Plants emission of Volatile Organic Compounds (VOCs) is involved in a wide class of ecological functions, as VOCs play a crucial role in plants interactions with biotic and abiotic factors. Accordingly, they vary widely across species and underpin differences in ecological strategy. In this paper, VOCs spontaneously emitted by 109 plant species (belonging to 56 different families) have been qualitatively and quantitatively analysed in order to provide an alternative classification of plants species. In particular, by using bipartite networks methodology from Complex Network Theory, and through the application of community detection algorithms, we show that is possible to classify species according to chemical classes such as terpenes and sulfur compounds. Such complex network analysis allows to uncover hidden plants relationships related to their evolutionary and adaptation to the environment story. Indeed, such “secondary” metabolites2 are the result of different plants responses, through the course of evolution, to specific needs. Among such metabolites, volatile organic compounds (VOCs) play a dominant role3. Being released by almost any kind of tissues5 and type of vegetation  as green leaf volatiles, nitrogen-containing compounds and aromatic compounds, plants VOCs can be emitted constitutively7 or in response to a variety of stimuli. They are involved in a wide class of ecological functions, as a consequence of the interactions of plants with biotic and abiotic factors8. Plants use VOCs to perform a variety of tasks, as different as: indirect plant defence against insects9; pollinators attraction10; plant-to-plant communication12; thermo-tolerance and environmental stress adaptation7; defence from predators13.Plants produce an amazing variety of metabolites but only a few of these are involved in “primary” metabolic pathways, that is to say common to all organisms. Others (indicated as “secondary” metabolites) are instead characteristic of smaller plants groups4O), acetone (C3H6O), acetaldehyde (C2H4O), methyl-ethyl-ketone  and methyl-vinyl-ketone 14; in few cases, sulfur compounds  and furanocoumarins and their derivatives  are also found16.According to their biosynthetic origin and chemical structure, plant volatiles can be grouped into isoprenoids or terpenoids, but also oxygenated VOCs (OVOCs), such as methanol 27, similarly metabolic networks are used to study the biochemical reactions which take place into living cells28. Furthermore, biological networks found important applications in medicine29, where they are applied as a solution to human diseases comorbidity analyses30, or to study the structural and functional aspects of human brain, by defining the reciprocal interactions of the cerebral areas31. With respect to the above activities, the application of Complex Networks Theory in botany is still scarce, apart some tentatives of comparing different ecosystems looking for steady  behaviours32. Recent applications of graph theory in botany deal with the attempt of assessing plants species similarities on the basis of both their diaspore morphological properties, and fruit-typology ecological traits33. Following the same approach, in this paper we perform network analyses with the goal of identifying communities of “similar” species, starting from their VOCs, a proxy of the different ways in which different species react to external stimuli.In this paper we apply complex networks analyses22.To this purpose, we arrange data in bipartite graphs, a method particularly suitable to study the relations between two different classes of objects and to group individuals according to the properties they share. In a bipartite graph vertices can be divided into two disjoint sets, such that every vertex of one set is connected only with a vertex of the other set. In this way no links are present between vertices of the same set. In our case, the plants species and their VOCs define the two independent sets of vertices built from botanical data. Bipartite graphs are then analysed by considering the two different projections of vertices of the same class. In this way we have a first graph by considering all plants species (as vertices) connected on the basis of the emitted VOCs and a second graph made up by VOCs connected if the two are produced by the same plants. On thee two graphs we perform a community detection to create a taxonomic treeThe present research work focuses on a group of 75 VOCs emitted by 109 different plant species in basal conditions, in order to understand if taxonomy-related plants emit a similar VOC composition. To assure the analysis to be robust and consistent, we measured the VOCs emitted by each plant species by a three times replication experiment . Complex networks analysis is then applied to the VOCs dataset represented as bipartite network, in order to easily define metrics and hidden statistical properties able to discriminate and classify plant taxonomy based on VOCs patterns.Asteraceae (8.26%), Solanaceae (6.42%), Rosaceae (6.42%), Fabaceae (5.5%), Brassicaceae (4.59%), and Polygonaceae (3.67%). All the other families are present at lower percentages.The 109 plants species analysed are representative of 56 families, and the dataset is quite homogeneous in terms of families percentages. The most copious families are: To evaluate the data statistical structure we plotted for each protonated mass the emission recorded for all the 109 plants species. Figure 35 . Outliers are shown in Fig. From a statistical point of view the same result was confirmed by the presence of outliers inside each record, that can be easily visualised by boxplot methodologyNotwithstanding the clear dominating behaviour of some species emissions with respect to the other plants, for a given VOC (i.e. the outliers described in the previous paragraph), the statistical procedure that takes into account just the highest recorded values among the outliers  turned out to be too restrictive. Actually, not even a small emission of a protonated mass can be neglected from an experimental point of view. A low emission can also be a signal from a wounded leaf, and it has to be evaluated when comparing the species reciprocal behaviour with respect to an external wounding perturbation.In order to investigate the relationships among plants according to their VOCs emissions, we consider bipartite graphs properties which turn out to be particularly suitable to solve communities detection issues. The final goal of our analysis is to cluster plants in a relatively small number of statistically stable communities, each one made up by those plants emitting the same VOCs and to see if this reveals hidden properties of the species analysed. Standard clustering methodologies have been taken into account as a quantitative guide for a more critical analysis. In the binary analysis all species have been considered irrespective of the strength of their emissions. At the same time, all VOCs were equally taken into account during the analysis of the emerging plants network structure. A detailed description of the applied methodology can be found in the section “Materials and methods”.ncps) and we considered significant all the emissions larger than it ). In the second case, we applied a more severe criterion, and we decided to take into account just the emissions above the third quartile G2). Figure To represent in a suitable way the connections and relationships between the available plants species starting from our experimental data, we considered two different ways of building the plants network, depending on the statistical measure used to represent the highly not-Gaussian behaviour of the series. In the first case, we set a fixed threshold for the signal intensity  and G2, respectively as indicated above), made up by V = 184 vertices and divided into two layers (or set of vertices): the first category of vertices is made up by the VP = 109 plants species and the second one is composed by the VPM = 75 emitted VOCs. Intuitively, as the name suggests, a bipartite graph is composed by qualitatively two different sets of vertices. The first layer is made up by the individuals/entities , and the second one is composed by the properties they share . In this way, by connecting each vertex of the first set to all its properties in the second set, we can create a 2-layer-network. Plants-to-plants species networks are subsequently defined by considering as vertices the plant species in the database. From a mathematical point of view, that approach translates into computing the bipartite projection of both G1 and G2. Two vertices are connected if they share at least one common property, in other words, two plants are connected in the network, if they emit almost the same amount of a specific VOC. For every network, we considered size (number of edges), order (number of vertices), degree (average and its distribution), density , clustering and finally the community structure.According to the case we create two distinct bipartite graphs . In the resulting graph ncps. V1 = VP = 109 vertices (plant species), and E1 = 5,886 edges. Species i and species j are linked if they share at least one common emitted protonated mass. The weight wij of each link eij is given by the total number of shared VOCs between species i and species j. The plants graph corresponding to the first method was created as a bipartite projection of graph wij = 24, in agreement with the dense structure of the network.That network structure is poorly able to extract information about the dominant behaviour of one species with respect to the others, in terms of their emissions. Concerning the links weights distribution, the maximum number of protonated masses shared by two species is 66, and in average species are connected by links of weight G2 graph. Again, the common emitted VOCs determine the presence or not of a (weighted) link between two nodes. V2 = VP = 109 vertices (plants species) and E2 = 2,343 edges. Links are less by construction: in that case, for each VOC just the emissions larger than The plants graph corresponding to the second test was analogously constructed as the species-vs-species bipartite projection graph of P(k), representing the fraction of vertices with degree K > k. A log-line plot is chosen to display the degree complementary cumulative distribution function (CCDF). The graph strength distribution is also shown in Fig. s of a vertex corresponds practically to its weighted degree, thus it takes into account the total weight of the vertex connections, and it allows one to identify high and low concentration edges-regions inside an undirected graph. The maximum strength value is equal to smax = 1,624 and it corresponds to Lavandula spica L. (Lavender) species, while the minimum value is equal to smin = 27 and it is common to Humulus lupulus L. (Wild hop), Actinidia arguta (Siebold & Zucc.) (Hardy kiwi), Ficus benjamina L. (Weeping fig), Magnolia liliiflora (Desr.) (Japanese magnolia), and Diospyros lotus L. (Date-plum) species. Finally, Fig. In that case the majority of species share few common VOCs emitted (i.e. the mean of the edges weights is around 5). On the contrary, some vertices are connected by heavy links . Figure Taken as a whole, Fig. 37 possibly made during the preparation of the experimental samples were excluded. Indeed, the volatiles produced in consequence of wounding tend to be quite conserved among different species39; their inclusion in the database could lead to misinterpretation. Furthermore, other compounds that turned out to be less powerful in the aggregation features, as highlighted by the above described analyses, were removed from the dataset. As a result, a selection of 30 protonated masses was taken into account.A third test was performed on a reduced version of the original database. All those volatiles strictly associated to the mechanical woundingncps was used to distinguish between relevant and negligible emissions of a specific VOC: all the emissions larger than 0 ncps were considered comparable. The corresponding bipartite network G3 was made up by V = 139 vertices subdivided in two sets: VP = 109, i.e. the plants analogously to previous graphs, and VPM = 30 . In order to study plants network, the bipartite projection V3 = VP = 109, while the edges are equal to E3 = 2,522, similarly to the third-quartile-based graph In order to compensate the filter introduced by that selection of relevant VOCs, a threshold equal to 0 P(k), while Fig. smax = 746, but it still corresponds to Lavandula spica L. (Lavender) species, which again emerges as the most connected node. On the other side, the strength minimum value is smin = 23 for Cyperus papyrus L. (Papyrus), Salicornia europaea L. (Glasswort), and Solanum quitoense Lam. (Naranjilla) species. Further, k and thus the strength s of an isolated node are equal to 0 by definition.By following the previous approach for estimating the graph basic metrics, Fig. A first attempt to group plants on the basis of the VOCs emitted was performed by applying the community detection to both the dense mentclass2pt{minimwij > 0.5 in Table Notwithstanding some discrepancies in the results depending on algorithms optimization after pruning the network, two big communities emerge from mentclass2pt{minimmentclass2pt{minimi and j is proportional to the link’s weight, wij. Nodes colours refer to cluster membership.The situation improved by analyzing the communities of Brassicaceae family mainly, exception made for the Brassica oleracea L. var botrytis species (Cauliflower) which belongs to another community  and PM73  (20 species), followed by PM55 , PM89 , PM115  (19 species), and PM53 , PM81  (18 species). In general, the more informative VOCs for this cluster are compounds belonging to several chemical classes. Notice that from m/z = 123 (PM123) to m/z = 205 (PM205), where peaks deriving from terpenes, sesquiterpenes and their fragments are found, the emissions are null for all the species. One species can emit more than one VOC, so that all the species can be counted more than once to assess how many species share the same protonated mass emission. Gossypium herbaceum L. (Cotton), Plantago lanceolata L. (Plantain), and Inula viscosa L. (Inula) species are between the highest weighted degree nodes in Fig. cluster 1: 31 species  grouped in 21 families; prevailing families: Asteraceae, Apiaceae, Cannabaceae, Lamiaceae. The species belonging to that cluster emit, taken as a whole, a large amount of VOCs. They share in particular the emission of VOCs which are or refer to terpenes compounds, which are among the principal odour-like molecules emitted by plants flowers and leaves. In details, 28 species share PM123 and PM135, both terpenes or sesquiterpenes fragments (Tp/STp-f); 27 species share PM93 (Tp-f), PM95 (STp-f), PM105 , PM109 (Tp-f), PM119 (Tp-f), PM121 (Tp-f), PM137 (Tp/STp-f), PM143 , PM149 (Tp/STp-f), PM163 (STp-f), PM205 (STp); 26 species share PM91 , PM107 (HeArC), PM111 , PM153 (Tp-f).cluster 2: it is the biggest community, made up of 44 species  grouped into 27 families; dominant families: Lavandula spica L. , Foeniculum vulgare Mill. , Crithmum maritimum L. , and Liquidambar styraciflua L. . A more detailed description of cluster 2 is supplied hereafter.Accordingly, that community includes plant species characterised by intense flavour, such as Brassicaceae (dominating family with 4 species), Actinidiaceae, and Fabaceae. Interestingly, the Brassicaceae Cauliflower belongs to the previous community . Indeed, Cauliflower is, among the Brassicacaeae species included in the present study, one of the richest in VOCs and terpenes41. This is the most homogeneous community in terms of family composition. PM63, a typical sulfur compound (SC), is the most emitted VOC, being released by 5 species (4 of them belonging to the Brassicaceae family), followed by another sulfur compound, PM49, and PM83  (3 species), PM87 (Ald/Alc). In particular Brassica rapa L. (Chinese cabbage) emits also PM85 (Alc-f), PM103 , PM117 (Alc), PM129 (Alc), PM143 . The latter protonated mass, tentatively identified as 2-Nonanone42 has been already reported in Chinese cabbage43. The emission of all the other VOCs is null for the whole species set.cluster 3: 6 species only  from 3 families: Polygonaceae, Rosaceae, Solanaceae, Araceae, Fabaceae. They do not share any emitted VOC with other plants, since they do not release any protonated mass at all. That result has to be interpreted taking into account cluster 4: 28 isolated species  belonging to 20 different families dominated by In that case two big and one small clusters emerge from a basic community detection. The two big clusters embed 44 and 31 species, i.e. respectively the 40.4% and 28.4% of the species present in the dataset , Foeniculum vulgare Mill. (Fennel), Crithmum maritimum L. (Samphire), Liquidambar styraciflua L. (Sweetgum), Chrysanthemum indicum L. (Chrisanth), Santolina chamaecyparissus L. (Cotton lavender), Curcuma longa L. (Turmeric), Cupressus sempervirens L. (Mediterranean cypress), Ocimum basilicum L. (Basil), Citrus x Aurantium L. (Bitter orange), Tetradenia riparia (Hochst.) Codd. (Ginger bush), Juniperus communis L. (Juniper), Artemisia vulgaris L. (Mugwort), Citrus x Limon L. (Lemon), Stevia rebaudiana (Stevia), Eucalyptus globulus L. , Quercus ilex L. (Holm oak), Hedera helix L. (Ivy).Cluster 2, besides being the biggest one, is made up by those species corresponding to the highest weighted degree vertices in Gossypium herbaceum L. (Cotton), Plantago lanceolata L. (Plantain), and Inula viscosa L. (Inula) are the most connected aqua nodes in Fig. Other species with as well a huge emission of VOCs are present in cluster 1: Brassicaceae family, characterised by the predominant emission of sulphur compounds.Cluster 3 , Foeniculum vulgare Mill. (Fennel), Santolina chamaecyparissus L. (Cotton lavender), Crithmum maritimum L. (Samphire), Cupressus sempervirens L. (Mediterranean cypress), Ocimum basilicum L. (Basil), Liquidambar styraciflua L. (Sweetgum), Eucalyptus globulus L. , Juniperus communis L. (Juniper), Curcuma longa L. (Turmeric), Hedera helix L. (Ivy), Dahlia pinnata Cav. , Brassica oleracea L. var botrytis (Cauliflower), Picea abies L. (Norway spruce), Tetradenia riparia (Hochst.) Codd. (Ginger bush), Apium graveolens L. (Celery), Stevia rebaudiana (Stevia), Artemisia dracunculus L. (Tarragon), Artemisia vulgaris L. (Mugwort), Quercus ilex L. (Holm oak). That result is fully in agreement with the previous one.Again cluster 2 , Cannabis sativa L. (Hemp), Citrus x Limon L. (Lemon), Humulus lupulus L. var. Cascade (Common hop), Ruta graveolens L. (Rue), Calycanthus floridus L.  and Psidium guajava L. (guava).Some highly connected nodes are also present in cluster 1 , PM109 (Tp-f) and PM137 (Tp/STp-f) (26 species), PM95 (STp-f), PM121 (Tp-f), PM123 (Tp/STp-f), PM149 (Tp/STp-f), PM205 (STp) (more than 20 species). The m/z listed above probably refer to terpenes compounds and almost all of them are found in plant belonging to cluster 2 of the previous Citrus spp. In this community the species that release sulfur compounds (PM49 and PM63) are also found, such as: Ruta graveolens L. (Rue), Inula viscosa L. (Inula), Psidium guajava L. (Guava), Gossypium herbaceum L. (Cotton), and Citrus x Aurantium L. (Bitter orange), which together with Cannabis sativa L. (Hemp), and Citrus x Limon L. (Lemon) are among the most emitting species. Interestingly, species from Brassicaceae family, typically rich in sulfur compounds41, are not included in this cluster.cluster 1: it is the biggest community, made up by 37 species  grouped into 23 families; dominant families: Asteraceae (5 species), Apiaceae, Lamiaceae, and Cupressaceae. This community is made up by those species which are the most active in terms of VOCs emission, in agreement with the species gathered in cluster 2 of the previous Lavandula spica L. (Lavender), Foeniculum vulgare Mill. (Fennel), Santolina chamaecyparissus L. , Crithmum maritimum L. (Samphire) . Cauliflower is also found here. Again, an high heterogeneity characterises the families distribution. Accordingly, the species belonging to this cluster release some volatiles already highlighted for the previous cluster 2; in fact, the most released VOC is PM153 (Tp), emitted by 24 species, followed by PM93 (Tp-f), PM95 (STp-f), PM121 (Tp-f), PM123 (Tp/STp-f), PM149 (Tp/STp-f) (released by 23 species), and PM109 (Tp-f), PM119 (Tp-f), PM133 (Tp), PM137 (Tp/STp-f), PM143 (K/Ald), PM151 (Tp/Tp-f), PM205 (STp) (emitted by more than 20 species). Except for the ketone PM143, they are all terpenes compounds.cluster 2: 25 species  grouped in 16 families; prevailing families: Brassicaceae, Actinidiaceae, and Fabaceae. All these species emit in particular sulphur compounds PM49 (SC) and PM63 (SC) , while just few of them also release PM93, PM95, and PM153 . Brassica rapa L. (Chinese cabbage) species again distinguishes, being the only one which emits PM143 (K/Ald). This cluster is the most stable and it corresponds to cluster 3 of the previous analysis. It shows an homogenous families composition, since it groups all the Brassicaceae species, exception made for the Brassica oleracea L. var botrytis (Cauliflower) species, in agreement with previous analysis.cluster 3: 19 species  from 13 families only: Solanaceae, Araceae, Fabaceae, Rosaceae. As for the previous analysis on graph cluster 4: 28 isolated species  belonging to 23 different families dominated by Hereafter the four communities are described in term of dominating families and clustering protonated masses.G3, i.e. the VOCs-to-VOCs graph of selected protonated masses Vb3 = 30 vertices (each corresponding to one protonated mass), and Eb3 = 435 edges. Usually a bipartite graph is based on the representation of different individuals according to the common properties they share. Here the emitted VOCs are the analogous of features, since the most two plants emit the same volatiles the most they are similar. More in details, the VOCs are the vertices of the network G3, since we obtained similar results for G2. Graph G1 is not considered since from the previous analyses it turned out to be less suitable to describe our experimental data as a network.The second bipartite projection of graph smax = 679), followed by PM93, PM109, PM121, PM149, PM135, PM123, PM137, PM205 , monoterpenes (C10), sesquiterpene (C15), homoterpenes (C11 and C16), some diterpene (C20) and triterpene (C30), that are easily released into the atmosphere. The highest amount of species shared between two VOCs is observed between all the following couples of VOCs: PM93–PM95 and PM93–PM109 (respectively 48 and 46 maximum numbers of species), PM95–PM109, PM109–PM137, PM93–PM121, PM95–PM121, PM95–PM135, PM95–PM137, PM95–PM149, PM109–PM121, PM93–PM123. Their corresponding links are the thickest ones (highest link weights) in Fig. 44, thus it is more expectable that a plant species emits, simultaneously, two or more VOCs of the same class instead of the combination of VOCs of different classes. However, terpenes biosynthesis is very complex45 and uses many separated pathways, and cases of plants producing isoprene (terpenes building unit) but not other monoterpenes (and viceversa) have been frequently reported46.Colors here help the reader to distinguish between the most and less interconnected VOCs. Such as for the species-based bipartite projection graph, some protonated masses are highly connected with their neighborhoods. The highest value of weighted degree is recorded for PM95 smax = 79, follo47.On the contrary, the two sulphur compounds PM49 and PM63, which considerably determine the assembling of the violet cluster in Fig. Finally, PM201 (Tp), PM169 , and PM159  are some of the less interconnected VOCs.3, have been analysed by bipartite networks methodology in order to classify plants species. In particular, several quantitative measures coming from Complex Network Theory25 have been applied to uncover eventual similarities between the species in term of their VOCs emissions. To assure the reliability and robustness of the results, different classical and advanced community detection algorithms have been applied, and only the comparable results were retained. Moreover data have been pre-processed by means of both descriptive and quantitative statistical methods, to better focus on data behaviour. VOCs time series, obtained by recording the emissions content for each available species, suggest the presence of spike-like pulses (corresponding to few species), exceeding from a quite flat background signal. Each VOC turns out to be emitted by few species in a very large quantity with respect to all the other species emissions of the same protonated mass.Volatile organic compounds (VOCs), that represent a crucial component of a plant’s phenotypeLavandula spica L. (Lavender), Foeniculum vulgare Mill. (Fennel), Santolina chamaecyparissus L. (Cotton lavender), Crithmum maritimum L. (Samphire), Cupressus sempervirens L. (Mediterranean cypress), Ocimum basilicum L. (Basil) (for cluster 1); Citrus x Aurantium L. (Bitter orange), Cannabis sativa L. (Hemp), Citrus x Limon L. (Lemon), Humulus lupulus L. var. Cascade (Common hop), Ruta graveolens L. (Rue), Calycanthus floridus L.  and Psidium guajava L. (guava) (for cluster 2). A third community clearly groups species belonging to Brassicaceae family, turning out to be quite homogeneous in terms of clusters families composition. Finally, a fourth community highlights all those species which, by network construction, are not sharing any VOCs emission with the other species. See previous Section “Community detection analysis” for more details.After a preliminary test performed on the whole dataset, some VOCs have been excluded. In fact, some volatiles, especially C6 compounds and acetaldehyde, can occur in response to external stress, including wounding; this should be taken into account when using these compounds for communities detection analysis. Using a reduced dataset, community detection suggested the presence of 4 clusters. Two communities are made up by highly VOCs-emitting species. We recall here the most interconnected nodes: 44; for example, species characterised by terpenes and nitrogen-containing compounds as floral volatiles are different from species releasing sulphur-containing volatiles48. Moreover, terpenes compounds emitted by plant species , are the major constituents of plants essential oils50, and can be used to distinguish different species; in this study, although the exact chemical definition of the compounds involved is beyond the purpose, community detection highlighted two well defined groups (clusters 1 and 2) of species that emit different terpenes compounds. It emerges quite clearly that many VOCs are common to phylogenetically distant plant species, suggesting that the metabolic pathways of their production have been preserved during times. Further investigation on the emission of volatiles in species representative of plant evolution will give insight on VOCs involvement with specific functions that appear at specific moments of the evolution, thus allowing to obtain more information on the biological sense of plants clustering.The second bipartite projection confirmed terpenes compounds and sulphur compounds to be the two chemical classes most responsible for species classification. Indeed, the chemistry of volatiles has been shown to be species-specific33. A better understanding of the plant relationships would take further advantages from the correlation between VOCs emitted by different plant organs. Such approach could be then extended to different fields in botanic framework, such as plant ecology, psychophysiology and plant communication.In conclusion, complex network analysis allows to measure and describe hidden plants relationships probably related to the way they react to their environment. That result strengthens previous findings obtained by applying Complex Network Theory to plants morphological features53. The compounds emitted by different leaves were transported from the air stream where collided with H3O+ reagent ion inside the drift tube. The analysis was carried out as follows: each leaf samples was placed into 3/4 L glass jar  provided of glass stopper fitted with two Teflon tubes connected respectively to the PTR-ToF-MS  and the zero air generator . An overview of the plants used is shown in Tables ® tube) were kept at 24 ± 1 °C and 12/12 hours day/night, respectively, allowing to obtain leaf samples from non-stressed plants grown under stable conditions. In order to standardise the procedure, representative mature and healthy leaves were harvested from three plants for each species . Leaves were cut in four pieces with a sterile razor perfectly shaved, in order to avoid excessive damages of the tissues. Cut samples of each plant species were divided in three replicates, weighted separately and analysed. PTR-ToF-MS results were then referred to the weight unity (1 g). Before each leaf sample analysis, the glass jar was exposed to 1 minute of purified air flux (100 sccm) to remove all the VOCs accumulated in the head space during the time between sample preparation; then, a blank air sample was taken and subsequently used for background correction. All measurements were conducted in an air-conditioned room, with temperature and humidity respectively set at 20 ± 3 °C and 65%54, and using the same PTR-ToF-MS instrumental parameters: drift pressure = 2.30 mbar, drift temperature = 60 °C and inlet temperature = 40 °C, drift voltage = 600 V, extraction voltage at the end of the tube (Udx) 35 V, which resulted in E/N ratio of 140 Td (1 Td = 10–17 V cm−2). This setup allowed a good balance between excessive water cluster formation and product ion fragmentation55. Moreover, the inlet flux was set to 100 sscm. The internal calibration of ToF spectra was based on m/z = 29.997 (NO+), m/z = 59.049 (C3H7O+) and m/z = 137.132 (C10ncps) as described by Herbig et al.56. Data were filtered following the procedure used by Taiti et al.57 and used for statistical analysis. In this manner, a dataset comprised of mean mass spectra for each sample analysed was compiled. Finally, the tentative identifications of peaks was performed on the basis of an high mass resolution and rapid identification of compounds with a high level of confidence58. Further characterization of VOCs belonging to certain chemical classes such as terpenes, which are prone to fragmentation, was attempted using literature data on fragmentation of standards during PTR-ToF-MS analysis61. Similar approach was performed for the other identified compound, e.g. following Papurello et al.62 and Liu et al.63 for sulfur compounds, Loreto et al.36, Brilli et al.37, Degen et al.38, and Wu et al.39 for wounding-related VOCs, and Schwartz et al.64 and Soukoulis et al.65 for aldehydes, ketones and alcohols.PTR-ToF-MS has been used in this study as the detector for the organic compounds emitted by leaf samples. A full description of this tool, with its advantages and disadvantages, can be found elsewhereTukey34. None a-priory assumption is made on the underlying statistical distribution. Boxplots show information about data location and spread, by starting from the estimation of the second quartile  and of the interquartile range (IQR), where IQR = Q3 − Q1, and Q3 and Q1 are the third and first quartiles, respectively. Boxplots are also known as box-and-whisker plots. The rectangular box is related to the data quartiles, and, more in details, the left and right sides of the rectangle correspond respectively to Q1 and Q3. The whiskers are lines extending from the box till lower and upper first outliers. It follows that the boxplot width visually shows the sample IQR, the vertical band drawn inside the box represents the median, and as a whole the box is a measure of the data dispersion and skewness. On the contrary, there is no common definition for the end of the boxplots whiskers. In the present work we adopt the following formalism: outliers are defined as those data points lying outside the range ; extreme events are defined as those data points exceeding the range . Several graphical solutions for boxplots are present nowadays, and generalised versions allow to apply them to skewed distributions, also, by assuring a robust measure of the skewness in the determination of the whiskers35. We recall here that the quartiles are also called quantiles of order 1/4, 1/2, 3/4, or Boxplots are an intuitive graphical non-parametric method particularly suitable to visualise the distribution of continuous univariate data, firstly proposed by G, where every plant species p is connected to its features, i.e. in that case the VOCs it emits. No connection is present between the two set of nodes, i.e. the plant species and the recorded VOCs. Usually, a bipartite graph can also be described by a binary matrix A whose element aij is 1 just if plant p shows the feature f. The most immediate way to measure correlation between species is counting how many VOCs the plants species share in term of significant emissions, and similarly how many plants emit the same VOCs. We refer to the Basic Network Analysis subsection for a proper description of the methodology. In formulas, this corresponds to consider the matrix of species P = AAT and the matrix of volatile organic compounds, F = AT A, i.e. the two bipartite projections of G. In the present work, we focused on the graph having as nodes the different plants, i.e. on the Plants graph GP whose edges weights are proportional to the number of commonly emitted VOCs between plants. Second, in order to catch the predominant similarities in terms of volatile organic compounds emissions, we analysed the second bipartite projection, i.e. the Features graph, GF, whose nodes represent the emitted VOCs. In that case edges weights were proportional to the number of plants sharing the same emitted compound.Data are represented as an undirected bipartite graph Graph density (D) is defined as the ratio between the numbers of existing edges and the possible number of edges. Given a N-order network, graph density is computed as D, is the graph average degree ki is the degree of each vertex in V, i.e. the number of edges incident to it.Network clustering coefficient (c) is the overall measure of clustering in a undirected graph in terms of probability that the adjacent vertices of a vertex are connected. More intuitively, global clustering coefficient is simply the ratio of the triangles and the connected triples in the graph. The corresponding local metric is the local clustering coefficient, which is the tendency among two vertices to be connected if they share a mutual neighbour. In this analysis we used a local vertex-level quantity20 defined in Eq. (20.d in Eq. :1\documeNetwork strength (s) is obtained by summing up the edge weights of the adjacent edges for each vertex20. That metric is a more significant measure of the network properties in terms of the actual weights, and is obtained by extending the definition of vertex degreeaij elements of the network adjacent matrix A. In formulas, As regards network analysis, we computed some global and local basic metrics described hereafter.66. More in general, hierarchy is a central organising principle of complex networks, able to offer insight into many complex network phenomena67. In the present work we adopted the following methods belonging to complex networks framework:Fast greedy (FG) hierarchical agglomeration algorithm68 is a faster version of the previous greedy optimisation of modularity22. FG gives identical results in terms of found communities. However, by exploiting some shortcuts in the optimisation problem and using more sophisticated data structures, it runs far more quickly, in time O(md log n), where d is the depth of the “dendrogram” describing the network community structure.Walktrap community finding algorithm (WT) finds densely connected subgraphs from a undirected locally dense graph via random walks. The basic idea is that short random walks tend to stay in the same community69. Starting from this point, WT is a measure of similarities between vertices based on random walks, which captures well the community structure in a network, working at various scales. Computation is efficient and the method can be used in an agglomerative algorithm to compute efficiently the community structure of a network.Louvain or Blondel method (BL)70 to uncover modular communities in large networks requiring a coarse-grained description. Louvain method (BL) is an heuristic approach based on the optimisation of the modularity parameter (Q) to infer hierarchical organization. Modularity , is 1 if i = j, and 0 otherwise.ity (Eq. ) measureLabel propagation (LP) community detection method is a fast, nearly linear time algorithm for detecting community structure in networks21. Vertices are initialised with a unique label and, at every step, each node adopts the label that most of its neighbours currently have, that is by a process similar to an ‘updating by majority voting’ in the neighbourhood of the vertex. Moreover, LP uses the network structure alone to run, without requiring neither optimisation of a predefined objective function nor a-priori information about the communities, thus overcoming the usual big limitation of having communities which are implicitly defined by the specific algorithm adopted, without an explicit definition. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities.Communities detection aims essentially at determine a finite set of categories (clusters or communities) able to describe a data set, according to similarities among its objects73 based on dimensionality reduction methods was also performed to assure the results robustness and reliability, by rejecting those solutions not independent from the statistical methodology applied.Besides the complex networks communities detection methodologies, a classic cluster analysis


Studies reporting left ventricular mass (LVMI) or left ventricular (LV) diastolic function  responses to RDN were included. Two randomized controlled trials (RCTs), 3 controlled studies and 11 uncontrolled studies were finally identified. In observational studies, there was a reduction in LVMI, E/e’ and N-terminal pro B-type natriuretic peptide (BNP) at 6 months, compared with pre-RDN values. LV ejection fraction (LVEF) elevated at 6 months following RDN. In RCTs, however, no significant change in LVMI, E/e’, BNP, left atrial volume index or LVEF was observed at 12 months, compared with pharmaceutical therapy. In summary, both LV hypertrophy and cardiac function improved at 6 months after RDN. Nonetheless, current evidence failed to show that RDN was superior to intensive  drug therapy in improving cardiac remodeling and function.The aim of the present study is to systematically evaluate the impact of RDN on cardiac structure and function in patients with resistant hypertension (RH) or diastolic dysfunction. We retrieved Pubmed, Embase and Cocharane Library databases, from inception to April 1 Resistant hypertension (RH) is defined as blood pressure (BP) above target despite the optimal use of at least 3 different classes of antihypertensive drugs (including a diuretic)3. Patients with RH are at increased risk for major cardiovascular events5. Left ventricular hypertrophy (LVH) is a common cardiac structural change in hypertension, and considered a more important risk factor than BP level itself6. The structural alternations accounts for LV diastolic abnormalities, which can be detected early in hypertensive heart diseases8. Like LVH, LV diastolic dysfunction has been associated with increased cardiovascular mortality9. Therefore, improvement of LVH or diastolic dysfunction is an important treatment target in RH patients.Hypertension is the most common risk factor for cardiovascular diseases11. The effect of RDN on cardiac structure and function has also been studied in several small clinical studies12, while the results remains controversial. The aim of the present study is to systematically evaluate the impact of RDN on cardiac structure and function in patients with RH or diastolic dysfunction, by collecting currently available clinical evidences.Sympathetic nerve system (SNS) plays a crucial role in the development of RH and LVH, and is considered a potential target. Catheter-based renal denervation (RDN), a novel interventional technique to reduce renal afferent and efferent sympathetic nerve activity, has been proven effective in reducing BP in certain patients13. We electronically retrieved Pubmed, Embase and Cocharane Library, from inception to April 1st, 2016, using the keywords as follows: “left ventricular dimensions”, “atrial dimensions”, “cardiac hypertrophy”, “cardiac dimensions”, “echocardiography”, “magnetic resonance imaging”, “cardiac imaging”, “echocardiogram”, “ventricular dysfunction”, “renal denervation” and “renal sympathetic denervation”. Related references of retrieved articles were also searched for potential eligibility. No language restriction was applied in the search process. However, we only included English-written full text in the final review. The whole search process was performed by two investigators independently.This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statementObservational studies reporting left ventricular mass  or LV diastolic function  before and after RDN were included. Also, randomized controlled trials (RCTs) that comparing the effect of RDN with that of pharmaceutical therapy (PT) on LVM or diastolic function were involved. Detailed inclusion criteria were: 1) studies using cardiac imaging, namely echocardiography or cardiac resonance imaging, to assess LVM or diastolic function; 2) studies with no less than 10 subjects; 3) follow up of at least 6 months. Conferences abstracts, reviews and case reports were excluded. We checked the authors, methods and results to identify duplicate reports, which were excluded unless they featured different follow-up durations.After eligible articles being identified, data were extracted by two separate researcher. The characteristics of included studies, involving study design, sample size, measurements, study population and follow-up interval were extracted. Also, baseline characteristics of included participants, including age, gender, commodity diseases, body mass index, and usage of antihypertensive drugs were collected. The outcomes of interest, i.e. change in LVMI or diastolic function (including LVMI or diastolic function before and after RDN) were extracted. Any disagreement was resolved by consensus. The primary endpoints were: 1) LVMI and E/e’ change following RDN in observational studies; the difference in LVMI and E/e’ change between RDN and DT in RCTs. Secondary endpoints were: 1) LV ejection fraction (LVEF) response; 2) left ventricular diastolic diameter (LVDD) response; 3) left atrial volume (LAVI) response; 4) BNP response. Data at different time points were collected separately.For observational studies, the mythological quality was assessed by means of the Newcastle-Ottawa scale. , thus when only 95% CI or standard error (SE) was reported, we converted 95% CI or SE to SD according to the Cochrane handbook. P < 0.05 was considered statistically significant. We performed data analyses by the mean of Review Manager (version 5.2). Meta-regression analyses were conducted in STATA software (version 11.0) to evaluate the relationship between changes in LV remodeling or function and blood pressure.For observational studies, including controlled and single-arm studies, the changes in LVMI and E/e’ before versus after RDN were pooled. For RCTs, the differences in LVMI and E/e’ changes with RDN and PT were pooled and analyzed. The same strategy was used to handle secondary outcome parameters. Weighted mean difference (WMD) with 95% confidence interval (CI) was taken as treatment effect measure when outcome measurements in all trials were made on the same scale. The standard mean difference (SMD) was applied when the trials all assessed the same outcome, but measured it in a variety of ways (referring to Cochrane handbook). I25 (including 11 uncontrolled studies and 3 controlled studies) and 2 RCTs27. The baseline characteristics of included subjects was shown in Table 26. As shown in Supplementary Tables Our primary search identified 435 records, while only 16 of these articles were finally included in our analysis . More pronounced reduction was observed at 12 months   but not 12 months (P = 0.48).In observational studies, echocardiography showed that LVMI was reduced at 6 months following RDN (WMD = −13.88 g/m2 = 0%)  Fig. .Table 3Cet al.  but not 12 months  after RDN   Fig. .Figure 52 = 44%) but not 12 months  following RDN  at 6 months following RDN  and SNS were over-activated in patients with RH. Previous experimental studies have demonstrated that RDN could block the over-activity of SNS and RAAS, and thus exert protective effects on cardiac fibrosis and remodeling31. In 2012, Brandt et al. reported that RDN improved LVH and diastolic function in patients with RH using echocardiography7. In 2014, Mahfoud et al. also found that RDN reduced LVMI in a trial using CMR18. Notably, these effects of RDN occurred, at least in part, independently of BP12. Following these pilot studies, many trials have been conducted to investigate the effect of RDN on LVH and diastolic function.RDN is a new interventional approach designed to treat RH. The value of RDN is still under debate, especially after failure of RDN to lower BP over and above the sham control group in the large randomized double-blinded Symplicity HTN-3 trial33, and only one involved the influence of RDN on LVH and atrial size34. That study, however, has included conference abstracts, of which the ultimate results are not available. Differing from that study, the present study included only full-text article and evaluated not only LVH but also LV diastolic function change following RDN. Furthermore, more important relevant trials, including two recent RCTs, were included in the current study, which may increase our understanding of RDN on cardiac remodeling and function.This is the first meta-analysis on RDN and cardiac structure and function. Prior to this analysis, there have been many meta-analyses assessing the BP-lowering effects of RDNThe group of trials in the present analysis is rather heterogeneous in many respects: 1) Some trials are observational, some are RCTs; 2) Imaging was performed by CMR or echocardiography; 3) Follow-up dates ranged from 6 months to 12 months. Therefore, it is of no surprise that we separately pooled data according to study design, imaging technology and follow-up duration.35. In our analysis, we found that both echocardiography and CMR revealed a reduction in LVMI at 6 months, indicating a consistency of echo and CMR in LVMI measurement at this time point. However, we also observed a discrepancies between echo and CMR at 12 months regarding LVMI. The small number of studies involved in CMR analysis at one year might explain this. Our study indicates a requirement for more studies with CMR analysis and long follow-up visits.Different cardiac imaging may produce inconsistent results24, and therefore it may be difficult to derive a conclusive analysis. More future studies should consider a longer follow up duration.Follow-up duration is also a pivotal factor that influence the final results. We found that LVMI change at 12 months was more remarkable than that at 6 months, suggesting that the effect of RDN on LVH may be sustained up to one year. With regards to cardiac function, both E/e’ and EF were improved at 6 months, but none of them changed at 12 months. One potential explanation is that the number of trials included at 12 months was too small (only 2)Study design is the most important factor that may impact the results. In observational studies, we found that LVH and diastolic function significantly improved, however, these results cannot be reproduced in RCTs. One reason is that observational studies have overestimated the treatment effects. Another explanation is that the two RCTs included all featured a small sample size. Large RCT remains to be needed.18. Our meta-regression analysis showed that the change in LV remodeling or dysfunction were not significantly associated with blood pressure lowering  drug therapy in improving cardiac remodeling and function. More RCTs focusing on the impact of RDN on cardiac remodeling and function are needed. Actually, there are several ongoing trials investigating the role of RDN in cardiac remodeling and HF , and we expect the publication of the results of these trials to provide more information and new insights into this challenging subject.Supplementary Table 1 and 2


In a rat hepatoma model, the increase of microvessel density (MVD) following arterial embolization  was partially limited by SOF/TIBA/PLGA MSs. This resulted in the SOF/TIBA/PLGA MSs group  showing a smaller tumor size increase and viable tumor portion compared to the TIBA/PLGA MSs group. These findings suggest that a developed SOF/TIBA/PLGA MS can be a promising therapeutic system for liver cancer using a transarterial embolization strategy.Sorafenib  and 2,3,5-triiodobenzoic acid -loaded poly(lactic-co-glycolic acid) (PLGA) microspheres (MSs) were fabricated. Embolization, drug delivery, and tracing the distribution of MSs for liver cancer therapy were accomplished with the developed MSs after their intra-arterial (IA) administration. SOF/TIBA/PLGA MSs with 24.8–28.5 µm mean diameters were prepared, and the sustained release of SOF from MSs was observed. Lower systemic exposure (represented as the area under the curve [AUC]) and maximum drug concentration in plasma (C According to current guidelines, HCC can be curatively managed by liver transplantation, surgical resection, and local ablation2, but, unfortunately, only 30% of new HCC patients are eligible for the curative treatments3. The remaining patients undergo palliative treatments including transarterial chemoembolization (TACE), oral sorafenib (SOF), and supportive care. Besides, initially curable patients may receive palliative managements afterwards, when HCC recurs. In this context, TACE and SOF are two key players in current HCC management systems.Hepatocellular carcinoma (HCC) is the second leading-cause of cancer-related mortality in the world4, TACE is the most frequently used first-line treatment for HCC worldwide. In addition, TACE is the most common second-line treatment for patients who have already received a treatment other than TACE. This oncologic interventional method involves selective delivery of chemotherapeutic agents and embolic materials into the tumor-feeding arteries. The beneficial effect of TACE is mostly derived from arterial embolization6, considering that HCC is typically hypervascular.According to a recent global cohort study7. Therefore, studies of TACE in combination with the oral angiogenesis inhibitor are conducted for inoperable patients with HCC8. SOF, a multi-targeted tyrosine kinase inhibitor, is the most commonly used oral angiogenesis inhibitor; it is used not only as a monotherapy but also in combination with TACE. However, recent clinical trials of TACE with SOF imply that this combination can be a double-edged sword, showing worrisome toxicity profiles as well as promising clinical efficacy9.However, tumor hypoxia induced by arterial embolization causes vigorous angiogenesis following up-regulation of the hypoxia-inducible factor (HIF)-1α pathway; this consequently influences tumor recurrence and the survival of patients17. In our previous study18, doxorubicin (DOX)-loaded poly(lactic-co-glycolic acid) (PLGA) microspheres (MSs), fabricated by the solid-in-oil-in-water (S/O/W) method, were prepared; their application in the TACE of a liver tumor was evaluated. However, it is necessary to trace the in vivo fate of MSs after their administration to the hepatic artery to confirm the embolism and predict the therapeutic efficacies. An imaging agent that has conjugated or included polymeric MSs has been developed for the therapy of liver cancer20. Notably, magnetic resonance imaging (MRI)-visible PLGA MSs, including SOF, have also been prepared and assessed in vivo22. In this study, PLGA MSs, including SOF and 2,3,5-triiodobenzoic acid (TIBA), were developed for transarterial embolization (TAE), locoregional delivery of SOF, and computed tomography (CT) imaging. They can maximize the embolic effect and angiogenesis inhibition, minimize the systemic toxicity of SOF, and monitor the lesion on follow-up CT imaging. PLGA has been widely used as a biocompatible and biodegradable polymer to prepare drug carriers24. Considering its hydrophobic and degradable characteristics, it can be used for the fabrication of MSs and the sustained release of poorly water soluble drugs; as a result, biosafety can be accomplished24. It seems that TIBA may be one of the appropriate iodine derivatives for preparing particles with suitable physicochemical properties , radiopacity, and biocompatibility27. Following fabrication, the particle size, biodegradability, drug release profiles, and CT imaging properties of MSs were characterized in vitro. After these initial in vitro characterizations, a rat model was employed to enable in vivo studies investigating pharmacokinetics, tumor responses, and CT imaging capabilities.Recently, various preparation methods of micro-sized carriers have been reported28. When TIBA was added alone to the drug and polymer solution, it was easily released from MSs during the fabrication process, and a kind of precipitates were formed. Thus, to reduce the crystal size of TIBA and increase its content in MSs, TIBA dispersed in polyethylene glycol (PEG) was used instead of only TIBA. After heating (for solvent evaporation), freezing, and hydrating, the excess amount of PEG was washed off, and TIBA/PEG was obtained. In addition, due to the poor solubility of SOF in dichloromethane (DCM), SOF and PLGA were first dissolved in acetone, and an SOF/PLGA film was acquired after solvent evaporation. Prepared TIBA/PEG and SOF/PLGA were dissolved in DCM, and it was subsequently added to poly (PVA) solution. By emulsification and the solvent evaporation process, SOF/TIBA/PLGA MSs were finally prepared.SOF/TIBA/PLGA MSs were fabricated using a modified oil-in-water (O/W) emulsification method, as shown in Fig. SOF/TIBA/PLGA MSs were administered to the hepatic artery for the embolization, therapy, and imaging of liver cancer Fig. . The meain vitro stability test  administration was estimated by an est Fig. . The morin vivo drug release pattern ; however, there was no significant difference between the two groups after dose normalization. The maximum concentration (Cmax) value of the oral administration group was also much higher than that of the IA administration group (p < 0.05).SOF concentrations in plasma according to time were determined after oral administration of the SOF solution and IA administration of SOF/TIBA/PLGA20 MSs  and IA administration of SOF/TIBA/PLGA20 MSs , drug concentrations in the normal liver and liver tumor regions were quantitatively determined Fig. . In the Compared with the pre-treatment status, the tumor sizes increased by 115%, 111%, and 154% in the SOF solution , SOF/TIBA/PLGA20 MSs (IA), and TIBA/PLGA MS (IA) groups, respectively. Viable tumor portions after the treatments were 29 ± 13%, 25 ± 11%, and 51 ± 13% in the SOF solution , SOF/TIBA/PLGA20 MSs (IA), and TIBA/PLGA MS (IA) groups, respectively. The average values of microvessel density (MVD) were measured to be 14, 27, and 31 in the SOF solution , SOF/TIBA/PLGA20 MSs (IA), and TIBA/PLGA MS (IA) groups, respectively  and alanine transaminase (ALT) levels were elevated at day 1 and then gradually decreased at day 3 and 7 in all groups, but the SOF solution  group showed higher peaks at day 1 and slower normalization during the follow-up period compared to the SOF/TIBA/PLGA20 MS (IA) and TIBA/PLGA MS (IA) groups Fig. . White bCT images on post-procedure 1 day clearly demonstrated hyper-attenuated spots, which had not been identified on the pre-procedural images in any of the five rats Fig. . The spo29. These unwanted systemic effects may lead to the reduction or discontinuation of doses30. IA administration of SOF, compared with the oral administration of SOF, can reduce its systemic exposure and related side effects. For simultaneous embolization and chemotherapy of liver cancer after IA administration, various approaches have been tried, including emulsion composed of an anticancer agent and imaging agents, hydrogel, and MSs33. After the treatment, administered MSs need to be monitored by a noninvasive modality, such as CT, to trace the distribution of MSs and to predict the treatment response. In this investigation, TIBA as a CT imaging contrast agent was encapsulated with SOF into PLGA MSs, and the preparation method was optimized  may hamper their direct physical loading onto the organic phase during the fabrication process of PLGA MSs. To prevent the precipitation of TIBA during the fabrication process of MSs and improve its encapsulation efficiency in MSs, PEG was used as a dispersing agent for TIBA. An excess amount of PEG was removed by the centrifugation step, and its dispersed form in PEG (TIBA/PEG) was obtained. Due to the poor solubility of SOF in DCM, an SOF/PLGA film was prepared by dissolving those in acetone and eliminating that solvent. TIBA/PEG and SOF/PLGA were added in DCM; subsequently, it was poured into the aqueous phase (PVA solution) to make O/W-type emulsion. By the emulsification-solvent evaporation step, hardened MSs were acquired.SOF has been used as an oral formulation but it can induce hand and foot syndrome, diarrhea, and hypertensionzed Fig. . Althoug34. In addition, it was known that particles with a 40 µm mean diameter were necessary for the embolization of liver cancer in a rat model. Particles less than 40 µm may be distributed to the other organs and tissues, such as the lung and spleen35. As reported previously28, the particle size distribution of developed MSs may be suitable for the chemoembolization of liver cancer via the IA route  with an approximately 24.8–28.5 µm mean diameters (about 90% were ranged from 12 to 48 µm according to the volume distribution profile)  solution, simulating the blood components. As shown in Fig. via the IA route, sustained drug release is crucial, as MSs have to block terminal arterioles and capillaries as well as provide a therapeutic agent for at least several days. In this study, drug release was maintained for 14 days and the drug release period in this study was extended further compared to the reported data with SOF-loaded PLGA MSs22. By the optimized fabrication process , the drug may be entrapped efficiently and dispersed homogeneously in the MSs. It can lead to the sustained drug release profile, enhanced drug accumulation in the tumor region, and improved therapeutic efficacies. The biodegradation of PLGA MSs, observed in Fig. SOF release from fabricated MSs was assessed at pH 7.4 Fig. . In genep < 0.05); however, there was no significant difference in dose normalized AUC (AUC/D) values between the two groups. Higher systemic exposure, judged from the AUC value of the oral administration group compared to the IA administration group, may induce more unwanted systemic effects . The much higher Cmax value of the oral administration group compared with the IA administration group may be also related to those side effects. Considering AUC and Cmax values, the IA administration of MSs have more advantages in respect to reducing side effects in comparison with the oral administration group.The pharmacokinetic properties of SOF after the oral administration of the drug solution and IA administration of SOF/TIBA/PLGA20 MSs were assessed in normal rats  seems to be limited by SOF/TIBA/PLGA20 MSs , which resulted in the SOF/TIBA/PLGA20 MSs group showing less tumor size increases and viable tumor portion compared to the TIBA/PLGA MSs group . This implies that SOF can elevate the antitumor efficacies in conjunction with the embolization property of MSs. Although the embolization of the tumor-feeding artery induces massive coagulation necrosis of the tumor, the activation of the HIF-1α pathway as an immediate response of hypoxia eventually causes local progression from the residual viable tumor4. Accordingly, clinicians have tried to combine the angiogenesis inhibitor  with TACE, but a recent randomized controlled trial revealed that the time to progression of HCC was not significantly improved by adding oral SOF to TACE36. This disappointing result may be due to the insufficient delivery of SOF to the tumor. By means of oral administration, the amount of SOF delivered to the tumor is relatively much lower than the total dose of intake. Furthermore, the blood flow to the tumor is very limited after TACE, suggesting that SOF in the blood may rarely reach the tumor region. In contrast, a much higher intratumoral concentration of SOF was presented in the SOF/TIBA/PLGA20 MSs group (compared to the SOF solution group) in this study, and it can explain the observed antitumor efficacies. Concordantly, post-embolization angiogenesis was less prominent in the SOF/TIBA/PLGA20 MSs group when compared to the TIBA/PLGA MSs group.The increase of MVD following arterial embolization  may contribute to the enhanced antitumor efficacy after IA administration. According to the pharmacokinetic parameters (AUC and Cmax) in normal rats, IA administration of SOF/TIBA/PLGA20 MSs , compared with the oral administration of SOF , can reduce the unwanted systemic effects  of SOF. IA administration of SOF/TIBA/PLGA20 MSs  exhibited superior antitumor efficacies in a rat hepatoma model compared with that of TIBA/PLGA MSs . It showed more selective delivery of SOF to the tumor region compared with the oral administration group of SOF . Developed SOF/TIBA/PLGA MSs can be multifunctional systems for the therapy of liver cancer using a TAE method.SOF- and TIBA-loaded PLGA MSs were fabricated by the modified O/W emulsification method. SOF/TIBA/PLGA MSs can be applied to the therapy of a liver tumor by the embolization, drug delivery, and monitoring SOF was purchased from LC Laboratories . TIBA was obtained from Tokyo Chemical Industry Co., Ltd. . PLGA , PEG , and PVA were acquired from Sigma-Aldrich Co. . FBS was purchased from Gibco Life Technologies, Inc. . All other reagents were of analytical grade.28. To improve the loading content of TIBA in MSs, TIBA dispersed in PEG (2 kDa) (TIBA/PEG) was prepared. TIBA (200 mg) and PEG (2 g) were dissolved in acetone (20 mL), and aliquots (1.8 mL) of TIBA/PEG were transferred to a tube. The organic solvent was eliminated by heating it to 80 °C under a gentle nitrogen gas stream. Melted TIBA/PEG was then rapidly moved into liquid nitrogen to be frozen. Distilled water  was added to each tube, vortexed for 5 min, and centrifuged at 13,200 rpm for 3 min. After centrifugation, the supernatant was removed; this procedure was repeated three times. Remaining pellets were lyophilized for further use. SOF  and PLGA (20 mg) were solubilized in acetone (0.5 mL) due to the poor solubility of SOF in DCM. The acetone was then removed by heating it to 70 °C under a gentle nitrogen gas stream for 1 h. The prepared SOF/PLGA film and TIBA/PEG were dissolved in DCM (0.5 mL) and mixed. This organic phase was added to the PVA solution , and the O/W emulsion was stirred for 5 min to remove the organic solvent. After centrifugation at 13,200 rpm for 3 min, the supernatant was removed, and the MS pellet was dispersed with DW. The dispersion of MSs was then freeze-dried and stored at −20 °C for further use.SOF and TIBA-loaded PLGA MSs were fabricated using a modified O/W methodThe mean diameter of the SOF/TIBA/PLGA MSs was measured using a laser diffraction particle size analyzer  according to the manufacturer’s protocol. The morphology of MSs was also observed by a FE-SEM . MSs were coated with gold using sputter, and their morphological shapes were observed by the FE-SEM.in vitro degradability of SOF/TIBA/PLGA20 MSs was investigated in the serum. Prepared MSs (10 mg) were suspended in 50% (v/v) FBS mixed with phosphate-buffered saline (PBS) (0.5 mL), and it was incubated at 37 °C at 50 rpm for 4 weeks. To monitor the biodegradation of MSs by the FE-SEM, the dispersion of MSs was centrifuged at 13,200 rpm for 5 min, and MSs were collected for freeze-drying. Lyophilized samples were then observed by the FE-SEM  as described above.The The amounts of SOF released from the MSs were measured at pH 7.4. SOF-loaded MSs containing 50 μg of the drug were suspended in PBS (pH 7.4), including 0.5% (w/v) Tween 80 (as a release medium), and incubated in a shaking bath (50 rpm) at 37 °C. Aliquots (200 μL) of the released media were collected at determined times , and an equivalent volume of fresh media was supplemented at each time. SOF concentration in the released media was determined by the high performance liquid chromatography (HPLC) method to understand the release profile of SOF. SOF was quantitatively determined using a Waters HPLC system  equipped with a separation module , a UV/Vis detector , and a column . The mobile phase was composed of DW containing 2% (w/v) triethylamine (pH 5.4 adjusted with phosphoric acid) and an acetonitrile mixture . The absorbance was detected at a 265 nm wavelength. The injection volume was 20 μL, and the flow rate was maintained at 1 mL/min. The lower limit of quantification (LLOQ) of the drug was 25 ng/mL, and the precision and accuracy of the used assay were within the acceptable range.2) in each tube. The SNR was calculated by the following equation:To investigate X-ray attenuation of SOF/TIBA/PLGA20 MSs, three different phantoms were made using 2 mL of 2% agar, 2 mL of 2% agar mixed with 10 mg of SOF/TIBA/PLGA20 MSs by manual pipetting, and 2 mL of 2% agar mixed with 10 mg of SOF/TIBA/PLGA20 MSs by sonication. Each group was added in the plastic tubes (1 cm in diameter) and scanned by a micro CT scanner . A radiologist selected three consecutive image slices that scanned the center of conical tubes, and the radiologist placed a round region-of-interest  and xylazine , a 1.6-French microcatheter  was inserted into the carotid artery. With the guidance of X-ray fluoroscopy, the catheter was introduced to the proper hepatic artery, and the MSs suspended in the mixture of the normal saline and iodine contrast agent  were infused via the microcatheter.The experimental protocols of animal studies were approved by the institutional Animal Care and Use Committee  and animal studies were conducted in accordance with those guidelines and regulations. Male Sprague-Dawley (SD) rats  with an approximate body weight of 400 g were utilized, and they were reared in a light-controlled room at 22 ± 2 °C and at 55 ± 5% relative humidity. IA administration of MSs was conducted by an experienced interventional radiologist according to the previously reported protocolIn vivo pharmacokinetics after oral administration of the SOF solution and IA administration of SOF/TIBA/PLGA20 MSs was assessed in normal rats . For oral administration of SOF solution, SOF was dissolved in an ethanol/PEG 400/DW mixture  at a concentration of 1.5 mg/mL. Considering the clinical dose of SOF and hepatic arterial blood volume of the rats, the doses of oral and IA administration were set to be 10 mg/kg/day and 1 mg/kg, respectively. Afterwards, aliquots of blood (200 μL) were collected from the tail vein at pre-determined times . Blood samples were centrifuged at 16,000 g at 4 °C for 3 min, and the supernatant was stored at −70 °C prior to the quantitative analysis of the drug.m/z 465.2 to 270.3, 192 V, 24 eV, and 5 V, respectively. In addition, the fragmentation transition, fragmentor voltage, collision energy, and cell accelerator voltage for the analysis of IS were m/z 436.2 to 291.2, 98 V, 14 eV, and 5 V, respectively. Chromatographic separation of SOF and IS was performed using a Poroshell 120 EC-C18 column . The mobile phase was composed of acetonitrile and a 0.2% (v/v) formic acid solution ; the flow rate was 0.4 mL/min. Retention times of SOF and IS were 2.5 and 1.9 min, respectively. Acquisition and processing for the quantitative analysis of SOF and IS were performed with a MassHunter Workstation Software Quantitative Analysis . Linearity was established in the range of 2.5–5,000 ng/mL SOF concentration. Precision and accuracy were shown within the acceptable ranges. The AUC, as a pharmacokinetic parameter, was calculated using WinNonlin software .SOF concentrations in plasma were quantitatively determined by a liquid chromatography-tandem mass (LC-MS/MS) system. Aliquots of plasma samples (50 µL) were blended with acetonitrile (450 µL) containing 0.5 µg/mL valsartan  by vortex mixing for 5 min. After centrifuging at 13,200 rpm for 5 min, the aliquot (2 μL) of the supernatant was injected into the LC-MS/MS system equipped with an Agilent Technologies 1260 Infinity HPLC and Agilent Technologies 6430 Triple Quad LC/MS systems . The electrospray ionization (ESI) source settings for the analysis of SOF and IS were optimized manually; gas temperature, gas flow, nebulizer pressure, and capillary voltage were 300 °C, 11 L/min, 15 psi, and 3,000 V, respectively. The fragmentation transition, fragmentor voltage, collision energy, and cell accelerator voltage for the analysis of SOF were 6 N1-S1 cells  in the liver of SD rats. To avoid spontaneous tumor regression37, cyclosporine A  had been subcutaneously injected for five days, from one day before the tumor implantation until four days after the tumor implantation38. Twelve days after the tumor induction, 30 tumor-bearing rats were scanned by a clinical magnetic resonance (MR) scanner, randomly divided into three groups, and treated by each protocol. Twice-daily oral administration of the SOF solution , IA administration of the SOF/TIBA/PLGA20 MSs group , and IA administration of the TIBA/PLGA MSs group  were performed. In the TIBA/PLGA MSs group, corresponding amounts of MSs to 1 mg/kg of SOF dose in SOF/TIBA/PLGA20 MSs were administered via the hepatic artery. The rats in each group were randomly divided into two subgroups, and each subgroup was sacrificed for further analyses on day 3 and day 7. The rats euthanized on day 7 underwent MR imaging just before their deaths. Based on the MR images, tumor sizes before and after the treatments were compared among the three groups. MR imaging was conducted with a T2-weighted imaging sequence . The normal livers and liver tumors were harvested after euthanasia. Each specimen was divided into two blocks; a half was submitted for histologic evaluation, and the other half was utilized to measure tissue distribution of SOF.A rat hepatoma model was induced by inoculating 5 × 10via the oral route or the IA route, the liver was dissected on days 3 and 7. The normal liver and liver tumor tissues were separated to measure drug concentrations in each region. To determine the SOF concentrations in the specimens, each tissue was mixed with DW  and homogenized by ball milling. Drug concentrations in each tissue were measured using the LC-MS/MS method, which was described previously.SOF concentrations in the normal liver and liver tumor regions of the SOF solution group  and the SOF/TIBA/PLGA20 MSs group  were determined. After drug administration 2), and the number of microvessels was counted. The mean value of three measurements per each rat was used for further analyses.Histologic analyses were conducted using the specimens obtained on day 7. Axial sections of the specimens were fixed in a 10% (v/v) buffered formaldehyde solution and were embedded in paraffin. Hematoxylin and eosin (H&E) and CD34 staining were conducted for each sample. Afterwards, a pathologist who was blinded to the treatment allocation visually assessed the percentage of viable tumor portion and MVD. To determine the MVD, the pathologist selected three hot spots per each rat with optical magnification , IA administration of the SOF/TIBA/PLGA20 MSs , and IA administration of the TIBA/PLGA MSs  groups. Blood sampling was performed at day 0 (just before the treatments), 1, 3, and 7 in the tail veins, and AST, ALT, WBC counts, hemoglobin and creatinine were measured to evaluate systemic toxicity of each treatment.Twelve normal rats were randomly divided by three treatment groups: twice-daily oral administration of the SOF solution . All experiments were repeated at least three times, and the data are shown as the means ± the SD.Statistical analysis of the data was done using a two tailed


To determine non-radiographic risk factors differentiating atypical lipomatous tumors (ALTs) from lipomas.All patients with deep-seated lipomatous tumors of the extremities treated from January 2000 to October 2010 were retrospectively reviewed. Factors reviewed included age, gender, tumor location, size, histology, local recurrence, dedifferentiation, and metastasis. Multivariate logistic regression models were used to evaluate the effects of patient characteristics on ALT status.p < 0.0001. One lipoma (1.04%) recurred at 77 months and five ALTs (10.9%) recurred at an average of 39 months (19–64 months). Two ALTs originally treated with wide resection recurred with a dedifferentiated component and were treated with wide re-excision and chemotherapy. No metastases or tumor-related deaths occurred in either group at the time of last follow-up. Patients older than 60 years, tumors greater than 10 cm, or thigh location, were more likely to be diagnosed with an ALT (p < 0.05).Ninety-four lipomas and 46 ALTs were included. Patients with an ALT were older (median: 60.5 vs. 55 years). Lipomas were evenly distributed between upper (48.9%) and lower extremities (51.1%), whereas ALTs predominately involved the lower extremities (91.3%). Median ALT size (22 cm) was greater than lipomas (10 cm), Lipomatous tumors were more likely to be ALTs when the tumor was at least 10 cm in size, located in the thigh, or found in patients that were 60 years of age or older. These risk factors may be used to guide management and surveillance strategies, when lipomatous tumors do not display characteristic radiographic features. Lipomatous tumors of the extremities are the most common soft tissue tumors encountered in clinical practice with the majority of these tumors being lipomas and atypical lipomatous tumors (ALTs) –3. ALTs Characteristic radiographic features differentiate ALTs from other liposarcoma sub-types, but differentiating between a lipoma and an ALT often presents a diagnostic dilemma. Lipomas and ALTs have relatively homogenous MR signal, usually equivalent to normal fat, and generally do not enhance after gadolinium administration. Some radiographic features that suggest an ALT rather than a lipoma are larger size, thick septae, more nodular appearance, less fat content, and calcification , 7, 9–12Diagnostic uncertainty arises when a large homogenous lipomatous tumor does not display radiographic features consistent with an ALT. When an asymptomatic lipomatous mass exhibits features consistent with the diagnosis of an ALT, the clinician may recommend excision, whereas a lipoma may be observed. Furthermore, length and intensity of follow-up may be affected by equivocal histologic diagnoses, whereby a definitive diagnosis of ALT cannot be established.Here, we present a large cohort of patients that underwent excision of their lipoma or ALT with the purpose of determining non-radiographic risk factors that may help to preoperatively risk stratify these two entities. These risk factors will help guide treatment for patients presenting with equivocal radiographic features.A retrospective chart review was performed in accordance with the MD Anderson Institutional Review Board. The orthopedic oncology database was queried to identify all patients with a histopathologic diagnosis of lipoma or ALT of the extremities treated at our institution from January 1, 2000 to October 1, 2010. Tumors were excluded if they did not have a final pathologic diagnosis of lipoma or ALT, or underwent previous excision at an outside facility. All tumors were deep to the fascia. Factors reviewed included: age, gender, location of lesion, size of lesion based on preoperative MRI (greatest single dimension measured in any direction), gross pathologic size, biopsy and final histologic diagnosis, and history of local recurrence, dedifferentiation or metastasis. Length of follow-up was determined as time from surgery to last clinic appointment, telephone follow-up, or survey. One hundred sixty-six deep-seated lipomatous tumors were marginally excised during the study period. Twenty-six patients were excluded; 10 ALTs and 4 lipomas had undergone previous excision, 6 tumors were diagnosed as lipomas but the initial histopathologic diagnosis could not exclude ALT nor could the independent blinded review, 1 ALT had myxoid features, 2 tumors had a final histopathologic diagnosis of fibrolipoma, 2 were diagnosed as hibernoma, and 1 lipoma had a hemagioma within the lesion. The size of the lesion in the greatest dimension was determined by measurements obtained from the preoperative MRI, ultrasound, or measurement of the gross specimen if no preoperative imaging was available. The location of the lesion was classified as upper extremity (arm and forearm), shoulder, or lower extremity (thigh and leg). A tumor in the hand (two lipomas) was classified as a forearm lesion, and a tumor in the ankle (one lipoma) or foot (one lipoma) was included in the leg classification.Two musculoskeletal pathologists performed a blinded, independent, randomized review of the histologic slides to obtain diagnoses for comparison to the original, unblinded diagnoses. Preoperative radiologists’ and intraoperative surgeons’ differential diagnoses were noted if included in the preoperative radiology report or operative report, respectively. These differential diagnoses were compared with the final pathologic diagnosis.p Values less than 0.05 were considered statistically significant. Statistical software SAS 9.1.3  and S-Plus 8.0  were used for all analyses.Fisher’s exact test or Chi-square test was used to evaluate the association of age or tumor location with the diagnosis of lipoma or ALT. Wilcoxon rank sum test was used to determine differences in age and tumor size between patients with a lipoma or ALT. BLiP plots were generated for overall tumor size by lipoma and ALT diagnoses, as well as thigh location only. Multivariate logistic regression models were used to evaluate the effects of patient characteristics  on ALT status. p = 0.017). The location of lipomas and ALTs differed significantly (p < 0.0001). Lipomas were evenly distributed between the upper and lower extremities, 48.9 and 51.1%, respectively. Whereas, ALTs predominately involved the lower extremity (91.3%), 78.3% of which were located in the thigh.One hundred forty lipomatous tumors of the extremities met inclusion criteria; 94 had a final diagnosis of lipoma and 46 were ALTs. Patient demographics and tumor location are shown in Table p < 0.0001). Almost all ALTs were greater than 10 cm, 93.5 vs. 53.2% of lipomas, p < 0.0001. In the lower extremity, ALT size was significantly larger than lipomas; median size = 22 vs. 11.8 cm, respectively (p < 0.0001). ALTs tended to be larger in the upper extremity but this was not significant as there were too few ALTs available for comparison. ALTs in the thigh (22 cm) were significantly larger than lipomas ; however, there was significant overlap between the two groups  and 61.3 months (1–166 months) for ALTs. Of the 140 tumors excised, there was 1 (1.04%) local recurrence in the lipoma group and 5 (10.9%) recurrences in the ALT group .In our cohort of 140 deep-seated lipomatous tumors, we found that age of 60 years or older, size at least of 10 cm, and a thigh location were significant risk factors for an ALT diagnosis. This is in agreement with others who have reported that larger tumor size , 13, 14 Histologic subtyping of the ALTs has been reported to provide important prognostic information regarding recurrence rates . AlthougWe note that our interpretations may be limited given all our patients were treated with surgery and we did not have a non-surgical cohort for comparison. Also, regular follow-up of these benign lipomatous tumors was highly variable during the study period. In a number of cases, patients either followed-up with their local physicians or were instructed to return on an as needed basis. Whether or not these patients subsequently developed undetected recurrences or were treated for recurrences at another facility is largely unknown. Given the higher recurrence rates and potential for dedifferentiation in ALTs even after excision, routine surveillance with MRI is now recommended.Differentiating between ALTs and lipomas may be difficult even after good MR imaging and routine histology. The non-radiographic features associated with the diagnosis of ALTs were location deep in the thigh, size at least of 10 cm, and patient age of 60 years or older. These features may be used to alert clinicians to the higher possibility of an ALT and help clinicians determine appropriate management and surveillance strategies.VL: formulation of project, concept development, analysis of data, and manuscript oversight/editing. JB: data generation and collection, analysis of data, and writing and revisions. LM: data generation and writing. LF: statistical methods and analysis and editing manuscript. W-LW: pathology review and review of all specimens. PL: concept development and manuscript review. BM: concept development and manuscript review. AL: pathology review and review of all specimens. RS: concept development. JM: radiographic assessment and review.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.


Variations in protein coding sequence may sometimes play important roles in cancer development. However, since variants may not express into proteins due to various cellular quality control systems, it is important to get protein-level evidence of the genomic variations. We present a proteogenomic strategy getting protein-level evidence of genomic variants, which we call sequential targeted LC-MS/MS based on prediction of peptide pI and Retention time (STaLPIR). Our approach shows improved peptide identification, and has the potential for the unbiased analysis of variant sequence as well as corresponding reference sequence. Integrated analysis of DNA, mRNA and protein suggests that protein expression level of the nonsynonymous variant is regulated either before or after translation, according to influence of the variant on protein function. In conclusion, our data provides an excellent approach getting direct evidence for the expression of variant protein forms from genome sequence data. Proteogenomics provides protein-level evidence of predicted genes and helps to improve genome annotations using integrated information of Exon/RNA sequencing and proteomic data123569The purpose of the present research is to suggest a new strategy of proteogenomics for getting protein-level evidence of genomic variations. Generally, proteomic data in proteogenomics are acquired based on shotgun proteomics, using liquid chromatography tandem mass spectrometry (LC-MS/MS)23121314171819As a proof-of-concept, we present an analysis of nonsynonymous variants at the protein level by using our STaLPIR method on gastric cancer cells. Briefly, we integrated the entire exome sequence data and STaLPIR data. Subsequently, we selected a set of 296 nonsynonymous variants and confirmed the expression of 147 variants at the protein level, with further information of gene expression pattern, gene regulation and their functional aspects. Until now, despite the rise of studies on variants using proteogenomics, few have attempted to address the expressed feature of variants at the protein level. Our results provide significant information for understanding the expression of variant genes from DNA to protein, and lay a foundation for future work to treat mutant proteins that might occur in various cancers.2 RPKM level  as a model system, and performed both whole-exome/RNA sequencing and proteomic analysis . We expesequential targeted LC-MS/MS based on prediction of peptide pI and Retention time (STaLPIR) (Escherichia coli (E. coli) lysate (E. coli) or after spiking into E. coli digests (with E. coli). Both mixtures were run in triplicate in DDA and Inclusion methods, but only once in TargetMS2. The number of identifications for the standard mixture without E. coli digests was almost identical in all the three methods  . The STa) lysate . The sta methods . The iden method . NotablyE. coli digests. We performed the DDA experiment at 0 hour, 16 hours and 24 hours without any intervening column-washings, and identified 29 overlapping peptides from three experiments. The relationship of RTs between each pair of experiments was highly linear (average r = 0.99) and a minor deviation (average difference = 0.1 min) was observed  during RPLC and isoelectric point (pI) by which prefractionation is accomplished. First, we assessed the reproducibility of our LC-MS/MS using the standard peptide mixture with observed . A totalobserved . Based oobserved . From thin silico digestion. Again, the proteins to which 298 of 1,029 variants belonged were identified by DDA and Inclusion, even though most of the proteins were identified by those other than variant peptides. Therefore, the 298 variants were selected as targets of STaLPIR in order to detect the variant sequence-containing peptides. In addition, the corresponding reference peptides (reference sequence-containing peptides) were also targeted for STaLPIR  using whole exome sequencing. Of these, expression of 1,153 genes were observed at the mRNA level, and 534 genes were identified at the protein level, regardless of detection of variant peptides ; 20% of nt genes . We obsent genes . In concFor analysis of allelic expression pattern at protein-level, not only the variant peptides but also the reference peptides were targeted for identification using STaLPIR . The allThere are six variations of nucleotide pair . The distribution of frequency versus variation type, did not change significantly by the cell type or dataset . Our resin silico algorithms such as MutationAssessor252627We also analyzed the possible functional effects of variations. We used This study proposes a comprehensive proteogenomic approach in mapping of nonsynonymous variants at the protein level. Furthermore, our study provides not only the identification of nonsynonymous variants at the protein level, but also the information about expression pattern, relationship between mRNA and protein, pattern of allele type and functional impact of variant. To identify variant, we developed a new proteogenomic approach (STaLPIR) with improvement in peptide identification. Nevertheless, there is a limitation to this study, which needs to be further explored. We identified 2224 variant from the genomic data, but only ~13% (298) were selected as targets. The presence of a unique tryptic peptide is essential for proteomic analysis. Some variants had changes to the amino acid of trypsin cleavage site such as K or R, and some changes were isobaric like I > L or L > I. In other cases, the tryptic peptide where the variation occurred was not unique and could not be distinguished from the peptides of other proteins. Therefore, it requires further analysis employing multiple proteases with different cleavage specificities.We considered allele types of variations at DNA, mRNA and protein levels for analysis of allele type change during gene expression. Our STaLPIR approach is suitable for such analysis because not only the variant form but also its reference form can be targeted in TargetMS2 runs, regardless of the read count of each allele type at the genomic level. We speculate that the expression level of variant proteins are regulated either before or after translation. Especially, our study revealed that the difference of protein levels between allele types was clearly depicted by the functional impact of variations that discerned the allele types . In euka31Although our present study has focused on identification of nonsynonymous variant proteins, STaLPIR is also applicable to study for the identification of other genomic aberrations such as fusion gene, novel splice junction, insertions and deletions. These application studies also need to consider the presence of unique peptides corresponding to the aberration sites at the genomic loci. Subsequently, the novel peptides of interest are targeted by STaLPIR to get protein-level evidence of such genomic aberrations. In conclusion, our result provides an outstanding approach by which we can obtain evidence for the expression of variants at the protein level, and helps to understand the relationship between gene and protein.® RPMI 1640 medium  supplemented with 10% fetal-bovine serum , 100 units/mL penicillin and 100 μg/mL streptomycin.Three gastric cancer cell lines, SNU1, SNU5 and SNU216 were used throughout the study. The cells were grown in Gibco® Mini kit (Qiagen). Libraries were constructed using Illumina TruSeq RNA Sample Prep Kit v2-Set A and sequencing was carried out using the Illumina Genome Analyzer IIx. Following this, the reads were mapped on hg19 using TopHat v2.0.6The genomic DNAs were isolated from the cells using the PuregeneTM DNA purification kit (Qiagen). Library construction and exome enrichment were performed using Illumina TruSeq DNA Sample Prep Kit-Set A, SeqCap EZ Human Exome Library v2.0, and SeqCap EZ hyb and wash kit (Roche NimbleGen). Next, exome enriched libraries were sequenced using Illumina Genome Analyzer IIx. The resulting sequencing reads were aligned on human reference genome 19 (hg19) with Burrows Wheelers Aligner (BWA)Proteins were extracted from the three gastric cancer cell lines and proteolyzed using trypsin. The resultant peptides were separated into 24 fractions according to their pI on an Agilent 3100 OFFGEL Fractionator equipped with pH 3–10 linear IPG strips . Next, peptides in each fraction were loaded onto a reversed-phase Magic C18aq  column  on an Eksigent nanoLC-ultra 1D plus system, and eluted with a linear gradient from 5% to 30% acetonitrile in 0.1% formic acid at a flow rate of 300 nL/min. The eluted peptides were analyzed using Q Exactive quadrupole mass spectrometer  operating in a data-dependent acquisition mode with (referred to as Inclusion) or without (referred to as DDA) an inclusion list or TargetMS2 mode. In DDA and Inclusion modes, survey full-scan MS spectra  were acquired with a resolution of 70,000. The MS/MS spectra of the 12 most intense ions from the MS1 scan with a charge state ≥2 were acquired with the following parameters: resolution, 17,500; automatic gain control (AGC) target, 1E5; isolation width, 2.0 m/z; normalized collision energy, 27%; dynamic exclusion duration, 30 s; and ion selection threshold, 4.00E + 03 counts. In TargetMS2 mode, MS/MS spectra were directly acquired without survey full scans. The TargetMS2 mode was operated at similar parameters with the exception of 2E5 for AGC target.We first constructed customized protein databases by appending human UniprotKB database (released May 2013) with the amino acid sequences of nonsynonymous variants identified by whole exome sequencing. MS2 spectra obtained from DDA, Inclusion and TargetMS2 experiments were searched against our customized databases using MSGF+ search engine with following settings: number of tolerable termini = 1; MS1 tolerance = 15 ppm; isotopeErrorRange = 0,2; and allowance of decoy database search. The false discovery rate (FDR) was set to 1% at PSM level. For protein assembly, search output files from DDA and Inclusion were submitted to IDPicker 3.1. Here, for further refinement of data, FDR at spectrum, peptide and protein level were set to ≤1% with minimum of 2 unique peptides and spectra.in silico digested using trypsin with the following options: two missed cleavages; full tryptic digestion and mass (MH+) range . Subsequently, the tryptic peptides containing variation sites were picked up and filtered against the following parameters: peptide length (8–25 amino acids); pI value (3–10.99) and no N-terminal cysteine (C), glutamine (Q) and glutamic acid (E). We arranged the target peptides as per pI value, and classified them into 24 OFFGEL fraction groups. Finally, the Inclusion and TargetMS2 lists were prepared by Skyline software (version 2.5)The sequences of reference and variant proteins were E. coli β-galactosidase . Skyline was used to generate extracted ion chromatograms (XICs) of target peptides. Then, peak areas  of XICs were normalized against the geometric mean of the peak areas of two β-galactosidase peptides (FNDDFSR and LNVENPK).Variant peptides as well as the corresponding reference peptides were measured by LC-MRM-MS on a 5500 Qtrap mass spectrometer equipped with an Eksigent nanoLC-Ultra 2D plus and a nanoelectrospray ion source . The instrument was operated in positive mode with the following parameters: ion spray voltage of 2,100 V, curtain gas at 20 psi, nebulizer gas at 25 psi, resolution at 0.7 Da (unit resolution) for Q1 and Q3, interface temperature at 150 °C, and scan mass range of 300–1,250 m/z. The collisional energy (CE), collisional cell exit potential (CXP) and declustering potential (DP) were optimized for each peptide by direct infusion using Turbospray. Quantification experiments were performed using a scheduled LC-MRM mode with MRM detection window of 480 s and cycle time of 1.5 s. Before analysis, samples were spiked with 400 fmol of digested http://www.broadinstitute.org/cancer/software/genepattern/esppredictor) was used to predict ionization efficiency of STaLPIR peptides during mass spectrometry. For functional annotation of proteins, DAVID  was used . Functional annotation charts were generated using each of the following GO terms: Biological Process, Molecular Function, Cellular Component, Uniprot Sequence Feature, and Protein Information Resource. Results were considered significant at P-value < 0.05 and visualized by the Enrichment Map plugin Cytoscape.ESP Predictor .