These sensor mechanisms link extracellular signaling molecules with gene regulation through a network comprised of membrane receptors, signal cascade proteins, transcription factors, and transcription factor biding DNA motifs. Modeling predicted four pre-mycorrhizal sensor complexes in aspen that interact with 15 transcription factors to regulate the expression of 1184 genes in response to extracellular signals synthesized by Laccaria. Predicted extracellular signaling molecules include common signaling molecules such as phenylpropanoids, salicylate, and jasmonic acid. This multi-omic computational modeling approach for predicting the complex sensory networks yielded specific, testable biological hypotheses for mycorrhizal interaction signaling compounds, sensor complexes, and mechanisms of gene regulation.In mycorrhizal symbiosis, plant roots form close, mutually beneficial interactions with soil fungi. Before this mycorrhizal interaction can be established however, plant roots must be capable of detecting potential beneficial fungal partners and initiating the gene expression patterns necessary to begin symbiosis. To predict a plant root—mycorrhizal fungi sensor systems, we analyzed  The nature of the symbiosis that forms between aspen roots and Laccaria is termed ectomycorrhizal (ECM) symbiosis. In ECM interactions, the plant roots and fungus remain separated by a narrow space called the apoplast, requiring that nutrients and signaling molecules exchanged by plant and fungus be transported across both fungal and plant cell walls by specific transmembrane transporters and receptors.Using Laccaria and aspen as model organisms for symbiotic interactions provides a unique opportunity to investigate the molecular mechanisms of mycorrhizal interaction. Aspen and Laccaria are capable of forming mycorrhizae in the laboratory, making them useful models of plant-fungus symbiotic interactions. The genomes for both Laccaria and the closely related aspen species We used several computational analysis approaches that accommodate multiple “omics” data sources to predict aspen mycorrhizal sensors. Using transcriptomics and metabolomic modeling, we identified possible signal compounds synthesized by Laccaria during pre-mycorrhizal interaction. Using transcriptomic data and published databases of known plant protein-protein interactions, we constructed possible protein-protein interaction networks that link transmembrane transporters to transcription factors via networks of signal cascade proteins. These sensor complexes can be linked, using transcriptomics data, to clusters of co-regulated genes that share common regulatory motifs. Together, these predictions can be combined into a system-scale model of pre-mycorrhizal interaction that spans Laccaria signaling compounds to the sensors that detect them, to the pre-mycorrhizal specific gene expression patterns in aspen root. This predicted system-scale interaction network model proposes molecular biological experiments that can validate these predictions and demonstrate the molecular mechanisms that mediate pre-mycorrhizal interaction.The conceptual model of mycorrhizal sensor mechanisms used as a framework for subsequent data analysis is represented in Figure The core component of this analysis is a laboratory experiment in which aspen and Laccaria are co-cultured in the laboratory. A crucial experimental aspect is the culturing of aspen and Laccaria together, but separated by a permeable membrane preventing direct contact between plant and fungus. In this condition, aspen and Laccaria can only interact via small signaling molecules. We hypothesize that observed changes in gene expression patterns in aspen root under these conditions, relative to aspen grown in monoculture, are potentially attributable to aspen root responding to diffusible signals generated by Laccaria. From laboratory cultures, RNA is extracted and sequenced. Sequenced mRNA is used to generate gene expression data for both Laccaria and aspen root. This transcriptomic data is analyzed in the context of prior transcriptomic and proteomic data sets, used to generate metabolomic models, and analyzed to identify significant genomic features associated with mycorrhizal interaction in later analysis steps.Laboratory cultures of aspen and Laccaria were used to identify gene expression patterns specifically associated with mycorrhizal interaction via diffusion of small signaling molecules from Laccaria Figure . There wP. tremuloides seeds were surface sterilized and germinated on McCown's woody plant media (WPM) in Petri dishes as previously described  Orton (strain S238N) culture was maintained on Modified Melin Norkan's (MMN) media at 20°C, as described in Kim et al. . P. tremd Cseke, . One weeThere were a total of 18 sequenced transcriptomes: 2 replicates of free living Laccaria, 2 replicates of free living aspen root, 6 interaction Laccaria (one sample per time point), 6 interaction aspen root (one sample per time point), and 2 replicates of fully-formed mycorrhizae. Fully formed mycorrhizae transcriptomes are comprised of comingled aspen and Laccaria transcriptomes. Of the 18 transcriptomes, 8 are Laccaria, 8 are aspen root, and 2 are mixed Laccaria and aspen root mycorrhizae.Total RNA was extracted from samples by CTAB method Cseke,  and RNA P. trichocapra as surrogates for P. tremuloides genes. Gene models and their annotations were taken from the DOE JGI website (www.jgi.doe.gov).Using a strategy we have previously employed . “BowStrap” performs a bootstrap analysis on the output of the short sequence-aligning program “Bowtie” (http://bowtie-bio.sourceforge.net/index.shtml) In “BowStrap,” both unique and multiply aligned reads are considered to generate a measure of gene model expression with an accompanying confidence interval and statistical significance of expression. Bowtie indexes were generated from sets of published JGI gene models for L. bicolor and aspen For mycorrhizal samples, which contain sequence derived from both plant root and fungal transcriptomes, sequence reads were aligned to each set of indexed gene models. The default Bowtie conditions were used to generate alignments for all sets of sequence reads to gene models, except for setting Bowtie to return all possible sequence alignments. Ten thousand “BowStrap” iterations were used for the calculation of average and standard deviations of RPKM values. Significantly detected gene expression was defined as a Cumulative Normal Distribution (CND) based, uncorrected p < 0.0001.Gene model expression was detected in the collected transcriptomics data using the application “BowWhile transcriptomic data is collected from both the aspen and Laccaria components of the biological system, that data will be utilized very differently for aspen than for Laccaria. From aspen transcriptomes, we identified groups of co-regulated genes, found transcription factor binding sites that are enriched in those gene clusters, and predicted possible protein-protein interaction sensor complexes that potentially link the ability to detect extracellular conditions and use that information to drive changes in aspen root gene expression.We anticipate that groups of genes that are co-regulated in response to pre-mycorrhizal interaction conditions share relevant common functions for symbiotic interaction. For aspen root transcriptomes, gene models that were expressed in all experimental samples and were highly variable in expression across conditions were grouped into co-regulated sets of genes using K-means clustering. K-means clustering is a method by which multiple observations can be partitioned into a number of clusters in which each cluster shares similar means. For inclusion in clusters, genes were identified as significantly expressed in all 10 aspen root transcriptomes  and differentially expressed as identified by a coefficient of variation >0.33 . K-means clustering was performed using Multiple Array Viewer  enriched Gene Ontology (GO) Biological Process annotations   was used to calculate statistical significance of enrichment for a transcription factor biding motif C with motif M in 1000 bp upstream region.fM = frequency at which motif M is found in 1000 bp upstream region for all differentially regulated genes.A significance threshold 0.01 and a requirement that at least 10 genes in a cluster to be associated with a particular motif were used to identify enriched motifs in co-regulated gene clusters. These values for significance and number of genes were empirically determined from the distribution of transcription factor binding motifs in this data. A moderate significance level of 0.01 was used due to the expectation that all genes in a co-regulated cluster are not likely driven by the same transcription factor. A minimum of 10 genes indicates that a potential motif regulates a cluster of genes actually differentially regulated in this experiment and not just co-occurring in genes that share biological function.Known transcription factor binding motifs for transcription factors present in predicted sensor complexes present in co-regulated gene clusters were identified. The upstream regions for co-regulated genes were searched for these motifs.Any signaling molecules synthesizes by Laccaria will have to be detected by membrane-bound sensors in aspen roots, then that signal relayed via signal cascade interactions to alter the activity of specific transcription factors. While no direct proteomic information was collected, the patterns of gene expression in roots in conjunction with databases of known plant protein-protein interactions will be used to identify possible sensor complexes that have the ability to link the synthesis of signaling molecules to resulting changes in aspen gene expression.in vitro transcriptomic data, genomic annotations, previously reported aspen root transcriptomes under a wide variety of growth conditions, and tens of thousands of previously validated plant protein-protein interactions was used to predict protein complexes in aspen root. We required the following conditions for inferring the existence of a Protein-Protein Interaction (PPI) involved in signal detection and gene regulation:All proteins in a PPI must be significantly expressed at every time point.Proteins in a PPI must be annotated with a function relevant to environmental sensing and signal transduction  were used to identify sets of genes that are co-expressed under multiple biological conditions. One hundred and sixty three transcriptomes for Populus roots (GSE20118) and 14 tissue-specific transcriptomes (GSE21481) were collected for a total of 188 aspen transcriptomic data sets.To identify putative expressed sensor protein complexes, we used the following computational approaches. We presume that if a gene model is detected as significantly expressed, then its protein product is present as well. Transcriptomic data archived in the Gene Expression Omnibus (GEO) (Arabidopsis Information Resource (http://arabidopsis.org/) for experimentally validated PPIs to build tables of LOI scores. As we consider a requirement of PPIs that physically interacting proteins must be co-localized in the cell into regions that permit physical interactions, the annotation used in LOI-score calculations was from cellular localization predictions by WoLF PSORT  is a computational method that defines and enables exploration of metabolite-space inferred from the transcriptome  annotations were used to define UEFs for this analysis. UEF counts were calculated as the sum of quantile-normalized RPKM expression levels of all gene models with UEF. KEGG metabolic reactions were used to identify all possible enzymatic transformations for EC annotations. The sets of allowed enzymatic transformations were limited to those present in KEGG pathways associated with aspen or Laccaria. Lists of allowed KEGG pathways are found in Table 2 fold change of all genes in co-expressed gene clusters. The log2 fold change is equal to the log2 for the average gene expression for a condition minus the log2 average expression of a cgene over all conditions. As a negative PRMT-score indicates a predicted increased capacity for a metabolites synthesis, we considered a strong negative correlation to be an indication of Laccaria metabolomic activity associated with aspen root gene expression, i.e., increased predicted synthesis of a metabolite (a negative PRMT-score) correlating with increased expression of aspen genes (a positive log2 fold change).We used Pearson's Correlation Coefficient to identity correlations between predicted metabolomic turnover in Laccaria and gene expression in aspen roots. For patterns of expression in plant root, we used the average logThe complete set of results from gene expression analysis, protein complex prediction, transcription factor binding motif analysis, and metabolomic model prediction can be assembled into a single model of the regulatory systems of aspen pre-mycorrhizal sensing. The following rules were used to combine multi-omics models into a single model for plant-fungus mycorrhizal interaction mechanisms.Connect sensor complex to differentially regulated gene cluster: When genes in a co-regulated gene expression cluster overlap with genes for proteins in a predicted protein sensor complex, then that sensor complex is presumed to regulate that gene cluster.Connect sensor complex to predicted Laccaria signal: When the gene expression pattern of genes in a co-regulated aspen gene expression cluster strongly correlated by Pearson Correlation Coefficient with calculated PRMT-score for Laccaria metabolites, then that Laccaria metabolite is presumed to be a regulator of that co-regulated aspen gene cluster. Strongly correlated is defined as being in the top and bottom 0.001 percentile for all possible correlation coefficients between gene expression patterns and PRMT-scores. The mechanism of regulation of aspen genes by Laccaria metabolite is presumed to be the aspen sensor complex associated with that co-regulated gene cluster.Connect sensor complex to differentially regulated gene cluster transcription factor binding motifs: The transcription factor binding motifs that link a sensor complex to its regulated genes is derived from the known binding motifs for transcription factors in the sensor complex and the significantly enriched binding motifs present in the co-regulated gene cluster.Connect differentially resulted gene cluster to predicted phenotype: The specific phenotypic response of a co-regulated gene cluster is presumed to be found in the statistically significantly enriched functional annotations in that gene cluster (CBD derived p < 0.05).2-tranformed RPKM values, were collected for all aspen and Laccaria biological samples. An average of 66.9% (SD 1.2%) of aspen genes were detected as significantly expressed across all samples. An average of 66.4% (SD 2.0%) of Laccaria genes were detected as significantly expressed across all samples. The gene expression values for aspen and Laccaria samples can be found in Tables Transcriptomic data, in the form of boot-strapped normalized, logWe examined gene expression changes at various plant-fungal interaction time points to regulatory identify regulatory changes associated with establishment of symbiosis. Aspen root gene expression was grouped into co-regulated sets of genes using K-means clustering Figure . The speThe majority of expression clusters in aspen roots showed a gene expression change during interaction time points and a constant level of gene expression for free living and mycorrhizal samples . Half of these gene clusters  are significantly enriched for annotations related to defense or pathogen response, suggesting that gene expression patterns differentially regulated during interaction may be associated with a general response to the presence of a fungus and not specifically associated with mycorrhizal interaction. Clusters 1 and 3 are enriched for regulation of transcription and are down-regulated during interaction samples. Only aspen root cluster 9 demonstrates an expression pattern that can be described as mycorrhizae-specific: up regulated in both interaction and mycorrhizal samples. This gene expression cluster is enriched for annotations of glucan metabolism, cell wall modification, and carbohydrate metabolism. Glucan metabolism is associated with plant-pathogen interactions , a transcriptional activator of heat shock genes  domains . It contains two MADS-box transcription factors and one Ethylene Response Factor (E2F1) cell cycle transcription factor. The complex also contains eight genes annotated as Curculin-like (mannose-binding) lectin (IPR001480). Lectin synthesis has been observed to occur in plants in response to specific environmental stresses and lectins are involved in stress signaling , N-Glycan Biosynthesis (map00510), Biosynthesis of Alkaloids Derived from Shikimate Pathway (map01063), Biosynthesis of Phenylpropanoids (map01061), and Biosynthesis of Plant Secondary Metabolites (map01060). These identified KEGG pathways correlate well with previously published observations of mycorrhizal interaction. The Biosynthesis of Plant Hormones (KEGG map07070) pathways include those for auxin, ethylene, jasmonic acid, and brassinosteroids which have all been previously reported as signaling compounds in plant-symbiote interactions  present in regulated gene cluster 3. This transcription factor is annotated as DNA-binding WRKY   program at Argonne National Laboratory. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This paper provides the first reported case of acute posterior chamber hemorrhage during fluorescein angiography (FA). This is a case review with serial color photographs of the anterior segment. A 76-year-old male was referred for angiographic control of age-related macular degeneration. He was pseudophakic OU, BCVA 20/40 OU. He had mild hypertension, but not diabetes. He had had two previous angiograms without adverse effects. Difficulty was experienced in obtaining the images owing to a progressive reduction in the transparency of the media. A dense hemorrhage in the posterior chamber of the right eye was found, involving the visual axis. Thorough biomicroscopy, gonioscopy, and ultrasonic biomicroscopy showed that part of one of the haptics of the right intraocular lens (IOL) was touching and tearing the posterior face of the iris, without any visible synechiae, iris, or angle neovascularization. Anterior segment FA and posterior ultrasonography were normal. No similar case has been described in the literature involving dense progressive bleeding located in the capsular bag and posterior chamber, without any detectable triggering ocular event other than mydriasis and fluorescein injection. Contact of the iris or sulcus with part of the intraocular lens, aggravated by the intense use of mydriatics during the FA procedure, probably caused bleeding to happen. Fluorescein angiography (FA) is a very safe procedure, with infrequent complications and low morbidity –4. BesidA 76-year-old male was referred for angiographic control of age-related macular degeneration. He was pseudophakic in both eyes (OU) , having best corrected visual acuity (BCVA) 20/40 OU and intraocular pressure (IOP) 11 mmHg OU. He had mild hypertension. He had had two previous angiograms without complications. In November 2009 he underwent FA at our clinic, following pupil dilation using tropicamide 1% plus 2.5% phenylephrine solution (3 instillations 30 minutes before the procedure). Before the photos were taken, he complained of floaters in the right eye (RE). Difficulty was experienced in obtaining the images owing to a progressive reduction in the transparency of the media. The images obtained during the procedure showed great confluence of drusen in the posterior pole of the right eye, with no signs of papillary, retinal, or choroidal neovascularization. Owing to the worsening of symptoms he was reexamined before leaving the clinic. A dense hemorrhage in the posterior chamber of the right eye was found, enveloping the visual axis . Bed resWe found no similar case in the literature involving dense progressive bleeding located in the capsular bag and posterior chamber, without any detectable triggering ocular event other than mydriasis and fluorescein injection. Intraocular hemorrhage months or years after cataract surgery has been described as SWAN syndrome, in which stromal vascular growth is detectable and produces recurrent bleeding approximately 36–48 months after surgery . However


The purpose of this study was to characterize the development of BPD symptoms across adolescence by evaluating the fit of several latent variable growth models to annual assessments of symptoms obtained from girls when they were ages 14 through 19 years. After determining the best fitting model, we examined prospective associations between the temperament dimensions of emotionality, activity, low sociability, and shyness and BPD symptom development.We utilized longitudinal data from the Pittsburgh Girls Study; one of the few large-scale, prospective studies of girls  in the United States. Parent- and teacher-reports of girls’ temperament were collected at Wave 1, when girls were ages 5–8 years. Child-reports of BPD symptoms were collected annually beginning at age 14 through 19 years.We found that a free curve slope intercept model provided the best model fit, with the course of BPD symptoms characterized by a large component of inter-individual stability and a smaller component representing within-individual changes across adolescence. Symptoms appeared to peak by age 15, decline through age 18, and remain steady between ages 18 and 19 years. Both parent- and teacher-reports of temperament emotionality, activity, low sociability, and shyness predicted the developmental course of symptoms.BPD symptoms in adolescence reflect trait-like differences between youth with less within-person variability across time. Childhood temperament dimensions of emotionality, activity, low sociability, and shyness predict adolescent BPD symptom development. Parent- and teacher-informants provide unique information about the course of BPD symptoms, underscoring the utility of collecting child assessments using multiple informants. Borderline personality disorder (BPD) emerges during adolescence or young adulthood and is characterized by multiple debilitating symptoms, including emotional dysregulation, tumultuous interpersonal relationships, and impulsive behaviors; all of which can interfere with occupational, academic, and social functioning –4. MoreoLittle research exists about the development of BPD symptoms prior to adulthood and about how childhood temperament might predict parameters of developmental course. In particular, few studies have assessed BPD symptoms in youth across the multiple waves of data collection needed to characterize the pattern of adolescent growth. Findings from community samples have demonstrated that BPD symptoms and features peak during mid-adolescence and decline during late adolescence and young adulthood –14. ThisGiven the developmental continuities between childhood temperament and personality, temperament is an obvious etiological feature to consider for the development of personality disorder symptoms in adolescence –23. ChilConsistent with DSM-5 Section III model of personality disorders, we can conceptualize personality disorders as extreme or maladaptive variants of normal personality traits, and, using this framework, BPD can be viewed as extremely high levels of neuroticism, low agreeableness, and low conscientiousness or high disinhibition –33. TherWe have previously examined the bi-directional associations between parenting and BPD symptom development in the context of child and parent characteristics using autoregressive latent trajectory models . These mN = 2,450) comprises an urban community sample of girls, ages 5–8 years at the first assessment, and their primary caregivers. To identify the study sample, low income neighborhoods were oversampled: neighborhoods that were characterized by at least 25% of families living at or below poverty level were fully enumerated and 50% of households in all other neighborhoods were randomly selected for enumeration as well  reflects more inter-individual, trait differences and less intra-individual differences. Moreover, the residual variance at each assessment occasion is relatively stable , suggesting similar levels of measurement error across this period of adolescence.The unstandardized parameter estimates for the intercept variance and slope loadings can be examined to determine the relative amounts of trait and trajectory components in BPD symptoms across adolescence. The intercept variance estimate  = 86.60, p < .01; RMSEA = 0.02, 90% CI = 0.01, 0.03; CFI = .99 and TLI = .98; AIC = 29921.43 and BIC = 3011.41. The final model accounted for 9% of the variance  in the intercept factor of BPD symptoms and 4% of the variance  in the slope factor.Table All parent reports of temperament domains were significantly associated with the intercept factor. Specifically, high emotionality, high activity, and low sociability at Wave 1 were significantly associated with higher elevations in BPD symptoms across ages 14 through 19. Parent reports of shyness at Wave 1 also predicted lower levels of BPD symptoms across this developmental window. Parent reports of temperament did not significantly predict the slope of BPD symptoms.Additionally, all teacher reports of temperament significantly predicted the intercept or the slope factors, suggesting teacher reports also explain unique variance in adolescent BPD symptom development. Teacher report of emotionality at Wave 1 predicted unique variance in the intercept factor, indicating that emotionality as measured by teachers predicted elevated BPD symptoms across adolescence. Moreover, teacher reports of activity and sociability at Wave 1 predicted the slope factor, suggesting that high activity and low sociability predicted increasing BPD symptoms across ages 14 to 19. Teacher reports of shyness at Wave 1 negatively predicted the slope factor, indicating that teacher reports of shyness predicted a slower rate of growth across this age span.Figure These analyses examined how childhood temperament impacts the developmental course of BPD symptoms throughout adolescence. We extensively evaluated various growth models of BPD symptoms across ages 14 through 19 and found that the FCSI model provided the best fit to the data. Comparing the assumptions of other growth models to those of the FCSI model extends our understanding of the course of BPD symptoms. Since the FM and FM-Shift models constrain the estimation of an intercept latent variable, the failure of these models suggests that there are significant amounts of inter-individual or trait-like differences in BPD symptoms from age 14 through age 19. Moreover, the failure of the MANOVA models to provide better model fit indicates that in addition to the within-individual trait component, a significant amount of within-person change in BPD symptoms also exists across this developmental period. Finding significant between- and within-person variability is consistent with our previous report of moderate levels of stability with some year-to-year fluctuations in BPD symptoms across ages 14 through 17 [Our finding that the FCSI model best captures the form of adolescent BPD symptom development is consistent with Wood’s 2011) re-analysis of temperament data measured in the first 5 years of life. Taken together, these findings support the notion that temperament and personality reflect processes characterized by “multiple short-term bursts of change, plateaus, or asymptotes in performance” across development 11 re-ana–12, 14 aWe also found support for our hypothesis that childhood temperament and adolescent BPD symptoms likely reflect the same basic underlying dimensions. Temperament predicted higher elevations of BPD symptoms as well as increases in symptoms across time. Emotionality was the strongest predictor of elevated BPD symptoms, with both parent- and teacher-reports predicting this pattern of development across time. Additionally, parent-reports of high activity, low sociability, and low shyness predicted elevated levels of symptoms while teacher reports of these dimensions predicted increases in the rates of symptoms across time. These findings are consistent with previous work documenting the links between infant temperament dimensions of emotionality and activity predicting BPD symptoms 28 years later . FindingAdditionally, our results indicate significant value in gathering multiple informants when assessing child temperament. These findings demonstrate that both parent and teacher reports of temperament explain unique variance in the trajectory of BPD symptoms across adolescence. Parents and teachers have access to different, yet complementary information about the child’s temperament that is useful in predicting clinical symptoms assessed 5–12 years later.This study is not without limitations. BPD symptoms were not measured prior to age 14 nor were temperament dimensions measured annually in adolescence so it is not possible to determine the co-occurring or reciprocal relationships among these constructs. However, the prospective nature of our assessments and the early age at which temperament was measured lessen concerns that girls’ BPD symptoms would be driving temperament. Another limitation concerns the generalizability of our findings. The sample only included girls so the findings may not generalize to male-only or mixed-gender samples. In addition, BPD was not measured repeatedly with a semi-structured interview but relied on interview guided self-reports. However, this measure has been widely used in adolescent community and psychiatric patient samples and its relationship to diagnosis is well established , 49. Morr = 0.15, p < .05) than parent-reported Sociability and any other teacher-reported temperament dimension, reflecting both convergent and discriminant validity, respectively. Moreover, the magnitude of this association is within the range obtained for the other temperament dimensions we assessed . Including Sociability was also of theoretical significance as it allowed us to examine associations between the full complement of temperament dimensions as articulated by Buss and Plomin (1984) and BPD symptom development. Removing this scale would only offer a fragmented approach to examining these relationships. Importantly, this brief measure of Sociability did indeed predict BPD symptom development, which supports the utility of this index in our sample.Another limitation concerned the abbreviated Sociability assessment in our sample. Specifically, Sociability was measured with only one item in parent and teacher reports, which may have reduced the validity of this index. However, we do have evidence supporting the validity of this index. For instance, the pattern of bivariate correlations between parent and teacher reports of temperament dimensions demonstrated construct validity for the Sociability index Table . Parent Despite these limitations, our study has several important strengths. The validity of our findings is supported by our prospective design, such that we assessed temperament prior to our measurement of borderline personality disorder symptoms. Additionally, we utilized a multiple informant design, and including parent and teacher ratings of temperament and child reports of BPD symptoms. This design feature eliminates concerns about shared method variance accounting for the observed pattern of associations. Finally, the use of community participants rather than clinical patients ensures that the prospective associations observed are more representative of the development of BPD that unfolds in the general population and are less likely to be biased by the effects of treatment or characteristics that are specific to those who seek treatment.In sum, BPD symptoms in adolescence reflect trait-like differences between youth with less within-person variability across time. Symptoms appeared to peak by age 15, decline through age 18, and remain steady between ages 18 and 19. Childhood temperament dimensions of emotionality, activity, low sociability, and shyness predict adolescent BPD symptom development. Specifically, children with high levels of emotionality may be at risk for particularly elevated course of BPD symptoms across adolescence. Parent and teacher informants provide unique information about the course of BPD symptoms, suggesting the utility of collecting child assessments using multiple informants.


With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data. Recent studies have demonstrated the ability to detect disease-causing copy number variants (CNVs) in exome sequencing data. However, exonic CNV prediction programs have shown high false positive CNV counts, which is the major limiting factor for the applicability of these programs in clinical studies.We have developed a tool (cnvScan) to improve the clinical utility of computational CNV prediction in exome data. cnvScan can accept input from any CNV prediction program. cnvScan consists of two steps: CNV screening and CNV annotation. CNV screening evaluates CNV prediction using quality scores and refines this using an in-house CNV database, which greatly reduces the false positive rate. The annotation step provides functionally and clinically relevant information using multiple source datasets.We assessed the performance of cnvScan on CNV predictions from five different prediction programs using 64 exomes from Primary Immunodeficiency (PIDD) patients, and identified PIDD-causing CNVs in three individuals from two different families.In summary, cnvScan reduces the time and effort required to detect disease-causing CNVs by reducing the false positive count and providing annotation. This improves the clinical utility of CNV detection in exome data.The online version of this article (doi:10.1186/s12864-016-2374-2) contains supplementary material, which is available to authorized users. With advances in next generation sequencing technology and analysis methods, single nucleotide variants (SNVs) and indels can be detected with high sensitivity and specificity in exome sequencing data , 2. WhilcnvScan enables users to evaluate CNVs predicted from any program and provides robust CNV quality assessment to reduce the false positive count. As a comparison, the false positive count of SNV prediction was reduced with the availability of variant quality assessment and recalibration methods introduced by programs like GATK toolkit . While cWhen considering techniques used to improve variant discovery in exome sequencing, current SNV analysis pipelines use in-house SNV databases to filter out variants due to technical artifacts and population-specific variants. In order to provide similar methods for CNV analyses, we developed a novel method using an in-house CNV database to further evaluate the quality of CNV calls.In addition, existing CNV analysis tools such as ANNOVAR , VEP 1111, CNVAnTo assess the clinical utility of cnvScan, we used 64 exomes from primary immunodeficiency (PIDD) patients. cnvScan greatly reduces false positive CNVs and enabled the identification of three high-quality rare CNVs in two families. Both of these CNVs were confirmed as PIDD-causing variants. cnvScan thus provides both robust CNV quality assessment and a broad range of functionally and clinically relevant information for each CNV.As input, cnvScan can use a CNV results file from any prediction program. cnvScan then uses a two-step approach to improve the functional and clinical interpretation of computationally predicted CNVs: CNV screening and CNV annotation  using ExomeCopy , ExCopyDCNV prediction programs calculate quality scores which provide statistical support for the predictions  count. As a first step, we studied the relationship between the quality score and the false discovery rate (FDR). In order to calculate the FDR, we derived a set of true positive (TP) and FP CNVs by comparing CNV calls from exome sequencing and exon-focussed aCGH experiments (exaCGH ) from 17In order to test the applicability of quality scores for CNV quality assessment, we first studied the relationship between TP CNVs, FP CNVs and quality scores of each program. Here we calculated the cumulative TP (cTP) and cumulative FP (cFP) counts  Fig. 1)1). As exWe next calculated the FDR to evaluate the relationship between FP CNVs and quality scores. Fig. CNV prediction programs tested in our study call CNVs based on normalized coverage data extracted from a collection of exomes (reference collection) . High coThe in-house database was developed from the exomes used in the reference collection of the prediction program and consists of the location, quality score and sample ID of each predicted CNV  indicating low CNVQ for FPs were observed in ExomeCopy predictions. ExCopyDepth and ExomeDepth also showed higher CNVQ ratios in low database CNV counts (< 5). However, the CNVQ ratio decreases with increasing database CNV counts. For example, ExCopyDepth and ExomeDepth showed low CNVQ ratios (< 1) for higher database CNV counts > 7). XHMM also showed low CNVQ ratios (< 1) for all the database CNV counts. ExCopyDepth, ExomeDepth and XHMM are optimized to detect rare CNVs , 7, 9. T. XHMM al.5 indicaIn order to demonstrate how FPs and TPs can be differentiated using CNVQ, we compared FP and TP CNVQ distributions. The CNVQ range of FPs is lower than the CNVQ range of TPs Fig. , h, i. WIn summary, the analysis of CNV quality scores and in-house CNV databases, suggested that cnvScan is useful in identifying TP and FP CNVs. Identifying clinically relevant CNVs remains challenging due to the number of TP CNVs identified per exome Fig . TherefoIn order to assess the functional effect and clinical significance of predicted CNVs, cnvScan provides an annotation step which uses data from multiple external databases Table . These sFor each screened CNV, gene content (Gencode V.19) , level oKnown CNVs were identified using three datasets: Sanger high-resolution CNVs , 1000 GeClinically significant information was obtained from OMIM morbid map , DeciphecnvScan was developed to improve the clinical utility of CNV predictions. We propose a three-stage approach to implement cnvScan and detect clinically significant CNVs: (1) CNV prediction, (2) cnvScan screening and annotation and (3) CNV filtration and disease-causing variant detection.CNVs can be predicted from any program and the resulting CNVs used as input. Then cnvScan evaluates the CNV prediction quality and provides functional and clinical annotation. Finally, CNV filtration can be performed to detect rare, high-quality clinically relevant CNVs. Common and non-disease causing variants within the initial prediction can be filtered using cnvScan annotations . To exclude low quality CNVs, CNV quality scores (from prediction programs) and CNVQs (from cnvScan) can be used as filtration parameters.cnvScan was designed to considerably improve the time and effort required to detect disease-causing variants. To assess this, we implemented cnvScan with CNVs predicted from exomes used in the previous stage of the study (TP and FP CNVs from 17 exomes of PIDD patients). The total number of CNVs used in the cnvScan run was 1742 (ExCopyDepth predictions). Following the cnvScan run, the first filtration step identified 1004 (57.63 %) CNVs as common non-disease causing CNVs. CNVs were then filtered using CNV quality scores and CNVQs ranging from 10 to 40 Fig. . CNV quaWe then calculated the FP/TP CNV ratio and studied how the ratio changes when implementing cnvScan over a range of quality scores. Fig. n = 64). During cnvScan implementation, the database CNV count was not used as a filtration parameter to identify FPs since this could exclude disease-causing variants, which were predicted from multiple samples. However, the database CNV count improved the differentiation of TP and FP CNVs  for quality scores between 10 and 40 Fig. , d. ThercnvScan implementation and evaluation demonstrated the ability to reduce the FP CNV count and FDR in CNV prediction. This can improve the time and effort required to detect clinically significant CNVs from computational predictions. We then applied cnvScan in a patient exome collection to test the performance of our improved method.Having evaluated the efficiency of cnvScan, we wanted to study how cnvScan implementation can improve disease-causing CNV detection. We predicted CNVs using ExCopyDepth on 64 PIDD patient exomes and the resulting CNVs were assessed and annotated using cnvScan  section.n = 769) predicted to affect known PIDD genes (n = 475). Next, cnvScan filtration steps were applied to detect PIDD-causing variants from this PIDD call set.Since we are interested in PIDD-causing variants, we selected only the CNVs (n = 101 CNVs) and a high-stringency threshold . Thus by removing common CNVs and using a high-stringency quality filter, we removed 99.47 % of CNVs.The first filtration step that remove common and non disease-causing variants, identified 210 (27.3 %) CNVs. The second filtration step was performed to filter out FP CNVs using two CNV quality thresholds: a low-stringency threshold  were identified from both the low- and high-stringency filtered sets. Both deletions were evaluated genetically and clinically to assess the phenotype in the respective families.To detect PIDD-causing variants, the functional and clinical annotations provided in cnvScan were examined manually. Three patients with PIDD-causing variants  can be used effectively to reduce false positive CNV counts. Functional and clinical interpretation of predicted CNVs were improved with the wide range of information provided by cnvScan annotation .In summary, cnvScan enables researchers to use different programs to predict CNVs and apply suitable filtration thresholds to remove false positives and non-disease- casing variants. This reduces the time and effort required to detect disease-causing CNVs and improves the clinical utility of exonic CNV prediction.www.sequencing.uio.no). Sequence alignment was performed with NovoAlign (v2.07.17) [Exome capture was performed using the Agilent SureSelect Human All Exome capture kit v.5 (Agilent Technologies). DNA was extracted from whole-blood and 3 μg of DNA was prepared for exome sequencing according to manufacturer’s recommendations. The exome captured libraries were sequenced on an Illumina HiSeq 2500 at the Norwegian Sequencing Centre (2.07.17)  resultin2.07.17) , the finComputational CNV prediction was performed on exomes from 17 patients with primary immunodeficiency (PIDD) using ExomeCopy , ExCopyDThe main input file required for cnvScan is the CNV results file from the prediction program  designed to capture exonic regions . Here, eTo identify TP and FP CNVs, we compared CNV predictions to exaCGH results. TPs are CNVs detected by both methods. FPs are CNVs detected by the prediction program but not by the array. In order to be conservative in FP identification, CNVs that have at least 4 probes in the exaCGH design were selected as the final set of FP CNVs.Finally we calculated the cumulative TP (cTP) and cumulative FP (cFP) count to assess CNV quality scores. Quality scores of TP and FP CNVs were sorted from highest to lowest and cumulative TP and FP counts were calculated for each quality score.For the family studies, CNVs were confirmed by either exaCGH or MLPA. MLPA was preformed by Dr Dirk Roos and Mr. Martin de Boer at the Department of Blood Cell Reasearch, Sanquin Blood Supply Organization, Amsterdam, Netherlands.CNV screening is the initial analysis performed after reading the input files. This provides metadata that describes the quality of each CNV call. The reported metadata are: CNV quality score assigned by the prediction program and CNVQ from cnvScan.In-house database was designed to identify FP CNVs predicted due to technical artifacts in the reference exome collection. Thus, in-house database should contain CNVs predicted from the reference collection used in the prediction program. A method and script used to generate the in-house CNV database is discussed in Additional file Previous studies have shown that the use of male and female samples in the same reference collection influence the CNV prediction in X and Y chromosomes , 9. Thushttps://github.com/PubuduSaneth/cnvScan.In order to interpret the functional effect and study clinical significance of the CNV, predicted CNVs were annotated with data from external databases Table . AnnotatFollowing the cnvScan analysis, CNVs can be filtered to generate a set of high-quality rare CNVs. This CNV set can be further examined to identify clinically significant CNVs. Filtration is performed based on candidate gene lists (gene based filtering) and parameters reported in cnvScan analysis (variant based filtering). Separate scripts for gene and variant based filtering are available in our git repository.https://github.com/PubuduSaneth/cnvScan).cnvScan is written in python programming language and all the scripts are available via our git repository  and all the participants provided a formal written consent to participate in the study.All the participants provided a formal written consent to publish.https://github.com/PubuduSaneth/cnvScan/wiki/cnvScan-implementation).Genomic data of a person is considered sensitive data under the Norwegian Personal Data Act §2, point 8 and protected under Nordic data protection laws. Therefore the PIDD patient data (discussed in the article) cannot be made available in public data repositories. However, we have provided a dataset (CNV result files and CNV database generated from 14 exomes from 1000 genomes project) to test cnvScan at our github repository (


Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations. Despite increasing investments in pharmaceutical research and development, the rate of introduction of successfully translated drugs has decreased . ReasonsThe failure of single targets to successfully translate into clinical practice and the problem of development of drug resistance with single target cancer therapies has increased interest in discovery of effective drug combinations. Administering drug combinations has been effective in overcoming resistance to anti-microbial therapies for treatment of infectious diseases such as HIV and tuberculosis . In cancWhile combination therapies may dramatically improve efficacy of cancer therapies, the discovery of effective combinations is a challenging endeavor. With over 1,500 FDA approved compounds, experimentally testing every possible combination of these drugs would be unfeasible, even with high-throughput experimental methods . TherefoHere, we review computational and experimental methods for accelerating the discovery of effective drug combinations for complex diseases, with special focus on cancer. In addition, we include a list of publicly available resources as a reference for future drug combination studies Table .Table 1When presenting the results of drug combination studies, it is important to have a standard to statistically define synergistic drug pairs. Two commonly used methods for quantifying synergy between drug combinations are Loewe additivity and Bliss independence. Loewe additivity is based on the assumption that the two inhibitors act through a similar mechanism while Bliss independence assumes independent mechanisms .IA]X%, [IB]X%) can be used to calculate the theoretical concentrations of each inhibitor needed to achieve the same X% inhibition when combined .Using Loewe additivity, the concentration of two inhibitors (A and B) which alone results in X% inhibition of the target ([50) of [IA]50% and [IB]50%), respectively. The coordinates  and  represent the concentration for drugs A and B, respectively. The line of additivity is constructed by connecting these two points for a 50% effect isobologram plot. The concentrations of the two drugs used in combination to provide the same effect X% (e.g. X = 50%) will be denoted by point  and are placed in the same plot. Synergy, additivity, or antagonism will be determined when this point  is located below, on, or above the line, respectively. More generally, linear, concave, and convex isoboles represent non-interacting, synergy, and antagonistic drug combination, respectively  popularized by Chou and Talalay . Here, twhere CI value <1, =1, and >1 represent synergy, additivity, and antagonism, respectively.ET) as the product of the individual effects with drugs A (EA) and B (EB). The predicted combined effect (ET) is computed by:Bliss independence is based on probability theory and assumes the two inhibitors are working through independent mechanisms . The inhE) is expressed as fractional activity compared to control between 0 (100% inhibition) and 1 (0% inhibition). For example, if drug A and drug B each result in 40% tumor growth compared to control, then the predicted tumor growth when combined would be (0.4)*(0.4)*(100%) = 16% of control according to Bliss Independence. The predicted combined inhibition level would therefore be 100%-16% = 84% inhibition of tumor growth. If the actual tumor growth when drug A and B are combined is less than 16% of control (greater than 84% growth inhibition), then the compounds would be synergistic by Bliss Independence. If the tumor growth level is greater than 16% of control (less than 84% growth inhibition), then the compounds would be defined as antagonistic  and require selection of parameter values for concentrations of species in the network and rate constants controlling protein-protein associations . Many ofWhen training parameters to data, it is important to determine the importance of parameter selection on model predictions. For example, Chen et al. measured parameter sensitivity for several independent fits and saw that the rank order of the most sensitive parameters was nearly the same across the fits for a given output, therefore parameter uncertainty did not affect major model predictions . In anotMass-action network models have been used to predict new beneficial drug combinations for cancer. As an example, Faratian et al. used a mass-action model of heregulin-induced HER2/3 signaling through MAPK and PI3K to study the role of PIK3CA activation in Receptor Tyrosine Kinase (RTK) inhibitor resistance . Model rde novo resistant to ERBB2 inhibition. Another group developed a Boolean logic model of apoptosis signaling in Leukemic T-Cell large granular lymphocytes . Lo. Lo28]. phocytes . The autphocytes .50), and Hill coefficient. While these parameters can be fit to data, using default values generated highly similar quantitative predictions as a previously constructed detailed biochemical model of the same pathway which used 88 parameters from literature [While logic-based modeling approaches benefit from simple construction using network topology, results can be difficult to interpret due to assignment of discrete values to continuous variable such as concentration of active species, sensitivity to temporal node-updating schemes, and incompatibility with many systems analysis tools such as quantitative sensitivity analysis . To addrterature ,34. TherThe normalized-Hill modeling approach is a valuable tool for model construction of larger networks with more unknown parameters. As an example, Ryall et al. used this approach to model the cardiac hypertrophy signaling network, which contained 106 species and 193 reactions . Since cRyall et al.’s analysis of the hypertrophy signaling network also looked at the presence of different signaling motifs such as bi-fan and feed-forward loops. Motifs can affect network properties such as signal filtering, acceleration, pulse generation, ultra-sensitivity, stability, and robustness -40. Yin Network modeling approaches are useful when network topology is known, but these approaches can be biased towards established pathways and may miss novel interactions. Statistical association-based models do not depend on prior knowledge of pathways and instead use correlations and patterns in experimental data to predict network structure. As an example, Ryall et al. used data of correlations among cell shape features and expression of 12 genes relevant to cardiac hypertrophy to identify a network map linking input modules to output modules . Drug coMany effective drug combinations have been discovered using correlations in gene expression signatures. One useful tool for this is the Connectivity Map (CMap) database . The firin vitro with docetaxel and found to synergistically increase cytotoxicity. Wei et al. used a similar approach to predict drugs to overcome resistance to glucocorticoid treatment in acute lymphoblastic leukemia [in vitro were used to define a sensitive/resistant gene signature. Using CMap, mTOR inhibitor rapamycin was found to induce a highly similar signature, leading to the hypothesis that mTOR inhibition could induce glucocorticoid sensitivity. Follow-up experiments supported this hypothesis and showed that rapamycin conferred sensitivity through down-regulation of MCL1. Therefore, CMap is a useful tool for using gene signatures to predict drug combinations that may overcome drug resistance.As an example, Riedel et al. applied CMap to predict drugs that would prevent resistance to chemotherapy agents in lung cancer cell lines . Genes wleukemia . Microarin vitro experiments confirmed that bosutinib alone is a more effective agent than gefitinib, and that the combination of bosutinib and gefitinib had synergistic effects in EGFR mutant NSCLC cells [Inspired by the CMap concept, Kim et al. recently developed K-Map (Kinase Inhibitor Connectivity Map) that systematically connects a set of query kinases to kinase inhibitors based on quantitative profiles of the kinase inhibitor activities . InsteadLC cells . This de50 values [Other computational approaches have been developed to predict drug combinations using data from high-throughput screens and drug databases. Pal and Berlow developed an algorithm based on set theory that uses tumor drug sensitivities and kinase inhibition profiles for a set of individual drugs to predict the tumor sensitivity to new drugs or drug combinations . The alg0 values . Tang et0 values . While tGiven the high cost of exhaustive drug screens, Gujral et al. exploited the polypharmacology of kinase inhibitors by developing an approach to select the most predictive kinase targets from a smaller scale drug screen of multi-target drugs . They peHuang et al. used drug genomic profiles from the Connectivity Map database to construct a drug functional network and then grouped drugs into modules with similar transcriptional responses . They thMoreover, Liu et al. have developed a database of both successful and unsuccessful drug combinations (DCDB) . The curCheng et al. constructed a global human kinome interaction map by integrating kinase-substrate interactions, kinase-drug interactions, protein-protein interactions, and atomic resolution three-dimensional structural protein-protein interactions . Their aNetwork based approaches have also been used to study drug-drug interactions. As an example, Cheng and Zhao developed a comprehensive drug-drug interaction network incorporating 6946 interactions of 721 approved drugs using data from DrugBank . They thLarge scale knockdown screens using RNA interference (RNAi) can also be used to identify potential drug combinations . RNAi scPritchard et al. used RNAi signatures of eight cell death genes to determine the mechanism of drug combination effects in lymphoma cells . Single in vitro and in vivo patient-derived xenografts. This rational combination is now being translated into a Phase I clinical study . This illustrates the utility of integrating functional genomics screens with bioinformatics to identify and translate drug combinations into clinical study.Spreafico et al. identified non-canonical Wnt pathway mediated resistance to MEK1/2 inhibitor Selumetinib in colorectal cancer cells by integrating gene set enrichment analysis and synthetic lethality screens . Using cWhile the number of FDA approved drugs makes exhaustive drug screens unfeasible Figure , there hRoller et al. conducted a functional chemical genetic screen of 300 drug combinations in nine melanoma cell lines and identified pairs of compounds that synergistic increase cytotoxicity . InteresOne of the major ongoing initiatives at the Developmental Therapeutics Program of the National Cancer Institute, U.S. National Institute of Health, is the large-scale high-throughput drug combination screening of 100 FDA approved drugs. The first set of screening results generated 5,000 possible drug combinations in the NCI-60 cancer cell lines panel . The goaWhile substantial intratumoral heterogeneity has been detected in cancer patients using next generation sequencing technologies , currenta priori selection of important pathways [Given the experimental costs of exhaustively testing drug combinations, computational models of signaling networks will be especially useful in pre-clinical screening of combinations of compounds. Model simulations reveal non-intuitive effects of drug combinations . Due to pathways . One of pathways . This mepathways . Many ofin vitro and in vivo experiments would then be used for model refinement so more informed predictions of drug combinations can be made in future studies.In addition to larger network reconstructions, future modeling efforts will benefit from tighter integration of high-throughput sequencing, proteomics, and phenotypic screen data Figure A. This win vivo human disease models such as patient-derived tumor xenografts [Predicted drug combinations should be validated in cancer cell lines and in relevant nografts . These mnografts . Therefoin vitro discoveries to patients, and enhance discovery of pharmacodynamic biomarkers.Another opportunity for improved design of combination therapies is through quantitative systems pharmacology approaches integrating cell signaling network models with pharmacokinetic-pharmacodynamic (PK/PD) models . QuantitAdditionally, future work will benefit from parallel integration of computational modeling, preclinical testing, and clinical trials, where data from each approach can be used for refinement of the other. Computational models tuned to specific cancer cell lines using bioinformatics and experimental data could be perturbed to make predictions of effective drug combinations to validate in preclinical models Figure A. For clIt is becoming increasingly apparent that drug combinations will be essential for improving therapies for complex diseases such as cancer . The sig


Utilizing RNA interference as an innovative therapeutic strategy has an immense likelihood to generate novel concepts in precision medicine. Several clinical trials are on the way with some positive initial results. Yet, targeting of RNAi payloads such as small interfering RNAs (siRNAs), microRNA (miR) mimetic or anti-miR (antagomirs) into specific cell types remains a challenge. Major attempts are done for developing nano-sized carriers that could overcome systemic, local and cellular barriers. This progress report will focus on the recent advances in the RNAi world, detailing strategies of systemic passive tissue targeting and active cellular targeting, which is often considered as the holy grail of drug delivery. RNA interference (RNAi) is an innate cell machinery for regulation of gene expression. This process is preformed by double-stranded ribonucleic acid (dsRNA) that down-regulates the expression of precise genes with matching nucleotide sequences by decaying specific messenger RNA (mRNA) or by physically obstructing mRNA translation. RNAi can be triggered exogenously by uttering short hairpin RNA (shRNA) with virus-like vectors, or by integrating artificial small interfering RNAs (siRNAs) sequences straight into the cell cytoplasm –3.siRNA is a chemically synthesized double stranded RNA (dsRNA) of 19–21 base pairs with 2-nucleotides unpaired in the 5′-phosphorylated ends and unphosphorylated 3′-ends . MoreoveThe combination of silencing any preferred gene and the ability to treat various diseases by addressing potential non-druggable targets coupled with an improved safety and the reduced likelihood for meddling to the endogenous microRNA machinery, emphasizes the potential of RNAi to become a new potential modality for precision medicine.Aside from this potential, employing RNAi molecules as therapeutics is not a marginal assignment. For instance, for siRNAs, the ~ 13.5 kDa and the net negative charge with more than 40 phosphate groups, makes these molecules an obstacle for crossing  the plasCommon strategies used for delivery of RNAi molecules  for cellular applications are utilizing conventional transfection techniques. Studies with purely physical methods for example electroporation, gene gun or microinjections –17, in ain vitro is an essential tool for studying gene expression in normal and diseased validating new therapeutic targets [The use of RNAi strategies to silence genes of interest  targets , 37. Yet targets , 30.in vitro RNAi delivery shortly represented above, there are still numerous challenges for translating these strategies into clinical practice. The biggest hurdle confronting the translation of siRNAs therapeutic potential into the clinic is the ability to deliver these payloads in a safe- and cell-specific manner [Although there are large numbers of technologies available for c manner , 30.Native delivery of siRNAs has been shown in different disease models , 38, 39 Systemic delivery of RNAi molecules is currently considered as the ‘Holy Grail’ of the field. Whereas topical and local RNAi delivery strategies have to employ internalization strategies and endosomal release approaches, systemic RNAi delivery strategies need to really on methods to evade the immune system, frame-up within small blood capillaries, uptake by cells of the mononuclear phagocytic system (MPS), degradation by RNAses, renal clearance, anatomical barriers (such as the liver), immune activation, extravasation from blood vessels to target tissues, and permeation within the tissue to name a few .Naked siRNAs can be delivered systemically via hydrodynamic injection. This strategy, with an unknown mechanism, involves rapid intravenous administration of a large volume of siRNAs in physiologic solutions (about 10% of the body weight administered within 5–10 seconds) , 40, 41.Naked siRNAs can be employed for kidney targeting. Naked siRNAs are known to be eliminated by the glomerulus, which secrete molecules with weights that are below 40 kDa. Free siRNAs are accumulated in the kidney around 30–40 times more than in any other organ , 3. ThisUtilizing naked siRNAs for systemic application is appropriate only when the target organ is the kidney. Else, delivery of siRNA payloads for systemic applications must relay on nano-scale delivery platforms. These nano-scale delivery platforms have to be made from materials that are degradable, non-immunogenic and compatible to the biological environment. In addition, the ability to direct these delivery platforms to specific organs and cells will provide them with additional advantage since only diseased cells will be targeted.The parental siRNA delivery strategies can be generally divided into two categories: passive tissue and active cellular-based deliveries. While, passive tissue delivery strategies use the propensity of macromolecules and nanoparticles to accumulate in organs of the MPS. The MPS, an integral part of the immune system, consists of phagocytic cells located in reticular connective tissue, primarily monocytes and macrophages , 34. CelStable nucleic acid-lipid particles (SNALP) Figure  are ~ 90Different types of adverse effects have been documented with the use of different amino lipid-based nanoparticles , 52, 53 Lipidoid (synthetic lipid-like molecules)-containing liposomes are additional siRNA delivery strategy that has been shown to induce effective genes silencing ( < 80% reduction in ApoB and Factor VII mice’s mRNAs levels) in the liver . Single Histidine-Lysine (HK) peptides are an additional, simple and effective RNAi delivery strategy. While lysine is important for binding the RNAi payloads, histidines stabilizes these particles and have an important role in buffering acidic endosomes, thereby enabling endosomal disruption and payload release . HK peptAtelocollagen (AC) consists of a no- to minimal fraction of pepsin-digested collagen type I from calf dermis. AC is rich in positively charged amino acids such as lysine and hydroxylysine. It condenses the negatively charged RNAi payload and interacts with the plasma membrane, hence helps to incorporate the RNAi into the cells’ cytoplasm . Indeed siRNAs conjugated to different molecules that can aid in cellular targeting is a common strategy for active cellular delivery. One such example is the Cholesterol – siRNA conjugate , 30. ThiAn additional strategy in this category is the dynamic polyconjugates (DP) , 67. Thivβ3 and αvβ5) [Polyethyleneimine (PEI) complexes carrying RNAi payloads have also shown to trigger in vivo gene silencing when administrated subcutaneously into human tumors implented in nude mice. These PEI complexes composed of RGD (Arg-Gly-Asp) tripeptide that endow them specificty to integrins (adhesion receptors highly expressed on tumor cells and extracellular materix around the tumor) and coupled via PEG  to PEI  . When RGnd αvβ5) .cyclodextrin-containing polycation (CDP) particles have been successfully used for siRNAs delivery into mice’s subcutaneous tumors  and haveAn innovative platform strategy based on a fusion protein carrier having a specific ligand (mAb fragment) fused to human trucated protamine (RNA condenser) was developed to deliver RNAi payloads in a receptor-specific manner  Figure . ProtamiAptamer-siRNA chimeras Figure  are RNA-Various approaches of targeted amino-based LNPs serve for targeting of stellate cells in the liver. These cells are involved in the formation of scar tissue in response to liver damage such as fibrosis or as nurse-like cells in the case of a liver cancer (hepatoma) . These sManipulating gene profiling in leukocytes holds great promise for drug discovery and for enhancing the development of new therapeutic modalities for leukocytes implicated diseases such as inflammation, hematological malignancies, and leukocytes-tropic viral infections. Yet, systemic delivery of RNAi to leukocytes has proven to be extremely challenging since leukocytes are intrinsically resistance to conventional transfection mechanisms and since they are dispersed in the body and thus need specific cell surface markers to be used as targeting moieties , 47.stat3 by using the RNAi payload and simultaneously activated TLRs responses using its agonist. Consequently, the tumor microenvironment was changed from pro-oncogenic environment to an anti-oncogenic (by causing activation of tumor-associated immune cells and potent antitumor immune responses) [siRNAs that are linked  to a CpG oligonucleotide agonist of Toll Like Receptor (TLR)9 for targeting myeloid cells and B cells (both are key components of tumor microenvironment) that express this receptor were reported by Kortylewski et al. . These csponses) .An interesting strategy simillar to the protamine fusion protein startegy was developed to treat viral-tropic infections. scFv against CD7, a surface antigen present on the majority of human T cells was modified to include a Cys residue for conjugation to a 9 arginine (R) residue. The concept was that the scFv will target a specific cell surface receptor while the 9R will aid in complexing the RNAi payload. This elegant strategy was utilized for specific delivery of CCR5 (a chemokine receptor that function as co-receptor for HIV) and Vif/Tat (HIV replication proteins) -siRNAs payloads into T cells, and had shown to suppress HIV infection in humanized mice without inducing toxicity in target cells , 80, 81.The strategy we developed for targeting subsets of leukocytes is based on leukocytes’ integrins, which are the most characterized cell adhesion molecules that mediate cell-cell and cell-matrix interactions , 83. Uti7 integrin (which is highly expressed in gut leukocytes) to the HA [To substantially enlarge the amount of the RNAi payload and attain a superior and more selective gene silencing, we have formed LNPs decorated with anti-integrin mAb termed ‘Integrin-targeted stabilized nanoparticles (I-tsNP)’ that successfully delivered RNAi payloads into subsets of leukocytes involved in gut inflammation . These po the HA . Utilizio the HA  and did o the HA  – it wilSeveral RNAi-based clinical trials utilize naked RNAi molecules with local delivery Table , yet, a The first sets of clinical evaluations introduced naked siRNAs to patients suffering from age-related macular degeneration (AMD) and diabetic macular edema (DME). The administration of these siRNAs was done intraviteral and the siRNAs were against vascular endothelial growth factor (VEGF). Other local strategies to deliver RNAi payloads were conducted, but many of these strategies were ended because of safety issues , 97. SinRRM2 mRNA and RRM2 protein levels after several cycles of dosing was demonstrated providing promise and hope to the field of systemic RNAi delivery strategies [The first-in-human systemic administration trial engaging the use of targeted NPs for RNAi payloads to solid tumors – named CALAA-01, was conducted in 2008 , 71. CALrategies .Meanwhile several other systemic delivery strategies entered the clinical trials, six are based on SNALP strategy , 48. A rAnother intriguing therapeutic strategy, which was clinically tested in patients with advanced colon cancer, targets Protein Kinase N3 (PKN3), an important mediator of the PI3K pathway. To deliver PKM3-siRNAs, Atu027 lipoplexes were used. Atu027 was reported to activate the complement system in clinical trials, and thus needs to be further optimized for future clinical testing. A novel strategy targeted against Ebola virus (TKM-EBOLA) and a strategy to reduce hypercholestrolemia (ALN-PCS02), both SNALP-based formulations entered clinical evaluation in 2011 , inflammation, and viral infections. Yet, the lack of in deep understanding of molecular pathways in health and diseases together with general toxicities related to engineered nanomedicines can hamper the full potential for precision RNAi-based medicine and restrain the development of clinically approved novel RNAi modalities.Although the great progress achieved towards personalizing disease care, the current personalized therapies targets a single specific molecular marker on tumor cells and thus neglecting the vast heterogeneity presents within the primary tumor, between different metastases, within each metastasis and between tumors of different patients. Consequently, current treatment usually results in apparent partial or complete responses, which in most cases follow by a resistant tumor relapse and mortality of the patients. Therefore, personalized treatment requires multi-layer characterization of the molecular signatures of tumor cell subpopulations together with individualized pharmacotyping  to achieve maximal efficacy.We envision that the growing efforts for fine-tuning NPs design, optimizing intracellular payload release, successful approaches in minimizing adverse effects, and functionalizing the NPs with selective targeting moieties, along with the comprehensive understanding of our genome and transcriptome, would collectively tile the way for specific, safe and targeted NPs that carry RNAi payloads for precision medicine.


Pyrethroid pesticides cause abnormalities in the dopamine system and produce an ADHD phenotype in animal models, with effects accentuated in males versus females. However, data regarding behavioral effects of pyrethroid exposure in children is limited. We examined the association between pyrethroid pesticide exposure and ADHD in a nationally representative sample of US children, and tested whether this association differs by sex.Data are from 8–15 year old participants (N = 687) in the 2001–2002 National Health and Nutrition Examination Survey. Exposure was assessed using concurrent urinary levels of the pyrethroid metabolite 3-phenoxybenzoic acid (3-PBA). ADHD was defined by either meeting Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition criteria on the Diagnostic Interview Schedule for Children (DISC) or caregiver report of a prior diagnosis. ADHD symptom counts were determined via the DISC. Multivariable logistic regression examined the link between pyrethroid exposure and ADHD, and poisson regression investigated the link between exposure and ADHD symptom counts.Children with urinary 3-PBA above the limit of detection (LOD) were twice as likely to have ADHD compared with those below the LOD . Hyperactive-impulsive symptoms increased by 50 % for every 10-fold increase in 3-PBA levels ; effects on inattention were not significant. We observed possible sex-specific effects: pyrethroid biomarkers were associated with increased odds of an ADHD diagnosis and number of ADHD symptoms for boys but not girls.We found an association between increasing pyrethroid pesticide exposure and ADHD which may be stronger for hyperactive-impulsive symptoms compared to inattention and in boys compared to girls. Given the growing use of pyrethroid pesticides, these results may be of considerable public health import.The online version of this article (doi:10.1186/s12940-015-0030-y) contains supplementary material, which is available to authorized users. Attention Deficit Hyperactivity Disorder (ADHD), the most common neurobehavioral disorder in children , 2, is aAlthough pyrethroid pesticides are often considered a “safer” choice because they are generally not as acutely toxic as organophosphates , animal et al. found that postnatal pyrethroid exposure was associated with general behavioral difficulties  were tabulated for those who had urinary 3-PBA levels below and above the limit of detection.This study was determined to be exempt from review by the institutional review board (IRB) at Cincinnati Children’s Hospital Medical Center (Study 10-transformed due to skewed distributions) and ADHD, adjusting for potential confounders. To examine the consistency of pyrethroid pesticide associations, additional analyses, available in Additional file The association between ADHD and urinary 3-PBA (levels below versus above the limit of detection) was analyzed via logistic regression, adjusting for potential confounders. Logistic regression was used to examine the association between continuous urinary 3-PBA levels (log10-transformed levels). For each model, adjusted count ratios were calculated to determine the mean percent increase in symptom counts beyond that of the reference group for each stratum of the 3-PBA variables.We used Poisson regression to assess the relationship between urinary 3-PBA levels and the number of inattentive and hyperactive symptoms endorsed as being present ‘often’ in at least two settings, adjusting for potential confounders. The Poisson distribution accounts for the skewed and count nature of these data. Analyses investigated urinary 3-PBA levels as both a dichotomous predictor (levels below versus above the limit of detection) and a continuous predictor , adjusting for potential confounders. Analyses using restricted cubic polynomial splines provide flexibility in modeling the relationship between the exposure and the outcome, and avoid assumptions about model linearity ). Of note, we had reduced statistical power to detect sex*pyrethroid interactions in dichotomous models due to relatively small numbers of participants having the ADHD diagnosis. This pattern suggesting possible sex-specific effects is consistent with prior animal studies that reported greater impulsive and hyperactive symptoms in exposed male but not female mice [et al. found a stronger adverse association between pyrethroid exposure and a measure of total behavioral difficulties as well as oppositional/conduct-specific problems in girls compared to boys [et al. did not find any significant interactions  between sex and pyrethroid exposure, and it is not clear if they observed any patterns suggesting possible sex-specific effects despite the failure to meet thresholds for statistical significance. Certainly, further study of sex-specific vulnerability to pyrethroid exposure is warranted, given mounting evidence that the adverse effects of some ADHD risk factors may be accentuated or limited to certain groups [We observed sex-specific effects of pyrethroid exposure. Urinary pyrethroid biomarkers were associated with increased odds of an ADHD diagnosis and number of ADHD symptoms for boys but not girls in logistic and poisson regression as well as spline analyses. The sex*pyrethroid interaction had a borderline significant p-value of 0.09 when 3-PBA was modeled as a logarithmically-transformed continuous variable. In contrast, the interaction was not statistically significant  and later development of ADHD, as data from animal studies suggest that both the prenatal and postnatal periods may represent susceptible phases for pyrethroid neurotoxicity [An additional limitation of our study is that pyrethroid exposure was assessed using 3-PBA concentrations in a single spot urine sample. Given that pyrethroids are non-persistent and rapidly metabolized, serial measurements would provide a more accurate measurement of typical exposure , and areociation , 43. Subtoxicity –23. AnotMisclassification of ADHD may also have played a role in analyses for which case ascertainment included caregiver report of the child’s prior diagnosis of ADHD, as we cannot verify that doctors in community-based settings used DSM-IV criteria when establishing ADHD diagnoses . HoweverOur results suggest an association between childhood urinary pyrethroid pesticide biomarkers and ADHD, particularly hyperactive-impulsive symptoms, and these associations may be stronger in boys than girls. Given the growing use of pyrethroid pesticides and the perception that they represent a safer pesticide alternative, these results may be of considerable public health importance. However, replication of findings is warranted in prospective, longitudinal studies with serial measurements of pyrethroid pesticide exposure.


The degradation of HIV-derived proteins into epitopes displayed by MHC-I or MHC-II are the first events leading to the priming of HIV-specific immune responses and to the recognition of infected cells. Despite a wealth of information about peptidases involved in protein degradation, our knowledge of epitope presentation during HIV infection remains limited. Here we review current data on HIV protein degradation linking epitope production and immunodominance, viral evolution and impaired epitope presentation. We propose that an in-depth understanding of HIV antigen processing and presentation in relevant primary cells could be exploited to identify signatures leading to efficient or inefficient epitope presentation in HIV proteomes, and to improve the design of immunogens eliciting immune responses efficiently recognizing all infected cells. Protein degradation by the antigen processing machinery is a continuous process regulating all cellular functions by defining the lifespan of all proteins, discarding misfolded proteins, recycling amino acids to make new proteins, and initiating immune monitoring .Proteins and misfolded or incomplete translation products such as defective ribosomal products  are degrDespite rather extensive knowledge of protein degradation pathways and high-throughput methods to sequence HIV in large cohorts of patients, our capacity to rapidly identify or predict T cell epitopes in pathogen-derived proteins after infection or vaccination is still cumbersome, mostly relying on screening of T cell responses by Elispot using PBMCs from infected persons. The identification of potential anchor residues for various MHC-I isotypes in HIV sequences shows that there are more putative epitopes than actual immune responses, suggesting limitations in either production of peptides, their presentation by MHC or available TCR repertoire. We still do not know which HIV peptides are actually processed and presented by HIV-infected cells. These studies are still technically difficult due to the high number of infected cells required for peptide extraction. The first evidence of presentation of HIV peptides by HIV-infected cell lines came through acid elution and HPLC purification of peptide fractions reactive against HIV-specific CD8 T cells . A seconIn vitro degradation of HIV proteins by purified proteasomes [In vitro proteasomal degradation of Nef showed production of numerous peptides in epitope-rich areas of Nef enriched in hydrophobic residues [To study epitope production and presentation several experimental systems have been developed. The use of HIV-infected cells as targets for assays measuring HIV-specific T cell functions  measures both endogenous processing and presentation of epitopes and variability of MHC-peptide-TCR, and is limited by the availability of HIV-specific T cell clones. teasomes ,47,48, Eteasomes ,50 or byteasomes  enables residues . Degradaresidues ,50. Degrresidues . Single residues . Howeverresidues ,54, and residues ,53,55,56residues .HIV virions enter cells by fusion at the plasma membrane and by endocytosis or antibody-mediated phagocytosis for immune complexes. Vaccines such as viral vectors or nanoparticles decorated with peptides or proteins enter dendritic cells by endocytosis. Degradation of HIV Envelope after deglycolysation showed poor proteasomal degradation and efficient degradation by cathepsins, in accordance with intrinsic differences in antigen degradation according to peptidases’ substrate preferences . Side-byin vitro and presentation to T cells [A corollary to studies on efficiency of epitope production is to better understand mechanisms leading to impaired epitope presentation in the context of viral evolution. HLA-restricted mutations occur frequently in HIV-infected individuals during acute and chronic infection ,63,64,65 T cells . In a po T cells . In anot T cells . Thus, H T cells , leadingThe accumulation of data on protein degradation by purified proteasomes led to the development of prediction tools such as NetChOP or PaProC to define potential cleavage sites in proteins of interest ,85,86,87HIV infection induces massive changes of cellular metabolism  and creaIn vitro degradation of HIV p24 by purified proteasomes from mature DC or activated CD4 T cells showed differences in the degradation patterns of p24 and production of HIV epitopes, supporting intrinsic differences in the hydrolytic activities of peptidases from different cell subsets [HIV infects CD4 T cells, monocytes, macrophages and dendritic cells but we do not know if they similarly process and present HIV epitopes during infection. Whereas all cells contain proteasomes and post-proteasomal peptidases, their levels of expression and hydrolytic activities in primary cell subsets from a given individual are poorly defined. We showed that primary CD4 T cells present lower proteasomal and aminopeptidase activities than monocytes isolated from the same donor . These d subsets . HIV canHIV infection profoundly affects the transcriptional programs of cells  and trigin vivo in mice as well as in human cells in vitro and LCMV-specific T cell responses in treated mice [in vitro degradation assays in extracts treated with PIs, and in HIV-infected cells endogenously processing and presenting HIV peptides to HIV-specific T cells [HIV Protease inhibitors (PI) are designed to block HIV protease catalytic sites to prevent HIV maturation into fully infectious particles. First generation PIs such as Saquinavir and Ritonavir present some cross-reactivity with proteasome catalytic sites and alter proteasome activity ted mice ,105,106. T cells .In vitro HIV infection alters antigen processing activities in monocyte-derived macrophages [in vitro studies [In vitro studies revealed that uptake of HIV Tat altered epitope processing activities and viral epitope presentation but the impact on the processing of HIV is not known [Besides these external stimuli, HIV infection itself affects antigen processing. In cells isolated from HIV-infected donors CD4 T cells present lower activities than monocytes as seen in cells isolated from healthy donors. Peptidase activities in cells from HIV-infected donors were heterogeneous with a trend toward decrease in CD4 T cells and increase in monocytes compared to healthy donors . The verrophages ,109. Int studies ,111. In ot known . Howeverin vitro studies, preclinical animal studies and clinical trials include designing and testing various types of immunogens  [A commonly accepted approach to HIV vaccine design (including prophylactic vaccines protecting from infection or therapeutic vaccines clearing or controlling established infection) would combine a potent antibody response to prevent establishment of infection and sustainable T cell responses to control viral replication . Challenc acids) , viral vc acids) ,117, vecc acids) , and adjc acids) . Since ci.e., live persistent virus with attenuated capacity to replicate) led to clearance of high or low dose SIV infection in 50% of Rhesus macaques up to 3 years after infection. The attenuated vector led to broad CD4 and CD8 T cell responses with an effector phenotype that were detected both in blood and lymph nodes of vaccinated monkeys [HIV infection leads to a predictable hierarchy of T cell responses with narrow immunodominance of one or several T cell responses for each HLA type ,120. Imm monkeys ,125,126. monkeys . The mec monkeys  support  monkeys . As both monkeys ,131,132. monkeys .in vivo validation.Breaking immunodominance could be accomplished through different approaches. Eliminating known immunodominant epitopes through deletions or mutations preventing presentation , or limiThe high variability of HIV in the infected population and across clades may require either the design of vaccines specific for clades or to focus the immune responses on the most conserved areas of the virus . The emeThe choice of vectors and adjuvants and the specific subset of dendritic cells endocytosing the vaccine and could also alter conditions in which the peptides are processed ,153,154.Therapeutic vaccines aiming at clearing or substantially reducing HIV infection is urgently needed to replace lifelong treatment of the HIV-infected population. One approach consists in combining reactivation of HIV provirus with drugs such as HDAC inhibitors with vaccines boosting HIV immune responses in HIV-infected persons receiving ART ,160. LatFurther studies are needed to identify protective immune responses for prophylactic and therapeutic vaccines, specifically with regards to epitope specificity covering various HLA, ratio or phenotypes of CD4 and CD8 T cell responses, and how to specifically induce them during vaccination. Guiding principles such as encompassing viral diversity or focusing on conserved areas, adequate matching epitope presentation between infected cells and DC receiving vaccines are defined, and multiple tools to study epitope production and immune responses are available. Regardless of the vector or adjuvants used for vaccination, the HIV immunogen will have to be processed in professional antigen presenting cells for the priming of immune responses. It is necessary to ensure that—in the context established by the specific vaccination strategies discussed above—this HIV immunogen will be properly degraded into MHC-I and MHC-II epitopes corresponding to sustainable protective immune responses able to recognize infected cells. Assays to follow protein degradation into epitopes and motifs to optimize epitope provide a way to test epitope production from vaccine immunogens before larger studies in animal models and clinical trials, and guidelines to the design of immunogens leading to optimized epitope presentation.