Dose–Time Network Identification (DTNI) was applied to gene sets from exposed primary human hepatocytes using four stress pathways, namely endoplasmic reticulum (ER), NF-κB, NRF2, and TP53. Inferred GRNs suggested case specificity, varying in interactions, starting nodes, and target genes across groups. DILI and carcinogenic compounds were shown to directly affect all pathway-based GRNs, while non-DILI/non-carcinogenic chemicals only affected NF-κB. NF-κB-based GRNs clearly illustrated group-specific disturbances, with the cancer-related casein kinase CSNK2A1 being a target gene only in the carcinogenic group, and opposite regulation of NF-κB subunits being observed in DILI and non-DILI/non-carcinogenic groups. Target genes in NRF2-based GRNs shared by DILI and carcinogenic compounds suggested markers of hepatotoxicity. Finally, we indicate several of these group-specific interactions as potentially novel. In summary, our reversed-engineered GRNs are capable of revealing dose dependent, chemical-specific mechanisms of action in stress-related biological networks.Perturbation of biological networks is often observed during exposure to xenobiotics, and the identification of disturbed processes, their dynamic traits, and dose–response relationships are some of the current challenges for elucidating the mechanisms determining adverse outcomes. In this scenario, reverse engineering of gene regulatory networks (GRNs) from expression data may provide a system-level snapshot embedded within accurate molecular events. Here, we investigate the composition of GRNs inferred from groups of chemicals with two distinct outcomes, namely carcinogenicity [azathioprine (AZA) and cyclophosphamide (CYC)] and drug-induced liver injury , and a non-carcinogenic/non-DILI group . For this, we analyzed publicly available exposed  In the last few years, investigation of biological processes disturbed by chemical exposure and its potential adverse effects has become the main goal in toxicological assessments targeting hazard identification and/or drug development. This strategy encompasses two steps: the first being the identification of such processes/pathways and the second, the estimation of dose–response, dynamic relationships that may define the boundaries between adaptive and adverse responses .in vitro models, the ability of current methods to address the aforementioned challenges has proven insufficient. Take, for instance, pathway analysis – omnipresent in HT studies as means to identify biological processes affected across different conditions. First, functional annotation does not reflect the diversity of the human genome, with the repertoire of pathways across multiple databases being either comprised of general processes  or very specific responses   technologies  to exposed athways) . The choSaccharomyces cerevisiae have shown that network interactions undergo critical changes after challenging with a DNA damaging agent, leading to extensive network rewiring   using expression data; in contrast to pathways, GRNs are case specific, referencing multiple types of physical and biochemical interactions among genes and gene products , allowinactions) . Other ractions) .Recently, our research group has developed and validated a tool for inferring GRNs from HT gene expression data during chemical exposure, taking possible time and dose dependencies into account . By usinDose–Time Network Identification (DTNI) is a method for inferring network interactions among genes through ODEs that relate changes in gene expression over time and dose and an external perturbation. DTNI requires measurements from multiple doses and time points – not necessarily sampled at equal intervals – and expression values of a reduced gene set . DTNI can be applied to single chemicals but also allows the use of group-wise approaches – in which a consensus network is inferred for multiple compounds. A detailed description of the method, its validation, and script availability for MATLAB is described elsewhere .1). TG-GATEs contains microarray data of hundreds of compounds, generated in vitro (human and rat) and in vivo (rat), tested at multiple doses and time points. We therefore mined TG-GATEs for compounds matching the criteria of full availability of sets  and specific classification regarding carcinogenicity or DILI. To also avoid methodological biases, we aimed to create groups with fairly equal number of compounds; based on these constraints, the carcinogenic group comprised two chemicals [azathioprine (AZA) and cyclophosphamide (CYC)], while DILI  and non-carcinogenic/non-DILI  groups contained three chemicals each (Table 1). Table 1 contains detailed information on chemicals and evidence for their inclusion in their respective groups.In order to link network changes to chemical effects, we targeted compounds with well-known (adverse) effects in humans. We opted for three groups, two with different mechanisms of toxicity – carcinogenicity and DILI – and one with no weight of evidence for human carcinogenicity or DILI. Since DTNI requires datasets with a minimum of three doses and three time points, we used the Japanese database TG-GATEs  through R scripts provided on ArrayAnalysis   is available on Supplementary Data Raw files from each chemical set were downloaded from TG-GATEssis.org) . Probes Given our goal to identify common features underlying (non-)toxic mechanisms across chemicals, and DTNI’s requirement for reduced gene sets, we opted for a group-wise approach for evaluating how carcinogenic, DILI, and non-carcinogenic/non-DILI compounds may  affect networks involved in toxicological responses. Our selection involves known stress pathways indicative of DNA damage (TP53), oxidative stress (NRF2), endoplasmic reticulum (ER) stress, and inflammation (NF-κB). From KEGG, we retrieved gene components from pathways ER , TP53 , and NF-κB  – entries hsa04141, hsa04115, and hsa04064, respectively. NRF2 genes  were obtained from WikiPathways (entry WP2884)   from each chemical using the database ConsensusPathDB (CPDB) and its overrepresentation analysis tool ( < 0.05) .p-value) set to 0.05. The final network for each pathway within a chemical group was obtained after determining the intersection among the DTNIs . The consensus network for NF-κB in the DILI group, for instance, was generated after overlapping the results from DTNI with all three compounds plus LOOCVs . Cytoscape was used to generate and visualize the networks.For DTNI, we used scripts developed for use on MATLAB . For thiTo assess differences across networks generated by DTNI, we considered four aspects of the inferred GRNs: direct perturbations to the pathway being analyzed, number of interactions, genes involved, and overall direction of expression (up- or downregulation). Furthermore, while starting nodes  may be used as basis to investigate potential molecular initiating events (MIEs), target genes may offer a clue to potential downstream effects. Therefore, those two categories of genes were investigated in more detail within the GRNs.Furthermore, to validate the interactions found by our method, we used the “induced network modules” tool available on the database CPDB. For this, we used gene lists from each pathway as input. We allowed for intermediary nodes and limited our search to only high-confidence protein interactions in addition to genetic, biochemical, and gene regulatory ones. By comparing our inferred GRNs to those obtained from CPDB, we were able to detect indirect, direct, and potentially novel interactions. A predicted interaction was labeled “direct” when the same edge was present in CPDB and “indirect” when a third node mediated an interaction between two nodes. Indirect interactions may appear due to reactions that happen faster than the timescale measured  . Potentip-value < 0.05) found for each pathway in each chemical group is summarized in Table 2. DILI and carcinogenic compounds affected all networks tested, with the former frequently resulting in the highest number of gene interactions. In contrast, non-carcinogenic/non-DILI compounds only affected NF-κB genes. When generating the networks, we observed that those from DILI are more connected in comparison with the other groups. TP53 and ER resulted in sets of unconnected modules in the carcinogenic group. An overview of such network layouts is represented in Supplementary Data GRNs reconstructed from gene expression data showed strong variation across carcinogenic, DILI, and non-carcinogenic/non-DILI groups. The number of significant edges .Although input gene lists were identical, the resulting inferred networks showed great variation across chemical groups. To enable visualization and direct comparison, pathways were represented as the union of all interactions predicted in each chemical group, in which edge colors express different chemical groups  , as well as DILI and carcinogenic (CXCL12 and CARD10), and carcinogenic and non-DILI/non-carcinogenic . NRF2 showed the highest overlap, with 12 genes shared between DILI and carcinogenic compounds: BLVRB, SLC39A4, HGF, SLC39A3, CBR3, CYP4A11, GSTA1, HSPA1A, SLC2A1, MGST2, SLC5A8, and RXRA. GTSE1 was the only gene shared between DILI and carcinogenic chemicals for the TP53 pathway, while EDEM2, DNJB1, and DNJA1, from the ER pathway, were found shared by the aforementioned groups.Since only a few interactions were shared among all groups, we decided to investigate whether this low overlap was due to distinct starting nodes and/or target genes. These are indicated as different sections in the networks, with the upper part containing starting nodes and the lower part the target genes; the latter is further divided into clusters to highlight genes that are specific or shared by more than one chemical group. This arrangement allowed us to detect striking differences among groups. First, we observed that the only pathway that resulted in shared target genes among all three groups was NF-κB – Overall, we also detected distinct starting nodes, frequently targeting specific clusters of chemical groups, suggesting that these potential MIEs may result in different responses within the same pathway.To validate the edges predicted by DTNI, we compared our results to databases sourcing protein, genetic, and gene regulatory interactions as well as biochemical reactions. The detailed list of edges analyzed is provided in Supplementary Data Figures 4). With that, we aimed to understand the regulation of such processes during exposure. Also, by generating individual chemical group/network graphics interchange format files, we confirmed these biological interactions as time- and dose dependent, showing clear oscillatory and/or progressive gene expression profiles  in non-human To investigate the behavior of pathway activation in exposed models, we investigated the effects of chemicals with different adverse outcomes (acute organ injury and carcinogenicity) compared to drugs not implicated in these pathologies. We aimed to reconstruct pathways based on gene expression data; for this, we selected four pathways  due to their established involvement in mechanisms of toxicity . Our firFigures 4). This can be illustrated by the GRNs inferred for genes from the NF-κB pathway . Although all groups directly affected this process, GRNs indicate that while non-DILI/non-carcinogenic compounds targeted the inhibition of NF-KB1 and NF-KBIA, those from the DILI group resulted in activation of NF-KB2 and RELA. In addition to differences in direction of expression of these targets, studies have also shown that some NF-κB units may act independently from each other, controlling proliferation and immune responses . Among these genes, we identified hepatocyte growth factor (HGF), known to play a role in tumorigenesis and tissue regeneration , the gene coding, which converts biliverdin to bilirubin. Bilirubin, which has long been regarded as a cytotoxic waste product of heme metabolism, was recently discovered to possess strong antioxidant activity , comprised mostly by fragmented subnetworks. Since carcinogens AZA and CYC also share DNA-damaging properties, we expected a large amount of disturbances in GRNs from both pathways. In view of this, we hypothesize that due to the different mechanisms underlying their effects , common significant interactions could not be inferred from the available data. The fact that these chemicals have stronger effects on dividing cells may yet be another cause, since human hepatocytes show limited proliferative capacity when cultured . The fact that TP53 and NF-κB have been extensively studied over the years due to their clinical relevance may explain why they showed the highest number of direct or indirect positive hits . Therefore, our results indicate the need for further studies targeting the validation of such interactions as means to expand the repertoire of biological interactions and assess their relevance as potential markers of toxicity.It should be pointed out some of the limitations of GRN methods, in particular ODE-based methods such as DTNI. Although ODE-based methodology describes time-series data well, its deterministic nature does not account for statistical fluctuations in concentrations and kinetic parameters that greatly influence biological systems . The genNonetheless, our approach demonstrated some unprecedented mechanistic aspects of GRNs upon exposure to chemicals with different toxic potential. First, GRNs are usually condition dependent, indicating distinct molecular events depending on the type of exposure. To some extent, however, there are similar gene targets shared by GRNs inferred for toxic groups – but not present in compounds considered non-DILI/non-carcinogenic – that may point toward relevant molecular events indicative of toxicity. Finally, the fact that disturbances in these molecular targets evolve with increases in dose reinforces the value of DTNI as an asset in network-based, HT investigations. Anchoring these dose-dependent events to apical measurements may therefore reveal molecular signatures and clarify the tipping points leading to adverse outcomes.TS analyzed the data and wrote the manuscript. JK and DJ wrote the manuscript.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. The reviewer KG and handling Editor declared their shared affiliation.


Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method. These genetic changes as well as gene-expression profiling (GEP) have been widely used in the molecular classification of newly diagnosed patients to define diagnostic entities and identify promising new therapeutic targets7. However, at present a standard of classification based on subgroups that could be targeted therapeutically is still being debated. Clearly, there is a need for innovative tools to improve the identification of the prognostically relevant entities, clinically and biologically, in newly diagnosed MM patients. It is tempting to use the mutational spectrum based on whole-exome sequencing as a gold standard; however we have previously shown that a large number of exome mutant alleles are not expressed clinically or biologically8. In addition, exome sequencing may miss potential driver mutations in the non coding regulatory elements known to affect enhancer activity, which thereby affect the transcriptional program9; therefore GEP remains a tool of choice. However, GEP alone is limited and must be integrated with innovative approaches that use biological regulatory networks to extract biological information relative to gene expression datasets to provide significant clues about the etiology of myeloma.Multiple myeloma (MM) is a neoplasm of plasma cells with an incidence rate of approximatively 5/100,000 in Europe. The median survival of MM patients has improved substantially over the past decade. Owing to the establishment of high-dose therapy followed by autologous stem cell transplantation as a routine procedure, significant improvements in supportive care strategies, and the introduction and widespread use of the immunomodulatory drugs thalidomide and lenalidomide, and the proteasome inhibitor bortezomib. Nevertheless, almost all MM patients ultimately relapse, and new drugs and new combinations for the treatment of MM are warranted. MM is a heterogeneous disease at both the clinical and molecular levels. Recent large scale genomics analysis based on the landscape of copy-number alterations and on whole exome sequencing have revealed the hallmarks of genetic changes in MM such as hyperdiploidy, translocations involving the IgH locus, and mutations in the RAS/MAP and NF-kB pathways and in TP53pathway analysis or active pathways detection have been developed. These methods use as a knowledge base a biological pathway or regulatory network, that compiles a series of molecular phenomena that lead to activation (or inhibition) of gene expression, a cell product such as a hormone, or a physical modification of the cell. Regulatory network information is currently available through databases such as Gene Ontology (GO)10, the Kyoto Encyclopedia of Genes and Genomes (KEGG)11, the Pathway Interaction Database (PID)12, Wikipathway13, Transfac14, and Causal Biological Networks (CBN)15. The main objective of pathway analysis methods is to confront or integrate GEP data with regulatory networks or pathways to distinguish two or more classes of cells  from GEP data by inferring a specific signature for each class. We can identify three principal categories of approaches that have been used to associate GEP with specific pathways16.During the past decade, many methods of so-called 18 includes approaches that are based on differentially expressed (DE) genes. These approaches score single pathways based on the proportion of DE genes  contained in each pathway. In most cases, these methods use a hyper-geometric test17 to score each pathway. Moreover, the majority of ORA approaches that use functional annotation (GO) or pathway maps (KEGG) consider the consequences of the DE genes  in the associations between gene and pathway. Martin et al.19 called this type of reasoning forward assumption compared to the backward assumption18, which considers the causes of those DE genes in the gene-pathway association.The Over-Representation Analysis (ORA) group of approaches21 or backward23 reasoning. Although these methods improve the problem of genes selection, the pathways in which individual genes are involved are still studied independently. Moreover, the position of the genes in the topology is not used in the analysis.The Functional Class Scoring (FCS) group of approaches uses the full datasets without any pre-selection, allowing integration of the effects of low gene expression variations in the identification of the pathways involved. FCS approaches can use forward27, others consider different types of relationships between genes28, generally activation and inhibition. The majority of methods study each pathway independently. Within this group, we can also identify methods that use both forward27 and backward28 reasoning.The Pathway Topology (PT) approaches are very similar to the FCS approaches, but in addition, they score genes according to the pathways to which they belong. Whereas some of these approaches only include interactions between genes29, a tool based on logic programming (Answer Set Programming) that confronts a node coloring (GEP) with labeled and directed graphs. Our combined approach could be considered to fall within the PT category since it takes into account the causality and activation/inhibition logic of graph edges. However, unlike previous methods, it uses a global logic to analyze experimental and pathway data. In this formalism, both forward and backward modes are included as reasoning modes (causes-consequences). IGGY allows us to check the consistency of the information and to generate predictions based upon automatic repairs for upstream non-measured species. It uses DE data as well as the identically expressed genes across classes (invariant genes) in its analysis. The proposed method does not correlate protein activation with gene expression; the two entities are identified separately in the graph. The non-measured protein activations necessary to satisfy the GEP according to the entire pathway database topology are used later to propose a signature for each dataset profile. This global signature can be used to characterize the dataset classes. Moreover, our model also allows us to in silico quantify the effect of perturbations on this global pathway for each single patient. We show how this type of method, which combines large-scale information in terms of number of patients, the complete GEP, and the entire compendium database, can be applied to identify new specificities of MM disease compared to normal cells. As a result, we inferred information on the states of specific proteins in the cell that may cause these disorders, and we identified specific markers of MC compared to NPC that can be used to identify survival markers. Furthermore, these markers can be studied as therapeutic targets because of their over-representation and their impact on the involved pathways.In this work, we propose to integrate the GEPs obtained from myeloma cells (MC) of 602 MM patients and from normal plasma cells (NPC) of 9 healthy donors with the whole compendium of the PID-NCI public pathway repository so as to better understand the mechanisms of plasma cell carcinogenesis. To integrate this data, we first automatically build a directed (and labeled) graph using the whole compendium of the PID-NCI public pathway repository. This graph connects signaling pathways to the transcription of the genes in the GEP dataset. We then integrate the graph with the expression data by reasoning on its logic using IGGY30. Purified plasma cells were frozen at −80 °C in lysis buffer. Approval for this study was obtained from the University Hospital of Nantes. The study fulfilled the requirements of the Declaration of Helsinki.Plasma cells were isolated from the bone marrow of 602 newly diagnosed cases of MM. The samples were obtained during standard diagnostic procedures conducted at the Intergroupe Francophone du Myélome (IFM) centers. The subjects included patients younger than 65 years of age who were enrolled in either the IFM 2005–01 trial (n = 311) or the IFM-2007-02 (n = 128) trial, older patients enrolled in the IFM-2007-01/Multiple Myeloma 020 trial (n = 76) and 9 normal donors. The experiments were undertaken with the understanding and written informed consent of each subject. Plasma cell purification was performed as previously describedlog2 normalized with Expression Console Affymetrix software v1.1 using an RMA algorithm.RNA was extracted using the AllPrep DNA/RNA MiniKit or the RNeasy Micro kit  in accordance with the manufacturer’s instructions. RNA quality and quantity were assessed using Agilent 2100 Bioanalyzer  and a Nanodrop Spectrophotometer , respectively. MM samples from which 50 ng of total RNA was available were processed according to manufacturer’s instructions  before labeling and hybridization onto an Affymetrix Human Exon1.0 chip according to the manufacturer’s instructions . Data were analyzed and graph coloring model requires invariant genes, classical discretization methods cannot be used in our study. To identify over-/underexpressed and invariant gene expression for each profile, we used two thresholds: k1 for invariant genes and k2 for variant genes. For each gene g, we computed a vector, pg, composed of its differential expression, i. A profile i refers to each of the 611 cells of type MC or NPC considered in this study. k1 and k2.Since our i;If i;if i.and if k1 and k2 , we obtained 150 sets of vectors that contain the discrete overexpressed (+), underexpressed (−), or invariant (0) values for all the genes expressed in each dataset. We discarded combinations of values leading to pg vectors with a sign proportion greater than 50%. Each k1 − k2 combination was used to test the precision of our approach. We did this 100 times by using 50% of the discretized {+, −, 0} genes’ expression to predict the other 50% for each MC dataset, after which we comparing the measured and predicted data using a precision matrix  was lower than that obtained using the selected thresholds  and 27,976 edges (activation or inhibition). To orient our analysis to the expression profiles and to the biological problem at hand, we built a subgraph with signed edges by extracting the downstream events from three signaling pathways , all of which are known to include cellular receptors involved in MM31, to the over- and underexpressed variant genes from all datasets by the shortest paths. This cycled, directed subgraph was then filtered by deleting all nodes that are not observed and with one predecessor or one successor32. This filtering step involves no loss of information with respect to the graph coloring model and allowed us to reduce the complexity of the analysis while maintaining the dependencies between the nodes.We used the 2012 version of the complete pathways database PID-NCI (Pathway Interaction Database)In Fig. α) in which V is the set of nodes, E is the set of edges and α is a function labeling the edges as α:E → {+, −}, let β be a set of observed data with β:V → {+, −, 0}. In our case, β is obtained from GEP and labels only the nodes that are preceded by a “transcription” event as reported by the PID-NCI database. Thus, there are nodes in V, such as proteins or complexes, that remain unlabeled. Our reasoning framework expresses that there is at least one state or coloring model of this biological system. A coloring model is an assignment μ:V → {+, −, 0} of each node in V to a sign in {+, −, 0}. Let us denote by S the set of all possible coloring models; note that |S| = 3V||. When imposing the restrictions of β to S, we reduce the size of all possible coloring models  with the same (opposite) sign.Each {+, −} variation associated with a node m (associated with sign 0) has to be explained either by the fact that (i) all direct predecessors of m are associated with an invariant sign, or (ii) at least two direct predecessors of m are associated with opposite variant signs {+, −}.Each invariant node The sign consistency imposes a reasoning mode over a graph β, then a set β and respect the consistency rules.When a graph G is consistent with G is not able to explain the labeling β according to the three previously explained consistency rules. In this study, we used two approaches to restore the consistency.When a consistent solution does not exist, the graph topology of MCOS-repair: This repair mode corrects the graph topology. Considering that the graph is not complete , we can suppose that some inconsistencies are caused by events that are missing from the graph. It is possible to correct the graph by adding a set of artificial influences  of artificial influences that can be added to restore the consistency. The MCOS is in general not unique.SCENFIT-repair: This repair mode corrects β by considering wrong information in the observed data. β will be corrected by switching the sign of the observed nodes so as to minimize the number of switches. The switch of an observed node is quantified by a cost, as described below. The set of possible minimal SCENFIT-repairs is not unique.Changing a variant sign  to the opposite variant sign will have a cost of 2.Changing an invariant (respectively variant) into a variant (respectively invariant) sign will have a cost of 1.union of the consistent coloring models under each minimal repair. Usually, the number of consistent coloring models is very large, and we use a projection of these models to deduce and propose insights from the graph-observations confrontation. We distinguish 7 sign projections classes:After applying a repair operation, the set of consistent coloring models will be the strong, meaning that the node has the same sign in all consistent solutions {+, −, 0}.1. 3 classes are weak, meaning that the node has 2 signs in the consistent solutions: Not+ , Not− , change .2. 3 classes are 3. The last class means that a node has three signs in the consistent solutions:? .i in V into a triplet of boolean values. The boolean value expresses whether the couple , where s ∈ {+, −, 0}, belongs to the sign-projection result. Since we only focused on sign-projections, the nodes observed in β were not considered. To reduce the number of variables, we excluded the boolean value that refers to invariant couples (nodes coupled with “0”). In this way, we represent the sign-projections obtained for each GEP as a boolean matrix M of size 2 × m × (NMC + NNPC), where m represents the number of nodes in G that were never observed in any GEP and NMC (respectively NNPC) represents the number of profiles in class MC (respectively NPC). Mij stands for the decomposed prediction of node i under profile j; note that Mij can be separated into i is predicted to be of sign s in profile j. To identify specific markers of MM, we analyzed M and looked for overrepresented values when comparing the vectors belonging to MC with those belonging to NPC. For this, we used two approaches, a machine-learning approach based on supervised learning and a statistical approach based on frequency classification. For the supervised learning, we used a decision tree33 and a random forest classification34. Due to the underrepresentation of the NPC, we increased the weight of each NPC by 67 so as to have the same order of population in each group (9 NPC and 602 MC). For the frequency approach, we calculated the frequency score (FS) for each group (MC or NPC) and for each assignment  as follows:C represents the class MC or NPC and s represents the {+, −} sign assigned to i. We then sorted our results based on a Fisher test between the proportions for NPC and MC to determine the most specific node assignments for the MC datasets.In this analysis, we used the sign projections computed after restoring the consistency using the MCOS-repairs. To compare predictions across individuals using statistical and machine-learning approaches, we decomposed each sign projection result over a node in silico the activation or inhibition of a protein. The quantification of these in silico perturbations was performed in two steps. First, we considered the set of assignments in M (see previous section), where i is predicted to be of sign s in profile j, with s ∈ {+, −}. For each assignment j (βj) except that we added an observation on node i fixed to s ∈ {+, −}. We then computed the SCENFIT score G and i, s) according to its j, as follows:In this analysis, we quantified the effectiveness of a node perturbation  with 320 nodes and 1.3To RAM.For the sign consistency analysis, we used IGGYAll graphs used in this study are available online using cynetshare. The subgraphs of NCI-PID before (goo.gl/upfzwC) and after compaction (goo.gl/SfNSv4). The subgraph from Fig. 31 to the variant genes, we generated an induced subgraph from NCI-PID containing 2,269 nodes, 2,683 edges and connecting 529 variant genes. This graph was then compacted to a new graph with 596 nodes and 960 edges . Independently, our discretization method . This result is illustrated in Fig. FSMC > FSNPC. For these assignments, we checked the number of input/output variant genes connected to each node in the graph  for our predictions (see Methods section). The complete list of the FS obtained is shown in Table in vitro studies demonstrating that in myeloma cell lines IL6 protects against apoptosis via AP-1 inactivation39.The FOS and JUN proteins form a heterodimer complex that is responsible for AP-1 activity. This activity is known to play a role in tumorigenesis because it has been implicated in the induction of apoptosis, in the promotion of cell survival and in proliferation. The classification methods showed that  is the best assignment to distinguish MC from NPC and revealed that AP-1 activity is lower in almost all MM patients than in normal controls. Inspection of individual patients’ subgraphs showed predominantly underexpression (65% of the observed expression in MC) of the proapoptotic protein BIM  for OS determination51. In the multivariate analysis, estimation of hazard ratios for death indicates that both  and  were independent powerful prognostic factors  and the three strongest known prognostic variables in MM Table ; these w−7 (null model vs 3 parameter model) whereas the parameters  and  increase the log-likelihood from −496.62 (3-parameter model: AIC3p = 999.2) to −486.90 (5-parameter model: AIC5p = 983.8) with p-significance <10−4 (3-parameter model vs 5-parameter model) and <10−10 (null model vs 5-parameter model). Therefore, we can conclude that the 5-parameter model provides more prognostic information than the 3-parameter model (AIC5p < AIC3p and p5p vs 3p <10−4). In term of the global increase in the log-likelihood between the null model and the 5-parameter model, the specific impact of the selected pairs represents more than 34% of the total.The multivariate model with the known prognostic parameters shows that these factors increase the log-likelihood from −515.16 (null model) to −496.62 (3 parameter model), with p-significance <10in silico node perturbations (see Methods section), we evaluated the impact of perturbing the key nodes found with the FS method (Table in vitro JUN overexpression in MM cell lines results in cell death and growth inhibition41. A similar tendency (more conflicts in MC than in NPC) is observed when FOXM1 is activated, but the difference cannot be considered significant. Nonetheless, we note that of the 36.4% of profiles in which the activation of FOXM1 is top-ranked, 96.8% correspond to patient profiles with the prediction  , we can associate this node with the proliferation pathway. The computed prognostic model showed that the prediction of inhibited proliferation can be a protective factor for MM patients. The second node, , was also identified as a protective factor by the 5-parameter model. This complex is known to be involved in the RB pathway, which influences cell growth pathways by regulating the initiation of DNA replication. This pathway is usually altered in cancer, leading to a loss of function54, and current therapeutic approaches aim to activate this pathway55.Our analysis of the predictions made by the method allowed us to identify nodes associated with a sign specific to MC compared to NPC datasets. Among these assignments, we found the inhibition of JUN/FOS[n] and FOXM1*[c]. These proteins are known to be involved in cancer in generalin silico node perturbation method, we were able to estimate the effect of perturbing a node within a particular dataset (i.e. single patient cancer cell). This method represents a powerful tool for analyzing the consequences of perturbations of oncogenic pathways in a given patient, especially as in-vitro experiments are limited due to the small amount of viable myeloma cells that are obtained after bone marrow aspiration. The results of this in silico analysis show that activation of JUN/FOS[n] had a significant impact on 75% of MC profiles; all of these JUN/FOS[n] = “+” sensible MC profiles had the prediction . In addition, activating FOXM1*[c] had a significant impact on 36.4% of the profiles; 96.8% of the FOXM1*[c] = “+” sensible MC profiles had the prediction . The difference in the percentages of JUN/FOS[n] and FOXM1*[c] can be explained by the graph topology and the connectivity of the individual nodes. JUN/FOS[n] is connected to eight genes through a distance of 1 molecular species  assignments representing the specific states of biological entities. Using two approaches of classification, we were able to identify specific assignments for MC datasets compared to NPC datasets. Finally, taking advantage of our modeling framework, we studied the effect of performing single in silico perturbations.In this study, we used a specific approach to study and understand the heterogeneous gene expression profiles of approximately 600 multiple myeloma (MM) patients. Our primary goal was to provide mechanistic scenarios by identifying protein activity states of molecules that may be central to the diversity of gene expression. Our approach relies heavily on reasoning based on graphs and on changes in gene expression in the form of logical programs that combine these two types of information. The method proposed here can be summarized in the following steps. First, we obtained a directed graph, allowing us to connect significantly up-/down-regulated genes to upstream MM-related cellular receptors. Second, we confronted this graph to transcriptomic data with IGGY, which is a tool that reasons on the logic of the graph and on shifts of expression in the data so as to predict  and , that are of potential biological interest in the understanding of the molecular mechanisms underlying resistance to treatment. Note that these nodes can only be predicted with the graph and coloring model, since they are a  consequence of the GEP. Our results on Supplementary Materials


In multiple plant lineages a modified system known as C4 photosynthesis has evolved. To better understand the molecular patterns associated with induction of C4 photosynthesis, the genus Flaveria that contains C3 and C4 species was used. A base to tip maturation gradient of leaf anatomy was defined, and RNA sequencing was undertaken along this gradient for two C3 and two C4Flaveria species. Key C4 traits including vein density, mesophyll and bundle sheath cross-sectional area, chloroplast ultrastructure, and abundance of transcripts encoding proteins of C4 photosynthesis were quantified. Candidate genes underlying each of these C4 characteristics were identified. Principal components analysis indicated that leaf maturation and the photosynthetic pathway were responsible for the greatest amount of variation in transcript abundance. Photosynthesis genes were over-represented for a prolonged period in the C4 species. Through comparison with publicly available data sets, we identify a small number of transcriptional regulators that have been up-regulated in diverse C4 species. The analysis identifies similar patterns of expression in independent C4 lineages and so indicates that the complex C4 pathway is associated with parallel as well as convergent evolution.Most terrestrial plants use C C3 photosynthesis is considered the ancestral form and is found in the majority of species. All three photosynthetic pathways use Ribulose-1,5-Bisphosphate Carboxylase Oxygenase (RuBisCO) to catalyse CO2 fixation in the Calvin–Benson–Bassham (CBB) cycle and, in so doing, two molecules of the three-carbon molecule 3-phosphoglycerate are formed. However, RuBisCO is not completely substrate specific, catalysing an oxygenation reaction that produces 2-phosphoglycolate as well as 3-phosphoglycerate  initially fixes carbon in mesophyll (M) cells, and then, after release of CO2 in the bundle sheath (BS) cells, RuBisCO re-fixes CO2 in the CBB cycle. The production of CO2 within the BS leads to high concentrations of CO2 around RuBisCO and minimizes oxygenation. PEPC generates oxaloacetate, which is rapidly metabolized to malate and/or aspartate prior to their diffusion to the BS. Three C4 acid decarboxylase enzymes known as NADP-malic enzyme (NADP-ME), NAD-malic enzyme (NAD-ME), and phosphoenolpyruvate carboxykinase (PEPCK) have been shown to release CO2 around RuBisCO and, for many years, based on their relative abundance, these decarboxylase enzymes were used to define three C4 biochemical subtypes. More recently it has been shown that this division into three types is less rigid than previously thought .Most species of e leaves . C4 specoid type . Phyloge3 and C4Flaveria species, and set out to link the gradual maturation of C3 and C4 characteristics in leaves to underlying alterations in transcript abundance. By linking the development of the C4 phenotype to changes in gene expression in two C4 species, and comparing these findings with equivalent data from two C3 species in the same genus, we aimed to identify common traits associated with C4 photosynthesis, and to remove species-specific characteristics from our data sets. Using the maturing leaf as a dynamic system, we show that in both C4 species studied, the induction of Kranz anatomy occurs along a base to tip developmental gradient in leaves of >2 cm length. We sampled this maturation gradient and undertook RNA sequencing to correlate the underlying patterns of gene expression with anatomical development.In this study, we used two pairs of CFlaveria bidentis (L.) Kuntze, Flaveria pringlei Gandoger, Flaveria robusta Rose, and Flaveria trinervia (Spreng.) C. Mohr were grown in a glasshouse on the roof of the Department of Plant Sciences, Cambridge. Temperature was maintained above 20 °C and supplemental lighting was provided to ensure at least 250–350 µmol m–2 s–1 photon flux density for 16 h d–1. Seeds were sown directly onto soil  in covered pots as seeds need high humidity for germination. Covers were removed when seedlings were 1 cm high.2 to 1 cm2 were fixed in 4% (w/v) formaldehyde at 4 °C overnight and then placed on ice and dehydrated prior to being placed in 100% (v/v) ethanol, followed by 1:1 ethanol/Technovit mix and then 100% Technovit 7100 . Samples were subsequently left in Technovit solution plus hardener I (1 g of hardener per 100 ml) for at least 1 h. Disposable plastic resin embedding moulds  were filled with a mixture of Technovit plus hardener I and II (15 ml of Technovit plus hardener I were mixed with 1 ml of hardener II). Samples were arranged within the embedding moulds which were then covered with unstretched Parafilm® M to seal them from air, and left to harden overnight. Samples were removed, heated to 80 °C, and trimmed for sectioning. Sections of 2 µm thickness were produced using a Thermo Scientific Microm HM340E microtome. Ribbons were mounted onto SuperFrost®white microscope slides , left to dry, and then stained with 0.1% (w/v) toluidine blue. All sections were analysed with a BX41 light microscope , usually using the bright-field setting.Samples of 4 mmTo clear leaves, they were placed into 70% (v/v) ethanol and heated to 80 °C. The next day samples were placed in 5% (w/v) NaOH for ~15 min to clear leaves further, and then mounted in water and analysed by light microscopy.–2.To quantify leaf anatomical characteristics, Photoshop CS5 was used. The program was calibrated with scale bars and the lasso tool was used to measure cell area for both BS and M cells. Measurements of M cells were taken between BS cells, with 30 BS and 30 M cells being measured per leaf section. This was done for all six leaf stages and for three biological replicates for each species. Images derived from leaf sections were assembled using Photoshop. The background of sections was averaged and in some cases the contrast was increased to improve visibility of cell borders. Vein density was determined using images of cleared leaves. In mature sections of the leaf there was no ambiguity in collecting these data, whereas in the most basal sections of some leaves, the estimates may be underestimates of the extent of venation as immature veins are not always visible. Three independent leaves were measured per species. The length of the veins was measured using Q-Capture Pro7, and vein density was expressed in mm mmSamples for TEM were processed by the Multi-Imaging Centre in the Department of Physiology, Development and Neuroscience (Cambridge). Sections of ~1 mm thickness of all six stages along a developing leaf were used for embedding. Tissues were fixed in 4% (w/v) glutaraldehyde in 0.1 M HEPES buffer with a pH of 7.4 for 12 h at 4 °C. Subsequently they were rinsed in 0.1 M HEPES buffer five times, and then treated with 1% osmium ferricyanide at room temperature for 2 h and rinsed in deionized water five times before being treated with 2% (w/v) uranyl acetate in 0.05 M maleate buffer with a pH of 5.5 for 2 h at room temperature. They were rinsed again in deionized water and dehydrated in an ascending series of ethanol solutions from 70% to 100% (v/v). This was followed by treatment with two changes of dry acetonitrile and infiltration with Quetol 651 epoxy resin. Sections 50–70 nm thick were cut with a Leica Ultracut UCT and stained with lead citrate and uranyl acetate. Images were taken with a Tecnai G2 transmission electron microscope  and operated at 120 kV using an AMT XR60B digital camera  running Deben software . Images were taken at ×1700, ×3500, and ×5000 magnification.Flaveria species. Total leaf RNA was sent to the Genomics & Transcriptomics Laboratory (GTL) in Düsseldorf (Germany) for paired-end sequencing. RNA samples were prepared following the TrueSeq RNA sample preparation v2 guide (Revision F) and sequenced using an Illumina/Solexa HiSeq 2000 machine. Prior to assembly, reads were trimmed using Trimmomatic v0.32  reads from for each gene across the four species. Expression patterns of genes of interest were analysed using a custom-made R script to extract the data of interest and to visualize expression patterns. Gene Ontology (GO) term analysis was performed according to Deep sequencing was carried out to analyse transcriptomes associated with the leaf developmental gradient defined by the anatomical analysis. Opposite leaves from the third leaf pair after the cotyledons were harvested in the morning between 10:00 h and 11:00 h (4 h after the start of the photoperiod in the glasshouse). The leaves were harvested when they had reached 1.8–2 cm length (from base to tip). Leaves were measured on an ice-cold glass plate and cut into six portions of equal length (3–3.3 mm). Each sample was then immediately placed into liquid nitrogen. Sections of 12 leaves were collected before extraction of RNA using the Qiagen Plant RNeasy Mini Kit. The optional DNA digestion step with RNase-free DNase was performed. Three biological replicates were analysed for each of the four ic v0.32  were use-HIT-EST . Annotat3F. pringlei and F. robusta as well as C4F. bidentis and F. trinervia showed characteristic C3 and C4 anatomy under our growth conditions. In all cases, the third leaf pair was chosen as the first and second leaf pairs have previously been described as juvenile . SHR and SCR have been implicated in the development of C4 Kranz anatomy   (4 species at the base of the leaf (RETINOBLASTOMA-RELATED PROTEIN (RBR1) to activate cell cycle progression  , and multhe leaf . Good caGA2ox2 , which catalyses the first step of gibberellin synthesis, and a spike in transcripts of GA2ox2 , which d4 BS commonly develop dimorphic chloroplasts with M cells containing stacked thylakoids (grana) and the BS cells containing fewer grana   to promoters.The control of photosynthesis gene expression and chloroplast development has previously been linked to a pair of transcription factors known as GOLDEN-LIKE 1 and 2 (GLK1 and GLK2) . Unfortu complex . Transcroroplast , was low4 plants alter photosystem activity to adjust ADP/ATP and NADPH ratios to ensure proper functioning of the carbon pump. Consistent with this was the C4-specific up-regulation of SIG3, which initiates transcription of psbN , as well as genes involved in cyclic electron transport (PGR5 and PGRL1A), were more abundant in both C4 species, and these differences became more apparent from base to tip  gene family, REC2 and REC3, knockout of which causes a reduction in chloroplast size  and G. gynandra (C4) from the Cleomaceae ; AT2G05160, a CCCH-type zing finger family protein with an RNA-binding domain; AT2G21530, a SMAD/FHA domain-containing protein; RNA Polymerase Sigma Subunit C , and BEL1-Like Homeodomain1  (4 species in the monocots and dicots [both rosids (Cleomaceae) and asterids (Asteraceae)]. This strongly supports the notion that independent lineages of C4 plants have recruited homologous trans-factors to induce the C4 system.Previously, transcript abundance in developing leaves of the C5G67030) . The fiv3 and C4 species have relied on either sampling one congeneric C3 and C4 species pair, or analysis of mature leaf tissues. The number of genes that are differentially expressed between any two species can be very high simply due to species-specific differences, which do not relate to their photosynthetic pathway. In this work, we identify genes that are consistently differentially expressed in multiple C3 and C4 species from the same genus along a leaf maturation gradient. In the Flaveria genus, the data set therefore indicates that ~200 genes are consistently up-regulated in mature tissue using C4 compared with C3 photosynthesis. In young tissue that is still undergoing differentiation, the number of genes up-regulated in the C4 species is around double this number. Leaf maturation and the photosynthetic pathway were responsible for the greatest amount of variation in transcript abundance. In Flaveria, the evolution of C4 photosynthesis is not associated with an expansion of BS cell size, but rather a reduction in the cross-sectional area of M cells in C4 compared with C3 species. Overall, the analysis therefore provides quantitation of changes in gene expression associated with C3 and C4 photosynthesis, candidate genes underlying the alterations in leaf characteristics associated with these pathways, and, through comparison with independent C4 lineages outside of the Asteraceae, insight into the extent to which parallel evolution underlies the convergent and complex C4 trait.Most previous analysis derived from comparisons of RNA-seq of CJXB online.Supplementary data are available at Flaveria genus.Fig. S1. Phylogeny of the Fig. S2. Mature leaf vein density and cell size.3 and C4Flaveria.Fig. S3. Leaf anatomy maturation gradient in CFig. S4. RNA quality from sections of each species.Fig. S5. TPM correlation matrix.Flaveria species.Fig. S6. GO term analysis presented as heatmaps for each Flaveria.Fig. S7. Cell division and vein development in the base samples of Fig. S8. Chloroplast maturation in mesophyll and bundle sheath cells for each species.Data S1. TPM values for all four species for all stages and all replicates.3 species or both C4 species at each stage.Data S2. Summary of transcripts that were up-regulated in both CData S3. Transcript abundance of SHR, SCR, and SCR-like genes as well as auxin response genes.Data S4. Transcript abundance of genes associated with cell division.Data S5. Transcript abundance of genes associated with chloroplast maturation and development.Data S6. Comparison of data derived from this study with genes identified by Data S7. Comparison of data derived from this study with genes identified by Table S1. Number of reads and transcripts, per stage and species.de novo transcriptome assembly and annotation of contigs; BMCJ, SJB, and IR-L analysed the RNA-seq data; BMCK, SJB, and JMH wrote the paper.BMCK and JMH designed the study. BMCK grew the plants, undertook the anatomical analysis, and isolated the RNA; RS-U and CB undertook the Supplementary_Figures_S1_S8Click here for additional data file.Supplementary_Table_S1Click here for additional data file.Supplementary_Dataset_S1Click here for additional data file.Supplementary_Dataset_S2Click here for additional data file.Supplementary_Dataset_S3Click here for additional data file.Supplementary_Dataset_S4Click here for additional data file.Supplementary_Dataset_S5Click here for additional data file.Supplementary_Dataset_S6Click here for additional data file.Supplementary_Dataset_S7Click here for additional data file.


Studies focusing on taxonomic compositions of the gut microbiota are plentiful, whereas its functional capabilities are still poorly understood. Specific key functions deserve detailed investigations, as they regulate microbiota-host interactions and promote host health and disease. The production of butyrate is among the top targets since depletion of this microbe-derived metabolite is linked to several emerging noncommunicable diseases and was shown to facilitate establishment of enteric pathogens by disrupting colonization resistance. In this study, we established a workflow to investigate in detail the composition of the polyphyletic butyrate-producing community from omics data extracting its biochemical and taxonomic diversity. By combining information from various publicly available data sets, we identified universal ecological key features of this functional group and shed light on its role in health and disease. Our results will assist the development of precision medicine to combat functional dysbiosis. Lachnospiraceae and Ruminococcaceae along with some Bacteroidetes, were detected in >70% of individuals, encompassing ~85% of the total butyrate-producing potential. Most of these key taxa were also found to express genes for butyrate formation, indicating that butyrate producers occupy various niches in the gut ecosystem, concurrently synthesizing that compound. Furthermore, results from longitudinal analyses propose that diversity supports functional stability during ordinary life disturbances and during interventions such as antibiotic treatment. A reduction of the butyrate-producing potential along with community alterations was detected in various diseases, where patients suffering from cardiometabolic disorders were particularly affected.Given the key role of butyrate for host health, understanding the ecology of intestinal butyrate-producing communities is a top priority for gut microbiota research. To this end, we performed a pooled analysis on 2,387 metagenomic/transcriptomic samples from 15 publicly available data sets that originated from three continents and encompassed eight diseases as well as specific interventions. For analyses, a gene catalogue was constructed from gene-targeted assemblies of all genes from butyrate synthesis pathways of all samples and from an updated reference database derived from genome screenings. We demonstrate that butyrate producers establish themselves within the first year of life and display high abundances  in adults regardless of origin. Various bacteria form this functional group, exhibiting a biochemical diversity including different pathways and terminal enzymes, where one carbohydrate-fueled pathway was dominant with butyryl coenzyme A (CoA):acetate CoA transferase as the main terminal enzyme. Subjects displayed a high richness of butyrate producers, and 17 taxa, primarily members of the IMPORTANCE Studies focusing on taxonomic compositions of the gut microbiota are plentiful, whereas its functional capabilities are still poorly understood. Specific key functions deserve detailed investigations, as they regulate microbiota-host interactions and promote host health and disease. The production of butyrate is among the top targets since depletion of this microbe-derived metabolite is linked to several emerging noncommunicable diseases and was shown to facilitate establishment of enteric pathogens by disrupting colonization resistance. In this study, we established a workflow to investigate in detail the composition of the polyphyletic butyrate-producing community from omics data extracting its biochemical and taxonomic diversity. By combining information from various publicly available data sets, we identified universal ecological key features of this functional group and shed light on its role in health and disease. Our results will assist the development of precision medicine to combat functional dysbiosis. Firmicutes families and some Bacteroidetes  were analyzed after establishing a gene catalogue via gene-targeted assemblies considering all major known pathways, terminal enzymes, and associated taxa. The data sets derived from North America, Europe, and Asia and involved eight diseases and specific interventions as well as one study that followed bacterial community succession after birth  and taxonomic diversity. We aimed to investigate the role of butyrate producers in health and disease and to extract ecological key features of this functional group in order to assist the design of precision medicine that combats functional dysbiosis. To this end, 15 publicly available metagenomic and metatranscriptomic data sets n = 2,37 samplesAn overview of all pathways and genes involved in the formation of butyrate is presented in 10.1128/mSystems.00130-17.9TABLE S1 rplB/recA/pyrG for all major taxa considering all samples included in this study  are shown. Download TABLE S1, XLSX file, 0.1 MB.Sheet A gives an overview of individual data sets included in our analyses. All metagenomic (metaG) and metatranscriptomic (metaT) data derived from fecal samples. The genome name, taxonomic classification based on RDP taxonomy on the genus level, and taxonomic affiliation used in the study are given for all butyrate-producing candidates in sheet B. In sheet C, correlations (Spearman’s rho) between mean abundances of pathway genes and mean abundances of the corresponding three housekeeping genes (hkg) Copyright © 2017 Vital et al.2017Vital et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the n = 826).In order to gain a global overview of colonic bacteria exhibiting butyrate-producing pathways, we analyzed metagenomes derived from three continents—North America , Eubut) as the terminal enzyme were dominant (74.3% ± 13.7%) over butyrate kinase (buk)-exhibiting bacteria (7.1% ± 8.8%). Taxa containing both but and buk (13.4% ± 9.1%) as well as bacteria lacking these genes  were observed as well (Firmicutes families Lachnospiraceae (47.8% ± 18.4%) and Ruminococcaceae (43.2% ± 17.6%), whereas Porphyromonadaceae (Bacteroidetes) (3.8% ± 6.2%) and other taxa  were observed to a minor extent  and two (4A) specific taxa associated with protein-fed pathways were present in >70% of samples, representing 94.8% ± 12.3% (Gl), 80.7% ± 22.5% (4A), and 83.1% ± 20.4% (Ly) of respective communities  and Flavonifractor (Ly) were detected in >90% of samples  displaying abundances of >5% . Other p as well . The majr extent . Three tmunities ; all are samples .Anaerostipes, Coprococcus, Eubacterium hallii, and Subdoligranulum variabile, whereas levels of Clostridium XIVa and Roseburia inulinivorans were elevated in Chinese individuals .Overall, mean pathway abundances were similar in all data sets, except for the Human Microbiome Project (HMP) (study I), which displayed lower Ac pathway levels . The samividuals . Oscilli10.1128/mSystems.00130-17.1FIG S1 n = 826) are displayed ; all taxa shown are known butyrate producers. Bacteria exhibiting the acetyl-CoA pathway are arranged according to their terminal enzymes butyryl-CoA:acetate CoA transferase (but) and butyrate kinase (buk); bacteria exhibiting but and buk (both) or lacking both enzymes  are also indicated. Members of the Lachnospiraceae are indicated in bold, Ruminococcaceae are indicated in italics, and Bacteroidetes are underlined. Error bars represent standard deviations. For the key to Roman numerals referring to individual studies, see FIG S1, JPG file, 0.5 MB.Characterization of the butyrate-producing community in samples derived from healthy individuals encompassing nine metagenomic (studies I to IX) and two metatranscriptomic (X and XI) data sets. Panel A displays the abundances of pathways as percentages of total bacteria . The mean number of observed taxa associated with each pathway is shown in panel B. Panels C to F show relative abundances of taxa encoding/expressing respective pathways. Only taxa that were detected in >70% of all samples . For the key to Roman numerals referring to individual studies, see FIG S2, JPG file, 0.4 MB.Differences in butyrate-producing communities between continents. Panel A shows nonmetric multidimensional scaling analyses of butyrate-producing communities from individuals derived from the United States (I and II), Europe (III to VII), and China (VIII and IX). Tips of lines illustrate individual samples, whereas ellipses represent standard deviations of points. Relative abundances of acetyl-CoA pathway groups, i.e., “enzyme”  and “family” , and of individual taxa are shown in panel B. Pathway affiliations of taxa are indicated by the color bars; members of the acetyl-CoA pathway are arranged on the family level. Acetyl-CoA pathway groups and all taxa shown differed significantly between continents  were detected in 72% of samples (data not shown).In metatranscriptomic data (studies X  and XI ), expresbut and buk correlated in two data sets (I and VI), which was below the set threshold of ≥3 studies to record correlations. Nodes of individual taxa clustered according to their taxonomic affiliations, where members of the Lachnospiraceae and Ruminococcaceae grouped distinctly in the main network module. A few Lachnospiraceae and Bacteroidetes members formed small, separate groups  revealed that abundances among protein-fed pathways correlated well (four to six correlations), whereas the Ac pathway showed a unique behavior . Overalle groups .n = 826) resulted in low stratification for Ac pathway communities, and most samples displayed diverse compositions dominated by F. prausnitzii, yet a few specific community types enriched in certain taxa such as Butyrivibrio crossotus, E. rectale, Oscillibacter, and R. faecis were detected . Samples are arranged based on results from complete-linkage clustering of individual pathway communities . Relative abundances of major taxa and their taxonomic affiliations  are given. Results from clustering are shown at the top of each panel, whereas origins of samples (continent) are indicated below. Download FIG S3, JPG file, 1.2 MB.Stratification of pathway communities derived from healthy individuals of studies I to IX  are shown in panel A; at the top, mean abundances  of three taxa—Clostridium sensu stricto, Erysipelotrichaceae incertae sedis, and Flavonifractor—harboring the acetyl-CoA pathway are displayed, whereas abundances of Flavonifractor additionally containing the 4-aminobutyrate (4A) and lysine (L) pathways are indicated below. No differences in taxon abundances with diet were observed in 12-month-old infants. Nonmetric multidimensional scaling (NMDS) analysis of communities from mothers and their infants (4 months and 12 months after birth) is displayed in panel B. Only samples exhibiting at least three taxa harboring butyrate-producing pathways were considered for analyses. Samples derived from infants receiving breastfeeding (at the age of 4 months) and any breastfeeding (at the age of 12 months) are highlighted in green. In panel C, Bray-Curtis dissimilarities of butyrate-producing communities between infants (at the age of 12 months) and their mothers as well as between all mothers are illustrated. Due to the low abundance of butyrate producers in newborns, no analyses were performed in that group. In NMDS plots, tips of lines illustrate individual samples, whereas ellipses represent standard deviations of points. Download FIG S4, JPG file, 0.6 MB.Succession of the butyrate-producing community after birth (study VI). Taxa that differed significantly in abundance with diet or delivery mode in children at the age of 4 months , in particular due to a decline of several Lachnospiraceae; in study VIII, a few abundant Ruminococcaceae (F. prausnitzii and S. variabile) decreased as well. The reduction was governed by taxa containing but and buk, whereas bacteria exhibiting “alternative” terminal enzymes were more abundant in T2D samples. Certain taxa of that pathway, such as Oscillibacter and Pseudoflavonifractor, together with  protein-fed pathways showed increased levels in T2D patients. Accordingly, the butyrate-producing community structure of T2D subjects was distinct from those of controls in both studies (P = 0.06). Pathway abundances in patients characterized by impaired glucose tolerance (study IV) were not different from those in healthy controls (data not shown). Treatment with metformin, a T2D therapeutic agent, resulted in elevated levels of the Ac pathway (compared with untreated T2D patients) in all data sets, with various key taxa such as Coprococcus, R. inulinivorans, and S. variabile consistently increasing. Significant changes (P < 0.05) were, however, observed only in a meta-analysis  as sample sizes of individual studies were low. Community structures during treatment were altered as well (P = 0.22), respectively  of studies VIII (−13.2% ± 4.8% [standard error {SE}]) and XII  or tending to be different  based on permutational ANOVA are indicated. Tips of lines illustrate individual samples, whereas ellipses represent standard deviations of points. Download FIG S5, JPG file, 1.8 MB.Nonmetric multidimensional scaling analyses of butyrate-producing communities in diseased individuals and healthy controls. Panels A to C display butyrate-producing communities derived from type 2 diabetes patients and healthy controls of studies VIII, XII, and IV, respectively. Samples from type 1 diabetes patients (XII) were included in panel B. The influence of metformin treatment on community structures  is presented in panel D. Panel E illustrates communities derived from obese individuals from studies III, IV, and V compared with lean controls. Communities of atherosclerotic (study VII) and cirrhotic (study IX) individuals as well as from ulcerative colitis and Crohn’s disease patients (study III) compared with healthy controls are shown in panels F to H. Panel I displays butyrate-producing communities derived from colorectal cancer patients (II and V) and healthy controls. For the key to Roman numerals referring to individual studies, see Copyright © 2017 Vital et al.2017Vital et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the III,IV,V], and CVD, −19.9% ± 12.2% [SE], P = 0.08) compared with healthy controls, mainly due to a decrease of certain Lachnospiraceae (n = 25), and despite large reductions observed for many taxa in the patient group, accompanied by significant alterations of the community structure , displayed reduced Ac pathway abundances , comprising three pathways  and a multitude of taxa, except for controls . Accordicontrols .n = 21; CD, n = 4; healthy controls, n = 14), limiting the extraction of statistically robust signals.Samples from UC and CD patients displayed distinct results where the mean abundance of the Ac pathway was reduced only in the latter patients compared with healthy controls  antibiotic treatment (cefprozil for 7 days  and 90 days [control_d0/d90]) from study XIII and dissimilarities between individuals within different groups (for study XI) are shown as well. Significant differences  and trends  based on FDR-corrected pairwise Wilcoxon signed-rank tests for before-after treatment (study XIII) and plant versus base 1 (b1/pl.) or animal diet (an./pl.) (study XI) comparisons are illustrated. Pathway affiliations of taxa are indicated by the color bars; members of the acetyl-CoA pathway are arranged on the family level. Lachnospira., Lachnospiraceae; Ruminococ., Ruminococcaceae; o., Porphyromonadaceae/other families. In NMDS plots, communities derived from individual subjects are connected and ellipses represent standard deviations of points. Download FIG S6, JPG file, 1.3 MB.Analysis of the butyrate-producing communities during antibiotic treatment and during dietary interventions. Nonmetric multidimensional scaling (NMDS) analyses and heat maps showing abundances /expression (relative to three housekeeping genes [hkg]) of acetyl-CoA pathway groups, i.e., “enzyme”  and “family” , and main individual taxa of all pathways are displayed. Panel A shows communities before (day 0) and after treatment [day 7 (cefprozil)] with the antibiotic cefprozil for 7 days  over baseline samples. Accordingly, measured butyrate concentrations were reduced in samples derived from diets rich in animal products compared with the plant-based diet (P < 0.1) (nmax = 8), and despite the reduced expression of genes of major but-containing taxa, namely, Coprococcus, E. rectale, F. prausnitzii, and R. faecis, during the former diet in all individuals , changes did not reach statistical significance (P > 0.05).Expression of butyrate-producing pathways was sensitive to dietary interventions, as the Ac pathway was upregulated during plant-based diets compared with animal product-based nutrition . Expresssed diet , despitesed diet . Gene exP < 0.1) . Howevern = 21) and Prader-Willi syndrome  did not significantly affect mean abundances of the Ac pathway in either group  in 3/2 (PWS/SO) and 5/7 (PWS/SO) individuals, respectively, sustaining similar overall abundances of the Ac pathway throughout the experiment  obesity  and diet-related “simple” obesity  with a high-fiber, low-protein diet (study XIV). Samples were taken before (day 0) and after 30 days, as well as after 60 and 90 days (only for PWS) during interventions. Bray-Curtis dissimilarities of pairwise comparisons between individual groups are displayed in panel B. The heat map (C) shows abundances  of acetyl-CoA pathway groups, i.e., “enzyme”  and “family” , and of main individual taxa of all pathways. Significant differences (P < 0.05) in taxon abundances compared with controls  based on FDR-corrected pairwise Wilcoxon signed-rank tests are illustrated as colored dots in panel B, where no edge indicates a decrease during interventions and black edges illustrate significant increases. In SO patients, only the abundance of Alistipes changed significantly (blue diamond). Pathway affiliations of taxa are indicated by the color bars; members of the acetyl-CoA pathway are arranged on the family level. Lachnospira., Lachnospiraceae; Ruminococ., Ruminococcaceae; Por./oth., Porphyromonadaceae/other families. Ellipses in NMDS plots represent standard deviations of points. Download FIG S7, JPG file, 1.4 MB.Nonmetric multidimensional scaling (NMDS) analysis of butyrate-producing communities (A) during dietary interventions treating Chinese children suffering from genetic obesity  and their donors (green) are illustrated . Samples were taken before transplantation  and 2, 14, 42, and 84 days after intervention. Red dashed lines connect donor communities with communities of recipients 2 days after transplantation. Bray-Curtis (BC) dissimilarities between the butyrate-producing community of each sample and (1) the donor community (blue) as well as (2) the original community  are given in panel B. Download Copyright © 2017 Vital et al.2017Vital et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the n = 6]). Thus, while butyrate-producing taxa behaved highly disparately in untreated individuals, the results suggest that their responses to (unknown) temporal alterations in the gut environment were partly correlated (concurrent decreases/increases). Individuals subjected to specific interventions, namely, antibiotic treatment (study XIII) and dietary interventions (study XIV), displayed strongly directed behavior where the bulk of taxa (~70 to 75%) decreased , XIII , which represents the overall abundance change of the entire pathway (the direction of the cumulative abundance change was omitted), with the “total change,” i.e., the cumulative absolute abundance change of individual taxa disregarding the direction . If all In this study, we provide a comprehensive overview of the butyrate-producing communities of the human colon. Overall, our results do largely agree with findings reported in original studies , encompassing ~85% of the total butyrate-producing potential; a few taxa were even detected in >90% of subjects, including een taxa  and low een taxa , suggestThe high taxon richness observed, especially for the Ac pathway, indicates great functional redundancy. However, the extent to which individual butyrate producers are indeed redundant depends on their biochemical characteristics. Most importantly, we consider redundancy of individual pathways as limited since the Ac pathway is a major fermentative route for bacteria transforming vast amounts of substrate, whereas substrate fluxes via protein-fed pathways are expected to be of minor importance . Thus, tMegasphaera/Anaerostipes, which, in contrast to most other taxa, increased during dietary intervention (study XIV), maintaining the Ac pathway at similar levels throughout the experiment in several individuals   . Further(n = 15) . Proteinn = 12) that distinguished between CD in the ileum and the colon also reported lower abundances of genes for butyrate formation along with their reduced expression in both patient groups compared with healthy controls , whereas others displayed diverse behavior. For instance, several Roseburia species decreased in T2D samples; however, their abundances were hardly affected in obese individuals. Abundances of other butyrate producers were not altered in any disease or even displayed increased levels . This highlights the complex nature of functional dysbioses that are based on taxonomically diverse, functionally redundant communities and demands precision treatment to combat disease. Administration of abundant, barely reduced taxa is most unlikely to be a promising approach, and interventions specifically boosting various niches of highly reduced key taxa appropriately filling the entire spectrum of this functional group in each individual will be superior in increasing butyrate production effectively. In any case, establishing a diverse, functionally redundant community that has the potential to adjust to specific  conditions should be considered for the development of sustainable treatment strategies.Only a few taxa were reduced across various diseases  and used to screen (hmmsearch) 67,134 genomes provided by PATRIC  for the presence of respective pathway genes  was no selection criterion, as some butyrate producers have alternative routes for the formation of acetoacetyl-CoA . Our database now contains 1,716 genomes encompassing 19,284 genes, and all sequences can be downloaded at http://193.175.244.101/Butyrate/.We updated a previously published butyrate synthesis gene database, applying the original workflow encompassing a multilevel screening approach  with som n = 27, . Genomesetyl-CoA . FinallyrplB), recombinase A (recA), and CTP-synthase (pyrG) were retrieved from FunGene  and quality filtered for an average Q score of ≥20 and length of ≥70 , where the filter size was adjusted to ensure a false discovery rate (FDR) below 1% as suggested by the developers  of the Ly pathway, the minimum contig size (90 aa) and bit score (30) were reduced to consider its short length . In order to maintain all analyses on the nucleotide level, the following steps were performed outside the Xander pipeline. Merged nucleotide sequences (nucl_merged) from all samples were dereplicated, filtered for a length of ≥70% median gene length of respective reference sequences from genomes, and subjected to chimera removal  and FraE v. 4.2. using HMferences ), proteivery-sensitive) (Bacilli [~70%] and Enterobacteriaceae [>20%]) were included at this step, and reads that mapped to those sequences were filtered out in order to avoid the possibility of false-positive counts derived from those genes. Mapped reads were retrieved by SAMtools  . Paralog -S -F 4 ).bcd-etfAB, which is present in all pathways) were used to calculate pathway abundances except for gcdB (encoding the beta subunit of glutaconyl-CoA decarboxylase) of the Gl pathway  (abfD (encoding 4-hydroxybutyryl-CoA dehydratase/vinylacetyl-CoA delta-isomerase) recruited proportionally high numbers of reads, and the median of length-corrected read counts was used for calculating its abundance.All genes of a pathway  . Resultssitives) , obtaineRoseburia and Lachnospiraceae incertae sedis at the species level, as sequences of individual species displayed high phylogenetic distances for all pathway genes . This was also the case for B. crossotus compared with other Butyrivibrio spp. For genera that encompassed only one species, such as F. prausnitzii and S. variabile, the species name is displayed. Genomes that could not be classified at the genus level were treated separately. A complete list of PATRIC identifiers (IDs) associated with individual taxonomic bins is presented in Clostridium XIVa is distinct from the Clostridium XIVa cluster as defined by Collins et al.  that are important for this study is given in sheet B. Download Copyright © 2017 Vital et al.2017Vital et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the gctA  as well as atoA/D (only very low counts were obtained as many taxa are devoid of those genes) and kal (its short length limited detection of low-abundance taxa) of the Ly pathway was not required. Taxon abundances  were calculated based on gene-length-corrected median read counts of all pathway genes compared with mean read counts of housekeeping genes. As for overall pathway calculations, length-corrected gene counts were largely consistent between all genes of a pathway within a taxon. The proportions of members from the same family  exhibiting the Ac pathway as well as the proportion of bacteria exhibiting distinct terminal enzymes  were calculated as well. The abundance of taxa containing both but and buk was proportionally split according to obtained abundances of each terminal gene. Similarly to our previous study (abfD).A taxon was considered present if all genes of a pathway were detected for that taxon. The absence of specific pathway genes in certain taxa was accounted for based on data from genomic references. Due to the low sequencing depth of metatranscriptomes of data set XI , one misus study , high pemetaMDS), and permutational analysis of variance (ANOVA) (adonis) were calculated in R (v. 3.1.2) using the package vegan (v. 2.3 to 4) and were based on square-root-transformed relative abundance data of the entire butyrate-producing community . Heat maps were created in R using the package gplots (v. 2.17.0) on log-normalized data [log(x + 1)]. FDR-corrected Mann-Whitney U and Kruskal-Wallis tests were done in QIIME . All numbers following the ± sign are standard deviations, except for results derived from comparisons with healthy controls  considering correlations  that were detected in at least three data sets (n = 8).Bray-Curtis dissimilarities, nonmetric multidimensional scaling (v. 1.9.1 ) considecontrols , where t


Depression as a common complication of brain tumors. Is there a possible common pathogenesis for depression and glioma? The most serious major depressive disorder (MDD) and glioblastoma (GBM) in both diseases are studied, to explore the common pathogenesis between the two diseases. In this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma. Glioma is the most common tumor in the central nervous system, mostly occurring in the brain, and the diagnosis and treatment of glioma are incomplete, inaccurate, and easily reappeared. The current study ,2 shows Aiming at this problem, this study uses GBM and major depressive disorder (MDD) as the research object to study the overlapping genes, miRNA, biological pathways, and so on. Is the statistical analysis of the correlation between MDD and GBM feasible? With the implementation of the human genome project (HGP), the Human Proteome Project (HPP), and the Human Connectome Project (HCP), more and more ion channels, cytokines, growth factors, neurotransmitters and neurotransmitter receptors, enzymes, other proteins, and miRNA associated with the development of depression and glioblastoma diseases, have been identified and validated . TherefoThrough COREMINE platform text mining tools, MDD and 1826 genes have co-occurrence relationship, Glioma and 1826 genes have co-occurrence relationship, GBM and 4510 genes have co-occurrence relationship. Among them, 57% of MDD co-occurrence genes and 23.1% of GBM co-occurrence genes were identified as common genes, with a total of 1041 genes . Besidesp-value < 0.01) significantly associated with MDD, and 823 DEGs  were significantly associated with GBM. A simple statistical analysis of DEGs revealed that a total of 27 genes were not only significantly associated with MDD but also closely related to GBM. It was found that five genes  in these 27 genes are down-regulated genes, and six genes  are up-regulated genes.After the DEGs was screened out, the DEGs of different platforms of the same disease were combined as the final DEGs of the disease. There are 463 DEGs , including 704 GO biology process terms, 35 GO cellular component terms, 47 GO molecular function terms, and 18 KEGG pathway terms. DEGs in GBM are significantly enriched in 1681 terms, involving 1207 GO biology process terms, 201 GO cellular component terms, 224 GO molecular function terms, and 48 KEGG pathway terms. These results show that MDD and GBM have 264 BP, 18 CC, 16 MF functional annotations overlap in GO, and seven biological pathways overlap in KEGG. The R tool is used to analyze and enrich the DEGs. DEGs in MDD were significantly enriched in 804 terms . After a series of screening criteria, 18,656 pairs for MDD mRNA—miRNA, 52,413 pairs for GBM mRNA—miRNA are obtained. Data analysis of MDD reveals that there are 370 different mRNA corresponding to 2455 different miRNA. Data analysis of GBM reveals that there are 754 different mRNA, corresponding to 2586 different miRNA. Through the enrichment of 27 common DEGs in MDD and GBM, three KEGG biological pathways are enriched, which are Cytokine—cytokine receptor interaction, Chemokine signaling pathway, Bladder cancer. There are 128 GO biology process (BP) terms, two GO cellular component (CC) terms, three GO molecular function (MF) terms are significantly enriched . The resThe common miRNAs are analyzed by miEAA, and their enrichment and annotation results are shown in To comprehensively and accurately identify the pathogenesis of MDD and GBM, all available transcript data for both of the diseases are downloaded. The purpose is to horizontally merge large amounts of transcriptome data to expand the sample size and obtain a larger sample size dataset. Functional analysis of the differential expression genes of the two diseases is carried out from three aspects.From the perspective of coding genes, MDD and GBM differentially expressed genes are enriched in seven common biological pathways, namely Melanoma, Pathways in cancer, mitogen-activated protein kinase (MAPK) signaling pathway, Endocytosis, p53 signaling, Focal adhesion, Bladder cancer. As a result, it has been found that five common pathways are associated with the development of cancer, suggesting that the two diseases may also be closely related to other diseases, particularly cancer. Due to the complexity of cancer, the five pathways are temporarily serve as the common pathway for the two diseases. At the same time, it can see that the other two common pathways, MAPK signaling pathway and Endocytosis, are reported to be associated with both diseases. The MAPK pathway may be initiated at the cell surface and continue during endosomal sorting, while more recent studies suggest that MAPK signaling is a required element of endocytosis . Li Kai From the perspective of miRNA, the corresponding miRNAs are predicted by the mRNA of the two diseases, and the number of common miRNAs has been found to be 2453. In addition, various miRNAs have been demonstrated to be either upregulated or downregulated in glioma tumors, and played critical roles in regulating glioblastoma proliferation, migration, and chemosensitivity . Most ofFrom the perspective of protein interaction, the significant core gene modules in the MDD and GBM protein interaction networks were enriched to 10 common biological pathways. The discovery of significant core gene modules in protein—protein interaction networks allows for a more accurate and comprehensive understanding of the function of DEGs in disease. The γ-aminobutyric acid (GABA), glutamic acid, and acetylcholine (Ach) are three common amino acid neurotransmitters, which are specific chemicals that act as “messengers” in synaptic transmission. Salvadore et al. confirm that amino-acid neurotransmitter system dysfunction plays a major role in the pathophysiology of major depressive disorder . Panosyahttp://www.coremine.com/medical/), a medical ontology information retrieval platform, was used to search for key words, such as major depressive disorder and glioblastoma.In the biomedical field, text mining has been widely used to identify biological terms, such as genes, disease names in the literature, and even reveal the relationship between biological terms. In this study, COREMINE  (Availabhttp://www.ebi.uk/ArrayExpress). Including transcriptome data sets of major depressive disorder (excluding bipolar disorder) and glioblastoma. A total of 47 series, 2093 samples of raw data, 11 series, 367 samples were associated with MDD, 36 series, and 1726 samples were associated with GBM  database was used to construct the Protein-Protein Interaction (PPI) between proteins encoded by differentially expressed genes. The STRING database is a database that collects protein—protein interactions, gene regulatory relationships, document mining analysis, and protein co-expression analysis, and calculates physical interactions and predicts interaction relationships. The protein interaction threshold was set at 0.4. The protein interaction data obtained from the online STRING database is imported into the Cytoscape software, and the node with the degree greater than 2 and the BC was greater than 0.01 was obtained by using its Network Analysis plug-in tool. The node as a network centre node (Hub). The protein represented by the central node was usually the key protein (Hub gene) [In this study, the STRING (Available online: ub gene)  with imphttp://www.targetscan.org/vert_71/) tool for differentially expressed genes. The standard for screening predicted miRNAs was 8 mer—a (exact match to positions 2–8 of the mature miRNA followed by an “A”) and the percentage of context ++ score (CS) should not be less than 95%. This CS is the cumulative sum of 14 features for a particular site, including the type of site, complementary pairing, minimum distance, length of open reading frame (ORF), conserved target probability (PCT), and so on. To further analyze and explore the pathogenesis of the disease. The miEAA  online tool was used to enrich and annotate the predicted miRNAs by combining the two diseases. The miEAA’s p-value adjustment method was error detection rate , the category of p-value less than 0.1 was significantly related.Numerous studies have confirmed that alterations of specific microRNAs (miRNAs) levels are closely related to human pathologies . A smallIn this article, we first rely on transcriptome data to obtain reliable and useful differentially expressed genes (DEGs) by differential expression analysis. Then, we used the transcriptomics of DEGs to find out and analyze the common pathway of MDD and GBM from three directions. At present, more and more miRNA are the biomarkers of disease, which are related to the pathophysiology of various diseases, including MDD and GBM. However, due to the large number of predicted miRNAs, further studies are needed to find suitable biomarkers for MDD and GBM. Finally, we determine the important biological pathways that are common to MDD and GBM by statistical knowledge. It is worth mentioning that, Chemokine signaling pathway not only found in functional enrichment of coding genes is a common pathways between MDD and GBM, also found in the core gene module of the protein interaction network. Our findings provide the first direct transcriptomic evidence that common pathway in two diseases for the common pathogenesis of the human MDD and GBM. Our results provide a new reference methods and values for the study of the pathogenesis of depression and glioblastoma.