In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for  Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software. We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications. Over the last decades, systems biology has enabled the comprehension of the different layers of biological processes, enabling the interpretation of the data generated by several emerging high-throughput technologies. The recent growth in both the diversity and quantity of data generated by these technologies led to the need of novel approaches for data processing, analysis and modelling to take full advantage of these data. Genome-scale tools for genomics, transcriptomics or proteomics allowed scientists to generate new approaches for experimental design and analysis, with important tools such as Genome-Scale Metabolic Models (GSMMs) granting the ability of simulating cellular processes and infer its phenotype, saving time and costs .Due to the recent evolution of systems biology, the study of metabolism has experienced significant advances throughout the last years. Metabolism is crucial for the study of cellular function and its disturbance is linked to several diseases, ranging from diabetes or hypertension to cancer , 3. SomeCells can be described as an interconnected network of components, making it challenging to understand processes, such as metabolism, in an isolated way, since it has interactions with other sub-systems, such as the genome or the transcriptome. In recent years, significant efforts have been made to better understand these connections. One relevant effort has been the development of GSMMs, a tool which allows the computation of fluxes through metabolic reactions, and has been heavily used in metabolic engineering , 8 and tRecon1  was the S depicts the main structure of the model, with columns defining reactions and rows metabolites, where Sij represents the coefficient of the i-th metabolite in reaction j. The assumption of the steady-state can be expressed as: S ⋅ v = 0, where v is the flux distribution vector. In addition to these constraints, every reaction in the model has an upper and a lower bound, restraining the maximum and minimum amount of flux passing through it. These constraints define an admissible space of flux distributions, i.e. the rate at which every metabolite is either produced or consumed for each reaction [GSMMs’ potential for phenotype simulation can be attained through constraint-based modelling (CBM) methods, which allow for fast calculations over large algebraic models, under the assumption of a steady state , since most kinetic parameters for metabolic reactions are not known. A stoichiometric matrix reaction .v). A common approach is to define an equation (pseudo-reaction) representing the growth of a cell, which will be maximized [These models can be used to support the definition of linear optimization problems, setting their constraints, and allowing different formulations to be achieved through appropriate objective functions over the flux distributions , a method that relies on the assumption that flux distributions that demand the lowest overall flux through the network and are quicker to grow are selected, leading to improvements in the assumption made by FBA .GSMMs enable the integration of several types of information, including distinct omics data. A relevant application of integrating these data is the generation of tissue/cell specific metabolic models , startinSeveral methods were developed to build draft cell/tissue-specific metabolic models throughout the past years, taking as inputs a template generic GSMM and different types of omics data. Although these methods use different approaches, their final objective is to obtain a draft model and/ or a flux distribution which tries to match the omics data provided . In thisAs reviewed by Robaina-Estevéz and colleagues, these methods can be classified into three families, GIMME-, iMAT- and MBA-like. The main objective in the GIMME family is to try to reach fluxes obtained from the model consistent with omics data, while maximizing a Required Metabolic Function (RMF), such as growth. For the iMAT family, the objective is the same without the need of a RMF. For the MBA family, methods try to achieve model consistency according to a predefined core of reactions, which may come from literature or from the omics data (e.g. the most expressed genes) . It shouThe connection of expression data (transcriptomics or proteomics) to the GSMMs is possible due to the gene-protein rules included in the models, which contain information relating the genes encoding the enzymes associated with each reaction (if they exist), in the form of Boolean expressions. However, problems that may affect the accuracy of the reconstructed model, such as experimental and inherent biological noise, several possible platforms to obtain expression data, bias on the process of detection and non curated relationship between gene expression and reaction fluxes, are still a main concern .Since the steps to reconstruct tissue-specific metabolic models address a combination of the aforementioned issues, it is necessary to establish a pipeline to integrate omics data (which will be referred to as “preprocessing” from now on). In the most common pipelines, there are three main steps to be fulfilled . The firAnother important factor to take into account when reconstructing or simulating a metabolic model is the medium composition. By default, most of the GSMMs do not have a predefined composition, allowing the user to define which one to use. However, for most cases, it is difficult to discriminate all metabolites and their concentration, possibly leading to false predictions when the model is simulated . SeveralIt has been proven that different media can lead to changes in the phenotypic behaviour of the cells, how they respond to stress and change their epigenome or transcriptome. Specially in humans, there are some added components to the medium, such as fetal bovine serum (FBS) whose composition is not clear and add another unknown factor to how the model should behave on its presence . AlthougAs impactful as the other raised issues, the choice of an algorithm to reconstruct a tissue-specific model is another source of variability in the final model, mainly due to the differences on the reconstruction algorithm principle. As a validation method for the reconstructed models, they can be tested with a set of required metabolic tasks, such as production of lipids and vitamins . Since nWhen considering the issues described above, we understand that there are some limitations that are troublesome to overcome, such as the lack of a standardization of preprocessing methods, lack of certain types of omics data to fully characterize cells’ metabolism (such as fluxomics) or even limitations of the methods themselves, like the assumption of a steady-state or missing information for certain metabolic reactions. Alongside with the development of the troppo package, we tried to tackle some of the previous problems. We sought to aggregate dispersed methods, both for tissue-specific reconstruction and thresholding, and to provide an open-source alternative to proprietary software, such as MATLAB, where most of the methods are implemented.With this in mind, we developed a generic pipeline for context-specific model reconstruction, allowing to test several ways of performing data preprocessing, as well as to choose the algorithm used for model extraction and validation. In a first set of experiments, we applied this pipeline to generate multiple reconstructions of the MCF7 breast cancer cell line using recent transcriptomics data and knockout screenings from the Cancer Cell Line Encyclopedia (CCLE) –42, as wThrough this initial analysis, we were able to identify the best-performing set of parameters, which were applied to a larger case study. So, in a second set of experiments, the best configurations were used to reconstruct models for the whole set of the CCLE cell lines (over 700). We validated the resulting reconstructed models with CRISPR gene essentiality screens. With this work, we aim to establish a pipeline to find the optimal tuning of parameters for a given dataset, to help to reconstruct a more insightful tissue-specific metabolic model. An important advantage of this work is the use of open-source software in all steps of the pipeline, unlike previous studies mainly using proprietary software.The context-specific metabolic reconstruction (CSMR) pipeline employed in this work contains four essential steps: input preprocessing (1), context-specific reconstruction (2), refinement (3) and validation (4).An overview of the work is present on The inputs for any context-specific reconstruction always involve a template genome-scale metabolic model capable of yielding a non-zero flux distribution and only containing reactions capable of carrying non-zero flux, as well as a set of omics measurements integrated in the model via CSMR algorithms.find_blocked_reactions function from the COBRApy package. We also removed any boundary metabolites—usually added to balance exchange reactions—prior to running any reconstruction, gapfill, or analysis method. This model must be feasible in steady-state conditions and must be capable of allowing flux through the biomass pseudo-reaction.We first ensured the model was consistent by identifying blocked reactions—whose maximum and minimum fluxes are null under open exchange conditions—with flux variability analysis, using the In this pipeline, we focused specifically on transcriptomics data that is mappable with the model’s gene associations, although some methods allow integration of other data types.Similarly to previous approaches , 36, 44,Using transcriptomics to characterize enzyme activity is not trivial, since the relationship between messenger RNA and protein expression is not fully understood, despite ongoing progress in quantifying both of these biological entities. However, Nusinow et al. have recently quantified the proteome for a subset of the CCLE panel and found a moderately positive correlation (mean Pearson c.c. = 0.48) between mRNA and protein abundance . In thisRNA-Seq technologies typically produce transcript-level measurements represented as proportions of the entire transcriptome. However, we intended, for the RASs used in this work, to obtain a positive or negative value relative to reference thresholds calculated across all samples. To this end, we processed expression measurements into transcript activity scores (TASs) that can better represent this dichotomy. All the thresholding calculations were done over the transcript-level measurements.We first defined the concept of a global threshold, where the expression of all genes contributes. This is useful in filtering out transcripts whose expression is low or high enough for them to be assigned as inactive or active, respectively, with a high degree of confidence. However, this type of thresholding does not take into account the variability of measurements between transcripts. While originally available for microarray-based transcriptome quantification , a generA local thresholding approach can also be considered to mitigate the aforementioned problems. Rather than condensing the entire measurements into a single value, thresholding can be performed on a per gene basis, yielding a value for each gene independently. Similarly to the global thresholding approach, local thresholds can also be used as a reference for fold change calculations.In this pipeline, both thresholds were calculated by first determining transcript-wise quantiles for various percentages (from 10% to 90%), yielding multiple sets of local thresholds, one for each percentage. To convert the latter to global thresholds, we used the mean value of a local threshold set to obtain a single representative value for the entire expression dataset.After establishing appropriate thresholds, we then combined them to establish rules that can be used to determine whether a transcript is active and calculate its TAS to better represent that activity level. We implemented an approach based on the work of Richelle et al , where tgmin). The expected TAS values will always be negative.Inactive: transcript expression is inactive with a high degree of confidence—assigned when expression values are lower than a global lower threshold (gmax). The expected TAS values are always positive.Active: transcript expression is active with a high degree of confidence since its value exceeds a global upper threshold (l(y)) and the expected TAS value is constrained between -1 and 1, with its sign reflecting whether it is considered active.This also implies the existence of an intermediate state of uncertainty for cases where transcript expression lies between the two thresholds. In these cases, we distinguished between active and inactive transcripts by comparing the transcript’s expression with its transcript-specific local threshold  rules provided with the model. GPR rules are Boolean expressions that describe, for a given reaction, which combinations of transcripts are involved with the synthesis of one or more enzymes capable of catalyzing it. These rules are often expressed or can be converted into disjunctive normal form, where multiple conjunctions (expressions with the AND operator) denoting the various enzymes or isoforms involved are bound by a disjunction (OR operator).minimum function—an enzyme’s activity is limited by the lowest expressed transcript/subunit—while OR operations could be replaced with either sum or maximum functions. When using sum, we assumed the reaction activity correlates with the combined activity of all enzymes and isoforms catalyzing it, while the maximum function equates reaction activity with the highest expressed enzyme’s score.RASs must be presented as continuous scores and, thus, the Boolean operators in GPR rules must be replaced with numerical values. We replaced AND operators with a This step includes conversion of RASs into inputs accepted by the different CSMR algorithms, given their diverse nature, and we have implemented two alternatives to perform this conversion in our routines. Although our pipeline is generic, we chose the FASTCORE  and tINIMethods such as tINIT, where scores mirror the reactions’ states as present or absent, can take RAS as input without any further processing. On the other hand, algorithms such as FASTCORE require a set of core reactions as input. In this case, a further threshold must be applied for these to be obtained. In our work, we emphasized the division between positive and negative scores to represent activity, and as such, core reactions are those with a RAS above 0.r of size n , indicating reactions’ presence. Indeed, this vector includes Boolean flags indicating whether each reaction should be kept or removed in the context-specific model.With the inputs appropriately adapted, the output of each algorithm is always a binary vector The models generated by the CSMR algorithms were then checked for consistency with expected phenotypes. For each of these models, we knocked out (set lower and upper bounds to 0) reactions flagged for removal, before performing any simulation or analysis. We first checked whether the model is capable of allowing non-zero flux through the biomass reaction, to ensure lethality can be tested. When growth medium formulations are available, we can additionally ensure that the model is feasible and capable of growth if the compounds present in growth media are the only ones allowed to be consumed. This was achieved by constraining exchange reactions that do not involve medium metabolites to only allow positive flux values, thus only allowing medium metabolites to be consumed by the model.When the preliminary checks described above fail, gap filling approaches can be employed to infer sets of missing reactions that can expand the solution space and enable expected phenotypes.troppo Python package. This algorithm leverages efficient elementary flux mode (EFM) enumeration algorithms to find minimal sets of active fluxes required for feasibility under a specific condition. Assuming a stoichiometric matrix S of m metabolites and n fluxes, the flux vector v and an identically sized vector y, and a set K of reactions available to fill gaps, the LP formulation employed in EFMGapfill can be defined as follows:Gap filling was performed using a novel EFMGapfill approach, which was implemented in the y to the fluxes in the vector v. In this expanded solution space, the variables in y will hold a value of 1 if their associated flux in the vector v is greater than 1. Otherwise, both variables are set to 0. These variables and indicator constraints are then used to discretize fluxes into active and inactive states. The objective function is dependent on the set of reactions K available for the algorithm to add as a gap filling solution, although the objective is always to minimize the sum of a subset of the vector y whose indices are contained in K.In the formulation represented in LP1, constraint 1 defines the steady-state constraint, similarly to other CB approaches, such as FBA. Constraints 2 and 3 associate the binary variables in K according to a Boolean vector r (a set of reaction indices). K will have all reactions from the template model not included in r. The vector r is typically the output of a previously determined CSMR reconstruction. Furthermore, we also defined and constrained an objective reaction u  to always carry non-zero flux, representing a phenotype that is expected to be maintained upon tailoring the model to the subset of reactions in r.We adapted the input K. The resulting solution from our gap filling approach is the smallest set of intracellular reactions not found in r that should be included so that the context-specific model is capable of carrying flux through u.We identified two possible gap filling scenarios that can be accomplished using this approach. In the first, we did not assume constraints on external metabolite exchanges and thus, we also excluded these reactions from r must not be manipulated, but the model still requires gap filling to predict growth. The growth medium, rather than the enzyme content of this model must be the target for manipulation. In this case, all intracellular reactions not in r must be constrained and exchange reactions must be split into forward and reverse reactions carrying flux in opposite directions. To find the minimal set of extracellular metabolites required for the model to carry flux through u, the set K must be defined as the set of reverse exchange reactions in the model.An alternative scenario may arise where the set of reactions An important question arising from any CSMR process is ensuring the reconstructed models are capable of capturing the metabolic context of the cell or tissue, as represented by their corresponding omics measurements. Although literature review may reveal expected behaviours and phenotypes associated with the specific context to be modeled, a truly systematic validation of these models can only be achieved with large-scale datasets covering a wide range of measured biological entities. Such experiments should clearly point out the effect of perturbations that can be mapped onto the model on cell metabolism so that simulated fluxes become directly comparable. In this section, we describe how gene knockout screens and fluxomics can be integrated in our pipeline to validate these models.Gene essentiality screens, such as those performed with CRISPR, provide a directly quantifiable measurement of the impact of gene deletions on cell viability, which can be modeled on CBMs through metabolic tasks . The biog. To this end, we first obtained a mapping ω, which evaluates the GPR expression of reaction r with every gene marked as active, except for g. To apply the gene knockout, we must first determine the set K = {r|r ∈ R, ∀¬ω}, which identifies the reactions that are disabled upon deletion of g; then, we set the lower and upper bounds of each reaction in K to 0. Adding these constraints to the model, the simulation can be run using FBA, yielding predicted growth rates for each gene deletion.The CBM workflow used to predict essential genes used GPRs to determine the set of reactions to exclude given a knocked-out gene Finally, flux distributions resulting from gene knockouts can be evaluated. It is useful to always compare predicted mutant growth rates with wild-type levels. We considered several growth rate thresholds based on the wild-type value to represent viability, but we assumed that values of the mutant biomass flux below 0.1% of the wild-type biomass flux were considered a knockout lethal. Additionally, infeasible solutions are considered as non-viable. We then discretized each gene knockout’s result as essential or non-essential and compared them with the experimental screening.scikit-learn package is presented on K classes, t as a vector with the amount of true positives and p the vector with the amount of predictions each class k, while c is the total amount of true positive samples for all classes and s is the number of samples.We used Matthews’ correlation coefficient (MCC) to assess the predictive ability of our models. The multiclass definition of MCC as implemented in the Alternatively, flux distributions obtained from the model using an appropriate phenotype prediction method can be directly compared with experimentally measured fluxes, obtained from techniques such as isotope labeling coupled with metabolic flux analysis. In this work, we employed parsimonious enzyme usage flux balance analysis (pFBA) to predict phenotypes using our context-specific reconstructions. We have chosen this method since it requires no prior knowledge and reduces the admissible solution space of FBA by assuming cells not only attempt to achieve the predefined cell objective, but also minimise the overall sum of metabolic fluxes to do so.forward active, if the flux is positive, reverse active if it is negative , or null when there is no flux. Although less precise, this discards the usage of experimentally measured external metabolite consumption rates. The model’s predictive ability can then be ascertained by using metrics suitable for multiclass predictive models such as Matthews’ correlation coefficient or weighted F1 scores.A key limitation in using CB models to predict flux values is the lack of reliable measurements for substrate uptake fluxes. This directly influences the predicted growth rate and intracellular fluxes. A more reliable comparison can be made by discretizing flux values into three classes: s(x)   that maiRelevant fluxes were selected before using supervised or unsupervised algorithms by eliminating fluxes with low variance. Furthermore, we also selected an arbitrary number of features ranked by their significance in explaining the variance of the data relative to a discrete clinical feature using one-way ANOVA tests.One of the methods used to analyse predicted fluxes was Principal Component Analysis (PCA), which we used to further reduce the high-dimensionality of the metabolic model’s solution space. We also inspected principal component loadings to identify groups of fluxes that were relevant with the clinical features in the biological samples from which the models were reconstructed.Finally, we also used predicted fluxes to train supervised learning classifiers. We used Random Forest classifiers with varying number of Decision Tree estimators. K-fold cross-validation (CV) was used to assess the classifiers’ predictive performance using Matthews’ correlation coefficient as our metric. In some instances, we trained classifiers using several pFBA flux distributions for the same cell line. To avoid the inclusion of models from the same cell line in both training and testing folds, we implemented a custom k-fold CV routine that splits datasets by cell lines rather than by individual flux distributions.We assessed the effect of the model reconstruction parameters on each validation task by fitting a linear regression model with parameters as inputs (independent variables) and performance metrics (MCC) as the output (dependent) variable. First, we built a feature matrix with one-hot encoded parameters for all reconstructed models. We then fit the linear regression using this feature matrix to predict the MCC value. This model’s coefficients for each parameter are then used to infer its impact on the predictive performance.https://github.com/BioSystemsUM/human_ts_models/. The packages cobamp, troppo, cobrapy, pandas, seaborn, scikit-learn and matplotlib libraries are required to replicate the results and analysis featured in this work.The software featured in this work was developed using the Python programming language. Although compatibility between language sub-versions should not cause any problems, we recommend using Python 3.6 and above. The entire source-code to perform all steps of the pipeline featured in this work is accessible through the GitHub repository at troppo framework [https://github.com/BioSystemsUM/troppo. This software package provides an environment for omics data processing and subsequent integration with constraint-based metabolic models. This software is structured around two main parts: the omics layer handles data parsing, labeling and normalization, as well as mappings to previously loaded constraint-based metabolic models; the reconstruction layer contains routines to easily adapt omics inputs into appropriate reaction-level scores and run context-specific model reconstruction algorithms using novel implementations of existing methods.A significant part of our model reconstruction pipeline has been implemented using the ramework , developcobrapy package [pandas package. These routines have been generalized and included in the source-code of this work as auxiliary functions, although most parts of the input preprocessing pipeline are fully accessible through troppo.We also used the  package  to read troppo. Both fastCORE and tINIT algorithms used in this work were run using in-house implementations, which had been validated in a previous work [cobamp [The remaining parts of the context-specific model reconstruction have also been implemented in several components of ous work . The EFM [cobamp . This pamatplotlib and seaborn libraries.The plots featured in this work were generated using the Our first study evaluated the influence of different input processing methods on model reconstruction, using the MCF7 breast cancer cell line as a case study. The tested input processing methods consisted of several values for thresholding transcript-level measurements with the aforementioned strategies, global, local-T1 and local-T2, with different assigned values for the threshold. The different strategies tested will help to highlight the best combinations of parameters to obtain a more accurate context-specific model . Since tIn a second stage, using knowledge from these MCF7 models, we selected the best performing preprocessing options for each algorithm, and reconstructed various models for all cell lines, validating them with gene essentiality predictions. In the absence of fluxomics measurements, we also assessed whether such models could be used to generate relevant information for other tasks by using the result of several pFBA simulations as features for supervised machine learning approaches.https://github.com/SysBioChalmers/Human-GEM.We used the Human-GEM (version 1.5.0) genome-scale metabolic reconstruction as our template model, stemming from a recent effort by Robinson et al. to provide a consensus metabolic model for Homo sapiens . The modThe experimental data used in this work was obtained from two different sources. The Cancer Cell Line Encyclopedia provides RNA-seq transcriptomics data for over 56000 genes across 1270 unique cell lines. These expression values are represented in transcripts per million (TPM) and are already pre-processed using standardized GTEx pipelines. TAS calculations were performed across the entire dataset, although the only integrated scores were those whose associated genes were mapped to the template metabolic model.These datasets are complemented with the Achilles dataset, characterizing lethal effects of over 18000 gene knockouts through CRISPR experiments , 42. GenFor this work, after some extra processing, we considered five essentiality thresholds evenly distributed across the range between -1.5 and -0.5. As of the first quarter of 2020, 739 cell lines had been included in the Achilles dataset , which wFluxomics measurements for MCF7 cell lines were obtained as part of the dataset used in the analysis of the work of Katzir et al. , where tWe first reconstructed models of the MCF7 cancer cell line by considering every possible combination of the parameters displayed on We obtained 320 models from this reconstruction effort and assessed their ability to correctly predict essential genes and flux activity. To understand the influence of parameterization on the models’ performance, we observed the distribution of values across multiple parameter options and evaluated parameter importance numerically using a linear regression.The results summarized on gmin parameter when using the localT2 strategy seems to have little impact on the models’ predictive power. This supports the claim that when both local and global strategies are combined, they provide a positive effect on the TAS calculation strategy, since localT1 does not show good results, and the ones from the global ones are improved when coupled with the localT2 strategy. The complete results are provided on Despite this similarity when comparing the average of all models for each strategy, the best predictions were achieved using the localT2 strategy, which is a clear indicator that a combination of both thresholding approaches are useful to estimate RASs. Although the performance achieved using the localT2 strategy could be attributed to the usage of two (rather than one) global thresholds, we observed that the gmax and local thresholds at the 25th percentile seem to have the most positive effect on predictive ability in all models. Although the CERES score threshold representing the median essential gene knockout was set at -1, our models show slightly increased predictive power at -0.75.We were also able to infer some of the properties associated with the dataset, where Aside from data preprocessing related parameters, we have found the best parameter combinations are to use the tINIT algorithm in conjunction with the maximum function as replacement for the AND operator. We also observed that refining the model with EFMGapfill to allow growth using only the defined growth media metabolites as substrate did not result in better gene essentiality predictions.We also performed a similar assessment on the ability of our models to correctly predict reaction activity/inactivity and directionality for the MCF7 cell line under three growth medium compositions with associated fluxomics measurements. Note that MCC is used given that the predicted variable is discrete. We did not address a quantitative prediction, as the preliminary results showed poor results in directly predicting flux values.For each parameter combination, we generated three corresponding predictions using the growth medium as an additional flux constraint and calculated the MCC between the measured and predicted flux activities. In The relationship between average MCC and its standard deviation is shown in FASTCORE appears as the more consistent tool to extract context-appropriate sets of reactions from a template model. Due to its low computational demand, these reconstructions can be repeated with alternative parameters or to sample large amounts of models. tINIT, on the other hand, shows consistently lower sensitivity to the parameters selection, as see on Figs We used 10 of the highest scoring parameter combinations from the MCF7 cell line case study to reconstruct the entire panel of cell lines available in CCLE with associated gene knockout effect screens (n = 739). A similar reconstruction pipeline was employed in this larger case study, although we did not perform gap filling relative to the growth medium, due to heavy computational demand and an expected negative impact in phenotype predictions. Another important aspect to take into consideration is the lack of fluxomics data, which was used in the optimization of the parameters for the MCF7 cell line. This was not employed in the large-scale study since this type of data is rarely available. Still, we used it in the previous study to demonstrate the importance that it can have in improving the reconstruction of context-specific models. Another limitation of this pipeline is the use of a shared objective function, a generic biomass reaction. Due to the heterogeneity and different types of tissue being reconstructed, the ideal scenario would be to have a specific objective function for each tissue, which is very hard to attain.We performed a large-scale analysis to demonstrate the predictive accuracy of the reconstructed MCF7 cell lines. There are slight differences in gene essentiality prediction performance between the 10 selected parameter combinations, with tINIT models reconstructed displaying slightly higher scores. In all of these scenarios, the selected pipeline parameterization choices improved gene essentiality predictions, when comparing with the models reconstructed in the work of Robinson et al, which asserts the importance of using more complex scoring strategies involving global and local thresholds. A deeper analysis can be found at the We used the models and their respective predicted fluxes to explore the metabolic heterogeneity among various breast cancer cell lines. To do so, we retrieved molecular subtype annotations from the DepMap repository and used PCA to project these flux distributions using reduced features and obtain relevant information on the flux patterns present in different breast cancer subtypes. These results are summarized on The subset of models belonging to breast cancer were extracted and the 200 fluxes  with most statistically significant differences among subtypes were used to decompose the dataset. We included all reconstructed models, regardless of their parameters, and reduced the latent space to 3 principal components (PCs) capable of explaining 33% of the observed variance. The loadings of each principal component were obtained, with each flux being summarised in their corresponding pathways by calculating the average of the absolute ratios between the weights of each flux and the maximum observed values in the PC loadings.Firstly, we can conclude that the decomposition of predicted fluxes can adequately distinguish between major molecular subtypes of breast cancer. The second PC (PC2) marks a good distinction between basal and luminal cell lines and correlates with reported prognosis and aggressiveness , with luThe lack of fluxomics data for the whole set of cell lines featured in DepMap does not allow to carry out a large-scale systematic comparison of the pFBA flux distribution predictions with experimental data. However, we set out to assess whether or not these predicted fluxes could be useful in predicting several clinical features associated with each sample. To do so, we established a supervised classification task, where the disease’s primary location would be predicted using various datasets as inputs, namely, (1) standardized expression values (TPM from RNASeq) using the entire gene set, as well as only those genes that can be integrated in the metabolic model, (2) TASs generated for each sample, (3) predicted fluxes (using pFBA over reconstructed models) and (4) the reaction presence (binary) from the CSMR algorithm outputs. Our results, using random forests as the machine learning model to address these tasks, are summarized on Classifiers trained with standardized transcriptomics data showed good relative performance  with the subset corresponding to metabolic genes only slightly outperforming it. Processing these data and generating TASs slightly increased predictive capabilities , which further justifies applying our preprocessing workflows before analysing and integrating omics data. However, the outputs of CSMR algorithms, namely, the presence or absence of each reaction resulted in models capable of predicting a cancer cell line’s primary site with an average MCC of 0.525 (sd = 0.031). Furthermore, pFBA simulations resulted in even worse classifiers that could only reach an average MCC of 0.298 (sd = 0.042).Overall, our results show that context-specific model reconstruction and flux balance analysis approaches are not yet consistent enough for accurate quantitative flux predictions, as predicted metabolic fluxes by themselves did not appear to be relevant features for complex classification tasks.We built upon several previous efforts to generate constraint-based models of differentiated human tissues, being capable of assembling a generic pipeline that can be useful in standardizing the process of integrating transcriptomics data into human metabolic models for the scientific community. Furthermore, this pipeline is available as part of an open-source software tool providing a generic framework for the implementation of context-specific model reconstruction tasks.We were able to leverage large-scale multi-omics experiments with cancer cell lines and a state-of-the-art human metabolic reconstruction to generate meaningful models capable of capturing the metabolic diversity among, and within, multiple types of cancer. We were also able to validate the models using experimentally determined essential genes and fluxomics data.The usage of decomposition methods to understand flux predictions allowed us to establish a link between metabolic phenotypes and breast cancer prognosis, and by making use of the interpretability of constraint-based models, we were also able to pinpoint key enzymes and metabolites associated with dysregulated growth. This elicits the potential for similar approaches to assist in contextualizing transcriptomics profiles into metabolic phenotypes, with the purpose of understanding the intricate mechanisms responsible for human diseases, especially for personalized medicine applications.troppo, in a way that these datasets can be easily integrated and used with appropriate methods.The availability of metabolomics and proteomics data is still lower in comparison with RNA-Seq technologies used for transcriptomics quantification. As such, we have developed this work to only consider the latter omics type, and we argue that the reconstruction of models based on transcriptomics data results in computational tools that can be more easily adapted to a clinical setting since they do not rely on generating multiple omics datasets. However, we have also built the computational tools, namely Although encouraging, our results show the difficulty in closing the gap between experimentally measured and predicted fluxes. We argue that there is value in building representative models using gene expression alone, since the techniques used to obtain these measurements are far more ubiquitous and less costly. However, naturally, this lack of information implies some limitations when interpreting the model. This was evident when using model simulations to predict a cell line’s disease, where classifiers trained with these predictions displayed poor predictive performance. This leads to the conclusion that although we may have some interesting results with the reconstructed models obtained, this was mainly due to the characteristics of them, not so much for their predictive capabilities. As described before, there is still a long way for metabolic models, independent of their type, to be able to simulate the metabolism with a high accuracy. So, for now, we try to obtain the most of what the current technology can offer.In the absence of precise exo-metabolome uptake or secretion rates, CBMs in their original definition, are merely capable of predicting metabolic pathways on a discrete level, and thus, flux distributions must always be interpreted relative to a given original state or model context rather than assuming these fluxes are numerically comparable. A related challenge also appears when considering the biomass objective function, which is usually too generic to describe different tissue types, and hinders the ability for these approaches to generate meaningful models for cells whose metabolic objective is difficult to define.Recent works that have incorporated exo-metabolite measurements , metabolWe must, additionally, acknowledge the importance of using a fluxomics data source for a reference cell line to serve as a basis for subsequent reconstructions. Although we know this type of data is rare, we were able to prove it can be useful to improve the process of reconstruction with some degree of validation, when available. In this work, this led to a significant decrease in computational resource usage, as well as a better choice of parameters without exhaustive reconstructions, even if for only a specific cell line.troppo in such a way that reconstructing a context-specific metabolic model is a simple task, even for users with limited programming skills.The implementation of this complex pipeline in a modular framework allows for the usage of different methods that might fit a particular purpose. Previous works have reported the heterogeneity in outputs from various CSMR algorithms and our case study clearly shows that this choice impacts the type of phenotypes to predict and, as such, we extended Other similar studies have been done to try to establish a generic pipeline to reconstruct and evaluate several context-specific models. StanDep is an alternative strategy to threshold/integrate data for the reconstruction of context-specific tissue models (NCI-60 cancer cell line panel) . They toS1 File(CSV)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.


The non-photosynthetic chlorophyte alga Polytomella sp. SAG 198.80 was found to be able to assimilate butyrate fast. To decipher the metabolic pathways implicated in butyrate assimilation, quantitative proteomics study was developed comparing Polytomella sp. cells grown on acetate and butyrate at 1 gC.L−1. After statistical analysis, a total of 1772 proteins were retained, of which 119 proteins were found to be overaccumulated on butyrate vs. only 46 on acetate, indicating that butyrate assimilation necessitates additional metabolic steps. The data show that butyrate assimilation occurs in the peroxisome via the β-oxidation pathway to produce acetyl-CoA and further tri/dicarboxylic acids in the glyoxylate cycle. Concomitantly, reactive oxygen species defense enzymes as well as the branched amino acid degradation pathway were strongly induced. Although no clear dedicated butyrate transport mechanism could be inferred, several membrane transporters induced on butyrate are identified as potential condidates. Metabolic responses correspond globally to the increased needs for central cofactors NAD, ATP and CoA, especially in the peroxisome and the cytosol.Volatile fatty acids found in effluents of the dark fermentation of biowastes can be used for mixotrophic growth of microalgae, improving productivity and reducing the cost of the feedstock. Microalgae can use the acetate in the effluents very well, but butyrate is poorly assimilated and can inhibit growth above 1 gC.L Other permeases may also be involved in these processes  into the cell. Two different mechanisms have been described in yeast, (i) passive transport rocesses . The rolrocesses  but the rocesses . At pH v acetate . Therefoartments . HoweverDesulfosarcina cetonica (Rhodospirillum rubrum (Candida ingens (Yarrowia lipolytica (via the monocarboxylate transporter (MCT), a 45-kDa plasma membrane protein with 12 transmembrane segments that symports H+ with the butyrate anion . It was grown on synthetic media referred to as HAP (acetate) or HBP (butyrate), based on Tris-Acetate-Phosphate (TAP) medium used for the green alga C. reinhardtii (−1 leading to an ammonium (NH4+) concentration of 7.5 mM, 0.6 mM of MgSO4 and 0.3 mM of CaCl2. To adjust nutrients to the Redfield C:N:P ratio of 106:16:1, corresponding to 83.3:12.6:0.8 mM for 1 g carbon per liter (1.0 gC.L−1), the proper amount of 1 M NH4Cl (5 ml) and 1 M K2HPO4 (0.8 ml) stock solutions were added. Hutner’s trace elements were used at 1 ml.L−1  and pH medium was adjusted to 7.0 (HCl) prior to sterilization at 121°C for 20 min. After cooling, 100 μl.L−1 was added of a stock of vitamin B1 (50 mM), biotin (1 mM) and cyanocobalamin (1 mM), sterilized over a 0.2 μm filter. Precultures were maintained on HAP medium containing 20 mM HEPES and were used to inoculate HAP and HBP media to initial optical density at 750 nm (OD750) = 0.05. The inoculum was prepared by collecting preculture cells in their exponential phase via centrifugation at 2500 g for 10 min, and resuspending them in Phosphate Buffered Saline to a final OD750 = 5. Cultures were done in 500 ml Erlenmeyer flasks filled with 200 ml medium under dim light at 25°C and without agitation.nhardtii , in whic1 ml.L−1 . As carb−1 dry weight (DW) deduced from OD750 determination. To calculate the biomass DW from OD750 values, a correlated factor was used, determined to be 1.0774 (R2 = 0.977). This correlation factor was obtained from a calibration curve gathering 70 data points collected during separated experiments, at various growth stages (stationary phase excluded) and growth conditions (HAP or HBP medium). To determine biomass DW during these experiments, between 5 and 25 ml (depending on growth stage) biomass was centrifuged . Supernatant was discarded, and pellet rinsed with one volume of phosphate buffer saline. Biomass was centrifuged again, supernatant discarded and pellet resuspended in 10 ml distilled wated. The biomass was transferred in a pre-dried and pre-weighed aluminium crucible and dried overnight at 105°C.Biomass growth was followed by measuring optical density at 750 nm (OD750), using a Helios Epsilon spectrophotometer. OD750 was measured by placing 1 ml of liquid culture in a cuvette and comparing to distilled water. The sample was diluted when necessary so that OD750 < 0.6. Biomass production was expressed in g.L−1). The GC system consisted in a Perkin Clarus 580 model equipped with capillary column Elite-FFAP crossbond®carbowax® (15 m) maintained at 200°C and with N2 as the gas vector (flow rate of 6 ml·min−1) with a flame ionization detector (FID) maintained at 280°C .For VFA measurements, samples from fresh cultures were immediately centrifuged, the supernatant filtered over 0.2 μm cut-off filters and frozen at −25°C until analyzed. The VFA concentrations were determined by gas chromatography. A 500 μl aliquot of supernatant was mixed with 500 μl of internal standard solution . The resulting reagent was stored in the dark. First, 2 ml H2SO4 (98%) were added in the tubes containing microalgae cells. The tubes were heated 10 min at 100° C and cooled on ice. The reaction was initiated by addition of 5 ml phosphovanillin reagent prior incubation for 15 min at 37° C. Tubes were periodically shaken by inversion. After cooling, optical density of suspensions was measured at 530 nm with an Aqualytic® spectrometer and compared to distilled water. Calibration curves were obtained using canola oil.Samples from fresh cultures were immediately centrifuged, the supernatants were discarded and the pellets stored at −25°C until used. Prior to analysis, the pellet was thawed, resuspended in 100 μl distilled water and added to 10 ml glass tubes for either lipid or sugar measurement. Total lipid concentrations in the algal samples were determined by the phosphovanillin method . Phospho2SO4 (98%). Two mille liter of anthrone reagent were added in the tubes containing microalgae. Tubes were cooled down on ice and then incubated at 100° C for 10 min. After cooling, absorbance was measured at 625 nm with an Aqualytic® spectrometer and compared to distilled water. Calibration curves were obtained using glucose solution.Total sugars were measured by the anthrone method . Anthronx (gdw.L−1.d−1) and the specific growth rate xμ (d−1) were calculated according to The biomass productivity PfX and 0X are the biomass concentrations at t = final (h) and at t = 0 h.where Y (gdw.gsub−1) was estimated according to The biomass yield fS and 0S are substrate concentrations (g.L−1) at t = final (h) and at t = 0 h.Where Statistical analysis was performed using GraphPad Prism V 8.0.2.Polytomella sp. grown either on acetate or butyrate  were stacked in the top of a 12% SDS-PAGE resolving gel before in-gel digestion using modified trypsin , as previously described , and the corresponding reversed databases. Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set at, respectively, at 10 ppm and 25 mmu. Peptide modifications allowed during the search were: Carbamidomethyl , Acetyl  and Oxidation . The Proline software  through concomitant searches against a homemade software  was usedStatistical analysis was performed using the ProStaR software .via the PRIDE . Statistical testing was conducted with limma, whereby differentially expressed (DE) proteins were sorted out using a log2 (fold change) cut-off of 0.6 and a value of p cut-off of 0.004, allowing to reach a false discovery rate < 1% according to the Benjamini-Hochberg method.Intensity-based absolute quantification iBAQ,  values wPolytomella sp. was described previously  and Chlorella sorokiniana (Uniprot UP000239899) to obtain equivalent enzyme identities in these two model microalgae. For subcellular localization predictions, DeepLoc  homology searches at KEGG DeepLoc  and Pred DeepLoc  were usePolytomella sp. was grown on either acetate or butyrate at a fixed C concentration of 1.0 gC.L−1 . For several Chlorella strains it was reported that butyrate assimilation, when it occurred, was slower than acetate assimilation  was obtained in both conditions  on acetate and 55.6 ± 3.1 mg.L−1 on butyrate.A similar maximum biomass yield of 1.07 gnditions . This innditions . In the y starch . Lipid aPolytomella sp. relies on intracellular carbon reserves to survive in absence of external carbon source, growth on butyrate may ultimately negatively impact its survival in oligotrophic conditions. Accumulation of high levels of starch during the exponential growth phase is another distinctive trait of Polytomella sp. as compared to green species, which tend to accumulate storage compounds especially when growth conditions become suboptimal.Although this was not directly measured, the reduced cellular sugar content in the butyrate condition suggests an increase in protein content that may be required to actively metabolize this substrate. Since Polytomella sp. growing on acetate and butyrate were sampled during the exponential phase  between the acetate and butyrate conditions, the above global trend is confirmed but a more detailed picture emerges . RelativPolytomella sp., MapMan was used to project the FC values of individual proteins onto the different metabolic pathways. In section 3.5 these pathways will be discussed in detail. In the category lipid metabolism, the highest FC values are for enzymes of fatty acid synthesis, fatty acid degradation and the glyoxylate cycle  enzyme  enzyme  but alsoC. reinhardtii where it occurs in the mitochondria (The amino acid metabolic pathway most affected by the C-source is the formation and subsequent degradation of branched chain amino acids (BCAAs). It appears to take place largely in the peroxisome based on the subcellular targeting predictions of the corresponding proteins, unlike in its close relative chondria . It is nchondria , which sPolytomella sp. cells growing on butyrate relative to acetate. The most pronounced enrichment of enzymes involved in butyrate assimilation is found in the peroxisomes, small spherical organelles (0.2–1.5 μm) that lack DNA and are surrounded by a single membrane that derives from the endoplasmic reticulum   . The propathways .Polytomella enzyme using BLAST (45–50% ID), including medium chain acyl-CoA synthase and even bacterial 3-methylmercaptopropionyl-CoA ligases. We thus changed the annotation of the enzyme to fatty acyl-CoA synthase (FACS). This enzyme is the prime candidate for the production of butyryl-CoA in Polytomella sp., pending further experimental confirmation. A true LACS isoenzyme was also detected , but since its expression level is much lower than FACS, this enzyme is unlikely to act as the first step of butyrate assimilation. Two acyl CoA oxidases (ACX) were also highly butyrate-specific, with a FC value of 0.001 for ACX4 and 0.021 for its paralog ACX. ACX4 was predicted to be localized in the plastid but homologs in other algae including C. reinhardtii retrieved by BLAST searches (~51% ID) are annotated as peroxisomal  (via phosphate butyryltransferase (EC 2.3.1.19) + butyrate kinase (EC 2.7.2.7)  . Convers2.7.2.7)  or butyr2.7.2.7) . The pre2.7.2.7) . In cont2.7.2.7) .C. reinhardtii, the glyoxylate cycle allows growth on acetate  and isocitrate lyase  are induced on butyrate, but at a lower level than citrate synthase  and malate dehydrogenase    . AconitaFC 0.03)  but noneFC 0.03) . CIS2 isFC 0.03) ; Table 1FC 0.03) . PolytomX5-H/QA) . In C. rrobodies . Earlierytosolic . Since tCA cycle .via a malate/OAA transporter  it can currently not be excluded that MDH2 is cytosolic, and if so, this would indicate an increased need for malate/oxaloacetate shuttle activity to sustain increased production and export of citrate from the peroxisome. A cytosolic localization for MDH, ACO and ICL occurs in yeast and does not impact glyoxylate cycle function  fumarate 10-fold lower, but hardly any citrate was formed  into malate, which can be later exported to the cytosol  via a mansporter . Export nsporter . Despitefunction . The facs formed . Convers by ACLY . In our C. reinhardtii (A0A2K3CWL4/Cre15.g637761) which was confirmed to be involved in the import of activated long-chain fatty acids from the cytosol to the peroxisomal matrix, similar to the yeast peroxisomal ABC transporters PXA1 and PXA2  in the cytosol. Another transporter identified is homologous to the mitochondrial dicarboxylate/tricarboxylate transporter DTC (FC 0.6) in A. thaliana , an oligomeric complex of approximately 150 kDa in the inner mitochondrial membrane which constitute the sole entry point into the mitochondria of pyruvate, produced by glycolysis or from malate. The upregulation of this carrier is of fundamental importance in establishing the metabolic programming of a cell . Once inthaliana . DTCs trPolytomella sp. genome. However, a member of the formate/nitrite transporter (TC 2.A.44) family  was found to be induced by butyrate. FNT transporters transport monovalent anions and are not strictly selective as they can use nitrite or formate but also larger organic anions such as lactate and acetate  superfamily and homologous to bacterial acetate/succinate channels were found to be induced by acetate in C. reinhardtii and possibly implicated in intracellular acetate transport  was found in the proteome induced by butyrate at FC 0.636 (but with p > 0.004).Furthermore, two general substrate transporters of the Major Facilitator Superfamily were identified . MFS transporters can only transport small solutes in response to chemiosmotic ion gradients and can be found anywhere in the cell, so their proposed placement in the peroxisomal membrane for transport of carboxylates is speculative . The mon acetate . It may ransport . In the + -ATPase  that exports cellular protons , was found markedly induced on butyrate (FC 0.003), while a second CAT1 ortholog was far less induced. No clear ortholog was found for the C. reinhardtii ER-localized CAT2. Catalase upregulation probably relates to the β-oxidation of butyrate, where it allows the detoxification of H2O2 produced by ACX into H2O and O2. Using density gradients,  are important in cellular signaling, but stress conditions may cause increased ROS production and result in oxidative damage . Various2O2 . The butoxisomes  and simiadients,  found thoxisomes .2O2 to H2O. Other GAC enzymes were also induced on butyrate, such as glutathione S-transferase , while glutathione reductase (GR) was not. The GAC can be found in different cellular compartments such as plastids, mitochondria, peroxisomes and the cytosol,  was found highly induced on butyrate. PRXs are important for cellular redox signaling and antioxidant defense as they detoxify organic hydroperoxides (R-O-OH) that can be formed from the reaction of organic molecules with H2O2 .The localization of PRX2 and M/FSD1 is uncertain as they lack typical targeting signal and were predicted to be cytosolic by DeepLoc. Intriguingly, the levels of plastid type alternative oxidase , which functions in plastid redox homeostasis by oxidizing plastoquinol to reduce O2 to H2O in photosynthetic organisms  was highly induced by butyrate. It is part of the glutathione-ascorbate cycle (GAC) that uses electrons from NAD(P)H for the reduction of Hcytosol, . Since nith H2O2 . Anotherrganisms , and thee system .2O2 levels that exert a positive regulatory influence  is predicted to be mitochondrial, the Polytomella ortholog is predicted to be peroxisomal  into acetyl-CoA, so the degradation of isoleucine into MS (via propionyl-CoA) does not demand the presence of MMSA in the peroxisome when MS is imported into mitochondria. Mitochondrial MMSA can convert MS into acetyl-CoA which can be further utilized without problems. In the case of valine degradation, the absence of MMSA in the peroxisome would block the pathway at the level of the conversion of MMSA to propionyl-CoA, and in that case MMS would have to be exported to the mitochondria. Here, MMSA would convert MMS into propionyl-CoA, but its fate in the mitochondria would not be obvious: the enzymes necessary for its conversion to succinyl-CoA (propionyl-CoA carboxylase producing (S)-methylmalonate-CoA, methylmalonyl-CoA epimerase producing (R)-methylmalonate-CoA and methylmalonyl-CoA mutase to convert it to succinyl-CoA) could not be identified in the Polytomella genome. This is similar to the case of P. tricornutum where the epimerase step was not detected , somewhat induced by butyrate, was found to be a promiscuous enzyme that produces also thioesters of malate, fumarate and glutarate among others , which serves as precursor for fatty acid synthesis (see 3.5.6). It is noted that even if ALD5/ACSF3 are not located in the mitochondria, the products can be imported into the organelle. Alternatively, the BCAA degradation pathway may be streamlined when MMSA is dually targeted to both peroxisome and mitochondria, which is known for other enzymes such as CAT , whereby sugars and lipids may serve to produce amino acids such as BCAAs. We propose that butyrate is metabolized and yields acetyl-CoA and further di/tricarboxylic acids at a slower rate than acetate, which is compensated for by the degradation of BCAAs to produce acetyl-CoA and possibly succinyl-CoA that feed into carbon metabolism. A factor in the induction of the BCAA degradation pathway specifically may be the fact that it shares several enzymes with the β-oxidation of butyrate .In the non-sulfur purple bacterium butyrate . Interesrivation . In thesPolytomella caeca can grow on acetaldehyde (1 mM) as sole external carbon source  and produces the acetyl-CoA that is a direct substrate for ACC. Biotin has been described to exert regulatory influences in cell signaling, for example the upregulation of glucose metabolism (A clear upregulation is found of two of the four enzymes of the type II fatty acid synthesis (FAS) system, which uses an acyl carrier protein (ACP): 3-oxoacyl-ACP reductase  and enoyl-ACP reductase . In o plants . Fatty ao plants . The FAso plants . Fatty ao plants . Four ouo plants . The mtFtabolism , which wvia consecutive protein phosphorylations by serine and threonine protein kinases  rather than to specific metabolic pathways. Among the over-represented categories are “RNA processing” and “Protein biosynthesis, modification and homeostasis.” This includes heat-shock proteins, protein kinases, maturation-, elongation-, and assembly factors. Although butyrate induced more proteins compared to acetate, there may also be downregulation signals produced in response to specific metabolic needs imposed by butyrate. A specific perception of an acetate-linked metabolite and associated signal cascades may also be involved. Some of the proteins suppressed by butyrate may suggest the implication of a mitogen-activated protein kinase (MAPKs) signal transduction pathway, which modulates important cellular processes such as proliferation, stress responses, apoptosis and immune defense  kinases .We note the induction of a protein of uncertain function, being either protein kinase (MAP3K-RAF) or dual specificity kinase splA isoform B (FC 4.663). It may suggest activation of a signal transduction pathway for positive regulation of gene transcription from a receptor on the cell surface . The preOf note are the three plastidial small heat shock proteins  for which little data exist in microalgae but in plants play a central role in the protection against stress damage, in the folding, intracellular distribution, and degradation of proteins, as well as in signal transduction chains . The HSPThe mitochondrial organization/maturation factor  is the most induced protein compared to butyrate. Its function is uncertain, but may relate to protein translation or post-translational maturation of cytochrome c oxidase. Finally, although most enzymes involved in amino acid synthesis were mildly induced by butyrate, a few enzymes involved in production of aromatic amino acids and methionine were more abundant on acetate (FC ~2.5). Methionine is a direct precursor of S-adenosylmethionine (SAM), an important posttranslational regulator of many cellular processes, including autophagy, the recycling of cellular components in response to stress . ButyratPolytomella sp., and the relation with butyrate metabolism in other organisms is discussed as well as the potential implications of these findings for the capacities for butyrate assimilation of other -green- algae. In addition, a major step has been made in our understanding of peroxisomes in Polytomella sp. and in relation to its close relative C. reinhardtii.The key hypotheses that have been proposed in the past concerning limitations in the trophic metabolism of microalgae relate to cell permeability, toxic products formed from the substrate, lack of enzymes necessary for effective dissimilation of the substrate or their improper cellular location, lack of transcriptional control, effect of low-intensity light in stimulating heterotrophic growth and respiratory deficiency, etc. . In thisvia peroxisomal β-oxidation resulting in acetyl-CoA and di/tricarboxylates for cellular use via the glyoxylate cycle. We found that multiple transporters are induced to facilitate the metabolic interplay between peroxisome and other cell compartments. Although no monocarboxylate transporter was identified for butyrate transport, a formate/nitrite transporter was put forward as candidate for this function. We hypothesize that butyrate causes a major antioxidant defense response related to the production of H2O2 and NADH in β-oxidation. An increased turnover of BCAAs to propionyl-CoA and acetyl-CoA was suggested, which may, together with an overproduction of pyruvate from glycolysis, serve amino acid or cofactor production. Butyrate lowers accumulation of carbohydrates and lipids while fatty acid synthesis was found induced, probably in the mitochondria. This all may serve organellar reorganization (peroxisomes) and the production of cofactors for several central metabolic enzymes to accommodate butyrate utilization. In contrast, acetate utilization seems to stimulate activities that relate to the biosynthesis and homeostasis of proteins.Based on our data, we propose that butyrate is assimilated Polytomella sp. a good model for the study of VFA metabolism, but the high starch levels even during the exponential growth is another distinctive trait that allows continuous cultivation on dark fermentation effluents with potential for biofuel production. However, dark fermentation effluents are much more complex substrates than the model media used in this study, with variable composition depending on the fermentation process. As such, potential effects of dark fermentation effluents on biomass growth remain to be studied. Our proteomics approach revealed in many instances the induction on butyrate of multiple proteins belonging to the same pathway or similar metabolic activities suggest their importance in butyrate metabolism. Other omics and biochemical approaches should be employed to further explore the specificities of butyrate vs. acetate as a carbon source. In particular, metabolomics and fluxomics should be used to reveal the assimilation pathways and quantify metabolic fluxes. This study allows to better define the main issues that remain to be tackled in order to understand why butyrate is problematic especially for green algae, which relate to the import of VFAs into the cell as well as the role and regulation of β-oxidation and associated antioxidant activities in relation to photosynthetic metabolism. As a non-photosynthetic alga, Polytomella can serve as a metabolic reference for efficient butyrate assimilation, to which other (green) algae may be compared. Special focus should be on the root causes of the poor use of butyrate especially by green algae, that this study allows to define, i.e., the identity of butyrate transporter and the role of antioxydant reponses. Since Polytomella sp. does not seem to appear to possess novel metabolic capacities per se, it should be considered that this alga owes its fast butyrate assimilation in some way to the loss of another major metabolic capacity: photosynthesis.Its fast butyrate assimilation makes The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/JL performed experiments, data acquisition, data curation, formal analysis, and writing of the original draft. AA contributed to conceptualization, experiments, data acquisition, data curation, formal analysis, validation, review, and editing of the original draft. SB performed experiments, data acquisition, data curation, and formal analysis. YC performed data acquisition, data curation, formal analysis, validation, review, and editing of the original draft. OV performed data acquisition, data curation, formal analysis, validation, review, and editing of the original draft. JPS contributed to supervision, funding acquisition, validation, review, and editing of the original draft. RL designed original experimental plan and performed experiments, data acquisition, data curation, formal analysis, supervision, funding acquisition, validation, review, and editing of the original draft. All authors contributed to the article and approved the submitted version.JL received a PhD fellowship from European Union from the Occitanie region, France, with complementary funding from FEDER. This study was funded by the National Institute of Agriculture, Alimentation and Environment (INRAE), the CNRS, and was supported by the FermALip project, funded by the Carnot institute 3BCAR as well as by the “Initiative d’Excellence” program from the French State . The proteomic experiments were partially supported by Agence Nationale de la Recherche under projects ProFI  and by GRAL, a program from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Identifying the interrelations among cancer driver genes and the patterns in which the driver genes get mutated is critical for understanding cancer. In this paper, we study cross-sectional data from cohorts of tumors to identify the cancer-type (or subtype) specific process in which the cancer driver genes accumulate critical mutations. We model this mutation accumulation process using a tree, where each node includes a driver gene or a set of driver genes. A mutation in each node enables its children to have a chance of mutating. This model simultaneously explains the mutual exclusivity patterns observed in mutations in specific cancer genes (by its nodes) and the temporal order of events (by its edges). We introduce a computationally efficient dynamic programming procedure for calculating the likelihood of our noisy datasets and use it to build our Markov Chain Monte Carlo (MCMC) inference algorithm, ToMExO. Together with a set of engineered MCMC moves, our fast likelihood calculations enable us to work with datasets with hundreds of genes and thousands of tumors, which cannot be dealt with using available cancer progression analysis methods. We demonstrate our method’s performance on several synthetic datasets covering various scenarios for cancer progression dynamics. Then, a comparison against two state-of-the-art methods on a moderate-size biological dataset shows the merits of our algorithm in identifying significant and valid patterns. Finally, we present our analyses of several large biological datasets, including colorectal cancer, glioblastoma, and pancreatic cancer. In all the analyses, we validate the results using a set of method-independent metrics testing the causality and significance of the relations identified by ToMExO or competing methods. Cancer progression is an evolutionary process where somatic mutations in so-called driver genes provide the harboring cells with certain selective advantages. Identifying the interplay among the driver genes is critical for understanding how cancer evolves. In this paper, we introduce a method for analyzing cohorts of tumors. Our approach is based on a novel probabilistic model, which can identify the temporal order of mutations in driver genes, and how they may exhaust each other’s selective advantages. We introduce an efficient likelihood calculation procedure and build an MCMC algorithm for making inferences based on our model. Our computationally efficient inference algorithm enables us to work with hundreds of genes and thousands of tumors. Using a broad set of synthetic data experiments, we demonstrate the performance of our inference algorithm in various scenarios. We also present our analyses of several biological datasets. Our results agree with a set of well-known relations among the driver genes and suggest new interesting such relationships. PLOS Computational Biology Methods paper.This is a Cancer is a disease caused by evolutionary processes involving the accumulation of somatic mutations in the genome . Some mudriver pathways and a linear progression structure over these pathways. In Pathway Lk iterations throughout our synthetic data experiments.For each generative progression model, we explored 16 settings, with equal false positive and false negative probabilities in {0.001, 0.01, 0.05, 0.1} and the number of tumors in {50, 100, 200, 500}. We sampled 10 datasets for each setting using the generative process explained in section 1.2. We have used our inference algorithm with 100g1 and g2 are placed together in a common node of a model T, we say the pair  is in the set of mutual exclusivity relations implied by T. Similarly, if the node including g1 is an ancestor of the node including g2, we say  is in the set of progression relations implied by T. We calculate our precision and recall in recovering these two sets of relations and calculate two F-scores. Let FME be our F-score for identifying the mutual exclusivity relations. Similarly, let FPR be our F-score for identifying the progression relations. We define our overall score, denoted by Foverall, as the harmonic mean of FME and FPR, i.e.,Foverall ∈  and Foverall = 1 implies perfect identification of the generative model, while the star tree  has Foverall = 0, as its recall is zero for both mutual exclusivity and progression relations.We can compare our results against the known generative progression models to evaluate our performance during the synthetic data experiments. To this end, we focus on the sets of mutual exclusivity and progression relations implied by the models. If two genes Foverall scores in the cases with ϵ = δ = 0.1. Firstly, when the error rate is this high, the star tree  provides a competitive posterior against the generative model. As a result, it’s much harder to improve the posterior with small modifications, leading to the chains getting stuck close to the initial state. We also observe that the Foverall score is a very fragile metric that can easily drop with the slightest errors in the recovered progression model.Further details on our evaluation metrics, the individual precision and recall values, the performance in terms of likelihoods, and the inferred error values are provided in Section 2 in In this section, we present our experiments on several biological datasets. We start this section by explaining our metrics to assess the results. We then compare our method ToMExO against two state-of-the-art methods, pathTiMEx  and MHN To have a method-independent metric to assess the progression models, we use probabilistic causation tests introduced in  as explaProgression scores: if node u is a parent of node v, mutation in u should increase the chance of mutation in v. We define our progression score asp(v|u) and v, among the tumors with and without mutation in u, respectively. Note that if a mutation in u increases the chance of mutation in v, then a mutation in v also increases the chance of mutation in u. The progression score defined in u and v  and in the case of λPR > 0, a mutation in u or v increase the chance of mutation in the other one. As discussed in  and if λME(u) > 0, the genes in u have some levels of mutual exclusivity, depending on how high λME(u) is.u, v) be an edge with positive λPR, where λPR > λPR. Consider a null hypothesis that says v is independent of u and gets mutated with a probability equal to p(v) in each tumor (p(v) is the empirical mutation rate of v across all tumors). Having observed nu,v tumors with mutations in both u and v, we can calculate how likely it is that the null hypothesis generates nu,v or more tumors with a mutation in v, among the tumors which have a mutation in u, i.e.,In the following, we define p-values for the progression and mutual exclusivity signals to check how likely it is that the observed patterns have happened by chance. Starting from the progression patterns, let . Having ng,w tumors with mutations in both g and w, we can calculate how likely it is that the null hypothesis generates ng,w or fewer tumors with a mutation in w among the tumors which have a mutation in g:For the mutual exclusivity relation among a pair of genes, we consider the two directions of the relation separately. Assume that a mutation in gene k samples and reported the sample with the maximum posterior among all visited samples.We used ToMExO to analyze a glioblastoma dataset previously analyzed by pathTiMEx  and MHN r and column c shows the effect of event c on event r. The interrelations coded by the MHN can be shown using a graph. PR = 0.93), in the correct direction, with a pretty significant p-value. Note that the same relation, but in the opposite direction, was reported by the MHN.In the following, we present a selection of our analysis on a set of larger TCGA datasets, which are impossible to investigate by MHN or pathTiMEx due to the large numbers of genes (mutation events) in the data. We downloaded mutation-called TCGA data from the GDAC firehose and limited our focus to the somatic mutations in the cancer-type specific lists of driver genes suggested by IntOGen . Note thk samples and reported the maximum a posteriori sample in all the following analyses. We analyzed all the 30 cancer types available in GDAC firehose. To construct our binary input matrix, we consider a tumor mutated in a gene if it has at least one non-silent mutation. Similar to the previous section, we ran ToMExO with 10 MCMC chains and 10037, corresponding to a per-tumor ratio of 1.347. The GBM dataset with more than 7.5 thousand bits can fully conform with our progression model, assuming only 17 false positives and 77 false negatives.Our GBM dataset includes 290 tumors and 26 genes. The resulting progression model is shown in As shown in PR = 1) from TP53 to IDH1. However, EGFR is placed in a separate first-layer node. We tested the progression relation from EGFR to IDH1 and found a significant negative progression score (λPR = −0.67), which means that mutations in EGFR highly reduce the chance of mutation in IDH1. This was expected since mutations in TP53 and IDH1 are not common in the classical subtype, where EGFR is highly mutated, as mentioned above. We emphasize that the other significant progression relation of our model, from TP53 to RB1, is also interesting as these two tumor suppressors are known to cooperate in glioblastoma tumorigenesis [The other multi-gene node in the first layer includes EGFR and NF1, which are considered the main drivers of the classical and mesenchymal subtypes of GBM, respectively . These tigenesis .78, corresponding to a per-tumor ratio of 2.246. The dataset with more than 9.5 thousand bits can fully conform with our progression assuming 67 false positives and 107 false negatives.Our COADREAD dataset includes 223 tumors and 43 genes. The resulting progression model is shown in PR(KRAS → TP53) = −0.06 and λPR(APC → TP53) = 0.02. Therefore, TP53 is almost independent of APC and KRAS, as suggested by our progression model.As recovered by our progression model, KRAS mutations in colorectal cancer are known to happen after mutations in APC . MutatioOur progression model for colorectal cancer  includes21, corresponding to a per-tumor ratio of 1.381. The dataset with 2.7 thousand bits can fully conform with our progression, assuming 26 false positives and 40 false negatives.Our PAAD dataset includes 150 tumors and 18 genes. The resulting progression model is shown in Mutations in KRAS and TP53 are present in 91 and 69 percent of our samples, respectively. These two genes are known to be highly associated with oncogenesis in pancreatic cancer . We haveIn another branch independent from KRAS and TP53, we have SMAD4. We calculated a progression score of 0.08 from KRAS to SMAD4, which agrees with the separated branch for SMAD4. The mutations in SMAD4 are well-known to be important in pancreatic cancer . Our modhttps://github.com/mrmohaghegh/tomexo). Our complementary analyses of another set of 27 cancer types can be found in our repository.In this paper, we introduced ToMExO, a probabilistic method for analyzing cross-sectional mutation data from cohorts of tumors. Our method is designed to simultaneously infer the mutual exclusivity patterns among the driver genes and place them into a tree-like structure coding the temporal order of events. We introduced a computationally efficient inference algorithm for investigating huge datasets with hundreds of genes and thousands of tumors. After an extensive set of synthetic data experiments, we presented our results on a dataset previously analyzed by two state-of-the-art methods, showing our superior performance. Finally, we presented our analyses on larger TCGA datasets that the opponents cannot approach due to the large numbers of genes in the datasets. Our inferred progression models for glioblastoma, colorectal, and pancreatic cancers presented in this paper recover several well-known mutual exclusivity and progression patterns among specific genes and suggest a broad set of new such relations. ToMExO is publicly available in our GitHub repository Click here for additional data file.