We found that four trajectories in both samples provided the strongest model fit. Identified populations included animals that acquired and retained avoidance behavior on the first day (Rapid Avoiders: 22 and 25%); those who never successfully acquired avoidance ; a modal class who acquired avoidance over 3 days ; and a population who demonstrated a slow pattern of avoidance, failed to fully acquire avoidance in study 1 and did acquire avoidance on days 4 and 5 in study 2 . With the exception of the Slow Avoiders in Study 1, populations that acquired demonstrated rapid step-like increases leading to asymptotic levels of avoidance. These findings indicate that avoidance responses are heterogeneous in a way that may be informative for understanding both resilience and PTSD as well as the nature of instrumental behavior acquisition. Characterizing heterogeneous populations based on their response to threat cues would increase the accuracy and translatability of such models and potentially lead to new discoveries that explain diversity in instrumental defensive responses.Individuals exposed to traumatic stressors follow divergent patterns including resilience and chronic stress. However, researchers utilizing animal models that examine learned or instrumental threat responses thought to have translational relevance for Posttraumatic Stress Disorder (PTSD) and resilience typically use central tendency statistics that assume population homogeneity. This approach potentially overlooks fundamental differences that can explain human diversity in response to traumatic stressors. The current study tests this assumption by identifying and replicating common heterogeneous patterns of response to signaled active avoidance (AA) training. In this paradigm, rats are trained to prevent an aversive outcome (shock) by performing a learned instrumental behavior (shuttling between chambers) during the presentation of a conditioned threat cue (tone). We test the hypothesis that heterogeneous trajectories of threat avoidance provide more accurate model fit compared to a single mean trajectory in two separate studies. Study 1 conducted 3 days of signaled AA training ( Animal models of stress are thought to provide information about the course and etiology of stress psychopathology such as Posttraumatic Stress Disorder (PTSD) , which combines sequential Pavlovian and instrumental conditioning , i.e., the number of times animals cross between divided chambers in between AA trials, which is a measure of inter-trial exploratory behavior. We predict that rapid avoiders identified by LGM should also show increased ITRs.n = 81 rats) consisted of 3 days of signaled AA training while Study 2 (n = 186 rats) utilized identical procedures with training extended through 5 days. Rats were individually housed in plastic tubs with ad libitum access to food and water, and kept on a 12 h light/dark cycle (lights on at 8 AM). All procedures were approved by the NYU University Animal Welfare Committee. Animals in Study 1 had intracranial guide cannula implants as previously described  weighing 250–300 g at the time of arrival. Animals were used in two separate AA studies. Study 1  separated into two equal compartments by a metal divider placed halfway along the length of the chamber (Coulbourn Instruments). A passage in the divider  allowed animals to move freely between compartments. The floor was comprised of conductive stainless steel bars. The CS was a 5 kHz, 70 db tone delivered via two speakers mounted on opposite walls of the chamber. The US was a 0.7 mA footshock administered via the floor by a scrambled shocker. The chamber was lit by two 0.5 W light bulbs, one in each compartment. The shuttle box was housed within a larger sound-attenuating cubicle.Shuttling (movement from one compartment to the other) was monitored by two infrared arrays, each comprised of 5 emitter-detector pairs, located on either side of the metal divider. Sessions were also recorded on DVD by a pair of black and white infrared cameras, one in each compartment.On the day prior to the initiation of training, all animals were habituated to the shuttle box for 1 h. Shuttling between compartments was recorded as a measure of baseline activity. Twenty-four hours later the first of 3 or 5 consecutive daily avoidance-training sessions began. Each session started with a 5 min acclimation period in which no stimuli were presented. The 1st trial of the 1st session was a Pavlovian trial—a 15 s tone CS preceded a 1 s foot shock US regardless of whether the animal performed the avoidance response (shuttling) during CS presentation. This allowed all animals to acquire the Pavlovian contingency at the same point in training. All subsequent trials were avoidance trials. The CS lasted a maximum of 15 s and was followed immediately by a US lasting a maximum of 15 s. The shock begins immediately after the 15 s CS (if the rat fails to shuttle during the CS) and remains on until an escape shuttle occurs or 15 additional seconds elapse. If the animal shuttled during CS presentation, the tone terminated immediately, and the US was not delivered; this was scored as an avoidance response. Each session was comprised of 30 avoidance CSs with a varying inter-trial interval that was, on average, 120 s. Both CS and US duration depended on the behavior of the animals. If the rat shuttled during the CS presentation, it immediately terminated and thus the CS was less than 15 s. If no shuttle occurred during the CS, then the shock was presented until an escape shuttle occurred or until an additional 15 s elapsed. Thus, if the rat escaped the US then the US presentation was less than 15 s. There were no minimum CS or US durations programmed, only maximums. Avoidance responses were recorded as number of successful shuttles in response to the CS. Inter-trial shuttling responses (ITRs) were recorded simply as the number of non-CS shuttles per session.The current study attempts to identify latent (not directly observable) classes of active avoidance acquisition using LGMM. LGMM allows for the empirical exploration of underlying heterogeneity that may otherwise be treated as error when assuming population homogeneity across the sample under study , are useful for identifying and studying homogeneous stress response patterns without reliance on a priori assumptions to define cutoff scores for populations , with diminishing reductions in scores through five classes. Class solutions demonstrated marginally significant differences in model fit through four classes based on the LMRT and significant improvement through five classes based on the BLRT compared to the four class solution. Entropy values remained in the high range across solutions. The addition of a fifth class produced an additional small class (3.3%) following an erratic pattern without significantly altering the other classes. Based on this evidence and evidence from the literature that fixed effects can lead to over-identification  demonstrated an initial intercept that was significantly different from 0 , a non-significant slope on Day 1 , a negative slope on Day 2 , and a marginally significant negative slope on Day 3 . Class 2 (Slow Avoiders: 21%) demonstrated an initial intercept that was significantly different from 0 , and a significant increase in avoidance behavior on Day 1  but no increase in learning on Day 2  or Day 3 . Class 3  demonstrated an initial intercept that is significantly different from 0  and growth in avoidance learning across all 3 days of training . Finally, Class 4 (Rapid Avoiders: 22%) demonstrated an initial intercept that was significantly different from 0 and elevated compared to the other classes  along with significant positive growth on Day 1  and flat slopes for Day 2  and Day 3 , which in this case was not substantively distinct from another identified class. Entropy values remained high across solutions. Based on these observations, a four class solution was retained  demonstrated an initial intercept that was marginally significantly different from 0 , significant positive growth on Day 1 , non-significant growth on Day 2  and Day 3 , and significant positive growth on Day 4  and Day 5 . Other classes included Non-Avoiders (16%) who demonstrated an intercept that was significantly different from 0 , non-significant growth on Day 1 , positive growth on Day 2 , and non-significant growth on Day 3 , Day 4 , and Day 5 . Modal Avoiders (50%) demonstrated a significant intercept , significant positive growth across Day 1 , Day 2 , and Day 3  and non-significant growth on Day 4  and Day 5 . Rapid Avoiders (25%) once again demonstrated an elevated intercept compared to the other classes , significant growth on Day 1 , and non-significant growth on Day 2 , Day 3 , Day 4 , and Day 5  = 57.94; p < 0.001) and a significant interaction between time and class membership  = 6.45; p < 0.001). Post-hoc analyses using Least Squared Differences correction for multiple comparisons revealed significant differences by class on the course of ITRs that closely resembled trajectories of avoidance response  behavior in response to a two-way shuttling paradigm. Previous studies drawing on distinct AA populations  have made important advances in understanding competition in brain circuits mediating threat and avoidance conditioning formation resulting in individual differences in instrumental behavior  who acquired AA within 10 trials and achieved asymptotic levels of avoidance within the first day of training, Modal Avoiders , who demonstrated step-like increases in AA acquisition for roughly 3 days before converging with Rapid Avoiders, and a class of animals (Non-Avoiders) that failed to acquire AA learning through both 3 and 5 days of training . In data from both studies, a Slow Avoider group was identified  that demonstrated slight gains through the first 3 days of training and in Study 2 demonstrated a step-like increase in avoidance acquisition across days 4 and 5.Specifically, The use of LGMM provides statistical evidence for consistent population heterogeneity in AA acquisition. Comparing the identified phenotypes to the population mean provides important insights into the limitations of assuming population homogeneity. Characterizing these populations comes with a number of benefits for future research.The population mean provides the appearance that acquisition is linear and gradual over time, while acquisition among disaggregated phenotypes follows a step-like pattern where animals learn rapidly during some days of training and demonstrate little or no increase on other days. The use of a piecewise modeling approach with a separate slope each day of training allows for the identification of non-linear patterns of acquisition. Disaggregating populations provides evidence that the mean pattern of learning is an artifact resulting from collapsing multiple qualitatively distinct populations, including those who very rapidly acquire AA and those who consistently fail to do so. Further, related to both of the above limitations, the population mean obfuscates important information about the characteristics of signaled AA acquisition. By disaggregating populations, we can observe and characterize asymptotic levels of avoidance both prior to and following acquisition. By identifying latent populations we observe that animals en route to asymptotic levels of active avoidance successfully avoid in ≥to 50% of trials. Further, rapid acquisition appears to occur following the initiation of a new day of training indicating that memory consolidation is likely occurring between training sessions, possibly during sleep. The population mean, however, provides no clear evidence in this regard, as the mean for early trials is influenced by those who rapidly acquire and the mean of the latter trials is influenced by those who fail to acquire  supported LGMM results in finding distinct differences between classes, with a consistent direction indicating that increased ITRs early in training facilitate AA acquisition. In particular, Rapid Avoiders were distinguished from Modal Avoiders by markedly more ITRs on Day 1 of training. Increased ITRs on Day 3 also distinguished Slow- from Non-Avoiders. This result is consistent with previous reports of decreased ITRs in an extreme group selected for poor AA and frequent freezing . More generally, any strategy that assumes homogeneity and uniformity of variance (Fox, Modeling statistical heterogeneity is also valuable when analyzing the results of experimental stress interventions, such as immobilization, which are used to studying the neurobiology of stress pathology e.g., Andero et al. . While ence Fox,  fails toThe current findings and approach can significantly aid in the discovery of behavioral and neurobiological differences associated with clinically relevant learning and coping strategies. Further, the approach can be generalized to other behaviors that are of clinical importance such as appetitive responses to addictive substances. Identifying heterogeneous populations allows for the exploration of circuit functioning that differentiates populations, the identification of time dependent changes that cause shifts in behavior, and examination of manipulations that alter the proportions of the identified populations. For example, naturally occurring differences in basal CREB, a key protein required for memory formation, has been shown to be associated with distinct behavioral responses to threat conditioning training. Further, direct augmentation of amygdala CREB causes shifts from one extreme to the other (Cowansage et al., Several lines of evidence suggest that impaired AA in Slow- or Non Avoiders may owe to a reduced capacity to suppress Pavlovian defensive reactions. Slow AA avoiders show increased rates of freezing (Choi et al., Rapid Avoiders constituted a robustly distinct minority. It is of interest to consider neural circuits that may mediate enhanced AA acquisition. It is plausible this subpopulation has a markedly superior capacity to suppress freezing, compared to Modal Avoiders. Alternatively, or additionally, Rapid Avoiders may have enhanced motivational control of instrumental performance, or enhanced reward processing, perhaps due to more efficient neural interactions between the amygdala and nucleus accumbens (Cain and LeDoux, Identifying distinct trajectories of AA acquisition provides an inroad for translational research through comparison to quantitatively and qualitatively related patterns in clinical populations. Naturalistic studies of the course of stress pathology identify a similar heterogeneity in trajectories of symptom non-remission following trauma exposure (Bonanno et al., 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.


Background: Diabetes mellitus and depression are highly prevalent conditions throughout the world and have significant impact on health outcomes. It has been estimated that diabetes mellitus type 2 affects about 246 million people in the world; nevertheless, incidence varies among countries. There is evidence that depression is associated with a poor metabolic control in patients with type 2 diabetes mellitus that present other health problems (such as hypertension and obesity). The aim of this study protocol is to determine if obesity increases the risk for depression in patient with diabetes type 2.Methods: The analysis will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).The studies suitable for inclusion will be assessed by the Newcastle-Ottawa Scale (NOS) to determine their methodological quality. To identify the studies of interest, we will search on PubMed and EBSCO databases. We will use the following keyword combinations: "Diabetes Mellitus type 2 AND obesity AND depression", "depression AND Diabetes Mellitus type 2", "Diabetes Mellitus type 2 AND body mass index cross sectional study", "depression AND obesity cross-sectional study". Causes for exclusion will be publications that studied patients diagnosed with diabetes mellitus type 1; articles that focused on the treatment and complications of diabetes mellitus type 2; publications that have studied other clinical or psychiatric conditions .Conclusion: The results of this study will form the basis for a better understanding of the association between obesity and depression in patients with diabetes mellitus type 2, and will allow development of prediction tools and better interventions. It is evident that several modifiable and non-modifiable risk factors play an important role in the pathogenesis of diabetes among population. Currently, evidence for the deleterious effects of diabetes mellitus type 2 are based on cross-sectional or other observational designs. Therefore, this study will have important implications for future research and public health guidance. It has been estimated that diabetes mellitus type 2 affects about 246 million people in the world4; nevertheless, incidence varies among countries6. The International Diabetes Federation has anticipated an increase of 366 million people by 2030, giving a total of 552 million people with diabetes type 2 in the world7.Diabetes and depression are highly prevalent conditions throughout the world and have significant impact on health outcomes. Diabetes mellitus is a chronic-degenerative disease, characterized by high levels of blood glucose9. Literature suggests there is a correlation between type 2 diabetes and mood alterations such as depression and neuropsychiatric disorders; for instance, major depressive disorder11, schizophrenia13, mild cognitive impairment15 and suicidal behavior16. It also has been observed that depression could cause an increase in all-cause mortality risk (approximately 70%)17; it is also the most common mental disorder and generates a great impact on people and society in terms of suffering, disability and economic costs, a phenomenon that seems to occur in many parts of the world; in this context, it has been reported that depression affects 350 million people worldwide18; for example, a research by Talbotet al. suggests that depression is not only a direct consequence of diabetes; depression may also be a risk factor for the onset of diabetes type 219. Patients with diabetes mellitus type 2, often present a careless attitude towards their disease, resulting in metabolic decompensation, with high and low glycemic levels which could trigger mood alterations21. Diabetes mellitus 2 is also associated with a higher risk of comorbid depression compared with the general population22. It has been suggested that diabetes type 2 could be conditioned by depression, anxiety or anguish25; nevertheless, the reason for this association is not clear27. The neurobiological mechanisms that could explain the association between depression and diabetes mellitus type 228 could include 1) the alterations involved in the metabolism of biogenic amines (serotonin and norepinephrine), from the adrenal-pituitary-hypothalamus axis (by increasing cortisol)30; 2) trophic agents such as Brain Derived Neurotrophic Factor (BDNF) through Glycogen Synthase Kinase-3 (GSK-3)32. The GSK-3 is a serine/threonine protein kinase that mediates the addition of phosphate molecules into serine and threonine amino acid residues. It consists of two isoforms, α and β34. It is possible that an over activation of GSK-3 play an important role in the pathogenesis of the development of schizophrenia and mood disorders such as bipolar disorder and major depression in patients with diabetes mellitus type 236. Furthermore, it has been suggested that the presence of metabolic alterations in patients with diabetes type 2 such as obesity, could increase the severity of depression40. The distinct mechanisms that link obesity to insulin resistance and diabetes mellitus type 2 are related to an increased production of adipokines and more adipose tissue as a result42; these molecules are involved in many clinical manifestations of diabetes mellitus type 2 and they are also associated with arterial hypertension and cardiovascular disease43. First, the adipose tissue of the obese patient becomes resistant to the action of insulin due to the effect of some of these adipokines; for instance, the tumoral necrosis factor alpha (TNF-α) and interleukine-6 (IL-6)44. Secondly, this resistance occurs in other tissues; therefore, insulin and glucose levels increase. This increase, along with high adipokines levels (that occur in diabetes mellitus type 2), lead to different adverse events, such as endothelial dysfunction45, increase in oxidative stress46, impairments in lipoprotein metabolism and increase in blood pressure47. For a review see Antuna puenteet al.48. For example, a research by Svenningssonet al. suggests an association between depression and obesity in patients with diabetes mellitus type 2 in both genders; this study reported that at least one in five men and one in three women showed depression in diabetic type 2 patients with obesity49. Recently, a report showed that there is a positive association between having a high body mass index and the risk to develop diabetes mellitus type 250. In general, literature shows evidence that depression is associated with metabolic disorders in patients with type 2 diabetes mellitus27.The diabetes type 2 is a complex disease, where hereditary and metabolic factors interfereIn this work we will focus on searching a correlation between obesity and depression in patient with diabetes type 2. The aim of this study is to determine if obesity in patients with diabetes type 2 increases the risk of depression.PICOT QUESTION: Does obesity increase the risk of depression in Diabetes Mellitus type 2 patients?51. In accordance with the guidelines, our study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO)52, on 08 October, 2014 (registration number CRD42014014034).The study protocol will be conducted and reported in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines51. We will search on PubMed and EBSCO databases. We will further scan reference lists in relevant reviews and publications retrieved for the purpose of our study protocol. There will be no initial limit on the date of publication. We will use the following keyword combinations: "Diabetes Mellitus type 2 AND obesity AND depression", "depression AND Diabetes Mellitus type 2", "Diabetes Mellitus type 2 AND body mass index AND cross sectional study", "depression AND obesity AND cross-sectional study". The bibliography of the articles chosen will also be examined in order to find more articles that might not be on the electronic databases. We will only include case-control, cross-sectional and cohort studies. The planned procedure is illustrated inThe selection of publications and the reporting of results for the study protocol will be conducted in accordance with the PRISMA guidelinesTitles and abstracts will be screened for eligibility according to the following inclusion and exclusion criteria.2) and severity of depression in patients with diabetes type 2.For the purpose of this study protocol will be included publications in English language that examined the relation of body mass index .This study protocol will include case-control, cross-sectional and existing cohort studies up to date.The participants will be adults (aged 18 years and over), diagnosed with diabetes mellitus type 2. For the purpose of this review, only overweight and obese type 2 diabetes mellitus patients with symptoms of depression will be included.53. Results will be analyzed using a narrative synthesis. To give more support to our analysis, we will consider the GRADES scale procedures (http://www.gradeworkinggroup.org).First, Two independent reviewers will read the titles of all the citations retrieved from the electronic database searches and removed all citations that are clearly not related to our study. Next, the abstract will be assessed to determine if the study satisfies the inclusion criteria. If from the abstract it is unclear whether the selection criteria are met or not, the full article will be scanned. Any discrepancy for inclusion will be discussed with a third or fourth author. Once the appropriate articles have been chosen for further analysis, two or three authors (independently) will be involved in the assessment of each article and data extraction. Further studies may be excluded as a result of not being relevant for our study. Further studies may be included through searching the reference lists in publications selected for the review. All the studies included will be read in detail and the relevant information extracted. The degree of agreement between the observers will be calculated by the Kappa coefficient; the studies that cause disagreement will be reviewed again, then the observers will decide the inclusion/exclusion together. The studies selected will be evaluated for quality and incorporation of gender perspective. Studies deemed for inclusion will be scored for methodological quality using the Newcastle-Ottawa Assessment Scale (NOS)A descriptive synthesis of important characteristics will be undertaken independently, including, author, year study, sample characteristics, type of study design, length of follow-up (for cohort studies), exposure variable characteristics, dependent variable characteristics, method used to ascertain diabetes status and body mass index; assessment of depression, relative risk or equivalent associated with diabetes mellitus type 2 and obesity. A quantitative synthesis of effects will not be attempted because of substantial methodological heterogeneity among studies.2 ≥30) the data for each subgroup will be additionally extracted. Authors will be contacted via email for any missing relevant information. Also, data will be analyzed descriptively. The systematic review will be presented in tables comparing quality measurements and the data previously mentioned.Whenever possible, adjusted relative risk (RR) or equivalent and associated 95% CI will be extracted directly from studies. For studies that present RR by subgroups  and emotional alterations of diabetes mellitus type 2 patients57. Finally, depression and obesity appear to be linked with poorer behavioral management of diabetes and glycemic control; therefore, the need for comprehensive interventions worldwide that target depression in conjunction with the type 2 diabetes mellitus management. The findings from this study protocol will be widely disseminated through discussions with stake-holders, publication in a peer-reviewed journal and a conference presentation. This study protocol on diabetes and depression will bring to light knowledge gaps in the area and will offer directions for future researches.The aim of this study protocol is to verify if there is a direct relation between depression and obesity in patients diagnosed with Diabetes Mellitus type 2, with the aim of improving the treatment of these patients, through an updated and quantitative estimate of the risk of depression associated with obesity. This study protocol will include a wide number of study designs; therefore, a subgroup analysis will be performed, to understand the relation between depression and obesity in patients with type 2 diabetes according to type of study. Furthermore, literature suggests that age is associated with depression, as well as other emotional alterationsBDNF: Brain Derived Neurotrophic Factor; BMI: Body Mass Index; CRH: Corticotropin-Releasing Hormone; GSK-3: Glycogen Synthase Kinase-3; IL-6: Interleukine-6; NOS: Newcastle-Ottawa Scale; PRISMA: preferred reporting items for systematic reviews and meta-analyses; PROSPERO: Prospective Register of Systematic Reviews; RR: risk ratio; TNF-α: Tumoral Necrosis Factor alpha; T2DM: Type 2 Diabetes Mellitus. The aim of this paper is to make a systematic review and meta-analysis of the association between obesity and depression in patients with diabetes mellitus type 2. The results of the study may allow development of prediction tools and better interventions in such condition. The rationale for the study is provided and the methods proposed seem correct.et al, 2012, Glauset al, 2013, Chou and Yu, 2013). Therefore, I would suggest that the authors may take this kind of depression into account in their analysis.My only reservation is that, in psychiatric patients and in general population, obesity has been found to be mostly associated with a specific kind of depression, i.e. depression with atypical features or atypical depression ( LevitanI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In the study plan of De la Cruz-Cano and co-workers the parameters for a structured literature research about the relationship between diabetes type II, obesity and diabetes are reported. The rational for this planned investigation is stated very clearly, the methods proposed seem adequate, the purpose of the planned investigation is highly welcomed.Therefore the study protocol should be indexedin order to stimulate the discussion about this very important topic and the planned literature review.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Alterations in the redox balance are involved in the origin, promotion and progression of cancer. Inter-individual differences in the oxidative stress regulation can explain a part of the variability in cancer susceptibility.The aim of this study was to evaluate if polymorphisms in genes codifying for the different systems involved in oxidative stress levels can have a role in susceptibility to breast cancer.We have analyzed 76 single base polymorphisms located in 27 genes involved in oxidative stress regulation by SNPlex technology. First, we have tested all the selected SNPs in 493 breast cancer patients and 683 controls and we have replicated the significant results in a second independent set of samples (430 patients and 803 controls). Gene-gene interactions were performed by the multifactor dimensionality reduction approach.OGG1), rs406113 and rs974334 (GPX6), rs2284659 (SOD3), rs4135225 (TXN) and rs207454 (XDH) were significant in the global analysis. The gene-gene interactions demonstrated a significant four-variant interaction among rs406113 (GPX6), rs974334 (GPX6), rs105213 (OGG1) and rs2284659 (SOD3)  with high-risk genotype combination showing increased risk for breast cancer .Six polymorphisms rs1052133 (The results of this study indicate that different genotypes in genes of the oxidant/antioxidant pathway could affect the susceptibility to breast cancer. Furthermore, our study highlighted the importance of the analysis of the epistatic interactions to define with more accuracy the influence of genetic variants in susceptibility to breast cancer. Despite breast cancer (BC) being the most frequent cancer in women in western countries and the second cause of cancer death after lung cancer, the risNumerous evidence links carcinogenesis and oxidative stress regulation, including prooxidant and antioxidant defense systems–7. OxidaPrevious studies with knockout animals that lack antioxidant enzymes support the view that ROS contribute to the age-related development of cancer. For instance, mice deficient in the antioxidant enzyme CuZnSOD showed increased cell proliferation in the presence of persistent oxidative damage contributing to hepatocarcinogenesis later in life. AnotherIn this context, single nucleotide polymorphisms (SNPs) in components of the cellular redox systems can modify the redox balance and take part in both the BC initiation and/or progression, as well as determine possible therapeutic treatments–17.Despite the importance of oxidative stress in the development and progression of cancer, few studies have evaluated the relationship between genetic modification in genes coding for enzymes relatives to the redox system and the susceptibility to develop BC. The previous studies had focused mainly on the analysis of genes related to antioxidant defense enzymes, 19, butThe aim of this study was to evaluate the association between common variants in genes coding for proteins related to the redox system (antioxidant and oxidant systems or proteins) and the susceptibility to develop BC. We hypothesized that common SNPs related to the redox pathway are associated with an altered risk for BC. We chose 76 SNPs on which to perform a two-step study: one first exploratory set and a second, independent, validation set. We also decided to investigate the impact of complex interactions between SNPs at different genes of the stress oxidative pathway. To address this issue, we analyzed the effects of gene-gene interactions by the multifactor dimensionality reduction (MDR) approach. This analysis was carried out in four SNPs that were statistically significant in the combinatorial set.The underlying analyses were carried out in a Caucasian Spanish population. The study was carried out in two steps with two population groups. A first group of 1176 samples was composed of 493 female patients diagnosed for BC between the years 1998–2008 at La Paz Hospital and Foundation Jimenez Díaz (Madrid), and 683 healthy women controls recruited at the Hospital of Valladolid (Spain).Thereupon, we chose the polymorphisms that showed marginally significant association , and we replicated the procedure in a second independent group (n = 1233) where we included 430 female patients diagnosed for BC between the years 1988–1998 at the Clinic Hospital of Valencia (Spain) and 803 samples from cancer-free women recruited at the blood donor bank at the same Hospital. Blood was collected between 2010 and 2011 during periodical patient visits. The blood from controls was extracted between the years 2009 and 2012. In both groups, the controls were women without pathology or history of cancer. Controls were not matched to cases, but were similar in age. In group 1, cases’ mean age was 57.5 (range 23.5-89.5), and that for donors was 52.7 (21.5-96.5). In group 2, cases’ mean age was 54.1 (20.5-86.5) while in donors, it was 54 (22.5-92.5).We selected this staged approach because it allowed us to analyze only those polymorphisms with indicative results and reduced the number of genotyping reactions without significantly affecting statistical power, 20.The research protocols were approved by the ethics committee of the INCLIVA Biomedical Research Institute. All the participants in the study were informed and gave their written consent to participate in the study.http://www.ncbi.nlm.nih.gov/projects/SNP/) and HapMap (http://www.hapmap.org). The selection of polymorphisms was performed by SYSNP in a second independent set.Set 2: Subsequently, and in order to better identify those polymorphisms that could be associated with BC, we replicated the 10 SNPs with a OGG1 gene maintained the statistical significance  after the Bonferroni correction.Set 1 + Set 2: Finally, we analyzed the 10 polymorphisms in the global population set 1 + set 2 . The results are listed in Table GPX6) and rs1052133 (OGG1) was the most significant (p = 0.041). The best three-loci model included rs406113 on the GPX6 gene, rs1052133 on the OGG1 gene and rs2284659 on the SOD3 gene, and it showed statistical significance (p < 0.0007) with an OR = 1.82 and 95% CI = 1.28-2.58. A four-way interaction found that between rs406113 on the GPX6 gene, rs974334 on the GPX6 gene, rs1052133 on the OGG1 gene and rs2284659 on the SOD3 gene predicts breast cancer with a testing balance accuracy of 0.5267. This four-loci model had a chi-square value of 11.284 (p = 0.0008) and an OR of 1.75 [95% CI = 1.26-2.44]. The four polymorphism combinatory model showed a higher predisposition to BC than the polymorphisms rs406113, rs974334 and rs2284659 did individually  and had values similar to the ones of polymorphism rs1052133 (ORX5 = 1.82). The summary of the multi-factor dimensionality results are listed in Table There is growing evidence that epistasis interactions between genes may play a role in cancer risk, and different variable selection approaches have been developed to analyze the potential gene-gene and gene-environment interactions. The fout p = 0.01. The beThe combined genotype AA for rs406113, CC/CG for rs974334, CC/CG for rs1052133 and GG/ GT for rs2284659 showed a higher risk for BC, which is consistent with the models described for the polymorphisms individually. Figure SOD2) gene A16V (C/T) (rs4880) led to structural alterations in the domain responsible to target the mitochondria, giving a reduction in the antioxidant potential) and p-value = 0.0004). Our results are in concordance with the meta-analysis by Yuan and collaborators that suggests that the hOGG1 326 Cys allele provides a significant protective effect for BC in European women. The impOGG1. This enOGG1, 48, 49.carriers. This daCAT gene was not significant in our analysis, but it presented a borderline tendency in the Cebrian et al. study. Moreover, they found a significant difference in genotype distribution between cases and controls in rs4135179 (TXN). We, however, were unable to confirm this in our global analysis, although we detected a marginal significance in set 1. The reason for this discrepancy can be found in the population’s characteristics, in the superior age of the population included in the Cebrian study[The epistatic analysis of the four most significant polymorphisms in relation to the susceptibility to BC was performed with the MDR method. This is a reliable approach that has been widely used, 51–54. an study.Our study has several limitations to take into consideration. Firstly, there is no data available about the lifestyle of controls and patients that could be related to oxidative stress, such as diet, exercise and the consumption of tobacco and alcohol. Secondly, polymorphisms that were not explored in our study may affect the risk to develop BC and should be taken into account in the analysis of our data and in further studies. Nevertheless, the association between SNPs and risk for BC is reliable since that power exceeded 95% in all the cases. All samples are from the same country and ethnicity, and the adjustment for age reduces variability.Additionally, MDR has 80% statistical power to detect true interactions in two-, three-, and four-way gene-gene interactions, even with a small number of cases and controls. FurtherDespite these considerations, our work, as far as we know, is the largest study in the Spanish population that analyzes the influence of polymorphisms in oxidative genes in susceptibility to BC. Overall, our data, together with that published in the bibliography, 55–62, OGG1, GPX6, SOD3, TXN and XDH genes significantly associated with predisposition to breast cancer. These associations have not been described previously, except for rs1052133 (OGG1). Concerning this polymorphism the published results in breast cancer were contradictory, and some authors found only a significant association with the risk of developing lung cancer. We have found an increment in the risk of developing breast cancer in the carriers of at least one Ser allele (recessive model) in concordance with a meta-analysis of breast cancer susceptibility in European women. In this particular case, an incorrect expression of the protein encoded by the OGG1 gene could interfere with the suitable repair of the genetic material. Furthermore, our study highlighted the importance of the analysis of the epistatic interactions in order to define the influence of genetic variants in susceptibility to breast cancer more precisely. Further studies on the relevance of these and other polymorphisms in the development of breast cancer should be performed.Our results suggest that different genotypes in genes of the oxidant/antioxidant pathway could affect the susceptibility to breast cancer. We have found six polymorphisms in


Acetabular cup loosening is associated with pain, reduced function, and instability of the implant. If such event happens while the femoral implant is in a satisfactory position and is well fixed to the bone, isolated acetabular revision surgery is indicated. The aim of this single-center retrospective study was to evaluate the clinical and radiological results over the medium term  of isolated acetabular revisions surgery using a porous hemispheric revision shell matched with a cemented all-poly cup and large diameter femoral head (>32). 33 patients were enrolled. We collect any relevant data from the clinical board. Routine clinical and radiographic examinations were performed preoperatively; the postoperative follow-up was made at 1, 3, and 6 months and yearly thereafter. At the last available follow-up, we report satisfactory improvement of functional scores in all the patients; 2 patients (6.1%) showed thigh pain and only 4 hips (12.11%) presented mild groin pain; all the femoral components are well fixed and there were no potential or pending rerevisions. With bias due to the follow-up and to the retrospective design of the study, we report clinical, functional, and radiological satisfactory results.  The most common reason for failure of total hip arthroplasty (THA) is periprosthetic osteolysis and loosening of hip implants . The ratThere are two main problems to solve when an orthopaedic surgeon has to approach acetabular revision. First of all, even with bone loss due to the loosening of the previous implant, obtain primary fixation of the new prosthesis; then reach postoperative implant stability.This second issue could be more difficult when isolated acetabular revision is performed , 4.Effectively in these cases not only does the presence of the stem limit surgical options, but also repeated surgical incision, soft tissue damage, and in some cases extended synovectomy can reduce the stability of the implant .To address this risk, industries and surgeons have developed a variety of surgical hardware and strategies such as jumbo femoral heads , constraAnother way to face these problems is, in order to obtain fixation, the implantation of a shell in the better position allowed by the bone defect; then obtain stability cementing a polyethylene liner in the shell with a partially independent version and verticality.The present study was conducted to evaluate the clinical and radiological results over the medium term  of isolated acetabular revisions surgery using a porous hemispheric revision shell matched with a cemented all-poly cup and large diameter femoral head (>32).This single-center retrospective study was approved by our local ethical committee, and the patients gave written consent to participate. A review of our database between January 2009 and December 2012 for revision hip arthroplasty was done. In this period we performed 86 THA revisions; we selected patients that had isolated acetabular revisions with a porous hemispheric revision shell matched with a cemented all-poly cup and large diameter femoral head (>32).Information of ages, sex, clinical history, drug treatment, and preoperative and postoperative X-ray studies were collected and recorded. The acetabular defects were classified as described by Paprosky et al. .We included patients with (1) loosening or malpositions of the acetabular components, (2) a well fixed and well positioned femoral stem. We excluded (1) patients with septic loosening, (2) patients that required revision for both components, and (3) patients with monoblock stem. All the patients were operated on in the lateral decubitus position, and the surgical approach was posterolateral. We checked the stability of the stem and then we removed the acetabular cups, liners, and screws. If required before implantation of the revision components pelvic bony defects were grafted with tricalcium phosphate hydroxyapatite and morcelized bone graft. In all the index patients, the Regenerex revision shell  was implanted. This is a porous titanium construct, with multiple holes to maximize intraoperative screws fixation, designed to accept a cemented all-poly cup. During the revision surgery, the shell was implanted in the better position allowed by the bone defect; then in order to obtain the stability of the implant, the polyethylene liner was cemented into the shell in order to obtain verticality of about 45 degrees and summed of about 35 to 50 degrees of anteversion (stem plus liner) .At the end of the surgical procedure, we obtained an intraoperative acceptable stability; in our opinion this is defined as 45° or more of internal rotation at 90° of flexion and 20° or more of external rotation in 10° of hyperextension. The wound was drained in each case.In the postoperative period all the patients performed thromboembolic prophylaxis, with low molecular weight heparin; we did not use nonsteroidal anti-inflammatory drugs to prevent heterotopic periprosthetic ossification.Partial weight bearing was allowed since the first postoperative week in the majority of patients; in some due to poor patient's bone stock we delayed it to the first radiological and clinical follow-up (six weeks) and gradually advanced it to full weight bearing. No postoperative bracing was used.Routine clinical and radiographic examinations were performed preoperatively; the postoperative follow-up was made at 1, 3, and 6 months and yearly thereafter. Patients were scored as routinely in our practice preoperatively with the Harris Hip Score (HHS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) , 11; theRadiographs were evaluated by two of the senior authors (Enrico Bonicoli and Paolo Domenico Parchi), with consensus attained for reporting of all measurements. The radiographic follow-up was performed in order to evaluate the position of the implant and to search for any signs of osseointegration or loosening of the components.The de Lee and Charnley classification  was usedThe Wilcoxon signed-rank test was used to compare preoperative and postoperative hip scores.33 patients were enrolled for this study. There were 23 women  and 10 men ; the mean age at the revisions was 67 years (range 40–81 years); 17 were left  and 16  were right. The mean clinical follow-up was 36 months ; one died from unrelated illnesses; 2 patients were lost to follow-up.The preoperative diagnoses were 26 with aseptic loosening , 3 with implant instability , and 4 revisions due to metallosis .We treated 9 with type 1 defect of the Paprosky classification [Early complications (during the in-patient stay) occurred in 4 of the 33 patients . These included a superficial wound infection in two, deep-vein thrombosis in one, and postoperative early dislocation in one.The postoperative early dislocation happened during a possible wrong movement of the patient from the operative room to the ward in a patient under spinal anesthesia. We reduced the dislocation with external manoeuvres and this event also at the last follow-up never happened again.P = 0.008). The mean Harris Hip Score improved from 59 points (range 43–74) preoperatively to 88 points (range 67–92) (P = 0.002). At the final follow-up, 2 patients  showed thigh pain and only 4 hips (12.11%) presented mild groin pain; all the femoral components are well fixed and there were no potential or pending rerevisions.The mean WOMAC score in 25 patients improved significantly from 52.1 preoperatively to 62.27 postoperatively  leg length discrepancy. At the most recent follow-up no one patient had experienced a further dislocation.In this study we evaluate the clinical and radiological results over the medium term of isolated acetabular revision with a porous hemispheric revision shell matched with a cemented all-poly cup and large diameter femoral head (>32).Besides the lack of a control group there are some limitations in this study, such as the short follow-up of same patients and the retrospective design.However we think that our study can improve the literature's knowledge about this issue also because as reported in the Australian registry, the revision of the acetabular component is the most common cause of repeated surgery in total hip replacement . IsolateThe author's choice to face the problem of achieving fixation of the implant is to choose porous materials such as Regenerex during revision surgery. The advantage of these materials is not only to provide a primary stability due to scratch fit but also to allow long-term implant stability related to bone ingrowth (osteoconductivity) , which iIn this way it is possible to obtain the correct geometry of the revised implant  issue that is important to reduce the risk of dislocation which has been associated with hip revision surgery and rises in isolated acetabular revision.The author's opinion supported by biomechanical tests and several clinical series with short-term follow-up of liner cementation is that such technique is sure. The cemented polyethylene liner was found to have an initial fixation strength exceeding that of the conventional locking mechanism if 2 and 4 mm thick cement mantles were built up around the liner . It is uSchneider et al.  had 10 c


In silico B cell epitope mapping approaches have been widely pursued, though success has not been consistent. Antibody mixtures in immune sera have been used as handles for biologically relevant antigens, but these and other experimental approaches have proven resource intensive and time consuming. In addition, these methods are often tailored to individual diseases or a specific proteome, rather than providing a universal platform. Most of these methods are not able to identify the specific antibody’s epitopes from unknown antigens, such as un-annotated neo antigens in cancer. Alternatively, a peptide library comprised of sequences unrestricted by naturally-found protein space provides for a universal search for mimotopes of an antibody’s epitope. Here we present the utility of such a non-natural random sequence library of 10,000 peptides physically addressed on a microarray for mimotope discovery without sequence information of the specific antigen. The peptide arrays were probed with serum from an antigen-immunized rabbit, or alternatively probed with serum pre-absorbed with the same immunizing antigen. With this positive and negative screening scheme, we identified the library-peptides as the mimotopes of the antigen. The unique library peptides were successfully used to isolate antigen-specific antibodies from complete immune serum. Sequence analysis of these peptides revealed the epitopes in the immunized antigen. We present this method as an inexpensive, efficient method for identifying mimotopes of any antibody’s targets. These mimotopes should be useful in defining both components of the antigen-antibody complex.Antigen-antibody complexes are central players in an effective immune response. However, finding those interactions relevant to a particular disease state can be arduous. Nonetheless many paths to discovery have been explored since deciphering these interactions can greatly facilitate the development of new diagnostics, therapeutics, and vaccines.  Characterizing the interactions between disease-specific antibodies and their cognate antigens has proven highly informative in the study of host-pathogen relationships and critical in the development of effective biomedical products. The discovery of modified antigens or autoantigens that are specifically recognized by patient antibodies is of growing importance in disease research and target development for diagnostics, vaccines, and therapeutics. These complexes are typically found by querying immune sera against possible ligands in lysates, or in libraries of proteins or peptides made recombinantly or synthetically. Myriad of binding assays such as immunoblots , ELISAs In its original description, phage display was used to survey a library of peptides for binding to a given antibody. It has In vitro combinatorial synthesis of peptides on beads and microarrays of either proteins or peptides have been explored as library formats for surveying target binding , 10. BotHowever most of the applications of the above methods focused on known biological targets. This limited their applications. Each high throughput library can only cover limited proteome. On the other side, the antigens or epitopes within the antigen may not be in the known proteome. Studies can be confounded by the fact that immune sera often carry antibodies to mutant, unknown, or even exogenously-derived antigens of a host or pathogen proteome.Random peptide libraries, whether in vivo or in vitro, provide a means for identifying mimotopes of unknown antigens and are far more economical since one library can be used for all screens. For example, an antibody panned against phages displaying random sequence peptide fusions might select recombinants that mimic or hold similarity to a previously unknown ligand . MicroarRecently, the immunosignatures technology has been developed as a universal platform to profile complex antibody mixtures . This teHere we employ the IMS as a universal platform to design a method for identifying mimics of disease-relevant epitopes and then show that these mimotopes can capture the disease-relevant antibodies for the further investigation, such as identification of the unknown biological antigens.Unique from other platforms that focused on analyzing the purified targets , 7 the mA number of free peptides were synthesized by Sigma, Inc. Beads for antibody affinity purification were prepared in the lab. Peptides were directly synthesized on the TentaGel Beads. The sequences of these peptides and their assigned names are presented in Anti-human SMC1Afs serum was generated by Global Peptide Service LLC . The 17 amino acid (a.a.) SMC1A frame shift mutant sequence (SMCfs), identified in human tumor cDNA (CCGIYCHEEPQREDSSI), was synthesized by the peptide synthesis lab at Arizona State University and then conjugated to keyhole limpet hemocyanin (KLH) by Global Peptide Service LLC.A New Zealand white rabbit was immunized with the SMCfs-KLH conjugate. Blood for the experiment was collected at exsanguinations after two immunizations.ELISA plates were coated with 50 μL of 10 μg/mL of peptide or protein in carbonate coating buffer and incubated at 4°C overnight. The coated plates were washed 3X with PBST and blocked with 200 μL of 3% BSA in PBST at 37°C for 30 minutes. The blocked plate was washed 3X with PBST and 50 μL of primary anti-serum or purified antibody diluted in 3% BSA in PBST was applied. The plate was then incubated at 37°C for 1 hr. After the incubation, the plate was washed 3X with PBST. The antibody was detected with 50 μL HRP-goat anti-rabbit IgG diluted 1:2000 in 3% BSA in PBST. After the plate was incubated at 37°C for 1 hr, the plate was washed 3X and developed with 50 μL TMB for 10 minutes at room temperature. The development was stopped by adding 50 μL of 0.5 N HCl, and the plate was read with a SpectraMax 190 Molecular Devices instrument at OD 450 nm.Specific antibodies were absorbed from the rabbit anti-SMCfs sera by applying the sera to the SMCfs coated plate. The rabbit sera was diluted 1:250 with 3% BSA in PBST and incubated with the SMCfs peptide coated plate at 37°C for 1 hr. The unbound antibody in the supernatant was then removed and applied to another peptide coated well to remove more antibody specific for the peptide. This process was repeated up to 20 times, and this serum was then later applied to the peptide microarray at a dilution of 1:500. This same method was used to produce negative control antibody absorbed sera using the negative control CP1 peptide.The 10,000 non-natural randomly generated peptide sequences were custom synthesized by Sigma, Inc. These 20-mers were designed with 17 non-natural sequence a.a. residues (excluding cysteine) and a 3 amino acid (GSC) linker sequence on the C terminus. The C terminal cysteine binds to a sulfo-SMCC coated aminosilane glass slide. The solutions of different non-natural sequence peptides were printed onto the glass slide using a Nanoprint 60 instrument (Array It Technologies).Rabbit sera samples were applied to the non-natural sequence peptide microarray using a Tecan HS 4800 Pro microarray hybridization station. Slides were first washed for 30s with TBST, and then blocked with a blocking buffer consisting of BSA, mercaptohexanol, Tween 20, and PBS for 1 hr at 23°C. Duplicate samples of sera were diluted 1:500 in an incubation buffer consisting of BSA, Tween 20, and PBS and incubated with the slide for 1 hr at 37°C. The slide was then washed, and 5 nM of goat anti-rabbit IgG conjugated with Alexa Fluor 647 dye was applied for 1 hour at 37°C. The slide was then washed and dried for 5 min.The slides were scanned with an Agilent Technologies DNA Microarray Scanner with Surescan High-Resolution Technology instrument and analyzed with GenePix Pro 6.0 software to determine the fluorescence intensity of each spot. GeneSpring GX 7.3, Microsoft Excel, simple custom Java code, and GraphPad Prism 4 were then used to perform further analysis of this data.Total IgG of the rabbit anti-SMCfs sera was purified using Pierce Protein A/G Agarose beads according to the protocol of the manufacturer. 1 ml of the TentaGel beads with specific peptides were embedded in a column. 3 ml of the purified total IgG was added to the column and incubated with the beads for 1 hr at room temperature. The column was then washed with 10 ml PBST. The specific IgG was eluted with 4 fractions of 1 ml IgG Elution Buffer  and each fraction was neutralized with 100 ul 1M TRIS (pH8.0). All of the eluted fractions were measured at an absorbance of 280nm. The two fractions with the highest absorbance were combined and used for further analysis at a 1 to 40 dilution in 3% BSA in PBST.GLAM2 software  was usedTo identify non-natural sequences recognized by the immune serum, both naïve and SMCfs-immune rabbit sera were applied to the array. We identified a total of 108 non-natural sequence peptides which exhibited high specificity to the immune serum relative to the naïve rabbit control serum based on the t-test (p<0.0001). The antSince the KLH is a large and highly immunogenic antigen, the rabbit anti-serum contained high titer antibodies to the KLH protein. Mimotopes to KLH among the 108 SMCfs peptides were investigated . When thCompared to KLH, the SMCfs peptide is much smaller and lower immunogenic. To distinguish the mimotopes of the SMCfs peptide from that of KLH, we specifically depleted the anti-SMCfs antibodies from the immune serum through iterative antibody absorption steps. ELISA analysis confirmed that anti-SMCfs antibodies were partially depleted from the original serum after 11 rounds of absorption and further depleted after 20 rounds .The anti-SMCfs antibody-depleted samples, as well as the negative control serum, were applied to the CIM10K array. Reactivity profiles of these samples against the 108 peptides selected in the original screen were compared . Differemax is the maximum signal intensity of the peptide with unabsorbed SMCfs sera. S20X is the intensity of the peptide with sera after SMCfs binding antibodies have been absorbed twenty times. SCP is the intensity of the peptide with sera after CP1 binding antibodies have been absorbed twenty times. The CP1 peptide is a negative control peptide with no sequence similarity to SMCfs.A high score indicates that the given random peptide binds to antibodies with high specificity for the SMCfs sequence, and a low score indicates the opposite. STo assess the specificity of anti-serum binding to the four putative mimotopes, we first analyzed the reactivity of the selected peptides to the immune serum by ELISA . CompareTo test the possibility that RP1-RP4 are mimotopes of a KLH protein epitope rather than SMCfs, we measured the reactivity of the four selected random peptides, the original SMCfs peptide, and CP1 to the polyclonal KLH immune serum . All sixTo determine whether RP1 to RP4 can be used to selectively purify antibodies specific to the original immunogen, we prepared affinity purification columns with each of four random peptides (RP1 to RP4), SMCfs, and an irrelevant peptide as negative control (CP2-B). The binding specificities of the affinity-purified antibodies were measured by determining the activity of the eluted antibodies against SMCfs-27 or KLH antigen . The SMC4 different peptide sequences; whereas 1021 unique 17-mers are possible. Third, even for a peptide as short as 17aa, the mimotopes can resolve two different binding sites.The antibody samples that were affinity-purified from the SMCfs immune serum with the SMCfs peptide or with the array-selected mimotopes display more than one cross-reactivity group as measured by ELISA . AntibodFrom the mimotpe identification to the validation, we did not use the sequence information of the original SMCfs peptide. To test the possibility of using the sequence analysis of the mimotopes to identify epitope of the unknown antigen, we did the sequence analysis of both the SMCfs and RP1 to RP4 peptides. The epitope search software Bepipred was used to predict strong B cell epitopes within the unique SMC1A frameshifted sequence. The highest amino acid score was assigned to the proline within HEEPQRE. The GLAM2 software was used to identify sequences in RP1 through RP4 with similarity to sequence stretches found in SMCfs. The RP1 peptide contains two motifs: HEE and YXXXXPMRQ, although they are in reverse order relative to those sequences of SMCfs. Note that previous experiments have also identified high binding peptides with inverted sequences relative to the original epitope, but these inversions typically involved just two amino acids . RP2 conNote that the RP1-4 peptides which have been the focus of this paper were not the only peptides among the 108 selected peptides which exhibited good mimotope matches of the SMCfs peptide . When thThere was a good correspondence between GLAM2 scores for RP1-4 aligned with the SMCfs peptide and scores from the peptide microarray. The GLAM2-assigned similarity scores of the mimotopes to SMCfs led to a ranking of the top 4 peptides in the same order that they were ranked by the array absorption scoring described in section 3.2. For example, the RP1 peptide holds two motifs in common with the original antigen, exhibits the highest intensity on the peptide microarray, and scored the highest by the GLAM2 software. By contrast, the absorbance intensities obtained in the ELISA experiment did not yield rankings identical with the other readouts. This suggests that the array format may enable higher resolution detection of antibody-peptide interactions. Taking together, the sequence analysis of the specific array peptides showed the advantage of our high density random peptide array for identifying the epitopes of unknown antigen.The techniques demonstrated here could be inexpensively and generally applied for both mimotope and true-epitope discovery. We have demonstrated that non-natural sequence peptide arrays can be used to identify mimotopes of specific antibodies without knowing the sequence information of the primary epitope. This high density pepetide array platform is sensitive enough to identify at least two different specific antibodies to a simple peptide from the highly reactive antibodies which recognize KLH, a highly immunogenic antigen. The sequence analysis of the identified mimotopes can help identify the true B cell epitopes of an immunogen. Furthermore, the selected mimotopes can be used to purify this specific polyclonal antibody from the complex milieu of a serum sample for further analysis. In addition to be being a universal platform for the epitope exploration of any antibody, the non-natural sequence peptide array requires no assumptions or information relative to the true epitope of an immunogen, and therefore even mimotopes of newly mutated antigens can be discovered. As the SMCfs peptide was not an annotated, but frame-shift antigen, this method may allow discovery of the immunogenic epitopes of non-annotated antigens, such as novel mutations in tumor cells.We suggest that mimotopes can be used to identify medically important antibodies and their natural targets. Additionally, mimotopes can be used as alternatives to a true epitope if they are found to carry advantageous properties. These may include greater stability, simpler production, or differential affinity relative to an autoantigen. These are all important attributes for successful drug or therapeutic development.S1 TableThe intensities of all 10,000 peptides are available upon request.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 TableThe intensity of all 10,000 peptides are available upon request.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


VPS13B, a gene mutated in the rare neurodevelopmental disorder called Cohen syndrome, and recently reported with recessive variants in autism. We identify a small set of promising candidates for causal variants, including two loss-of-function variants and a rare, homozygous probably-damaging variant that could contribute to autism risk.Pinpointing the small number of causal variants among the abundant naturally occurring genetic variation is a difficult challenge, but a crucial one for understanding precise molecular mechanisms of disease and follow-up functional studies. We propose and investigate two complementary statistical approaches for identification of rare causal variants in sequencing studies: a backward elimination procedure based on groupwise association tests, and a hierarchical approach that can integrate sequencing data with diverse functional and evolutionary conservation annotations for individual variants. Using simulations, we show that incorporation of multiple bioinformatic predictors of deleteriousness, such as PolyPhen-2, SIFT and GERP++ scores, can improve the power to discover truly causal variants. As proof of principle, we apply the proposed methods to  Sequencing technologies allow identification of genetic variants down to single base resolution for a whole human genome. The vast majority of these variants (over 90%) are rare, with population frequencies less than 1%. Furthermore, in a specific study, many of the variants identified are not associated with the disease of interest, and identification of the small proportion of truly causal variants is a difficult task. Clearly, for causal variants that are rare enough to only appear a few times in a study, observed frequencies in cases and controls are not enough to distinguish them from the vast majority of random variation, and rich functional annotations can help identify the causal variants. Here we propose to develop a set of statistical methods that leverage diverse functional genomics annotations with sequencing data to identify a small set of potentially causal variants and estimate their effects. Pinpointing a subset of potentially causal variants is crucial for understanding precise biological mechanisms, and for further experimental functional studies. The tremendous progress in massively parallel sequencing (aka ‘next generation’ sequencing) technologies enables investigators to obtain genetic information down to single base resolution on a genome-wide scale in a rapid and cost efficient manner Both empirical and theoretical studies suggest that rare genetic variants are an important contributor to disease risk The fundamental challenge in pinpointing rare causal variants is that these variants are observed very infrequently in any given dataset and these sparse frequencies on their own are insufficient to provide meaningful risk predictions. In particular, for singletons or doubletons it will be necessary to incorporate prior functional and evolutionary conservation information in order to prioritize them as likely causal. Hierarchical modeling offers a natural strategy to leverage collective evidence from rare variants with sparse data. This can be accomplished in the presence of hierarchical covariates that are associated with disease risk and which can be used for implicitly aggregating the rare variants to permit stronger inferences about individual variants. These hierarchical covariates are characteristics of the variants themselves, such as the degree of conservation across species, the position in the gene, and other features that can be represented using bioinformatic measures. Indeed, many annotation tools  identify a list of likely causal variants, and (2) estimate the effects of these variants on disease. The first approach is a backward elimination procedure based on groupwise association tests that leads to the identification of a small set of “interesting” variants in the gene, which are enriched in causal variants. The second approach complements the first by employing hierarchical models First, we review the basics of groupwise association tests, and then we describe in detail the two complementary methods we propose for prioritizing variants for follow-up functional studies.n subjects have been sequenced in a region of interest , that contains m variants. Let ith individual, and m genetic variants.We assume that β's have essentially the same value, say j and k. To test the null hypothesis of no genetic effects We are interested in testing the null hypothesis of no genetic effects: The null distribution of The groupwise association tests described above test for association at a gene level, but are not able to pinpoint individual causal variants in the gene. However, once a gene has been shown to contain variants associated with disease (e.g. using the Burden or SKAT tests), identifying the individual causal variants among the many variants in a gene is of considerable interest as it can lead to a better understanding of the molecular mechanisms underlying a complex trait, and is essential for further experimental validation work.Starting with a groupwise association test, one natural way to identify causal variants that are individually of weak effect is to evaluate their contribution to a given set of variants by removing the variant from the set, and assessing the resulting effect, e.g. the p value for the reduced set. The following iterative algorithm  is designed for this purpose.Backward Elimination Algorithm: Step 1. Start with a set of Step 2. Remove each of the Step 3. If The current set becomes Step 4. Return the current set of variants.The results on a typical simulated example are shown in This algorithm is applicable when the number of variants we start with in Step 1 is not too large . However, sequencing a gene in thousands of individuals can lead to the detection of potentially hundreds of variants, or more. Therefore, we use a resampling procedure, whereby each time a small number of variants is chosen . For such variants, observed frequencies in cases and controls are clearly not enough to distinguish them from the vast majority of random variation ; A second stage model relates the individual variant risks to prior  information known about the variants:Combining the two models, one obtains the following generalized linear mixed effects model:The parameters of this model can be estimated using a hybrid Bayesian pseudo-likelihood approach which performs Bayesian estimation of the variance component of the model and then conducts pseudo-likelihood estimation of the fixed and random effects using this estimated random effects variance We combine the two complementary methods, the backward elimination procedure and the hierarchical modeling framework, as follows. We identify the list of “interesting” variants from the backward elimination procedure, and for each of these variants we report the effect size estimate, and the associated standard error obtained from the hierarchical model . The variants in the “interesting” list can be naturally ranked according to these effect estimates. We show below that restricting attention to only the list of “interesting” variants can improve the ranking of causal variants, and that this combined approach performs well in the scenarios we investigated.http://www.columbia.edu/~ii2135/).Both methods are computationally efficient and have been implemented in software available on the authors' website  for non-synonymous variants is defined as a binary indicator whether a variant is predicted to be damaging or not. Following the empirical results in Cooper et al. 2) is also defined as a binary indicator whether a variant is predicted to be probably damaging or not. Again, as in Cooper et al. Furthermore, additional variant annotation tools for non-synonymous variants exist, and are able, for example, to predict the damaging effect of an amino acid substitution (PolyPhen-2 and SIFT), and to assess the extent of evolutionary conservation at a position (GERP++). Therefore, for non-synonymous variants we simulate two additional predictors, as follows. The first bioinformatic predictor . We identify these two groups using an expectation-maximization (EM) algorithm for nonparametric estimation of mixture models and declare as “interesting” those variants belonging to the right tail component (which corresponds to the larger return counts). This is illustrated in For synonymous variants, since only a small proportion of causal variants are expected to be synonymous, the sample distribution of return counts often fails to exhibit separate groups in our simulations; therefore we select the variants with the top 20% return counts as “interesting” to be further investigated; although we expect less than 20% of synonymous variants to be causal , we conservatively choose the threshold of 20% to increase the probability of selecting causal variants, especially since for synonymous variants, unlike the non-synonymous ones, we do not make use of additional information in prioritizing variants.S). For each simulation we take the median of the ranks of the causal variants involved and then compute the median of these estimates across simulations. The ranking for the synonymous variants is done similarly.We employ the backward elimination procedure as well as fit a hierarchical model including the full set of variants and assuming a single functional predictor in the second stage, namely whether a variant is non-synonymous or synonymous. Due to the expected difference in enrichment of causal variants among non-synonymous versus synonymous variants we evaluate the overall ranking of the causal variants separately among non-synonymous and synonymous variants. More explicitly, among the non-synonymous variants selected as “interesting” by the backward elimination procedure we rank the causal variants based on their return counts (this approach is denoted as BE in the figures below). Furthermore, we also use the S). The hierarchical model (titled “HM” in the figure) results in higher median rank (worse performance) than that of the backward elimination procedure (titled “BE” in the figure). This is of course expected due to the smaller number of variants that the backward elimination procedure returns as “interesting” (S”) is similar to the one based on return counts in the backward elimination procedure . Note that synonymous variants were assigned a bioinformatic predictor of 0.1 or B2; see 2, due to the higher specificity of this predictor. For example, for model M1, a non-synonymous to synonymous ratio of 1.4 and 20% causal variants in a region, the median number of causal variants among the top 10 ranked non-synonymous variants increases from 5  to 8 when using bioinformatic predictor B2. Since we do not always know which of several available bioinformatic predictors may have higher accuracy, the hierarchical model allows us to combine multiple bioinformatic predictors. When combining three bioinformatic predictors , we find that the ranking of causal variants is now similar or superior to the ranking obtained when using only the better of the two bioinformatic predictors (i.e. B2). Similarly, when using a combination of four bioinformatic predictors (four predictors with the same sensitivity and specificity as B1), the ranking of causal variants is better than using just a single predictor B1, and similar to using the more accurate predictor B2. These results suggest that using multiple bioinformatic predictors with different accuracies (even multiple weak predictors) can help detection of the causal variants. Similar results are obtained when the proportion of causal variants in a region is 10%  together with a non-informative one does help improve the performance. Again, the ability of the hierarchical model to incorporate multiple functional predictors of varying accuracy is an important feature when the best predictors are not known a priori.We have also evaluated the effect of including a non-informative predictor in the analysis, although in practice we expect that functional annotations are correlated with the causal status of a variant. The results are reported in 1 or B2, in the backward elimination procedure directly (as a weight in the Burden test statistic). We found that for the case of only one bioinformatic predictor, the backward elimination procedure performed similarly with (or slightly worse than) the hierarchical model . This shrinkage is even more pronounced for model M2, which assumes higher odds ratios for the causal variants (compared to M1), resulting in the poorer performance noted with model M2. An additional source of bias comes from our analyses being conditional on the groupwise (gene-based) test being significant.ANGPTL4 for 3,551 individuals of varied ethnicity from the Dallas Heart Study. Rare and low-frequency variants in this gene have been previously associated with low serum triglyceride levels We first show an application of the proposed methods to a well studied re-sequencing dataset for In We next show an application to a gene with a larger number of functional variants, and for which not much is known on the likely causal variants. Hence the next application is a more difficult example for the proposed methods.VPS13B, also known as COH1, MIM #607817) is a gene associated with Cohen syndrome , a rare autosomal recessive neurodevelopmental disorder overrepresented in Finland and common in Amish, Irish travelers and Greek/Mediterranean founder populations VPS13B mutations, including nonsense, missense, splicing, indels, microdeletions and microduplications VPS13B mutations have been found in individuals with autism VPS13 gene family , cause chorea-acanthocytosis The Vacuolar Protein Sorting 13 homolog B  case/control dataset . Of these, the backward elimination procedure selects 42: 2 of them are LoF , and of the missense PolyPhen-2 predicts that 14 are probably damaging, 1 possibly damaging, and 25 benign . In FiguIn nherited . In one ed child . The  vae mother , and thechildren .VPS13B, all 42 variants deemed “interesting” by the backward elimination procedure were projected on the protein topology, reconstructed with the Pfam domains , the experimentally ascertained Golgi targeting domain VPS13B insufficiency or mutations are still unknown, fibroblasts isolated from individuals with CS show severe fragmentation of the Golgi apparatus into ministacks To understand the impact of the variants on the molecular functions of Most of the variants selected are singletons (34 out of 42). As previously mentioned, for singletons accurate bioinformatic predictors about their likely functional effects are essential in order to identify such variants as promising, and hierarchical modeling is a natural framework to incorporate such information. Naturally the false discovery rate among these singletons can be high, and dependent on the sensitivity and specificity of the bioinformatic predictors used to characterize the variants in the hierarchical model. For comparison, in Pinpointing the rare causal variants among a large number of variants that occur in a genetic region is a difficult challenge, but crucial for follow-up functional studies, and for a better understanding of the molecular mechanisms that lead to disease. For many causal variants that occur only a few times in a dataset, incorporation of external information characterizing the variants (such as bioinformatic predictions on the deleteriousness of a variant) is essential to help prioritize these rare variants. We have described here two complementary statistical methods, that are able to integrate diverse functional annotations on individual variants in a region, and produce a selected list of candidates for causal variants, ranked according to their estimated effect sizes. The backward elimination procedure offers a natural way to select a set of promising variants, while using multiple functional predictors in the hierarchical modeling approach provides more in depth characterization of variants’ effects on disease, and can help boost the power to identify causal variants. We have focused attention here on some of the commonly used annotations for coding regions; however we acknowledge that there are other possible functional genomics annotations available both for the coding and non-coding regions VPS13B. For this gene, we show that among the top selected variants are two LoF variants, and one rare, probably damaging variant that is homozygous in one affected individual. Autosomal recessive mutations associated with autism have been recognized for decades VPS13B is indispensable for the Golgi apparatus, and genes important for Golgi morphology and function have been linked to autism disorders, including RAB39B, mutated in a X-linked intellectual disability associated with autism, epilepsy and macrocephaly UBE3A, responsible of Angelman syndrome VPS13B in Cohen syndrome and autism and further strengthen the connection between Golgi homeostasis and autism.We illustrate the proposed methods through an application to a gene implicated in Cohen syndrome and autism, A rather large number of selected variants in the backward elimination procedure are singletons. Causal variants that appear as singletons in a dataset are difficult to distinguish from random genetic variation, and accurate functional predictions on such variants are crucial and will help in identifying those singletons more likely to be causal. Currently, it is not uncommon for predictions on the deleteriousness of a variant to be discordant (e.g. predictions from PolyPhen-2 and SIFT), and combining such multiple predictors can be difficult, although aggregate deleteriousness scores, such as Condel The use of next-generation sequencing technologies may lead to higher error rates compared to a traditional Sanger sequencing platform. Sequencing errors may be disproportionately present among singletons or very rare variants, especially for larger sample sizes, although for a single gene the number of errors is expected to be relatively small. Therefore, as a first step toward the characterization of the top ranked variants, Sanger sequencing can be used to validate the variants.Classical variable selection methods . Similarly, for the hierarchical model, variants with low functional score and higher frequency in controls compared with cases will be candidates for protective variants.Further improvements to the backward elimination procedure are possible. For example, instead of partitioning variants into two groups based on a binary bioinformatic predictor, such as non-synonymous and synonymous, an alternative would be to calculate stratified false discovery rates The estimates of a rare variant's effect on disease from the hierarchical model can have substantial bias. This happens because in any particular gene or region only a small proportion of variants are expected to be disease causing, and most of the variants represent random genetic variation. Therefore, when estimating odds ratios of causal variants, there is a strong shrinkage effect toward the overall estimate. Incorporation of accurate functional predictors in the hierarchical model is one possible way to help attenuate this bias; further work is needed on finding better ways to reduce the bias.In addition to their ability to pinpoint likely causal variants, the proposed methods can be used to prioritize variants for genotyping in independent datasets for the purpose of replication or validation. This is relevant when re-sequencing the gene or region in additional datasets is too expensive, and one chooses instead to genotype variants discovered in the original study The proposed methods are applicable to case-control or population-based designs. However, family-based designs represent a natural way to identify causal variants. For example, in multiplex families, significant sharing of a non-synonymous mutation among multiple affected relatives can be an important indication of causality. Bayesian approaches in this context have been developed before In summary, we have proposed and investigated two complementary statistical methods to identify causal variants among the naturally occurring genetic variation at a locus. They are able to incorporate sequencing data with various functional predictors on variants, and select a small number of variants that are enriched in causal variants. In the current study, we applied the proposed methods to a gene known to contain risk variants for ASD as proof-of-principle, and identify several interesting variants, including two LoF variants and a homozygous probably damaging variant likely important to autism risk.S1 FigureThe effect of removing causal vs. non-causal variants on the p value for the reduced set (Step 2 of the backward elimination procedure). Results are shown for one  simulated dataset with 2000 individuals, in a 25 kb region with 10% causal variants, under model M2. Removing the non-causal variant corresponding to the red circle leads to the largest drop in p value for the reduced set, and hence this variant is being removed in this iteration from the current set (Step 3 of the backward elimination procedure).(EPS)Click here for additional data file.S2 FigureNon-parametric mixture fit to sample distribution of return counts in the backward elimination procedure (simulated example). The return counts are for non-synonymous variants, and two groups can be distinguished, one corresponding to the “non-interesting” class, and the other to the “interesting” class.(EPS)Click here for additional data file.S3 FigureS refers to the ranking of causal variants only among those non-synonymous variants selected by the backward elimination procedure, with ranks based on the estimated effects from the hierarchical model. (b) The number of causal variants in Top 10 for non-synonymous variants.(a) Median rank of causal variants among the non-synonymous variants for two disease models (M1 and M2) and three values for the NS:S ratio . The proportion of causal variants in the region is 10%. HM refers to the original hierarchical model with ranking of the causal variants among the non-synonymous variants, based on their estimated effects, BE refers to the backward elimination procedure for non-synonymous variants, and HM(EPS)Click here for additional data file.S4 FigureS refers to the ranking of causal variants only among those synonymous variants selected by the backward elimination procedure, with ranks based on the estimated effects from the hierarchical model. (b) The number of causal variants in Top 10 for synonymous variants.(a) Median rank of causal variants among the synonymous variants for two disease models (M1 and M2) and three values for the NS:S ratio . The proportion of causal variants in the region is 20%. HM refers to the original hierarchical model with ranking of the causal variants among the synonymous variants, based on their estimated effects, BE refers to the backward elimination procedure for synonymous variants, and HM(EPS)Click here for additional data file.S5 FigureS refers to the ranking of causal variants only among those synonymous variants selected by the backward elimination procedure, with ranks based on the estimated effects from the hierarchical model. (b) The number of causal variants in Top 10 for synonymous variants.(a) Median rank of causal variants among the synonymous variants for two disease models (M1 and M2) and three values for the NS:S ratio . The proportion of causal variants in the region is 10%. HM refers to the original hierarchical model with ranking of the causal variants among the synonymous variants, based on their estimated effects, BE refers to the backward elimination procedure for synonymous variants, and HM(EPS)Click here for additional data file.S6 FigureS refers to the hierarchical model with ranking of the causal variants among the selected non-synonymous variants, based on their estimated effects, B1 refers to the hierarchical model with one bioinformatic predictor , mB1 refers to the hierarchical model with three bioinformatic predictors , and mB2 refers to the hierarchical model with four bioinformatic predictors (four B1). (b) The number of causal variants in Top 10 for non-synonymous variants.The effect of multiple bioinformatic predictors for non-synonymous variants. Ranking is done only within the set of variants selected by the backward elimination procedure. (a) Median rank of causal variants for two disease models (M1 and M2) and three values for the NS:S ratio . The proportion of causal variants in the region is 10%. HMctor B1, , B2 refe(EPS)Click here for additional data file.S7 Figure1 and B2) for non-synonymous variants in the backward elimination procedure (BE1 and BE2), and in the hierarchical model (B1 and B2) on the number of causal variants in Top 10. Ranking is done only within the set of variants selected by the backward elimination procedure. Results for two disease models (M1 and M2) and three values for the NS:S ratio  are shown. mB refers to the hierarchical model with three bioinformatic predictors . (a) 20% causal. (b) 10% causal.The effect of incorporating bioinformatic predictors (B(EPS)Click here for additional data file.S8 FigureVPS13B; overlaid is a fitted mixture with two components.Distribution of return counts from the backward elimination procedure applied to synonymous variants in (EPS)Click here for additional data file.S9 FigureMolecular validation and transmission pattern analysis for two cases with the c.9592C>T (p.Arg3198Trp) variant , the c.2650+2T>G singleton LoF (C) and the c.10148C>G (p.Ser3383Ter) singleton missense (D). For each family, the electropherogram of the relevant region is shown below the corresponding individual.(EPS)Click here for additional data file.S10 FigureSchematic representation of VPS13B (Q7Z768-2) in the Golgi membrane and the variants identified in this study. The Chorein N-terminal domain is shown in magenta, DUF1162 in cyan, and ATG C-terminal domain in green. The Golgi targeting domain overlaps with the ATG C and is shown in purple. Amino acids affected by LoF are shown in black, probably damaging missense in red, possibly damaging in yellow, and benign in grey. The arrow points to the homozygous probably damaging variant (p.Arg3198Trp).(EPS)Click here for additional data file.S11 Figure1 refers to the hierarchical model with two bioinformatic predictors . (a) Median rank of causal variants among the non-synonymous variants. (b) The number of causal variants in Top 10 for non-synonymous variants.The effect of incorporating a random bioinformatic predictor R for non-synonymous variants in the hierarchical model . Ranking is done only within the set of variants selected by the backward elimination procedure. Results for two disease models (M1 and M2) and three values for the NS:S ratio  are shown. RB(EPS)Click here for additional data file.S12 Figure1 refers to the hierarchical model with two bioinformatic predictors . (a) Median rank of causal variants among the non-synonymous variants. (b) The number of causal variants in Top 10 for non-synonymous variants.The effect of incorporating a random bioinformatic predictor R for non-synonymous variants in the hierarchical model . Ranking is done only within the set of variants selected by the backward elimination procedure. Results for two disease models (M1 and M2) and three values for the NS:S ratio  are shown. RB(EPS)Click here for additional data file.S13 FigureROC curves of the z-values estimated from a hierarchical model including PolyPhen-2 scores and an indicator for the non-synonymous vs. synonymous status as the higher level covariates (solid curves), and ROC curves based on ranking variants using the PolyPhen-2 scores alone (dashed curves); 10% of variants are assumed truly causal variants; associations between the PolyPhen-2 scores and the causal status vary from odds ratio of 2 (blue) to 4 (red); (EPS)Click here for additional data file.S1 Table1, B2, and a scenario with three functional, independent predictors: two B1's and one B2.Absolute biases and coverage probabilities when estimating variant effects in the hierarchical model, for the simulation scenarios in (PDF)Click here for additional data file.S2 TableVPS13B; only a functional vs. synonymous indicator is used as a binary functional predictor. The variants are sorted according to return count. Top 20 functional  variants in (PDF)Click here for additional data file.S1 TextDescribes details about the Autism Sequencing Dataset.(PDF)Click here for additional data file.S2 TextDescribes details about the nonparametric estimation of multivariate mixtures.(PDF)Click here for additional data file.S3 TextPresents comparisons between variant ranking from hierarchical model and variant ranking from PolyPhen-2 scores.(PDF)Click here for additional data file.