Leishmaniasis is a global concern especially in underdeveloped and developing subtropical and tropical regions. The extent of infectivity in host is majorly dependent on functional polarization of macrophages. Classically activated M1 macrophage can eliminate parasite through production of iNOS and alternatively activated M2 macrophages can promote parasite growth through by providing shelter and nutrients to parasite. The biological processes involved in immune signaling and metabolism of host and parasite might be responsible for deciding fate of parasite.Using systems biology approach, we constructed two mathematical models and inter-regulatory immune-metabolic networks of M1 and M2 state, through which we identified crucial components that are associated with these phenotypes. We also demonstrated how parasite may modulate M1 phenotype for its growth and proliferation and transition to M2 state. Through our previous findings as well as from recent findings we could identify SHP-1 as a key component in regulating the immune-metabolic characterization of M2 macrophage. By targeting SHP-1 at cellular level, it might be possible to modulate immuno-metabolic mechanism and thereby control parasite survival.https://github.com/cran/FactoMineR/tree/master.Mathematical modeling is implemented as a workflow and the models are deposited in BioModel database. FactoMineR is available at:   Leishmania , mucocutaneous (ML), or cutaneous leishmaniasis (CL) are the three different clinical manifestations of leishmaniasis. The World Health Organization (WHO) estimates that in 2020, VL was the primary form in more than 90% of cases that occurred in Brazil, Ethiopia, India, Kenya, Somalia, South Sudan, and Sudan. In 10 nations—Afghanistan, Algeria, Brazil, Colombia, Iraq, Pakistan, Peru, Syria Arab Republic, Tunisia, and Yemen—more than 84% of the cases recorded in 2016 were due to CL . When a sandfly harboring Leishmania protozoa takes a blood meal, it injects saliva that prevents blood clotting  and the M2 macrophage (anti-inflammatory) are the primary (Leishmania spp. They are characterized by the impaired tricarboxylic cycle (TCA cycle), production of pro-inflammatory cytokines such as Interleukin-12 (IL-12), interferon gamma (IFNℽ) and by generation of reactive oxygen species (ROS) and nitric oxide (NO) that help in efficient parasite killing. The M2 macrophages are identified by intact TCA cycle, production of anti-inflammatory cytokines such as Interleukin-4 (IL-4), Interleukin-10 (IL-10) etc., which promote tissue repair and parasite survival  convert tryptophan to kynurenine, reducing the availability of tryptophan to amastigotes. TDO levels are negatively correlated with parasite load  of parasite interacts with toll-like receptors (TLRs), TLR2 and TLR6, that activate myeloid differentiation primary response 88 (MyD88) which promotes nuclear factor kappa B (NFκB) activation and IL-12 synthesis. Crucial transcription factors, including interferon regulatory factor 1 (IRF1), c-Rel and nuclear factor of activated T cells 5 (NFAT5), are involved in the synthesis of IL-12 . IFNγ siite load . Later, Leishmania also secretes some proteases like GP63. GP63 activates host protein tyrosine phosphatase, non-receptor type 6 (SHP-1) that later inhibits NFAT5 and prevents IL-12, IFNγ, and NO production  and the other functioning as the early stage of M1 to M2 polarization in which the macrophage resident amastigotes leads to IL-10 expression and also use host immuno-metabolic pathways to produce Arg1, trypanothione, and ergosterol for their survival and proliferation (diseased state).Both models were reconstructed in Cell Designer and exported as systems biology markup Language (SBML) file. The biological reactions in the model were mathematically defined and quantified by including the kinetics rate law, parameter estimation, initial component concentration , a biochemical network simulator. It solves mathematical model ODEs and determines the flux of each reaction in the reconstructed biological network. The biological system’s outcome is determined by the rate at which components are produced, hence a reaction with a higher flux is crucial in the network  is a multivariate approach for analyzing a data table in which observations are characterized by numerous inter-correlated quantitative dependent variables. Its intent is to obtain important information from the data table and represent it as a set of new variables known as principal components. PCA also depicts the similarity pattern of the data and variables by presenting them as points on maps . We usedThe PCA input file contains information regarding each of the components present in M1 and M2, such as the name of the model in which that component is present, as well as the concentration of production/utilization.Cytoscape is an open-source software platform for building molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data. It also provides different plug-ins for the analysis of our network , maximum neighbourhood component (MNC), density of maximum neighbourhood component (DMNC), maximal clique centrality (MCC) and six centralities  based on shortest paths.P-value was adjusted at 0.01.The Cytoscape plugin program Biological Networks Gene Ontology tool  collected GO annotations for the M1 and M2 components. BiNGO retrieved the overrepresentation of GO categories in a sub-graph of a biological network, which is displayed on Cytoscape, using the input list of M1 and M2 components. The Benjamini and Hochberg correction was used to give tight control over the false discovery rate under positive regression dependence of the test statistics, and the hyper geometric test The healthy state mathematical model had four compartments including cell membrane, cytoplasm, mitochondria and nucleus. A total of 97 components and 74 reactions were used to construct the model. Simulation for 20 time units was performed, using SBML ODE Solver Library SOSlib (1.7.0) available in Cell Designer (4.4.2).In the M1 model, we observed that LPG bound to TLRs activated MyD88-mediated signaling, which in turn triggered the transcription of IL-12 through NFκB and lead to the activation of IFNγ, which further produced NO. Succinate dehydrogenase participated in the formation of ROS through ETC when the Krebs cycle was simultaneously hindered, which causes succinate accumulation. In addition, tryptophan was being catabolized in order to use it for the formation of ROS and prevent the parasite from using it as a nutrient source. Moreover, ROS also activated GSK-3b, which in turn prevented IL-10 from being transcribed. These combined processes are what allow for the elimination of parasites . We obseThe diseased state mathematical model had four components which included cell membrane, cytoplasm, and nucleus and parasite cell. Model consists of 103 components and 85 reactions. The simulation was performed for 100 time units, using SBML ODE Solver Library SOSlib (1.7.0) available in Cell Designer.In the M2 model, we found that the concentration of IL-10, ergosterol, and trypanothione had increased which showed parasite survival state . ConcurrAccording to comparative flux analysis, the reaction associated with M1 model that had high flux are cytokines producing reaction such as IL-12, IFNγ, NOS2 activation, NO production, citrulline to L-arginine conversion, succinate accumulation through impaired Krebs cycle, tryptophan catabolism, ROS production, activation of GSK-3b. The flux value of each high flux reactions is mentioned in the table below . Flux ofThe reactions associated with M2 model that had high flux were reactions that lead to production of IL10, polyamines production, trypanothione, and ergosterol. Activation of SHP-1, and AKT downstream pathway also had high flux. The flux value of each high flux reactions is mentioned in the table below . Flux ofWith only two datasets, the scree plot does not show the elbow, which means that maybe both datasets are equally important. . FurtherUnique components from M1 model which stood out from comparative analysis of both models were IFN-g (nucleus), IL12 (cytoplasm), succinate, ROS, and NO. In case of M2 model, they were SHP-1, AKT, trypanothione, and ergosterol . Matrix Two networks were constructed based on the connections present between the components of M1 and M2 models . The conThe entire network was analysed using parameters such as betweenness centrality, degree of nodes, edge betweenness, closeness centrality, clustering coefficient, and others to present a condensed and robust nature of the components with each other in both the networks.The Cytohubba was used to identify the top-ranked nodes in the entire network based on each topological analysis methods. MCC, DMNC, MNC, EPC, radiality, degree, closeness, betweenness, eccentricity, bottleneck, clustering coefficient, and stress centrality are the 12 scoring methods that were used to identify top-ranked nodes in the network .Each method yielded the top 10 nodes in the network. Based on their occurrence in each of the 12 scoring methods, the top 10 components are represented in Based on the occurrence of nodes in each of the 12 scoring methods, the top 10 components are represented in The BiNGO, version 3.0.5, plugin was used for identifying gene ontology (GO) categories that were substantially over-represented in a set of genes-metabolites or a biological network sub-graph of M1 network and M2 network. We investigated and displayed the biological functions and cellular elements of discovered components of M1 network. Arginine metabolic process, glutamine family amino acid catabolic process, nitric oxide biosynthesis process, superoxide metabolic process, response to cytokine stimulus, regulation of IL-12 production, regulation of transcription, and regulation of cellular respiration .The BiNGO, version 3.0.5, was used for GO categories for immune-metabolic biological network sub-graph of M2 network. We investigated and displayed ontologies associated with M2 components which highlighted significant involvement in cytokine signaling immune system, arginase activity, regulation of host lipid pathways, regulation of ROS, negative regulation of animo acid glutathionlylation, decreased IL-12 signaling, decreased NO production, and increased IL10 production . Gene liImmuno-metabolic response of host macrophages may decide fate of parasite infection; it may either promote parasite elimination or parasite survival. The extent of infection is dependent on multiple factors, including the species involved, host genetics, early innate immune response, adaptive immune response and crucially, the polarization of macrophages . As NO production is inhibited, L-arginine is shown to be used for the production of polyamines like spermidine in the presence of Arg1 which is synthesized through IL-4 mediated PPAR-γ activation. Spermidine is one of the precursors for trypanothione synthesis. Since NFAT5 is dephosphorylated, it may not bind to IL10 promotor, leading to activation of IL-10 synthesis.Flux analysis defines the flow of molecules at a specific time , which pThrough PCA we identified that both the dataset may be essential. From variable plot, the inverse correlation between M1 and M2 models signifies essentiality of components from both the datasets as cos2 value of both the datasets is same. We distributed the PCs based on significance of their concentration and identified 12 components for further studies. SHP-1 was the principal component from M2 dataset as it is directly activated by GP63 to inhibit NFAT5 through phosphorylating it at TYR143 residue thereby inhibiting its localization to nucleus affecting production of NO, IFNγ, and IL-12 in M1 model . GSK-3b The purpose of the network analysis was to look into the important components in terms of connectivity and its functioning present in both the network. For the M1 network, the top 10 components were oxaloacetate, L-arginine, aspartate, NFAT5, citrulline, citrate synthase, arginosuccinate, CoA, acetyl CoA, and citric acid. Oxaloacetate had highest frequency of occurrence score, which is one of the initial components for the impaired TCA cycle that later leads to the production of ROS. Other than oxaloacetate, acetyl CoA, citrate synthase, CoA, and citric acid were also part of the same impaired TCA cycle, which is important for ROS production . FurtherFor the M2 network, the top 10 components were trypanothione, trypanothione reductase, spermidine, spermine, putrescine, trypanothione synthase, HMG-CoA, NADPH, T[SH]2, and SHP-1. Trypanothione had highest frequency of occurance score that crucially helps in parasite survival by neutralizing the ROS. Trypanothione synthase, NADPH, T[SH]2, trypanothione reductase are all responsible for continuous production of trypanothione. Spermidine and spermine are polyamines required for parasite proliferation and spermidine also acts as one of the initial components for trypanothione synthesis. Furthermore, HMG-CoA is intermediate for ergosterol production that strengthens the parasite cell membrane. SHP-1 is the only host component that was enriched in Cytohubba analysis. SHP-1 functions are associated with inhibition of pro-inflammatory cytokine synthesis through transcription factor inhibition that enhances IL-10 production, which helps the parasite in its survival. Hence, from our model we identified that SHP-1 has role in promoting and inhibiting different processes, which promote parasite survival.Through our model and network analysis, we could infer that SHP-1, (a host factor) may promote parasite survival. This also suggests that parasite hijacks host machinery to regulate host associated transcription factors through chromatin remodeling. Thus, SHP-1 can be taken as a potential candidate for further studies to investigate parasite survival mechanism.As a conclusion, we assert that computational modeling and network construction of immuno-metabolic response against leishmaniasis helps us to understand the signaling mechanism responsible for parasite survival. Thus, system’s biology approach gave us the necessary lead in a specific reconstructed biological network. By regulating SHP-1 through synthetic biology approach we might be able to find a better therapeutic for treating leishmaniasis by gaining better control over immune-metabolic signaling of host.Our study has laid an insight into inflammation and macrophage associated infectious disease that is leishmaniasis. Such approach can be useful to study diseases which show dynamics in parallel with infection and inflammation, where cytokine expression and host pathogen metabolism might regulate the disease.vbad125_Supplementary_DataClick here for additional data file.


Lemna gibba cultures to different nutritional conditions.Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts.To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that The online version contains supplementary material available at 10.1186/s12870-023-04480-9. Lemnaceae, commonly known as duckweeds, are small, fast growing aquatic flowering plants. Prior to the emergence of Arabidopsis as a model organism, duckweeds were an important model system for plant biology -glucose and [U-13C6]-glucose in a molar ratio 80:10:10 . Mass isotopomer composition was determined by gas chromatography/mass spectrometry (GC/MS) in 16 analytes mostly derived from hydrolysis of biomass polymers  cycle seems to be very active as well. Based on the measured 13C enrichments in biomass  a carbon flux metabolic network of L. gibba central metabolism was defined based on genomic information and based on a generic plant carbon flux network . An initial draft model of central carbon metabolism was constructed based on 21830 proteins predicted from a draft genome of L. gibba 7742a as part of the Lemna Genome Sequencing Project -glucose and [U-13C6]-glucose  in a molar ratio 80:10:10. In another labeling experiment where 13C enrichment was determined in total biomass carbon, 40% of medium glucose was [U-13C6]-glucose (at 99%).Axenic cultures of  DWC130)  and were DWC130)  with 5 ghttps://office.microsoft.com/excel). The growth rate was calculated by the following equation:t is culture time (h), A0 and At are the frond surface area at culture time 0 and t, respectively. The doubling time of duckweeds growth is Growth of cultures was quantified based on increase in frond surface area, using Scanalyzer BS . The images were analyzed using LemnaLauncher image analysis software  to obtain the total fronds surface area. Curve fits shown in Fig. 13C6 glucose (30mM), U13C6 sorbitol (3mM) and D5-Gln (4.5mM). Then 2 mL CHCl3 was added, and the two phases were kept at 4°C for 20 min. After centrifuge at 3750 rpm for 20 min the upper phase was analyzed by direct infusion into a 4500 QTRAP mass spectrometry system  in MRM mode. The mass spectrometry settings are listed in Additional File To measure the uptake rates for medium glucose and Gln, growth experiments were repeated with unlabeled glucose. The initial and final medium glucose and Gln concentrations were measured to calculate glucose and Gln depletion and uptake rate. Glucose and Gln concentration were measured by mass spectrometry with internal isotope standards. To correct the water evaporation during the culture, a known amount of sorbitol was added to the medium after the culture. Briefly, after culture, 1 mL of sorbitol (0.7M) was added to a culture flask with originally 100 mL medium and mixed well. 100 µL medium and 1 mL water were then added to 100 µL of an aqueous solution of US0 and S being the µmol amount of substrate in the culture vessel at the beginning and end of culture, respectively, and D0 and D the dw of fronds at the beginning and end of culture, respectively.For exponentially growing cultures a substrate uptake flux can be calculated by multiplying the reciprocal of a yield coefficient with the specific growth rate. Accordingly, we define specific substrate uptake rates by:2 is accessible to growing fronds as another carbon source. The CO2 uptake rate was determined based on the measured glucose and Gln uptake rates (above) and the fractional enrichment of 13C in biomass. For this purpose, fronds were grown with 40% of medium glucose replaced by [U-13C6]-glucose enriched at 99% 13C (average 13C-enrichment 40.3%). Dry biomass of harvested fronds was analyzed by elemental analysis – isotope-ratio mass spectrometry, resulting in measurements for 13C enrichment . The analysis was performed at the University of California Davis Stable Isotope Facility (UCD SIF) using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a PDZ Europa 20–20 isotope ratio mass spectrometer. Calculation of the CO2 uptake (vCO2_up) was done by the following relation. f13C,BM depends on all carbon-molar uptake fluxes and the 13C-enrichment in each of the carbon sources (40.3% in case of glucose):vGLC_up and vGln_up are the uptake rates for glucose and Gln, respectively. Knowing f13C,BM, vGLC_up and vGln_up, vCO2_up can be calculated after rearranging Eq. Besides the medium supplied organic substrates glucose and Gln, atmospheric COvCO2_up is the uptake rate of unlabeled atmospheric CO2, which is not identical to the net CO2 balance since cellular CO2 can also be released. To estimate the statistical uncertainty in vCO2_up, Eq. f13C,BM, vGLC_up and vGln_up according to their mean and standard deviations [Note that viations .3 to result in a biphasic solvent system  [3 phase), free metabolites (methanol/water phase) and cell pellets  were obtained by weighing dry weight of each phase. The total nitrogen amount in cell pellets was measured by CHN elemental analysis . The nitrogen was then converted to protein weight based on the amino acid composition of duckweed protein [Cultures were kept in tissue culture flasks with vented seal caps. Cultures grew just until the fronds covered the entire culture flask surface area to ensure they were under exponential growth stage. Fronds were harvested by filtration, extracted and dry weight fractions of cell pellet, lipids and free metabolites content were determined as described before in detail . Briefly, v/v/v) . The bio protein .Starch content was measured by modified protocol . Briefly−1 and from 170°C to 210°C at 5°C⋅min−1. To quantify the concentration of fatty acids, 100 µg heptadecanoic acid (17:0) was added prior to transmethylation to each sample as an internal standard. Quantification was done by peak area comparison with the internal standard using MSD Chemstation Data Analysis software to integrate GC–MS peak areas .The lipid fraction was derivatized by boron trichloride methanol to fatty acid methyl esters (FAMEs) . Fatty a3/methanol/H2O fractionation of frond biomass was analyzed by Hydrophilic interaction chromatography (HILIC), using an aminopropyl column  according to a protocol by Bajad et al. (2006) [The free metabolites fraction from the biphasic CHCl. (2006) . A liquiFluxes from central metabolism into cellular biomass components (biomass fluxes) were calculated based on the biomass proportions of lipid, protein, starch, the cell wall fraction and 30 most abundant free metabolites. The weight of the polymers was divided according to their respective monomer composition. For this, the protein composition was estimated by averaging 10 literature reports on the amino acid composition in duckweed . The wei13C labeling pattern of various metabolites was measured by GC–MS  with select ion monitor (SIM) mode as previously reported [−1 with an HP-5MS column . Amino acids were derivatized by N-methyl-N-(tert-butyldimethylsilyl) trifluoroacetamide (MTBSTFA) [−1; to 300°C at 10°C⋅min−1; final time, 5 min. Glucose from digested starch was converted to methyl ester alditol acetates (MAAs) as previously reported [−1; final time, 2 min. In order to analyze sucrose the free metabolites fraction was derivatized by methoxyamine-HCl and followed by N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) [−1; final time, 10 min. Fatty acid methyl esters (FAMEs) were further reduced to saturated fatty acid methyl esters by PtO2 under H2 pressure and only the carboxyl group carbon (e.g. C1 and C2) labeling information were measured. To suppress the contaminant fragments around base peak of fatty acid methyl esters (m/z 74), the ionization energy for quadrupole mass analyzer was reduced from the standard 70eV to 15eV [−1; final time, 15 min. Glycerol was converted to trifluoroacetic acid ester by trifluoroacetic acid anhydride (TFAA) as previous report [−1; to 250°C at 20°C⋅min−1; final time 1 min. Altogether, 29 selected fragments in 17 analytes were monitored using the selected ion monitoring mode of the mass spectrometer. Correction for naturally occurring isotopes  in the derivative side chains was applied as reported before [The reported . All anareported . The inj (MSTFA) . Injectos report , the injd before .Lemna gibba draft genome v0.5.1 [Fronds grown on inorganic nitrogen or on organic nitrogen medium were harvested and immediately frozen in liquid nitrogen. For each condition three biological replicates were analyzed. Total RNA was extracted by grinding 200 mg frozen tissue to a fine powder in liquid nitrogen with a mortar and pestle, followed by extraction with TRIzol Reagent  and ethanol precipitation. Total RNA was resuspended and treated with RQ1 RNase-free DNase  on an RNA Clean & Concentrator-5 column . The purity and concentration of RNA were determined by a NanoDrop Nd-1000 spectrophotometer  and a Qubit fluorometer . Polyadenylated RNA was enriched from total RNA with the Dynabeads mRNA Purification Kit . Indexed, directional RNA-seq libraries were prepared with ScriptSeq v2 reagents  from 40–50 ng of polyA-selected RNA according to the manufacturer’s instructions. Libraries were sequenced on an Illumina HiSeq 2000 instrument  generating 101 bp paired-end reads. Reads from each sample were mapped against the 21,830 predicted transcripts of the e v0.5.1  using Boe v0.5.1 .p-value ≤ 5% (Benjamini–Hochberg adjustment for multiple testing) and a more than four-fold change in gene expression between conditions.Differential expression analysis for the two conditions  was performed using the DESeq package , 83. ThiLemna gibba genes were identified by BLAST search against the predicted transcripts. The primers for real-time PCR were designed and synthesized by Integrated DNA Technologies . The sequences of primers for reference and target gene are listed in  as described in the manufacturer's user manual. First-strand cDNA was synthesized by using cDNA synthesis kits from Fermentas, . Two reference housekeeping genes from Arabidopsis  were chosen from literature , 84 and ftp://ftp.uniprot.org) [–20 were kept, which associates 12,673 of the 20801 L. gibba protein sequences with 8,597 unique Uniprot sequence identifiers .Analysis of Gene Ontology (GO) terms which are enrichment in differentially expressed genes was performed with the online bioinformatics resources given by the Database for Annotation, Visualization and Integrated Discovery (DAVID) (v6.8) , 85. To rot.org)  using Prrot.org) . Top hitLemna gibba v0.5.1 draft genome [L. gibba gene loci were given as input, returning a total of 6875 proteins with a predicted enzyme function. 4436 of these are enzyme functions acting on small molecules. A network of central carbon metabolism in Lemna gibba to be used for 13C-metabolic flux analysis was then defined by mapping the E2P2-predicted reactions onto a carbon flux network originally developed for Brassica napus [13C-MFA network for B. napus have been discussed elsewhere [B. napus model can be understood here as a generic scaffold for the central metabolism of higher plants. A manual curation process was applied to improve the reaction-gene associations. If multiple isoforms of an enzyme were identified by E2P2, the likely subcellular localization could often be determined based on finding the closest Arabidopsis homologs in the reference model by bidirectional BLAST searches (L. gibba protein sequences against Arabidopsis TAIR10 predicted proteins and vice versa). In many cases the subcellular localization of L. gibba protein sequences was predicted using the online tool TargetP [L. gibba and Spirodela polyrhiza we could identify gene homologs for all four subclasses, except genes encoding the xylulose 5-phosphate translocator (XPT). Based on phylogenetic analysis of the phosphate translocator family across about 100 sequenced plant genomes it was found that a loss of the XPT occurred within subphyla of the monocots [L. gibba genome, our analysis of the genetic data left open whether enzyme isoforms exist for both the cytosol and the plastid. Despite this, the model was designed with complete cycles in both compartments in order to allow the OPPP to work in both compartments in absence of XPT. In light of these uncertainties in network definition, the interpretation of fluxes through OPPP and glycolysis at subcellular resolution must be done with caution. Nevertheless, we kept the compartmentalized scaffold. An aggregated uncompartmentalized view can always be generated and inspected. Any flux solution with statistical measures of uncertainty attached can easily be projected onto a non-compartmentalized or otherwise lumped network version, based on linear combinations of fluxes . Flux ratios can be generated in the same way. When using a Monte Carlo approach to generate the statistics (repeated iterative flux parameter fitting with noise-added measurement data), the aggregation of the best fitting flux solution and the flux solution vectors representing the statistical uncertainty is done in the same way. This means that besides the summation of corresponding flux values, no special statistical procedure needs to be developed to obtain flux statistics for the aggregated network.A metabolic network was derived from a t genome , 14 by ut genome , an enzyt genome . 21830 pca napus . Plant clsewhere . Most of TargetP . In addi TargetP  and phyl TargetP . Searchimonocots . It thermonocots , 95. If Spirodela polyrhiza that under photomixotrophy, when organic carbon substrates are available and may be catabolized, RuBisCO oxygenation and operation of the photorespiratory pathway are most likely largely suppressed due to elevated intracellular CO2 levels [Regarding the activities of ribulose-1,5-bisphosphate carboxylase/oxygenase , the ratio of carboxylation to oxygenation is largely dependent on the concentrations of both substrates present at the site of RuBisCO activity. Under normal atmospheric conditions, about 25% of the reaction catalyzed by RuBisCO is oxygenation . It has 13C-MFA, i.e., isotope tracer-based metabolic flux analysis under metabolic and isotopic steady state [13C-Metabolic Flux Analysis [13C-Metabolic flux analysis was mostly performed with the software tool 13CFLUX2 [2 value was calculated based on the general model information. Degree of freedom is the number of mass isotopomer measurements minus the number of measurement groups, plus the number of measured flux rates minus the number of free net and exchange fluxes in the model . This pool can exchange CO2 with atmospheric CO2 by independent uptake and release reactions while it can also exchange with the common cellular CO2 pool that is used by carboxylase and decarboxylase reactions other than RuBisCO  and the commercial software package ZEN [Living plants of kage ZEN . The help < 0.05.Comparisons between two metabolic growth conditions were performed using one-way ANOVA with 3 or 4 biological replicates per condition . Unless stated otherwise, mean values were deemed to be statistically significantly different for Additional file 1: Table S1. Increase in fronds area (cm2) during culture time (hours). Means and standard deviations represented three independent replicates. Table S2. Metabolite analysis by Liquid Chromatography / Mass Spectrometry. See also Table S13. Table S3. Biomass composition data (weight % in dry weight).  Means and standard deviations represented three independent replicates. Table S4. Measurements of 13C-enrichment in metabolites by GC/MS. Table S5. Measurement of 13C-enrichment in total frond biomass by Isotope-Ratio Mass Spectrometry. L. gibba fronds were grown with medium glucose consisting of 40% U13C6 glucose (99% 13C) and 60% unlabeled glucose . Table S6. Definition of carbon flux model with indication of change in gene expression and change in flux. Table S7. Metabolite names used in the flux model. Table S8. Assessment of step wise model improvement. The four model configurations  correspond panels A, B and C in Fig. S1, respectively. Table S9. Best fit flux results for inorganic nitrogen condition (INS) and organic nitrogen condition (ONS). Table S10. Flux values with statistical confidence measures. Table S11. Differential expression analysis data . Table S12. List of Uniprot accession numbers associated with Lemna gibba protein IDs . Table S13. GO term enrichment analysis for significantly differently expressed genes of Lemna gibba. Table S14. Mass spectrometer parameter settings for analysis of medium substrate levels. Table S15. Mass spectrometer parameter settings for metabolites measurements. Liquid chromatography coupled to mass spectrometer. Table S16. Primer sequences for qRT-PCR.Additional file 2: Fig. S1. Schematics for different model configurations for which the best fit between 13C-MFA simulation and experimental data was determined. Fig. S2. Central metabolism network of growing Lemna gibba fronds. Fig. S3. Central metabolism Flux map of growing Lemna gibba fronds. Fig. S4. Statistical uncertainty in the flux values for the oxidative steps of the Oxidative Pentose Phosphate Pathway (OPPP). Fig. S5. Comparison the relative gene expression level by DEseq and quantitative real-time reverse transcription PCR (qRT-PCR).Additional file 3. Additional file 4.


Aspergillus fumigatus, an opportunistic human pathogen, frequently infects the lungs of people with cystic fibrosis and is one of the most common causes of infectious-disease death in immunocompromised patients. Here, we construct 252 strain-specific, genome-scale metabolic models of this important fungal pathogen to study and better understand the metabolic component of its pathogenic versatility. The models show that 23.1% of A. fumigatus metabolic reactions are not conserved across strains and are mainly associated with amino acid, nucleotide, and nitrogen metabolism. Profiles of non-conserved reactions and growth-supporting reaction fluxes are sufficient to differentiate strains, for example by environmental or clinical origin. In addition, shotgun metagenomics analysis of sputum from 40 cystic fibrosis patients  before and after diagnosis with an A. fumigatus colonization suggests that the fungus shapes the lung microbiome towards a more beneficial fungal growth environment associated with aromatic amino acid availability and the shikimate pathway. Our findings are starting points for the development of drugs or microbiome intervention strategies targeting fungal metabolic needs for survival and colonization in the non-native environment of the human lung. Aspergillus fumigatus and analyze fungal metabolism of infection of the lung of cystic fibrosis patients, finding that the fungus shapes the lung microbiome to promote its own growth.Here, the authors generate strain-specific genome-scale metabolic models of  The common mold Aspergillus fumigatus is essential for environmental decomposition but poses a serious threat to hospitalized patients, particularly those who are immunocompromised or have pulmonary diseases such as cystic fibrosis4. Annually, more than 1 million people have invasive aspergillosis (IA), a systemic multi-organ affecting disease starting by A. fumigatus infecting the lung, and 3 million have chronic pulmonary aspergillosis. Both conditions have high mortality rates and diagnosis remains challenging . In addition, A. fumigatus contributes substantially to fatal disease progression in chronic obstructive pulmonary disease, which appears to have a much higher prevalence than previously estimated5, while A. fumigatus is related to as many as half of the worldwide cystic fibrosis cases6.Fungal infections are an emerging and costly concern for human health and health careA. fumigatus isolates possess to cope with external stresses or accessible nutrient profiles in challenging environments such as the human lung remain largely unknown. Recently, we explored the genetic diversity of A. fumigatus and found a remarkably low fraction of core genes shared by all members of the species 7. How the genetic diversity of A. fumigatus influences phenotypic and metabolic heterogeneity, and particularly the ability to thrive in the non-native niche of the human lung, has not been addressed.The distinct characteristics that 8. We previously applied GEM analysis to reveal gut microbiome species that influence colonization levels of the opportunistic fungal pathogen Candida albicans9. The exponentially increasing number of available genome sequences makes the reconstruction of multistrain GEMs possible. The first multistrain-GEM collection of Escherichia coli enabled the definition of strain-specific adaptation to nutrition availability and the prediction of nutritional auxotrophies in some strains10. Protocols and databases were consequently updated to allow for bacterial GEM reconstruction at strain-level resolution12, although reconstructions of multistrain-GEMs remain to be explored in eukaryotes.One promising approach to studying the metabolic capabilities and growth dependencies of pathogens is genome-scale metabolic model (GEM) reconstruction and analysisA. fumigatus as a fungal model organism. By defining metabolic differences among 203 environmental and 49 clinical strain-specific GEMs we identified metabolic reactions that differ between the two populations. Subsequently, we performed shotgun metagenomics on sputum from 40 cystic fibrosis patients before and after they were diagnosed with an A. fumigatus colonization. Based on computationally defined metabolic output of the lung microbiome, we propose that the presence of A. fumigatus shapes the metabolic landscape of the lung microbiome to be favorable for fungal growth. Resolving the impact of genetic diversity on A. fumigatus metabolism is important to extending our understanding of adaptation mechanisms that likely involve aromatic amino acid metabolism and the shikimate pathway for ultimately guiding the development of new antifungal therapies.In this study, we provide multistrain-GEM reconstruction using A. fumigatus metabolism 14. This approach allowed us to acquire as many Aspergillus-associated reactions as possible in the core metabolism of A. fumigatus . It also allowed us to acquire a more comprehensive catalog of optional accessory metabolic reactions by defining strain subset diversity, which enabled subsequent strain-specific gap-filling curation 15. The largest fractions of the derived biomass function included carbohydrates and proteins . Additional essential components included lipids, DNA, and energetic co-factors  or MetaCyc . The remaining 2370 metabolic reactions (excluding exchange reactions) could not be mapped to any gene in our pan-GEM draft model and were removed from the generic pan-GEM. These reactions were retained for subsequent strain-specific refinement steps, which require accessory information e.g., for gap-filling of fragmented metabolic pathways 18 based on relaxed flux bounds of exchange reactions.Subsequently, we screened available es Figs.  and 2003ays Fig. . Concurrays Fig. . For theays Fig. , MethodsA. fumigatus wild-type (Af293 strain) and four mutant strains affecting nitrogen or carbon metabolic components, and adapted the model to publicly available gene essentiality information19  from our previous study addressing the global diversity of A. fumigatus7, we mapped strain-specific gene profiles to the reference pan-GEM and subsequently derived strain-specific GEMs (Supplementary Data 22 and crosschecking against blocked reactions with FASTCC (Methods)23. Model size varied in the different A. fumigatus strains from 1321 to 1402 reactions (mean 1361).Using a genomic dataset of 252 A. fumigatus, we found a low number of core metabolic components shared by all GEMs  and 1108 metabolic reactions (76.9%) were shared by all strain-specific GEMs, resulting in a large degree of metabolic variation across all GEMs (296 accessory genes and 338 accessory reactions). Most relevant accessory content was involved in nucleotide, energy (including oxidative phosphorylation and nitrogen metabolism), and amino acid metabolic pathways p ≤ 0.05, Fig. A. fumigatus26. Combining the ability to convert chorismate and glutamine to anthranilate, pyruvate, and glutamate, with amino acid and energy metabolism-associated conversions of methionine, succinate, or tryptamine, and the ability to take up and grow on aspartic acid, appeared sufficient for strain origin classification. Specifically, the ability to add sulfur to methionine as well as the absence of the ability to convert selenocystathione to selenocysteine or tryptamine to Indole-3-acetaldehyde appeared a characteristic of environmental strains, which in part was not present in clinical strains . Using information from only 21 reactions, classifying environmental from clinical strains achieved an area under the curve for precision over recall of 0.92 and an accuracy of 0.79 . For all patients, we had an initial culture-negative sample and a subsequent sample that was positive for A. fumigatus growth. To investigate changes to the lung microbiota after A. fumigatus colonization, we performed shotgun metagenomic sequencing for all 80 sputum samples (A. fumigatus negative and positive), generating an average of 5.59 Gbp of sequencing data per sample (standard deviation 0.80 Gbp). By combining Kraken 2 and raspir30 for taxonomic profiling, we identified a total of 67 genera and 200 species. Although spontaneous expectoration of sputum is frequently used for sample acquisition32, this practice may introduce contamination from oropharyngeal flora. By applying FEAST33, which was developed to partition microbial samples into their source components facilitating the quantification of contamination or other potential source environments, we found a dominant source of our samples to be cystic fibrosis patients’ lung microbiome and a significantly higher contribution of them than the oral or clinical environment microbiome  showing an uneven distribution in the population (Supplementary Data Streptococcus mitis (65% occurrence in the 10 most abundant species), Haemophilus parainfluenzae (61.25%), Rothia mucilaginosa (57.5%), Prevotella melaninogenica (55%), and Staphylococcus aureus (52.5%) were most frequently found among the 10 most abundant species , we generated networks from differentially correlated microbial pairs in paired A. fumigatus-negative versus A. fumigatus-positive samples from cystic fibrosis patients  between A. fumigatus-negative and subsequently positive patient samples. For the top 5 differentially abundant species , existing edges . Interestingly, modules 3 and 4 were enriched in amino acid metabolism, for example, phenylalanine and tryptophan, but also valine and (iso-)leucine. Further enrichments included glycosaminoglycan and steroid biosynthesis (module 1), glycan biosynthesis (module 2), propanoate and fatty acid metabolism (module 3), and flavonoid metabolism  and positive correlations between FEV and a number of amino acid-involving pathways including biosynthesis of phenylalanine, tyrosine and tryptophan, and also with glycolysis  and L-Phenylalanine:2-oxoglutarate aminotransferase (EC2.6.1.1), which showed notably constrained flux-bound variability across most simulated strain GEMs  decreased metabolic activity significantly  and growth  on minimal media . Relative metabolic activity and radial growth increases based on minimal media were highest for glutamine  reconstructed a comprehensive pan-GEM of A. fumigatus metabolism in a data-driven manner, which we validated against phenotypic microarray and gene essentiality data; (ii) derived 252 strain-specific GEMs from individual genome assemblies and manually curated them towards growth feasibility and minimal fractioned network topologies; and (iii) determined metabolic differences differentiating clinical from environmental strains, such as metabolic reactions involving several amino acids, particularly aromatic amino acids as well as chorismate or thioredoxin. Chorismate is an important precursor for aromatic amino acids and is formed in the shikimate pathway. This seven-step pathway is not present in animals and enables the synthesis of the aromatic amino acids tyrosine, phenylalanine, and tryptophan. Thioredoxin is an important factor for DNA synthesis and is associated with A. fumigatus virulence42.In this study, we built a suite of Pseudomonas putida43, loss of fitness-relevant pathways for survival in the gastrointestinal environment in extraintestinal Salmonella spp.44, and strain-specific metabolic capabilities of Staphylococcus aureus linked to pathogenic traits and virulence acquisitions45. Here, we applied this strategy to explore metabolic diversity in a eukaryotic fungal pathogen. This strain-specific A. fumigatus GEMs platform is publicly available (BioModels ID MODEL2211100001) for investigating the metabolically relevant A. fumigatus gene set and its impact on the metabolic diversity influencing growth rate capabilities, metabolic adaptation, and pathogenicity in this important human fungal pathogen.Multistrain-GEMs have been used to elucidate the metabolic diversity of human-pathogenic bacteria. For example, they defined the pan-metabolic capabilities of A. fumigatus colonization. Clinical isolate-specific simulations and analysis showed significantly increased growth rates in the predicted lung microenvironment of cystic fibrosis patients after a confirmed A. fumigatus colonization, suggesting that the lung microbiome is remodeled to a state more favorable to fungal growth. Our analysis predicted 77 metabolic reactions associated with aromatic amino acid metabolism and the shikimate pathway would have significantly different flux ranges after A. fumigatus colonization of the lung. These reactions appear not only in aromatic amino acid metabolism but also in sulfur, nitrogen, and lipid metabolic pathways, highlighting the advantage of including topological pathway information when analyzing metabolic activity. Several of these reactions are shown to be essential for the growth of the fungus in knockout experiments, including knockouts for aroC, TrpC, and MET16, making these genes potential targets against the fungus25. However, we identified a challenge in drug design using potentially targetable biomarkers originating from lethality data of in vitro gene deletions in A. fumigatus. Our analyses using metabolic modeling coupled with growth experiments suggest that A. fumigatus might be able to grow without the function of targeted genes by obtaining the necessary metabolites externally. This might occur in patients, for example, with A. fumigatus acquiring metabolites generated by lung bacteria  used to build the strain-specific GEMs and the clinical strains in the cystic fibrosis patients in our study. In our previous study, however, we showed that genomic similarities of clinical strains are relatively high even when the strains originate from different countriesA. fumigatus and its metabolic interdependencies with bacterial communities in the human lung.Altogether, the presented analyses demonstrate that fungal, genome-scale metabolic modeling is feasible at the strain level. The results contribute towards a mechanistic understanding of the impact of genome diversity on the metabolic phenotype of A. fumigatus biomass composition based on several sources in the literature. First, we assigned proportions of main biomass components, as described,49 as 38.8% carbohydrates, 9.9% lipids, 30% proteins, 0.6% DNA, and 3.7% RNA. Since this resource neglected polyols, we added 4% polyols as reported for Aspergillus oryzae50 for a total of 42.8% carbohydrates. After adding 6.6% cofactors, these main components made up the total biomass composition together with the reported 6.4% ash fraction49. Next, we screened the literature to specify fractions of carbohydrate subcategories, e.g., glucans or trehalose53; lipids including sterols; phospholipids; neutral lipid and free fatty acid compositions55; amino acid composition of the protein content57; and co-factor content including energy carriers such as NADH and vitamins such as riboflavin59. After calculating the mmol/g content of each fraction, we added ATP demand according to models developed for Saccharomyces cerevisae and Aspergillus niger61. Furthermore, we added a nongrowth-associated ATP maintenance reaction and calibrated flux through it for reported growth rates of the fungus in batch-fermentation process in hypoxic and normoxic glucose-limited conditions .All reconstruction and analysis efforts used COBRApy (v0.17.1)13. We downloaded the Aspergillus CoReCo model for A. fumigatus (Biomodels ID MODEL1604280029) and further Aspergillus models from the CoReCo repository including A. oryzae (Biomodels ID MODEL1604280012), Aspergillus nidulans (Biomodels ID MODEL1604280008), A. niger (Biomodels ID MODEL1604280021), Aspergillus clavatus (Biomodels ID MODEL1604280016), Aspergillus terreus (Biomodels ID MODEL1604280019), and Aspergillus gossypii (Biomodels ID MODEL1604280044) from the BioModels repository (https://www.ebi.ac.uk/biomodels/). In addition, we incorporated metabolite and reaction information from a recently published A. fumigatus central metabolism model14. This yielded a base model of 7,606 reactions  and 3,578 metabolites. Of note, the high number of initial exchange reactions originated from models created with the CoReCo pipeline, which includes an exchange reaction for each defined metabolite by default. All subsequent curation efforts aimed to keep only reactions for which annotation information was available or that were necessary for model feasibility. Filtering duplicate reactions and metabolites reduced the model by 73 reactions and 210 metabolites. Filtering duplicate reactions was necessary because our initial merge of metabolic information included reactions and converted metabolites from different sources. Only when reactions or metabolites had different naming conventions we reduced to a single naming scheme. When different isozymes encoded the same metabolic reaction, we included all alternative genes as OR relationships (instead of AND) in the genes-to-protein rule of the affected metabolic reaction and removed the duplicate reaction. The biomass formation was modified based on the literature on A. fumigatus metabolism and enriched with information from closely related species if we did not find A. fumigatus-specific information (Biomass formulation section).We gathered and combined information from automatically generated draft reconstructions based on the CoRoCo pipelinehttps://www.kegg.jp/) and MetaCyc (https://metacyc.org/) databases for gene annotation for A. fumigatus metabolism and added 1444 genes to the model. When available, we adopted AND and OR relationships for genes for metabolic reactions and crosschecked with genes encoding reactions in the yeast consensus model61. During this step, 2370 reactions could not be mapped to any annotated gene and were removed from the template model but kept for subsequent gap-filling procedures. Further curation efforts were run in parallel, since modifications influenced other curation procedures. Curation included compartmentalization, resolving erroneous energy generating cycles (EGCs)17 and gene essentiality information19, and adaptation to phenotypic growth assays . We considered 0.001 mmol/grDW/h as an absolute minimum for growth rate to account for reported low-oxygen growth conditions, e.g., in glucose-limited media21. We identified essential carbon, nitrogen, sulfur, and phosphorus components  for subsequent GEM analysis . Simulation scripts for all analyses are at Github: https://github.com/mohammadmirhakkak/A_fumigatus_GEM/, which also holds the MEMOTE report (Github folder: https://mohammadmirhakkak.github.io/A_fumigatus_GEM/memote_report.html). Simulation-specific constraints are in Supplementary Data Last, we checked the compatibility of our model with metabolic modeling standards by running MEMOTE testsA. fumigatus was derived for 300 environmental and clinical strains with a global distribution7. By mapping the genomes for 252 strains to the Af293 A. fumigatus reference genome annotation, we identified metabolically relevant genes by requiring at least 95% sequence identity under the rationale that high sequence identity preserves metabolic function. Small deviations from the chosen sequence identity threshold did not change the results. The similarity analysis was done by BLAST analysis of protein sequences using diamond (v0.9.24.125)68. The presence or absence of metabolic reactions was deduced for each strain using the associated gene-protein-reaction rules from the pan-GEM and the relevant identified genes. The metabolic core comprised reactions and genes that occurred in all strains with other reactions and genes defining, respectively, the accessory reactome and genome. To ensure that all strain-specific GEMs showed nonzero growth capability on minimal media with glucose as the carbon source, we identified and resolved minimal sets of essential reactions required to operate when adapted to the minimal cut set concept22. Finally, we guaranteed a consistent network property by identifying and discarding blocked reactions for each isolate using FASTCC23. All models were deposited in BioModels24 and assigned the identifier MODEL2211100001.Recently, the pan-genome of pyrG-, ΔlysF, Δmet2, and ΔniaD were grown at 25 °C for 7 days before experimental assays on malt agar supplemented with 5 mM uracil. Uracil supplementation was required for growth of the CEA17 pyrG- strain (a uridine auxotroph) and was added to all media to ensure comparable growth. Mature conidia were harvested by rubbing plates with sterile distilled water and filtering the resulting solution through a 30-µm cell strainer to remove mycelial fragments. Spore purity was assessed and confirmed by microscopy. Spore solutions were adjusted to a transmittance of 75%. Phenotypic microarrays were performed using Biolog Phenotypic Microarray plates PM1, PM2, PM3, and PM4  prepared following the manufacturer’s protocol for filamentous fungi, including resuspending conidia in filamentous fungi (FF) media and the addition of 0.16 ml of Biolog Redox Dye D to the master mix of each plate to quantify fungal metabolic activity. By using PM1-PM4 we could investigate 379 different growth conditions rather than only 95 as available with Biolog’s FF plate (Fungi Identification Test Panel). The plates were incubated at 37 °C for 3 days and metabolic activity was measured colorimetrically using an OmniLog microplate reader with readings taken every 15 min. The incubation temperature was changed from the initial 25 °C to 37 °C for the phenotypic microarray experiments to mimic the change in temperature that occurs in the transition from the environment to the human host as used before71. Experiments were performed as biological duplicates or triplicates , CEA17 72. The diagnosis of cystic fibrosis was verified by established diagnostic criteria74. Spontaneously expectorated sputum was collected during visits to the Cystic Fibrosis Center at the University Hospital Heidelberg and frozen in liquid nitrogen on the day of visit. Sputum samples were collected from 40 cystic fibrosis patients before and after they had positive A. fumigatus colonization. Samples were frozen within 24 h after reception at the microbiology department. The cohort for 80 total samples was 15 females and 25 males aged 23.6 ± 4.96 years (mean ± standard deviation) before A. fumigatus colonization. Samples were taken during visits from patients without exacerbation or intravenous antibiotic treatment in the previous 3 months. Supplementary Data Burkholderia cepacia special agar  , and Sabouraud agar  . Two other media were used for anaerobic isolation (36 °C): Schaedler agar  and kanamycin-vancomycin agar . Colonization was defined as positive culture from a specimen from the sputum samples on at least one of the agar plates.This study was approved by the Ethics Committee of the University of Heidelberg and written informed consent was obtained from all patients or their parents or legal guardians (S-370/2011). Patients were treated according to the standard of careg (8000 rpm) for 1 min. To QIAamp Mini spin columns 500 μl Buffer AW2 was added before centrifuging at full speed  for 3 min. To the QIAamp Minispin columns, 200 μl Buffer AE or distilled water was added before incubating at room temperature for 1 min and centrifuging at 6000g (8000 rpm) for 1 min. Genomic DNA was randomly fragmented by sonication.DNA extraction procedures followed the QIAamp DNA Mini and Blood Mini Handbook. The QIAampl DNA minikit was used for DNA extraction. Sputum samples were placed in a 1.5 ml microcentrifuge tube and 180 μl Buffer ATL was added. Proteinase K (20 μl) was added, and samples were mixed by vortexing and incubated at 56 °C. After centrifuging, 4 μl RNase A (100 mg/ml) was added, followed by mixing by pulse-vortexing for 15 s, and incubating 2 min at room temperature (15–25 °C). After centrifuging to remove drops from inside the lid, 200 μl Buffer ATL was added. Samples were mixed by pulse-vortexing for 15 s and incubated at 70 °C for 10 min. After briefly centrifuging, 200 μl Buffer AL was added to samples, followed by mixing by pulse-vortexing for 15 s, and incubating at 70 °C for 10 min. After brief centrifugation, 200 μl ethanol (96–100%) was added and samples were mixed by pulse-vortexing for 15 s. After brief centrifugation, QIAamp Mini spin columns and 500 μl Buffer AW1 were added before centrifuging at 6000DNA fragments were end-polished, A-tailed, and ligated with full-length adapters for Illumina sequencing, followed by PCR amplification with P5 and indexed P7 oligos. PCR products for final construction of libraries were purified with the AMPure XP system . Libraries were checked for size distribution with an Agilent 2100 Bioanalyzer , and quantified by real-time PCR to ensure an amount of at least 3 nM). Qualified libraries were inserted into Illumina sequencers (MiSeq system). For quality checking, 1 ethidium bromide negative control was added for every 11 samples and treated with the same handling procedure as the experimental samples.30 was used to support our Kraken 2 results. Only the species that can be detected in at least one sample by raspir were kept 35 were also used to assess the reliability and for comparison of Kraken 2 results. FEAST 33 was used to check for possible contamination and to estimate the contribution of potential source environments using the cystic fibrosis lung microbiome dataset from three published studies 77, an oral microbiome dataset (ENA project ID PRJEB28422)78 and a clinical environmental dataset (ENA project ID PRJNA376580)79. Taxonomic assignment of the datasets followed the same pipeline used for our dataset. FEAST results  pipeline was used to assign nonhuman reads to KEGG pathways. R packages vegan (v2.5) and picante (v1.8.2) were used to calculate alpha diversity with a Shannon and phylogenetic diversity index for each sample using the read counts of species. Statistical differences between samples before (A. fumigatus -) and after (A. fumigatus +) colonization with A. fumigatus were obtained by Wilcoxon signed-rank test. For beta diversity, R package coda.base (v0.3.1) was used to calculate the pairwise Aitchison distance for samples using the relative abundance of species. Statistical differences between samples before and after colonization with A. fumigatus were calculated by PERMANOVA.Trimmomatic was used to clip adapter and low-quality bases . Remaining reads shorter than 30 base pairs were discarded. BWA (v07.17) was used to align quality-filtered reads to the human reference genome (hg38) for the removal of human-derived reads. From 1.9e + 07 ± 2.7e + 06 (mean ± standard deviation) metagenomic reads, 7.8e + 05 ± 8.9e + 05 remained after preprocessing samples. To estimate the taxonomic composition of the nonhuman reads, Kraken 2  was used with its standard database as the reference. To further control false positive rate, raspir (v1.0.2)A. fumigatus colonization . Subsequently, MEGENA (v1.3.7) was used to identify co-expressed modules  in the constructed network using differentially correlated microbial pairs. To identify molecular functions, we investigated the enrichment of KEGG pathway information (https://www.genome.jp/kegg/pathway.html) by permutation testing to determine if correlations between modules and KEGG orthologies were possible by chance81. First, for a given module, all correlation coefficients and p-values for a KEGG orthology and all species in the module were obtained using the Spearman correlation method. The sum of absolute correlation coefficients in the module was then calculated. Following that, a random set of species of the same size as any given module was chosen 1000 times from all species, calculating the sum of absolute correlation coefficients each time. Finally, the sum of correlation values for each module was evaluated. If higher than 95% of the sums of correlation values in the 1000 repeats of randomly selected species, we inferred a significant correlation between modules and KEGG orthologies.The co-abundance network was constructed based on the relative abundance values of species . DGCA (v2.0.0) was used to construct the network from differentially correlated microbial pairs in paired cystic fibrosis samples, comparing before to after 40. In brief, MAMBO optimizes a highly correlated metabolic profile with a given relative abundance profile using growth rate simulations of bacterial GEMs associated with a particular metagenomic sample. For 200 raspir-confirmed bacterial species, we downloaded 143 matching bacterial GEMs from the AGORA2 (https://vmh.life/files/reconstructions/AGORA2/version2.01/)82 and CarveMe collection (https://github.com/cdanielmachado/embl_gems/tree/master/models)83. Optimizations were run in a python environment (v3.7) using a high-performance cluster . Finally, 929 metabolites were obtained using MAMBO , we randomly sampled 50% of the majority group and oversampled the minority group using ADASYN implemented in R package imbalance (v1.0.2.1). Subsequent feature selection used Boruta (v7.0.0), VSURF (v1.1.0), MUVR (v0.0.973), and sPLS-DA . These steps were repeated 50 times and selected features and their selection frequency were recorded. Finally, the Extra Trees algorithm from PyCaret (v2.3.2) was run for the feature sets, scanning frequency cutoffs to optimize the cutoff value for ML performance. The best hyperparameters of the Extra Trees model were automatically selected by scikit-optimize .A. fumigatus was cultivated on Aspergillus minimal medium (AMM)84 agar plates for 5 days at 37 °C. Conidia were harvested with sterile phosphate buffered saline containing 0.01 % (v/v) Tween 80 and filtered through a 40-µm pore-size cell strainer . Conidia numbers were determined using a CASY cell counter . Spore purity was assessed and confirmed by microscopy.For conidia production, 85. In brief, in a 96-well cell culture microplate  190 µl AMM/resazurin containing, when indicated, 5 mM glutamine, glycine, phenylalanine or tryptophan, was inoculated with 10 µl of conidial suspension at 12 different concentrations, for 1.6 × 106 to 0.78 × 103 conidia per well. After 18 h, resazurin reduction was measured as fluorescence using a microplate reader (Tecan Infinite M200 Pro) at wavelengths 570 nm (excitation) and 615 nm (emission). Relative metabolic activity was determined as the number of conidia needed as inoculum to reach a fluorescence of 5000 at an optimal gain.Metabolic activity was determined based on a resazurin assay as described6 conidia ml−1) point-inoculated on AMM or cystic fibrosis media (SCFM2)86 agar plates supplemented with 5 mM glutamine, glycine, phenylalanine or tryptophan, when indicated. Radial colony growth of A. fumigatus cultures at 37 °C was determined after 24 h using a caliper.Radial growth was determined with 5 µl conidial suspension  coupled with a quadropole detector  controlled by ChemStation (Agilent). Raw data were converted to netCDF format using Chemstation (Agilent), before data were imported and processed in Matlab R2018b  using PARADISe software88.Sample analysis was performed by MS-Omics  as follows. Samples were derivatized with methylchloroformate using a slightly modified version of the Smart et al. protocolt test using a p-value cutoff of 0.05. Multiple test correction as described by Benjamini–Hochberg was applied when appropriate. Gaussian mixture model was used to identify significantly abundant lung species. Functional enrichment of KEGG terms was done using overrepresentation analysis. Spearman correlation was applied to report correlation of KEGG modules with forced expiratory volume of cystic fibrosis patients (uncorrected p ≤ 0.05). All codes and data to reproduce the presented results are described in the code and data availability section of this manuscript and are stored in zenodo, github, BioModels and ENA repositories.All statistical test procedures are described in the respective methods sections. No statistical method was used to predetermine the sample size. No data were excluded from the analyses. Testing group differences included two-tailed Wilcoxon or Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary Data FilesSupplementary Data S1Supplementary Data S2Supplementary Data S3Supplementary Data S4Supplementary Data S5Supplementary Data S6Supplementary Data S7Supplementary Data S8Supplementary Data S9Supplementary Data S10Reporting Summary


This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.To close the gap between the rates of computational screening and experimental realization of novel materials An autonomous laboratory, the A-Lab, is presented that combines computations, literature data, machine learning and active learning, which discovered and synthesized 41 novel compounds from a set of 58 targets after 17 days of operation. In contrast to conventional ML algorithms used for optimization, human researchers benefit from a wealth of background knowledge that informs their decision-making, and it is increasingly recognized9 that autonomy will require a fusion of encoded domain knowledge, access to diverse data sources and active learning.Although promising new materials can be identified at scale using high-throughput computations, their experimental realization is often challenging and time-consuming. Accelerating the experimental segment of materials discovery requires not only automation but autonomy—the ability of an experimental agent to interpret data and make decisions based on it. Pioneering efforts have demonstrated autonomy in several aspects of materials research, including robotic and Bayesian-driven optimization of carbon nanotube yield13, the A-Lab addresses the unique challenges of handling and characterizing solid inorganic powders. These often require milling to ensure good reactivity between precursors, which can have a wide range of physical properties related to differences in their density, flow behaviour, particle size, hardness and compressibility. The use of solid powders is well suited for manufacturing and technological scaleup, and the approach of the A-Lab to synthesis produces multigram sample quantities that facilitate device-level testing.Here we present the A-Lab, an autonomous laboratory that integrates robotics with the use of ab initio databases, ML-driven data interpretation, synthesis heuristics learned from text-mined literature data and active learning to optimize the synthesis of novel inorganic materials in powder form. Although autonomous workflows based on liquid handling have been demonstrated in organic chemistry14, the A-Lab generates synthesis recipes using ML models trained on historical data from the literature and then performs these recipes with robotics. The synthesis products are characterized by X-ray diffraction (XRD), with two ML models working together to analyse their patterns. When synthesis recipes fail to produce a high target yield, active learning closes the loop by proposing improved follow-up recipes. Over 17 days of operation, the A-Lab successfully synthesized 41 of 58 target materials that span 33 elements and 41 structural prototypes  screened using the Materials Project14 and cross-referenced with an analogous database from Google DeepMind. Because the A-Lab handles samples in open air, we only considered targets that are predicted not to react with O2, CO2 and H2O , an active-learning algorithm that integrates ab initio computed reaction energies with observed synthesis outcomes to predict solid-state reaction pathways17. Experiments are performed under the guidance of this algorithm until the target is obtained as the majority phase or all synthesis recipes available to the A-Lab are exhausted.For each compound proposed to the A-Lab, up to five initial synthesis recipes are generated by a ML model that has learned to assess target ‘similarity’ through natural-language processing of a large database of syntheses extracted from the literaturerature16 . If thesThe A-Lab carries out experiments using three integrated stations for sample preparation, heating and characterization, with robotic arms transferring samples and labware between them Fig.  and 2. T19. Because the target materials considered in this work have no experimental reports, their diffraction patterns are simulated from computed structures available in the Materials Project and corrected to reduce density functional theory (DFT) errors  following the methodology outlined in previous work20  intermediate phases that leave only a small driving force to form the target material should be avoided, as they often require long reaction time and high temperature29.The active-learning cycle of the A-Lab2P2O9 was optimized by avoiding the formation of FePO4 and Ca3(PO4)2, which have a small driving force (8 meV per atom) to form the target. This led to the identification of an alternative synthesis route that forms CaFe3P3O13 as an intermediate, from which there remains a much larger driving force (77 meV per atom) to react with CaO and form CaFe2P2O9, causing an approximately 70% increase in the yield of the target 2HPO4, which proceed through a series of decomposition reactions and ultimately evaporate above 450 °C (ref. 30). Still, recipes based on these precursors can succeed if the ammonium phosphate reacts with another precursor before its evaporation temperature, effectively locking the phosphate ions in the solid state. For example, volatility does not seem to be an issue for the Mn-containing phosphates targeted in this work, as each Mn oxides precursor reacts with the ammonium phosphates at low temperature (<500 °C) to form Mn2(PO4)3 as an intermediate. This precursor behaviour can, in principle, be learned when sufficient pairwise reaction data have been collected, after which the A-Lab may favour the selection of precursors that trap in phosphate ions at low temperature and therefore preclude unwanted volatility.Precursor volatility disrupted all synthesis experiments targeting CaCr3)5, whose synthesis attempts produced amorphous samples 5 with energies as low as 61 meV per atom above the crystalline ground state, a finding that is consistent with the widely reported glass-forming ability of phosphate-rich compounds34.Melting of samples at high temperature inhibited the crystallization of one target, Mo, even though it is widely reported in the literature to be experimentally accessible35. If the energy of LaMnO3 were lowered, consistent with its experimental stability, La5Mn5O16 would be destabilized (above the hull). Errors in the computed energy of LaMnO3 may arise from its strong Jahn–Teller activity36, compositional off-stoichiometry37 or the presence of f-states in La—all of which present challenges to conventional DFT. Problems with YbMoO4 were found to be because of a poor pseudopotential choice in the Materials Project that destabilizes the well-known oxide, Yb2O3, and it is likely that, in more accurate calculations, YbMoO4 is not stable. A similar lanthanide-related electronic-structure problem may also be responsible for the failure to synthesize BaGdCrFeO6. These examples demonstrate the ability of the A-Lab to provide important feedback to high-throughput computed datasets. With improved calculations that exclude the computationally problematic compounds in this work, our total success rate would increase to 78% (43/55 targets).Some failure modes result from inaccuracies in the computed stability of the target and therefore cannot be addressed by modifications to the experimental procedures. Fundamental-electronic-structure challenges are probably affecting LaIn 17 days of closed-loop operation, the A-Lab performed 355 experiments and successfully realized 41 of 58 novel inorganic crystalline solids with diverse structures and chemistries. This unexpectedly high success rate (71%) for the synthesis of computationally predicted compounds was achieved by integrating robotics with: (1) DFT-computed data to survey the energetic landscape of precursors, reaction intermediates and final products; (2) heuristic suggestions for synthesis procedures obtained from ML models trained on text-mined synthesis data; (3) ML interpretation of experimental data; and (4) an active-learning algorithm that improves on failed synthesis procedures. The study also revealed several opportunities to enhance the lab’s active-learning algorithm by addressing failures caused by slow reaction kinetics, which would enable an improved success rate of 74% with in-line solutions.Our paper demonstrates that autonomous research agents can markedly accelerate the pace of materials research. Researchers initialized the A-Lab by proposing 58 target materials, which were successfully realized at a rate of >2 new materials per day with minimal human intervention. Such rapid discovery points to a vast landscape of opportunities in materials synthesis and development. Although this work focused on a limited subset of all possible synthesis targets, many new candidates await evaluation. As the breadth of ab initio computations continues to grow, so will this list of novel materials.Advances in simulations, ML and robotics have intersected to enable ‘expert systems’ that show autonomy as an emergent quality by the sum of its automated components. The A-Lab demonstrates this by combining modern theory-driven and data-driven ML techniques with a modular workflow that can discover novel materials with minimal human input. Lessons learned from continuing experiments can inform both the system itself and the greater community through systematic data generation and collection. The systematic nature of the A-Lab provides a unique opportunity to answer fundamental questions about the factors that govern the synthesizability of novel materials, serving as an experimental oracle to validate predictions made on the basis of data-rich resources such as the Materials Project. In future iterations of the platform, such an oracle may be expanded to investigate factors beyond synthesizability, including microstructure and device performance. Although our current success rate for the synthesis of novel compounds is high, the remaining discrepancies between current predictions and their experimental outcomes is a crucial signal required to improve our understanding of materials synthesis.4+), as determined by pymatgen38.The 58 targets evaluated by the A-Lab were identified from the Materials Project database (version 2022.10.28). We first obtained all entries from the Materials Project that were marked as ‘theoretical’  and predicted to be thermodynamically stable (at 0 K) or very close to the convex hull (<10 meV per atom). We did not consider materials with ≤2 elements nor those containing elements that are radioactive , exceedingly rare  or toxic . Owing to concerns with the experimental handling of certain materials systems , we constrained our selection to only include the following types of material: oxides, carbonates, bicarbonates, hydroxides, sulfates, sulfites, bisulfates, silicates, fluorides, chlorides, bromides, orthoborates, metaborates, tetraborates, phosphates, phosphites, chlorates, chlorites and hypochlorites. Finally, we removed all compounds predicted to have uncommon and potentially challenging oxidation states  and temperatures ranging from 600 to 1,100 °C. We further checked for reactivity with CO2 and H2O under those same conditions by using the Interface Reactions module in pymatgen41. From the resulting list of 146 new compounds that were stable in air, we selected 58 targets for which precursors were readily available. Later in the process, we found literature evidence for a small number of these compounds, but most (52/58 compounds) are believed to have no previous reports . It operates autonomously once given the following information: which elements to consider in the target materials, how large an upper limit to impose on each material’s energy above the convex hull, the atmospheric conditions under which the materials will be synthesized and a threshold on the reaction energies that exist between each material and the gaseous species present in the specified atmosphere. The algorithm then scrapes the Materials Project and produces a list of candidate materials that satisfy these criteria. Further filtering may be considered on the basis of the availability and cost of precursors for each target. Although this is done manually in the current version of the algorithm, potential improvements could automate the process by using online data from chemical inventory lists and vendor websites.The algorithm we used for identifying potential synthesis targets is available on GitHub  target and each known material in the knowledge base is evaluated using the cosine similarity between their encoded vectors. After identifying the reference material that is most similar to the target, its precursors are included in the new recommendation. When these precursors do not cover all the elements in the target, we use a masked precursor completion model12 to account for such missing precursors. Subsequent recommendations are implemented by moving down the list of known materials ranked to be most similar to the target.We have established a pipeline for recommending synthesis recipes by using a knowledge base of 33,343 solid-state synthesis procedures extracted from 24,304 publications11. The target and its associated precursors are transformed into three sets of features: (1) precursor properties including melting points, standard enthalpies of formation and standard Gibbs free energies of formation; (2) target compositional features indicating which elements are present; and (3) the calculated thermodynamic driving force associated with pairwise reaction paths from precursors to target. Although the proposed synthesis temperature is dependent on the precursors, not just the target, it may vary for each recipe. However, to maximize the efficiency with which the A-Lab operates, we chose to use one fixed temperature for each target. This temperature was calculated by averaging the proposed synthesis temperatures for the top five precursor sets recommended for a given target. This allowed all such precursor sets to be batched in a single furnace.For each set of recommended precursors, the most effective synthesis temperature is predicted using an XGBoost regressor trained in previous workThe A-Lab performs fully automated solid-state synthesis and characterization. It is a bespoke robotic platform that consists of a precursor preparation station with a central robot arm (Mitsubishi) for powder dispensing and mixing (custom-made with Labman Automation Ltd.), a high-temperature heating station with four box furnaces , a product-handling station developed in-house for powder retrieval and sample loading, a characterization station with a powder X-ray diffractometer  and two collaborative robot arms  that transfer samples and labware between stations. Further details on the robotic platform are provided in Supplementary Note 2 balls and crucibles) and precursor dosing bottles containing between 50 and 100 g of powders are manually loaded before starting a new experimental campaign. Prescribed amounts of the precursor powders are dispensed into a plastic vial by an automatic dispenser balance . The precursor powders are then mixed thoroughly with ethanol and ten 5-mm ZrO2 balls in a dual asymmetric centrifuge  for 9 min. To ensure proper slurry viscosity, the ethanol amount is automatically calculated on the basis of the amount and density of each powder comprising the mixture. The resulting slurry is transferred with an automated pipettor  into an alumina crucible, which is then dried at 80 °C in a closed evaporation system. A UR5e robot arm on a linear rail (Olympus Controls) removes the dried samples from the precursor preparation station and loads them into one of four box furnaces. Heating is performed in batches, with each furnace containing up to eight samples on an alumina tray. Each batch is heated to 300 °C with a slow ramping rate of 2 °C min−1 to raise the likelihood that any phosphate precursor has time to react before it becomes volatile at higher temperature. The samples are then heated to the specified synthesis temperature with a nominal ramp rate of 15 °C min−1, followed by a 4-h dwell. After the dwell is complete, the samples are naturally cooled to 100 °C, at which point a UR5e arm removes the samples from the furnace and waits another 10 min to allow the samples to cool to room temperature.The synthesis process starts from the precursor preparation station, where the necessary consumables  consisting of six convolutional layers, with max pooling applied between each, followed by three fully connected layers with ReLU activation. Batch normalization and a dropout rate of 50% is applied between the fully connected layers for regularization. At inference, we apply Monte Carlo dropout to create an ensemble of 100 networks with 50% of their connections randomly excluded. The final prediction is taken as the phase that seems most frequently in the ensemble and its associated confidence is defined as the fraction of models that predict it.Given an XRD pattern obtained from an unknown sample, we apply XRD-AutoAnalyzer to identify the constituent phases and estimate their weight fractionsA unique model instance is trained on the chemical space defined by each target. Experimental-structure entries with elements shared by the given target are extracted from the ICSD, also including carbonates and hydroxides. For the DFT-calculated target, we apply a machine-learned volume correction to its lattice parameters  were trained using reinforcement learning based on a proximal policy optimization algorithm18. Such cases were still considered successful as long as the disordered version of the target retained the same crystal structure and overall composition as the ordered version.When the automated refinement gives a poor fit, manual analysis is performed. For targets for which we suspect the poor fit resulted from configurational disorder, we refined the XRD patterns using cation-disordered versions of the target’s structure taken from the Materials Project. The cations allowed to be exchanged (disordered) with one another were selected on the basis of the Hume-Rothery rules, as detailed in previous work3, our recently developed algorithm that learns from previous experimental outcomes to identify improved reaction pathways. Given the products obtained from a set of precursors proposed by our natural-language models at temperature TNLP, ARROWS3 first suggests that a lower temperature (TNLP − 300 °C) be tested for the same precursor set. The intent of this approach is to reveal which intermediate phases lead to the formation of each impurity observed at higher temperature. From the low-temperature-synthesis outcome, information is extracted about the pairwise reactions that occurred, including those between the precursors (to form the observed intermediates), as well as those between the intermediates (to form the high-temperature impurities). New synthesis experiments are then proposed on the basis of sets of precursors expected to avoid such reactions, giving priority to those with a maximal thermodynamic driving force to form the target. The driving force is calculated as the free-energy difference between a target and its associated precursors, in which all solid energies (at 0 K) are extracted from the Materials Project and corrected using a machine-learned descriptor that accounts for vibrational-entropy contributions at the specified temperature46.Active learning is performed using ARROWSTNLP − 300 °C), iteratively higher temperatures (ΔT = 100 °C) are examined until the target is obtained with a yield exceeding 50% or until the temperature reaches TNLP. At each step, the algorithm determines which pairwise reactions occurred and records them in a database that is referred to throughout all other experiments performed by the A-Lab. In subsequent iterations, ARROWS3 gives priority to sets of precursors containing pairs of phases that are expected to form the desired target, while avoiding pairs that form unwanted impurities. Moreover, to avoid testing redundant synthesis routes for which different precursors form identical products, the algorithm checks whether the low-temperature (TNLP − 300 °C) intermediates obtained from a given precursor set differ from those obtained with previous (unsuccessful) recipes. If not, then no further experiments are proposed for that set of precursors. This process is repeated until the target is successfully obtained or until all the available precursor sets have been exhausted. Further details on the active-learning process are provided in Supplementary Notes After testing a precursor set at low temperature  handling powders and slurries in the sample preparation station used to dispense and mix precursors before heating. The video is played at 20× speed.Supplementary Video 2Robot arm R2 (UR5e) moving crucibles from the sample preparation station to the box furnaces. The video is played at 20× speed.Supplementary Video 3Robot arm R3 (UR5e) retrieving powder samples  and cooperating with an Aeris X-ray diffractometer for their characterization. The video is played at 12× speed.