobutyrate to 2-oxovalerate [Escherichia coli BW25113, the authors add an overexpressed leuABCD pathway to increase yields of this precursor. Subsequently, 2-oxovalerate is converted to butanal by pyruvate decarboxylase and then to butanol by alcohol dehydrogenase. Using the standard BW25113 metabolic model retrieved from the KBase repository, RetSynth was unable to identify this pathway since the model did not contain a reaction for 2-oxovalerate synthesis. The lack of production of this metabolite in the model is unsurprising as natural yield of the precursor is so minimal in Escherichia coli [To produce 1-butanol in valerate . Becausehia coli . HoweverEscherichia coli strain BW25113. Our pathway converted native metabolite 2-keto-4-methylpentanoate to 3-methylbutanal and then subsequently produced 3-methylbutanol via added enzymes pyruvate decarboxylase and alcohol dehydrogenase . This matches the synthetic pathway used by [Our third validation example was to find the optimal pathway to production of 3-methylbutanol in  used by  to produEscherichia coli K-12 M1655 and MetaCyc reaction information. For every compound in the MetaCyc repository that was not native to Escherichia coli K-12 M1655, RetSynth identified an optimal pathway along with two levels  of sub-optimal pathways.The power of RetSynth lies in its ability to quickly identify optimal and sub-optimal pathways for a large set of target compounds. To illustrate this strength, a database was constructed consisting of a KBase metabolic network for Escherichia coli K-12 M1655, we found synthetic pathways for 3462 compounds. Optimal and sub-optimal pathways for methyl acetate and pterostilbene, both of which have economic value, are illustrated in Fig. Escherichia coli. The optimal pathway synthesizes acetone from the native compound acetoacetate and subsequently converts acetone to methyl acetate  for each reaction/enzyme we counted the number of compounds for which it was the first step in an optimal or sub-optimal pathway. Each reaction/enzyme was only counted once per compound even if it was in multiple optimal and/or sub-optimal pathways. Of the total 766 enzymes that were the first step in optimal and/or sub-optimal pathways, we identified 24 enzymes that were in 50 or more compound production pathways  catalyze reactions in Fig. Rhodotorula glutinis and Ustilago maydis respectively. Additionally, it was discovered that enzyme 4.3.1.25 can catalyze both these reactions and is found in Rhodotorula glutinis. By identifying enzyme additions that are in the highest number of target compound production pathways RetSynth can lead and enhance the development of efficient chassis organisms for optimal production of all types of economically and industrial target compounds.Using the RetSynth results for the 3462 target compounds, we can identify which reaction/enzyme is common to the highest number of them. This gene would be an advantageous gene addition for cultured strains of Escherichia coli K-12 M1655  can be discovered. With the addition of SPRESI, 413 more target compound production pathways were identified. The hybrid pathway for production of benzene in There are a number of other tools which can find synthetic pathways for target compounds, however none of these tools encompass all of the features of RetSynth Table . We perfOptStrain uses mixed integer linear programming (optimization-based framework) to find stoichiometrically balanced pathways that produce a target compound in a specified chassis organism . The desEscherichia coli. The 443 reaction were methodically classified into three different categories 1) reactions that use no co-substrates or co-factors, 2) reactions that are anabolic conversions (merging the substrate with a co-substrate), and 3) reactions that are catabolic conversions where the substrate breaks down into corresponding product and co-product. Additionally, thermodynamic analysis was performed for each reaction, calculating ΔG (KJ/MOL), as was a promiscuity analysis (determining if an enzyme could accept multiple substrates). Subsequently, GEM-Path implemented a pathway predictor algorithm, which works by 1) designating a target compound and setting predictor constraints , 2) applying reactions to the target in a retrosynthetic manner for generating the corresponding substrates, and 3) checking if the substrate matches a compound in the Escherichia coli metabolome. Subsequently, if a pathway is found FBA is run to validate production.The GEM-Path algorithm uses several different techniques to design pathways for target compound production in a chassis organism . This alΔG and promiscuity) than RetSynth uses to identify optimal solutions. This subsequently makes GEM-Path’s metabolic database substantially smaller than RetSynth and therefore is missing many synthetic pathway opportunities. Additionally, GEM-Path’s algorithm does not allow multiple pathways per target to be identified, limiting the potential pathways provided to the researcher.GEM-Path is not available for public use and there are other differences between the two software. GEM-Path integrates more detailed reaction parameters when predicting a pathway  were also the same for the two tools. Using RetroPath, which was run with the KNIME analytics platform with the pathway limit being 10 reaction steps (which matched the default pathway limit of RetSynth) we attempted to identify pathways for all MetaCyc compounds not in Escherichia coli. This query, however, was too large for RetroPath to handle, and subsequently RetroPath was employed to find pathways for a smaller set of target compounds including methyl acetate, pterostilbene . A RetSynth database was then built to match the reactions that were in the RetroPath MetaCyc reaction rules database so an equal comparison between the tools could be run. Extra RetroPath parameters such as maximum and minimum diameter and maximum molecular weight for source were all kept at their default values of 1000, 0 and 1000 respectively. Diameter is a measure of the depth and detail of the molecular reaction signatures (reaction SMARTS) used to identify pathways in RetroPath. The larger diameter the more detailed and strict the reaction SMARTS are and therefore are less able to predict novel reactions. Because RetSynth cannot predict novel reactions and we want to do a strict comparison between the two tools the maximum diameter of 1000 keeps the reaction SMARTS sufficiently strict to prevent novel reactions from being identified by RetroPath. Additionally, source compounds  is available for both MacOS and Windows Fig. . The GUIRetSynth is an open-source, stand-alone software tool for identifying optimal and sub-optimal pathways to biological, chemical and hybrid production of target chemicals. Additionally, RetSynth is able to rank pathways based on maximum theoretical yield which is calculated by flux balance analysis. Our tool exceeds the capabilities of any other current software available because it includes a graphical user interface, providing the ability for RetSynth to be used by scientists without a programming background, the capability to add new and proprietary biological reactions as well as synthetic chemical databases, efficient identification of optimal and sub-optimal pathways and clear images of pathways via our visualization module to allow quick interpretation of results.Project name: RetSynthProject home page: https://github.com/sandialabs/RetSynthhttps://github.com/sandialabs/RetSynthOperating system(s): Mac, Windows and LinuxProgramming language: Python and JavaOther requirements: GNU Linear Programming Kit (v4.64), libSMBLLicense: BSD 2-clause licenseAdditional file 1Supplementary Methods-Preventing Cyclic pathways from being identified as viable routes. Outlines how software prevents pathways with cycles from being identified. (DOCX 3 kb)


Gene and protein expression analysis demonstrated that whilst both monocultures of HPV‐positive and HPV‐negative cell lines, along with normal tonsillar fibroblasts (NTF), expressed low chemokine levels, NTF cultured with conditioned medium from HPV‐negative OPC cells expressed significantly higher levels of all chemokines tested compared to NTF incubated with the medium from HPV‐positive OPC cell lines. HPV‐negative OPC lines expressed IL‐1β mRNA whereas HPV‐positive cells did not, and NTF constitutively expressed IL‐1R1. Pre‐treatment with the IL‐R antagonist, anakinra or siRNA to IL‐1R1 significantly reduced chemokine secretion from NTF stimulated with conditioned medium from HPV‐negative tumour cells or recombinant IL‐1β (p < 0.05). These data suggest that secretion of chemokines is driven by the interaction between HPV‐negative OPC cells and stromal fibroblasts through an IL‐1/IL‐1R‐mediated mechanism that is less prominent within the HPV‐positive tumour microenvironment. These observations may explain differences in leukocyte sub‐populations recruited to HPV‐positive versus negative OPC and indicate that HPV status is a key determinant in controlling the inflammatory tumour microenvironment.Human papillomavirus (HPV) is now recognised as a major aetiological agent in the pathogenesis of oropharyngeal carcinoma (OPC). HPV‐positive tumours are associated with better outcomes compared to HPV‐negative tumours, possibly due to differences in their aetiology and/or the tumour microenvironment. Increased numbers of tumour‐associated leukocytes have been observed in many cancers including OPC, with variable influence on prognosis depending on the leukocyte subpopulation investigated. Whether HPV status influences leukocyte recruitment to OPC remains unknown. This  What's new?Rates of oropharyngeal cancer (OPC) are increasing, mainly due to human papillomavirus (HPV) infection. There is limited understanding, however, about the affects of HPV status in OPC on tumor‐stromal interactions, which influence tumor development and progression. In this study, HPV‐negative OPC cells were found to drive a robust chemokine secretion profile in stromal tonsillar fibroblasts, whereas HPV‐positive OPC cells exhibited relatively low chemokine expression. Increased chemokine expression in stromal tonsillar fibroblasts was mediated by the IL‐1/IL‐1R axis. The findings suggest that HPV status plays a role in the recruitment of leukocyte sub‐populations in the OPC tumor microenvironment, influencing inflammatory activity. ANOVAanalysis of varianceELISAenzyme‐linked immunosorbent assayFBSfetal bovine serumHLAhuman leukocyte antigenHNChead and neck cancerHPVhuman papillomavirusILinterleukinMAPKmitogen‐activated protein kinaseMTT‐2,5‐Diphenyltetrazolium Bromide)NTFnormal tonsillar fibroblastOPCoropharyngeal carcinomaPBSphosphate‐buffered salinePCRpolymerase chain reactionSCCsquamous cell carcinomaHead and neck cancers (HNC) are the sixth most common cancer globally and ninth most common malignancy in males in the United States.Several studies have consistently demonstrated that individuals with HPV‐positive OPC display better overall survival compared to their HPV‐negative, stage‐matched counterparts.versus HPV‐negative tumours may have important connotations for outcome and prognosis.The tumour microenvironment is instrumental in the progression of many cancers, particularly with respect to inflammation.in vitro model of HVP‐positive and HPV‐negative tumour/stromal cell interactions to determine if HPV status has an influence on the molecular drivers of leukocyte recruitment in OPC.Tumour‐infiltrating leukocyte recruitment is mainly driven by a large family of chemotactic cytokines (chemokines) that are often up‐regulated in cancer.l‐glutamine, 100 IU penicillin and 100 μg/mL streptomycin in a humidified incubator with 5% CO2, at 37 °C. All materials were purchased from Sigma‐Aldrich, UK, unless otherwise stated. Most studies were performed on NTF06 ; however, additional studies to confirm consistency across NTF from different donors were also performed , NTF319  and NTF322 ).OPC cells UPCI‐SCC89, UPCI‐SCC72 (HPV‐negative) and UPCI‐SCC90, UD‐SCC2 (HPV‐positive) were received through material transfer agreement with Professor Susanne Gollin, University of Pittsburgh. Hypopharyngeal carcinoma cell lines used included FaDu (HPV‐negative) and UPCI‐SCC152 (HPV‐positive). Cell lines were authenticated by DNA short tandem repeat analysis by the Genomics Core Facility at the University of Sheffield and HPV status confirmed by the HPV Cytology Screening Unit . Normal tonsillar fibroblasts (NTF) were isolated from patients during routine tonsillectomies undertaken at the Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust with written, informed consent7 cells in 75cm2 flasks for 24 h in serum‐containing medium, washed three times with phosphate‐buffered saline (PBS) then incubated with 6 mL serum‐free medium for 24 h. Conditioned medium was collected, centrifuged at 400g for 5 min to remove cell debris and stored at −80 °C. NTF were grown to 80% confluence in 75cm2 flasks, washed three times with PBS and then incubated with either cancer cell line conditioned media or serum‐free medium for 24 h. Cancer cell+NTF or NTF alone conditioned medium was then retrieved, centrifuged at 400g for 5 min and stored at −80 °C for later analysis. For IL‐1R blocking experiments, 6 × 105 NTF were incubated with 10 μg/mL anakinra or placebo control  for 2 h before stimulation with conditioned medium from SCC89 OPC cells or medium containing 5 ng/mL recombinant IL‐1β as a positive control for 4 h. Levels of anakinra or placebo were maintained throughout the experiment. A cytotoxicity assay using the tetrazolium dye MTT was performed on anakinra treated NTF as previously described.Tumour cell lines were seeded at 1 × 105 NTF seeded in 6‐well plates were incubated with transfection complex containing 300 nM siRNA  and Hi‐PerFect transfection reagent (both Qiagen) in serum‐free medium. The transfection complexes were added drop‐wise to cells and the tissue culture plates swirled to ensure equal distribution of the transfection complex. Mock‐transfected control cells received transfection reagent alone. Cells were incubated for 24 h at 37 °C in 5% CO2 with serum‐containing medium before washing twice with PBS and addition of conditioned medium from SCC89 cells. All medium was collected after 4 h and analysed for chemokine content by ELISA.siRNA for IL‐1R1 was performed according to the manufacturer's instructions . Briefly, 3 × 10Human chemokine arrays  were used to screen the medium from NTF cultured with conditioned medium derived from HPV‐positive or ‐negative OPC cell lines, according to the manufacturer's instructions. The relative expression levels of each chemokine were determined by densitometry using Quantity One software . Commercially available ELISA kits for CXCL8, CCL2 (BD Biosciences) and CCL5 (R&D systems) were used to quantify protein levels according to the manufacturer's instructions.‐ΔΔCt. For expression of IL‐1α and IL‐1β by cancer cells, gene expression was normalised to the abundance of the control gene transcript, β‐2‐microglobulin.Total RNA was isolated  and 500 ng reverse transcribed . qPCR was performed using Taqman gene expression assays as follows: 0.5 μL cDNA was amplified using 5 μL TaqMan master‐mix, 3.5 μL nuclease‐free water and 0.5 μL TaqMan gene probes for CXCL1 (Hs00236937), CXCL5 (Hs01099660), CXCL8 (Hs00174103), CCL2 (Hs00234140), CCL5 (Hs00982282), CCL7 (Hs00171147), IL‐1α (Hs00174092) and IL‐1β (Hs00174097), 0.5 μL β‐2‐Microglobulin (Hs00984230) was used as a reference control (Applied Biosystems). Reactions were performed using thermal cycles of 50 °C (2 min) and 95 °C (10 min) then 40 cycles of 15 s at 95 °C followed by 1 min at 60 °C. The threshold cycle (Ct) was normalised against the reference gene and then fold changes in expression relative to the untreated NTF group calculated by using the formula 2NTF were seeded on glass coverslips at 1 x10p < 0.05.All data are expressed as mean ± SD of at least three independent experiments unless otherwise stated. Statistical analysis was performed using GraphPad Prism‐v7.0 (GraphPad Software). Group‐wise comparisons were carried out using one‐way independent ANOVA with Tukey's multiple comparisons test and differences were considered significant when a,b,c). Upon stimulation with conditioned medium from HPV‐positive cancer cells  the amount of chemokine in the conditioned medium was increased compared to NTF06 alone, but was still relatively low. In contrast, incubation of NTF06 with conditioned medium derived from HPV‐negative cancer cells  significantly increased the amount of CXCL8 (SCC89 p < 0.001 and FaDu p < 0.01), CCL2 and CCL5  in the conditioned medium . For SCC89‐treated NTF06, the increase in CXCL8 was time‐dependent, with the levels of this chemokine still increasing after 24 h . To show that this stimulatory effect was not due to use of NTF06 alone, NTF isolated from 3 other donors  were stimulated with the conditioned medium from HPV‐positive SCC2 or HPV‐negative SCC72 OPC cells. These data show that, although some donor‐to‐donor variation was observed, HPV‐negative SCC72 conditioned medium consistently induced significantly (p < 0.01) greater chemokine secretion from all the NTF tested than for HPV‐positive SCC2 conditioned medium . The increased chemokine secretion by these NTF in response to SCC72 conditioned medium was much reduced compared to the levels observed for SCC89 conditioned medium  increased when NTF06 were cultured with conditioned medium derived from HPV‐negative compared to NTF06 treated with HPV‐positive‐derived conditioned medium or NTF06 cultured with medium alone . Similar observations were found for genes encoding the monocyte‐specific chemokines CCL2 and CCL7 . In addition, the gene encoding the T‐cell chemokine CCL5 was also significantly increased by HPV‐negative conditioned medium but only for SCC89 and FaDu cells . In all cases tested, the level of gene expression was directly related to cell type in terms of the ability to induce chemokine production by NTF06 with SCC89 < FaDu<SCC72. Taken together, these data suggest that HPV‐negative cancer cells secrete factor(s) that induce chemokine gene, and in turn protein, expression by NTF and that the factor(s) are largely absent from HPV‐positive conditioned medium.Quantitative PCR was next performed to determine whether induction of chemokine expression was extended to the mRNA level. Expression of genes encoding the neutrophil‐specific chemoattractants, CXCL1, CXCL5 and CXCL8 were all significantly . Differential expression of IL‐1β was even more evident; with HPV‐positive cells expressing negligible levels, whilst HPV‐negative cells all expressed abundant IL‐1β transcripts, with highly significant increases observed for SCC89 and FaDu . We further conducted a preliminary study using biopsy tissue sections from HPV‐positive and HPV‐negative OPC to examine IL‐1β expression in vivo by immunohistochemical analysis. We found that IL‐1β was minimally expressed in normal tonsillar epithelium. In contrast, the presence of IL‐1β was generally observed in most HPV‐negative OPC tumour cells, whereas expression was not observed in most HPV‐negative OPC tumour cells. However, staining was variable amongst sections and the presence of IL‐1β was observed in tumour sections from certain HPV‐positive biopsies .Chemokine gene expression is predominantly induced by pro‐inflammatory cytokines, usually by activation of the MAPK and NFκB signalling pathways.c). A similar staining pattern was observed for NTF isolated from different donors .Both IL‐1α and IL‐1β potentiate their pro‐inflammatory actions by binding to IL‐1R expressed by target cells. Immunofluorescence staining of NTF06 cells using a specific anti‐human IL‐1R1 antibody followed by confocal microscopy analysis showed punctate expression of IL‐1R1 in NTF, providing evidence of both cell surface and intracellular receptor expression Fig. c. A simvia IL‐1R, NTF06 were incubated with conditioned medium derived from SCC89 cells in the presence or absence of the highly specific IL‐1R1 antagonist anakinra, and levels of CXCL8, CCL2 and CCL5 secreted quantified by ELISA. Medium from SCC89 cells was used in these experiments because these cells were identified as expressing most abundant IL‐1 as well as generating a robust chemokine release profile by NTF. An MTT cytotoxicity assay was initially used to confirm that neither anakinra nor the placebo control were toxic to NTF in concentrations up to 100 μg/mL . As with previous data, both NTF and SCC89 when incubated alone expressed minimal CXCL8 and CCL2, whereas NTF06 incubated in SCC89 conditioned medium significantly induced the secretion of both chemokines . Pre‐incubation of NTF06 with anakinra significantly (p < 0.01) abrogated secretion of CXCL8 and CCL2, reducing levels to that of unstimulated NTF06. Interestingly, pre‐incubation with the placebo significantly (p < 0.01) reduced levels of CXCL8 whilst increasing levels of CCL2 (p < 0.01) although in both instances the levels of these chemokines were still significantly higher than that detected after anakinra treatment . The pattern of CCL5 expression differed to that of CXCL8 and CCL2. Overall, levels of CCL5 were much lower than those observed for CXCL8 or CCL2. CCL5 was detected in SCC89 medium alone, although secretion significantly increased when the media was incubated with NTF06 (p < 0.05). Treatment with anakinra significantly (p < 0.01) reduced CCL5 levels, although not back to baseline, whilst treatment with placebo had no effect . To confirm specificity, IL‐1R1 gene expression by NTF06 was reduced using gene specific siRNA oligonucleotide probes . When incubated with SCC89 conditioned medium, secretion of CXCL8 was significantly reduced from 8.0 ± 0.9 ng/mL in mock‐transfected NTF06 to 3.6 ± 0.9 ng/mL (p < 0.01) in siRNA treated cells . To determine that this phenomenon was not due to one individual NTF batch, we repeated the anakinra blocking experiment with NTF derived from 3 separate donors and tested for CXCL8 release in response to SCC89 conditioned medium. Treatment of all batches of NTF with SCC89 conditioned medium led to a significant increase in CXCL8 secretion (p < 0.001). In addition, pre‐incubation with anakinra resulted in a total abrogation of CXCL8 chemokine secretion (p < 0.001) in response to conditioned medium derived from SCC89 cancer cells for all NTF tested that was not evident in the placebo control (Fig. To determine whether the IL‐1 expressed by HPV‐negative cells was responsible for inducing chemokine release in vitro model to examine HPV‐positive versus HPV‐negative cancer‐stromal interactions, and how such interactions may influence micro‐environmental chemokine release.Despite the often late diagnosis of HPV‐positive OPC and lymph node involvement, prognosis is significantly better than that of its HPV‐negative counterpart.Chemokine protein array data demonstrated that the conditioned medium from HPV‐negative OPC when incubated with NTF contained increased amounts of chemokines encompassing most chemokine family members. Several previous reports have shown that OPC cells have the ability to express chemokines,et al. showed that silencing IL‐1β with lentivirus‐delivered shRNA significantly inhibited oral squamous cell carcinoma cell growth both in vivo and in vitro, heavily implicating IL‐1β as a key regulator of the tumour microenvironment during HPV‐negative oral cancer development.et al., showed that infection of primary keratinocytes with HPV16 E6, but not E7, abrogated IL‐1β processing and secretion in a proteasome‐dependent manner that was mediated by ubiquitin ligase E6‐AP and p53.in vitro cultured HPV‐positive and HPV‐negative OPC cells observed in our study. Indeed, in preliminary immunohistochemical studies on a small number of human HPV‐negative and HPV‐positive OPC biopsies, we found, in general, that HPV‐negative tumour cells appeared to express more abundant levels of IL‐1β than HPV‐positive tumour cells. However, we also observed IL‐1β expression by some HPV‐positive tumour cells, suggesting that loss of IL‐1β in vivo is not absolute. It is plausible that expression levels of IL‐1β may differ significantly by HPV status in vivo and therefore a larger cohort study examining IL‐1β levels in HPV‐negative/positive OPC is warranted.The most studied and potent of inflammatory mediators belong to the TNF and IL‐1 families. We found transcripts for IL‐1α and IL‐1β preferentially in HPV‐negative cells. Indeed, IL‐1β was expressed exclusively in HPV‐negative OPC. Constitutive expression of IL‐1β has been detected previously in keratinocytesvia IL‐1β secreted by HPV‐negative OPC. In support of these findings, siRNA ablation of NTF IL‐1R1 also significantly reduced chemokine secretion in response to HPV‐negative SCC89 conditioned medium. Inhibition of chemokine secretion using siRNA to IL‐1R was not as effective as inhibition with anakinra. This may be due to more efficient blockade of IL‐1R by anakinra compared to siRNA treatment where IL‐1R was reduced by 88%, and so signalling may occur through the IL‐1R still expressed by si‐RNA treated cells. In addition, we used siRNA specific for IL‐1R1 and so IL‐1β may exert its actions on other IL‐R family members expressed by NTF. Our data provides evidence that OPC HPV status may radically influence tumour biology by altering the inflammatory microenvironment. Indeed, increased levels of IL‐1β have been found in the cell constituents of HPV‐negative compared to HPV‐positive OPC, supporting the observations in our study.Although our study is the first to identify IL‐1R1 expression on tonsillar fibroblasts, this receptor has been detected previously on gingival fibroblasts.One of the major roles of chemokines in tumour biology is to attract leukocytes to tumour tissue. Although no study has reported data specific to HPV‐positive/negative OPC, abundant tumour‐associated macrophages and neutrophils have been found in HPV‐negative tumours in animal models, and in human HNC where they have been linked to poor prognosis.in vitro are not representative of tumours in vivo due to tumour hypoxia, nutrient exhaustion, and changes in vasculature supply. The data presented in our study will therefore need confirmation through in vivo and human cancer tissue studies.Whilst chemokines driving myeloid cell infiltration appear to promote tumour progression, there is evidence that recruitment of certain T‐cell populations is associated with improved prognosis. Indeed, in a prospective study, the presence of elevated levels of tumour‐infiltrating CD8+ T‐cells correlated with a good prognosis in HPV‐positive compared to HPV‐negative OPC.via specific interaction with IL‐1R, whereas this molecular mechanism was less prominent in HPV‐positive OPC cells. IL‐1‐driven chemokine release may have a key role in the increased neutrophil and macrophage burden observed in HPV‐negative cancers, which is often associated with poor prognosis.In summary, we have found for the first time, that HPV‐negative OPC cells constitutively express IL‐1 that stimulates significant chemokine release from tonsillar fibroblasts Appendix S1: Supporting InformationClick here for additional data file.


Laparoscopic-adjustable gastric band (LAGB) complications are increasingly recognised as follow-up time increases. These are most commonly related to the gastric band or port site, but complications of the connecting tubing are also reported. We present a case of LAGB tubing penetration through the transverse colon causing abdominal sepsis in a complex surgical abdomen and review prior published cases of abdominal viscus penetration by LAGB tubing. Like complications involving all LAGB components, these often present with non-specific abdominal signs and symptoms and undergo abdominal CT as an early investigation. This makes knowledge of normal and pathological imaging features of LAGB components important in radiology practice. LAGB complications typically present with non-specific abdominal symptoms and signs meaning a CT scan of the abdomen is often an early investigation.4Surgical management of obesity has proven superior to any non-surgical approach.We present the case of a 67-year-old female with a complicated surgical history. She initially presented to our facility with a strangulated large bowel containing ventral hernia, 10 years after undergoing LAGB procedure elsewhere. At this time, she remained morbidly obese and had been lost to follow-up. She underwent emergency surgical hernia repair with resection of necrotic transverse colon and loop ileostomy formation. One year later she presented with a LAGB port-site infection and had the port removed with the tubing sutured to the abdominal wall. During both admissions, the LAGB tubing was repeatedly seen in a stable position adjacent to the transverse colon on CT , with noElective reversal of the ileostomy was then performed 6 months following this but was complicated by abdominal sepsis in the early post-operative period. Abdominal CT demonstrated transection of the remaining proximal transverse colon by the LAGB tubing . This re3 Previously reported cases of LAGB tubing erosion into visceral structures are described in 18 Migration of different forms of intra-abdominal catheter into bowel wall has also been reported, and port system infections and the presence of a free end of tubing are predisposing factors.10 Thus this case, in combination with previous literature, suggests that in patients in which the LAGB is no longer functioning intra-abdominal tubing should be removed when port-site removal or other abdominal surgery is performed. This is perhaps especially important when the tubing is seen on imaging to lie near a viscus which may be vulnerable to erosion over time. Awareness of this can help guide radiology reporting and surgical practice.Intragastric migration/erosion of the band is a well-recognised LAGB complication reported to occur in 3.9% of cases, but penetration of the connecting tubing into an abdominal viscus is rare.10Although the number of new LAGB procedures being performed is reducing steadily there are a large cohort of patients with this device already in place. As LAGB tubing complications are primarily related to mechanical stress they are likely time-dependent and prevalence will continue to increase with ongoing follow-up.19,20 This may be diagnostic as in our case, but as demonstrated in While plain radiographs and upper gastrointestinal series may be used to evaluate LAGB complications, most cases will undergo abdominal CT as part of investigation.Complications related to LAGB tubing are increasingly recognised in clinical practice. When combined with the ever-expanding use of CT for investigation of abdominal complaints, this highlights the importance of the imaging features of all LAGB components for radiologists. Clear visualisation of LAGB tubing penetration through the colon as demonstrated here has rarely been reported.LAGB complications can be related to any of the components including the connecting tubing, and tend to present with non-specific abdominal signs and symptoms and undergo abdominal CTLAGB tubing complications often occur in patients who have had previous revision or removal of the LAGB port site due to infectionLAGB tubing has the potential to penetrate through abdominal structures and may cause severe illnessThe large numbers of patients with LAGB devices in place coupled with the increase in tubing-related complications as follow-up time increases makes knowledge of the normal and abnormal imaging features of LAGB tubing important in radiology practice


Background: A National human papilloma virus (HPV) Vaccination Programme for the prevention of HPV infection and associated disease using the quadrivalent HPV vaccine (4vHPV) has been funded and implemented in Australia since 2007, initially for girls only and extended to boys in 2013, with uptake rates among the highest observed worldwide. Aim: We report on the impact of this national programme on HPV prevalence and associated disease burden and estimate the potential impact of adopting a nonavalent HPV (9vHPV) vaccine. Methods: We performed a non-systematic literature review of studies measuring the burden of HPV-associated disease and infection in Australia before and after introduction of HPV vaccination. We also included key national reports with estimates of HPV-related disease burden. Results: Substantial declines in high-grade cervical disease and genital warts among vaccine-eligible women have been observed. Reductions in genital warts incidence and HPV prevalence among heterosexual men of similar age were observed before introduction of the male vaccination programme, indicating a substantial herd effect. 9vHPV vaccine is expected to prevent up to 90% of cervical and 96% of anal cancers. Of an estimated 1,544 HPV-associated cancers in 2012, 1,242 would have been preventable by the 4vHPV vaccine and an additional 187 anogenital cancers by the 9vHPV vaccine. Conclusions: Vaccination using 4vHPV vaccine has had a large demonstrable impact on HPV-related disease in Australia. A switch to 9vHPV could further reduce the HPV-associated cancer burden. With continued high coverage among both males and females, elimination of vaccine-type HPV disease seems achievable in Australia. Human papillomavirus (HPV) infections result in a substantial burden of disease globally as cervical cancer, anogenital and oropharyngeal malignancies and anogenital warts in both men and women. Vaccination for the prevention of HPV infection has been available for more than a decade. In 2007, Australia became one of the first countries to fund and implement a National HPV Vaccination Programme. The ongoing school-based programme for girls (aged 12–13 years) and a community-based programme for women up to the age of 26 years (that concluded in 2009) have used a 3-dose schedule of the quadrivalent HPV vaccine Gardasil (4vHPV vaccine), which protects against HPV types 6, 11, 16 and 18. In 2013, the funded programme was expanded to boys , making Australia one of 22 countries and territories  that have implemented or announced their intention to implement a gender-neutral HPV vaccination programme. From January 2018, a 2-dose course of the nonavalent HPV vaccine Gardasil 9 (9vHPV vaccine), which targets 4vHPV types plus HPV 31, 33, 45, 52 and 58, replaced the 4vHPV programme.Given the changing approaches to HPV disease prevention in Australia, we provide an overview of the current epidemiology of HPV-associated disease from multiple Australian data sources. We also report on the progress made in the decade following commencement of the National HPV Vaccination Programme, and provide an estimate of the further impact achievable.We performed a non-systematic review of the published literature to identify recent epidemiological studies that measured the burden of HPV-associated disease and infection in Australia. We searched the Medline and Embase databases for studies published from January 2007 to April 2016 using key search terms related to disease outcome measures, including papillomavirus infection, genital warts or condylomata acuminanata, cancers, carcinomas and neoplasms associated with HPV  and recurrent respiratory papillomatosis. Searches were limited to studies in humans and those that included ‘Australia’ as a text word. We selected articles that measured the prevalence or incidence of HPV-associated disease and infection before and after introduction of the vaccination programme, focusing on publications from 2014 onwards to indicate contemporary HPV disease burden estimates. In addition to articles identified through the searches, our review included data from publications since April 2016 that the authors were aware of. Reports from the Australian Institute of Health and Welfare (AIHW) were reviewed to report the documented incidence of HPV-associated cancers and cervical disease burden nationally and to estimate the disease burden preventable with HPV vaccination. We calculated the incremental benefit of the 9vHPV vaccine (on top of the 4vHPV vaccine) by applying the proportion of cases attributable to 9v-non4vHPV types, reported in the published literature, to the number of HPV-attributable cancer cases.Following the implementation of the National Cervical Screening Programme (NCSP) in 1991, the incidence rate of cervical cancer in Australia decreased from ca 18 to 6–7 per 100,000 between 1991 and 2002 and has since plateaued for over a decade, reported at 7.4 per 100,000 women in 2014 .-20.16-20Declines in HPV prevalence have also been observed among men. Although most men do not seroconvert after exposure to HPV, persistent infections are associated with seroconversion. A retrospective HPV DNA analysis of stored urine and urethral specimens from heterosexual men 25 years and younger who were positive for chlamydia found that the prevalence of 4vHPV types declined between 2004 and 2015 : 12–25; post-vaccination: 7%, 95% CI: 5–9; adjusted prevalence ratio: 0.37, 95% CI: 0.22–0.60, p < 0.0001) . Two HPVDecreases in the incidence rates of high-grade cervical abnormalities (HGAs) in vaccine-eligible age cohorts of women are well documented by Australia’s cervical screening registers at both state and national levels. The greatest and earliest decline in HGA incidence occurred among the youngest age group (< 20 years) who, by 2014, had a rate of 5.0 per 1,000 women screened nationally, less than half the rate in 2007 . By 2011A linkage study using data from the Victorian Cervical Cytology Registry and the National HPV Vaccination Programme Register showed that incidence of HGAs among vaccine-eligible women (aged 12–17 years in 2007) who completed vaccination was lower than among unvaccinated women  . FurtherGenital warts are often emotionally distressing to the patient and treatment can be long and painful, with a proportion of cases occurring recurrently. Although only a minority of genital warts require hospital treatment, estimates before commencement of HPV vaccination indicate a substantial overall number of hospitalisations . The impA substantial herd effect on genital warts from the female vaccination programme has been observed in young unvaccinated heterosexual males . HospitaJuvenile-onset recurrent respiratory papillomatosis (JORRP) is a rare condition that develops in childhood, which is acquired via vertical transmission of HPV 6 or 11 and can be associated with significant morbidity and mortality . Recent Given the long latency period between HPV infection and progression to cancer, the full impact of HPV vaccination on rates of HPV-associated cancers is yet to be seen. A recent analysis of more than 800 invasive cervical cancer cases in women aged 20–99 years (median: 50 years) from seven pathology laboratories in Queensland, NSW and Victoria from 2005 to 2015 found that HPV 16 and 18 were detected in 51.6% and 19.6% of the specimens, respectively, comprising 71.8% of all cancers . The addThe vast majority (up to 95%) of anal SCCs are associated with HPV . A retroData on the proportion of penile, vaginal and vulvar cancers containing HPV by genotype are lacking in Australia, but are available from international meta-analyses. In a meta-analysis of 29 studies (mostly from North America and Europe), ca 70% of vaginal cancers were HPV-positive, with HPV 16 being the most commonly detected type (54%) followed by HPV 18 and 31 . A reporAmong an Australian hospital-based sample of 248 oropharyngeal cancers, of which 50 were HPV-positive, HPV 16 and 18 were the most prevalent types , with HPV 33 detected in two cases . Data onWe estimate that the replacement of 4vHPV vaccine with 9vHPV vaccine in the Australian vaccination programme can potentially prevent an additional 15% of cervical cancers and 11% of anal cancers . Based oOur paper summarises the current state of HPV disease epidemiology in Australia, providing a benchmark for the evaluation of imminent changes to HPV prevention strategies. Although it is not an exhaustive literature review, we highlight the progress made by the 4vHPV vaccination programme and the potential gains of including the 9vHPV vaccine in the programme. Similar successes have been observed in European countries that have implemented HPV vaccination programmes, particularly those achieving similarly high coverage. Declines in vaccine-type HPV prevalence have been observed among vaccinated women in Belgium, England, Italy, the Netherlands, Norway, Scotland and Switzerland -54. EvenClinical trials indicate that the 9vHPV vaccine is efficacious in the prevention of cervical, vulval and vaginal precancerous lesions and persistent infection with vaccine-type HPV , and thaThe success of any HPV vaccination programme hinges on achieving and maintaining high rates of vaccine uptake among both girls and boys. The effectiveness, cost-effectiveness and overall population impact is expected to be greatest if vaccination occurs before sexual debut, thereby affording protection against HPV prior to the age at which adolescents and young adults are at highest risk of being infected ,63. HPV Australia has been one of the leading countries in implementing public health programmes for the prevention of HPV, being among the first to introduce a National HPV Vaccination Programme for girls and young women and subsequently the first to expand eligibility for government-funded universal vaccination to boys. Inclusion of boys in the programme was considered vital for reasons of equity given the substantial disease burden in men, particularly MSM who would not benefit from a female-only vaccination programme, and the expected incremental reduction in HPV infections among women. A gender-neutral programme also assured the acceptability of the programme and allowed the prevention of HPV-associated cancer to be framed as an issue for all young people. The changes to the NCSP are an important advance in the prevention of HPV-associated disease. With continued improving vaccine uptake, Australia is moving towards elimination of vaccine-type HPV disease .


We investigated the infection status and genotype distribution of human papillomavirus (HPV) in women of different ages and various ethnic groups in the Yili region, Xinjiang, China. We checked the HPV genotypes of 3,445 samples of exfoliated cervical cells using the PCR–reverse dot blot method. The total infection rate of HPV was 25.60% . The ethnic stratification showed that the infection rates were 22.87% (196/857) in Uygur, 21.55% (122/566) in Kazak, and 27.89%  in Han individuals. The most prevalent high-risk genotypes were HPV16, HPV52, and HPV53 in Uygur and Kazak and HPV16, HPV52, and HPV58 in Han ethnic groups. The age stratification showed that the infection rates in Han, Uygur, and Kazak women were up to 40.9% (61/149) in those aged 26–30 years, 41.5% (22/53) in those over 61 years old, and 30.2% (29/96) in those 46–50 years old, respectively. Therefore, HPV infection and HPV genotype distribution varied among the different age groups of the three ethnic groups. In 2015, approximately 98,900 new cases of cervical cancer and 30,500 associated deaths were recorded in China2. Xinjiang is one of the areas in China with the highest incidence of cervical cancer. The prevalence of cervical cancer in Uygur women is significantly higher than that in Han and other ethnic groups. One of the highest incidence rates of cervical cancer in Xinjiang has been detected in Uygur women , involving mostly young women3. Human papillomavirus (HPV) is the leading cause of cervical cancer. Therefore, cervical cancer can be prevented, discovered in the early stages, and cured. HPV is widespread in humans, and more than 120 types of HPV have been isolated. More than 20 genotypes are classified as high-risk HPV, of which HPV16 and HPV18 are the most carcinogenic4. Together, these two genotypes cause ~70% of cervical cancer cases. The key risk factors for HPV carcinogenesis are persistent HPV infection with abnormal expression of HPV early proteins and instability of the HPV gene, resulting in host gene imbalance5. The distribution of HPV genotypes in different regions varies widely6. The HC2 High-Risk HPV DNA Test, approved by the US Food and Drug Administration (FDA), is a standard method for detecting the 13 high-risk HPV viruses; however, it does not distinguish among specific genotypes7. In 2014, the FDA approved the use of a PCR-based genotyping method for first-line screening of cervical cancer in women aged 25 years or older8. In 2015, the guidelines set by the Society of Gynecologic Oncology (SGO) and the American Society for Colposcopy and Cervical Pathology (ASCCP) addressed the issue of using HR-HPV testing alone as the primary screening method, and recommended genotyping for HPV16 and 18 as a classification method for HR-HPV-positive women9. Recently, Sun et al. showed that a PCR-reverse dot blotting HPV test could provide a reliable and sensitive clinical reference10. In the current study, we aimed to investigate the distribution of HPV genotypes in different ethnic and age groups in Yili, Xinjiang, China. This study will help us further to understand HPV infection and provide information about the diagnosis and treatment of HPV-related cancers and the development of vaccines in local regions in Xinjiang.Approximately 527,600 new cervical cancer cases and 265,700 cases of death due to cervical cancer were recorded worldwide in 2012. Cervical cancer is the third most common cause of female cancer deaths in developing countries. The incidence and mortality rates of cervical cancer in low-income countries are higher than those in high-income countries, and approximately 90% of cervical cancer deaths occur in developing countries2 = 13.797, P = 0.001) . The HPV infection rates in women of Han, Uygur, and Kazak ethnicities were 27.89% , 22.87% 196/857) and 21.55% (122/566), respectively. The HPV infection rate was significantly different among the three ethnic groups , followed by HPV52 with an infection rate of 4.06%  and HPV53 with an infection rate of 2.67% . The most common high-risk genotypes in the Han sample were HPV16, with an infection rate of 18.04% (140/776), HPV52, with an infection rate of 12.89% (100/776), and HPV58, with an infection rate of 7.09% (55/776). In the Uygur women, the most common high-risk genotypes were HPV16, with an infection rate of 25.19% (67/266), HPV52, with an infection rate of 9.40% (25/266), and HPV53, with an infection rate of 6.77% (18/266). The most common high-risk genotypes in the Kazak individuals were HPV16, with an infection rate of 24.08% (46/191), HPV53, with an infection rate of 10.99% (21/191), and HPV52, with an infection rate of 7.85% (15/191). The HPV genotype composition varied among the three ethnic groups, and HPV16 infection differed significantly among the three ethnic groups . The double infection rate, i.e. infection with two HPV genotypes, was 21.43% (189/882); the triple infection rate was 5.10% (45/882), and the rate for four or more infections was 1.7% (19/882). According to ethnic group, the rates of single infection, double infection, triple infection, and quadruple or more infection were 72.87% (411/564), 20.57% (116/564), 4.96% (28/564), and 1.59% (9/564) in the Han women; 71.94% (141/196), 21.43% (42/196), 4.59% (9/196), and 2.04% (4/196) in the Uygur women; and 63.11% (77/122), 25.41% (31/122), 6.56% (8/122), and 4.92% (6/122) in the Kazak women. Infection with a single HPV genotype was most common among all three ethnic groups  and ≥61 years   for the group aged 26–30 years and 31.3% (36/115) for the group aged ≤25 years. In the Han women, the HPV infection rates were 40.94% (61/149) for the group aged 26–30 years and 36.00% (27/75) for the group aged 56–60 years. In the Uygur women, the HPV infection rates were 33.87% (21/62) for the group aged 26–30 years and 41.51% (22/53) for the group aged ≥61 years. In the Kazak women, the HPV infection rates were 30.21% (14/53) in the group aged 46–50 years and 25.00% (5/20) in the group aged ≤25 years. The HPV infection rates differed significantly among the three ethnic groups aged 26–30 years . Of these 55 cancer patients, 21 were Han women, 20 were Uygur women and 14 were Kazak women. The prevalence rate was 1.04%  for Han women, 2.33% (20/857) for Uygur women and 2.47% (14/566) for Kazak women. Thirteen of the 55 patients (23.64%) were negative for HPV infection, and the other 42 (76.36%) were HPV positive. Of these 42 HPV-positive patients, 19 were Han, 14 Uygur and 9 Kazak. Furthermore, 14 of the 19 Han patients (73.68%) were HPV16 positive; 12 of the 14 Uygur patients (85.71%) were HPV16 positive; and 8 of the 9 Kazak patients (88.89%) were HPV16 positive. Of the 55 patients, 30 (54.55%) had single HPV infection; 10 (18.18%) had double HPV infection; and 2 (3.64%) had triple HPV infection. No quadruple or more than quadruple HPV infection was observed in these patients   had HPV infection. In 30 cases of cervical squamous cell carcinoma by TCT diagnosis, 24 (80.00%) had HPV infection. In 756 cases of cervical intraepithelial neoplasia (CIN), 118 (15.61%) had HPV infection. In 28 cases of cervical adenocarcinoma and polyps, 5 (17.86%) had HPV infection. A statistically significant difference was observed among the groups  . The HPV infection rate of G6P0–G10P10 patients was higher than the average infection rate of 18.06% (147/814). A significant difference was observed between the groups  was performed with PCR-reverse dot blot hybridization technology . All of the detection procedures were conducted in accordance with the manufacturer’s instructions. Detection involved HPV DNA extraction, PCR amplification, hybridization, membrane washing, color development, compliance with quality standards, and data interpretation. With this method, 23 different HPV genotypes could be detected: 17 high-risk genotypes, HPV16, 18, 31, 33, 35, 39, 45, 51, 52,53, 56, 58, 59, 66, 68, 73 and 82; and 6 low-risk genotypes, HPV6, 11, 42, 43, 81 and 83. The lower limit of the virus copy number of detections was less than 1.0 × 1022.A gynecologist swabbed the cervical cells, collected them in a tube, and sent them to our laboratory for testing as soon as possible. A pathologist reported the cytology test resultsP < 0.05 was considered statistically significant. The Bonferroni step-down procedure was used to compare two groups to minimize the risk of inflated type 1 error.Data were statistically analyzed with SPSS 17.0. Data were divided into the three different ethnic groups: Uygur, Kazak, and Han. The age group categories were ≤25, 26–30, 31–35, 36–40, 41–45, 46–50, 51–55, 56–60, and ≥61 years. The chi-squared test was used, and


Background: Assessment of human papillomavirus (HPV) type-specific viral load (VL) is a valid tool for determining the etiology of HPV-related skin tumors, especially when more than one HPV type is detected within one lesion.Methods: The causative HPV type was determined in 185 fresh-frozen tissue specimens of histologically confirmed common warts (CWs) collected from 121 immunocompetent patients. All tissues were tested using the type-specific quantitative real-time polymerase chain reactions (PCR) for the most common wart-associated Alpha-PV (HPV2/27/57) and Mu-PV types (HPV1/63/204). The presence of 23 additional low-risk HPVs was evaluated using a conventional wide-spectrum PCR.Results: HPV DNA was detected in 176/185 (95.1%) CWs and multiple HPV types in 71/185 (38.4%) lesions. Using the VL approach and a robust cutoff of one viral copy/cell established in this study, HPV2/27/57 were determined as causative agents in 41/53 (77.3%) and 53/71 (74.7%) CWs with single and multiple HPVs, respectively.Conclusions: CWs are mostly etiologically associated with HPV2/27/57 and only rarely with HPV1. In the majority of CWs containing multiple HPVs, a single HPV type was present in high concentration, indicating etiological association. No significant differences in VLs of lesion-causing HPV types in CWs containing single or multiple HPVs were found. Cutaneous warts are ubiquitous benign skin tumors   types belonging to genera To the best of our knowledge, only seven small-scale HPV prevalence studies performed on a total of 129 histologically confirmed CW tissue samples have been published in peer-reviewed literature to date  Gross e, with HP1, 1.1 × 104, and 4.4 × 104 viral copies/cell, respectively, suggesting an etiological role of these HPVs in a subset of CWs.Köhler et al.  suggesteThe aim of this study was to accurately determine the prevalence of several HPV types in by far the largest collection of fresh-frozen tissue samples of histologically confirmed CWs and to reliably assess the causative HPV based on estimation of type-specific HPV VLs using the robust cutoff value established in this study, especially in the most challenging warts containing multiple HPV types within one lesion.The study included 185 histologically confirmed fresh-frozen CW tissue samples prospectively collected from 121 immunocompetent patients, older than 5 years, referred to a single dermatology outpatient clinic in Slovenia between February 2014 and April 2016.The majority of patients  were older than 12 years, with a mean age of 29.3 years (standard deviation (SD) 16.4, median 25 years, range 5–78 years); 66/121 (54.5%) patients were males.n = 131), lower extremities including dorsal sides of feet (n = 26), upper extremities (n = 14), and head and neck region (n = 14). Mean duration of warts was 16 months , with a mean wart diameter of 8.4 mm .A single wart was collected from 76/121 (62.8%) patients and multiple warts from 45/121 (37.2%) patients: two, three, and five warts were collected from 28/121 (23.2%), 16/121 (13.2%), and 1/121 (0.8%) patients, respectively. Warts were collected from hands including wrists .Alpha-PVs (LR-HPVs): HPV2, 3, 6, 7, 10, 11, 13, 27, 28, 29, 32, 40, 42, 43, 44, 55, 57, 74, 77, 91, 94, 117, and 125 etiologically associated with various mucosal and cutaneous warts. Furthermore, in all samples, the determination of potential Mu-PV  infection was performed using three type-specific quantitative RT-PCRs, as described previously , following protocol for nucleic acid purification from mammalian tissues, and spectrophotometrically quantified using the NanoDrop 2000c spectrophotometer . The integrity of extracted DNA was verified by real-time polymerase chain reaction (RT-PCR) amplification of a 268-bp fragment of human beta-globin gene, as described previously  and HPV63 . Based on the clear bimodal distribution of estimated HPV VLs in 53 randomly selected CWs containing a single HPV, ranging from 5.0 × 10−4 to 7.1 × 106 copies/cell  CWs, and such an HPV was securely assigned as the causative HPV. In 4/71 (5.6%) warts, two HPVs present in the same lesion had high VLs and were both co-assigned as potentially causative HPVs (HPV1+27 and HPV1+57 in two CWs each). The causative HPV could not be reliably determined in 15/71 (21.1%) CWs containing multiple HPVs due to the fact that: (i) the VLs of all HPVs detected within the lesion were far <1 viral copy/cell (six samples), or (ii) in addition to targeted HPVs with minute VLs, the CW also contained additional HPVs , for which we were unable to measure VLs due to the lack of appropriate methods , and others were collected from legs , arms , and head . The most frequent causative HPV in all four anatomical regions was HPV57 and caused all four CWs collected from the head. Interestingly, all four CWs samples with co-assigned causative HPVs (HPV1+27 and HPV1+57 in two CWs each) were collected from hands.Among 45 patients with multiple CWs sampled, complete concordance of HPVs detected was found in only 14 (31.1%). However, at least one common HPV was present across all warts obtained from the same individual in 40/45 (88.9%) patients. In addition, in all 21 patients in whom multiple warts were available and HPV VLs could be estimated in all CWs sampled, 100% agreement across assigned causative HPVs was found.Although a broad range of HPV types and a relatively high frequency of multiple HPVs have previously been reported in CWs' tissue samples . Multiple HPVs were detected in 71/185 (38.4%) CWs, which is substantially higher in comparison to the majority of similar studies , with the most frequent combinations being HPV1+27 and HPV1+57, in line with the findings of some previous studies , which is in line with similar studies, in which HPV positivity ranged from 64.3 to 100%  , and significantly higher median VLs of HPV2/27/57 were observed in comparison to HPV1/63/204 , irrespective of single or multiple HPVs present in CWs. This phenomenon could be a consequence of different phylogenetic backgrounds of the species Alphapapillomavirus 4 and the genus Mu-PV  that could be used to reliably differentiate between causative and bystander HPV types in CWs, especially in challenging cases such as CWs containing multiple HPVs, have not been established yet. Based on the VLs measured in 53 randomly selected CWs containing a single HPV type, and clear bimodal distribution of the VLs obtained, in this study the cutoff value for causal determination was set at one copy/cell. Using this approach, HPV57 has been identified as the most important CW-related HPV causing a significant proportion of CWs . Three HPV types (HPV2/27/57) were determined to be causative HPVs in three-quarters of CWs irrespective of whether CWs contained single or multiple HPVs, whereas HPV1 was assigned as the causative HPV in ~4% of CWs, again irrespective of single/multiple HPV status. Similarly, a previous study performed on nine samples determined HPV2/27/57 and HPV1 to be causative HPVs in 6/9 (66.6%) and 1/9 (11.1%) CWs, respectively  CWs, we assigned two co-causative HPVs. In these CWs, we most probably had colliding wart lesions, each associated with an individual causative HPV, as recently demonstrated in cervical intraepithelial neoplasia lesions using laser capture micro-dissection and HPV genotyping, under the “one virus, one lesion” concept  CWs, and, in one-fifth of CWs containing either single or multiple HPVs, the causative HPV remained undetermined, suggesting that these CWs might be etiologically attributed to HPV types from other species such as Alphapapillomavirus 2 or 8, Gamma-PVs, Nu-PVs, or other novel, still-unidentified HPV types. Although the cumulative prevalence of HPVs from species Alphapapillomavirus 2 and 8  was relatively low  and their VLs were not estimated in this study, these HPV types could potentially be considered as the causative HPV of warts in question because HPV1 and 63, co-detected in these CWs, were present in minute quantities. In previous studies performed on histologically confirmed CWs, the reported detection rate of HPV4/60/65 (genus Gamma-PV) and HPV41 (genus Nu-PV) varied between 0 and 100%  as potential causative HPVs of nine warts, due to the lack of appropriate methods we were unable to measure the VLs of these HPVs, and consequently these CWs remain without a reliably assigned causative HPV.The main limitation of this study is potential selection bias because our cohort might not be fully representative of the general population due to enrollment of patients referred to the tertiary level of dermatological care, possibly due to more treatment-resistant warts or clinically atypical lesions. In addition, considering the benign nature of CWs and potential adverse events of the biopsy, including pain, allergic reactions to local anesthetics, and scarring  as causative HPVs of three-quarters of CWs irrespective of whether CWs contained single or multiple HPVs. This study provides further evidence for informed decisions about selecting the most appropriate targets for future vaccine(s) against cutaneous HPV-related benign tumors.All datasets generated for this study are included in the article/The studies involving human participants were reviewed and approved by Medical Ethics Committee of the Republic of Slovenia (consent number 63/10/13). Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.MP, KF, JM, and VB designed the study. VB and KF organized the database and performed the statistical analysis. LH and KF performed laboratory testing. RK and BL performed histological assessment. VB drafted the first version of the manuscript. LH, KF, and MP drafted individual sections of the manuscript. MP produced the final version of the manuscript. All authors revised manuscript, read it, and approved the submitted version.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.


Sequence logo plots have become a standard graphical tool for visualizing sequence motifs in DNA, RNA or protein sequences. However standard logo plots primarily highlight enrichment of symbols, and may fail to highlight interesting depletions. Current alternatives that try to highlight depletion often produce visually cluttered logos.EDLogo plot, that highlights both enrichment and depletion, while minimizing visual clutter. We provide an easy-to-use and highly customizable R package Logolas to produce a range of logo plots, including EDLogo plots. This software also allows elements in the logo plot to be strings of characters, rather than a single character, extending the range of applications beyond the usual DNA, RNA or protein sequences. And the software includes new Empirical Bayes methods to stabilize estimates of enrichment and depletion, and thus better highlight the most significant patterns in data. We illustrate our methods and software on applications to transcription factor binding site motifs, protein sequence alignments and cancer mutation signature profiles.We introduce a new sequence logo plot, the EDLogo plots and flexible software implementation can help data analysts visualize both enrichment and depletion of characters  across a wide range of applications.Our new The online version of this article (10.1186/s12859-018-2489-3) contains supplementary material, which is available to authorized users. At each position in the alignment, the standard logo plot represents the relative frequency of each character  by stacking characters on top of each other, with the height of each character proportional to its relative frequency. The characters are ordered by their relative frequency, and the total height of the stack is determined by the information content of the position. The visualization is so appealing that methods to produce logo plots are now implemented in many software packages  and web[WebLogo , Seq2LogSeq2Logo , iceLogo[iceLogo ).enriched; that is, at higher than expected frequency. In many applications such enrichments may be the main features of interest, and the standard logo plot serves these applications well. However, sometimes it may be equally interesting to identify depletions: characters that occur less often than expected. One example of this, highlighted in [N-X- S/T where X is any amino acid apart from proline P [H4AC and H3K4ME1 at the gene start and gene end regions in lymphoblastoid cell lines. The standard logo plot represents strong depletion(s) by the absence of character(s), which can be difficult to discern visually.Because the standard logo plot scales the height of each character proportional to its relative frequency, it tends to visually highlight characters that are ghted in , involveroline P , 9. Anotroline P  notes dey axis. However, we have found that the resulting plots sometimes suffer from visual clutter – too many symbols, which distract from the main patterns of enrichment and depletion.To better highlight depletions in amino acid motifs, Thomsen et al.  suggestsEnrichment Depletion Logo or EDLogo plot – that highlights both enrichment and depletion, while minimizing visual clutter. In addition, we extend the applicability of logo plots to new settings by i) allowing each “character” in the plot to be an arbitrary alphanumeric string ; and ii) allowing a different “alphabet” of permitted strings at each position. We also introduce Empirical Bayes statistical methods to stabilize estimates of enrichment and depletion, and thus better highlight the most significant patterns in data. All these new features are implemented in our R package, Logolas, which can produce generalized string-based logo and EDLogo plots. We illustrate the utility of the EDLogo plot and the flexibility of the string-based representation through several applications.Here we suggest a simple solution to this problem, producing a new sequence logo plot – the x axis, how a probability vector p compares with another probability vector q. For example, suppose that at a specific position in a set of aligned DNA sequences, we observe relative frequencies p== of the four bases {A,C,G,T}. The goal of the logo plot might be to represent how p compares with the background frequencies of the four bases, which for simplicity we will assume in this example to be equal: q==. Verbally we could describe the change from q to p in several ways: we could say “T is depleted”, or “ A,C and G are enriched”, or “T is depleted, and A,C and G are enriched”. While all of these are valid statements, the first is the most succinct, and our EDLogo plot provides a visual version of that statement. The second statement is more in line with a standard logo representation, and the last is in essence the approach in [In essence, the goal of a logo plot is to represent, at each position along the roach in  , let p= denote the probabilities of the n elements C1,…,Cn (which can be characters or strings) permitted at that position, and q= denote corresponding background probabilities. Define r= by: At a particular position, where j along the x axis, the EDLogo plot plots the element Ci, scaled to have height |ri|, and above the x axis if ri is positive, or below the x axis if ri is negative. Elements are stacked (from bottom to top) in order of increasing ri, so that the largest characters are furthest from the axis.Then at position The basic strategy has close connections to ideas in , but witEDLogo plot for p vs q is essentially a mirror (about the x axis) of the EDLogo plot for q vs p  sample from that population, conditional on an event E (e.g. a transcription factor binding). Indeed, by Bayes theorem, under this assumption we would have for some unknown (positive) “parameters” pi must sum to 1, Since the he model  implies λ. Even if p and q are observed without error, there is a non-identifiability in estimating λ: we can set λi=cpi/qi for any positive c. Equivalently, if we consider estimating the logarithms li:= logλi, we can set Now consider estimating the parameters k. Note that ri in  the definition of the median of r= with total stack height 2, but subtracting any number between the two central observations (0 and 1) would lead to the same total stack height. Thus, if we measure parsimony by total stack height, there exist multiple equally-parsimonious plots, giving the user a decision to make.When the number of classes mentclass2pt{minimacted in ), every Among these equally-parsimonious solutions, subtracting the smallest number corresponds to favoring an “enrichment” representation, whereas subtracting the largest number favors a “depletion” representation, and subtracting the median treads a middle ground between the two. See Additional file EDLogo plot described above typically works well provided that no probabilities pi or qi are very small (or zero!). Very small values of pi or qi can cause very large values of ri| which can undesirably dominate the plot.The basic ri|) is unstable estimates of small probabilities from low counts of rare events. We found that the simplest solution to this problem – use of pseudocounts to stabilize estimates of small probabilities [EDLogo plot from stabilized estimates for In practice we have found the most common source of this problem .Although our stabilization method is ideally-suited to settings where EDLogo plot, and compares it with the standard logo and the weighted Kullback–Leibler logo (wKL-Logo) plot [Figure go) plot , in fourdisc1 of Early B cell factor EBF1 from ENCODE [EDLogo plots, both of which aim to highlight depletion as well as enrichment: the EDLogo plot introduces less distracting visual clutter than the wKL-Logo plot, producing a cleaner and more parsimonious visualization that better highlights the primary enrichments and depletions. In particular, the EDLogo plot is most effective at highlighting depletion of bases G and C at the two positions in the middle of the sequence. This depletion is hard to see in the standard logo because of its emphasis on enrichment, and less clear in the wKL-Logo due to visual clutter. This depletion pattern is likely meaningful, rather than a coincidence, since it was also observed in two other previously known motifs (known3 and known4) of the same transcription factor [The first application (panel (a)) is a setting where the standard logo plot is widely used: visualizing transcription factor binding sites (TFBS) –18  is essentially impossible to see in the standard logo plot, is visually detectable in the wKL-Logo plot, and clearest in the EDLogo plot. Again, the EDLogo plot is more parsimonious than the wKL-Logo plot, and consequently the primary N-X- S/T motif stands out better in the EDLogo plot. In addition to showing depletion of P at the expected position (+1), the EDLogo plot also highlights depletion of P at position +3, suggesting an extended motif N-X- S/T-X.The second example (panel (b)) shows an amino acid motif corresponding to niProtKB ; see AddniProtKB  as an exEDLogo plot is particularly effective at highlighting depletion as well as enrichment.The next two applications (panels (c) and (d) of Fig. x axis), together with base frequencies at the ± 2 flanking bases. We also follow the common convention of orienting the strand so that the mutation is from either a C or a T, yielding six possible mutation types: C→T, C→A, C→G, T→A, T→C, T→G. This Figure panel illustrates two important points. First, it illustrates the flexibility of our software package Logolas, which allows arbitrary strings in a logo. For all three logo plots  we use this to represent the six mutation types by six strings of the form X→Y, and we find the resulting plots easier to read than the pmsignature plots in [EDLogo plot is a better visual summary than the other plots. Specifically the EDLogo plot best highlights the primary aspects of this signature: enrichment of C→T mutations, and depletion of G at position +1. Here the depletion of G at +1 may be a bi-product of the enrichment of C→T mutations combined with the overall depletion of CpG sites in the genome due to deamination [Panel (c) shows logo plots representing an estimated cancer mutation signature profile (signature 12) from a clustering analysis of a large number  somatic mutations by   are plotted on a log scale, whereas in the standard plot they are on an absolute scale. This means that in the EDLogo plot it is differences in the heights of characters that matter , whereas in a standard plot it is ratios of heights. In this case the frequency of C→T is 0.96 and T→C is 0.03, with other mutations essentially absent. Consequently T→Cis enriched relative to other mutation types, but nowhere near as strongly as C→T. When these enrichments are plotted on the raw scale, as in the standard plot, essentially only the C→T enrichment is visible. On the log scale, both are visible. Which representation is preferable depends on how much one wants to emphasize subtler vs stronger enrichment patterns.In this example the EDLogo plots for all 27 mutational signature profiles reported by [For readers interested in other cancer mutation signatures, we provide orted by  in AdditEDLogo and wKL-Logo plots seem more informative than the standard logo plot. Specifically, the standard logo plot is dominated by the high deviation from background frequencies at the intergenic, exon and intron regions, and the differences in enrichments and depletions among regions are difficult to discern. In comparison, the EDLogo and wKL-Logo plots highlight a number of differences among regions  shows logo plots summarizing the relative abundance of 5 different histone marks in different genomic contexts . For exN-linked glycosylation sites above involves very small (e.g. zero) counts of some amino acids at some sites, and provides an example of both the need to stabilize estimates of The example of pi, of amino acid i is estimated by counting the number of times, mi, that amino acid i occurs in this central position in an observed data set of m=5422 sequences. The maximum likelihood estimate (mle) for pi is mi/m. However, every observed amino acid at this central position is an Asparagine (N), so the mle for pi is 1 for the N amino acid, and is 0 for all other amino acids. These 0 estimates for pi lead to unreasonably large – indeed, infinite – values for Before explaining the benefits of our EB approach, we first motivate the need for stabilization, using the central position of this motif as an example. At this position  the frequency, pi is estimated by EDLogo plot can be constructed from the pseudo-count-based estimates of p,q. However, we found the resulting plot  to each observed count before computing estimates. For example, using a pseudo-count of 0.5 for each of the 20 amino acids that could occur, lot Fig. a somewhap and q and then taking their log-ratio. Consequently, the method produces estimates of N alone in the center position, highlighting that the data can be explained purely by strong enrichment of N.Our EB stabilization method takes a different approach. Specifically, it directly stabilizes estimates of ple Fig. b the ploEDLogo strategy of using a median adjustment in   [In addition, the tment in  to reducth = 11) , 23EDLogo plot, designed to highlight both enrichment and depletion of elements at each position in a sequence (or other index set). We have also developed statistical methods that can improve these plots by stabilizing enrichment estimates for rare events. We have implemented these methods, as well as standard logo plots, in a flexible R package Logolas, which offers many other features: the ability to use strings instead of characters; various customizable styles and color palettes; several methods for scaling stack heights; and ease of integrating logo plots with external graphics like ggplot2 [We present a new sequence logo plot, the  ggplot2 .Project Name : Logolashttps://github.com/kkdey/Logolas)Software Download page : Github R package (https://kkdey.github.io/Logolas-pages/Project Home page : Operating system : Platform independentProgramming Language : R (≥ 3.4)License : GPL (> = 2)Any restrictions to use by non-academics: Nodata function.Data : The data used in this paper are reported in Additional file TFBS example Statistical justification of the Empirical Bayes stabilization of Additional file 2Figure S2.EDLogo plots for six different motifs of the EBF1 transcription factor. The PWMS for known1 and known2 come from the TRANSFAC database [known3 from the JASPAR database [known4 from [disc1 and disc2 were discovered by the ENCODE project [known3, known4 and disc1) show depletion of G and C in the middle of the binding site.database ; known3 database ; known4 wn4 from ; disc1 a project . Three oFigure S3. Comparison of the EDLogo plot (a) with pmsignature [EDLogo plot highlights the depletion of G at the right flanking base more clearly than does the pmsignature plot. The use of strings to represent mutations in the center is arguably more intuitive than the pmsignature representation.ignature  plot (b)ignature . The EDLFigure S4. Illustration of EDLogo for all mutation signatures from Shiraishi et al. EDLogo plots for the 27 mutation signature profiles estimated by [C→G, C→T, etc at position 0 on x axis) reflect the relative frequency of each substitution type among somatic mutations contributing to the signature profile, while the heights of the bases at flanking positions on either side reflect the relative frequency of each base at these flanking positions.mated by  using daFigure S5. Illustration of median adjustment of a position specific scoring matrix (PSSM). The PSSM shown here is for the binding motif of the protein D-isomer specific 2-hydroxyacid dehydrogenase, catalytic domain (IPR006139) . The data has been obtained from the 3PFDB website [bottom panel) is arguably less cluttered than the non-adjusted version (top panel). website , 23. TheFigure S6. Choice of median. An illustration of how the choice of median value used for centering the EDLogo representation of the EBF1-disc1 transcription factor binding site example from Fig. top), whereas choosing the largest median value favors depletion (bottom) and choosing the mid-point of the interval treads a common ground between enrichment and depletion (middle). As default option in our software and for all the EDLogo plots in this paper, we use the smallest median centering. (PDF 2615 kb)entclass1pt{minimaAdditional file 3Supplementary Table. Tables of positional frequency and weight matrices used for creating the different EDLogo plots in the Figures and the Supplementary Figures of the manuscript. (PDF 184 kb)