Progression of systemic scleroderma (SSc), a chronic connective tissue disease that causes a fibrotic phenotype, is highly heterogeneous amongst patients and difficult to accurately diagnose. To meet this clinical need, we developed a novel three-layer classification model, which analyses gene expression profiles from SSc skin biopsies to diagnose SSc severity. Two SSc skin biopsy microarray datasets were obtained from Gene Expression Omnibus. The skin scores obtained from the original papers were used to further categorize the data into subgroups of low (<18) and high (≥18) severity. Data was pre-processed for normalization, background correction, centering and scaling. A two-layered cross-validation scheme was employed to objectively evaluate the performance of classification models of unobserved data. Three classification models were used: support vector machine, random forest, and naive Bayes in combination with feature selection methods to improve performance accuracy. For both input datasets, random forest classifier combined with correlation-based feature selection (CFS) method and naive Bayes combined with CFS or support vector machine based recursive feature elimination method yielded the best results. Additionally, we performed a principal component analysis to show that low and high severity groups are readily separable by gene expression signatures. Ultimately, we found that our novel classification prediction model produced global gene signatures that significantly correlated with skin scores. This study represents the first report comparing the performance of various classification prediction models for gene signatures from SSc patients, using current clinical diagnostic factors. In summary, our three-classification model system is a powerful tool for elucidating gene signatures from SSc skin biopsies and can also be used to develop a prognostic gene signature for SSc and other fibrotic disorders. Systemic Scleroderma (SSc) is a multifaceted disease that exhibits heterogeneity and clinical variability among patients, often complicating diagnosis and decisions regarding treatment. SSc causes skin fibrosis, systemic vascular alterations and collagen accumulation from chronic hardening and tightening of the skin and connective tissues, eventually leading to organ failure and poor prognosis . One levClinical progression of SSc is most often measured by skin thickening using the modified Rodnan skin score (mRSS), which entails a 17-point assessment of skin thickness on various areas of the body, culminating in a 51-point maximum scale of severity. Expression levels of several genes have been associated with skin score –12. HoweIn this current study, we constructed classification models with the goal of predicting SSc skin severity based on gene expression profiles and identifying marker gene sets that correlate with high or low severity patients. Ongoing and future studies will focus on the importance of genes identified herein in disease and how they relate to patient-to-patient heterogeneity. Our classification models were capable of readily parsing patients, based on genomic profile alone, into high or low severity groups as defined by mRSS and led to the identification of gene sets associated with disease progression. Thus, our model is a powerful tool not only for diagnosis but also to find previously unknown disease-related genes that warrant further investigation.http://www.ncbi.nlm.nih.gov/geo/). Dataset 1 (GEO Accession: GSE9285) [The two datasets used in this study were obtained from the Gene Expression Omnibus (GSE9285)  containsGSE9285) . Data weDataset 2 (GEO Accession: GSE47162) [In all data sets a two-class classification was used to establish classification models that distinguish between "high group" and "low" group ". Skin scores between 18 and 51 were categorized as "high severity", and those less than 18 were categorized as "low severity". An analysis of over 900 patients indicated that a mRSS threshold between 18 and 25 was optimal for distinguishing between patients that would progress or regress . We tookFor the Dataset 1, microarrays were visually inspected for defects or technical artifacts, and poor-quality spots were manually excluded in the original study. Spots with fluorescent signals less than two-fold of local background in both Cy3- and Cy5- channel were excluded. Following this pre-processing step, 28,495 probes with at least 80% of their data points were included in downstream analysis. Background correction and quantile normalization were performed using limma package . The datWe used a five-fold cross-validation design to compare the performance of the various classification methods. To find the best parameters for the model, we performed another “nested” leave-one-out (LOO) cross-validation on the five original training sets. We identified performance information for each combination of classification method and parameter set and selected the optimal combination to be used for the testing set. This methodology has been described and aims to limit the effects of overfitting the model while objectively evaluating model performance .Three classification methods were applied to each of the two datasets: support vector machine (SVM), random forest (RF) and naive Bayes (NB). We used the SVM methods implemented in the e1071 package in R, and selected the RBF  kernel, which has been shown to produce outstanding classification performance in previous studies . We emplIn feature selection method, a small subset of features are identified that, together with the classification methods, are most effective in distinguishing samples belonging to different groups. There are three broad categories of selection methods: filter, wrapper, and embedded methods. Filter methods rank the features or genes regardless of the model. They select the most significant feature or gene based on univariate measure. Wrapper methods evaluate subsets that are optimal with respect to a subset evaluator such as a classifier. Embedded methods incorporate the search algorithm into the classifier. In this study, two filter methods and two wrapper methods were applied. The Chi-Squared method is a filter method that evaluates features or genes individually by measuring their chi-squared statistic with respect to the classes. The correlation-based feature selection method (CFS) method is also a filter method, which measures the correlation between attributes and recognizes those feature subsets in which each feature is highly correlated with the class but uncorrelated with other subset features. The SVM-based recursive feature elimination method (SVM-RFE) is a wrapper method used in microarray data analysis. It eliminates unessential genes and selects better and more compact gene subsets. Random forest-based backward feature elimination method (RFVS) is another wrapper method that constructs RFs in an iterative manner. Upon each iteration, RFVS builds a random forest after discarding genes with the smallest importance values in the last iteration. The returned subset of genes is the one with the smallest out-of-bag error .Principal component analysis (PCA) was performed using function pca in R (version 3.2.2) package FactoMineR (version 1.31.4). Configuration: scale.unit = TRUE, ncp = 5, ind.sup = NULL, quanti.sup = NULL, quali.sup = NULL, row.w = NULL, col.w = NULL, graph = TRUE, axes = c .The component loadings used in PCA are the correlation coefficients between the variables (rows) and factors (columns) and the values for the genes of PC1 and PC2 indicate the "weights" for the genes. The higher the loading value, the higher weight a gene carries in composing the PC. Background correction and quantile normalization were performed using limma package . Data waHeat maps showing gene expression for the patients by severity group were constructed using normalized and scaled expression levels. Quantile normalization was performed on the raw microarray data, and the normalized expression levels were then Log2 transformed, centered (on 0), and scaled to the same range across genes, so that each gene is evaluated with equal weight.Ingenuity Pathway Analysis (Qiagen) (Version 43605062) was used to perform Canonical Pathway Analysis and Upstream Regulator Prediction. Analysis was performed on four lists of microarray probe IDs: identified by CFS classifier in Dataset 1 and differentially expressed, identified by SVM-RFE classifier in Dataset 1 and differentially expressed, identified by CFS classifier in Dataset 2 and differentially expressed, and identified by SVM-RFE classifier in Dataset 2 and differentially expressed. Differential expression was considered statistically significant if the t-test p-value was < 0.05 following Bonferroni correction based on the number of probe IDs identified for each classifier-dataset combination. The Bonferroni correction was applied to limit the false positive discovery rate associated with multiple hypothesis testing. Probe IDs that were successfully mapped by IPA were included for analyses. The direction of expression was positive for probes with higher expression in high severity patients and negative for probes with higher expression in low severity patients. For each dataset, the appropriate reference probe set was based on the microarray used in the original study.Several studies, including retrospective cohort analyses and prospective clinical trials, have shown that the severity of skin sclerosis, as assessed by the modified Rodnan skin thickness score (mRSS), is predictive of disease outcome . HoweverWe first tested various two-class classification models to determine which could best distinguish between samples designated as low or high severity. The accuracy, sensitivity, specificity, and Mathew’s correlation coefficient (MCC) were determined for each of the classification models . MCC valThe CFS selection method identified 84 probe IDs in Dataset 1 and 89 probe IDs in Dataset 2 as being differentially expressed between low and high severity groups. Likewise, the SVM-RFE method identified 450 probe IDs in Dataset 1 and 50 probe IDs in Dataset 2 . SpecifiWe performed principal component analysis (PCA) using all targets to pass data cleaning and filtering to visually interpret the variability within and between severity groups. PCA based on all four feature selection methods showed clear separation between severity groups based on gene signature for both Datasets . The PCAIn addition, the Log2 normalized expression values were used to create heat maps for each Dataset. Heat maps were generated to show probe IDs from Dataset 1  and DataBased on the high performance of our models, we hypothesized that the genes identified by the models and differentially expressed between severity groups may have biological significance. To further explore the relationship between these groups of genes, we used Ingenuity Pathway Analysis (IPA) to analyze the lists of genes that were both identified by the classifier and that showed differential expression between severity groups . This study is one of the first to describe an independently validated classification prediction model for gene signatures in SSc using current clinical diagnostic factors. We used applied three well-established classification methods, SVM, RF, and NB, coupled with four different feature selections, to clinically derived microarray data and compared the relative efficiency of the models. Interestingly, RF and CFS showed high MCC values of 0.96 and 1.00 for both of the independent datasets that we used, suggesting an efficient diagnostic two-layered system for SSc. This two-layered model system is capable of reducing the dimensionality of large independent data to find a more useful subset for further exploration. Moreover, due to the heterogeneity of patients with SSc, a prediction model that can deduce disease severity in an unsupervised manner is essential. To get an idea of how the gene lists generated by our classifiers compared to conventionally-derived differentially expressed genes, we also performed a formal differential gene expression analysis comparing low and high severity patients from each dataset (data not shown), which identified many probe IDs that were statistically significant between groups. While some genes identified by the feature selection methods overlapped with this list of conventionally derived differentially expressed genes, in each case our classifiers also identified several genes that were not found with the conventional differential gene analysis. This highlights the value of our classifiers in finding genes that can be useful for classification purposes but may not be identified by conventional methods.Further validating the efficiency of the two-step model system, principal component analysis showed a clear separation of patients with high and low skin scores when subjected to the model. This clustering of datasets further substantiates the efficiency of the model system to reduce complexity and distinguish between high and low severity gene profiles. After performing the PCA, we examined which microarray probe IDs were associated with the highest loading values to gain a sense of which were the most important in separating the severity groups. The most highly weighted probe IDs were associated with a broad array of biological processes. In Dataset 1, Tetraspanin 8 showed one of the highest loading levels. Tetraspanins have previously been shown to alter fibrosis through the regulation of epithelial cell-basement membrane interaction . For DatOur model assigned patients into high and low severity groups using skin score, the standard diagnostic measurement for SSc. This bolsters the efficiency and effectiveness of our model system to determine outcome, which should help decrease the lack of specificity that currently hampers clinical treatment. The analytical pipeline reported herein highlights the potential applicability of an unsupervised, tiered correlation method, whereby disease severity can be classified based upon genetic signature alone. Furthermore, the novel gene sets that have been identified as correlating with disease severity may be further investigated to provide insight into the molecular mechanisms underlying a clinical trait of SSc, a complex fibrotic disease. Our methodology can be used to stratify patients to assess response to therapy and to guide appropriate recruitment to clinical trials.in vivo. Additionally, it is possible that these genes may interact in pathways that are not yet known. However, our analysis of potential upstream regulators did identify one small network, controlled by the cytokine oncostatin-m (OSM), that could drive relatively high expression of P-selectin (SELP), cholesterol 24-hydroxylase (CH25H), c-c motif chemokine ligand 2 (CCL2), and angiopoietin 2 (ANGPT2) and low expression of heat shock protein family B member 3 (HSPB3) in higher severity SSc patients. Evidence in the literature shows that OSM is capable of regulating these genes with directionality suggesting that OSM positively regulates the genes associated with the more severe skin scores [To evaluate the functional relevance of these clusters in the context of disease, we used various tools within Ingenuity Pathway Analysis to explore interactions between genes that were both identified by our classification methods and showed differential expression between low and high severity groups. Our analysis did not return any pathways that were significantly enriched by the genes in our lists, showing that the genes identified here to do not belong to known biological pathways. This result indicates that our classification methods are capable of identifying genes that, when taken together, can accurately be used to predict disease severity although they may not participate in the same pathways n scores –32. Studn scores –36. Relen scores –40. TherWe observed some differences in the probe IDs identified by our classification methods and in subsequent downstream analyses. For the two datasets, the CFS method identified similar number of probe IDs that were used for classification . On the other hand, the SVM-RFE method identified 450 probe IDs for Dataset 1 and 50 for Dataset 2. We believe that these differences stem from the fact that CFS and SVM-RFE use intrinsically different methods for feature selection. The CFS method is a filter method that gives priority to genes whose expression is highly correlated with a class. SFM-RFE is a wrapper method that discards genes that have only a small impact on classification. Interestingly, when only the genes that have significantly different expression levels between severity groups are considered, the numbers of genes identified by the classifiers are more analogous. This indicates that the majority of the 450 probe IDs identified when the SVM-RFE method was applied to Dataset 2 were useful for classification, despite not being considered differentially expressed between groups.Our novel three-classification model system of SVM, RF, and NB is an efficient and powerful tool for developing gene signatures from SSc skin biopsies and can also be used to develop a prognostic gene signature for SSc. Our models achieved high accuracy, specificity, and sensitivity, demonstrating their potential use as diagnostic indicators. Furthermore, our model system can be used to model other fibrotic disorders by substituting different phenotypes in the training cohort. Current ongoing and future studies using alternative methodologies will investigate specific genes identified by the classifiers to determine how they may relate to disease pathogenesis and progression.S1 TableB: Back biopsy, FA: Forearm biopsy, F: Female, M: Male, W: White, A: Asian, AA: African American, H: Hispanic. Shading indicates individual patients.(DOCX)Click here for additional data file.S2 TableF: Female, M: Male, W: White, A: Asian, AA: African American, H: Hispanic.(DOCX)Click here for additional data file.S3 TableProbe IDs included in analysis were both identified by the classification method and significantly differentially expressed between low and high severity groups . CFS: correlation-based feature selection method, SVM-RFE: SVM-based recursive feature elimination method. Ratio: number of genes from input list that occur in pathway. B-H p-value: Benjamini-Hochberg adjusted p-value of right-tailed Fisher’s Exact Test.(DOCX)Click here for additional data file.S1 FigResults based on Chi-squared feature selection method are shown in A (Dataset 1) and B (Dataset 2). Results based on RVFS feature selection method are shown in C (Dataset 1) and D (Dataset 2).(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.


Impairments in translation have been increasingly implicated in the pathogenesis and progression of multiple neurodegenerative diseases. Assessing the spatiotemporal dynamics of translation in the context of disease is a major challenge. Recent developments in proteomic analyses have enabled the resolution of nascent peptides in a short timescale on the order of minutes. In addition, a quantitative analysis of translation has progressed in vivo, showing remarkable potential for coupling these techniques with cognitive and behavioral outcomes. Here, we review these modern approaches to measure changes in translation and ribosomal function with a specific focus on current applications in the mammalian brain and in the study of neurodegenerative diseases. RNA translation is a dynamic process that regulates many baseline cellular functions and adaptations in response to environmental cues. Studies in the early 1960s demonstrated the involvement of protein synthesis in memory formation and recall, thereby providing evidence that translation links mechanisms at the cellular level to complex brain function ,3. As teProtein synthesis in eukaryotes relies on a complex network of coordinating mechanisms that facilitate RNA translation and ribosome function . ModeAccordingly, several techniques to analyze translation at the protein level have been developed, and these can be paired with mass spectrometry (MS) analysis of the whole proteome to correlate an altered translation with total protein levels . These nOriginal methods to study translation began with metabolic labeling of polypeptide chains with radiolabeled methionine in 1953, a practice still used to validate changes in the overall levels of protein synthesis . Currentde novo protein synthesis across conditions by tracking the incorporation of the different tags over time. For example, one can stably culture primary neurons under media containing a light isotope of arginine, induce membrane depolarization with a potassium chloride treatment, and in parallel supplement with heavy arginine-containing media for an hour. MS analysis of these samples with proper controls would exhibit the background levels of light arginine-incorporated proteins compared to newly translated, heavy arginine-incorporated proteins. Multiplexing with multiple amino acid isotopes is also possible, enabling the MS detection of nascent peptides across multiple timepoints or experimental conditions within the same sample.Stable isotope labeling with amino acids in cell culture (SILAC), which was developed nearly two decades ago, provides a relatively simple technique to quantitatively measure proteins via MS . This meStable isotope incorporation is one of the most accurate techniques that assess protein synthesis in the living human brain, and it has recently been used to directly study translation in excised human cortical and hippocampal tissue . HoweverThe Holt group recently published two articles utilizing pSILAC to assess changes in the axonal nascent proteome in Xenopus retinal ganglion cells ,32. In tAs an alternative to SILAC-based nascent proteomic experiments, the Schuman group developed a method in which synthetic, bio-orthogonal amino acids are incorporated into the proteome B 35]. T. T35]. TIn the seminal study of BONCAT by Dieterich et al., human embryonic kidney (HEK) cells treated with AHA for two hours showed stable and reproducible incorporation into over 100 proteins which were confirmed as nascent peptides using pSILAC . Since tAs MS and affinity purification techniques have progressed, BONCAT has grown to a remarkable depth of peptide capture across a variety of input samples. By combining BONCAT with CA1 neuropil micro-dissection, Hodas et al. identified the local dynamics of the neuropil nascent proteome following a dopamine receptor agonist treatment . BioinfoAs with pSILAC, shorter timescale proteomic analyses struggle to enrich sufficient labeled material for broad coverage in MS for in vivo use. In 2014, the Cline group developed a method to enrich biotin-tagged proteins from BONCAT-click chemistry experiments in vivo . This mede novo proteome in multiple mouse models of tauopathy within four hours of treatment . T. T52]. TThe Klann group recently followed up on their study by using BONLAC to assess the nascent proteome differences in acute slices prepared from fragile X syndrome (FXS) model mice, which do not express Fmr1, or fragile X mental retardation 1 . Here, BThough NCATs enable the greater interrogation of translation in neurological contexts, a limiting factor is the poor sensitivity inherent in using non-canonical forms of methionine results in nearly 500 times lower incorporation compared to regular methionine . ReportsPuromycin is an aminonucleoside antibiotic analog of tyrosine-tRNA that irreversibly binds to the C-terminus of a polypeptide, terminating synthesis for that protein . InitialOther methods utilizing puromycin-based nascent protein capture include treatment with puromycin analogs, which are amenable to click chemistry or fluorescent-based approaches ,70. HoweWhile the duration of tag incubation can be tailored to investigate translational dynamics across a variety of temporal ranges from several minutes to several hours, it is important to also consider the methodological limitations for the nascent proteomic techniques described here. As further development enhances the resolution of nascent proteomics to shorter timescales, reproducibility becomes a greater concern, both due to limitations in the technique and in the possible inherent biological variance. Experimentally determined Pearson’s correlation coefficients (PCC) between replicates are very high (>0.95) in medium (one to six hours) and long (over six hours) incubation times. However, short durations (under one hour) of tag incubation potentially suffer from technical variability stemming from differences in tag availability across cell compartments as well as fundamental kinetic differences between the exogenous, tagged amino acid and the endogenous version. As these sources of variation cause differences in tag incorporation, these would introduce experimental bias detected by downstream MS. For example, the shortest tag incubation time described in this review used a five-minute pSILAC incubation window coupled with SP3 . The expTranslation is a crucial component of neuronal health as well as memory and cognition, and it has increasingly been implicated in neurodegenerative diseases. However, little is known about how translational dynamics contribute to neuronal function. We briefly discussed a variety of methods  develope


Integrins are contributors to remodeling of the extracellular matrix and cell migration. Integrins participate in the assembly of the actin cytoskeleton, regulate growth factor signaling pathways, cell proliferation, and control cell motility. In solid tumors, integrins are involved in promoting metastasis to distant sites, and angiogenesis. Integrins are a key target in cancer therapy and imaging. Integrin antagonists have proven successful in halting invasion and migration of tumors. Overexpressed integrins are prime anti-cancer drug targets. To streamline the development of specific integrin cancer therapeutics, we curated data to predict which integrin heterodimers are pausible therapeutic targets against 17 different solid tumors. Computational analysis of The Cancer Genome Atlas (TCGA) gene expression data revealed a set of integrin targets that are differentially expressed in tumors. Filtered by FPKM (Fragments Per Kilobase of transcript per Million mapped reads) expression level, overexpressed subunits were paired into heterodimeric protein targets. By comparing the RNA-seq differential expression results with immunohistochemistry (IHC) data, overexpressed integrin subunits were validated. Biologics and small molecule drug compounds against these identified overexpressed subunits and heterodimeric receptors are potential therapeutics against these cancers. In addition, high-affinity and high-specificity ligands against these integrins can serve as efficient vehicles for delivery of cancer drugs, nanotherapeutics, or imaging probes against cancer. Integrins are heterodimeric receptors consisting of alpha and beta subunits. 18 α- and 9 β-subunits are transmembrane glycoproteins that combine non-covalently to form 24 distinct receptors. Integrins mediate cell adhesion to the extracellular matrix (ECM), and interact with distinct ECM proteins to remodel, migrate and respond to extracellular changes. Integrins function to regulate important biological processes such as proliferation, gene expression, cell survival and motility. The distribution of integrin proteins on the cell surface determines the type of ECM proteins able to bind to the cell and therefore impacts how a cell senses and responds to its microenvironment [The myriad of cellular responses initiated by integrin signaling stem from their ability to differentially recognize distinct sets of ligands. In normal tissues, cell migration and morphogenesis are regulated by interactions of cell surface integrins with the extracellular matrix proteins, such as collagen, fibronectin, laminin, osteopontin, vitronectin, tenascin, fibrillin and VCAM-1 , 3. Thise.g., fibroblast growth factor receptor) and integrins , resulting in the promotion of tumor growth [Ligated integrins enhance cell survival through several mechanisms, some of which include activation of the PI3K-AKT pathway, NF-κΒ signaling cascade, and p53 inactivation , 5. Convr growth . Antagone.g., WNT, MYC, AKT), integrins contribute to sustaining this aggressive subset of cells and regulating expression of stem cell specific markers. Drug resistance is dependent on tumor cells utilizing survival-inducing pathways, which can relate to ECM-integrin interactions. The integrin-ECM drug resistance strategy selects for cells that express integrins capable of adhering to the matrix proteins of the host organ thereby activating alternative pro-survival anti-apoptotic pathways.Cancer stem cells (CSCs) are therapeutically resistant cells that reside within the main tumor population, and are responsible for long-term tumor survival and proliferation. Integrin signaling is crucial to maintaining stem cell properties within CSCs , 9. PairIntegrins are prime targets for imaging and therapy in cancer . We haveSmall-molecule drugs and biologics directed against overexpressed cell surface proteins such as EGF receptor and HER2 in tumors have been effective in treating cancers . In the In this study, we sought to identify actionable integrins in 17 different cancer types by computational analyses of the transcriptome data obtained from The Cancer Genome Atlas (TCGA). Using RNA-Sequencing (RNA-Seq) expression data, overexpressed integrins were found, followed by a prioritization scheme involving the application of a ranking metric and filtering by FPKM absolute expression level. In addition, immunohistochemistry data from the Protein Atlas was integrated for validation of results and the most prominent integrin heterodimers on each cancer were predicted.1) logarithmic fold change of expression between cancer and normal samples, and 2) false discovery rate. The following sections detail the results of the selection process and the outcome of the individual targetable integrin subunits as well as the obligated integrin heterodimers. Accompanying this ranking system, the expressed integrin genes were filtered by the total level of expression, denoted by FPKM values, also obtained from TCGA. By comparing the predicted integrin targets against existing experimental immunohistochemistry data obtained from The Protein Atlas, the resultant targets were confirmed. Then, using the rules of heterodimeric integrin subunit pairing, therapeutic target integrins for each cancer were predicted.The strategy that was employed to determine integrin anti-cancer drug targets across 17 different tumor subtypes is depicted in the flowchart in Figure 1) overexpressed in cancer , 2) high ranking Metric score, and 3) having at least a moderate level of absolute expression. As described in the Introduction, the complete repertoire of integrin receptors is constituted of 27 integrin genes encoding for 18 α- and 9 β-subunits. These α- and β-subunits form 24 different known obligated heterodimers of integrins   are indicated by blank, or white boxes   . Integris Figure . The res) Figure .ITGA9 is the most widely underexpressed subunit across the 17 profiled cancers, ranging from −1.41 to −5.89 fold reduction in cancer compared to normal. In contrast, ITGAX is the most widely overexpressed subunit among the profiled cancers (1.54 to 8.11 linear fold change). Aggressive carcinomas with low 5-year survival rates display unique and diverse expression patterns. For example, cholangiocarcinoma (CHOL) overexpresses a large variety of subunits , whereas, the equally deadly pancreatic adenocarcinoma (PAAD) overexpresses a very limited set of integrins, including ITGA6 and ITGB4. Although both CHOL and PAAD have low 5-year survival rates, they display completely different integrin expression profiles. Interestingly, tumors originating from the same organ such as kidney chromophobe (KICH), kidney renal cell carcinoma (KIRC) and kidney papillary renal cell carcinoma (KIRP) share few integrin expression features with one another  for the integrin subunits are displayed for glioblastoma multiforme, hepatocellular carcinoma, and pancreatic adenocarcinoma Figure . The ranA ranking system for determining the best potential integrin drug targets was developed Figure ; the folFragments Per Kilobase of transcript per Million fragments mapped) values [In addition to considering integrin gene differential expression as a major selection criterion, candidate integrin subunits were further filtered on the basis of transcript expression levels. Normalized gene-expression level reported in FPKM  values  for all https://www.proteinatlas.org/humanpathology). The overexpressed integrins can be highlighted by integrating the results of RNA-seq differential expression  is not possible. IHC does not discriminate between different cancer subtypes within the same organ when pathologically examining samples for integrin expression. Extensive similarities between the IHC and RNA-seq expression data, however, do exist. In both methods of analysis, ITGA6 is overexpressed in head and neck, lung, liver, and pancreatic cancers.As one approach to validating that the expression of the candidate integrins at mRNA level were also expressed at the protein level, and elevated, we analyzed immunohistochemical data from the Human Protein Atlas . Both the IHC and the computational analysis methods  overexpresses a large variety of subunits possibly to increase migration and promote metastasis whereas pancreatic adenocarcinoma (PAAD) overexpresses a very limited set of integrins Figure . The relE Figure .The computational analysis Figure  and immu1) RNA expression does not always correspond to protein expression, and 2) non-TCGA samples were used. Comparing non-TCGA data (IHC data) to TCGA samples  can lead to discrepancies in analysis simply because a different data source was used. It is curious that there are inconsistencies with the immunohistochemistry data. For example, when staining ITGβ4, antibodies HPA036348 and HPA036349 showed reliable results whereas antibodies CAB002422 and CAB005258 do not stain normal glands that should typically be positive for ITGβ4. In order to combat this source of inconsistency and be stringent in the criteria for IHC analysis, only validated antibodies supported by immunoblotting evidence were used  may exhibit overexpression of a marker while adjacent sections may not express the marker. Moreover, consideration of intra-tumor heterogeneity is critical in the context of drug development and tumor targeting, since the overall drug responses can be influenced by the target being expressed in a generalized manner throughout the tumor or in a more limited, focal area. In this regard, single cell analyses, spatial transcriptomics, and immunohistochemistry can be vital in discerning these patterns of expression.Many solid tumors, such as GBM, LIHC, and PAAD exhibit marked cellular and molecular heterogeneity. This is critical to consider when analyzing transcriptome data as certain focal areas in the tumor  had only tumor samples. Abbreviations of cancer types used throughout this paper are in concordance with TCGA abbreviations and the full name of the cancer type can be found in the legend to Figure The data for the 27 integrin subunits  was extracted from the entire RNA-seq expression datasets. The DESeq2 R package was utilized for differential expression analysis . As the p-value, and a false discovery rate (FDR) adjusted according to the Benjamini-Hochberg method  data was sourced from the Human Protein Atlas –60. UsinIn order to begin comparing IHC expression data, the qualitative descriptors were converted into quantitative values. Each descriptor for expression was paired with a value . A weighted average was calculated according to the equation shown below.A weighted average for each integrin for each tumor type was generated using only the results from validated antibodies . OverexpFPKM absolute expression values were obtained from the TCGA dataset analyses on The Protein Atlas. For the subunits most highly ranked according to the metric, FPKM values were used to filter this set of integrin targets further. A cut-off of 10 FPKM was employed, as that threshold defines the moderately expressed transcript.Since integrin subunits associate as heterodimers to assemble a productive receptor, the top genes ranked by the metric were used to predict the protein heterodimer drug targets. Rules for integrin α and β subunit association were used as summarized in Figure


Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Optimization of molecular protocols has enabled the molecular biologist to produce next-generation sequencing libraries in several hours, leaving the analysis of sequencing data as the primary obstacle to wide-scale deployment of accessibility profiling assays. To address this obstacle we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.We executed a multi-dimensional grid-search on the NIH Biowulf supercomputing cluster to assess the impact of parameter selection on biological reproducibility and ChIP-seq recovery by analyzing 4560 pipeline configurations. Our analysis improved ChIP-seq recovery by 15% for ATAC-seq and 3% for DNase1-seq and determined that PCR duplicate removal improves biological reproducibility by 36% without significant costs in footprinting transcription factors. Our analyses of down sampled reads identified a point of diminishing returns for increased library sequencing depth, with 95% of the ChIP-seq data of a 200 million read footprinting library recovered by 160 million reads.We present optimized ATAC-seq and DNase-seq pipelines in both Snakemake and bash formats as well as optimal sequencing depths for ATAC-seq and DNase-seq projects. The optimized ATAC-seq and DNase1-seq analysis pipelines, parameters, and ground-truth ChIP-seq datasets have been made available for deployment and future algorithmic profiling.The online version of this article (10.1186/s12864-018-4943-z) contains supplementary material, which is available to authorized users. Regulatory control of RNA expression is a key component of the central dogma of molecular biology. Under the central dogma, protein and RNA products regulate RNA expression. Modules of expression compose expression states associated with specialized biological activities. The information contained within a genome is expressed in a temporally and spatially coordinated program of ever increasing differentiation. This program starts with the embryonic stem cell and grows progressively more specialized. Control over RNA expression is predominantly achieved through the cooperative behavior of multiple proteins that bind DNA called transcription factors (TF) which modify chromatin and recruit RNA polymerase II (RNAP) prior to transcriptional elongation and mRNA expression .Higher order control over RNA expression has been postulated to exist in terms of an ‘epigenetic landscape’  in whichChromatin accessibility profiling was developed to interrogate the genomic circumstances that drives a given expression state . These thttp://hpc.nih.gov) for each run. The results from each of the 4560 argument combinations was then assessed against ‘ground truth’ ENCODE chromatin immunoprecipitation (ChIP)-seq results describing TF binding within the GM12878 lymphoblastoid cell line for both DNase1 hypersensitivity and ATAC-seq chromatin accessibility profiling [The process of cellular gene regulatory network (GRN) reconstruction via TF footprinting is computationally intensive and requires multiple data transformations, each of which have multiple possible parameters that may or may not significantly impact the quality and accuracy of the network generated. To assess the impact of parameter selection on network generation, we executed a computational grid search across eight parameter arguments and five different pipeline steps, with a total of 4560 different pipelines evaluated. The process of evaluating 4560 different pipelines collectively utilized 72,960 CPUs with 3.75 GB RAM each. The mean total time that each pipeline took was 2 h, and running all pipelines on the Biowulf supercomputing system consumed more than 100,000 CPU hours  to investigate the relationship between read depth, number of footprints detected, and ChIP-seq recovery. Reads were sampled from 20 to 200 million single-end reads; the number of open chromatin regions, the number of footprints, and the ChIP-seq AUC was calculated at each sampling Fig. . InspectAs part of the procedure for ATAC-seq and DNase-seq, the enzymatically-digested genomic fragments are PCR amplified. This process of PCR amplification can introduce bias into the composition of the library via stochastic ‘PCR jackpotting’ in which randomly selected genomic intervals are amplified by the DNA polymerase . We evalWhen PCR duplication removal was introduced, the reproducibility of footprinting data alignments measured as Pearson correlation between two replicates’ genomic bins increased from 0.6 to 0.82 Fig. . Introdui) peak size, controlling the size of the bins; and ii) minDist, controlling the minimum distance allowed between two peaks. Analysis of grid search results indicates that peak size altered the biological reproducibility of detected open chromatin regions .Optimized Snakemake pipelines for ATAC and DNase1 footprinting are available for download at the Chiorini Lab github page  and technical (ATAC-seq) replicates. Similarly, smaller peak sizes of open chromatin regions (OCRs) lead to reproducibility improvements. Because of these small peak sizes, there is no reproducibility-optimized Wellington pipeline, as Wellington requires the sizes of shoulder and footprint sequences to sum to a value greater than 50 base pairs. Picard PCR duplicate removal is used in some AUC-optimized pipelines, whereas small peak sizes are not used in any AUC-optimized pipelines.PCR duplicate removal takes out identically-ended sequencing reads (reads that look like the result of PCR bias). For example, in a chromatin accessibility assay performed on a population of 50,000 cells, it is expected that the enzyme cuts the same base pair position in multiple genomes at some unknown rate. Pipelines that used PCR duplicate removal exhibited as much as a 50% reduction in the number of footprints found relative to their untreated counterpart pipelines. In our data, TF footprinting detection by Wellington was negatively impacted by PCR duplicate removal, with a mean reduction of 84% of the AUC above random. Similarly, larger peak sizes increase the genomic interval in which footprinting is detected while decreasing the degree of biological reproducibility. Lost opportunities to call footprints, in the case of peak size, or lost information to call footprints, in the case of PCR duplicate removal, may explain the apparent contradictions we observed between increased reproducibility and diminished AUC scores for certain pipelines.We noted that pipelines optimized for reproducibility in alignment and peak calling do not score well on AUC, and vice versa. This tradeoff is explicable by the two argument differences between these pipelines: HOMER peak size and bias correction. High reproducibility was found in samples with small peak sizes, and visual inspection determined those peaks overlapped with only the highest points of alignment, leading to safe and reliable peak assertions. However, as peaks are the only regions in which footprints are searched for, fracturing of open chromatin regions limits footprinting in regulatory regions and can reduce AUC. HINT’s known DNase bias correction increases reproducibility in DNase-seq footprints but reduces mean AUC. This phenomenon is currently inexplicable. Interestingly, none of the evaluated bias correction methods improved ATAC-seq footprint reproducibility or mean AUC.It is worth noting that overlap of footprints between replicates was higher in DNase1-seq pipelines optimized for reproducibility relative to AUC-optimized or default pipelines, and lower in ATAC-seq pipelines optimized for reproducibility relative to AUC-optimized or default pipelines. It is not clear why this is the case. Using an AUC-optimized pipeline with HINT results in the highest biological information possible gleaned from footprinting.We also found that AUCs representing ChIP recovery for ATAC-seq data in GM12878 were universally lower than ChIP recovery AUCs from DNase-seq data. Our optimized pipelines for ATAC-seq data used very sensitive alignment but were otherwise identical to optimized pipelines for DNase-seq for each algorithm. It may be the case that algorithms have been optimized on DNase-seq data and modes for using ATAC-seq data are not mature. Neither optimized pipeline using HINT utilized either known DNase1 bias or bias estimated from the data. As footprinting algorithms continue to include ATAC-seq training data, it is very likely that performance on this kind of data will improve.Chromatin accessibility profiling assays such as ATAC-seq and DNase1-seq offer the opportunity to rapidly characterize the regulatory state of the genome at a single nucleotide resolution. Significant optimization of molecular protocols has enabled the generation of sequencing libraries in several hours. The remaining computational analysis of next generation sequencing data is the primary obstacle to wide-scale deployment of accessibility profiling assays . To address this obstacle, we have developed an optimized and efficient pipeline for the analysis of ATAC-seq and DNase1-seq data.Our pipeline was designed to maximize ChIP recovery, the current ‘gold standard’ in transcription factor occupancy assessment. In addition, we performed a computationally intensive grid search across 4560 possible argument combinations to identify the impact of parameter selection on pipeline performance. Our analyses identified the optimal argument combinations for ATAC-seq and DNase1 analysis and these pre-optimized pipelines were translated into Snakemake pipelines for routine and reproducible deployment to supercomputing clusters. Each iteration of these analyses required approximately 11.5 CPU years to reach completion and are being made freely available at the Chiorini Lab Github to avoid duplication of effort within the scientific community. Additionally, the raw datasets and code for pipeline validation have been made publically available allowing investigators to easily profile, and rapidly improve, future pipelines and algorithms for transcription factor footprinting.Genome wide chromatin accessibility mapping offers a unique window into the regulatory state of the cell by allowing an investigator to rapidly map cellular genomic states. We anticipate continued development in algorithms and molecular protocols that will improve the accuracy and efficiency of gene regulatory network inference from ATAC/DNase sequencing. As chromatin profiling is a valuable tool to characterize the networks and TF binding events that define a cell’s operation, we expect that chromatin profiling assays will continue to see use in cancer genomics, cell engineering, and monogenic diseases cases evading exomic diagnosis. The optimized analysis pipelines and quantitative profiling metrics for construction of ATAC/DNase-seq networks provided here contribute to the scientific community by providing an easily accessed platform for analysis and pipeline engineering.http://www.bioinformatics.babraham.ac.uk/projects/fastqc). For ATAC-seq, FastQC adds additional information by identifying the tags remaining in the read from Tn5 tagmentation. Removal of tagmentation tags was accomplished via truncation of 20 bp of signal from the 5′ end of the transcript using Trimmomatic [The structure of our footprinting analysis pipeline is presented in Fig. mmomatic . For theFollowing quality control, the second step is to align reads to the genome using Bowtie2 . A seriehttp://broadinstitute.github.io/picard). These tools are then used to sort and index the aligned reads. If Picard is used to convert the reads to binary format, the aligned reads are also queued for PCR duplicate removal via the Picard MarkDuplicates function. PCR duplicates are identical copies of reads which can introduce PCR bias to the TF footprinting analysis by over-representing a small number of highly duplicated genomic regions. In genomic footprinting, PCR duplicate removal may lower bias or, alternatively, discard biological information.In the third step, aligned reads are converted to a binary format using (as the third argument) either SAMtools  or the BIn the fourth step, the aligned reads are analyzed by the HOMER  findpeakp-value cutoff and a desired false discovery rate. HINT is a hidden Markov model based footprinting algorithm that incorporates bias correction for known DNase1 digestion biases. Usefully, HINT also incorporates functions that enable de novo estimation of enzymatic bias from input data. The only parameter that can be passed to HINT is the mode of bias correction. Both Wellington and HINT return genomic intervals of predicted protein binding. These proposed protein binding intervals are then intersected on the genome with transcription factor position weight matrices to generate predicted transcription factor binding intervals and a final gene regulatory network.The final step of the analysis pipeline is to pass the aligned reads and OCRs to a TF footprinting identification algorithm. We evaluated two footprinting algorithms, Wellington and HINT. Wellington utilizes a beta-binomial distribution to estimate footprints and leverages the observation that enzymatically accessible genomic DNA in the nucleus results in strand-specific increases in enzyme activity around transcription factor footprints. Wellington requires two arguments: a https://www.encodeproject.org/experiments/ENCSR000EJD/ and https://www.encodeproject.org/experiments/ENCSR000EMT/, respectively. ATAC-seq data used to validate the pipeline was acquired from Buenrostro et al. [The data used to validate this pipeline came from GM12878, a lymphoblastoid cell line. DNase1-seq data used to validate the pipeline is ENCODE data  from theo et al.  via the Three metrics of data reproducibility were assayed across the pipeline. The first metric was the correlation of sequence read alignments. Under this metric, the genome is divided into 10 kilobase bins and the number of reads in each bin is compared between replicates for each bin. A summary Pearson correlation coefficient is then calculated, with higher Pearson correlation coefficients between genome bin sets suggestive of similar patterns of aligned reads across the genome.The second metric of reproducibility quantitatively assesses OCR reproducibility between replicates. Peaks are averaged across 10 kilobase bins and compared between replicates to produce a Pearson correlation coefficient that summarizes the reproducibility of the assay to detect OCRs across multiple replicates.The final reproducibility metric assayed was the percentage of TF footprints detected as present and overlapping in two biological replicates. This metric assesses the ability of the assay to detect biologically reproducible TF binding events.In addition to the three metrics of reproducibility, we assessed the ability of chromatin accessibility based data to recover transcription factor binding by comparing identified footprints to the ‘gold standard’ of ChIP-seq for each footprint. Transcription factor ChIP data for BHLHE40, BRCA1, CTCF, EBF1, IRF3, MXI1, NFE2, NFYA, NFYB, NRF1, STAT1, STAT3, USF2, ZNF143 and ZNF274 was downloaded from ENCODE. This data was compared to footprint coverage of the genome at all positions matching that transcription factor’s binding motif to assess the ability of transcription factor footprinting to recover known ChIP-seq binding information.http://www.bioinformatics.babraham.ac.uk/projects/fastqc), seqtk (https://github.com/lh3/seqtk), Bowtie2 v.2.2.9 [http://hpc.nih.gov).Software used in the pipeline includes FastQC  to 20, 40, 60, 80, 100, 120, 140, 160 and 200 million reads. Random seeds for seqtk were set to the same value as the number of reads being downsampled to and three permutations of the data were iteratively sampled. Downsampled samples were used to assess the impact of read number on the total number of transcription factor footprints detected and AUC recovery.ATAC-seq and DNase-seq data was downsampled using seqtk  curves were produced from these predictions and the area under curve (AUC) for each transcription factor was calculated. Across all of the transcription factors tested, there were on average 10 negative TF binding sites for each positive TF binding site.Additional file 1:Table S1. Parameters passed to each pipeline. Default, AUC-optimized and reproducibility-optimized pipelines for ATAC-seq and DNase1-seq are shown using each footprinting algorithm. Parameters for each pipeline are listed. These parameters correspond to Fig. github.com/ChioriniLab. (PDF 182 kb)Additional file 2:Figure S1. FastQC Reveals Tn5 Tags and PCR Chimerism. Non-random distributions of the first ~ 10 bases after tagmentation correspond to the Tn5 tagmentation after library primers are removed. This non-random region at the beginning of the read is characteristics of ATAC-seq data. In the ATAC-seq sample from Buenrostro, a second non-random region can be seen, suggesting PCR chimerism. (PDF 57 kb)Additional file 3:Figure S2. Trimming reads improves alignment of the GM12878 ATAC-seq reads. Tn5 transposase attaches mosaic end (ME) tags that need to be trimmed from the 5′ end of the read. Additionally, however, trimming low-quality base pairs from the 3′ end of the ATAC-seq reads so that all reads had the same length improved alignment to the genome (shown in green). With a 3 billion base pair genome, the chance that a sequence of a certain length will align randomly is high for sequences shorter than 17 base pairs. To minimize random alignment while removing low-quality base pairs for this ATAC-seq data, we trimmed the reads to a final length of 20 base pairs. (PDF 13 kb)Additional file 4:Figure S3. Visual inspection reveals consistent overlap between HOMER peaks and OCRs at peak size of 200. (a) Read coverage for DNase1-seq (top) and ATAC-seq (bottom) shown underneath open chromatin regions called by HOMER at peak sizes ranging from 10 base pairs to 2000 base pairs. Peak reproducibility between replicates was shown to be higher with lower peak sizes. Visualized using the Broad Institute’s Integrative Genomics Viewer (software.broadinstitute.org/software/igv/).  Metrics of reproducibility and biological information plotted against the HOMER argument minDist for all pipelines. Pipelines could have a minDist of 0, 50 or 500, and these values had no effect on correlation between replicates or recapitulation of known ChIP-seq. (PDF 1272 kb)


Autophagy is an evolutionarily conserved cellular process in which intracellular components are eliminated via lysosomal degradation to supply nutrients for organelle biogenesis and metabolic homeostasis. Flavivirus infections underlie multiple human diseases and thus exert an immense burden on public health worldwide. Mounting evidence indicates that host autophagy is subverted to modulate the life cycles of flaviviruses, such as hepatitis C virus, dengue virus, Japanese encephalitis virus, West Nile virus and Zika virus. The diverse interplay between autophagy and flavivirus infection not only regulates viral growth in host cells but also counteracts host stress responses induced by viral infection. In this review, we summarize the current knowledge on the role of autophagy in the flavivirus life cycle. We also discuss the impacts of virus-induced autophagy on the pathogeneses of flavivirus-associated diseases and the potential use of autophagy as a therapeutic target for curing flavivirus infections and related human diseases. Flaviviridae family, including hepatitis C virus (HCV), dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV) and Zika virus (ZIKV), that contain important human pathogens and thus exert a global burden on public health . N. N399]. Apart from repressing the antiviral response to support viral replication, several studies have suggested that HCV-induced autophagy may enhance viral RNA replication via interactions between autophagic machinery and viral proteins  411,412412. GuevAutophagy was also shown to promote HCV virion assembly. Tanida et al. showed that knockdown of ATG7 and Beclin 1 moderately reduces the extracellular infectivity of infected cells without affecting the intracellular expression of viral proteins and RNAs  415], i, i415], C47A, a mutant ATG4B that inhibits the lipidation of ATG8 family proteins, results in severe cytoplasmic vacuolation and cell death . A. A418]. Apart from the replication of viral RNA and infectious HCVcc, several HCV viral proteins have also been shown to induce autophagy. Su and colleagues reported that HCV NS4B is the only viral protein that sufficiently induces incomplete autophagy via residues 1~190  419]. G. G419]. The molecular mechanism by which HCV exploits to initiate autophagy has been investigated. Several groups have reported that HCV may activate ER stress, which is required for autophagy activation  401,405,409. In HCV was also shown to induce ER stress to interfere with protein kinase B (PKB)-tuberous sclerosis (TSC)-mTOR complex 1 (mTORC1) signaling and thus activate autophagy  439]. A. A439]. In addition to acting as a pro-viral factor benefiting viral growth, autophagy has also been shown to eliminate LDs and mitochondria  429,430430. VescLittle is known about whether HCV indeed activates autophagy to regulate virus-host interactions in vivo, although increasing evidence implies a major pro-viral role of autophagy in the HCV replication cycle in infected cells of the in vitro hepatocyte cell culture model. The lack of a reliable small animal system for investigating the HCV life cycle and host cellular response and the inconvenient monitoring of HCV-host interactions in HCV-infected liver specimens account for the slow progress in elucidating the detailed physiological role of autophagy in the HCV life cycle in HCV-infected cells. Notably, whether autophagy is activated in host cells infected by different HCV genotypes is still unresolved because the only infectious HCVcc used in the in vitro model, HCV JFH1 (genotype 2a), harbors an extraordinary replication cycle that is quite different from that of the prevalent genotypes 1 and 3 in most infected individuals ,402.In addition to promoting HCV growth, viral-triggered autophagy seems to degrade a panel of intracellular components, including LDs, mitochondria and host and viral proteins ,434,443.Autophagy has emerged as a critical player in balancing lipid metabolism by the selective degradation of LDs, that is, lipophagy  and by rAnalogous to HCV, DENV infection also activates host autophagy in infected cells ,464,465.Apart from viral RNA replication within autophagic machinery, the pharmacological modulation of autophagy in DENV-infected cells showed that DENV infection activates autophagy to facilitate the maturation of infectious particles  468]. N. N468]. In addition to playing a pro-viral role in the DENV viral life cycle, autophagy activated by DENV infection also alters cellular metabolism, as first demonstrated by Heaton et al.  463]. T. T463]. Several studies have investigated how DENV infection induces autophagy activation and DENV NS4A has been shown to activate autophagy in a PI3K-dependent manner  475]. A. A475]. Although autophagy was extensively shown to be activated by DENV infection, little is known about how DENV initiates this process. Additionally, the exact physiological significance of viral-induced autophagy in DENV-host interactions and the pathogenesis of DENV-associated diseases remain under further investigation. Comprehensively understanding the functional roles of DENV-activated autophagy in host cells will not only help unveil the progression of DENV-related diseases but also provide a molecular basis for the rational design of antiviral treatment.JEV infection was first reported to activate autophagy in human NT-2 cells , as demonstrated by increased levels of lipidated LC3  480]. T. T480]. In contrast to the pro-viral role of autophagy in DENV and JEV, autophagy is not likely required for activation of the WNV viral life cycle. WNV infection was shown to induce LC3 lipidation and the formation of LC3-labeled autophagic vacuoles in Vero cells  488]. T. T488]. In recent years, Zika was discovered to activate autophagy to induce the formation of autophagosomes and thus promote viral replication in human skin cells  494]. A. A494]. Suppression of autophagy has been demonstrated to inhibit viral growth in most of flavivirus-infected cells. Interference with membrane nucleation by the Vps34/PI3KC3 inhibitor, 3-MA reduces the viral replications of DENV ,467,471,Autophagy plays a pivotal role in balancing cellular homeostasis via the continuous degradation of intracellular components and recycling of nutrients for biogenesis. Upon the infection of cells with flavivirus, autophagy is exploited by the virus to reconstitute the membrane structure required for completion of the viral life cycle and degrade harmful organelles in the infected cells. Additionally, autophagy is activated to allow flaviviruses to escape innate immune surveillance and protect infected cells from death. Although the interactions between flaviviruses and host autophagy have been extensively studied over the past decade, several fundamental questions regarding how flaviviruses activate autophagy and how viral-induced autophagy influences the pathogeneses of flavivirus-related diseases remain unknown. Future investigations are needed to understand the clinical relevance of autophagy and flavivirus infections in infected patients. For instance, whether the conclusions drawn from these in vitro infected cell models can be recapitalized in real in vivo physiological conditions needs to be determined. Additionally, the lack of a feasible and conceivable mouse model for most flaviviruses substantially hampers the development of a dynamic and timely assay for assessing flavivirus-induced autophagy in vivo. Dissecting the interplay between flaviviruses and autophagy in an in vivo animal that supports the complete life cycle will permit the detailed elucidation of the functional roles of autophagy in viral infection, host defense regulation and disease progression. An innovative high-throughput screening strategy and bioinformatics analysis tools will improve the comprehensive understanding of autophagy-flavivirus interactions and help identify potential autophagy therapeutic targets to design an effective antiviral strategy for curing flavivirus infection in the clinic.


The authors should be commended for the stratified analysis aiming at providingpractical information for the cardiovascular community. Based on the current state ofevidence we can state that 1) in patients with ULMCAD, PCI can be considered analternative to CABG in patients with low anatomical complexity, and 2) patients with MVDhave better clinical outcomes when treated with CABG. When these two populations werecombined, the present meta-analysis showed an early (<30 days) benefit of PCI interms of mortality and stroke, and long-term advantage of CABG in death and myocardialinfarction.The accumulation of evidence has allowed to better understand which patients may benefitfrom a determined revascularization strategy.8 The SYNTAXscore II was developed to aid the heart team in the decision-making process consideringthe interaction of between clinical variables and anatomical complexity. The scoreincorporates the clinical variables with the anatomical SYNTAX score providing atreatment recommendation (i.e. PCI or CABG) based on predicted 4-yearmortality.10 Mortalityestimation based on individual patient profiles enhances heart team discussion,patient's information and shared decision making. Furthermore, the SYNTAX II score hasbeen validated in contemporary clinical trials; in the EXCEL trial patients randomizedto PCI in whom the SYNTAX score II recommended CABG had higher all-cause mortality at3-year follow-up.11 Moreover, in theSYNTAX II study, patients with MVD selected based on a mortality risk equipoise betweenPCI and CABG had similar outcomes compared to a matched population undergoingCABG.13 A practical recommendation, supported by the findingsof this meta-analysis are that: females, young patients, diabetics, low-ejectionfraction and MVD with high anatomical complexity have better prognosis when treated with CABG, whereas in old patients, with COPD orULMCAD with low anatomical complexity PCI is an acceptable alternative. Long term data(i.e. 10 years) from the original SYNTAX and FREEDOM have become available and showed apersistent advantage of CABG over PCI in patients with MVD.3 Long term clinical follow-up of patients with ULMCADincluded in EXCEL and NOBLE are awaited to further define the best treatmentstrategy.Heart team's interaction is the mainstay of the clinical decision-making process. Keyclinical factors such as age, sex, the presence of diabetes mellitus, chronicobstructive pulmonary disease (COPD) and left ventricular ejection fraction should beaccounted for in the selection between PCI and CABG. In addition, anatomicalconsideration based on the presence of isolated ULMCAD and/or MVD must also influencethe treatment decision.16 A Comparison of Fractional Flow Reserve-Guided PercutaneousCoronary Intervention and Coronary Artery Bypass Graft Surgery in Patients WithMultivessel Coronary Artery Disease (FAME 3) will further provide answers on the bestrevascularization strategy tailoring treatment decision based on coronary physiology. Inthe near future, virtual tool predicting functional improvement after PCI or CABG willfurther refine patients' selection potentially improving clinical outcomes in stableCAD.Further refinement in the evaluation of patients with ULM and MVD can be achieved usingcoronary physiology indexes. Systematic use of fractional flow reserve has been shown toreduce the number of lesions that appear to be angiographically significant, reclassifya significant proportion of patients to lower SYNTAX score tertiles and improve clinicaloutcomes compared to angiographic-guided PCI and optimal medical therapy.


Dengue Virus (DENV) and Zika Virus (ZIKV) are closely related flaviviruses, circulating in overlapping geographical regions. The recent ZIKV epidemic has been linked to an explosion in reports of microcephaly and neurological defects. It is conceivable that our knowledge of DENV might potentiate the development of a ZIKV vaccine due to the close phylogenetic relationship between these flaviviruses and cross-reactive antibodies, principally to the envelope protein (E protein). Alternatively, cross-reactive antibodies that are generated following vaccination or infection, might become damaging during subsequent infections.The aims of this review are to collate and analyse data from a recent series of DENV-derived monoclonal antibody (mAb) panels from different research groups. These panels measured DENV-mAb activity against ZIKV in terms of antibody-dependent enhancement (ADE) and neutralisation. Methodology used across groups was compared and critiqued. Furthermore, the specific antibody targets on E protein were considered and their therapeutic potential evaluated. Shortcomings of hmAb panels suggest ADE may be over-estimated and neutralisation underestimated, as compared to clinical situations. It remains unknown whether preference of enhancement or neutralisation by antibodies to ZIKV E protein is dictated by quantitative aspects of antibody titre or epitope specific variation. Additionally, little is known about how duration between flavivirus reinfections affect secondary antibody response.This review concludes that our current knowledge of cross-reactive antibodies to E protein is inadequate to anticipate the outcome of deploying an E protein based vaccine to regions co-infected by DENV and ZIKV. DENV is the leading arthropod-borne viral infection in the world;  estimateAedes genus of mosquito, specifically Aedes aegypti and Aedes albopictus, is the most common vector of both flaviviruses. [The viruses. , 8 Four viruses. , 10 A coE protein is a flavivirus structural glycoprotein that mediates receptor binding and virus-host cell membrane fusion, which is pivotal for enveloped viruses. E protein is composed of three domains with distinct functions – EDI, EDII and EDIII;  E proteiAntibodies from memory B cells created against DENV or ZIKV during previous infections, may cross-react with other flaviviruses to enhance infection, both clinically and immunologically. This is comparable to antibody-dependent enhancement (ADE) theory, whereby primary DENV infection exacerbates disease severity caused by subsequent heterologous DENV serotype reinfection.  Notably,In the last year, several studies have isolated human monoclonal antibody (hmAb) panels from ZIKV and DENV infected donors, to investigate the ability of cross-reactive antibodies to enhance heterologous flavivirus infection. , 13–15 TLimitations of in vitro work are the absence of the complete humoral response; antibodies in vivo might interact with immune system components, such as complement, to augment or suppress enhancing antibodies. Crucially, investigation of the in vivo enhancing ability of the anti-DENV hmAb, DV82, in the 129Sv/Ev immunocompetent mouse model did not increase lethality or symptom severity of ZIKV infection. This implies that cross-reactive hmAbs against EDI/II, namely DV82, enhance ZIKV infection in vitro but not in vivo. Virus tropism might be causal to the discrepancy. However, a more likely explanation is mouse model shortcomings.  ImmunocoAn important consideration of the human clinical enhancing antibody response caused by pre-existing, cross-reactive antibodies to ZIKV and DENV is that this response is polyclonal, unlike the monoclonal antibody responses typical of in vitro studies. Investigations of the worst possible scenario of ADE utilise convalescent DENV serum incubation of cells, prior to ZIKV challenge. This had led to inconclusive results, with some studies suggesting that DENV plasma has a potent ADE effect on ZIKV, , 15 and Paradoxically, some hmAbs have been shown to inhibit DENV plasma-mediated ADE of ZIKV infection in vitro, specifically antibodies to the envelope dimer-epitope (EDE) – a quaternary structure that bridges two E protein subunits. In contrast, hmAbs to the FLE have no such inhibitory effect on ADE.  Other stIn contrast, the panacea of cross-reactivity is the possibility that neutralising antibodies to flavivirus E protein could be exploited to create a ZIKV vaccine. Table Intriguingly, the neutralisation potency of antibodies to the FLE vary depending on the cell type in which virions are produced. Insect cells contain high levels of prM, the presence of which better exposes the FLE and leads to greater neutralisation by dengue virions produced in these cells compared to virions produced in human cells.  AlthoughA further target of hmAbs is the novel quaternary epitope - EDE; antibodies to this region have a higher neutralising capacity and lower ADE effect than antibodies to the FLE Tables  and 2. AA shortcoming of hmAb panels, in both neutralisation and enhancement studies, is that these may not fully represent the host antibody repertoire to infection as some antibodies may not survive the immortalisation process. Panels are also limited by uncertainty in accurate diagnosis of previous subclinical DENV and ZIKV infections. Furthermore, hmAb panels only characterise B cells from the blood, whereas splenic B cells are ignored.  AntibodyA vital discriminant between antibodies against EDE and FLE is the maturity of virus to which they bind. Crystallisation screen and X-ray diffraction datasets with molecular replacement suggests that 2A10G6 binds the ZIKV EDII tip at a perpendicular angle, binding immature or partially immature virus. Consequently, antibody binding to FLE is dependent on virion ‘breathing’ that transiently exposes the FLE hidden epitope.  In contrThe ability of DENV-derived mAbs that target EDE1, to protect against both DENV and ZIKV is currently being investigated. Studies using the AG129 mouse model show that all C10-protected mice survive following ZIKV challenge, in contrast to 60% mortality in control mice. The C10 mAb is a preferable immunoprophylaxis candidate than FLE mAbs, as only two doses of 10 μg are needed for a protective effect, in contrast to the 500 μg dose of 2A10G6.  The potePromising vaccine candidate models include virus like particles (VLPs) which lack viral genomes and thus are replication defective. Recent work developing subunit vaccines with ZIKV EDIII-displaying VLPs elicited potent humoral responses in immunocompetent C57BL/6 mice. Several questions remain unanswered regarding cross-reactive antibody neutralisation and enhancement to flavivirus E proteins. To anticipate the effects of ZIKV infection and vaccination in a DENV-infected region, we must first expand our knowledge of cross-reactive antibody responses; thus, I propose the following experiments Table . It is pThe pinnacle of cross-reactive neutralising antibody research would be the discovery of a universal immunogen of DENV and ZIKV; presently, EDE1 is a prominent candidate. Shortcomings of hmAb panels suggest ADE may be over-estimated and neutralisation underestimated, as compared to clinical situations. It remains unknown whether preference of enhancement or neutralisation by antibodies to ZIKV E protein, is dictated by quantitative aspects of antibody titre or epitope specific variation. Additionally, little is known about how duration between flavivirus reinfections affect secondary antibody response. The likelihood a ZIKV vaccine will be deployed to areas of DENV incidence indicate that understanding of cross-reactive antibody interplay is vital to an appropriate public heath response. Sanofi’s Dengue vaccine recently sparked safety concerns as data showed DENV-immune participants were at increased risk of severe dengue infection following vaccination.  Currentl


While epidemiological studies performed in Asian countries generally show that high levels of dietary soy are associated with reduced breast cancer risk, studies in Western countries have typically failed to show this correlation. In an attempt to model the preventative actions of soy on mammary tumor development, rodent models have been employed. Thirty-four studies were identified that evaluated the impact of soy products or purified soy isoflavones on mammary tumor initiation  and these studies were separated into mammary tumors induced by chemical carcinogens or transgenic expression of oncogenes based on the timing of soy administration. Regardless of when soy-based diets or purified isoflavones were administered, no consistent protective effects were observed in either carcinogen-induced or oncogene-induced mammary tumors. While some studies demonstrated that soy or purified isoflavones could reduce mammary tumor incidence, other studies showed either no effect or tumor promoting effects of soy products or isoflavones. Most importantly, only five studies found a decrease in mammary tumor incidence and six studies observed a decrease in tumor multiplicity, two relevant measures of the tumor preventative effects of soy or isoflavones. The variable outcomes of the studies examined were not completely surprising given that few studies employed the same experimental design. Future studies should be carefully designed to more accurately emulate soy consumption observed in Asian cultures including lifetime exposure to less refined soy products and potentially the incorporation of multigenerational feeding studies. It has been estimated that approximately 35% of human cancers are preventable through changes in lifestyle such as maintaining a healthy body weight, eliminating alcohol and tobacco, and adhering to cancer screening guidelines . One lifHowever, some of this variation may stem from the population examined, types of soy products consumed , as well as the timing and duration of soy consumption. Ideally, randomized human prevention trials should be performed; however, these types of studies would take decades to complete, may raise ethical concerns of feeding developing fetuses or newborns specialized diets without their consent, and it would be difficult to maintain compliance to specific diets for such a long duration.Since human randomized clinical trials are not feasible, animal models have been utilized with the most common being rodent models. Rodent mammary glands share features with the human breast including epithelial-lined ducts surrounded by myoepithelial cells, fibroblast and stroma . In addiThis review focuses on rodent models designed to assess the preventative actions of dietary soy products or purified isoflavones and includes papers published since 1995. For studies prior to 1995, please see the review by Barnes . The effSoy products are derived from soybeans and contain a number of compounds including protease inhibitors, phytosterols, saponins, and phytoestrogens known as isoflavones. Most of the work has focused on the isoflavone component of soybeans and the main isoflavones are genistein, daidzein and glycitein . IsoflavNot all sources of dietary soy are equal. Asian cultures typically consume minimally processed soybeans and more fermented soy products such as miso or tempeh where isoflavones are primarily in their aglycone form  . In contN-methyl-N-nitrosourea (NMU) and ethyl methanesulfonate (EMS), which are typically provided as a single administration to pubertal female rats and 2-amino-1-methyl-6-penylimidazo[4–b]pyridine (PhIP) is administered orally to rats four times per week for two weeks. Although chemically induced rat mammary tumor models share features with human breast cancer they frequently induce mutations in Hras [Pi3kca and Pten, two relevant human breast cancer genes [One of the first and most common rodent models used to evaluate the impact of soy isoflavones on mammary tumor develop is the administration of 7,12-Dimethylbenzathracene (DMBA) to rats. DMBA is typically given as a single dose to pubertal female rats through injection or oral gavage and tumor incidence is close to 100% with tumor onset ranging from 8–21 weeks, depending on the concentration of DMBA administered . Mammary in Hras ,48,49,50er genes . Chemicaer genes .neu transgenic mice. MMTV-neu mice express elevated levels of EbbB2  in mammary epithelial cells as well as other epithelial cells where the mouse mammary tumor virus (MMTV) promoter is expressed [neu mice develop mammary tumors with a median onset of 5–10 months [Wnt1. MMTV-Wnt1 transgenic mice develop mammary tumors expressing both luminal and myoepithelial genes and cluster most closely with normal human breast tissue [With the generation of genetically modified mice, alterations in specific oncogenes or tumor suppressor genes became possible. The most widely used transgenic mammary tumor model is MMTV-xpressed ,53,54. M0 months ,56,57 an0 months . Anothert tissue . One limt tissue ,60,61,62hGH. C(3)1/SV40 transgenic mice express the simian virus 40 large T antigen driven by the rat prostatic steroid binding protein C3(1) 5′-flanking sequence. Female mice develop mammary tumors by 4 months of age and male mice develop prostate tumors by 1 year of age [hGH transgenic mice express elevated levels of human growth hormone driven by a murine metallothionein promoter. These mice develop mammary tumors by 27–43 weeks of age [Two non-MMTV-driven transgenes have been used to study the impact of dietary soy on mammary tumor development: C(3)1/SV40 and Mt-r of age . Mammaryr of age . Mt-hGH s of age . Gene exs of age .In addition to the transgenic mice describe above, MTB-IGFIR transgenic mice  have alsA key difference in the studies evaluating the impact of soy isoflavones on mammary tumor development in rodent models is the timing of soy isoflavone administration. Most of the early studies and even some of the more recent studies initiate the feeding of soy diets or purified isoflavones in postnatal rodents. This experimental approach would presumably emulate the human situation where children or adolescents switch their diet to one containing high levels of soy. However, Asian cultures, where reduced breast cancer rates are observed, would presumably consume high levels of soy at all stages of life including during pregnancy and lactation as well as during childhood, adolescence and adulthood. Therefore, lifetime exposure  may more accurately model the isoflavone consumption of cultures with reduced breast cancer rates.Given the potential importance of the timing of soy exposure this review has been organized into two main sections: mammary tumor development following postnatal soy/isoflavone exposure, and mammary tumor development following lifetime soy/isoflavone exposure. There were also four studies that investigated soy exposure only during the perinatal developmental window, which have been included in this review. Twenty-three studies were identified that evaluated mammary tumorigenesis following the administration of soy-based diets or purified isoflavones during postnatal development; fourteen using chemical carcinogens in rats ,80,81,82neu mice with low (ovariectomized mice), normal or high (estradiol injection) levels of estrogen. In this study it was observed that diets high in soy increased tumor incidence in the low estrogen group, but the soy diet reduced tumor incidence in the high estrogen group (explaining why this study is referenced as soy-based diets having demonstrated both tumor protective and tumor promoting effects in the discussion above).The findings were also highly variable in the transgenic mouse models with four of the nine studies showing at least some protective effect against mammary tumorigenesis  ,86,87,88Genistein is the most studied individual soy isoflavone as it is the predominant isoflavone in soy and it can bind to estrogen receptors . When evFewer studies have evaluated the benefits of lifetime soy isoflavone exposure in rodent models of mammary tumor development and these studies are summarized in neu mice found that mice fed a diet containing the isoflavone-enriched product, Prevastein, had reduced tumor incidence and prolonged tumor latency in the group that were fed a high-fat diet based on corn oil, but not in the group with a diet based on fish oil. Meanwhile, the other study found no impact on tumor incidence in MMTV-neu transgenic mice fed a high soy diet compared to controls [There were also three studies using transgenic mice. One study using MMTV-controls . In the controls .neu transgenic mice [neu transgenic mice found no significant difference in the tumor incidence or onset in Prevastein-treated mice compared to control mice [For this review, perinatal exposure was defined by soy or isoflavone administration between conception and weaning. Using this definition, there were six studies identified, four of which used chemical carcinogens in rats ,97,98,99nic mice ,96. Thesnic mice ,98,99. Orol mice .The most appropriate measure of reduced breast cancer risk would be a reduction in tumor incidence  in rodents fed diets containing soy or isoflavones compared to rodents fed control diets. Of the 34 studies evaluated, only 5 demonstrated a significant reduction in tumor incidence in response to diets enriched with soy products or purified isoflavones. Tumor multiplicity was reduced in soy-fed mice in an additional 6 studies and thus 11 of 34 studies demonstrated that soy products or purified isoflavones reduced either the percentage of rodents that developed mammary tumors or the number of tumors that developed in each animal. When focusing on the studies that evaluated the administration of the purified soy isoflavone genistein, 4 of the 8 studies demonstrated that postnatal genistein reduced at least one tumor characteristic, with only 2 of these studies demonstrating a decrease in tumor incidence or multiplicity. Given that less than a third of the studies demonstrated a decrease in tumor incidence or multiplicity suggests that the current studies have failed to demonstrate a consistent, protective effect of soy isoflavones in preventing mammary tumor development.Before concluding that either rodent models are unsuitable for breast cancer prevention studies or high levels of dietary soy do not reduce breast cancer risk, further research should be encouraged. However, a number of factors require careful consideration, including (i) the type of soy , (ii) the experimental model , and (iii) when to initiate soy-based diets and how long these diets should be continued. Future studies should emulate the human data that most clearly implicates that the consumption of dietary soy reduces breast cancer risk, and these are the epidemiologic studies showing that lifetime/multigenerational exposure to diets containing unrefined, fermented soy products by some Asian cultures reduces breast cancer risk. Therefore, diets containing high levels of unrefined and possibly fermented soy products should be tested. The unrefined soy products would maintain most of the soybean components such as protease inhibitors, phytosterols and saponins that are typically lost during the refinement process. With respect to timing, lifetime exposure to soy-based diets should be the minimum and multigenerational exposure should be evaluated. No study investigating mammary tumor development following multigeneration soy exposure could be found, and it is possible that soy induces epigenetic alterations in the gametes of animals with lifetime soy exposure that then impacts the gene expression and tumor sensitivity of their offspring.The most relevant animal model is more debatable. Although non-human primates likely represent the best model, large dietary studies in non-human primates genetically altered to express relevant human oncogenes or lacking key tumor suppressor genes are not feasible and would raise ethical concerns. Therefore, genetically altered mice or rats expressing inducible, tissue-specific oncogenes or inducible, tissue-specific knockouts utilizing known human tumor suppressor genes probably represent the most appropriate model since this system (i) uses known oncogenes and tumor suppressor genes and thus the mechanisms of tumor initiation will be more relevant to human breast cancer, (ii) permits oncogene expression or tumor suppressor gene ablation in postnatal animals which presumably emulates the timing of spontaneous activation of an oncogene or loss of a tumor suppressor gene in humans, and (iii) several inducible mouse models are currently available and their genetic similarity to different human breast cancer subtypes is often known.However, a main concern with rodent models is that rodents and humans metabolize soy isoflavones differently ,101. The


The current definition of atypical femoral fractures (AFFs) excludes periprosthetic fractures. However, a few cases of bisphosphonates (BPs) -associated periprosthetic atypical femoral fractures (PAFFs) have been reported in the literature. Here, we report two rare cases of PAFFs that fulfilled the major criteria for AFFs in patients with prolonged use of BPs. Both cases progressed to a complete fracture with minor trauma from an incomplete fracture at the distal tip of the well-fixed femoral stem. The femoral stem effect on lateral femoral cortical bone, together with the decreased bone elastic resistance induced by BPs, was considered the cause of onset. In each case, we performed open reduction and internal fixation using a locking plate with cable grip and postoperatively prescribed teriparatide and low-intensity pulsed ultrasound (LIPUS). Both cases had a good clinical course. However, as conservative treatment was not effective in these cases, treatment such as non-weight-bearing exercises during hospitalization or prophylactic surgery may be necessary. Further studies are needed to determine the optimal treatment strategy. Atypical femoral fractures (AFFs) are stress or insufficiency fractures induced by low-energy trauma or no trauma and present specific X-ray findings. Interestingly, AFFs have emerged as potential complications of bisphosphonates (BPs) use over the past decade. The American Society for Bone and Mineral Research (ASBMR) published a Task Force report on AFFs in 2014 . Here, wμg teriparatide and low-intensity pulsed ultrasound (LIPUS). One year later, radiographs revealed nonunion (An 81-year-old woman with a left bipolar hemiarthroplasty performed 10 years previously presented with a left femoral shaft fracture that occurred without trauma . Her femnonunion . At the nonunion . At the μg teriparatide and LIPUS. The patient was allowed non-weight-bearing exercise. At the follow-up three months after surgery, the patient was using a wheelchair without pain and a radiograph revealed no displacement or loosening of the implants.An 85-year-old woman visited our hospital due to a right femur fracture that occurred after minor trauma as a result of having fallen down while walking. She had received a right total hip arthroplasty 18 years earlier due to rapidly destructive coxopathy and a revision arthroplasty 9 years earlier because of the loosening of the femoral stem. In addition, the patient had been taking alendronate for more than five years. Radiographs showed localized thickening of the lateral femoral cortical bone and complete transverse fracture with internal spikes. In addition, a third bone fragment was seen, showing the same findings as the atypical femoral fracture. We judged Vancouver type B1 periprosthetic fracture . In the We reported two rare cases of PAFFs that fulfill the major criteria for AFFs and that involve prolonged use of BPs. Both cases progressed to a complete fracture with minor trauma from an incomplete fracture at the stem tip of the well-fixed femoral stem.The 2013 ASBMR diagnostic criteria for AFFs require the presence of at least 4 of the 5 major criteria. The minor features include a generalized increase in cortical thickness of the femoral diaphysis, prodromal symptoms such as thigh pain and bilateral incomplete or complete femoral diaphysis fracture, and delayed fracture healing. The AFF definition excludes periprosthetic and pathologic fractures . A revieAssociations have been reported between AFFs and BPs, steroids, rheumatoid arthritis, femoral lateral bowing, and diabetes mellitus , 15. TheAlthough surgical treatment is generally performed for complete PAFFs, the best method of treatment for incomplete PAFFs is controversial. Curtin et al. reported three cases of incomplete PAFFs treated by discontinuing BPs and limiting weight bearing; later, teriparatide was added to two cases . Cross eThe efficacy of teriparatide in conservative treatment for PAFFs has not been demonstrated, although teriparatide treatment was used in several case reports of PAFFs , 10, 12.Daily administration of teriparatide enhances bone healing more than weekly administration in complete AFF patients . HoweverIn the light of the limited amount of research on PAFFs, further studies are needed to determine if conservative or surgical treatment is best for incomplete PAFFs, the methods of appropriate conservative treatment, and the necessity of prophylactic surgical treatment to prevent complete PAFFs.