A lack of physician scientists as well as a high female dropout rate from academic medicine and basic life sciences is a concern in many countries. The current study analyzes academic career intentions within a sample of recent doctoral graduates from medicine and basic life sciences (N = 1109), focusing on research self-efficacy beliefs as explanatory variable of gender and disciplinary differences. To ensure that differences in research self-efficacy could not be attributed solely to objective scientific performance, we controlled for number of publications and dissertation grade. The results of multivariate analyses pointed to a strong and significant association between research self-efficacy and academic career intentions . The lower academic career intentions of medical doctoral graduates were no longer significant when controlling for research self-efficacy. Within the field of medicine, female doctoral graduates expressed lower research self-efficacy beliefs and academic career intentions. When controlling for research self-efficacy, the correlation between gender and academic career intention was no longer significant. In contrast, no gender differences were found within the basic life sciences with respect to neither academic career intentions nor research self-efficacy. As with other countries, a lack of physician scientists –7), as wBoth in medicine and in basic life sciences, the PhD is considered the standard degree in Germany , 19, 20.Medical students usually start the PhD (Dr. med.) during undergraduate studies and the dissertation represents their first scientific thesis. Like the PhD, the Dr. med. is a sufficient precondition for pursuing an academic research career. In comparison to basic life sciences, the doctoral degree is usually attained within a shorter time frame and has been criticized in terms of its scientific quality , 26. AltWhen pursuing an academic research career in Germany, doctoral graduates from medicine and basic life sciences face similar circumstances. While academic research is a career path with an uncertain outcome in many countries, this uncertainty is particularly high in Germany . A permaWhile there are no data directly comparing academic career pursuit between medicine and other basic life sciences, a study of Briedis and colleagues  showed tAgainst this backdrop, the current article explores differences in academic career intentions between doctoral graduates from medicine and basic life sciences focusing on research self-efficacy as explanatory variable. Research self-efficacy beliefs designate a person’s beliefs to perform research related tasks successfully in the future , 32. SinResearch self-efficacy beliefs could also provide an explanation for a higher female academic career dropout: several studies have suggested that females hold lower research self-efficacy beliefs , 34, 35.Hence, the current article aims to explore the role of research self-efficacy in academic career intentions of medical and basic life sciences doctoral graduates and related gender differences. The article is structured as follows: first, an overview of the research literature on the effects of research self-efficacy and gender differences in research self-efficacy is provided. The derived hypotheses are subsequently tested on a sample of medical and basic life sciences doctoral graduates from Germany. In the last sections, the results and practical implications are discussed.Self-efficacy beliefs (short: self-efficacy) designate one’s beliefs to successfully conduct specific courses of actions in the future , 37. EmpWithin the context of academic research, empirical studies have shown that research self-efficacy beliefs are positively related to the intention to pursue an academic research career , 34, 48,Hypothesis 1Research self-efficacy beliefs are, beyond objective scientific performance , significantly related to academic career intentions in doctoral graduates from medicine and basic life sciences.Due to the averagely lower research experience of medical doctoral graduates, it is hypothesized that medical doctoral graduates express lower research self-efficacy beliefs in comparison to basic life sciences doctoral graduates:Hypothesis 2Medical doctoral graduates express significantly lower academic career intentions in comparison to doctoral graduates from other life sciences.It is further hypothesized that, when controlling for research self-efficacy, medical doctoral graduates and life sciences doctoral graduates do not significantly differ with respect to their academic career intentions:Hypothesis 3Research self-efficacy beliefs mediate the effect of field of study on academic career intention: under control of research self-efficacy, medical and basic life sciences doctoral graduates do not significantly differ with respect to their academic career intentions.Despite a high percentage of female doctoral graduates from medicine and basic life sciences, the female dropout within the postdoc phase through to professorship is very high, resulting in a below average percentage of female professors –10. WhilWithin a clinical-research training program, Bakken et al.  found siConsistent with these results Jöstl et al.  also fouInconsistent findings could be a result of different fields of study being analyzed, since previous results suggest gender differences to be domain specific , 54. MosVariations in used instruments may be another factor contributing to inconsistent findings. Some scales only focus on the research process itself , 56, 57 Hypothesis 4When controlling for measures of objective scientific performance, there are no significant gender differences in research self-efficacy beliefs.Hypothesis 5When controlling for measures of objective scientific performance, there are no significant gender differences in the intention to pursue an academic research career.http://www.med.uni-muenchen.de/forschung/ethik/index.html)The study was approved by the ethical committee of the Klinikum der Universität München  in medicine are considered the most demanding from a scientific point of view , having In addition to performance, we controlled for national and international conference contributions. While conference contributions do not have the importance of publications, they are an important part of academic research careers and represent experience in interacting with the scientific community. Being accepted at conferences might, specifically to doctoral students with little experience, boost confidence in their research capabilities / research self-efficacy. Moreover, we controlled whether the doctoral graduates were part of a working group during the time of conducting their dissertation (binary variable). Being a part of a working group, hence being in contact with other more experienced researchers, could affect research competencies and knowledge beyond the specific dissertation project, thereby affecting research self-efficacy as well.Analyses were carried out with the statistical package Stata, Release 12 . In the Before conducting multivariate analyses and hypotheses testing, descriptive and bivariate analyses were conducted to explore variable distributions, field, and gender differences in the dependent and independent variables. Further, medical graduates were less frequently associated with a working group (35 percent vs. 60 percent); however, there were no gender differences in this regard. With respect to achieved grade, 17 percent of life sciences doctoral graduates received a grade of summa cum laude opposed to 8 percent in medicine. In addition, male life sciences doctoral graduates more frequently achieved a grade of summa cum laude then their female counterparts (21 percent vs. 11 percent). This was also true for medicine: 9 percent of male medical graduates received a grade of summa cum laude opposed to 6 percent of females. Male medical graduates also conducted an experimental dissertation slightly more frequently than female medical graduates (46 percent vs. 40 percent).Regarding the dependent variables of interest (research self-efficacy and academic career aspirations), medical graduates expressed lower research self-efficacy beliefs and academic career aspirations. On these variables, no gender differences were apparent in the life sciences. However, female medical graduates expressed significantly lower research self-efficacy beliefs and lower intentions to pursue an academic research career in comparison to their male counterparts.To test hypothesized differences between medical and life sciences doctoral graduates (Hypotheses 1–3), a path analysis was carried-out with research self-efficacy as mediator variable and academic career intentions as dependent variable. The results of the path analyses are depicted in Moreover, objective performance indicators were significantly related to research self-efficacy and academic career aspirations. Doctoral graduates who received a grade of summa cum laude had significantly higher research self-efficacy and academic career intentions. Also graduates with the grade of magna cum laude had higher research self-efficacy beliefs, which were indirectly related to higher academic career intentions. The same applied to first author article publications, which were positively and significantly related to research self-efficacy, and thereby indirectly to academic career aspirations. A small negative direct effect of the duration of the doctorate on research self-efficacy was observed and a negative indirect effect on academic career intentions. With respect to conferences, specifically contributions to international conferences were associated with higher self-efficacy and academic career intentions, directly and indirectly. A small positive effect of national conference contributions on research self-efficacy was observed. Whereas contributions to national conferences were positively related to academic career intentions via self-efficacy, a small negative direct effect was simultaneously observed. Moreover, having belonged to a working group within the doctorate was related to higher research self-efficacy and hence, indirectly to academic career intentions.To test for the hypothesized gender differences in research self-efficacy, a stepwise multivariate regression analyses was carried-out for medicine and life sciences separately, controlling for performance and experience see . The resIn order to test Hypotheses 4 and 5, stepwise multivariate regression analyses were carried-out with academic career aspirations as the dependent variable, controlling for objective performance and experience in the first step and integrating research self-efficacy as the independent variable in the second step see . The resWhen controlling for research self-efficacy, gender differences in medicine disappeared , M2med. The goal of the current study was to explore the relationship between research self-efficacy beliefs on the intention to pursue an academic research career in the fields of medicine and basic life sciences, specifically addressing disciplinary and gender differences. An additional focus was laid on the control of objective scientific performance variables, which were neglected by several previous studies , 32, 52 In addition, several indicators of objective scientific performance were identified as possible sources of research self-efficacy: the grade of the doctorate and first author publications were positively and significantly related to self-efficacy beliefs in both medicine and basic life sciences. Having been part of a working group (positive) and the duration of the doctorate (negative) were only significantly related to research self-efficacy within the group of medical doctoral graduates. Due to the fact that medical students in Germany usually start their doctorate as undergraduates , they teInternational and national conference contributions were positively related to research self-efficacy and, thereby, had an indirect positive effect on academic career intention. However, contributions to national conferences had a direct negative effect on academic career intention. While this effect was comparably small, it is possible that graduates who contribute to national conferences meet more young researchers of their peer group and, as a result, are more aware of the competition in academic research. Another possible interpretation is that contributions to national conferences indeed offer an opportunity to practice ones’ presentation skills, but may not be understood as conducive in comparison to international conferences.Moreover, some indicators of objective scientific performance were directly related to academic career intentions: a grade of summa cum laude (both fields) and first author publications (only medicine). The direct relationship may be explained by higher objective chances of academic career success. The result of first author publications only being significantly related to academic career intentions in medicine could be explained by higher standards for dissertations in the basic life sciences. In our sample, the majority of basic life sciences doctoral graduates had achieved at least one publication as a first author. In medicine, a publication as a first author was rarer and, therefore, may be interpreted as a more meaningful achievement. Another possible explanation is that only medical doctoral students with a specifically high research interest publish as first author.Gender differences were apparent on the objective scientific performance level for both analyzed fields: women had fewer publications and attained lower grades. However, female life scientists did not express lower research self-efficacy beliefs or intentions to pursue an academic research career. This result differs from the study on biomedical PhDs by Gibbs et al. , who fouAlthough a multitude of scientific performance and experience variables were controlled for, it is still conceivable that the observed gender differences are a result of unobserved heterogeneity. For instance, gender differences in research experience before the doctorate or types of publications  were not assessed here. A further limitation of our results is that different experiences of supervision and mentorship and the anticipation of difficulties to combine an academic career with family duties were not measured. Relationships with supervisors and mentors might be another source of research self-efficacy and academic career intention. Empirical evidence suggests that females within the medical fields receive less mentorship , 75. BotSince the current study only analyzed a sample of doctoral graduates shortly after attaining their doctoral degree, the longitudinal development of research self-efficacy was not addressed. As a result of different levels of family duties or work experiences, research self-efficacy beliefs may evolve differently for male and female researchers in their ongoing careers. Accordingly, results of Abele  show no Another interesting question is whether there are differences in the pursuit of academic research between medical specialties. For instance, it is conceivable that physicians in fields with more clinical duties might be more likely to drop out of academic research than those in fields with lower patient contact. It is also possible that physicians wanting to pursue a career in academic research self-select themselves into fields in which the load of patient care is minimized precisely so that they can dedicate more time to research.A methodological limitation of our study is its cross-sectional design; therefore, the results need to be interpreted cautiously with respect to causality and causal direction. It may also be possible that graduates with high academic career intentions indicate higher research self-efficacy to avoid cognitive dissonance. However, the vast empirical evidence  on self-efficacy effects on achievement motivation and goal-setting, suggest that the correlations found within this study are not completely attributable to a reversed causality. While our measures of research self-efficacy were built upon already existing instruments, the instrument should be further validated with different samples of doctoral graduates.The results of this study suggest that research self-efficacy is an important predictor of academic career intentions for recent PhD graduates. Future studies should analyze to what extent these are able to explain career decisions in later career stages. The effect of research self-efficacy on academic career intentions could vary per one’s career stage and experienced scientists may be more aware of actual career prospects in academic research (e.g. difficulties in keeping employed in academic research). In this regard, it is important to keep in mind that career intentions are necessary to follow a certain career path, but the extent to which intentions can be realized is limited by opportunity structures. As mentioned before, physicians face very good career opportunities in clinical careers. When the threefold threat of academic medicine is too demanding, physician scientists have an “easy way out”. Creating the framework conditions for a better balance between patient care and research might be an important step toward keeping physician scientists in research. Another factor that might turn physicians away from academia in Germany is remuneration. While clinically working physicians receive comparatively high salaries, those who focus solely on research receive the same remuneration as researchers from other fields, which is significantly lower , 79. HowThe results of the study suggest that research self-efficacy beliefs are an important predictor of academic career intentions and, further, that medical doctoral graduates have a lower interest in academic research careers in comparison to their counterparts from basic life sciences. These differences were statistically explained by medical graduates’ lower research self-efficacy beliefs. As such, the study results suggest that the teaching of research related skills and knowledge in German medical education might not suffice to prepare medical students for their doctorate. This is in line with the recent critique of research content insufficiency in the current medical curriculum  and coulOur results further suggest that academic career intentions shortly after the PhD do not explain the higher female dropout from academic careers in basic life sciences. In contrast, female medical graduates expressed lower interest in pursuing a career in academic medicine and this difference was no longer significant when controlling for research self-efficacy. As the low amount of female physician scientists is of international concern, an explanation for their lower research self-efficacy beliefs is an important topic for future studies to analyze. Such results may be important for future policy recommendations with regard to fostering the new generation of researchers.In the current study, the separate analysis of gender differences for the fields of medicine and basic life sciences revealed that gender may not have an equivalent impact in different disciplines—even in related disciplines with a similar gender distribution. Many studies analyze various disciplines collectively, which may conceal important differences. Therefore, we suggest that previous studies re-analyze their data (when sufficient sample sizes are available) and that future research proceeds with differentiating the analyses per individual disciplines.S1 Appendix(DOCX)Click here for additional data file.


Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging. In 2017, it is estimated that there will be 222,500 cases of lung cancer and 155,870 deaths from lung cancer in the United States.–,–––In recent years, although needle biopsy and pathology analysis still remain the gold standard for definitive lung cancer diagnosis, rapid development in nonlinear optical imaging technologies has contributed to advancing diagnostic strategies in cancer, including autofluorescence bronchoscopy,The above-mentioned classification algorithms, however, require extensive image preprocessing, nuclei segmentation, and feature extraction based on the cellular and fibril structural information inherent in CARS images. A total of 145 features was directly calculated from the CARS images to separate fibril-dominant normal from cell-dominant cancerous lung lesions.–––To circumvent the complex image processing and feature engineering procedure and avert any possible human interventions, in this paper, we demonstrate the classification of CARS images on lung tissues using a deep convolutional neural network (CNN) that needs no hand-crafted features.22.1ex vivo using a modified confocal microscope. Each lung tissue was imaged at several different locations, and at each location a series of images was acquired at different image depths, called a Fresh human lung tissues were obtained from patients undergoing lung surgery at Houston Methodist Hospital, Houston, Texas, approved by the Institutional Review Board. The excised tissues were immediately snap-frozen in liquid nitrogen for storage. Frozen tissue samples were passively thawed for 30 min at room temperature, placed on a 2.2The schematic of the CARS imaging system was previously described.2.3While CARS images corresponding to the same tissue but from different viewpoints look very different, a To facilitate the training of deep neural networks, a large amount of labeled data is normally needed. Unlike image databases, such as the ImageNet2.4,Transfer learning refers to the process of using the knowledge learned on a specific task and adapting this knowledge to a different domain, based on the hypothesis that the features learned by the pretrained model are highly transferable.We randomly divided the training and validation set into nine partitions so that we could use ninefold cross validation to validate the performance of the algorithm. We then calculated and cached the bottleneck values for each image to accelerate the following training process. The bottleneck values are the information stored in the penultimate layer, which will be used multiple times during training.33.1,Commonly used pathological features can be identified in the three neoplastic lung types. Adenocarcinoma mostly contains cancer cells that have nested large round shapes and vesicular nuclei, with inhomogeneous cytoplasm forming glandular structures.3.2We first validated the effectiveness of transfer learning on CARS images using a nine-fold cross-validation approach. The entire training and validation dataset was split into nine folds (folds are split into disjoint sets of patients). We held one fold at each time as the test set and trained the model on the remaining eight folds of the data, until each of the nine folds had served as the test set. The overall accuracy that the retrained model achieves is We then tested the retrained model on the holdout test set that had never been seen by the model. The model also achieves 91.4% prediction accuracy, indicating its robustness to data. Given that the distribution of class labels in our dataset is not balanced, we plotted the receiver operating characteristic (ROC) curves see  for thre,To better understand the internal features learned by the retrained model, we visualized the penultimate layer using t-distributed stochastic neighbor embedding (t-SNE) in Neural networks have long been known as “black boxes” because it is difficult to understand exactly how any particular, trained neural network functions due to the large number of interacting, nonlinear parts.Finally, we proposed a majority voting strategy to further improve the performance of the model in predicting lung tissue classes. Since multiple CARS images at different imaging depths were acquired at each sampling point, we label the tissue type with the majority class to adjust the conflicting results among each 4–Automating image analysis is essential to promoting the diagnostic value of nonlinear optical imaging techniques for clinical applications. Thus far, we have explored the potential of CARS imaging in differentiating lung cancer from nonneoplastic lung tissues and identifying different lung cancer types.In this study, we exploited the recent advances in deep CNN and demonstrated how we can apply the model pretrained on the ImageNet data to a completely new domain such as CARS images and yet achieve reliable results. Our experimental results indicate that the proposed deep learning method has the following advantages when coupled with label-free CARS images. First, deep CNN can extract useful features automatically,We noticed that our CARS images have uneven background, most likely due to the chromatic aberrations. Interestingly, the traditional approach to remove background information of the microscopic images before feeding images into the deep CNN actually reduced the accuracy of prediction. This may be due to the fact that the background information contains subtle information about the foreground image features of interest, which normally are ignored by human inspectors. Meanwhile, it should be noted that most lung cancers are histologically heterogeneous among different patients, and the same cancer subtype may have different pathological characteristics at different stages, resulting much larger within-class appearance variations.in vivo applications at clinical settings.––in vivo to provide reliable architectural and cellular information without the need for invasive and sometimes repetitive tissue removal. We expect that combing a miniaturized fiber optics probe and a deep learning-based automatic image classification algorithm will have the potential to detect and differentiate lung tissue on-the-spot during image-guided intervention of lung cancer patients and save precious time in sending tissue specimens for frozen sectioning diagnosis in a pathology laboratory. Furthermore, deep learning is agnostic to the type of images, thus it can be applied not only on CARS images, but also some other nonlinear optical imaging and microscopy imaging modalities, such as two-photon-excited autofluorescence (TPEAF) and second harmonic generation (SHG). We have proven that the morphological difference shown in TPEAF and SHG images can be used to differentiate normal and desmoplastic lung tissues.In the past decades, increasing attention has addressed developing minimized optical fiber-based CARS imaging probes for direct In conclusion, we demonstrated the viability of analyzing CARS images of human lung tissues using a deep CNN that was pretrained on ImageNet data. We applied transfer learning to retrain the model and built a classifier that can differentiate normal and cancerous lung tissues as well as different lung cancer types. The average process time of predicting a single image is less than half a second. The reported computerized and label-free imaging strategy holds the potential for substantial clinical impact by offering efficient differential diagnosis of lung cancer, enabling medical practitioners to obtain essential information in real time and accelerate clinical decision-making. When coupled with a fiber-based imaging probe, this strategy would reduce the need for tissue biopsy while facilitating definitive treatment. This method is primarily constrained by data and can be further improved by having a bigger and more generalized dataset, to validate this technique across the full distribution and spectrum of lung lesions encountered in clinical practice.


Organisms are finely tuned systems that are resilient to perturbations but that must maintain some constants. For example, within the genome, the ratio between different gene products must be tightly constrained to ensure stoichiometric assembly of protein complexes and the like. Evolution has honed expression levels so that as long as there is the same number of chromosomes per cell, the relative transcription levels of such genes can be kept constant .But what happens if the ratio of chromosomes varies between individuals? This is precisely the case with heterogametic sex chromosomes (such as the human XY and XX chromosomes), which are common and have evolved independently from ancestral autosomes (non-sex chromosomes) several times in plants and animals. The evolutionary loss of recombination between X and Y chromosomes has led to distinct differences in gene content, and the resulting disparity in chromosome (and therefore gene) dosage between the sexes creates a number of challenges.Mammals solve this conundrum by inactivating one of the two X chromosomes in each female somatic cell. Flies, which also have X and Y sex chromosomes, albeit independently evolved, do things differently—they upregulate the single X chromosome in male somatic cells. The overall consequence, however, in both mammals and flies, is chromosomal dosage compensation. By contrast, in male germline cells, both the X and Y are transcriptionally repressed in a process known as meiotic sex chromosome inactivation (MSCI), for reasons that remain somewhat unclear.Drosophila male germline [While MSCI leads to a significant decrease in gene expression from the germline X chromosome, there are genes on the X chromosome that must be expressed for successful spermatogenesis. A recent paper published in PLOS Biology has revealed new insights into the transcriptional repression of the X chromosome in the germline . The mecPLOS Genetics shows that in addition to silencing protein-coding genes, MSCI represses the transcription of X-linked non-coding RNAs, including microRNAs with testis-specific expression [Mechanisms of MSCI vary between organisms, and in mice, most or all protein-coding genes on the X chromosome are silenced during MSCI. A study in pression . Using sBMC Biology presents evidence that the platypus uses a transcriptional repression regime more similar to birds than the rest of the therian mammals  [Humans, like mice, exhibit strong MSCI in the male germ line, but it appears that this was a relatively recent evolutionary event; a paper in mammals) . Thus, rPLOS Biology study noticed an interesting trend: genes residing on the X chromosome are disproportionately lowly expressed. They found that genes on the X chromosome have maximal expression levels about three times lower than that of autosomal genes, have a lower breadth of expression, and include fewer genes expressed in tissues requiring high levels of transcription (such as the liver) [While studying the distinct gene content of the mammalian X chromosome, authors of a e liver) . The autPLOS Genetics reveals how the X chromosome is identified in dipteran flies [An essential step in establishing dosage compensation is to single out the X chromosome from the autosomes for this transcriptional activation. Work appearing in an flies , which han flies . By compDrosophila, some components of the dosage compensation machinery are also involved in the transcriptional activation of genes within heterochromatin on autosomes. The authors of a PLOS ONE paper identify those components of the dosage compensation machinery that are required for this heterochromatic gene expression [In pression . InteresPLOS Genetics investigated whether there were different patterns of sex-biased expression in early Drosophila embryos [Sex-biased genes are those that have a higher transcript level in one gender compared to the other. A study in  embryos . At thisNature Communications study used a population genetic model and chicken transcriptomic data [One can think of dosage compensation as preventing sex bias, but it can also lead to sexual conflict. If a mutation leads to increased expression in the heterogametic sex  to balance expression, this could lead to hyperexpression in the homogametic sex , with negative fitness consequences. In birds, females are the heterogametic sex (with ZW chromosomes) and males are homogametic (ZZ) and—unlike flies, worms, and mammals—birds have incomplete dosage compensation. To understand why this less efficient mechanism evolved, a mic data  to show Drosophila melanogaster, a gene inserted into the X chromosome will be dosage compensated. However, as shown in this PLOS ONE paper, in the Australian sheep blowfly Lucilia cuprina, inserted genes are not dosage compensated even though the endogenous genes are [There are still many outstanding mysteries of sex chromosome transcriptional regulation, many of which stem from the extensive variation between organisms. Even closely related species show distinct differences. For example, in enes are . How dosFor more detailed reading please see the associated PLOS Collection .


Some populations will cope with human-induced environmental change, and others will undergo extirpation; understanding the mechanisms that underlie these responses is key to forecasting responses to environmental change. In cases where organisms cannot disperse to track suitable habitats, plastic and evolved responses to environmental change will determine whether populations persist or perish. However, the majority of studies consider plasticity and evolution in isolation when in fact plasticity can shape evolution and plasticity itself can evolve. In particular, whether cryptic genetic variation exposed by environmental novelty can facilitate adaptive evolution has been a source of controversy and debate in the literature and has received even less attention in the context of human-induced environmental change. However, given that many studies indicate organisms will be unable to keep pace with environmental change, we need to understand how often and the degree to which plasticity can facilitate adaptive evolutionary change under novel environmental conditions. Quantifying organismal capacities for such compensatory mechanisms is therefore paramount to forecasting responses to ongoing and future human-induced changes to the environment2. Of particular importance is quantifying the relative contributions of phenotypic plasticity, where a single genotype produces a range of different phenotypes in response to environmental variation, and evolution, where additive genetic differences underlie phenotypic change across generations or among populations. This distinction is critical, as plasticity and evolution often occur over different timescales and can operate under different constraints3.In the absence of sufficient compensatory mechanisms to cope with human-induced environmental changes, local extirpation of populations and possibly extinction of entire lineages will almost certainly occur12. Yet the distinction of plastic versus evolutionary contributions in a given population at a given time may be an oversimplification, as these mechanisms can interact to shape phenotypic changes in populations over time6. Basic research on evolution has long debated whether plasticity facilitates or constrains adaptive evolution13. However, this idea has only recently come into focus for theory15 and empirical work17 involving responses to human-induced environmental change. Here, we briefly review the interplay between plastic and evolutionary responses to human-induced environmental change and highlight new research areas for future development on the role of plasticity in shaping evolutionary responses to changes in climate, land-use, and environmental toxins. While we consider these environmental stressors separately for organizational purposes in this review, we acknowledge that many of these factors are not mutually exclusive, e.g. land-use changes can often include changes in environmental toxicity.Certainly much progress has been made over the last few decades in disentangling plastic from evolved changes in phenotypes under human-induced environmental change4, yet evolution in this sense might not achieve large enough shifts in trait values or be fast enough to keep pace with the scale and rapidity of human-induced changes to the environment before populations undergo extirpation. Limits on the amount of standing genetic variation in populations, mutation rates, fitness tradeoffs, the strength and consistency of selection, and the genetic correlations among traits can all serve to slow the rate of evolutionary change5.Evolutionary theory predicts that natural selection on environmentally insensitive  traits imposed by novel environments will drive populations toward their new local fitness optima18. There is also no guarantee that plasticity will be adaptive for the novel conditions generated by human-induced environmental change19. Whether plasticity is adaptive or not depends on whether the environmentally induced phenotypes are closer or farther from the local optimum. Because novel environments are, by definition, environments that organisms have never experienced previously, it is entirely plausible that adaptive plasticity in current environments will be maladaptive in the novel environment21. This prediction should, however, be tempered to some degree, as novelty can arise not only through environments not previously experienced but also through alterations to the frequency of environmental conditions experienced , then plasticity can weaken subsequent selection in the novel environment and hide genotypic variation from selection87.Plasticity may drive a population to extirpation in novel environments before adaptive evolution can occur if plasticity results in very low mean fitness , for example through developmental instability or a breakdown in homeostasisHow plasticity can facilitate evolution:87.Plasticity may facilitate evolution in novel environments when it acts to buffer populations from extirpation long enough for selection to act on standing or cryptic genetic variationcryptic genetic variation in novel environments when environmental variation falls outside the range generally experienced, exposing heritable phenotypes that were not expressed in the ancestral environment to selection. Since the speed of evolution is in part dependent on the degree of genetic variation underlying phenotypic traits under selection, evolution may proceed more rapidly when novel plasticity is revealed89.Gene by environment interactions may revealHow plasticity itself can evolve:genetic accommodation24. Under this broad umbrella, selection may act on genetic variation to change the slope of the reaction norm or the mean of a trait expressed across environments90. Selection to canalize the expression of a novel environmentally induced phenotype has been specifically termedgenetic assimilation92, and its reverse, the restoration of the ancestral phenotype in a novel environment, has been termedgenetic compensation20.Plasticity can evolve when selection acts on genetic variation in the phenotypic expression of individual genotypes across environments (i.e. reaction norms). This process has been named31. On the one hand, human-induced changes to the environment yield an imperative to understand plastic and evolutionary responses to new environments for the goal of biodiversity forecasting and conservation planning, but on the other hand, these often-rapid human-induced environmental changes may help overcome historical challenges in assessing plasticity’s role in evolutionary change. Specifically, many of the comparisons between ancestral and novel populations represent divergence processes that are relatively old32. Rapid human-induced environmental changes provide access to the early stages of plastic and evolved responses to new environments. Human-induced environmental novelty may also be more straightforward for attribution—that is, determining the proximate drivers of changes in the environment—as compared with historical comparisons between ancestral and novel populations where attribution is perhaps less clear and there is more time for suites of different environmental changes to accumulate.Human-induced novel environments are excellent sources to look for the exposure of cryptic genetic variation and to explore plasticity’s role in shaping evolution, but comparatively few studies have taken advantage of these opportunitiesIn this review, we discuss recent work on the interplay between plastic and evolved responses to human-induced environmental change. Specifically, we consider whether plasticity creates novel opportunities for selection to act, thereby facilitating evolution, or whether plasticity buffers environmental variation, dampening selection and constraining evolution . Because33 to rapid industrialization and global temperature rise within the last century34. The magnitude of recent human-induced changes, particularly to climate, has already led to the global redistribution of plants and animals in space and time36. For those organisms that cannot get out of the way of climate change, they must rely instead on plastic and evolved responses. A major focus has been on whether current amounts of phenotypic plasticity and evolutionary potential are sufficient to keep pace with projected climate velocities38. Of course, there are many effects of contemporary global climate change—from atmospheric deposition of elemental compounds to shifts in precipitation patterns—but temperature is perhaps the best studied and one that impacts organisms from biochemical rate processes up through species interactions, community dynamics, and ecosystem function39.Humans have made lasting impacts on the environment for thousands of years, from megafaunal extinctions during the late Quaternary period34. Studies of organismal capacities to cope with this change, either through plasticity or evolution, yield mixed evidence for whether organismal changes will be large enough and proceed fast enough to keep pace with climate change41. In general, there has been a strong research emphasis on existing plasticity in phenology, morphology, physiology, and behavior, perhaps because of the assumption that plasticity will operate over a timetable commensurate with rapid global climate change and perhaps also stemming from the added challenges of assessing evolutionary responses42. This emphasis is not without merit, as plasticity has been broadly shown to be a critical component of the response to climate change43. However, existing plasticity is not always sufficient to cope with climate change. For example, Anderson and colleagues found a substantial role for phenological plasticity in plant flowering time under recent climate change but concluded that plasticity alone is insufficient and evolutionary change is needed for these populations to keep pace44. While such variance partitioning studies of responses to climate change among plastic and evolved components is growing, there remain comparatively few studies assessing how this plasticity shapes evolutionary responses3.By the end of the century, global temperature is expected to rise by over 4°C under the highest greenhouse gas emissions scenarios, which we are currently tracking45. As a notable contrast, Logan and colleagues found evidence of selection on thermal performance traits when lizards were transplanted to a novel thermal environment46, but plasticity’s role in facilitating or constraining evolutionary change was not reported. It is worthwhile to consider why there are so few studies on genetic accommodation under climate change. One important limitation may be how the ancestral and novel populations are assessed under a temporal change in climate. Resurrection studies or studies which compare the leading edge of a rapid range expansion to the center range seem to be strong candidates for such an analysis47. Next, we consider two environmental changes—land-use and pollution—where the ancestral and novel populations are readily accessible.Those studies that have explored the role of plasticity in facilitating adaptive evolutionary responses to climate change have found little evidence in support of this mechanism, though this may be as much a product of the organisms and systems for which these data are available as it is a general biological pattern. For example, research on lizard responses to climate change from Buckley and colleagues indicates that plasticity in thermoregulatory behavior constrains lizard evolutionary responses to warming by shielding variation in thermal performance from selection48, urbanization is an increasingly important source of land-use change. Rates of urbanization are accelerating globally, with current levels of urbanization at three percent of the Earth’s landmass, excluding Greenland and Antarctica49. This may not seem like an exceptionally large number, but over half of the world’s population lives within these urbanized areas, and pockets of urbanization dot almost every corner of the globe50. Although studies have begun to quantify community composition and phenotypic changes in populations across urban and nearby rural habitats, few have explored the mechanisms that contribute to these changes. Because the urbanization process offers both novel urban habitats in close spatiotemporal proximity to ancestral rural habitats and the potential for replication across independent ancestral-novel comparisons52, urbanization gradients are excellent, but underused, sources of ancestral-novel comparisons needed for assessing plasticity’s role in shaping evolutionary change. Of particular interest is the rapid change in environmental temperature over space and time under the urbanization process, especially as urban warming can serve as a space-for-time proxy to understand responses to global climate change. Urban heat island effects are relatively consistent across many regions, with temperature increases in excess of several °C in the air column and over 10°C at the surface being possible . Though, of course, many other changes accompany the urbanization process, including changes in habitat structure, nutrient availability, and pollution54.Although there are many types of land-use changes occurring, including deforestation and habitat fragmentation, to which organisms respond through plasticity and evolutionary change56. More recently, Donihue and Lambert54 provided an excellent overview of how to assess and disentangle plastic and evolved responses to urban land-use change in addition to an exhaustive review of the studies that have done so in urban environments. They defined three criteria for demonstrating adaptive evolutionary change in response to urbanization, including quantifying phenotypic shifts in traits, measuring the fitness effects and genetic basis of those traits, and identifying drivers of trait changes. Only two studies met these criteria at the time of the review, including one study on killifish responses to elevated polychlorinated biphenyl pollution in urban environments57 and another on the reduction of dispersing versus non-dispersing plant seeds in urban environments58. Notably, the killifish study highlights the fact that the environmental stressors we consider in our review—climate change, land-use change, and environmental toxins—are not mutually exclusive, as, for example, killifish responses to polychlorinated biphenyl compounds may be considered in context of both land-use change and environmental toxicity. Relatedly, structural changes in urban environments, specifically replacing grassland and forest with impervious surfaces like roads, sidewalks, and buildings, contributes to the urban heat island effect; as the study on plant seed production demonstrated, these structural changes can also serve as an agent of selection themselves. Dispersing seed pods are selected against in urban environments because there are large gaps in suitable habitat owing to intervening sidewalks and roads58.In general, studies that explore plastic and evolutionary responses to urbanization have lagged behind climate change studies. This is surprising, since one of the best-known and earliest examples of rapid evolutionary change, industrial melanism in peppered moths, was itself driven by the fast progression of urbanization  associated with the industrial revolution59 have found phenotypic shifts in lizard morphology in urban areas where lizards use artificial surfaces as perches as opposed to vegetation in unaltered habitats ; a common garden experiment suggested a genetic basis for these changes in morphology, though the adaptive nature of these changes has yet to be demonstrated, and maternal effects cannot be fully excluded with their use of first-generation offspring from field-collected parents. Interestingly, evolutionary responses to urban heat islands appear to be one of the least-studied aspects of the urbanization process despite the ubiquity of the urban heat island signal among cities. Angilletta and colleagues60 demonstrated an increase in upper thermal tolerance in city ants compared with rural ants, though it is unclear whether this shift is adaptive and has a genetic basis. McLean and colleagues used a common garden experiment to demonstrate a genetic basis for shifts in growth rate of a chitinolytic fungus along an urbanization gradient, though, again, the adaptive nature of these changes is unclear, and only one urban-rural comparison was made61. Although urban evolution is gaining traction in the literature, the use of urban environments to assess the role of plasticity in shaping evolutionary responses has been used only to a marginal extent and remains open for future development. In particular, replicated urban-rural comparisons with multigenerational common garden studies are needed to explore the potential for urban evolution and its interplay with plasticity.Evidence consistent with an interpretation of urban evolution continues to accumulate. For example, Winchell and colleagues2 emissions and changes in land-use, more than a century of industrialization has led to the accumulation of toxins in the environment, notably heavy metal and pesticide contaminants62. Environmental toxins can have severe negative consequences for organismal fitness and population survival through a diverse range of effects and mechanisms of action63. Despite this diversity and in contrast to urbanization and climate change, the evolution of resistance to environmental toxins is well established66: for instance, the classic example of divergent evolution and speciation of plant populations living in and adjacent to toxic mine tailings70.In conjunction with increasing CO71 and pesticides72 can reduce the negative effects of later exposure. Interestingly, exposure to one toxin may induce a plastic response with positive fitness effects (i.e. an adaptation) to other toxins74, but the generality of this inducible “cross-tolerance” is currently unknown. This shared adaptive plasticity does not appear to be restricted to toxins with common mechanisms of action, contrary to expectations73. Indeed, this result suggests an important role of plasticity in the evolution of toxin tolerance, as cross-tolerance would induce initially adaptive rather than maladaptive plasticity in response to novel toxins, possibly facilitating subsequent evolutionary refinement.Phenotypic plasticity also contributes to organismal responses to environmental toxins, as prior exposure to heavy metals83.Like urbanization, the spatiotemporal heterogeneity of environmental toxins provides an opportunity to evaluate the role of plasticity in shaping evolution. Indeed, comparisons among populations differing in their exposure to pesticides provide some of our best evidence that genetic assimilation plays a role in evolutionary responses to anthropogenic change. Historically, studies focused on the evolution of tolerance in pest species directly targeted by pesticide application. A recent emphasis on non-pest species incidentally affected by pesticides has provided novel insights into the mechanisms underlying pesticide tolerance. By comparing pesticide tolerance among naïve populations to populations exposed to pesticides in contaminated environments (e.g. populations close to agriculture), numerous studies have now found evidence for the evolution of greater resistance to pesticides in affected populations of anurans and aquatic invertebrates compared to naïve populations. plastic) tolerances of wood frog populations varying in their distance from agriculture to an insecticide, Hua and colleagues found that evolved tolerance to a potentially lethal exposure of insecticide decreased with distance from agriculture79. In contrast, the degree of plastic tolerance induced from an initial sublethal exposure increased with distance from agriculture76. Together, these results are consistent with the predictions of genetic assimilation; exposure to novel environments induced the expression of phenotypic variation in ancestral populations, and this variation was then canalized in populations with a history of exposure to the novel environment29. We emphasize that while these studies provide indirect evidence of genetic assimilation, more direct tests are needed. To accomplish this goal, resurrection studies and the study of extant populations varying in their time of exposure to novel environments may provide powerful means of testing the role of plasticity and evolution for responses to human-induced environmental change.Using an agricultural land-use gradient in a space-for-time substitution, Hua and colleagues recently extended these findings to test key predictions of genetic assimilation : the can3 but accumulating. Although quantifying organismal capacities to respond to environmental change through plasticity and evolution is and should continue to be a major research priority, a key shortcoming in this area is a limited quantity of empirical data on the interaction of these mechanisms, specifically the role of plasticity in shaping evolutionary responses to environmental change31. Climate change, land-use change, and environmental toxins provide conservation and biodiversity forecasting challenges; however, these agents of environmental change also provide opportunities to explore the potential for novel environments to release cryptic genetic variation and how selection might act on that variation to enact evolutionary change. To move forward, we suggest researchers should especially focus on (i) quantifying the expression of cryptic genetic variation in the wild, (ii) assessing patterns of selection on cryptic genetic variation in natural populations, and (iii) quantifying the strength and direction of evolutionary versus plastic responses to novel environments. An especially powerful approach may be assessing evolutionary and plastic responses across a gradient of time rather than space. By identifying and studying populations exposed to novel selective environments for different lengths of time using multi-generational common garden designs, we can directly assess the mechanisms underlying these responses. Human-mediated changes via urbanization and environmental toxins may be especially amenable to such an approach where historical records can be harnessed to calibrate temporal variation in the selective environment.That plasticity is a powerful mechanism for allowing organisms to cope with rapid human-induced environmental change has widespread empirical support. Evidence in support of rapid evolution in response to environmental change is less abundant85. A major assumption underlying the genetic accommodation mechanism is that there will be a positive relationship between the degree of genetic variation revealed and the strength of natural selection in novel environments. However, this outcome is not guaranteed, and it is possible that genetic variance could be lower for the targets of selection when selection is stronger; in practical terms, this means that evolution could be slower, as trait heritability  would be lower in the novel environment despite stronger selection. There have been very few empirical examinations of the nature of the covariation between the amount of variation exposed and the strength of selection in novel environments. Wood and Brodie have examined this relationship with several agents of selection, including variation in environmental temperature27. Interestingly, while the authors found the strength of selection to vary with temperature, this variation was not correlated with genetic variance, as greater and lesser values of genetic variance were detected with increasing strength of selection. In contrast, environmental novelty, as defined by the study authors, consistently revealed increased genetic variance but the studies synthesized by Wood and Brodie did not concurrently measure selection in these novel environments. Going forward, we need additional empirical data on both the strength and targets of selection and the degree of heritable variation available for selection to act to determine when and how the assumption of a positive correlation between strength of selection and revealed genetic variance in novel or changing environments is upheld or broken. Evolution on contemporary timescales in response to human-induced environmental change provides at least as many opportunities to explore fundamental questions on interactions among plasticity, organismal ecologies, and the magnitude and direction of evolutionary change as it does challenges in predicting complex responses to environmental change for conservation planning.In addition to the issues with plastic and evolutionary responses to environmental change that we considered here, several related areas are also seeing further developments. One of these areas concerns the covariation between selection and genetic variation when both are affected by the same environmental factor


Notably, TorsinA lacks aromatic pore loops that are both conserved and critical for the processive unfolding activity of Clp/HSP100 proteins. Based on these distinctive yet defining features, we discuss how the apparent dynamic nature of the Torsin-cofactor system can inform emerging models and hypotheses for Torsin complex formation and function. Specifically, we propose that the dynamic assembly and disassembly of the Torsin/cofactor system is a critical property that is required for Torsins' functional roles in nuclear trafficking and nuclear pore complex assembly or homeostasis that merit further exploration. Insights obtained from these future studies will be a valuable addition to our understanding of disease etiology of DYT1 dystonia.Torsins are essential, disease-relevant AAA+ (ATPases associated with various cellular activities) proteins residing in the endoplasmic reticulum and perinuclear space, where they are implicated in a variety of cellular functions. Recently, new structural and functional details about Torsins have emerged that will have a profound influence on unraveling the precise mechanistic details of their yet-unknown mode of action in the cell. While Torsins are phylogenetically related to Clp/HSP100 proteins, they exhibit comparatively weak ATPase activities, which are tightly controlled by virtue of an active site complementation through accessory cofactors. This control mechanism is offset by a TorsinA mutation implicated in the severe movement disorder DYT1 dystonia, suggesting a critical role for the functional Torsin-cofactor interplay  Torsin ATPases are essential and broadly conserved AAA+ proteins whose discovery was tied to the characterization of the TorsinA DYT1 mutation found in patients with early-onset torsion dystonia, a highly debilitating hereditary movement disorder  lines  an alternating, symmetric Torsin/cofactor ring assembly; (b) homo-oligomeric Torsin rings; and (c) a Torsin/cofactor dimer Figure . Though Given that previous studies of Torsins were conducted primarily with “trap” variants that resulted in more static models, we propose a more dynamic model. This model is most strongly supported by the following evidence: only Torsins, but not LAP1 and LULL1, possess the C-terminal helix bundle that is essential for intra-protomer ring-forming contacts Figures  among thOne important point of discussion in the context of this model is how the cofactor luminal domains, which would effectively compete with other Torsin subunits in the ring for a nearly identical interface would manage to initially pervade the ring, gaining access to an ATP-bound Torsin subunit. One possibility Figure  is that Unlike the Clp/Hsp100 proteins which Torsins are most phylogenetically similar to, the Torsin structure   components, analogous to a process that has recently been described in yeast in which ESCRT proteins and the AAA+ ATPase Vps4 participate in a pathway that surveils NPCs (Webster et al., We envision two general mechanistic models to explain why blebs form in the absence of Torsin. In the first model, Torsin would act directly in NPC biogenesis. For example, Torsin might participate in the fusion of the inner and outer nuclear membranes during NPC assembly, probably in complex with other proteins. In the second model, Torsin would act upstream of NPC biogenesis or surveillance. Specifically, Torsins could act as trafficking chaperones by binding newly synthesized proteins in the endoplasmic reticulum and delivering them to sites of NPC assembly in the nuclear envelope Figure . Torsin Our model could also explain the accumulation of K48-ubiquitylated proteins in the nuclear periphery in Torsin deficient cells (Laudermilch et al., In conclusion, we have now reached a stage in our understanding of Torsin biology that is sufficient to begin formulating more precise hypotheses about their mechanism and their functions that can be tested by definitive experiments. The likelihood that further genetic experiments within a cellular context will yield the holy grail of the Torsin field—the elusive substrates that trigger the changes affected by Torsins in the ER and at the nuclear envelope—is more probable than ever. Merging these functional details with a structural understanding of the Torsins' action will provide the necessary basis for developing targeted DYT1 dystonia therapies.All authors listed, have made substantial, direct, and intellectual contribution to the work, and approved it for publication.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.


Shotgun proteomics utilizes a database search strategy to compare detected mass spectra to a library of theoretical spectra derived from reference genome information. As such, the robustness of proteomics results is contingent upon the completeness and accuracy of the gene annotation in the reference genome. For animal models of disease where genomic annotation is incomplete, such as non-human primates, proteogenomic methods can improve the detection of proteins by incorporating transcriptional data from RNA-Seq to improve proteomics search databases used for peptide spectral matching. Customized search databases derived from RNA-Seq data are capable of identifying unannotated genetic and splice variants while simultaneously reducing the number of comparisons to only those transcripts actively expressed in the tissue.We collected RNA-Seq and proteomic data from 10 vervet monkey liver samples and used the RNA-Seq data to curate sample-specific search databases which were analyzed in the program Morpheus. We compared these results against those from a search database generated from the reference vervet genome. A total of 284 previously unannotated splice junctions were predicted by the RNA-Seq data, 92 of which were confirmed by peptide spectral matches. More than half (53/92) of these unannotated splice variants had orthologs in other non-human primates, suggesting that failure to match these peptides in the reference analyses likely arose from incomplete gene model information. The sample-specific databases also identified 101 unique peptides containing single amino acid substitutions which were missed by the reference database. Because the sample-specific searches were restricted to actively expressed transcripts, the search databases were smaller, more computationally efficient, and identified more peptides at the empirically derived 1 % false discovery rate.Proteogenomic approaches are ideally suited to facilitate the discovery and annotation of proteins in less widely studies animal models such as non-human primates. We expect that these approaches will help to improve existing genome annotations of non-human primate species such as vervet.The online version of this article (doi: 10.1186/s12864-017-4279-0) contains supplementary material, which is available to authorized users. Shotgun proteomic approaches employ a database search strategy to compare experimentally observed mass spectra to an in silico-generated library of theoretical spectra derived from gene annotation information of the organism(s) being studied. The successful matching of peptides is thus predicated upon the accuracy of the search database being utilized to make these comparisons. The outcome of proteomics experiments is therefore driven by the quality and completeness of the genomic information of the organism being studied. Proteomic studies of genetically well-characterized species such as mice and humans benefit from robust proteomic search databases and extensive genome annotations which can account for known genetic variability such as splice variants and sequence variation altering the amino acid sequence of encoded proteins. However, protein identification of other research model organisms is limited by the quality of reference genome annotations.Proteogenomic methods attempt to improve the search library limitations by leveraging information about gene transcription to guide the curation of search databases customized to the tissues of individual organisms. Several groups have demonstrated that transcriptional profiling using massively parallel sequencing approaches (RNA-Seq) improves the detection of peptides in proteomics experiments in a wide range of different species, ranging from microorganisms –3 and plFirst, RNA-Seq data reveal sample-specific genetic sequences which may differ from the reference genome including nucleotide insertions, deletions, or substitutions. Single nucleotide polymorphisms (SNPs) comprise the majority of genomic variation within coding exons of genes, and these genetic variants are divided into two broad categories; SNPs which change a coding triplet but do not result in an amino acid substitution are referred to as synonymous SNPs, while variants which result in amino acid substitutions are called non-synonymous SNPs (nsSNPs). The identification and inclusion of nsSNPs has the potential to improve the search database annotation because these changes can alter the chemical properties of the fragmented peptides. Failure to account for the resultant mass and/or charge change arising from the amino acid substitutions introduces ambiguity in peptide matching because, unlike nucleotide sequencing where the base order and fragment size can be directly inferred from the raw data, proteomic matching of peptides is based solely on the atomic mass and charge expected to be derived from enzymatically fragmented proteins.Second, RNA-Seq reads can be used to identify splice junctions (SJs) which characterize mRNA isoforms absent from the reference gene model. SJs can result from genetic variation that alters how the spliceosome interacts with mRNAs, or SJs might arise as a result of alternative exon usage in tissue- or condition-specific contexts. RNA-Seq can also detect chimeric RNAs which arise from gene fusion events. Search databases that inadequately account for this isoform variability will fail to accurately identify their translated peptide products, and organisms with incomplete gene model information are therefore more susceptible to peptide misidentification.Finally, RNA-Seq reads can be used to estimate transcript abundance. Knowledge of which mRNAs are expressed can inform how search databases can be trimmed to minimize multiple testing that occurs within peptide spectral matching algorithms. As with any iterative comparison process, the likelihood of misidentifying peptides increases with the total number of comparisons made. Experiments which incorporate proteomics results are vulnerable to Type I error inflation because multiple comparisons are made first at the peptide spectral matching identification stage, and then again in the context of the experimental condition (e.g. identifying protein abundance differences between case and control groups).Based on this summary, it is evident that sample-specific search databases derived from RNA-Seq analyses from the same tissue sample as the proteomics data provide an improved search database, as it will include those peptides mostly likely to be found within the tissue being sampled, while excluding records derived from extraneous genomic information. This approach becomes even more valuable for analyses in samples from species with poorly annotated genomes , for reaSeveral bioinformatics pipelines have been described which facilitate the conversion of RNA-Seq reads into customized peptide databases which can be used to search mass spectrometry (MS) data. A well-established approach, developed and described by Sheynkman et al. , leveragChlorocebus aethiops sabeus), is one such example. The vervet monkey has long been an important model in AIDS research, as vervets are natural carriers of SIV yet display no symptoms of illness upon infection -1)).Interestingly, for two samples (samples 1347 and 1448), the search against the REFdb resulted in more peptide spectral matches (aon average 0.6%) when compared to the search using the SSdb. Similarly, for three samples  the analysis against the REFdb identified slightly more peptides compared to the search using the SSdb (on average 0.5%). Given the lower number of RNA-Seq reads or mass spectra obtained for some of these samples, it is conceivable that this difference is due to variation in sample preparation or sample quality. Partial degradation of tissue samples would affect both RNA and protein recovery, and may have impacted the analyses presented here.Next, we combined the search results across the 10 samples to compare the unique peptides and protein groups identified in vervet monkey liver samples by the REFdb versus the SSdb. These results are shown in the Venn diagram of Fig. The first set of these peptides represents peptides that match newly identified SJs not annotated in the gene models of the reference genome. Of the 284 SJ peptide search records identified and annotated from the RNA-Seq data, we identified 47 peptides by MS in more than one sample, which suggests these matches represent incompletely annotated genes of the vervet genome. Consequently, the results of these proteomics analyses could aid in the improved annotation of the gene models. Another 45 peptides mapped to a single sample, bringing the total number of distinctly SJ-mapped peptides identified in these samples to 92. A comparative analysis of these peptide sequences with other primates utilizing BLASTP revealed that the majority of the identified peptides (53/92) could be matched with an orthologous protein . The comA second set of peptides uniquely identified in searches using the SSdb are peptides that map to an SAV record where the amino acid variant resides within the predicted tryptic peptide that matched in the search. In total, 192 peptide matches representing 101 distinct peptides were found in the 10 samples by the sample-specific analyses using the SSdb. Of these 101 distinct peptides that conformed SAVs identified in the RNA-Seq data, 37 peptides were identified in more than one sample. A list of all peptides matching SAV records are included in Additional file These first two categories of peptides identified within the SSdb analyses represent search results we expected to obtain from the RNA-Seq-based proteogenomic approach because the mRNA read data create search records which accurately predict the respective peptide fragments seen in the MS dataset.While we might expect the same number of matches to reference protein entries contained in both the SSdb and REFdb searches, a total of 313 peptides were identified as matches to reference proteins in the search against the SSdb but not the REFdb. Morpheus, like many other spectral matching algorithms, uses a decoy-based searching approach to empirically estimate and maintain a 1% false discovery rate (FDR). The inclusion of these peptides may result from the adjustment of the absolute FDR that arises from the reduction in the absolute size of the SSdb compared to the REFdb. This is supported by the fact that the q-values of this subset of SSdb-identified peptides is higher, on average, compared to the overall average q-value of all peptides identified using the SSdb search. Of the “reference” peptides identified using the SSdb, only 16% (151 of 948) were seen in two or more samples. The peptides identified exclusively in the SSdb analyses are listed in Additional file While the minimization of the search database helps recover some true positive matches, as described above, concerns about restricting proteome search databases based upon RNA-Seq data could arise when a protein’s abundance is poorly correlated with its corresponding transcript abundance, or when proteins might be derived from a tissue of origin different from which they currently reside. Extracellular structural proteins or chromatin-associated proteins with long-half-lives would be likely candidates for the former category, while growth factors, cytokines or contaminating proteins might comprise the latter. We surveyed the list of peptides identified by REFdb but not SSdb analyses and identified 1950 total matches mapping to 891 unique peptides, of which 506 (57%) were identified in 2 or more liver samples. Those 506 peptides correspond to 238 unique protein entries in the vervet ENSEMBL database. Gene set enrichment analysis revealed that this list of proteins was indeed overrepresented by structural, cytoskeletal, and ribonucleotide binding proteins. Table Though distinct differences in the peptides identified by the SSdb versus REFdb have been revealed by this comparative exercise, it is perhaps equally important to highlight that SSdb analyses are capable of matching nearly 98% of the same spectra as the REFdb. The customized databases created from RNA-Seq robustly identify proteins from tissue samples, identify peptide variation that would otherwise be missed by searching against the reference annotation, and perform these functions using smaller file sizes and faster compute times.This work represents the implementation and proof-of-concept application of an established RNA-Seq proteogenomic approach to improve the identification of proteins in non-human primate proteomics experiments, such as the vervet monkey liver sample analyses reported here. Several non-human primate genomes have been drafted but incompletely annotated because a limited number of animals have been sequenced. Less common sequence and splice variants will continue to be incorporated as DNA sequencing sample sizes increase and more complete transcriptional profiles across tissue types are reported. In the meantime, a proteogenomic approach should improve protein identification in MS experiments in these species, and may offer robust and reliable data to improve the existing annotation using both transcript and protein data. We demonstrated that this analysis approach, implemented in Galaxy-P, is capable of identifying peptides derived from unannotated splice junctions and non-synonymous coding substitutions revealed from the RNA-Seq read data. These peptides would have gone unmatched by searching the MS data using the vervet reference genome annotation data. These novel peptides likely represent a combination of common, yet previously unannotated gene isoforms as well as isoforms and variants private to individual animals studied.As with other “omics” scale analyses, the iterative search process of shotgun proteomics presents challenges in balancing type I and type II error rates. The optimal search database would incorporate only those potential proteins which are likely to be found within a given sample; however, if the repertoire of proteins were already known, it would obviate the need for conducting proteomics experiments in the first place. Instead, RNA-Seq data can facilitate a compromise in the database curation process by predicting non-reference protein isoforms for inclusion while also utilizing information about transcript abundance to exclude individual gene sequences from the search records as the corresponding mRNA are not expressed and therefore the translated protein (or a peptide thereof) is unlikely to be identified by MS. While RNA-Seq data can be a useful benchmark for restricting the size of the MS search database, it should be noted that certain exceptions to the database exclusion process should be considered. Examples include proteins whose tissue of origin is different from the sampled tissue, such as blood-derived albumin, immunoglobulins, and complement proteins, or long-lived structural proteins such as collagen or ribosomal proteins, whose protein abundance is uncoupled from their corresponding mRNA expression in the tissue of interest at the time of sample collection. By comparing peptide matches made using RNA-Seq derived databases to peptides identified using the reference database, we have revealed a list of proteins routinely found in vervet livers that do not have corresponding mRNA abundance levels from RNA-Seq read data. Including these protein records in the construction of sample-specific databases for liver samples could mitigate the loss of information in future proteomics experiments.The reduction of the search database size and the included protein records significantly impacts the confidence with which peptides and proteins are identified. Due to the smaller number of records to compare an experimental spectrum to, the confidence with which individual peptides are assigned to the correct record is higher. However, these “improved” matches have another consequence: some experimental peptide spectra may not be confidently assigned to a specific sequence in the REFdb analysis since the match may not fall under the stringent 1% FDR commonly required to confirm a match. However, using the smaller SSdb, the same match is now made with a FDR of less than 1% , resulting in some additional “reference” proteins being identified in the SSdb searches at a 1% FDR but not in the REFdb searches. It is possible that these matches may include some low confidence peptide matches, but overall these additional peptide matches emphasize the additional power that is gained from reducing the search database to only relevant records of expressed proteins and peptide sequences.Our analysis only used one standard established analysis approach for these comparisons. The analysis pipeline implemented in Galaxy includes the Morpheus search algorithm which empirically calculates the FDR for peptide matches obtained in the search. Numerous other approaches have been proposed for the analysis of proteogenomic data, and the assessment of FDR in peptide and protein identifications. It is likely that some of these approaches, such as analyzing FDR separately for REFdb matches and matches to novel SSdb records derived from RNA-Seq  or alterChlorocebus aethiops sabaeus), demonstrates that the use of sample-derived RNA-Seq data, as anticipated, improves peptide identification and the accuracy and confidence of protein identification, while simultaneously reducing the search database space and the resulting computing effort required for the data analysis. Novel peptides including sequence variants identified by RNA-Seq, as well as new splice variants uncovered in the transcriptional analysis account for the majority of the novel peptides identified, highlighting the importance of proteogenomic approaches in species with limited available genome sequence data and gene annotation, such as non-human primates.A proteogenomic approach to the analysis of liver shotgun proteomic data from a nonhuman primate species, the vervet/African green monkey Additional file 2:Listing the SAV and SJ derived peptides identified exclusively by the sample-specific databases. (XLSX 46 kb)Additional file 3:Listing the reference genome peptides identified exclusively by the sample-specific databases. (XLS 343 kb)Additional file 4:Listing the peptides identified by the REFdb but not the SSdb searches. (XLS 541 kb)