Untargeted metabolomics commonly uses liquid chromatography mass spectrometry to measure abundances of metabolites; subsequent tandem mass spectrometry is used to derive information about individual compounds. One of the bottlenecks in this experimental setup is the interpretation of fragmentation spectra to accurately and efficiently identify compounds. Fragmentation trees have become a powerful tool for the interpretation of tandem mass spectrometry data of small molecules. These trees are determined from the data using combinatorial optimization, and aim at explaining the experimental data via fragmentation cascades. Fragmentation tree computation does not require spectral or structural databases. To obtain biochemically meaningful trees, one needs an elaborate optimization function (scoring).We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a Maximum A Posteriori estimator. We demonstrate the superiority of the new scoring for two tasks: both for the de novo identification of molecular formulas of unknown compounds, and for searching a database for structurally similar compounds, our method SIRIUS 3, performs significantly better than the previous version of our method, as well as other methods for this task.SIRIUS 3 can be a part of an untargeted metabolomics workflow, allowing researchers to investigate unknowns using automated computational methods. Liquid chromatography mass spectrometry (LC–MS) is one of the predominant experimental platforms for untargeted metabolomics. With advances in mass spectrometry instrumentation, it is possible to detect 1000s of metabolites simultaneously from a biological sample . UntargeTandem mass spectrometry data is usually searched against spectral libraries –6. Compuhttp://www.casmi-contest.org/) 2013, without using any fragmentation pattern information [Identifying the molecular formula of a compound is already a challenging problem: most peaks in an LC–MS run are ambiguous and can be explained by several molecular formulas, even when using instruments with high mass accuracy. This is particularly the case for compounds above 400 Da see Fig. . Moleculormation . In contormation –33 usualFragmentation trees (FTs) were introduced by Böcker and Rasche . FTs annwithout the need for a database of tandem mass spectra or molecular structures. We stress that FTs are computed using tandem MS data; one can also do so using multiple MS data [Clearly, the term “fragmentation tree” has been used much earlier than 2008 in the MS literature , 36; the MS data , 38 but  MS data  which so MS data  on the sFTs can be used to identify the molecular formula of a compound : for eacalignments can be used to derive information about a compound’s molecular structure, beyond the molecular formula of the compound: in particular, FT alignments can be used to search a spectral library for a compound which is structurally similar  to the query compound, in case the query compound itself is missing from the database [Rasche et al.  also shodatabase , 44.Maximum Colorful Subtree problem [The computational problem underlying FT computation has been coined the  problem ; unfortu problem . Neverth problem , 45, 46. problem , the ILPIn this paper, we report a systematic approach for choosing the fragmentation tree that best explains the observed data, based on Bayesian analysis and a Maximum A Posteriori estimation. Our Maximum A Posteriori estimate roughly follows the scorings from , 41, 42.not restricted to molecular formulas from any database. Evaluation is carried out on data from several 1000 compounds and two independent datasets.We evaluate FTs using two derived measures: both for the identification of molecular formulas of unknown compounds, and for searching a database for chemically similar compounds, the new FTs perform significantly better than state-of-the-art methods. In particular, SIRIUS 3 performs significantly better than its predecessors for molecular formula identification. We argue that this is due to an increase in quality of the FTs computed by SIRIUS 3. We stress that SIRIUS 3 is http://bio.informatik.uni-jena.de/software/) as version 3.0 of the SIRIUS framework for MS and MS/MS analysis.The implementation of the method presented here is freely available from our website  and “GNPS” (2005 compounds), see “may be contained in the compound: for example, for a compound with molecular formula We group compounds composed solely from C, H, N, O, P and S into the “CHNOPS” batch, and compounds containing halogen elements into the “contains FClBrI” batch. For batch CHNOPS, SIRIUS 3 is run using this alphabet of elements without any further restrictions. For batch “contains FClBrI” we assume that we know upfront which of the elements, besides CHNOPS, before computing the FT. This classification may be based on the exceptional isotope pattern of compounds containing chlorine and bromine, or the presence of certain losses in the fragmentation spectra for iodine. We argue that doing so is possible for ClBrI with high precision and recall; in fact, SIRIUS 3.0 offers an option to auto-detect chlorine and bromine by simple rules. But fluorine may pose a problem, as it has only a single stable isotope, and may be undetectable using characteristic losses (mass differences) in the MS/MS spectrum. To this end, simulation results may be too optimistic for the 344 compounds containing fluorine.The above evaluation setup implicitly assumes that we can determine if an element is putatively contained in an unknown compound, naïve method that returns the molecular formula with the smallest mass difference to the measured parent mass. This method completely ignores all fragmentation data, but will nevertheless in some cases find the correct answer, in particular if there are only few possible explanations of the parent mass. This strategy identifies the correct molecular formula for 14.6 % of the instances, and in 31.3 % the correct formula can be found in the top 5. Both of our datasets have no systematic mass error, see Fig. We evaluate the performance of SIRIUS 3 against existing methods for determining the molecular formula using MS/MS data. As a baseline method to evaluate against, we use the not restricted to molecular formulas from any database.Another common approach is to search the neutral parent mass in a compound database. If we restrict our search to molecular formulas that are contained in PubChem, and again rank molecular formula candidates by mass difference to the measured parent mass, we find the correct molecular formula for 17.1 % of the instances in top rank, and 59.8 % in the top 5. This approach is, by design, restricted to molecular formulas that are already known, and must naturally miss cases where no molecular formula is contained in a structure database. The improved performance is, hence, solely based on the reduced number of candidate molecular formulas, in particular for larger masses. We stress again that SIRIUS 3 is 2 (version 1.0)”, and will be referred to here as “SIRIUS2-DP”. SIRIUS2 does not use the Integer Linear Program proposed in [2 scoring with the ILP from [2-ILP” in the following.Second, we compare SIRIUS 3 against its predecessor, namely the computational method from  with theposed in  for compposed in  used herILP from ; this mek output of the different methods, for varying k. We find that SIRIUS 3 can correctly identify the molecular formula for 76.0 % of the instances, compared to 31.1 % for SIRIUS2-DP and 39.1 % for SIRIUS2-ILP. Using an ILP [2-DP, and roughly a twofold increase when compared to SIRIUS2-ILP.In Fig. g an ILP  instead 2-ILP and SIRIUS2-DP decrease with increasing mass: this can be attributed to the fact that more candidate molecular formulas have to be considered for larger masses, compare to Fig. Figure all molecular formulas. Up to 600 Da, identification rates are on par with SIRIUS 3 considering all possible molecular formulas. For larger mass, identification rates for the smaller PubChem candidate lists outperform the regular SIRIUS 3.To support our claim that the observed performance gain when searching large compounds in PubChem is simply an artifact, we also restricted SIRIUS 3 to molecular formulas from PubChem Fig. . We stre2-ILP, and SIRIUS2-DP as a function of the number of candidates in Fig. 2-ILP and SIRIUS2-DP for 8+ candidates. We have included “searching PubChem for the precursor mass” for comparison; clearly, the number of decompositions is not the number of candidates considered by this method. The improved performance for 2048+ decompositions is due to these compounds having large mass and, hence, fewer candidates in PubChem. Again, we also show identification rates for SIRIUS 3 considering only molecular formulas from PubChem.To further elaborate on this point, we show identification rates of SIRIUS 3, SIRIUS2-DP. MOLGEN-MS/MS is commercial, so we cannot estimate its performance on the datasets used here; but we evaluate SIRIUS 3 against MOLGEN-MS/MS on an independent dataset.To the best of our knowledge, the only other method that we could evaluate against, is MOLGEN-MS/MS  introducsimulate the isotope pattern of each compound. Since there is no generally accepted way of how to disturb this ideal data, we use a simple approach suggested repeatedly in the literature, see for example [Even better results for identifying molecular formulas can be reached if one combines fragmentation patterns and isotope patterns of an unknown compound. Unfortunately, no isotope pattern information is available for the GNPS and Agilent dataset. To this end, we have to  example : we ignoWe report results in Fig. Maximum Colorful Subtree problem (one instance corresponds to one compound) on a 2× Intel XEON 6 core E5-2630 at 2.30 GHz with 128 GB RAM. We compute 12 instances in parallel, so that only one CPU core is used per process. Running times are given per core, unless indicated otherwise. We solve Integer Linear Programs using Gurobi 6.0 (http://www.gurobi.com/). Processing all compounds from the two datasets requires 114 h of computing time . We find that 45 % of the instances are solved in less than a second, and 93.5 % of the instances require less than a minute. Computing the “easiest” 90 % of all instances requires only 2.8 h; in contrast, the 1 % “hardest” instances use more than 70 % of the total computing time. See Fig. We solve all instances of the We stress that computing a single fragmentation tree rarely takes more than a few seconds. But for some instances, we have to compute up to 20,000 FT to consider all possible molecular formulas. For example, the longest-running instance runs for 12.5 h and computes 3106 FTs; hence, each tree is computed within 15 s.Computation time is not a concern of this paper, as gathering the data required far more time than computing the FTs. There are numerous ways to speed up computations: first and foremost, White et al.  have recdo not re-estimate any hyperparameters for these evaluations but rather use those described above. Only mass accuracy and the set of elements are chosen appropriately.To show that we have not “overtrained” SIRIUS 3, we evaluate its performance on two independent datasets. We First, a preliminary version of SIRIUS 3 was used in the CASMI contest 2013 to determine the molecular formula of 12 unknown compounds. Using only the fragmentation tree analysis described here and ignoring the isotope pattern data, we correctly identified eight molecular formulas, and placed an additional three in the top 2 . In conj2-DP are taken from [2-DP. When omitting the isotope pattern data, SIRIUS 3 still identifies the correct molecular formula for 81.6 % of the instances, and for 18 out of 28 halogenated compounds.Second, we evaluate SIRIUS 3 on independent data by Stravs et al. . This daken from .1 For evken from . See TabThird, we use MS/MS spectra of 874 compounds from MassBank as independent data, see “not discarded but only penalized, as they are assumed to be less probable. Furthermore, prior probabilities are chosen conservatively, so that “unlikely” molecular formulas are penalized only slightly: if there is sufficient MS/MS data, SIRIUS 3 will “overrule” these priors, returning a molecular formula that violates one or even several prior assumptions. For example, we find that SIRIUS 3 correctly identifies the following molecular formulas, although the “hetero minus oxygen to carbon ratio” prior is violated: In “any biomolecule, and use the molecular structure database solely to estimate the hyperparameters.Second, although hyperparameters of the priors are determined from molecular structure databases, we do not train our method using these databases. Rather, we assume that the prior assumptions will hold for Third, SIRIUS 3 can ignore certain priors in its analysis. To evaluate this, we ignore priors not contained in the database. Varmuza et al. [average chemical similarity for each query in the top k for In case a query molecule is contained in the database  to search in, we can use any method of spectral comparison, such as peak counting or dot product, to identify the correct answer. We now want to capture the more challenging case of a search where the query molecule is a et al. , 44 suggrison in .search engine in the following): the search engine cannot decide to return more or fewer answers for certain queries; instead, it is forced to always return the same number of answers. We argue that this restriction is somewhat artificial: even for queries where the search engine cannot find anything remotely similar to the query, the top k answers are nevertheless taken into consideration for evaluation.But the evaluation setup suggested in , 44 forcX has size on average, the search engine will return q, x. For given Here, we suggest a novel evaluation setup that follows the same line of thought, but allows the search engine to return result lists of individual size. At the same time, this modification allows us to consider fractionate maximum ranks. Assume that our database 2-ILP combined with FT alignments. The idea of this evaluation is as follows: if the quality of SIRIUS 3 FTs is better than that of previous versions, then we may expect to also observe an improved search performance. We also evaluate against the method of estimating structural similarity using spectral comparison.We now evaluate whether FTs computed by SIRIUS 3, together with the FT alignments from , result http://sourceforge.net/projects/cdk/). We estimate search performance both for intra-dataset and cross-database search (inter-dataset). FT similarity is computed using FT alignments as described in [2-DP. We also evaluate against the method of directly comparing tandem mass spectra via shared peak counting.We determine chemical similarity of compounds using PubChem fingerprints and Tanimoto coefficients, as implemented in the Chemistry Development Kit (CDK) 1.5.8 , 52 . In comparison to direct spectral comparison, FT alignments improve the respective chemical similarities by up to 0.15 . For the inter-database search, improvements are less pronounced; we observe that all methods’ results are closer together.See Fig. “Chemical similarity” is not a well-defined measure, and there exist several approaches to estimate it. In particular, there exist other types of molecular fingerprints, such as MACCS fingerprints. We now use one type of molecular fingerprints (MACCS) to sort database compounds with respect to some query, and another type of fingerprints (PubChem) to evaluate results. This can be thought of as an upper limit of what can be reasonably reached by any method for this task. For the inter-database search, sorting compounds by their MACCS Tanimoto coefficient results in about 0.15 higher chemical similarity than the FT alignment method using SIRIUS 3. Hence, the quality difference between spectral comparison and FT alignments with SIRIUS 3, is about as large as the difference between FT alignments and the MACCS fingerprint method which has perfect knowledge about compound structure. This roughly holds true for the inter-database search, too.We stress that we optimized neither SIRIUS 3 nor the tree alignment method from  for thisWe use two datasets to train and evaluate SIRIUS 3. We concentrate on mass spectra in positive mode, as these constitute the majority of available spectra, and fragmentation spectra contain more peaks on average. For both datasets, we filter out compounds: for example, we discard compounds where no sufficient information is available in the database, compounds where the parent peak has large mass deviation, and large compounds. We stress that these compounds can also be analyzed with SIRIUS 3. We exclude them as only few compounds fall into these categories; the remaining compounds form more homogeneous groups that can be analyzed with identical parameters.http://gnps.ucsd.edu on January 12, 2015. For 5464 compounds we download positive ion mode spectra from publicly available GNPS libraries, excluding third party libraries. We delete 327 compounds with missing or inconsistent annotations of the ion mode, as well as 254 compounds for which we do not find a PubChem identifier, and 201 compounds that are not fully connected by covalent bonds. The dataset does not comprise MS1 data, so we remove 1290 compounds where fragmentation spectra have no parent peak, as we cannot asses the mass accuracy of these spectra. Note that in application, SIRIUS 3 can process such spectra without restrictions, as the mass of the parent peak is known from the MS1 measurement. From the remaining 3392 compounds, we use 2005 where fragmentation spectra contain at least five peaks with more than 2 % relative intensity, and the mass deviation of the parent peak is below the maximum of 10 ppm and 2 mDa, and the ion mass is below 1000 Da. Besides CHNO, compounds contain elements sulfur (313 compounds), chlorine (250), phosphorus (13), fluorine (168), iodine (9), and bromine (14). For each compound, GNPS provides a single collision-induced dissociation (CID) fragmentation spectrum at varying collision energies, mostly recorded on an Agilent QTOF with electrospray ionization. A few compounds were measured on a different experimental platform but are not excluded. We do not correct for any mass bias during preprocessing.The GNPS dataset was downloaded from the GNPS database website at Agilent dataset is commercially available under the name “MassHunter Forensics/Toxicology PCDL” (version B.04.01) from Agilent Technologies Inc., , and contains compounds of forensic and toxicological interest. In the commercial variant of the database, masses of peaks are corrected using masses of hypothetical fragments of the compound. We stress that our version of the database contains peak lists from fragmentation spectra without any corrections to masses or other parameters. This dataset contains 2318 compounds for which mass spectra were recorded in positive mode. We discard 53 compounds that contain deuterium, and 43 compounds that have parent mass accuracy worse than 10 ppm and five compounds with ion mass above 1000 Da. We also discard 67 compounds where the fragmentation spectrum containing <5 peaks with relative intensity 2 % or above. For 104 compounds no parent peak is recorded, so we have to exclude them from our evaluation. We end up with 2046 compounds. Besides CHNO, compounds contain elements sulfur (442 compounds), chlorine (381), phosphorus (78), fluorine (176), bromine (42), and iodine (27).The CID spectra were measured on an Agilent 6500 series instrument. For each compound, three different collision energies  were recorded. Unfortunately, only relative intensities were recorded, so we have to perform corrections to merge spectra recorded at different collision energies: we normalize each spectrum so that peak intensities sum up to one. Next, we “merge” the three spectra, and normalize the resulting peak list so that the highest peak has intensity 100 %. We then merge peaks from fragmentation spectra with different collision energies with mass difference up to 10 mDa : in thismay contain, the harder it is to identify its molecular formula: the decompositions of the monoisotopic mass of the compound constitute its candidate molecular formulas, and the number of decompositions increases significantly if we consider a larger alphabet of elements. For the purpose of evaluating SIRIUS 3, we split each datasets into two disjoint batches: the batch “CHNOPS” contains all compounds that use solely elements CHNOPS. The second batch, named “contains FClBrI”, contains all compounds with at least one atom from FClBrI.It is understood that the more elements an unknown compound http://massbank.jp). We use spectra of 1333 compounds. 958 compounds are measured on a QTOF instrument and provided by the Washington State University (266), the University of Connecticut (102), the Oswaldo Cruz Foundation (95), the Leibniz Institute for Plant Biochemistry (112), and the RIKEN Plant Science Center (383). 375 compounds are measured on a ITFT instrument and provided by the Max Planck Institute for Chemical Ecology (74), the Helmholtz Centre for Environmental Research (239), and the NAIST Graduate School of Information Science (62). We remove compounds for which we find no parent peak within 10 ppm or 2 mDa of the theoretical ion mass (252 compounds), and compounds for which the merged spectra contain <5 peaks with relative intensity over 2 % (207 compounds).The first independent dataset was provided by Stravs et al. . MS/MS dmuch easier and, hence, leads to favorable evaluation results for all available test data. But this approach must fail to detect compounds where not even the molecular formula is contained in any structure database.We have presented a Maximum A Posteriori estimator for the problem of computing fragmentation trees, that performs significantly better than previous approaches for the problem, roughly doubling the number of correctly identified molecular formulas. Beyond estimating the hyperparameters of the method, our method SIRIUS 3 does not rely on any  database. With recent methodical advances in the field –15, MS/Mhttp://www.csi-fingerid.org/), which is currently the most powerful tool for searching tandem MS data in molecular structure databases [Beyond molecular formula identification, FTs can assist in the structural elucidation of a compound, either manually or by automated means. We argue that this, in fact, is the main use of FTs; we have not discussed it in more detail here, as evaluating and comparing the performance of methods is highly elaborate and, for large-scale datasets, only possible using derived measures. As an example of an automated downstream analysis of FTs, Shen et al.  introducatabases .Identification performance can be significantly improved by adding isotope pattern information , 30, 41 Different from supervised Machine Learning, hyperparameters of SIRIUS 3 are not trained in a way that maximizes, say, correct identifications of molecular formulas. Instead, hyperparameters have a statistical interpretation such as “mean loss mass”. Besides the lists of common losses and fragments, only few hyperparameters of the method are estimated from the training data. Model assumptions such as using a log-normal distribution for modeling loss masses, can be evaluated using the data. To this end, there is only a faint possibility of overfitting the method to the training data. To further rule out this possibility, we have evaluated SIRIUS 3 on an independent dataset, reaching comparable identification rates for molecular formula identification.In our evaluations, we assume that we know in advance about the  presence of “unusual elements” FClBrI. SIRIUS 3 comes with a simple classifier to predict the presence of chlorine and bromine from the data. In the near future, we will integrate a more sophisticated Machine Learning approach for this task.We will repeat estimating the method’s hyperparameters when more training data becomes publicly available; this can further improve the method’s power in the future. In particular, when Orbitrap datasets of roughly the same size as the datasets used herein become available, we will release a version of SIRIUS that will incorporate this information.datam and a molecular formula with mass at most one peak in the measured spectrum. See below for the very rare exceptions to this rule.First, we will formally introduce fragmentation trees, allowing us to interpret fragmentation tree computation as a Maximum A Posteriori estimation in the next section. Our fragmentation tree (FT) V which are molecular formulas over some alphabet of elements, and directed edges (arcs) connecting these nodes. All edges are directed away from the root of the tree, and every node can be reached from the root via a unique series of edges. In small compound fragmentation, many fragments result from fragmentation cascades, that is, series of subsequent fragmentation events; these cascades are modeled by the tree structure of the FT. Nodes of the FT are molecular formulas of the parent ion and its fragments; edges correspond to losses. For any FT, each molecular formula can appear at most once as a node of the tree. For an edge f (either fragment or loss). This will usually be the mass of the lightest naturally occurring isotope of an element, such as A ses from .We report protonated ions as ron mass , 54. We For a given FT, we can simulate a fragmentation spectrum (without intensities), simply using the masses of all nodes’ molecular formulas. For the inverse direction, a FT is supported by a fragmentation spectrum of a compound if, for every node of the tree, we find a peak in the spectrum such that the mass difference between the molecular formula of the node and the peak mass is below some user-defined threshold. Recall from the above that there can be at most one such peak. Not all peaks of the fragmentation spectrum have to be explained by the tree, as we also have to model noise peaks in the spectrum. But we demand that for every node of the FT, there is a peak in the spectrum.a, b, and By modeling the compound fragmentation as a tree, we make the implicit assumption that each fragment in the fragmentation spectrum is generated by a single fragmentation pathway. In practice, different fragmentation pathways may lead to fragments with identical molecular structure. The most prominent example is that two fragmentation events happen independently and in arbitrary order: we call this a “parallelogram” spanned by the losses unique peak in the spectrum. In other words, no two nodes of the tree may correspond to the same peak. Allowing more than one node to explain a peak, would violate the vast majority of observations: in theory, it is possible that two fragments of a compound have different structure but very similar mass, so that both fragments explain the same peak. In practice, this situation is extremely rare, and excluding this “pathological” cases is again necessary to formulate our task as an optimization problem: the improvement by making this assumption outweighs the cases where it leads to a possible incorrect interpretation.There is one additional requirement we need: we demand that every node of the FT explains a supported by the observed data natural injective mapping from the FT nodes to the peaks by We now formalize our above considerations. We say that a FT fragmentation graph. Nodes are colored so that all molecular formulas corresponding to the same peak have the same color. Recall that we must use at most one vertex for each color (peak) in our FT. Edges are inserted whenever one molecular formula is a sub-formula of another. Edges are appropriately weighted using some score function. It is straightforward to check that there is a 1–1 correspondence between colorful subtrees, that use every color in the graph at most once, and FTs supported by the data. We search for a colorful subtree of this graph that has maximum weight.This formulation of the problem is not easily accessible by algorithmic means; to this end, we use an alternative formulation which, for additive scorings, is equivalent to the above : for eacTo identify the molecular formula of the unknown compound, we can add a super-root that is connected to all molecular formula explanations of the parent ion peak. As all of the corresponding nodes share the same color, only one interpretation of the parent ion peak will be present in the optimal solution. In practice, it turns out to be faster to instead consider one molecular formula for the parent ion peak at a time, compute for each such candidate an optimal FT, and rank the resulting trees according to their posterior probability.We have deliberately ignored that the mass difference between two measured peaks in prior probability of model Scorings in , 41, 42 not supported by the data We have stressed repeatedly that we are interested in those FTs only that are supported by the data. To this end, we demand We now introduce computations for prior probability and likelihood of the tree.fragment in a FT, and loss in a FT. The independence assumption is obviously violated in reality, but allows us to come up with simple yet meaningful priors. We can simplify this equation, noting that every node of the tree except the root has exactly one incoming edge. For molecular formulas u, v let vand loss u. Then,r is the root of We first concentrate on the prior r we use the molecular formula r, and the fact that certain molecular formulas are observed more often in molecular databases [For the prior atabases . We use atabases . This cohis rule ; if the In addition, we use three informative priors suggested previously , 34, 41,First, assume that the compound is not a radical, then the sum of valences is even . If the Second, the ratio between hetero atoms and carbon atoms is usually relatively small for biomolecules . We findm is the mass of the compound, is roughly normal distributed, see Fig. Third, the ring double bond equivalent (RDBE) values can be used as a prior : we obsespecial, as they occur less often in metabolites and natural products. We define n being the number of special elements in the molecular formula. We stress that this is not the number of atoms corresponding to special elements: for example, We add a fourth prior for penalizing molecular formulas containing “special” elements. We define all elements but C, H, N, O as Additionally, we add a prior for penalizing phosphorus-containing molecular formulas without oxygen or sulfur: we find that for more than 99 % of the phosphorus-containing compounds in the KEGG metabolite database, the sum of oxygen and sulfur atoms is at least twice the number of phosphorus atoms. We set The root priorWe do not use additional priors as suggested in , 34, 41.The prior probability implausible losses of an edge  using a prior do not want to see, there appears to be no reasonable way to learn such implausible losses from the data. Instead, we have to rely on expert knowledge and evaluation of FTs computed by the method, to collect this list. Also, priors for such implausible losses are chosen ad hoc as there appears to be no sensible way of learning such penalties from the data.We first penalize ation in , but thaentclass1pt{minimaRegarding the mass of a loss, we assume that large losses are less likely than small losses. Unfortunately, there is only a very small number of annotated FTs available in the literature, and these are usually measured on different instruments (and instrument types) using different experimental setup and, hence, mostly incomparable. To this end, we estimate the loss mass distribution using FTs determined by SIRIUS 3. We will try to bring in agreement the observed distributions with the distribution used for scoring.x. See “Different from , 41, 42 Some losses turn up more often than we would expect from the loss mass distribution. Instead of relying on an expert-curated list we learn common losses and their prior probabilities from our training data, see “The must be noise. The fragment mass prior favors peaks with small masses,ad hoc and without any further optimization; we expect that choosing, say, a threshold of 100 Da will not result in significant differences.For a FT to be informative, it is useful that the FT includes fragments of small masses, even if the corresponding peaks have small intensities and, possibly as a result, larger mass deviations. In addition, one can relatively easily identify the fragment’s correct molecular formula, as well as distinguish fragment peaks from noise, due to the small “combinatorial diversity”: the chance that the mass of a noise peak coincidence with the theoretical mass of a molecular formula is very small for small masses. As a theoretical example, consider masses below 15 Da: in this mass region, reasonable molecular formulas are H, v in comparison to its parent u. As proposed in [and common fragments, clearly violates the independence assumption: if we know the molecular formulas of a fragment and one of its losses, then this also tells us the molecular formula of the child fragment. To this end, we use a “cautious” prior that rewards only few and small common fragments which are observed very often, whereas the vast majority of fragments receive a flat prior. See “We use both the hetero minus oxygen to carbon ratio (HMOTCR) and the RDBE value of a fragment posed in  we do noThe FT we will compute should explain a large number of peaks; to this end, we want to favor large trees over small ones. The priors we have introduced so far do exactly the opposite: many edges result in many probabilities we have to multiply, and small trees are favored over large trees. To this end, we introduce one last prior: we assumem(v) in the fragmentation spectrum. All peaks in the spectrum not assigned to a node of the FT, are noise peaks and also contribute to the likelihood of the tree. Also recall that each node u, v) corresponds to a loss Recall that each considered FT lth peak and to its mass. Furthermore, we may assume that for each peak, the probability of the tree to generate some peak depends only on the corresponding hypothetical fragment, so To simplify our computations, we assume independence between the measured peaks in Unassigned peaks cannot be scored in the FT optimization, as only those nodes and edges are scored that are actually part of the tree. To get rid of the probability of unassigned peaks, note again that each node is assigned a unique peak, and that no two nodes are assigned the same peak. We reachstructure but only its molecular formula. Similarly, there exists no sensible model for the mass of noise peaks. To this end, we will use only the peak mass to assess the probability of signal peaks; and only peak intensity to assess the probability of noise peaks. The intensity of peak m is I(m);  for brevity we write We will now show how to compute the probability of signal peaks and noise peaks. Currently, there exists no general model for the intensity of signal peak in small compound MS. Here, the problem is even harder, as we do not know the fragment’s molecular m(v) for node/fragment v can be estimated asm(v) will occur by chance. Here, “It has been frequently observed that relative mass deviations are roughly normally-distributed , 58. We I(v) cannot be used for estimating the probability of fragment peaks. Similarly, we have no model for noise peak masses. But we can estimate the probability that a certain peak is noise, by observing that noise with high intensity are much rarer than noise peaks with small intensity.As we have no model for the intensity of fragment peaks, Böcker and Rasche  proposedx. See “m with intensity I or higher, isLet From the above we infer thatv that are within the specified mass accuracy v as the nodes of the fragmentation graph, each colored with the corresponding mass m, and set u, v) for each pair u, v) we set its edge weightM. These Given a fragmentation spectrum To improve the quality of FTs, and to increase the chance that the FT with the correct molecular formula root will receive the highest score, we use a hypothesis-driven recalibration . We are a of f as the median of the slopes b to be the median of the values f to all masses.Recalibration is carried out using an affine mass correction  \documenstimator , 60 to fdiscarded, returning to the original measured sample spectrum, and the next root molecular formula is processed.We then compute the optimal FT for the recalibrated sample spectrum and the candidate molecular formula, and use this score to evaluate which root molecular formula best explains the data. Then, the recalibration is We note that our hypothesis-driven recalibration (HDR) is fundamentally different from, say, the recalibration proposed in : using HWe now describe how to estimate the (hyper)parameters for priors and the likelihood estimation. Parameters for mass error and peak intensity can be chosen individually for each dataset, and are required for computing the likelihood of the data. In contrast, hyperparameters are only estimated once for the SIRIUS 3 method, and are not retrained for a new dataset; they are required for computing the prior probability of a FT.Mass accuracy parameters mentclass2pt{minimFor both datasets in our evaluation, we estimate Is our assumption correct that mass errors are normally distributed? To verify this claim, we have to know the true  mass of the fragments that resulted in some peak in the spectrum. To estimate the true mass, we use FTs computed for the correct root molecular formula, after setting all hyperparameters as described below. We compare the theoretical mass of each FT node with the observed mass of the peak. For both datasets, we observe that mass errors roughly follow a normal distribution see Fig. . We findWe have to determine the noise intensity parameters individually for each of the two datasets, because spectra in the Agilent dataset only provide relative intensities, whereas GNPS spectra provide absolute intensities.We use the peak intensity threshold Recall that hyperparameters are estimated only once for the SIRIUS 3 method, and are not retrained for a new dataset. We estimate hyperparameters from FTs that we have previously computed by SIRIUS 3. Clearly, for a FT to carry useful information, it has to be the FT that carries the true molecular formula of the compound as its root. For the rest of this chapter, FTs will always be computed for the true molecular formula. We estimate the hyperparameters only once using all FTs from both datasets, instead of estimating hyperparameters for each dataset individually.rounds: for the first round, we manually set parameters 2-ILP [We re-estimate hyperparameters in an iterative procedure, consisting of IUS2-ILP , 41, 42.ten rounds.Using these FTs, we estimate the loss mass prior, the common losses, the common fragments, and the tree size prior as described below. We then iterate: using these new priors, we again compute FTs, and proceed by recomputing the hyperparameters. We repeat this for We now consider the set of all losses that have been observed in at least one tree, together with their number of appearances (frequency). But instead of purely counting losses, we want to give more weight to losses that correspond to intense peaks. To this end, any loss receives weight corresponding to the maximum peak intensity of the two peaks that are responsible for this loss.Loss mass distribution and the list of common losses are jointly determined in an inner loop: the loss mass distribution dictates what losses we regard as being “more common than expected”. But these common losses, in turn, have to be made “uncommon” for determining the loss mass distribution. We proceed in six runs.w(l) be the total weight of some loss total weight of all observed losses. We set Let The observed sum of weights for this loss is at least 1.3-fold of what we would expect from .Large losses will be very rare and, using only the above rule, all of them would be regarded as “common”. To this end, we also demand that the frequency have to be at least five above the expected value from .Certain losses appear significantly more often than we would expect from the loss mass distribution. To this end, we use the following two rules to decide whether some loss Common losses are outliers, in the sense that their frequency is far higher than we would expect for a loss of this mass. To this end, we now correct their weight in a straightforward manner: for each identified common loss final round of fitting the hyperparameters, we reach l in the identified list of common losses, we set:After the See Fig. After loss mass distribution and common losses are determined for the current round, we determine a list of common fragments that show up significantly more often then what we would expect by chance.For each fragment, we compute its weight as the sum of peak intensities of the corresponding peaks. Then, we compute a frequency of each fragment, dividing its weights by the total weight of all fragments. Unlike losses, the diversity of fragments is very high . To avoid overfitting, we use only the 40 most common fragments and set the common fragment prior to their weight divided by the weight of the 80th most common fragment (39). Both numbers are chosen ad hoc. All other fragments get a flat prior of 1. See Table could be explained by a theoretical fragment, that is, m and molecular formula mass Finally, we determine tree size priors After the final found, we reach tree size priors


Malassezia dermatitis, flea infestation, hypothyroidism, keratinization disorder (seborrhea), combination of Malassezia dermatitis and tick infestation, and a combination of scabies and tick infestation. Therapy was given with cefpodoxime with clavulanic acid along with appropriate simultaneous medication for the underlying associated conditions. In all the cases response to therapy was excellent. Improvement was noticed by 9 to 19 days and 17 to 21 days in recurrent superficial and deep pyoderma, respectively. In one dog, relapse occurred by 45 days due to the associated condition of hypothyroidism which was confirmed through laboratory findings. Cefpodoxime with clavulanic acid proved to be an effective, safe, and convenient antibiotic for the treatment of recurrent pyoderma in dogs without any side effects.In the present study on recurrent pyoderma, dogs with a history of more than three episodes of skin infections in a period of one year were selected. The associated conditions and (or) underlying factors revealed upon thorough investigation were demodicosis,  Malassezia dermatitis and dermatophytosis), genodermatoses , occult neoplasia (solar-induced squamous cell carcinoma), and immunodeficiency  [Dogs suffer from a variety of skin infections. Canine pyoderma is one of the most common diseases. Pyoderma literally means pus in the skin and can be caused by infectious, inflammatory, and/or neoplastic etiologies; any condition that results in the accumulation of neutrophilic exudates can be termed pyoderma. Most commonly, however, pyoderma refers to bacterial infections of the skin. Pyodermas are common in dogs and less common in cats . Pyodermcquired) . Cephaloμg/kg body weight as incremental doses, looking for any toxic symptoms [μg/kg body weight, twice weekly till the skin scrapings became negative besides clinical improvement. Ticks and lice were treated with ivermectin at 200 μg/kg body weight, subcutaneously once, followed by external application of cypermethrin once a week to prevent recurrence of external parasitic infestation. Flea infestation was treated with fipronil spray twice a month [μg/kg body weight twice a day. Malassezia dermatitis was treated with oral ketoconazole at 5 mg/kg body weight per day given along with food and treatment for seborrhea was carried out with skin tonic containing essential fatty acids.The present investigation was carried out on the dogs referred to C.V.Sc, S.V.V.U., Tirupati, Andhra Pradesh, India, and dogs presented at the major veterinary hospitals around Tirupati. Dogs with a history of more than three episodes of skin infections in the past one year were included in the study as also done by Bensignor and Germain . The dogsymptoms . Treatme a month . HypothyMalassezia dermatitis, flea allergic dermatitis, and hypothyroidism were noticed in 2 dogs each followed by keratinization disorders (seborrhea) in one dog. Mixed conditions, that is, combination of Malassezia dermatitis and tick infestation, and a combination of scabies and tick infestation in one dog, were also noticed. However, no associated conditions could be noticed in the remaining 2 dogs. Simultaneously, Bloom and Rosser [In the present study, out of 13 dogs selected, seven were females and six were males. Recurrent superficial pyoderma and recurrent deep pyoderma were noticed in ten and three dogs, respectively. Age of dogs ranged from 1 to 8 years, with breeds like German shepherd, Labrador, Lhasa Apso, Dachshund, Rottweiler, Bullmastiff, Doberman, Pug, and nondescript and weights ranging from 8 to 40 kg. Duration of clinical signs ranged from 3 to 18 months. The dogs were previously treated with different antibiotics such as penicillin, lincomycin, enrofloxacin, amoxicillin clavulanate, cephalexin, and amikacin but for a shorter period of about one week. Thorough anamnesis revealed that failure to identify and treat the underlying factors, use of a narrow spectrum or an inappropriate antibiotic(s), and therapy of an insufficient duration might be responsible for recurrent pyoderma in the dogs presented. Upon thorough investigation, it was found that, out of 13 dogs with recurrent pyoderma, demodicosis, Out of thirteen dogs with initiated therapy, ten dogs could be monitored fully with complete recovery in all of them indicating that this antibiotic was 100 per cent efficacious. Observations of clinical recovery made at weekly intervals are presented in Demodex.Two dogs with deep recurrent pyoderma (cases 8 and 10) showed only some improvement by the end of 7 days with the presence of deep pustules, folliculitis, and ulcers. Though these lesions healed by two weeks, secondary lesions such as erythema, hyperpigmentation, and pruritus were still observed in this dog. However, complete recovery was observed with the continuation of antibiotics for 21 days. In another case of recurrent deep pyoderma (case 10), with generalized demodicosis as an underlying factor, resolution of primary and secondary lesions was observed only on 21 and 28 days of therapy, respectively. Prolonged recovery time in this case could be due to the severity and extent of the lesions and the longer time taken for eliminating the 4 and free T4 levels at a time through ELISA, as the work forms a part of postgraduate study.) However, the recurring condition was treated with the same antibiotic, at the same dosage but along with levothyroxine at 20 μg/kg body weight twice daily. Efficacy of cefpodoxime as reported by previous workers  was 96.In conclusion, this study suggested that cefpodoxime with clavulanic acid is safe and effective in the treatment of recurrent superficial and recurrent deep pyoderma in dogs. The once daily dosing makes cefpodoxime with clavulanic acid a very convenient antibiotic for dog owners, which should increase compliance. Addressing the underlying factors and following proper dose and duration of the antibiotic might have prevented recurrence of the disease in the present study.


Vibrio vulnificus, a resident in the human gut, is frequently found in seafood, causing food-borne illnesses including gastroenteritis and severe septicemia. While V. vulnificus has been known to be one of the major food-borne pathogens, pathogenicity and virulence factors are not fully understood yet. To extend our understanding of the pathogenesis of V. vulnificus at the genomic level, the genome of V. vulnificus FORC_017 isolated from a female patient experiencing a hemorrhagic rash was completely sequenced and analyzed.Three discontinuous contigs were generated from a hybrid assembly using Illumina MiSeq and PacBio platforms, revealing that the genome of the FORC_017 consists of two circular chromosomes and a plasmid. Chromosome I consists of 3,253,417-bp (GC content 46.49 %) containing 2943 predicted open reading frames (ORFs) and chromosome II of 1,905,745-bp (GC content 46.90 %) containing 1638 ORFs. The plasmid pFORC17 consists of 70,069-bp (GC content 43.77 %) containing 84 ORFs. The average nucleotide identity (ANI) value of the FORC_017 and CMCP6 strains was 98.53, suggesting that they are closely related.vvhA, rtx gene cluster, and various hemolysin genes were present in FORC_017. In addition, three complete secretion systems  as well as iron uptake-related genes for virulence of the FORC_017 were detected, suggesting that this strain is pathogenic. Further comparative genome analysis revealed that FORC_017 and CMCP6 share major toxin genes including vvhA and rtx for pathogenesis activities. The genome information of the FORC_017 provides novel insights into pathogenicity and virulence factors of V. vulnificus.Pathogenesis-associated genes including The online version of this article (doi:10.1186/s13099-016-0104-6) contains supplementary material, which is available to authorized users. Vibrio vulnificus infections in humans via either the consumption of contaminated seafood or contact of a wound with seawater generally cause mild gastroenteritis with diarrhea, vomiting, and abdominal pain [V. vulnificus is one of many pathogenic bacteria in the human gut microbiota [Vibrio vulnificus has been frequently detected in sea water as well as various kinds of seafood [Vibrio vulnificus was detected in 66 of 68 oyster samples (97 %) and 27 of 30 clam samples (90 %) from Long Island Sound, California [V. vulnificus-contaminated seafood causes clinical food-borne outbreaks. In South Korea (2000–2011), 34 individuals who consumed V. vulnificus-contaminated seafood were hospitalized and 16 died, which suggests that this pathogen is widespread and highly virulent and causes food-borne outbreaks [V. vulnificus have had their whole genomes sequenced and analyzed to date . Genome information has revealed the presence of various virulence factors in the genomes, such as an extracellular hemolysin/cytolysin, iron uptake receptors, capsular polysaccharide related to antiphagocytosis, and secretion systems [V. vulnificus need to be sequenced and analyzed.nal pain . In addicrobiota , 3. Vibr seafood . Vibrio lifornia . The consamples 9 % and 27V. vulnificus in South Korea, a clinical strain, V. vulnificus FORC_017, was isolated from a 63-year-old woman with the symptom of a hemorrhagic rash after consuming a raw dotted gizzard shad on Jeju Island. The genome was completely sequenced and analyzed using various bioinformatics tools. In addition, comparative genome analysis of the FORC_017 strain and other clinical isolates was performed. The results of this study would provide genomic insights into the main factors associated with food-borne outbreaks and pathogenesis, and is probably useful for further control of this highly virulent pathogen in seafood.To elucidate the pathogenesis of The FORC_017 strain was cultivated aerobically at 30 °C with modified Luria-Bertani medium (LB) supplemented with 1 % (w/v) NaCl for 12 h. The genomic DNA was extracted using DNeasy Blood & Tissue Kit , according to the manufacturers protocol.V. vulnificus FORC_017 were obtained using two different platforms of PacBio RS II platform  and Illumina MiSeq platform . The sequencing results were summarized in Additional file V. vulnificus FORC_017 strain were predicted and characterized using the basic local alignment search tool (BLAST) in the Virulence Factor Database . The circular genome maps of the FORC_017 strain were drawn using the GenVision program .The whole genome sequencing and assembly were conducted at ChunLab, Inc. . Sequences of V. vulnificus using 16S rRNA sequencing and its identity was confirmed with comparative phylogenetic tree analysis using MEGA6 [Vibrio. Phylogenetic trees were constructed from the aligned sequences using the neighbor-joining method with 1000 bootstrap replicates via MEGA6 software. Shewanella baltica OS678 was used as the out-group. ANI phylogenetic tree analysis of complete genome sequences of V. vulnificus  was conducted using the JSpecies program [V. vulnificus strains. The ANI values were calculated using JSpecies by comparing whole genome sequences between V. vulnificus strains, which were fragmented into 1020-bp, based on BLAST algorism. The tree was constructed using the R program. The comparative genome analysis was conducted with the Artemis Comparison Tool (ACT) [The strain was identified as ng MEGA6  with var program  to show ol (ACT) .V. vulnificus, including a short rod with a single flagellum  were predicted to be functional and 617 ORFs (20.96 %) to encode hypothetical proteins. Chromosome II consists of 1,905,745-bp with a GC content of 46.90 % containing 1638 predicted ORFs, 13 tRNA genes, and 3 rRNA genes. Among the ORFs, 1256 ORFs (76.68 %) were predicted to be functional and another 382 ORFs (23.32 %) to encode hypothetical proteins. The plasmid, pFORC17, consists of 70,069-bp with a GC content of 43.77 % containing 84 predicted ORFs. 36 ORFs (42.86 %) were predicted to be functional and another 48 ORFs (57.14 %) to encode hypothetical proteins. Based on the bioinformatics analysis of these chromosomes and a plasmid, circular genome maps were drawn  exotoxin and a repeats-in-toxin (Rtx)  and an rtx gene cluster , suggesting that FORC_017 is pathogenic. The FORC_017 strain has various hemolysin genes such as FORC17_0475, 1369, 2364, 2546, 2587 and 2903 in chromosome I, and FORC17_3057, 3239 and 4284 in chromosome II. Subsequent cytotoxicity test of the FORC_017 strain supported its pathogenesis activity . However, the FORC_017 strain does not have this cholera-like toxin in the genome. Therefore, T2SS may be associated with protease/lipase secretions, which is probably related to the damage of the membrane in the human cells for infection of V. vulnificus [Various secretion systems including Types I, II, IV and VI  were observed in the genome sequences of the FORC_017 strain. T1SS secretes gram-negative bacterial RtxA exotoxins in lnificus . While Tlnificus . TherefoV. vulnificus since iron loss caused by V. vulnificus negatively affects the heme protein integrity, resulting in host cell death [Iron uptake from host cells plays a key role in the survival of ll death , 23. TheVibrio strains using 16S rRNA sequences showed that the FORC_017 strain belongs to V. vulnificus  revealed that the FORC_017 strain has the closest evolutionary relationship  with a clinical isolate, V. vulnificus CMCP6, from a patient in South Korea (Fig. Comparative phylogenetic tree analysis of the FORC_017 strain and other cus Fig. a. FurtheV. vulnificus, which is probably involved in the seafood-borne outbreaks. In addition, the FORC_017 strain could provide further genomic information for development of rapid detection and control methods against V. vulnificus in various kinds of seafood. Therefore, genome information can be applied for rapid detection, epidemiological survey and prevention of food poisoning caused by this strain.The genome information of the FORC_017 strain provides genomic insights into its pathogenicity and various virulence factors as well as their virulence mechanisms in


Hyaluronan (HA) is a high molecular weight glycosaminoglycan of the extracellular matrix (ECM), which is particularly abundant in soft connective tissues. Solutions of HA can be highly viscous with non-Newtonian flow properties. These properties affect the movement of HA-containing fluid layers within and underlying the deep fascia. Changes in the concentration, molecular weight, or even covalent modification of HA in inflammatory conditions, as well as changes in binding interactions with other macromolecules, can have dramatic effects on the sliding movement of fascia. The high molecular weight and the semi-flexible chain of HA are key factors leading to the high viscosity of dilute solutions, and real HA solutions show additional nonideality and greatly increased viscosity due to mutual macromolecular crowding. The shear rate dependence of the viscosity, and the viscoelasticity of HA solutions, depend on the relaxation time of the molecule, which in turn depends on the HA concentration and molecular weight. Temperature can also have an effect on these properties. High viscosity can additionally affect the lubricating function of HA solutions. Immobility can increase the concentration of HA, increase the viscosity, and reduce lubrication and gliding of the layers of connective tissue and muscle. Over time, these changes can alter both muscle structure and function. Inflammation can further increase the viscosity of HA-containing fluids if the HA is modified via covalent attachment of heavy chains derived from Inter-α-Inhibitor. Hyaluronidase hydrolyzes HA, thus reducing its molecular weight, lowering the viscosity of the extracellular matrix fluid and making outflow easier. It can also disrupt any aggregates or gel-like structures that result from HA being modified. Hyaluronidase is used medically primarily as a dispersion agent, but may also be useful in conditions where altered viscosity of the fascia is desired, such as in the treatment of muscle stiffness. It is composed of disaccharides of alternating D-glucuronic acid and N-acetyl D-glucosamine connected by β-1,3 and β-1,4 glycosidic bonds, respectively. In most healthy tissues, HA has an average molecular weight of approximately 6–8 million2. HA has a high turnover rate, but homeostasis is normally maintained by similar rates of synthesis and degradation4. It can be enzymatically cleaved by hyaluronidases, or chemically degraded by hydroxyl radicals and peroxynitrite during inflammation7. It has a wide variety of physiological functions in the mammalian body, including maintenance of a viscoelastic cushion to protect tissues, control of tissue hydration and water transport, lubrication of biointerfaces, creation of large assemblies with proteins and proteoglycans in the ECM, and receptor-mediated signaling roles in cell detachment, mitosis, migration, tumor development, and inflammation9. HA is ubiquitous, but is particularly abundant in soft connective tissues, including between deep fascia and muscle, within muscle12, and also between the collagen layers that compose the deep fascia. This tissue is a multilayered structure formed by two to three layers of densely packed collagen fibers14, spaced by a layer of loose connective tissue 17. The proposed function of HA is to facilitate smooth gliding between these structures during movement, and in the transmission of force generated from muscle contraction.Hyaluronan (HA) is a high molecular weight glycosaminoglycan polymer of the extracellular matrix (ECM) in vertebrate tissuesThe aim of the present study was to examine more closely the viscoelastic properties of HA in association with these structures, and to evaluate if the above mechanisms can be affected by HA viscoelastic variations.18 and references therein). These properties may affect the movement of HA-containing fluid layers within and underlying the deep fascia. Additionally, the concentration and molecular weight of HA affects its contribution to the lubrication of biological interfaces. Changes in the concentration, molecular weight, or even covalent modification of HA in inflammatory conditions, as well as changes in binding interactions with other macromolecules, can have dramatic effects on the sliding movement of fascia.Solutions of high molecular weight hyaluronan can be highly viscous with non-Newtonian flow properties (see for example the review by Cowman and Matsuoka1.8). This means that, the larger the polymer, the lower the average density because the volume grows faster than the mass. For HA, with molecular weight normally in the millions, this leads to extremely large chain volumes. In contrast, the volume of a compactly folded globular protein chain increases only in direct proportion to the number of amino acids and is therefore proportional to the molecular weight to the first power. The expanded shape of a flexible polymer in solution is a key reason for the high viscosity of "unfolded" polymer solutions.For a semi-flexible polymer such as HA, the volume occupied by each chain is very large. Most of the volume is water, not bound by the polymer, and the polymer shape is constantly changing, but the water still contributes to the effective size of each molecule because the solvent movement is affected by frictional interaction with closely spaced polymer segments. Due to its rapid chain motions, the time-average shape of the molecule can be described as a sphere, with greatest density of chain segments near the center. Furthermore, the effective sphere-like volume of a wormlike HA chain in a good solvent grows approximately as the molecular weight raised to the power of 1.8  as proportional to the product of the number of spherical particles per unit of solution volume,n, and the volumes of the particles themselves,V. This product corresponds to the volume occupied by all the particles, divided by the solution volume, or the volume fraction,φ, of the solution that is occupied by particles. The occupied volume fraction can also be expressed in terms of the mass concentration of the polymer  multiplied by the specific volume of the polymer (in cm3 occupied/g). The specific volume (inverse of the density) is proportional to the intrinsic viscosity[η]. As discussed above, the density of the polymer domain decreases with increasing molecular weight, so the intrinsic viscosity is a sensitive measure of the molecular weight. For HA in neutral aqueous salt solution at physiological ionic strength, the intrinsic viscosity is proportional to M0.819.The specific viscosity,φ, is equal to0.4c[η]. When the productc[η] is 2.5, the volume fraction is 1, and the solution can be considered to reach the "coil overlap" point. This is the nominal point at which the chains fill the solution and are forced to touch each other, although they already interact at lower concentrations/hydrodynamic volumes, and can interpenetrate at higher concentrations/hydrodynamic volumes because the coil volumes contain mostly solvent. An ideal solution should be much more dilute than the critical concentration for coil overlap. Experimentally, the coil overlap point is usually identified as the value ofc[η] above which the specific viscosity begins to dramatically increase.From20. Some example chain parameters are given below . For HA with a molecular weight of 1 million, the coil overlap concentration would be about 1400 µg/cm3. For comparison, the concentration of HA in human synovial fluid is usually 2000–3000 µg/ml, and the average molecular weight is close to 6 million, so the HA chains are well above the coil overlap point.In order to estimate the concentration at which a HA solution might exceed coil overlap, we can consider the hydrodynamic size of the polymer at different molecular weights 20. Somen below 20. For c[η]. A comparison of the observed behavior of HA solutions to the ideal case  protein is excluded from the space  surrounding a fiber, by its own radius, and the thickness of the fiber. The center of the spherical protein defines its position, and the center cannot approach the fiber more closely than the sum of the radius of the sphere and the finite radius of the fiber. The probability of a protein finding space in a random suspension of such fibers (corresponding to the interior of a gel bead) is exponentially decreased as a function of the excluded volume. More fibers, or bigger proteins, mean less available space, and lower probability of being inside the beads. A similar picture can be imagined for the crowding of globular proteins by HA chains  that occurs between coiled HA chains in solution. Each chain crowds the others, by an amount related to the hydrodynamic volume it occupies, rather than just its physical chain length and thickness .18.The reduced probability of finding space for movement as a function of increased total concentration makes the effective concentration of the HA greater. The effective concentration is exponentially increased with HA real concentration and with the intrinsic viscosity. Since intrinsic viscosity is a measure of hydrodynamic volume, it is connected with molecular weight. The viscosity of HA solutions should then increase exponentially with concentration and molecular weight (as measured by intrinsic viscosity). We expanded the exponential term into a series. The first four terms of the series provide an excellent approximation of the observed specific viscosity. The first term is the ideal case, and the next three terms provide the nonideality contribution as shown in29.Now the extremely high viscosity of HA solutions can be successfully rationalized on the basis of mutual macromolecular crowding, which increases the effective concentration of the HA, and substantially increases the viscosity. There is no need to invoke intermolecular association or ordered structures of the HA molecules. It is also of great interest to note that, depending on the starting point on the curve, increasing or decreasing the HA concentration or intrinsic viscosity (and thus molecular weight) can have enormous impact . The absence of ordered structures in pure semi-dilute HA solutions is also supported by HA diffusion coefficients measured by the confocal Fluorescence Recovery After Photobleaching (FRAP) studies of Hardingham and coworkers30 and Fouissac, Milas, and Rinaudo31 show that the intrinsic viscosity of high molecular weight HA is decreased by about 25% as the temperature is increased from 25° to 65°C. Hoeflinget al.32 showed that incorporating the 25% decrease in intrinsic viscosity intoThe large hydrodynamic volume of HA chains depends on the stiffness of the chain, which is due to steric hindrance to rotation about the linkages between sugar residues, and to the dynamically forming and breaking hydrogen bonds across those linkages. With increasing temperature, rotations about the linkages are easier, and the chains gain flexibility. This shrinks the molecular volume, and consequently reduces the viscosity. It is possible to predict the extent of viscosity reduction, based on35. At low pH of about 2.5, at physiological ionic strength, an interesting viscoelastic putty (nearly like a gel) is formed as a result of interchain association37. But between pH values of 6.5–8.0, the expansion of the hyaluronan chains is nearly constant, and the intrinsic viscosity is not changed38.The viscosity of HA solutions is not very sensitive to pH in the physiological range. At very high pH, above about pH 11, the rotational freedom at the glycosidic linkages is greatly increased due to breakage of residual hydrogen bonds, and the chain volume shrinks, reducing the solution viscosity39.When a solution containing flexible polymers is flowing, the molecules can become distorted and stretched in the direction of the flow. The more slowly the molecules recover to their undisturbed shapes, relative to the rate of shear, the more they become aligned with the flow. The aligned molecules have a reduced contribution to the solution viscosity. In steady shear conditions, the solution viscosity is highest when the rate of shear is low, and molecules can reorient and relax to the undisturbed shape as rapidly as they move. But with increasing shear rate, the molecules cannot relax fast enough, and the viscosity drops.[η]M) and the solvent viscosity. The more viscous the solvent, the longer the time needed for relaxation. In semi-dilute, or crowded solutions, the relaxation time is much more strongly increased because molecules must find space to move past each other. Again, the probability of finding space is exponentially related to the excluded volume. The higher the molecular weight, or the higher the concentration, the longer the relaxation time, and the more dramatic the loss of viscosity (shear thinning) with increasing shear rate18.This shear rate effect is seen for both dilute and semi-dilute solutions. In dilute solutions, the relaxation time depends on the molecular volume , whereas fluid film lubrication occurs at right end of the curve (high velocity and low loads with a large film thickness). While this curve was generated using classic hard, non-porous, engineering materials (e.g. steel), and may not be completely applicable to soft, porous, hydrated tissues or materials42, it is still useful in understanding general conditions under which different modes of lubrication are operative.The three main modes of lubrication are boundary, fluid film or hydrodynamic, and mixed. In boundary mode lubrication, surface-to-surface contact occurs between articulating surfaces, and molecules bound to the surface mediate friction. In fluid film lubrication, a thick (relative to the surface roughness of the articulating surfaces) viscous fluid film supports the load and separates the surfaces allowing motion with little resistance to shear. Mixed mode lubrication is where both boundary and fluid film mode lubrication are operative. The conditions under which each mode operate are classically defined by a Stribeck curve , which d43. This has been speculated to be due to a ‘viscous boundary layer’ of HA at the surface of cartilage44.The viscosity of HA solutions can affect the mode of lubrication in which HA reduces friction as its relative effectiveness in reducing friction at tissue biointerfaces. For example, in a boundary mode of lubrication at a cartilage-cartilage biointerface, onto which HA is able to bind, relative effectiveness of friction reduction  has been shown to be dependent on the molecular weight of HA, with higher molecular weight resulting in lower friction 43. ThisConversely, in fluid film lubrication where a thick film of HA separates articulating surfaces, friction would be predicted to increase with viscosity, potentially reaching very high levels. In deep fascia, the thickness of the HA-containing fluid layer is apparently on the order of tens of microns, which is large compared with the diameter of the molecules and even the roughness of the surfaces. In such a case, if HA concentration and/or molecular weight are high, the resistance to flow due to high viscosity can negatively affect lubrication.45, potentially by binding and shrinking the hydrodynamic domains of HA molecules, enabling them to flow more easily. In cases where HA concentration becomes high and viscous flow is reduced, lubricin could facilitate increased motion and thus decreased friction.Lubricin is a lubricating mucin-like glycoprotein present in various body fluids, such as synovial fluid , that can alter the viscosity of HA solutions. We have shown that lubricin is able to reduce the viscosity of a high molecular weight HA solution when both components are present at physiological concentrations 45, pote2. The HA content of injured skeletal muscle is known to be elevated46. Steccoet al.17 documented, with a highly specific HA-binding peptide, the deposition of HA inside the loose connective tissue in three different fasciae of the body: fascia lata, rectus abdominis sheet and sternocleidomastoid (SCM) fascia. Steccoet al.20 also documented an increase of the thickness of the loose connective tissue in the SCM fascia in patients complaining of chronic neck pain syndrome. If the HA content of fascia is increased, the viscosity and elasticity of the HA-containing fluid would be increased, and its fluid film lubricating properties reduced.A common observation in inflamed tissues is an increase in the concentration of HA49. This transfer is normally a protective function that can stabilize the pericellular coat of cells. The HC domains can dimerize, and effectively act to hold HA chains noncovalently together51. It could be imagined that the HA, modified by HC, becomes gel-like and immobile in the deep fascia. HC-modified HA can also be found aggregated into fibers or cables53. An increase in both HA and TSG-6 has been reported in cultured vascular smooth muscle cells subjected to mechanical strain54, and proliferating smooth muscle cells in rat neointima after injury express high levels of TSG-655. Recently, an increase in HA, TSG-6, and HC-modified HA was observed in damaged mouse skeletal muscle tissue46.Possibly more important might be the covalent modification of HA by heavy chain domains derived from plasma inter-α-inhibitor (IαI). An increase in the expression of TSG-6 protein is commonly observed during inflammation. TSG-6 acts catalytically to transfer heavy chain (HC) domains from the chondroitin sulfate chain of IαI to HAThe fascia assumes a fundamental role with its two components: dense connective tissue (collagen fibers type I and III) and loose connective tissue , and HA). HA is an important component of the loose connective tissue in fascia. In this review, we have considered the physico-chemical properties of HA solutions, and how they depend on factors such as concentration, molecular weight, and modification by covalent linkage to HC derived from IαI, or noncovalent interactions with proteins such as lubricin.56. Changes documented in rat soleus muscle due to one week of immobilization include increase in HA concentration and shortening of sarcomere length57. These changes were postulated to increase the number of cross bridges attached during contraction59. In the early stages of this process, the arrangement of collagen fibrils in the endomysium may remain longitudinal, however, by about 4 weeks, the collagen fibrils became arranged circumferentially, which signals pre-contracture. Thus subtle changes in the turnover of HA and in the properties of the extracellular matrix with immobility can lead to structural and eventually functional changes in the muscles with significant consequences on movement60.Immobilization of a limb or body segment can lead to an increase in the concentration of HA within and between the fascial and muscular compartments, which can increase the fluid viscosity. The increased fluid viscosity within the loose connective tissue can in turn decrease the gliding between the layers of collagen fibers, which may be perceived by the subject as stiffness65. Due to the fundamental role of HA in determining the viscoelasticity of fluids in soft connective tissues, its alteration could therefore modify the activation of the receptors, producing non-specific musculoskeletal pain.The interdependence of mechanoreceptor activation and viscoelasticity of the surrounding tissue has been previously noted17. It may also reflect impaired turnover via flow toward the lymph. A high HA concentration would increase the viscosity of the HA-containing fluids. When the viscosity of the fluid in the loose connective tissue increases due to increased HA concentration or its covalent modification, the dense connective tissue can spread the stiffness throughout the surrounding areas, driving even further the sensation of muscle stiffness. Deep friction manipulation may aid outflow of HA if the effective shear rate within the fluid layers generates a drop in the viscosity. This may explain the reduced perception of stiffness that is reported by both therapist and patient during this manual treatment. This review suggests a basis for the typical finding in manual therapy: the more chronic is the stiffness, the higher the concentration of HA may be, and the greater the effort and time required for manual treatments65.The increased HA content of fascia and the underlying muscle may result from increased HA synthesis, due to a stimulation of the fibroblast-like cells that were previously suggested to be the biosynthetic source of hyaluronan6. For medical applications, a preparation containing a recombinant fragment of human PH20 hyaluronidase is currently available. It hydrolyzes HA (and susceptible linkages in chondroitin sulfate glycosaminoglycans) by splitting the glycosidic bond between C1 of an N-acetylhexosamine moiety and C4 of a glucuronic acid moiety. It reduces the molecular weight, and would be expected to lower the viscosity of the extracellular matrix fluid and thus make outflow easier. It can also disrupt aggregates or gel made by HA crosslinked via HC chains. The products of enzymatic cleavage may include small oligosaccharides of HA, which have been reported to trigger specific inflammatory responses and have a pro-inflammatory effect67. That possibility has been disputed68. In any case, the presence of fragments generated by the action of hyaluronidase is expected to be short lived as restored flow can wash the small polymers away. Hyaluronidase is used primarily as a dispersion agent, but may now be considered for use in conditions where altered viscosity of the fascia is desired, such as in muscle stiffness.There are a number of HA-cleaving enzymesThe physico–chemical properties of HA are modulated by its concentration, molecular weight, solvent ionic composition, temperature, and covalent or noncovalent binding of proteins and other species. If HA is forced to exist in a highly crowded environment, or more generally if its density within the loose connective tissue inside the fascia is increased as a result of injury or other pathological process, the behavior of the whole deep fascia and of the underlying connective tissue epimysium and perimysium could be compromised. Treatments that address the role of HA may hold promise. et al., 2007Richter,), lipid membranes , nanoparticles  and pulmonary surfactants .This expertly constructed review article on hyaluronan (HA) provides an in-depth analysis of the relationship between its structural and physicochemical properties. The article also contains a well-informed update on the effects of modifications, such as protein binding, cross-linking and molecular weight on these properties. In particular, this is discussed in relation to its role in regulating the viscoelasticity of loose connective tissues (fascia) required for effective organ separation. For example, the authors point out that abnormally high HA-dependent viscosity in muscle fascia can lead to local and more generalized stiffness, and potentially mechanosensor-mediated pain. Indeed, this section could be expanded to underline the importance of future research on the effects of structural modifications on catabolic processing of HA, both within the extracellular matrix and in fluid compartments. On the general topic of hyaluronan-mediated effects at interfaces, readers will also find interesting the papers from Richter and colleagues who are examining the effects of end-grafted HA on the properties of glycoconjugate cell coats (We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Competing Interests: No competing interests were disclosed.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. This is an excellent review of the current biophysics and biology of hyaluronan. It presents an analysis of the complex non-ideal behaviour of hyaluronan characterised by different biophysical techniques and interprets this technical information in a way that enables the non-specialist to understand the consequences these properties have in biology. It's up to date and presents a very readable insight into the complex behaviour of this essentially simple biopolymer.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


The realization of artificial molecular motors capable of converting energy into mechanical work is a fascinating challenge of nanotechnology and requires reactive systems that can operate away from chemical equilibrium. This article describes the design and construction of a simple, supramolecular ensemble in which light irradiation causes the directional transit of a macrocycle along a nonsymmetric molecular axle, thus forming the basis for the development of artificial molecular pumps. Since ancient times, man has tried to construct devices that facilitate life. With the advent of nanoscience and nanotechnology, which aim at controlling matter on the nanometer scale, chemists have started to develop devices of molecular size capable of performing simple functions . In thisThe research field of artificial molecular machines, which began in the early 1990s , has rap8Multicomponent molecular systems such as rotaxanes and related species are frequently used to realize molecular devices and machines –8. In thToday, the stimulus-controlled threading and dethreading of pseudorotaxanes are well known processes that have been realized with a wide variety of chemical structures –8. They Our group recently developed a simple, self-assembling system in which a macrocycle is displaced unidirectionally along a nonsymmetric molecular axle in a repetitive fashion using light as the sole energy source . The sysThe vast majority of molecular devices studied so far belong to the class of molecular switches. A molecular or supramolecular system behaves as a switch if it can exist in at least two different forms, each characterized by given physical and chemical properties, which can be reversibly converted into one another in consequence of the application of an external stimulus. In a switch, the transformation that leads from state 1 to form 2 follows a path that is the exact reverse of the transformation from 2 to 1 . This feTo understand the meaning of this statement we can use an example from everyday life. Let us imagine a person using a pulley to lift a weight by repeatedly exchanging the relative position of the hands with respect to the rope between state 1  and state 2 . The operation of the switch can be compared to the situation in which the person has one hand bound to the rope: irrespective of the exerted force, the weight will not be lifted because the exchange in the position of the hands will put down the load by the same amount that has just been pulled up. To reach the goal the operator needs to pull the rope with one hand and move the other hand forward, alternating hands such that the work previously done is not cancelled out. This means that the conversion between states 1 and 2, each corresponding to a different hand configuration relative to the rope, must take place by following nonequivalent routes. Similarly, in a molecular motor, the transformation between the two mechanical states occurs through different configurations  so that Molecular motors, like macroscopic ones, need an energy source to operate. In reference to Light is an energy source of great interest for use with molecules that has been widely exploited to operate molecular switches for a long time. For example, in photochromic compounds , the tratrans configuration, can be temporarily converted in a photochemical manner into the metastable cis isomer, endowed with significantly different steric and electronic properties + complex than in the [cis-]+ one.For example, the photostationary K2,hν = K4,hν), the mechanism is energy ratcheting, whereas upon irradiation at 365 or 287 nm, information ratcheting is also present. Different physical phenomena, however, account for the information ratcheting effect at these two wavelengths. The 365 nm light is not absorbed by the ring, but the presence of the latter enhances the absorption of the trans-axle, causing the preferential, photoinduced closure of the azobenzene gate in the pseudorotaxane with respect to the axle. The excitation of the ring with 287 nm light is followed by energy transfer to the trans-axle, a process that can effectively take place only if the components are associated, and causes the trans→cis isomerization. Hence, upon irradiation at either 287 or 365 nm, the information about the relative position of the ring and axle components  contributes to the gating.These data enable an insightful discussion about the mechanisms used by this system to rectify Brownian fluctuations . If irrakBT , which is about one fourth of the energy of ATP hydrolysis utilized by natural molecular motors. It can also be calculated that the system consumes on average 430 photons of 365 nm light  to complete a cycle, corresponding to an upper limit for the energy conversion efficiency of 9 × 10−5. Despite such a low conversion efficiency, this study clearly demonstrates that the synergy between photochemical reactions and self-assembly processes can lead to innovative methods for the conversion of sunlight into chemical energy [From the analysis of the free energy change of the system, it can be estimated that the maximum amount of work that could be performed by the pump in a cycle under the employed conditions is 5.1·l energy .The most recent progress in the area of molecular machines has revealed the importance of designing and realizing molecular and supramolecular systems that can operate continuously away from equilibrium. The development of an artificial molecular pump powered by light following a minimalistic approach is an important step towards this goal and enabled the study and detailed understanding of some fundamental principles underlying the operation of molecular motors.The structural simplicity and synthetic accessibility of the employed components are indeed important elements to favor the progress of this research and its possible applications. The present system, however, has a few important limitations. For example, because of the symmetry of the crown ether used as the macrocycle, it is impossible to establish which side of the ring cavity is pierced by the axle. Moreover, the device in its current form does not perform work but merely dissipates the light energy input into heat. The main challenge for this research is to modify the system so that the molecular movements can be used to generate concentration gradients or to transport chemical species along predetermined directions for significant distances in comparison with typical diffusive paths in solution.


Inositol phosphates (InsPs) are intricately tied to lipid signaling, as at least one portion of the inositol phosphate signaling pool is derived from hydrolysis of the lipid precursor, phosphatidyl inositol  bisphosphate. The focus of this review is on the inositol pyrophosphates, which are a novel group of InsP signaling molecules containing diphosphate or triphosphate chains  attached to the inositol ring. These PPx-InsPs are emerging as critical players in the integration of cellular metabolism and stress signaling in non-plant eukaryotes. Most eukaryotes synthesize the precursor molecule, myo-inositol -hexakisphosphate (InsP6), which can serve as a signaling molecule or as storage compound of inositol, phosphorus, and minerals (referred to as phytic acid). Even though plants produce huge amounts of precursor InsP6 in seeds, almost no attention has been paid to whether PPx-InsPs exist in plants, and if so, what roles these molecules play. Recent work has delineated that Arabidopsis has two genes capable of PP-InsP5 synthesis, and PPx-InsPs have been detected across the plant kingdom. This review will detail the known roles of PPx-InsPs in yeast and animal systems, and provide a description of recent data on the synthesis and accumulation of these novel molecules in plants, and potential roles in signaling. Myo-inositol (inositol) signaling is much like a language in that each molecular species used in the pathway, whether lipid or soluble in nature, can convey specific information to the cell, like a word. In this analogy, each combination of different numbers and positions of phosphates on the inositol ring and the presence of diacylglycerol linked via the C1 of inositol, also convey unique information . The high energy pyrophosphate bonds present in PPx-InsPs may serve as a way to store energy in the cell, with the standard free energy of hydrolysis of InsP7 and InsP8 higher than that of ADP and ATP, respectively 2-InsP4 or 1/3,5(PP)2-InsP4, and in these cases the slash indicates one of the pyrophosphates present can occur at either of two carbons . The ratios of these different PPx-InsPs differ in various Dictyostelium species examined , but was later conclusively identified as 1/3PP-InsP5 2-InsP4 (in vivo) and 5PPP-InsP5(in vitro) . Vegetative tissue from Arabidopsis was also analyzed and PPx-InsPs were found in both seedlings  and mature leaves (1.00% InsP7). InsP7 was detected in other plant species as well, including another member of the Brassica family, Camelina sativa (1.40% in seedlings), and an unrelated dicot, cotton (Gossypium hirsutum) in the leaves and in shoots and roots of seedlings. PAGE analysis was used to detect PPx-InsPs in both Arabidopsis and maize seed in this same work  is a high affinity ABC-binding cassette transporter that specifically binds to InsP6 PP-InsP7, similar to Dictyostelium. Efforts were made to purify the plant PPx-InsPs, however no informative NMR data was obtained , and the kinase activity of these enzymes phosphorylates the 5-position of InsP5, InsP6, and InsP7, yielding 5PP-InsP4 or 5PP-InsP5 and two possible forms of InsP8: 1/3,5PP-InsP5 and 5PPP-InsP5  mutant  has a PxxxDxKxG (“PDKG”) catalytic motif and includes the InsP3 kinases (IP3Ks), IP6K, and inositol polyphosphate multikinases (IPMKs). Recent phylogenetic studies on these enzymes has suggested that the InsP kinase family common ancestor is an IP6K precursor 2-InsP4  in animals P3 signaling pathway is activated  and AtVip2 (At5g15070) and the encoded proteins have 94% similarity to each other, but only 50% and 59% similarity to yeast ScVIP1 and human HsVIP1 respectively , which as in yeast and humans, is necessary for activity  and may represent a unique mechanism for regulation of VIP activity specific to plants 2-InsP4 and 5PPP-InsP5, both require phosphorylation at a C5 position 2-InsP4 synthesis , and the ability of each gene product to restore InsP7 is known to modify or interact with proteins in other model systems, and how these actions lead to changes in known signaling functions in yeast and animals. Although little is known about PPx-InsPs function in plants and mechanisms regulating inositol signaling differ between plants and animals, it is likely that InsP7 conveys plant signaling information by virtue of modifying or interacting with proteins. Drawing parallels from the known and hypothesized roles of InsP7 in other organisms, we speculate that PPx-InsPs function in several plant signaling pathways, including, but not limited to energy homeostasis, phosphate (Pi) sensing, and immune responses. In the following sections, we will elaborate on published data from other model systems that indicates a role for PPx-InsPs in these pathways.If we continue the analogy of InsP as words, our next challenge will be to understand what these words mean and what information they convey. We will describe how InsPPPx-InsPs are involved in energy homeostasis both at the cellular and organismal level. Maintaining energy homeostasis, or the balance of intake/production, storage and use of energy often in the form of ATP or sugar, is essential for all living organism. In animals, the AMP Kinase (AMPK) is often named as an energy sensor. Under low energy conditions, AMP is bound, activating the AMP kinase and reprograming the cell to maximize energy acquisition and minimize energy use Hardie, . In oppokcs1 mutant mouse embryonic fibroblast (MEF) cells, where levels of ADP and AMP are low. Further studies showed that the yeast kcs1 and kcs1/vip1 mutants as well as MEF ip6k mutants have reduction or loss of functional mitochondria. This loss of mitochondrial function with high ATP levels can be explained by an increase in glycolysis and a reduction of ATP used in metabolic pathways. Thus, the lack of InsP7 synthesis in these mutants leads to changes in ATP synthesis and utilization. InsP7 in this system most likely affects ATP levels by altering transcription of genes that control glycolysis. Specifically, in yeast promoters of glycolytic regulatory genes have a CT-box that binds to the General Control Response 1 (GCR1) transcription factor. InsP7 may function to regulate GCR1 directly by a non-catalytic transfer of the β-phosphate from InsP7 to an already phosphorylated serine residue in GCR1, resulting in a pyrophosphorylated serine. This modification likely causes a conformational change in GCR1, allowing it to bind the CT-box, thereby regulating the expression of glycolytic regulatory genes  pathway, reducing glucose uptake, insulin sensitivity and protein translation. In response to growth factors, Akt is normally phosphorylated by a protein kinase named PDK1 (3-phosphoinositide-dependent protein kinase 1), which activates the GSK3β (Glycogen synthase kinase 3) and mTOR signaling pathways. Activation of Akt requires binding of its PH domain to PtdInsP3, associated with the plasma membrane. When bound to PtdInsP3, Akt undergoes a conformational change which exposes its activation domain, allowing Akt to be phosphorylated and activated by PDK1  and the kinase that activates Akt, PDK1. Most studies indicate that plants do not synthesize PtdInsP7 in plants regulates transcription via pyrophosphorylation or whether InsP7 can compete with binding to PH domains of plant proteins, however, both are potential mechanisms by which InsP7 could act. Determining whether plants use InsP7 to regulate metabolism or growth, and the mechanistic details of such regulation will benefit from the development of genetic resources to examine Atvip loss-of-function mutants. In addition, we need to know whether PPx-InsPs levels are altered by changes in energy or metabolic status. Answering these questions is now possible as the basis for detecting and measuring PPx-InsPs in plants has been established, and the AtVip genes have been cloned and shown to encode active proteins.We do not yet know whether InsPi levels and numerous studies link PPx-InsPs to low Pi responses in other organisms. Plant Pi homeostasis is a highly regulated process  signaling pathway, including the Pho80-Pho85 cyclin-CDK (cyclin dependent kinase) complex  gene was isolated and confirmed to increase Pi uptake when expressed in Xenopus oocytes  are regulated at a transcriptional and post-transcriptional level. A recent study has shown that a sub set of PSR genes involved in Pi uptake, translocation and remobilization are up regulated in the ipk1 mutant under Pi sufficient conditions  compared to wild type , do not show an accumulation of Pi, indicating that InsP6per se is probably not the molecule utilized for sensing Pi    by the host pattern recognition receptors (PRRs) on the cell surface or cytoplasm. In plants, pathogen detection, signaling, and immune response takes place in most cells, while animal immune systems have evolved specialized mobile immune cells. Detection of PAMPs by PRRs initiate signaling cascades which can result in CaThe innate immune system in plants and animals share many similarities in the use of PAMPs and PRR as a method of detecting pathogens. Plants have a large diversity of PRR and Resistance (R) genes, however homologs of the RIG-1/IRF3 pathway have not been found in plants. Therefore, while it is interesting to speculate that PPx-InsPs may regulate specific defense transcription factors in plants, the lack of RIG-1 and IRF-3 homologs suggests that this pathway may be unique to animal innate immunity signaling.5 acting as a negative regulator. As described previously, 5PP-InsP5 competes for binding with Akt through the PH domain. Upon infection, 5PP-InsP5 levels drop allowing Akt to translocate to the membrane and allow for the induction of PtdInsP3 signaling, leading to neutrophil activation and superoxide production. ip6k mutant neutrophils have increased bactericidal activity and ROS production , showed a compromised defense response, including decreased expression of defense genes and a reduction in the systemic acquired immunity in response to a bacterial pathogen 2-InsP4 or 1PPP-InsP5. Further work is needed to identify the stereochemistry of plant PPx-InsPs and to clarify the regulatory components involved in their synthesis and metabolism. Drawing parallels to known roles of PPx-InsPs in other eukaryotes, plant PPx-InsPs may have a role in energy, Pi sensing, and innate immunity signaling pathways. Thus, identification of PPx-InsPs in plants presents a new avenue and tool that may be useful for improving crop yield, reduced fertilizer demand, and improved growth under stress.PPx-InsPs have recently been identified in higher plants, adding new molecular players in the plant inositol signaling pathway. Both InsPThe 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.