s2fieldsofstudy: Biology Medicine

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Widespread Molecular Patterns Associated with Drug Sensitivity in Breast Cancer Cell Lines, with Implications for Human Tumors

Background Recent landmark studies have profiled cancer cell lines for molecular features, along with measuring the corresponding growth inhibitory effects for specific drug compounds. These data present a tool for determining which subsets of human cancer might be more responsive to particular drugs. To this end, the NCI-DREAM-sponsored DREAM7: Drug Sensitivity Prediction Challenge (sub-challenge 1) set out to predict the sensitivities of 18 breast cancer cell lines to 31 previously untested compounds, on the basis of molecular profiling data and a training subset of cell lines. Methods and Results With 47 teams submitting blinded predictions, team Creighton scored third in terms of overall accuracy. Team Creighton's method was simple and straightforward, incorporated multiple expression data types (RNA-seq, gene array, RPPA), and incorporated all profiled features (not only the “best” predictive ones). As an extension of the approach, cell line data, from public datasets of expression profiling coupled with drug sensitivities (Barretina, Garnett, Heiser) were used to “predict” the drug sensitivities in human breast tumors (using data from The Cancer Genome Atlas). Drug sensitivity correlations within human breast tumors showed differences by expression-based subtype, with many associations in line with the expected (e.g. Lapatinib sensitivity in HER2-enriched cancers) and others inviting further study (e.g. relative resistance to PI3K inhibitors in basal-like cancers). Conclusions Molecular patterns associated with drug sensitivity are widespread, with potentially hundreds of genes that could be incorporated into making predictions, as well as offering biological clues as to the mechanisms involved. Applying the cell line patterns to human tumor data may help generate hypotheses on what tumor subsets might be more responsive to therapies, where multiple cell line datasets representing various drugs may be used, in order to assess consistency of patterns.

Introduction
Response to targeted therapy may vary from patient to patient, depending on the active pathways within the cancer being treated. These active pathways might be inferred, using the molecular profile of the cancer. As a step towards cataloguing molecular correlates of drug response, which might eventually yield markers for personalized therapy, recent studies have provided molecular profiling data (including gene expression and mutation) on large numbers of cancer cell lines (including ,60 breast cancer cell lines), along with measurements of growth inhibitory effects for specific drug compounds [1,2,3]. These data represent a valuable resource for the possible development of molecular ''signatures'' that might eventually be used to predict drug response in patients.
While data are available for deriving candidate predictive signatures of therapeutic response, there are a multitude of ways in which the data may be analyzed. With the goal of identifying analysis methodologies that may be applied here, the NCI-DREAM consortium (DREAM standing for ''Dialogue for Reverse Engineering Assessments and Methods'') recently sponsored a challenge (''sub-challenge 1'' of the DREAM7: Drug Sensitivity Prediction Challenge), for research teams to use molecular data to predict the sensitivity of breast cancer cell lines to previously untested compounds. The Challenge participants submitted their blinded bioinformatics-based predictions, which were then evaluated empirically against the measured results, to see which algorithms had the best performance. As stipulated by the organizers, NCI-DREAM Challenge participants were invited as collaborators in the main NCI-DREAM consortium paper [4], which highlighted the top performing method, while providing high level descriptions of the methods used by the other teams.
The purpose of this paper is to describe in more detail, what ended being the third best performing method in the NCI-DREAM challenge (out of 47 submissions in all). The method was rather simple and straightforward in its approach, and did not make much effort to select the ''best'' predictive molecular features from the data, but rather weighted all available features according to their correlations with drug response. In this paper, we also explore the potential of using this method to ''predict'' drug response in human breast tumors, making use of data from The Cancer Genome Atlas (TCGA), by which clear distinctions based on tumor subtype could be observed.

Basic approach
As part of the NCI-DREAM Challenge (''sub-challenge 1''), drug sensitivity measurements were made for 31 different drugs on 53 breast cancer cell lines. For 35 cell lines (the ''training set''), the drug sensitivity values were made available, along with molecular data from a variety of platforms, including mRNA expression by both sequencing (RNA-seq) and gene array, protein expression by Reverse Phase Protein Arrays (RPPA), DNA methylation arrays, exome sequencing, and SNP arrays. For 18 cell lines (the ''test set''), the drug sensitivity values were withheld from the Challenge participants. The identities of the drugs were also withheld until after submission. Figure 1 outlines the basic approach used by our NCI-DREAM Challenge Team #398 (Creighton), for predicting drug response based on molecular features. Of the molecular datasets provided for breast cancer cell lines, three were used: gene expression array, RNA-seq, and RPPA; the exome-seq and SNP array data were thought, perhaps, to be too sparse for the purposes of prediction, and DNA methylation data could optionally have been incorporated into our method but was not for the actual Challenge submission. Each dataset was first analyzed individually, in order to generate a set of predictions for the relative sensitivities across cell lines for a given drug; the resulting prediction scores from each platform were then averaged to obtain the final scores for submission.
The analytical method used was rather simple and straightforward, which to a degree may set it apart from other methods. For a given expression profile dataset, a matrix of correlations (by Pearson's) was constructed (across cell lines in the training set), between the drug sensitivities (2logged GI50 concentrations) and expression values. Within the matrix, each feature (e.g. gene or protein) would have a correlation value for each of the 31 drug compounds; a strong positive correlation would suggest that the feature might be a marker of sensitivity (e.g. ERBB2/GRB7 for lapatinib 2logGI50), and a strong negative correlation, a marker of resistance. In the prediction phase of the analysis, the Pearson's correlation was computed between each drug sensitivity profile (derived from the training cell lines) and each genomic profile (e.g. of gene/protein expression) of the test cell lines. A high correlation between drug sensitivity training profile and test sample genomic profile, would suggest that the test sample would be more sensitive to the drug (at least relatively speaking).
Notably, for each dataset, all features profiled were used in the scoring; in other words, there was no filtering or pre-selection for the ''best'' correlates or predictive features. Instead, all features were weighted by their correlation with drug senstivity, with features having little or no correlation pattern being weighted near zero. The assumption here was that the molecular patterns associated with drug sensitivity would be widespread, with perhaps on the order of hundreds of genes that could be used to drive the predictions.

Gene classifier significantly predicts drug sensitivity in blinded test
Despite the simplicity of its approach, the prediction method of Team Creighton performed well in the Challenge. Out of 47 teams that submitted predictions, Team Creighton ranked third overall in terms of aggregated predicion accuracy. Using the global wac-index [4], Team Creighton had a score of 0.570 (P = 2.06E-05), while the Challenge winner, ''teamfin'', had a score of 0.583 (with 0.5 being the expected score by chance). In addition to the aggregate score, the individual scores by compound could also be examined by team (Table 1). In addition to overall scoring, teamfin frequently performed well for individual drugs, while for a few drugs, team Creighton performed just slightly better over teamfin. Drugs where team Creighton performed notably less well, compared to teamfin, include Chloroquine, FTase inhibitor I, and Mebendazole.
By team Creighton's approach, predictions were made based on each individual molecular platform (gene array, RNA-seq, RPPA), then averaged for the final predictions. At the same time, the individual molecular platforms yielded predictions that were largely concordant with each other, and each platform-specific set of predictions performed similarly well as compared to chance ( Figure 2). This suggests that, regarding drug sensitivity, each molecular data type examined contains potential information; at the same time, averaging the three sets of results might be helpful, in order to offset any relative deficiencies as observed in one platform by the relative strengths in another.

Widespread molecular correlations with drug response in cell lines
Our successful prediction results would suggest that the molecular patterns underlying drug response may be widespread. While our actual Challenge submission used all available features, here we considered whether the predictive accuracies might have been improved, by using only those features having the strongest correlations (Table 2). Interestingly, based on analysis of the gene array dataset, predictive accuracies were just slightly worse (though still significant), when using the more restrictive feature sets. It would seem (at least using our method) that the potential improvement that might be had, by using only the top predictive Figure 1. Schematic of the basic approach used to predict drug sensitivies. In the training phase of the analysis (using cell lines in the ''training'' set), the expression of each gene is correlated with the drug sensitivity measurements (e.g. GI50s). For the given drug, the associated molecular correlation profile of sensitivity is then correlated with each of the expression profiles from samples in the ''test'' set (e.g. from the NCI-DREAM test cell lines, or from human tumors in TCGA). The resulting prediction scores provide a relative measure for inferring drug sensitivity. doi:10.1371/journal.pone.0071158.g001 Table 1. Concordance indices by drug for the top five performing teams. features, could be outweighed by the detriment of losing information from the rest of the features that may miss an arbitrary statistical cutoff. In addition, the RPPA dataset in pariticular had only 131 protein features, and using rigorous cutpoints there would leave us few features for assessing correlation patterns. In addition to its potential for prediction of drug response, the molecular data may also provide biological clues as to the mechanisms of drug response, in terms of the individual drug-tofeature correlations. For example, using the NCI-DREAM RPPA data, combined with the drug sensitivity measurements by Heiser et al. that corresponded to the cell lines [2], the top correlations between proteins and drug sensitivity could be visualized as a twodimensional matrix (Figure 3). A clustering of the correlations indicated that there were at least two major groups of proteins and or drugs, in terms of overall similarity or dissimilarity of patterns; Figure 2. For the NCI-DREAM cell lines, drug response correlation patterns are concordant across different molecular platforms. For the Challenge submission by Team #398 (Creighton), the gene array, RNA-seq, and RPPA datasets were first analyzed separately, and the resulting prediction scores from each platform were then averaged to obtain the final scores for submission. Heat maps show correlations (by Spearman's) between drug sensitivity predictions for any two platforms (RNA-seq vs RPPA, gene array vs RPPA, RNA-seq vs gene array). Table shows overall prediction accuracy score (c-index), for predictions based on either an example random model (with many iterations expected to average around 0.5), gene array data alone, RNA-seq data alone, RPPA data alone, or the average of all three platforms. (The official Challege submission had training scores ordered by GI50, as prescribed by the Challenge organizers, while this figure has training scores ordered by molecular correlation-based predictions.) doi:10.1371/journal.pone.0071158.g002 from what can be inferred from the markers and drugs involved, these groups should roughly coincide with the differences between estrogen receptor-positive (ER+) and ER2 cell lines. The matrix visualization of marker-drug correlations highlighted a number of interesting associations (many of which could have been anticipated), one of the most prominent being that between expression of Her2 protein and sensitivity to HER2-inhibitors such as Lapatinib or BIBW2992. Also, EGFR expression correlated with sensitivity to Gefitinib and Erlotinib. ER and progesterone receptor (PR) proteins did correlate with Tamoxifen sensitivity, but interestingly, this was not one of the stronger correlations observed (perhaps due in part to the relative underrepresentation of ER+ versus ER2 breast cancer cell lines).

Drug sensitivity correlations within human breast tumors
Ultimately, we would like to be able to predict which subsets of human cancers may be responsive to therapy. In this sense, the ''testing'' phase of our basic approach (Figure 1) could involve data from human tumors as well as cell lines. To this end, breast cancer cell line data, from public datasets of expression profiling coupled with drug sensitivities-from Barretina et al. [1], Garnett et al. [3], and Heiser et al. [2]-were used as training data to ''predict'' the drug sensitivities in human breast tumors. The human data here were from The Cancer Genome Atlas [5], which provides comprehensive molecular profiles for over 500 tumors, at data levels that include mRNA, protein (by RPPA assay), microRNA, gene promoter methylation, DNA copy, and somatic mutation.
For each drug represented in the cell line dataset, the correlations with gene or protein expression were projected onto Figure 3. Global correlations between proteins and drug sensitivity measurement (using NCI-DREAM RPPA dataset and GI50 data from Heiser et al. [2]). The subset of protein represented here, are those having correlations with significance of P,0.01 for at least two compounds. Rows, proteins; columns, drugs. Blue, correlation with drug sensitivity; red, correlation with resistance. doi:10.1371/journal.pone.0071158.g003 the human tumor dataset, yielding a matrix of drugXtumor sensitivity correlations. These sensitivity correlations could be viewed as a heat map representation, for each cell line dataset that was used to derive the molecular feature correlates (Figure 4). For the Heiser dataset, both mRNA data and RPPA data were used separately to generate correlation patterns.
As might be expected, based on previous analyses of the cell line data [2], drug sensitivity correlations within human breast tumors showed clear differences by expression-based subtype (e.g. luminal A, luminal B, Her2-enriched, basal-like, and normal-like [5]). A number of these subtype-specific associations appeared to be in line with what might have been expected. For example, the Her2- Figure 4. Drug sensitivity correlation patterns within human breast tumors. Three different datasets of cancer cell lines were examined (Barretina, Garnett, and Heiser), each dataset consisting of both molecular profiles and drug sensitivity measurements. For each cell line dataset, molecular correlation profiles of drug sensitivity were generated and applied to a separate profile dataset of human breast tumors (from TCGA). For each heat map, rows represent drugs, and columns represent human tumors (ordered by mRNA expression-based subtype); blue, correlation with drug sensitivity pattern; red, correlation with resistance pattern. For Barretina, all drugs with available data are shown, while for Garnett and Heiser, drugs shown include Tamoxifen, Her2 inhibitors, and chemotherapeutic agents represented in multiple studies. For Heiser, both mRNA data and RPPA data were used separately to generate correlation patterns (gray, no data). doi:10.1371/journal.pone.0071158.g004 enriched subtype showed sensitivity to anti-Her2 agents (e.g. Lapatinib and BIBW2992), the luminal subtype showed sensitivity to Tamoxifen, and the basal-like subtype showed sensitivity to chemotherapeutic agents [6]. Overall, the separate results sets using data from the three different cell line studies were concordant, though individual descrepancies could be seen as well (e.g. Docetaxol associations between Garnett and Heiser), which illustrates the utility of using multiple data sources in order to identify patterns of consensus.

Basal-like tumors show patterns of resistance to PI3K inhibitors
While many of the drug sensitivity associations, as observed in human breast tumors, could be verified using prior knowledge, other associations could invite further consideration and study. For example, when focusing on the subset of drugs that target the PI3K pathway, basal-like tumors, in general, showed patterns of resistance to PI3K inhibitors ( Figure 5). The consensus pattern was evident when considering not just multiple independent datasets, but multiple drugs thought to target the same pathway. There were some notable exceptions to the overall pattern, such as the drug BEZ235 showing higher sensitivity in the basal-like group, as has previously been observed in the cell lines [2]. Interestingly, previous analyses of TCGA data, using gene and protein signatures of pathway activation, had indicated that the PI3K pathway was most active in the basal-like group. Genetic and genomic alterations in the PI3K pathway were previously found in all breast cancer subtypes, with basal-like cancers having more loss of pathway inhibitors PTEN and INPP4B, and with luminal A cancers having more mutations in PIK3CA.

Discussion
We may conclude that cancer cells have a molecular signal indicative of potential drug response, based in part on the results of the NCI-DREAM Drug Sensitivity Prediction Challenge, where multiple analytical approaches, from various independent research teams, could yield significant accuracies of prediction. Notably, the Challenge used a rather small sample set, and even more robust patterns would likely emerge as more data on additional cell lines are considered. More data would also be needed, in order to assess whether the level of precision such molecular-based predictions can offer, would be useful for guiding treatment decisions for specific patients. One caveat of these types of analyses is that the predicted sensitivity rankings are relative, and in the case of patients, translating the predictions into recommended therapeutic doses may be difficult in some cases.
Given the good performance of our method in the Challenge results, we can give some consideration to the patterns associated with drug sensitivity in the human breast tumors. We find that these patterns segregate tumors by expression-based subtype, which should not be suprising, given the observed differences in drug sensitivities in the cell lines by their subtype [2], as well as the widespread molecular patterns that define the tumor subtypes [5]. These drug sensitivity-associated patterns may be helpful in generating hypotheses, of what patient subgroups might best respond to given therapy, though careful evaluation of all available data (including any clinical trials data) would be needed to better establish any associations.
Multiple cell line datasets representing various drugs that target similar pathway may be considered, in order to assess consistency of patterns as observed in human tumors. In particular, the observation, of basal-like breast tumors showing patterns of resistance to PI3K inhibitors, is intriguing, given that these tumors were found previously to show elevated PI3K signaling [5,7]. One might expect, given our previous experiences with targeting ER and Her2, that those tumors having greater activity for the pathway would be more sensitive to therapies blocking that pathway; however, it is conceivable that not all pathways may follow this model of oncogene addition. Figuratively speaking, it may be, in some cases, that more water is needed to put out a bigger fire. More data, and more analysis and integration of the existing molecular datasets (from both cell lines and human tumors), are needed, regarding the overall question of drug response; however, this present study, coupled with the results of the NCI-DREAM Challenge, might be considered to represent a step forward.

Prediction of drug sensitivity in breast cancer cell lines
Prediction of drug sensitivity in breast cancer cell lines was carried out as part of the NCI-DREAM Drug Sensitivity Prediction Challenge [4], using the datasets provided by the Challenge organizers (including Growth Inhibitor, or GI, measurements, gene expression array, gene-level RNA-seq, and RPPA proteomic datasets). Methods are described elsewhere (NCI-DREAM manuscript in preparation). Briefly, for each dataset, features were first logged and centered on the median across samples. For a given dataset and the known GI values, a matrix of correlations (by Pearson's) was constructed (across cell lines in the training set) between negative logged GI concentration values and expression values. Using this matrix of [gene X drug] sensitivity correlations, the Pearson's correlation was then computed between each drug sensitivity profile and each genomic profile (e.g. of gene/protein expression) of the test cell lines, a high positive correlation for a given cell line denoting relatively higher drug sensitivity. Sensitivity predictions made using the three individual datasets were then averaged, in order to derive the final scores for submission. Example calculations in Excel, using the RPPA dataset, are available in Data S1. The ''weighted average probablistic c-index,'' used to assess the overall accuracy of a given set of predictions, is elsewhere described [4] and was computed here, using code provided by the Challenge organizers.

Prediction of drug sensitivity in human breast tumors
Prediction of drug sensitivity in human tumors was carried out using public datasets [1,2] [3] [5], using a very similar approach used for the Challenge submission as described above. The sets of tumors analyzed in TCGA cohort were the same as those analyzed previously as part of the initial data freeze [5], having either mRNA data or data on multiple platforms (mRNA, mutation, and RPPA data), as indicated. For TCGA datasets (gene array and RPPA), gene features were normalized across samples to standard deviations from median. Using a given cell line expression dataset (Barretina mRNA, Garnett mRNA, Heiser mRNA, NCI-DREAM RPPA), Pearson's correlation was computed between the expression of each gene and log (GI50 or IC50) across the cell lines; each TCGA breast tumor expression profile (either gene array or RPPA) was then correlated with each (cell line-derived) drug response profile. For Barretina and Garnett datasets, only the data from breast cancer cell lines were used in the analysis. For the ''Heiser RPPA'' correlations, the NCI-DREAM RPPA dataset was projected onto the Heiser drug sensitivity measurements. Java TreeView [8] represented matrix patterns as color maps. For PI3K pathway, transcriptomic and proteomic signatures of activity were previously applied to TCGA data [5,9].


extfieldsofstudy:
Computer Science
Medicine

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A unified framework for the organization of the primate auditory cortex

In non-human primates a scheme for the organization of the auditory cortex is frequently used to localize auditory processes. The scheme allows a common basis for comparison of functional organization across non-human primate species. However, although a body of functional and structural data in non-human primates supports an accepted scheme of nearly a dozen neighboring functional areas, can this scheme be directly applied to humans? Attempts to expand the scheme of auditory cortical fields in humans have been severely hampered by a recent controversy about the organization of tonotopic maps in humans, centered on two different models with radically different organization. We point out observations that reconcile the previous models and suggest a distinct model in which the human cortical organization is much more like that of other primates. This unified framework allows a more robust and detailed comparison of auditory cortex organization across primate species including humans.

INTRODUCTION
One of the oldest and best characterized organizational features in the auditory system is its cochleotopic or tonotopic organization. Tonotopy is the ordered representation of sound frequency in auditory areas. It has been shown at all levels of the auditory pathway including the cochlea, the auditory brainstem nuclei, and the auditory cortex in at least mammals and birds. In the cortex of non-human primates, multiple areas can be defined neurophysiologically by gradients of neuronal sound frequency preference and by reversals of the frequency gradient between neighboring auditory cortical areas (Hackett et al., 1998;Kaas and Hackett, 2000;Hackett, 2011). More recently, non-invasive imaging using functional MRI (fMRI) has defined mirror-symmetric tonotopic gradients that allow division of the auditory cortex of non-human primates (Petkov et al., 2006;Baumann et al., 2010;Tanji et al., 2010) and humans (for a recent review see: Woods and Alain, 2009) into distinct areas.
Given the broadly conserved role of tonotopy in the auditory system of mammals, it is surprising that in all of the investigated species the organization of tonotopy in the human auditory cortex is one of the least understood. This is in spite of several decades of neuroimaging studies and efforts to understand the human auditory cortical organization. Moreover, even after a recent resurgence in human tonotopic studies we seem to be getting further from an agreed model of human auditory cortical organization. Although a considerable body of anatomical data appear to support an organization of the primary auditory (core) areas along the length of Heschl's gyrus (HG; Brodmann, 1909;von Economo and Koskinas, 1925;von Economo and Horn, 1930;Galaburda and Sanides, 1980;Rivier and Clarke, 1997;Morosan et al., 2001Morosan et al., , 2005Wallace et al., 2002), for which early studies of tonotopy appeared to provide support (Ojemann, 1983;Lauter et al., 1985;Howard et al., 1996;Wessinger et al., 1997;Bilecen et al., 1998;Lockwood et al., 1999;Talavage et al., 2000;Schonwiesner et al., 2002;Formisano et al., 2003), recent studies of tonotopy can be interpreted in terms of a fundamentally different organization of these core areas (Humphries et al., 2010;Woods et al., 2010;Da Costa et al., 2011;Striem-Amit et al., 2011;Langers and van Dijk, 2012). In this, the mirror-symmetric gradients in the auditory core is effectively perpendicular to the one suggested by anatomical and early functional studies. This has led to a fundamental reappraisal of the homology between non-human and human auditory areas.
Here, we critically examine studies of non-human and human primates that led to the traditional view of tonotopic field organization in the human auditory cortex. This view is based on a tonotopic axis that runs along the HG, the most characteristic anatomical feature in the human auditory cortex. We contrast this view with more recent interpretations based on a tonotopic axis that runs perpendicular to the HG. Taking all the current evidence from human but also non-human primates in account, we propose a unifying interpretation. This reconciles the apparently conflicting evidence supporting both previous views, and emphasizes the striking similarity of the human tonotopic maps to those in non-human primates.

TONOTOPIC ORGANIZATION OF CORTICAL FIELDS IN NON-HUMAN PRIMATES
As in other mammals, electrophysiological studies in non-human primates have established tonotopically organized areas at multiple levels of the auditory pathway including the inferior colliculus (IC) (Ryan and Miller, 1978;Zwiers et al., 2004), the medial geniculate body (MGB) (Gross et al., 1974), and the auditory cortex (Merzenich and Brugge, 1973;Morel et al., 1993;Kosaki et al., 1997;Rauschecker et al., 1997;Bendor and Wang, 2008). More recently, the existence and detailed organization of tonotopic fields has been confirmed by fMRI in the monkey IC (Baumann et al., 2011) and auditory cortex (Petkov et al., 2006;Baumann et al., 2010;Tanji et al., 2010). Cytoarchitectonic mapping, histochemical-and anterograde staining studies in the primate auditory cortex have established a concentric organization of auditory core areas that receive input mainly from an auditory pathway via the central nucleus of the IC (ICc) and the ventral portion of the MGB (MGv) and surrounding belt areas that receive input mainly from a distinct auditory pathway via the dorsal and lateral cortices of the IC and the dorsal MGB (MGd) [reviewed in Hackett (2007b)]. An influential organizational scheme (Hackett et al., 1998;Kaas and Hackett, 2000;Hackett, 2011) combined anatomical and functional data and subdivided core and belt areas into 2-3 core fields (primary-like fields) and 7-8 belt fields based on reversals of the tonotopic gradients running along a largely anterior-posterior axis (Figure 1). The scheme suggests that gradient reversals to mark functional area borders are a fundamental feature of cortical sensory organization analog to the visual cortex system where retinotopic gradient reversals mark functional area borders (Gattass et al., 1988;Sereno et al., 1995).
The tonotopic gradients are most obvious in the core fields A1 and R. In the macaque, the high to low frequency gradient of A1 starts typically in the midline of the posterior superior temporal plane and runs antero-laterally to the cusp of the circular sulcus FIGURE 1 | Functional field organization of the monkey auditory cortex superimposed over the frequency response areas and anatomical features of the macaque superior temporal plane. Left: a sagittal view of the macaque brain (top) with the superior temporal plane (dark gray shadings) exposed by cutting out the operculum (bottom). Right: within the superior temporal plane, the core area, highlighted with bold stroke, is surrounded by belt fields. Anatomical features are designated by light gray color shade. Main gradient directions from low to high of the frequency response areas are indicated with green arrows left of the scheme. CS, circular sulcus. Auditory field labeling according to Hackett et al. (Hackett et al., 1998;Hackett, 2011). (Merzenich and Brugge, 1973). A slight protuberance in the superior temporal plane at the cusp of the circular sulcus is a consistent anatomical predictor of the location of the low frequency reversal (Baumann et al., 2010). From there, the reversed low-tohigh tonotopic gradient of R typically turns slightly inward and covers the slope into the circular sulcus and part of its depth. Consequently, the tonotopic axis of the two main core fields A1 and R is not straight but features a considerable kink (Morel et al., 1993;Kosaki et al., 1997;Kaas and Hackett, 2000;Baumann et al., 2010), which has also been seen in marmosets (Bendor and Wang, 2008). A number of monkey studies (Morel and Kaas, 1992;Petkov et al., 2006;Bendor and Wang, 2008;Baumann et al., 2010;Tanji et al., 2010) report a further gradient reversal in the depth of the circular sulcus, an area that is designated RT in the Hacket and Kaas scheme (Hackett et al., 1998). However, there has been inconsistency in the earlier data on which way the tonotopic gradient would run in RT (Morel and Kaas, 1992). More recent monkey fMRI studies, however, consistently identify RT with a mirror reversed gradient in relation to the more posterior field R (Petkov et al., 2006;Baumann et al., 2010;Tanji et al., 2010). A microelectrocorticographic study in macaques showed that mirror-symmetric tonotopic gradients in the fields A1, R, and RT are even detectable in the spatial covariation of spontaneous activity in the absence of stimuli (Fukushima et al., 2012). Histochemical markers for core areas such as parvalbumin, myelin, and acetylcholinesterase density show values for RT that are somewhat between the typical values for auditory core and auditory belt areas (Hackett et al., 1998), and it features a cell packing density closer to belt areas (Hackett et al., 2001), thus it is also referred to as "core-like" area rather than core proper (Hackett et al., 2001).
The tonotopic gradients of the core fields extend into the adjacent belt fields (Rauschecker et al., 1995;Kosaki et al., 1997). Thus, the reversals of the tonotopic gradients can provide the anterior/posterior borders between the belt fields similar to the core (Hackett et al., 1998) (Figure 1). For instance, monkey fMRI studies (Petkov et al., 2006;Baumann et al., 2010) have delineated most of the belt fields including some for which there was only structural evidence. Nevertheless, the tonotopic gradients in the belt areas tend to be less robust than the core areas since the belt fields respond weakly to tones and responses to band-passed noise can elicit responses in a larger area making the determination of borders more difficult. While tonotopic arrangements beyond the belt fields (e.g., in the adjacent parabelt) requires further investigation, the tonotopic organization of the auditory core and belt fields in non-human primates is generally uncontroversial to date.

ORGANIZATION OF THE HUMAN AUDITORY CORTEX-THE CLASSICAL CONFIGURATION
Before the advent of non-invasive imaging methods, functional studies from the human auditory cortex were limited [see Ojemann (1983) and Howard et al. (1996)]. For most of the 20th century post-mortem anatomical studies provided the only clues on the organization of the human auditory cortex. Beginning with Brodmann (1909), a number of studies mapped cytoarchitectonically distinctive fields in the human auditory cortex (von Economo and Koskinas, 1925;Beck, 1928;  Horn, 1930; Galaburda and Sanides, 1980;Rivier and Clarke, 1997;Morosan et al., 2001Morosan et al., , 2005Wallace et al., 2002) [reviewed in Hackett (2007a), Figure 2]. The different studies vary in the nomenclature and the number of identified auditory fields. However, the different studies share several findings. All show one (or several adjacent) fields in the central part of HG, which are called koniocortex because of a well-developed layer 4 characteristic of primary sensory cortex. The koniocortex is similar in structure to the core areas of the non-human primate in the scheme above (Hackett et al., 1998) (Figure 1). This core region is surrounded by areas that are closer in terms of cytoarchitechtonics to the belt areas of other mammals. Hence, cytoarchitecture suggests an auditory cortex organization in humans that is similar to the non-human primate core and belt model. In this overall organization the studies mainly differ in the extent of HG that the core is covering. In the majority of studies the core is restricted to the central portion of the HG and the most medial and the most lateral portion is attributed to the belt (von Economo and Koskinas, 1925;von Economo and Horn, 1930;Galaburda and Sanides, 1980;Rivier and Clarke, 1997;Morosan et al., 2001Morosan et al., , 2005. However, in some of the studies (Brodmann, 1909;Beck, 1928) the core also extends to the most medial part of the HG or even encompasses the entire HG (Brodmann, 1909). Of particular relevance to the recent controversy about the location and direction of the main axis of the tonotopic areas are the anterior and posterior borders of the auditory core areas. While most newer studies based on cytoarchitecture (Beck, 1928;Galaburda and Sanides, 1980;Rivier and Clarke, 1997;Morosan et al., 2001) suggest core areas that are within the anterior and posterior limits of HG, the core areas in Brodmann (1909), von Economo and Koskinas (1925), and von Economo and Horn (1930) are more extensive and stretch clearly beyond the HG to part of the planum temporale (PT) situated immediately behind. Although these cytoarchitectonic studies showed differences in the precise borders of the primary or core area with respect to HG [this is emphasized in a further multi-subject study ], HG became established early on as an easy-to-identify marker for the location of the core areas in humans. Thus, when non-invasive neuroimaging methods allowed for the first time detailed functional investigations in the human auditory cortex, the direction of the tonotopic gradient along the HG seemed to be the main question that needed to be resolved. There was little expectation that the main tonotopic axis would not be in HG. The similarity of the elongated shape of HG with the elongated (but differently orientated) non-human primate core area likely provided another strong bias.
Early Positron Emission Tomography (PET) and fMRI did not provide the resolution and power for detailed tonotopic maps in the auditory cortex. However, a number of PET and fMRI studies consistently demonstrated significantly activated clusters or voxels responding to high frequency tones in the vicinity of the medial HG and to low frequency tones in the lateral HG (Lauter et al., 1985;Wessinger et al., 1997;Bilecen et al., 1998;Lockwood et al., 1999;Talavage et al., 2000). Given the previous anatomical evidence for a primary auditory area in the HG and the fact that homologous areas in other mammals are tonotopically organized, the common interpretation of these early imaging studies was that the HG contained a single tonotopic gradient progressing in antero-lateral direction from high frequencies in the medial HG along the gyrus to low frequencies in the lateral portion. This tonotopy configuration in humans was, with minor modifications, widely accepted. We will call this model here the "classical configuration" of human auditory cortex organization.
In 2002 some fMRI workers (Schonwiesner et al., 2002) already questioned whether human tonotopic organization might be more complex than the classical configuration suggested, despite finding very similar activation patterns to the previous imaging studies. In contrast to previous studies a range of tones at

Frontiers in Systems Neuroscience
www.frontiersin.org April 2013 | Volume 7 | Article 11 | 3 frequencies from 0.25 to 8 kHz were presented in this work. The derived response pattern did not form a simple and continuous gradient along the HG that could be easily compared to the primate model, nor could the response pattern be reconciled with the multiple mirror-symmetric tonotopy gradients from the data in non-human primates. A year later a study performed at high magnetic field with high resolution (seven Tesla MRI fieldstrength) (Formisano et al., 2003) seemed to answer all these questions. This high resolution dataset was derived from six subjects presented with six tones of different frequency, which was analyzed using flat-mapping techniques and a gradient analysis similar to those used in visual system (Van Essen and Zeki, 1978;Sereno et al., 1995). This study also represented the three dimensional curvature of the auditory cortex in a plane from rendered brains. The results were detailed, planar maps covering the central part of the auditory cortex with several highfrequency clusters that progressed in a continuous gradient into low-frequency areas. In one subject ( Figure 3 in the report) the direct line between two high frequency maxima was approximately located in the medial two thirds of the HG and, by crossing a low-frequency maxima, marked two mirror-symmetric gradients (high-low-high) roughly in the direction of the long axis of the HG. Further subjects ( Figure 5 in report) showed a similar although slightly more complex pattern without such precise alignment of the tonotopic axis along the length of HG. These results were not only compelling because they strikingly differed in their detail and completeness from previous studies but they also fulfilled the expectation of a monkey-like, mirrorsymmetric tonotopic gradient reversal along HG representing hA1 and hR, potential human homologs of the monkey core areas A1 and R. Furthermore, this interpretation allowed room for a third gradient reversal to be detected representing the area RT in the lateral HG. Hence this study provided an updated "classical configuration" that still featured a tonotopic axis along the HG but accommodated all the core fields known from nonhuman primates with at least one gradient reversal instead of single tontopic gradient (see Figure 3A, left). Although a further detailed study in (Talavage et al., 2004) suggested a more complex arrangement including a number of tonotopic axes that differed in location and direction from those of Formisano et al. (2003), this classical configuration remained widely accepted until recently.

REORIENTING THE GRADIENT AXIS IN HUMANS-THE "PERPENDICULAR CONFIGURATION"
The classical tonotopy configuration was first fundamentally challenged by Humphries et al. (2010). This study used flat mapping of the fMRI responses to multiple narrowband stimuli, as in Formisano et al. (2003) and Talavage et al. (2004). However, Humphries et al. (2010) proposed two principal mirrorsymmetric tonotopic gradients with an axis running in a posterolateral to antero-medial direction formed by high frequency areas posterior to HG (in the PT) and anterior to HG separated by a low frequency area on the crest of the HG. It was suggested that the posterior gradient represented a homologous field to A1 in non-human primates whilst the anterior gradient represented a homologous field to R. According to this proposal, a human homolog of the monkey core area would cross the HG perpendicularly in antero-medial orientation ( Figure 3A, middle). Thus, we call the proposed configuration the "perpendicular configuration." Compared to the previous classical configuration with a tonotopic axis along HG this new model essentially proposes an orthogonal tonotopic axis direction.
The perpendicular configuration of tonotopy stands not only in fundamental contrast to the classical configuration but it also deviates considerably from anatomical studies suggesting that koniocortex co-localizes with HG (Figure 2). The study of Humphries et al. (2010) has been followed by several tonotopy studies (Woods et al., 2010;Da Costa et al., 2011;Striem-Amit et al., 2011;Langers and van Dijk, 2012) in an effort to settle the question of tonotopic field arrangement. A number of methodological refinements have yielded maps of considerable detail and clear frequency specificity with a continuous progression between high-and low-frequency fields within the superior temporal plane. All of these studies showed at least two mirrorsymmetric gradients in the center of the auditory cortex. They also suggested a tonotopic axis that crossed the HG either perpendicularly (Striem-Amit et al., 2011) Formisano et al.'s (2003) suggestion of a gradient along HG. Furthermore, since these new studies agreed with Humphries et al. (2010) in the assignment of the posterior gradient to an A1 homolog and the anterior gradient to an R homolog, they are similarly at odds with the previous cytoarchitectonic studies which suggested an auditory core area co-localized with the HG. All the tonotopy studies (Woods et al., 2010;Da Costa et al., 2011;Striem-Amit et al., 2011;Langers and van Dijk, 2012) since Humphries et al. (2010) suggested tonotopic gradients or corresponding core fields that extend significantly posterior to HG into the PT and anterior to HG into the first transversal sulcus (FTS). How can the different interpretations of these functional imaging studies be reconciled with each other, and with the data from anatomical studies?

A UNIFIED MODEL OF PRIMATE AUDITORY CORTEX REPRESENTATION: WHAT CAN WE LEARN FROM THE MONKEY?
Irrespective of different experimental details and despite the variety of interpretations of gradient directions, the recent studies (Formisano et al., 2003;Humphries et al., 2010;Woods et al., 2010;Da Costa et al., 2011;Striem-Amit et al., 2011;Langers and van Dijk, 2012) show a surprisingly consistent pattern of preferred frequency response areas. If we focus on the large scale Frontiers in Systems Neuroscience www.frontiersin.org April 2013 | Volume 7 | Article 11 | 4

FIGURE 3 | Debated configurations of auditory cortical organization in humans and non-human primate configuration for comparison. (A)
The two main configurations of auditory core fields under debate (left, middle) in comparison with the "oblique" configuration proposed by the authors (right). The main frequency response areas based on the summary of recent evidence (Formisano et al., 2003;Humphries et al., 2010;Woods et al., 2010;Da Costa et al., 2011;Striem-Amit et al., 2011;Langers and van Dijk, 2012)  pattern of preferred frequency responses, dominant high frequency areas posterior and anterior to HG become obvious with a dominant low frequency area in the HG as originally suggested by Humphries et al. (2010). However, these preferred frequency areas do not simply form parallel stripes of alternating high and low frequency preference zones nor is the HG necessarily aligned with these stripes as Humphries et al. (2010) implied. Rather, we find that the posterior and anterior high frequency areas are in closer proximity to each other on the medial side than on the lateral side, almost joining in some subjects on the medial HG. This "V" or "U" shaped pattern formed by the high frequency areas provides space for a roughly wedge-shaped low frequency area on the lateral HG (Figure 3A, right). Although the macrostructure of the HG does not strictly separate high and low Frontiers in Systems Neuroscience www.frontiersin.org April 2013 | Volume 7 | Article 11 | 5 frequency areas, it is nevertheless predictably positioned relative to the low-frequency region. Apart from the low frequency area on the lateral HG [or between HG1 and HG2 if a duplication occurs (Da Costa et al., 2011)], the location and orientation of the anterior high frequency area is reliably predicted by the first transverse sulcus that confines HG on its anterior side. Finally, the posterior high-frequency area consistently extends on the lateral side beyond the posterior bank of HG into the PT (even in the case of a HG duplication) but often overlaps with the HG on the medial side. This common frequency response pattern with a predictable relationship to the macro-anatomy (illustrated in Figure 3) is particularly obvious in the group data of the different studies. It is not surprising that the inter-individual variability of the tonotopic pattern is higher in humans than in non-human primates given the more pronounced and variable gyrification in humans. Adaptation to speech is a further possible driving force to functional reorganization and variability. Nevertheless, the general aspects of this pattern are well discernible in both hemispheres even at the individual subject level.
Retrospectively, the frequency response pattern outlined based on recent studies is also consistent with the results from Formisano et al. (2003) and even the preceding imaging studies. While the V-shape pattern is probably most obvious in Figure  5B of Formisano et al. (2003), Figure 3 of the current article illustrates how a high-low-high gradient in the general direction of HG suggested by Formisano et al. (2003) is also achievable in most variations of the common pattern if the gradients are positioned selectively into the frequency response pattern. Furthermore, as has been pointed out before (Langers and van Dijk, 2012), most of the studies preceding Formisano et al. (2003) showed a tendency for medial high frequency clusters to be situated just anterior to HG (in the first temporal sulcus) or posterior (in Heschl's sulcus) and low frequency areas to be on lateral crest of the HG. This is precisely what the pattern in Figure 3 predicts.
Concluding that there is a frequency response pattern that accommodates the previous functional data well, how are the tonotopic gradients positioned within this framework? And more importantly, how does the scheme of auditory fields that is well established in other primates fit into the human functional organization? While most human tonotopy studies emphasize the similarities of their gradient and field model to the non-human primate scheme, the range of different interpretations [with the extremes of Formisano et al. (2003) and Humphries et al. (2010)] suggest that a clear concept of this scheme, and how it can be applied to the human homolog, is lacking. Based on our experience in generating tonotopic maps in macaques in the context of the macro-anatomy of the individual animal, we will highlight a few often overlooked features of the non-human primate auditory cortex that help in understanding the relationship of functional and structural anatomy in humans and monkeys. These comparative insights highlight how surprisingly close the human tonotopy pattern is to that in non-human primates in most aspects, providing further justification for the attempt to find a similar field organization framework in humans that has proven so useful in other primates.
The first feature we look at is the direction of the tonotopic axis defining fields A1 and R in monkeys. In contrast to the most frequent interpretation in the human tonotopy studies this axis shows neither a simple posterior-anterior direction nor are the two respective gradients strictly collinear. As outlined above, non-human primates demonstrate gradients that progress from medial high frequency maxima to a more laterally located lowfrequency maximum leading to a kink in the tonotopic axes. As we can see in Figure 3B, human and non-human primates share this feature and reveal a very similar frequency response pattern. The second aspect we want to highlight is the relationship of macro-anatomy and functional organization common to human and non-human primates. At the center of the human tonotopy debate is the orientation of the tonotopic gradients and the auditory core fields with respect to HG. A common understanding is that monkeys [in contrast to apes (Hackett et al., 2001)] do not feature a gyrus homologous to HG but rather a mostly flat superior temporal plane. This is a simplification at best in the case of the macaque. Even though this species does not feature proper gyrii, we consistently find a protuberance that separates the depth of the circular sulcus from the posterior planum. In addition to the anterior limitation of the circular sulcus this protuberance is in some cases additionally limited by a further groove (Intercalated sulcus) in the posterior planum forming a minigyrus (Annectant gyrus) which is located in the center of the auditory core (Jones et al., 1995) (Figure 3B). We postulate that the circular and intercalated sulci define a precursor to human HG. As discussed above this HG-like protuberance is a good predictor for the low frequency response maximum, a feature that it shares with the human HG ( Figure 3B). Given these homologies in functional pattern and anatomical features it should be straightforward to identify the human homologs of the auditory core fields in the human superior temporal plane. The human homolog of A1 stretches from the posterior high frequency area in the vicinity of the medial HG in antero-lateral direction to the low frequency area on the crest of the HG (Figure 3). There it borders the human homolog of R which stretches from the crest into the depth of the first transverse sulcus in the same way the monkey R extends into the circular sulcus (the FTS is essentially an extension of the human circular sulcus). In conclusion, we find that the HG is a good predictor of the approximate location of the human core area but, in contrast to the classical and the perpendicular configuration, the core's orientation is in a rather oblique relationship to this prominent anatomical feature. Furthermore, the human scheme matches the non-human primate scheme in frequency response pattern, global gradient orientation and even in the relative position of the macro-anatomical features. Thus, we call this alternative model the "oblique configuration." While this proposed model, similar to the perpendicular configuration, suggests that the human core area extends beyond the banks of the HG, this is limited to relatively small areas postero-medial and antero-lateral to the HG. Thus, the resulting pattern is very close to the results of the earlier cytoarchitectonic studies (Brodmann, 1909;von Economo and Koskinas, 1925;von Economo and Horn, 1930) and, given the individual variability in humans, it is only subtly different from and not necessarily irreconcilable with a core area confined to the middle portion of HG as suggested by other cytoarchitecture studies (Beck, 1928;Galaburda and Sanides, 1980;Rivier and Clarke, 1997;Morosan et al., 2001); see also ( Figure 2). Once the location of the auditory core fields is defined within the human tonotopic pattern, it is also straightforward to predict the human homologs of the belt fields by using the tonotopic reversals analogous to those that define the non-human primate scheme (Figure 3B, right). Furthermore, a number of the human tonotopy studies reported some evidence for additional low frequency reversals anterior and posterior of the high frequency areas of the common tonotopy pattern. The core-like field RT, caudal belt areas CM, and CL can be estimated with this additional information (Figure 3B). Taken together, an entire scheme of auditory functional fields in the superior temporal plane is derived that, as has been outlined above, shows a remarkable similarity to the situation in non-human primates in functional (frequency response) and anatomical terms. This similarity in the orientation of auditory fields and gradient axes across primates seems to us biologically more plausible than human core areas that deviate considerably in orientation from monkey homolog as suggested by the classical configuration and to some degree by the perpendicular configuration. Given that the perisilvian areas and superior temporal sulcus seem to share many response features across primates, a reorientation of auditory core and belt in respect to its immediate cortical environment would lead to a discontinuity at its borders that would be difficult to explain in evolutionary terms.

OUTSTANDING ISSUES
The evidence above based on tonotopy suggests a clear organization of human core areas within the auditory cortex. There is an immediate need to reconcile models in which core extends beyond HG and cytoarchitectonics or staining studies in which the core is largely confined to HG. This effort is hampered by intersubject variability and the absence of studies of tonotopy and anatomy in the same subjects. Also potential alternative approaches to functionally delineate core-belt borders have not yet been calibrated to the anatomical definitions of core areas because the traditional staining techniques can only be applied to post-mortem brains. New methods to allow "in vivo cytoarchitechtonics" in human and non-human primates have the potential to achieve this. For example, (quantitative) T1 mapping (Bock et al., 2011;Glasser and Van Essen, 2011) seems particularly promising to us because it allows the definition of more heavily myelinated core cortex, a feature that was first mapped in the anatomical studies of Flechsig (1920).
Further work is required to better establish the organization of areas beyond the core, possibly by applying novel stimuli that drive belt areas better than the narrowband stimuli used for tonotopic mapping. In general, remaining differences in frequency response pattern and gradient locations across the cortex might be addressed by agreeing on standard practices for data co-registration (including flat-mapping) and gradient quantification.
The goal of this work is a robust scheme for the definition of functional areas in humans that might in future properly justify the application of primate nomenclature to human studies and allow the development of better-defined primate models for human auditory cognition. Here we suggested a unified primate model of core and belt fields which provides testable hypotheses for future functional and anatomical comparative studies in primates.


extfieldsofstudy: Biology Medicine

text:
Analysis of Virion-Incorporated Host Proteins Required for Herpes Simplex Virus Type 1 Infection through a RNA Interference Screen

Viruses are strictly dependent on cells to propagate and many incorporate host proteins in their viral particles, but the significance of this incorporation is poorly understood. Recently, we performed the first comprehensive characterization of the mature herpes simplex virus type 1 (HSV-1) in which up to 49 distinct cellular proteins were identified by mass spectrometry. In the present study, we sought to identify if these cellular factors are relevant for the HSV-1 life cycle. To this end, we performed a small interfering RNA functional screen and found that 15 of these host proteins altered HSV-1 proliferation in cell culture, without any significant effect on cell viability. Moreover, the siRNA used had no negative consequences for Adenovirus type 5 propagation (with one exception) indicating that the modulation was specific for HSV-1 and not merely due to unhealthy cells. The positive host proteins include several Rab GTPases and other intracellular transport components as well as proteins involved in signal transduction, gene regulation and immunity. Remarkably, in most cases when virions were depleted for one of the above proteins, they replicated more poorly in subsequent infections in wild type cells. This highlights for the first time that both the cellular and virion-associated pools of many of these proteins actively contribute to viral propagation. Altogether, these findings underscore the power and biological relevance of combining proteomics and RNA interference to identify novel host-pathogen interactions.

Introduction
Herpes simplex virus type 1 (HSV-1) virions are composed of a DNA core within an icosahedral capsid surrounded by a heterogeneous and poorly characterized layer of proteins called tegument, which is itself wrapped in an envelope. Many of the tegument components are critical at an early stage of the infection. For example, the binding of incoming viral capsids to microtubules and their transport to the nucleus are dependent on components of the tegument, including the viral proteins UL36 and UL37 [1,2,3,4]. Furthermore, the incoming virion host shut off protein (Vhs; UL41) quickly down regulates the expression of several host proteins following viral entry [5,6] while VP16, also a tegument protein, regulates the impending cascade of viral gene expression [7]. Interestingly, two other transactivators, namely ICP0 and ICP4, have also been reported in the viral tegument and may play an early role upon entry of the incoming virus [8]. In principle, the incorporation of these molecules should be beneficial to the virus to facilitate the next round of infection.
The importance and complexity of the HSV-1 tegument is illustrated by a recent mass spectrometry study of highly purified extracellular virions, which revealed they contain 23 potential viral teguments and up to 49 distinct cellular proteins [9]. This analysis showed that roughly half of the host proteins found in HSV-1 virions are proteins that had not yet been reported in any herpesviruses. In contrast, the presence of members of the annexin and heat shock protein families as well as cyclophilin A, DDX3X and components of the cytoskeleton have been documented in other Herpesviridae [9,10,11,12,13,14,15,16,17,18,19,20], suggesting a common function for these proteins. Moreover, host proteins have been documented in numerous other viral particles, including human immunodeficiency virions (HIV [21,22,23]), influenza [24], vesicular stomatitis virus [25] and vaccinia [26]. However their biological relevance is, at best, unclear.
The incorporation of host proteins within mature viral particles presumably benefits the virus and may for example jump-start its replication cycle. In contrast, viruses also avoid the incorporation of proteins that can inhibit their replication. One such case is the HIV protein Vif that binds to the host APOBEC3G protein to prevent its inclusion into nascent virions and prevent deamination of the viral genome by this RNA editing protein [21]. It is thus likely that the incorporation of host proteins in viruses is not random but rather a regulated process. Unfortunately, few of these studies have examined the relevance of this phenomenon in the course of an infection [23,26,27,28,29].
In the present study, we aimed to identify amidst the 49 host proteins found in HSV-1 mature particles those that influence its replication and proliferation. To this end, we designed and validated a functional screening assay using small interfering RNAs (siRNA). We now report that 15 of these proteins have a significant impact on HSV-1 propagation in cell culture, with limited siRNA-associated toxicity or effect on the propagation of another double-stranded DNA virus, the human Adenovirus type 5. Positive hits include proteins involved in vesicular transport, gene regulation, signaling and immunity. Furthermore, we show functional evidence that the incorporation of most of these proteins within mature virions is biologically relevant for HSV-1 infectivity.

Inhibition of HSV-1 life cycle by drugs
143B cells were seeded overnight in a 24-well plate at a 2.5610 5 cells/well density. Cells were then mock treated or infected at a multiplicity of infection (MOI) of 5 with HSV-1 K26GFP. After adsorption, cells were washed twice in PBS and fed with complete DMEM containing MG132 (25 mM, Calbiochem), phosphonoacetic acid (PAA; 400 mg/ml; Sigma-Aldrich) or Brefeldin A (BFA; 5 mg/ml; Sigma-Aldrich). In the case of cells treated with MG132, the drug was first added 15 minutes prior to infection and during adsorption in order to prevent the transport of capsids to the nucleus. The infection proceeded for 24 hours at which time supernatants were collected and viral output measured as below (see HSV siRNA screen).
HSV siRNA screen siGENOME SMARTpool and select ON-TARGETplus siR-NAs targeting the human proteins previously identified in mature extracellular virions [9] were purchased from Dharmacon (Table 1; Thermo Fisher Scientific). The siRNA targeting the HSV-1 protein VP16 was used as a positive control [33] and a scrambled sequence of the VP16 siRNA was used as a negative control. Twenty-four hours prior to transfection, 143B cells were seeded in 24-well plates at a concentration of 5610 4 cells/well. siRNA transfections were then performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions, using 25 or 100 nM/well of siRNA diluted in Opti-MEM or only Opti-MEM as control. The siRNA treated cells were incubated for 5 hours before the medium was replaced with complete DMEM and incubated for 43 more hours for a total of 48 hours of transfection. At that point, cells were mock treated or infected with HSV-1 K26GFP at a MOI of 5. After a one hour adsorption period, cells were washed twice in phosphate-buffered saline (PBS), complete DMEM was added to the wells and the cells were incubated for a further 24 hours. Cells and supernatants were then collected. The viruses in supernatants were inactivated using formaldehyde (Sigma), concentrated 2 hours at 180006 g and the resulting viral pellets resuspended in 100 ml of PBS pH 8. They were then transferred into a 96-well black bottom mClear plate (Greiner Bio-One) and their fluorescence was measured using a Gemini EM spectrofluorometer and SoftMax Pro 4 (Molecular Devices). Alternatively, the infectious virions were titrated on Vero cells as described before [34]. Each experiment was performed in triplicates and repeated three times.
Adenovirus siRNA screen 143B cells transfected for 48 hours with Lipofectamine alone or 25 nM siRNA as described above were mock treated or infected with the Adenovirus pIX-DE3 EGFP at an equal MOI for 48 hours. Supernatant were collected and viral DNA was purified using the GenElute Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich) according to the manufacturer's instructions. qPCR was performed with a Rotor-Gene 6000 (Corbett) using the FastStart SYBR Green Master (Roche Diagnostics). For the amplification, the primers pairs specific for EGFP were used: forward, 59 ACG TAA ACG GCC ACA AGT TC 39; reverse, 59 AAG TCG TGC TGC TTC ATG TG 39. The data are expressed in relative genome copy compared to the Lipofectamine-treated control and were pooled from three independent experiments. For these experiments, siRNA targeting VP16 served as negative control whereas a siRNA targeting the Adenoviral hexon protein [35] was used as positive control.

Viability assays
143B cells were mock treated with Lipofectamine alone or transfected with siRNA for 44 hours. Cells were washed twice and the medium replaced. alamarBlue (AbD Serotec) was subsequently added to the cells or to DMEM medium as a control according to the manufacturer's instruction. The cells were further incubated in accordance with the manufacturers' instructions before viability was measured by spectrofluorometry.

Two-steps infection assays
143B cells were transfected for 48 hours as in the siRNA screen or with Lipofectamine only. Cells were then infected for a further 24 hours with HSV-1 K26GFP at a MOI of 5. Supernatants, containing viruses depleted of individual host proteins, were collected and titrated on Vero cells. Fresh 143B cells were either mock treated or transfected with the siRNA for 48 hours. They were then infected with the depleted virions at an MOI of 0.1 for 48 hours. The supernatants form this second round of transfection/infection were collected and titrated once again on Vero cells.

Western blotting
43B cells were transfected for 48 hours with Lipofectamine alone or siRNAs then were mock treated or infected with K26GFP at a MOI of 5 for 24 hours. After a centrifugation of 5 min at 10006g, the cells were collected, washed in PBS and lysed by 3 cycles of rapid freeze-thaw. Infected cell's supernatants were concentrated for 2 hours at 18 0006 g and resuspended in 10 ml of PBS. Unless specified otherwise, equal amount of proteins were analyzed by SDS-PAGE [9] [12]. Antibodies against the viral proteins VP5 and VP16 or the human DDX3X, ARF1, eIF4H and c-tubulin proteins were used to blot the respective proteins. When indicated, protein expression levels were quantified using ImageJ (version 1.45b).

Statistical analysis
Fluorescence and virus titers were normalized to the values obtained for the controls as indicated in the figure legends. Bilateral Student T tests were performed using GraphPad Prism 5 (GraphPad Software).

Validation of the siRNA-based assay
Our strategy to first examine the physiological relevance of the host proteins identified in mature HSV-1 virions involved a novel viral propagation assay based on RNA interference and HSV-1 K26GFP, a wild-type virus in which the Aequorea victoria green fluorescent protein (GFP) is fused to the capsid protein VP26 [30] ( Figure 1A). This approach enabled us to easily and rapidly measure viral output and to quantitatively screen many targets without resorting to the classical but time-consuming and cumbersome plaque assays. We selected a human cell line for this screen because it is the HSV-1 natural reservoir, it is compatible with our previous proteomics report [9] and a human siRNA library is commercially available. We opted for the human osteosarcoma-derived 143B cell line since it is more resistant to the cytopathic effects of the virus and produces significantly greater quantities of extracellular viruses upon infection than the HeLa cells originally used in our proteomic study ( [34,36] and data not shown). In addition, 143B cells have a greater than 80% siRNA transfection rate (data not shown). Cell plating density, infection conditions, harvesting time, assay buffers, plate format and parameters of the plate reader software were all extensively optimized (data not shown) to ensure that quantification of the virus from the supernatant was accurate, linear and sufficiently sensitive to detect extracellular virions ( Figure 1B).
We next sought to validate that the assay could indeed detect the impact of known inhibitors of the HSV-1 life cycle. We therefore pretreated cells with MG132, an inhibitor of the proteasome that perturbs the post-entry delivery of HSV-1 to the nucleus [37], phosphonoacetic acid (PAA) which prevents viral replication [38] and brefeldin A (BFA) which arrests viral egress of newly synthesized viral particles [39,40]. As expected, HSV-1 output was drastically lower in drug-treated cells than in untreated ones ( Figure 2A). As a second control, cells were transfected with siRNA targeting the HSV-1 protein VP16 (UL48), since its inhibition by siRNA is known to efficiently reduce VP16 expression and viral production [33]. Cells were thus transfected for 48 hours prior to infection with the single most effective siRNA targeting VP16 [33] or with Lipofectamine only. A scrambled sequence of the VP16 siRNA was used as negative control (scVP16) Since the scVP16 siRNA does not have any homology to any human or viral sequence as determined by blast (data not shown), it also served as a non-targeting control. The knockdown of VP16 expression was assessed by Western blotting and quantified with ImageJ, which revealed the high efficacy of the siRNA employed (up to 88% inhibition; Figure 2B). Though the inhibition of VP16 was not absolute, the extracellular viral yields were significantly lower upon treatment with siRNA against VP16 than for cells treated with the transfection agent only or the scrambled control ( Figure 2C). Taken together these results demonstrate the sensitivity and specificity of the assay.

Assessment of host proteins critical for the viral infection
To evaluate the functional relevance of the cellular proteins identified in mature extracellular virions, we examined them using the above siRNA screen. Since some of the peptides that were identified in the course of the proteomic study are common to different protein isoforms, we screened them together but also individually, thus lengthening our original list of proteins to 55 (Table 1). Furthermore, we added two additional specificity controls that were not in our initial proteomics study. They are the adaptor proteins (AP1 to AP4) and GGA proteins (1 to 3), all molecules regulating protein transport and relevant in the view of the many transport molecules reported here. The siRNA targeting VP16 and its scrambled version (scVP16) were once again used as controls. For each of the human targets identified in our proteomic study [9], we used a pool of four different siRNA duplexes to ensure the most effective knockdowns. These siRNA pools are commercially available and predefined by the manufacturer according to their proprietary algorithm (see Table 1 for catalogue numbers). Cells were transfected in triplicates with these siRNA for 48 hours, to allow endogenous levels of the host proteins to significantly decrease, and subsequently infected with GFP tagged HSV-1. Twenty-four hours later, viral yields were measured. Though both the intracellular and extracellular viral pools could be quantified, we opted to only probe the latter since free VP26-GFP is inevitably present in infected cells and would contribute to a higher background. Each experiment was normalized to the mean fluorescence obtained in supernatants from cells transfected with the transfection agent only and results from three independent screens were compiled. This method allowed us to identify several cellular proteins whose absence statistically altered viral production (p,0.05 or better; Figure 3). To evaluate the toxicity of the siRNA reagents and rule out nonspecific effects, the viability of the cells was monitored in parallel using the redox indicator alamarBlue, which quantitatively monitors cell metabolism and, consequently, overall cell viability (Figure 3, dots). Among the proteins that affected viral output, only the inhibition of ubiquitin C resulted in high cytotoxicity under our experimental conditions and was not pursued further. It is relevant to note that targeting some of the proteins with siRNA resulted in lower cellular viability without any impact on viral production (e.g. KRT1 and RAB5C), further highlighting the genuine implication of the other cellular proteins on the HSV-1 viral life cycle. Given that the GFP-based assay used in this study detects total viral particles rather than infectious virions, single positive isoforms were confirmed by conventional plaque assays to measure viral titers following siRNA treatment. 143B cells were transfected with siRNA, infected as before and the infectious particles in the supernatant were titrated on Vero cells. As expected, infected cells treated with the scrambled control produced similar levels of extracellular infectious particles as the Lipofectamine-treated sample. Moreover, all the siRNA tested lead to a statistically significant reduction in viral yields and were thus fully coherent with the GFP detection assay (Figure 4). Since Rab4B inhibition reduced viral yields by less than 25%, it was therefore excluded from further analysis. Taken together, these data validated the GFP-based readouts and show that the individual depletion of host proteins incorporated in mature HSV-1 virions impacts both total and infectious particle yields.
Thus far, our data support the potential involvement of several host proteins in the HSV-1 life cycle. To evaluate if these cellular proteins specifically affect HSV-1 propagation rather than nonspecifically perturb cell metabolism, 143B cells were transfected with siRNAs targeting the positive hits and infected with human Adenovirus type 5, another double-stranded DNA virus that replicates in the nucleus. In this experiment, the siRNA targeting the HSV-1 protein VP16 served as negative control whereas a siRNA targeting the hexon protein [35] was used as positive control. As expected, siRNA targeting the adenoviral hexon reduced viral yields (68% inhibition) while siRNA targeting VP16 did not ( Figure 5). Interestingly, only Arf1, Rab6A and Rab10 Figure 1. Screening method. A) 143B cells were seeded in 24-well plates 24 hours prior to transfection. Cells were then transfected with siRNA and incubated 48 hours before being infected with HSV-1 K26GFP for an additional 24 hours. Supernatants were collected and their fluorescence was measured using a Gemini EM spectrofluorometer. As a cytotoxicity control, cell viability was assessed in parallel using alamarBlue 2 days post-transfection. In addition, the cells were lysed and used for Westerns to validate the siRNA knockdowns. B) The fluorescence of pre-titered infectious HSV-1 K26GFP particles serially diluted in PBS was quantified using a spectrofluorometer. The fluorescence obtained was linear with the titers through the entire selection (r 2 = 0.9968), thus demonstrating the advanced sensitivity of the device. The gray area denotes the typical of values obtained in the RNA interference screen below. RFU: relative fluorescence units. doi:10.1371/journal.pone.0053276.g001 statistically influenced Adenovirus proliferation. This strongly suggests that the positive hits found in our screen specifically modulated HSV-1 proliferation and supports the conclusion that the effects observed were not due to some general cellular defects.

Importance of the proteins present in the mature virions
Several viruses have been reported to incorporate cellular proteins but the significance of this incorporation is often unclear. There are various reasons why cellular proteins could be incorporated in virions. Some of them may simply be incorporated in virions because they are abundant and/or happen to be at the right place and time but are not valuable to the virus. A second possibility is they may be included as a consequence of their function during viral assembly, intracellular transport or egress. In such cases, the proteins would not necessarily be important to initiate the next round of infection. Finally, some proteins may specifically be required to promote the infection of neighboring cells. In this exciting scenario, the depletion of the cellular proteins from the viral particles should stop or delay the next replication cycle, much the same way VP16 is incorporated into mature virions to act early during the infection as transactivator of immediate early genes [7]. We thus wished to directly address the functional significance of the virion-associated host proteins.
Since the virion-incorporated host proteins are present in both mature virions and the cell, the respective contribution of both of these pools to the viral life cycle was assessed. To this end, a twostep approach was employed. In the first step, viruses individually depleted for each of the positive targets identified above were produced on siRNA-treated cells. As controls, we also produced virions on Lipofectamine-treated cells or cells treated with VP16 or scVP16 siRNAs. The efficacy of the depletion was then examined by Western blotting for four of the proteins. As shown in figure 6, the siRNA strongly reduced the amount of the proteins in extracellular virions . It was thus possible to produce stocks of virions depleted on a given host protein. These viruses were then titrated and used to infect a new monolayer of cells (second step; Figure 7A). To reduce the risk of complementation between depleted virions and wild type virions produced by cells that may not have taken up the siRNA, a low MOI of 0.1 was employed. To sort out the importance of the viral and cellular pools of the proteins, all four combinations of wild type/depleted virions and wild type/depleted cells were probed. As expected, the infection of VP16 siRNA-treated cells with wild type virus harvested from Lipofectamine-treated cells strongly reduced viral yields, in accordance with the idea that newly expressed VP16 does regulate the viral life cycle ( Figure 7B). Though not as extensive, the infection of Lipofectamine-treated cells with VP16-depleted virions also significantly reduced viral production, consistent with past reports that the virion associated pool of VP16 is actively involved in the early stages of the infection process [7]. Moreover, the depletion of both newly expressed and virion-associated VP16 (i.e. infection of VP16 siRNA-treated cells with VP16-depleted virions) further reduced viral output compared to the virion-only depleted sample, hence reiterating the importance of both newly expressed and virion-associated VP16 pools during the HSV-1 infection. Interestingly, the individual depletion from the mature virions of 13 of the 15 target tested statistically reduced viral production in Lipofectamine-treated cells, 8 of which did so by at least 50% (DDX3X, HSPA8, KRT10, MIF, RAB5A, RAB6A, RAB10 and YWHAZ ( Figure 7B). One notable case was Arf1, for which the cellular pool clearly participated in greater proportion to viral propagation, despite the significant contribution of the viral pool of this protein. Given the above findings occurred in the presence of a normal complement of host proteins, this reempha-  sized the specific involvement of these proteins in HSV-1 replication (Figures 3 and 4) rather than an indirect metabolic defect. Altogether, this directly demonstrates for the first time that both the cellular and the virion-incorporated pools of these proteins do indeed participate in the HSV-1 replication cycle.

Discussion
Viruses are obligatory intracellular parasites that depend on many cellular functions to complete their life cycle. These interactions are complex and often poorly understood. While the contribution of host proteins to HSV-1 has been partially elucidated, genomic screens with other viruses suggest this may only be the tip of the iceberg [41,42,43,44,45]. Recently, we reported the detection of up to 49 distinct cellular proteins within mature extracellular HSV-1 virions [9], adding an additional layer of complexity to this scenario. Since some of these proteins may play pivotal roles during the viral life cycle, they constitute prime targets to identify and characterize novel host-pathogen interactions in the context of an HSV-1 infection.
The siRNA assay reported in this study is sensitive, rapid, linear and correlates with classical plaque assays (Figures 1 to 4). The data obtained by both fluorometry and plaque assays suggest that at least 15 of the cellular proteins tested are involved in the HSV-1 replication cycle in cell culture (Figures 3 and 4). Moreover, the incorporation into mature extracellular virions of most of these proteins seems necessary to ensure an optimal new round of infection ( Figure 7). It should be noted that overexpression of several of these proteins did not influence viral yields, presumably because they are not rate limiting in the cell (data not shown). These results are unlikely due to side effects of the depletion of the host proteins within the cells or to off-targeting effects of the siRNA. Indeed, first of all, only limited cytotoxicity was noted under our experimental conditions, which is consistent with other genomic screens [44,46]. Secondly, even when siRNA were somewhat toxic (e.g. KRT1 and Rab5C), this did not cause a reduction of viral yields. Only the inhibition of ubiquitin C, which did perturb HSV-1 output, correlated with high siRNA-associated cytotoxicity and was therefore not considered further, though it may still be relevant for the HSV-1 replication cycle ( Figure 3). Third, we could single out specific protein isoforms that affect viral proliferation, while other isoforms of the same proteins had no impact. For instance, inhibition of Arf1 significantly reduced viral production while the inhibition of Arf3, 4 or 5 failed to do so, hinting at the specificity of the assay and the lack of pleiotropic effect of the siRNAs (Figure 3). Forth, the same siRNA, with the sole exception of Arf1, Rab6A and Rab10, did not significantly impact the replication of Adenovirus type 5 ( Figure 5). In that case, it remains to be seen if these proteins are essential to viral growth under our experimental conditions or if both HSV-1 and Ad5 propagation depends on these host proteins. Finally, depleting these host proteins from the virions, but not the cell, still had an impact on viral yields (Figure 7). We therefore conclude that these molecules are most likely involved in the viral life cycle and that our observations cannot be credited to some pleiotropic effect.
It is important to note that the actual number of proteins regulating the HSV life cycle in the original RNA interference screen is likely higher than reported in the present study. Missed targets could include cellular proteins with long half-lives unperturbed by the siRNA, proteins with more subtle impacts on the virus as well as proteins that may be complemented by their virion-associated counterpart or by other cellular proteins. Consistent with this latter scenario, it is worth noting that depletion of either the cellular and virion pools of the proteins often affected viral propagation. Furthermore, the screen only considers host proteins involved in HSV-1 replication in tissue culture and may exclude some virulence factors and immune or latency-related proteins. Finally, the screen initially measured GFP output and quantified total viral particle release rather than infectious virions as determined by plaque assays. It is thus possible that some siRNA reduced the production of infectious viruses without affecting the total amount of viral particles, as noted when VP16 is inactivated [47]. Despite these limitations, the positive rate from this study is very high at 31% (i.e. 15/49). For comparison, genome-wide siRNA screens for HIV, influenza and West Nile virus have positive rates below 1.5% [41,42,43,44,45,48]. The data thus constitute a strong validation of the relevance of our strategy to unmask novel cellular players by combining proteomics [9] and a targeted siRNA screen (this study). Furthermore, this study and those of others [5,33,49,50,51,52] highlight the potency of siRNA as a useful tool to study HSV-1 host-pathogen interactions, despite of the ability of the virus to downregulate RNA-induced gene silencing [53].
The current GFP-based assay is less sensitive than traditional plaque assays but is a good complementary approach to screen large numbers of targets. While it is clear that the reductions are not as spectacular as when some viral genes are deleted, important differences have to be considered when analyzing the impact of host proteins instead of viral proteins. For instance, host proteins are often complemented by another gene copy, another isoform, a parallel pathway or other means, which implies that smaller effects upon depletion should not be surprising. This is even true for some HSV-1 genes, for which multiple deletions are required to see a significant phenotype. Moreover, cellular proteins finely exert their actions with impacts typically in the lower fold range (2-4 folds) rather than logs (10-100 folds). In agreement with this view, a study by Hill and colleagues [54] showed that HSV-1 itself modulates the expression of 3,123 host proteins by only 1.4 to 14.2 folds, changes that are presumably beneficial for the virus. Our results are also in the same range (i.e. less than a log difference) observed by others in recent studies analyzing the impact of cellular proteins on HSV or HIV proliferation Figure 8. Cellular functions of virion-incorporated host proteins. Several of the positive hits of our functional screen are implicated in pathways that are most likely relevant for the virus. While all of them were found in HSV-1 mature viral particles, many of these proteins (in green) have also been found incorporated into other herpesviruses, suggesting a common role for these proteins throughout the family [12,13,14,17]. doi:10.1371/journal.pone.0053276.g008 [55,56,57,58,59,60,61]. Multi-log inhibitions where therefore not expected in the present study but it does naturally make it more difficult to ascribe a functional role to present host proteins. However, the significant, albeit moderate, impact of depleting the host proteins in the virions themselves, while leaving the cellular pools intact, strongly supports their contribution to the HSV-1 replication cycle. The present data suggest that many of the tested proteins are indeed subtle modulators of viral production. The positive hits found in this study identified several potential players in the HSV-1 life cycle. Many of these proteins are involved in pathways that are most likely used in the infection process ( Figure 8). Among them, several Rab GTPases and other intracellular transport components as well as proteins involved in signal transduction were identified, some of which have already been proven necessary for viral production. For instance, it has recently been shown that Rab6A is necessary for the cellular trafficking of HCMV viral protein pp150 and efficient virus assembly [57], while Rab11 modulates the intracellular transport of influenza genome to the plasma membrane [62]. Similarly, Rab1 and Rab43 have been implicated in HSV-1 secondary envelopment [55]. It is thus highly possible that some of the proteins identified in the present study are involved in the numerous transport events occurring in the course of HSV-1 infection. The present screen is therefore very useful to pursue novel host-pathogen interactions in the context of HSV-1 infections. Moreover, 6 of the proteins; 14-3-3 f/d (YWHAZ), Arf1 (ARF1), DDX3X, eIF4H, keratin 10 (KRT10) and CD59 have previously been found in HCMV, KSHV and/or PRV viral particles in addition to HSV-1 [10,12,13,14,17], suggesting that they might have a common roles throughout the herpesvirus family.
Many studies have reported the incorporation of host proteins within mature virions but very few have addressed the significance of the incorporated fraction of cellular proteins in viral infectivity. To verify the importance of the pool of host proteins present in the extracellular virion, we depleted these proteins from virions by RNA interference and used them to re-infect untreated cells. This method enabled us to identify for the first time 8 proteins whose absence from the virions reduced viral production by at least 50%, with evidence of contributions by another 5 host proteins (Figure 7). This highlights the very close relationship between the virus and its host. In contrast, the depletion of some host proteins (i.e. Rab11A and YWHAG) in the virions caused no significant impact on viral production. It may be relevant to highlight that the present study was performed in 143B cells and it is likely that the pool of host proteins incorporated in mature virions or their roles varies among cell types. Though difficult to perform, a comprehensive study of host proteins in virions produced on neuronal cells would be particularly interesting.
The present study highlights the very close relationship between HSV-1 and its host. However, the full extent of host-pathogen interactions between HSV-1 and the cell remains elusive at the moment. Our approach constitutes an innovative and powerful strategy to identify potential novel cellular proteins that modulate the HSV-1 life cycle. The characterization of their functions at the molecular level is therefore of the upmost interest. Since all of these proteins have previously been detected in mature extracellular HSV-1 virions [9], they constitute a new class of proteins to study as potential therapeutic targets.