The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries. Observing the technological progress of the recent decades one can notice that it facilitated an access to vast biomedical data resources describing different molecular levels . Consequently, our comprehension of biological processes becomes more profound as well as reliable. These facts open up a broad field of data integration, that aims to infer from various data collection platforms taking into account known biological dependencies between them.Motivations. In the literature, it is broadly emphasized that the integration of -omics data improves understanding of various phenomena. The modern studies and corresponding literature emphasize the significant role of the integration of different -omics data types for better comprehension of a phenomenon of interest [interest . Howeverinterest . Importainterest ,4.In this work, however, the aim is to focus on a particular problem of the transcriptomic and metabolomic data integration. We report an interesting phenomenon related to the analysis of individual metabolic networks in the context of transcriptomic data. The conducted study allowed to detect a common and robust artificial pattern identifiable for each data cohort from the Cancer Genome Atlas that splits cancer-specific patients into two to four clusters. Interestingly, each cluster is characterized by a peculiar activity pattern among reactions hubs. In the article, we prove that these observations result mostly from the uneven metabolic information distribution within the metabolic network. To the best of our knowledge, no one has yet commented on the problem that the structure of a metabolic network can introduce when used along with integrative methods, in particular when used in the context of the flux balance analysis (FBA) .Related research. One of the first attempts to this type of integration was suggested by Covert and Palsson, where authors infer the binary enzymatic activity from the transcriptomic continuous signal [s signal . Next, as signal , GIMME [s signal , GIMMEp s signal , iMAT [1s signal , INIT [1s signal , MADE [1s signal , mCADRE s signal , PROM [1s signal , RELATCHs signal . Apart fs signal ,17,18,19Using this type of models, where a general metabolic network becomes context-specific through integration with a transcriptome of specific tissue or organism few groups of researchers have already reported some interesting discoveries. With nearly 2000 samples of breast tumor Leoncikas et al. discovered a novel poor prognosis cluster characterized by local production of serotonin along with active extracellular matrix and membrane remodeling reactions . In theiIn summary, the general biomedical aim of this approach is to model the gene–protein–reaction interactions in the form of a metabolic network and infer multiple biological properties, e.g., post-transcriptional gene activities or intensity and activity of metabolic reactions, that can explain the nature of analyzed sample.Our results. In this paper, we want to draw attention to a specific problem related to the task of the data integration and analysis based on the general metabolic networks along with transcriptomic data. First, we present how the specific structure of sub-networks can inflate the importance of specific groups of enzymatic reactions, which may lead to incomplete or questionable conclusions. Next, in order to cope with that obstacle, we suggest a possible routine that can track and eliminate the unwanted problem influencing the metabolic knowledge obtained from the integration procedure. Furthermore, we present our results from the analysis of the TCGA renal cell carcinoma (RCC) dataset, which point out reactions discriminating patients in the context of the observed clinical factors. Additionally, we report the discovery of a poor-prognosis cluster of patients along with its characterization. Finally, we point out and discuss some already existing methods of analysis of metabolic networks that are likely to be influenced by this subtle yet significant network structural property. The preliminary version of this study was published as an extended abstract in the proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 96–101; Madrid, Spain.In general, a metabolic network is a set of reactions among elements of a given set of metabolites. Each reaction may be associated with a specific genetic rule that needs to be met in order for reaction to occur. If a reaction has no genetic rule assigned to it, it can be triggered whenever all substrates are available. A genetic rule usually describes which genes/proteins need to be active/present in the system to carry out the reaction. In particular, a rule may require simultaneous presence of several enzymes , or alternative enzymes that can catalyze the reaction . Finally, each reaction has also a lower and upper bound describing minimal and maximal flow of metabolites through this reaction.On the other hand, formally, the network can be considered as a Petri net with conditional transitions of places, where by a conditional transition we mean additional Boolean formula that needs to be met in order for transition to occur  composed of the 897 primary tumor and 129 normal tissue samples. Tumor tissues were also classified by their morphological type subtype into four groups: 527 clear cell RCC, 290 papillary RCC, 66 chromophobe RCC and 14 unclassified RCC samples. For 201 patients the survival data were also available. Additionally, we used the brain cancer dataset (707 samples) to showcase the artifacts of the clustering results obtained from the metabolic landscapes.In order to determine the activity state of each reaction in a personalized model we made use of the approach proposed by Shlomi et al. . We formThe solution of the described problem provided a binary vector of activity states for each reaction which we refer to as a metabolic landscape.In the described setting the FBA problem was formulated as a linear program and solved using the simplex algorithm. However, it should be emphasized that the structure of the underlying graph was crucial in context of the flux analysis and the existence of dense sub-graphs loosely communicating with neighbouring vertices strongly influenced the final outcome of the algorithm.To detect the possible sources of the structural redundancy in a metabolic network we applied the measure of entropy on a graph structure. To account for the context of the metabolic fluxes we made use of the entropy based on the inner and outer neighbours of vertices within a given sub-graph .v that: belong to v. Based on that we define the entropy v as:Namely, for a graph Given the above definitions, we determine the entropy of a group of enzymatic reactions within the RECON 2.2 metabolic network. Additionally, we evaluate the entropy induced by groups of random reactions sampled from the entire network. These steps aim to depict the graph in terms of the entropy and thus connectivity and the flow of metabolic information, that is subject to the metabolic landscape analysis.Since the final outcome of a linear programming problem is a set of binary vectors, it is necessary to introduce specific methods to analyze them. In the literature there are described various statistical methods that are specifically dedicated to the analysis of binary data, including: the non-negative matrix factorization , the spaNonetheless, in our case, in order to explore the data, track the potential latent variables and visualize results we use the standard principal component analysis (PCA), which provides numerically stable and robust results and is also used to define a function for separating variables selection.m observations and n variables, P scores  matrix, satisfying P is a representation of M in a new basis that is composed of vectors of L. For the purpose of binary landscapes analysis, we construct a set of highest valued coordinates from first k directions of the rotation matrix. The general parametrized function f is the function determining the threshold for the value of a coordinate to be assumed significant and k is the number of vectors from loading matrix that are considered. The function is used to select the groups of reactions with the highly correlated activity pattern between all samples. In parallel, the results from PCA Loadings-based variable selection were compared with the overall entropy analysis of the network structure considering the interaction between the groups of enzymatic reactions. It turned out that the groups requiring the adjustment belong to the group of clusters characterized by the low entropy and loosely connected to the other vertices in the network.Let To determine differentiating variables in the studied datasets, two measures were used. First, the Jaccard Index is used to determine differentiating reactions between two groups of samples. For any Additionally, the Tanimoto similarity measure of two binary vectors The first step was to apply the procedure suggested by Shlomi et al. to the cohort of the TCGA data in order to verify if there existed a common metabolic pattern among various types of tumors, or alternatively, if there existed any metabolic biomarkers which may be of diagnostic importance.SLCO1A2  into, out of or within compartments of a cell, or between cells. Additionally, as a co-enzyme with SLC3A1 coordinates group of five transportation/exchange reactions of L-Cystine, L-Alanine and L-Ornithine, that were also detected as differentiating the cancer data. This observation implies activation of SLC3A1, that was recently reported to promote breast cancer tumorigenesis [As mentioned in the introduction, our preliminary results suggested that, surprisingly, each data set was composed of two to four clearly separable clusters of patients. In the case of 13 out of 20 TCGA cancer datasets, the corresponding set of metabolic landscapes was well-separated into four clusters that, on average, were differentiated by the activity of 180 reactions. Additionally, for the remaining seven cancer-specific landscape datasets there were on average 95 reactions differentiating samples into two clusters (see eactions ) and SLCeactions ) genes.  tissues . On the igenesis .Even though these literature reports may sound promising, we report another observation related to the analysis performed on a dataset with 500 randomly generated gene activities that were subjected to the metabolic analysis . Even though these reactions were biologically non-redundant , they very often formed well-connected sub-networks that were loosely connected with the rest of the network. As a consequence they made it problematic to apply the FBA methods. As the way to identify such structures we proposed to apply the notion of entropy induced by sub-networks. It turned out that these sub-networks minimized the entropy.In the For this reason we proposed two adjustment methods that take into account this phenomena. The aim was to transform the data so that the statistical analysis did not detect artificially induced data separation, but rather may result in a discovery of subtle differences in metabolic activity between samples possibly related to novel metabolic biomarkers.Due to the discovery of the artifacts in the results of the metabolic landscape analysis we propose the following pipeline of procedures, that aims to reduce the statistical redundancy immersed in the RECON 2.2 network structure.As the prerequisite, the entropy levels of each group of enzymatic reactions are determined with the tools described in the Materials and Methods section. These are the base of the control for redundant activity patterns in the statistical inference from the reaction activities.Gurobi solver based on the simplex algorithm. The solution was composed of the sample-specific metabolic landscapes, which were subjected to statistical analysis. The analysis of the data was supported by the two-fold verification if the redundancy among groups of reactions existed. On one hand, using the PCA loadings-based variables selection First, we processed the RNA-seq transcriptomic data and converted it to the gene activity matrix using the threshold of five counts. The matrix was then used to create a personalized metabolic network model. For each of these, a MILP problem was formulated and solved with the The data transformation was based on the aggregation of the activity states for groups of reactions with respect to compartments to which belong their substrates and products. Namely, each reaction was labeled with a name of form https://github.com/storaged/metabolic-landscape the code and the scripts used in this workflow are available. For the purpose of this study the workflow was executed on the machine with Mac OS.Finally, inference from the transformed data structure through the hierarchical clustering using the binary distance measures , correlation with the clinical data, selection of discriminatory features and functional analysis of determined clusters was performed. The outline of the described workflow is also depicted in the In order to validate the proposed workflow we performed the metabolic landscape analysis on the TCGA dataset of renal cell carcinoma (RCC). After performing the adjustment methods on the metabolic landscapes we reported a significant improvement in samples clustering, both in the sense of unwanted network structure-dependent clusters composition and correlation with clinical data see . AdditioWe removed the amplified activity pattern of reactions coordinated by the same genetic rule, that influenced the clustering of samples in an unwanted way. After the reduction there were no significant, discriminating reactions associated with the same genetic rule. This step also resulted in more reliable clustering of data according to clinical observations, e.g., normal or tumor tissue type or morphological type of a tumor sample.SLC6A3 as a experimentally confirmed biomarker for RCC [SLC6A3 in our data was clearly discriminating biomarker of Clear Cell RCC subtype (6.89 logFC).The results of our analysis of the renal cell carcinoma TCGA dataset were consistent with latest reports, that indicated  for RCC . The traCXCL16 gene as a significantly expressed in papillary RCC with others still waiting for their validation [SLC6A3 or SLC6A2, other member of the same Solute Carrier family. The transportation activity state of dopamine and norepinephrine via sodium symport between cytoplasm and extracellular space are well separating the Papillary RCC subtype from other samples. Namely, these reactions are predicted to be inactive in Papillary samples. Our results reported 247 out of 291 (≈ 85%) papillary samples and 42 out of 737 (≈6%) other samples characterized by inactivation of these reactions. This observation was also confirmed by the purely transcriptomic data, which suggested down regulation of both genes  for papillary samples. This observation suggested a simultaneous drop of the activity of both SLC6A2 and SLC6A3 as a potential diagnostic biomarker.However, thanks to the analysis of metabolic landscapes we further suggest potential biomarkers that correspond to specific, known RCC subtypes . The litlidation . NonetheSLC3A1 and SLC7A9  and (ii) reactions controlled by SLC7A9 that was involved in 79 reactions. However, after the adjustment two potential biomarkers were found: inactivation of sodium-dependent transport of (i) phosphate, supported by SLC17A1 and (ii) ascorbate supported by SLC23A1. Inactive phosphate and ascorbate transport characterized, respectively, 65 and 62 out of 66 ChRCC samples; 45 and 28 out of 774 other tumor samples. Even in the literature reports it is still not clear how the phosphate transportation or concentration level and absorption via SLC23A1 of ascorbate influences the cancer cells [In case of the Chromophobe RCC (ChRCC) subtype, before the adjustment of the metabolic landscapes two genetic rules were in fact separating this subtype from the other cancer samples: (i) the extracellular space and cytoplasm exchange reactions supported by the complex of er cells . We poinp-value: 0.002) difference in the survival time in one of the clusters , PRODH2 (4), PKLR (5) and SLC1A2 (94), see SLC1A2 gene  in order to describe a metabolic as well as genetic nature of this cluster. We determined a set of 106 differentiating reactions coordinated by four genes: Additionally, we performed the functional analysis of top 100 differentiating genes, using the DAVID on-line tool. The analysis provided a consistent output indicating functions and keywords commonly related to modifications in transport and symport reactions highlighting transmembrane transport activity. Finally, pathway analysis performed with KEGG implied deregulations in the glycan biosynthesis and metabolism pathway.SLC1A2 belonging to the organic-anion-transporting polypeptide (OATP) family, because it is responsible for transport of anticancer drugs  [OAT2 was reported to influence a poor response to antitumor UFT-based chemotherapy in colorectal cancer patients [PRODH2 was also reported in cancer [The above observations can be preliminarily verified with literature reports. In a metabolic sense, abnormalities in glycan biosynthesis that we observe were reported as a significant factor of cancer cells phenotype and biology almost two decades ago . It shoutherapy)  and overpatients . Loss ofn cancer , howeverConcluding, in our article we recall an important yet barely discussed data analysis and integration problem of the two-fold nature. Using the TCGA datasets and the metabolic network model we presented how the bioinformatical and statistical data analysis may lead to outwardly interesting biological and medical observations, which may be justified by the literature reports. However, not only do we prove that these observations are the inevitable consequence of the model assumptions , but also we show that these assumptions conceal the current state of knowledge about cell metabolism, as many discovery claims may testify.We believe that the identified problem is important enough to look at alternative approaches and verify if they are not exposed to the similar obstacle. However, as it was already mentioned, there is a strong evidence that the problem highlighted in this work may be present in other researches that dealt with the analysis of the FBA outcome. The reason, even though is very subtle, is related to the linearity of the solving method applied to the non-linear topology of the problem support. In particular, the existence of internally strongly connected sub-graphs that are loosely connected with their neighbouring vertices influences the graph flow approaches. The metabolic information cannot be flawlessly passed on between reactions and thus interferenced by particular information-flow blockers. Here, we briefly discuss several alternative approaches as an extension to the literature overview presented in the Introduction.Saccharomyces cerevisiae and show that their method outperforms the traditional approach upon maximisation of the rate of biomass production.To the scope of the traditional linear programming approach to solve the FBA problem we add what Lee et al. proposed in their work. The redefinition of the main objective function as well as an emphasis put on the more informative nature of absolute gene expression measurements provided by RNA-seq data was presented. Authors set an optimization problem to maximize the correlation between an observed transcriptomic profile and levels of reactions fluxes, that is solved using the MILP solver. Authors provide a case study based on estimation of exometabolic flux in Escherichia coli authors present how their model tracks changes in pathway responsiveness for variuos experimental conditions.Additionally, there is also a bunch of approaches that extends the approach to the FBA problems onto the field of Bayesian statistics. One example of an extension of the FBA via Bayesian factor modeling was suggested by Angione et al. . The extClostridium acetobutylicum from 13C data based on a small set of intracellular flux measurements.Lately, in  authors E. Coli case studies presenting the improved accuracy of predictions compared to the standard FBA.Interestingly, the problem of flux estimation was also approached from the perspective of the thermodynamic analysis. Zhu et al. introduce a novel, two-step optimization method termed as thermodynamic optimum searching . First, What should be emphasized here is that none of the recalled articles mentioned any observation on the relationship between the structure of the metabolic network and their final outcome. Additionally, an alarming fact is the use of methods such as the categorization of the transcriptomic signal, linear (or semi-linear) methods of solving the flow problem, binarization of data and their interpretation based by clustering. All of these observations allow the reader to ask if any control for artifacts was performed. In order to compare the methods, approaches, objectives of all the mentioned works in this article a table summary is provided in the To conclude, the future directions in which we would like to conduct this research include incorporating other metabolic networks for the landscape analysis, but also improved adjustment methods, that would define and take into account the confidence of the reaction. One idea in that direction, would be to incorporate some experimental validations from the literature reports. Ideally, we would like to reach the gold standard using measurements from the 13C-fluxomics technology, where metabolic precursors enriched with 13C and quantified by mass spectrometry or NMR ,51, as w


Saccharomyces cerevisiae is an attractive industrial microorganism for the production of foods and beverages as well as for various bulk and fine chemicals, such as biofuels or fragrances. Building blocks for these biosyntheses are intermediates of yeast central carbon metabolism (CCM), whose intracellular availability depends on balanced single reactions that form metabolic fluxes. Therefore, efficient product biosynthesis is influenced by the distribution of these fluxes. We recently demonstrated great variations in CCM fluxes between yeast strains of different origins. However, we have limited understanding of flux modulation and the genetic basis of flux variations. In this study, we investigated the potential of quantitative trait locus (QTL) mapping to elucidate genetic variations responsible for differences in metabolic flux distributions (fQTL). Intracellular metabolic fluxes were estimated by constraint-based modelling and used as quantitative phenotypes, and differences in fluxes were linked to genomic variations. Using this approach, we detected four fQTLs that influence metabolic pathways. The molecular dissection of these QTLs revealed two allelic gene variants, PDB1 and VID30, contributing to flux distribution. The elucidation of genetic determinants influencing metabolic fluxes, as reported here for the first time, creates new opportunities for the development of strains with optimized metabolite profiles for various applications.The yeast  Saccharomyces cerevisiae has been used for millennia for the production of various fermented foods and beverages1. In modern times, yeast has become popular in new applications, ranging from the biosynthesis of ethanol (for biofuels) and other raw materials  to the production of fine chemicals used as fuel additives, flavours and fragrances, or medical components2. Advances in metabolic engineering are constantly expanding the range of yeast’s applications.The yeast S. cerevisiae relevant to industrial processes are dependent on the functional and regulatory properties of central carbon metabolism (CCM)3. The yeast metabolic network that involves a large number of intracellular reactions is highly conserved and has evolved to be organized as a bowtie structure, meaning that all carbon sources are converted to 12 different precursor metabolites, which are then used by the cell for the biosynthesis of macromolecules that compose cellular biomass5. In addition, these precursors form the basis for the synthesis of extracellular metabolites, e.g., succinic acid, which is industrially produced by yeast fermentation as a building block for polymer production7. Furthermore, they serve as starting points for the heterologous production pathways of renewably produced fine chemicals9. The tight reduction of cellular carbon core metabolism to a small number of important metabolites results in a high carbon flux through these compounds10, controlled by a complex regulation on genetic  and metabolic levels. Therefore, understanding how metabolic flux distribution is controlled is a key requirement for increasing product biosynthesis by metabolic engineering11.Many phenotypic traits of 13. Commonly used constraint-based models (CBM) formulate metabolic networks as a stoichiometric matrix to predict intracellular fluxes through the application of experimental constraints on input and output fluxes. Depending on the network size and number of constraints, this approach, which is termed metabolic flux analysis (MFA), can be sufficient for estimating fluxes. However, in most cases, adding constraints on input and output data is not sufficient to estimate all fluxes of a network. One way to address this insufficiency is the 13C-MFA approach. It tracks 13C from labelled substrates across cellular metabolites with the aim of generating information to constrain and estimate intracellular fluxes. Another way is to apply the assumption that cellular functions of biochemical networks in a steady state are limited by physico-chemical constraints14. In this case, the flux balance analysis (FBA) approach chooses the best fitting solution through linear optimization out of a narrowed solution frame defined by the stoichiometric matrix of the CBM15. The outcome of this flux prediction depends on an applied objective function 16.While metabolite turnover rates are difficult to determine experimentally, they can be estimated by modelling13C-MFA/FBA approach to estimate the intracellular fluxes of S. cerevisiae CCM in conditions of modified intracellular redox balance18. Another example of the application of FBA is the study of Quirós et al. (2013), which used a model developed by Vargas et al. (2011) to evaluate changes in yeast metabolism in high sugar must20. In both studies, glycolytic fluxes showed the least variation, whereas the fluxes of the pentose phosphate pathway (PPP) were highly variable. 13C-MFA, on the other hand, was used to study network robustness21 or the effects of deletion mutants22. The latter study demonstrated interesting links between networks, e.g., a positive correlation between the PPP and biomass yield.To study how metabolic fluxes are modulated by genetic or environmental determinants, we previously used a combined S. cerevisiae by comparison of growth parameters in different media28. Furthermore, several studies extended the characterization of diversity to a greater number of phenotypic traits, including life history traits and metabolic traits, showing that origin has a broad phenotypic impact and that part of these phenotypic differences can be explained by adaptation to the ecological constraints imposed by origin31.In recent years, studies have generated vast amounts of information about the genotypic and phenotypic diversity of S. cerevisiae strains from different origins32, using the constraint-based model developed by Celton et al. (2012) to estimate CCM flux distributions between 43 strains grown under wine fermentation conditions18. The study showed a contrasting image regarding flux variability with quasi-constancy of glycolysis and ethanol synthesis on the one hand, but large variations in other fluxes, such as the PPP and acetaldehyde production, on the other hand. In addition, the fluxes’ multimodal distributions related to ecological origin revealed an association between genetic origin and flux phenotype32.Recently, we assessed the diversity of flux distributions between 38. Therefore, more knowledge about the impact of genomic variation on metabolic flux distributions can potentially assist with the selection or improvement of strains for diverse applications, in the food and beverage industries as in the field of biotechnology.Results from flux analysis have been used for strain improvement  by metabolic engineering based on flux predictionsS. cerevisiae and decipher the impact of genomic variation on yeast complex traits39. This application includes investigations of genetic determinants influencing the formation of industrially relevant traits, which have led to the discovery of allelic variants accounting for variations in these traits48. In all these studies, the assessed traits were straightforward to quantify. However, difficulties remain in detecting QTLs for traits with small variations or those that are more challenging to determine, such as intracellular metabolic fluxes.Quantitative trait locus (QTL) mapping, which has been applied in numerous existing studies, has become an important approach to more deeply understand the genomic complexity of S. cerevisiae yeast strains for their production of extracellular main metabolites during the exponential phase. We modelled the intracellular fluxes of yeast CCM by applying these experimentally determined metabolite concentrations to a constraint-based model18. Subsequently, we used these estimated fluxes as phenotypic data to perform QTL mapping on metabolic flux distributions. With this approach, we were able to detect four fQTLs that influenced various metabolic fluxes. By performing reciprocal hemizygosity analysis (RHA), we confirmed the robustness of the method by validating the role of two genes, PDB1 and VID30, within two fQTLs. The allelic variants of these genes show different effects on the fluxes of glycolysis, ethanol synthesis, glycerol synthesis, the tricarboxylic acid (TCA) cycle and the excretion of TCA cycle metabolites.The possibility of using QTL mapping to decipher genomic variations impacting metabolic profiles (rather than those affecting single metabolites) would create ways to understand the mechanisms behind metabolic flux distributions and to engineer strains with superior metabolic properties for various applications. To achieve this aim, we phenotyped 130 meiotic F2-segregants from a cross of two et al. 200749 in order to produce a correlation matrix . Because of the structure of the network, some fluxes are directly correlated. To assess for flux correlations, we analysed the relationships between steady-state reaction fluxes as described by Poolman For the production of extracellular main metabolites, the corresponding estimated excretion fluxes were chosen as representative. For main metabolic pathways, either the first flux  or the last flux of the pathway (lower glycolysis) were chosen. In the case of parallel fluxes leading to the same metabolite , both fluxes were chosen for evaluation.Principal component analysis (PCA) of selected flux reactions was performed to assess flux correlations and to evaluate the variation between parent and segregant strains Fig. .Figure 1With the first two dimensions explaining 69.1% of trait variation, the PCA of estimated fluxes adequately depicts the variation among strains. The parent strains behave similarly and show only minor differences in their flux profiles, whereas the segregant strains are more divergent. This finding is confirmed by the visualization of trait distributions . This finding confirms the conclusion drawn by PCA Fig. , which iα-ketoglutarate, pyruvate and ethanol excretion. The distributions of two fluxes differ from a normal distribution, namely, the synthesis of acetyl-CoA from acetate (Ac_Accoa) and the NADP-dependent mitochondrial flux from isocitrate to AKG (Icit_Akg_m_nadp) of the TCA cycle oxidative branch. In the case of Icit_Akg_m_nadp, the analyses indicate that this flux is inactive in the majority of segregant strains and both parents .Many 50.To this end, we used a population of 130 F2-segregants obtained from a cross of two wine yeast strains. For these strains, intracellular carbon fluxes were estimated by applying concentrations of extracellular metabolites during the exponential growth phase to a constraint-based stoichiometric model of yeast CCM. Analysis of flux deviations within the population of segregants indicated a positive correlation between the fluxes of the PPP, the TCA cycle oxidative branch and biomass formation, whereas these fluxes were negatively correlated to the fluxes of upper glycolysis. Negative correlations were furthermore found between the fluxes of lower glycolysis/ethanol synthesis and glycerol formation  was detected by RHA for the alleles of PDB1.Assessment of the two detected fQTLs with the highest LOD scores by RHA revealed the implication of is Table , Fig. 5,VID30, possibly influences the fluxes of glycolysis/ethanol synthesis and has an impact on the TCA cycle reductive branch and the excretion of TCA cycle metabolites  of glutamate and glutamine from AKG and ammonia, therefore explaining the role of Vid30 in central carbon metabolism since AKG is part of the fluxes of the TCA cycle oxidative branch. Gdh1, Gdh3 and Gln1 catalyse reactions from AKG to glutamine56, whereas Gdh2 catalyses the conversion of glutamate to AKG57. Glt1 synthesizes glutamate from either AKG or glutamine58. In low ammonia environments, Vid30 behaves as a positive regulator of GDH1, GDH3 and GLT1, which increases the flux from AKG to glutamate59. Since decreased AKG production was detected for the MTF2621 allele of Vid30  protein complex that performs the ubiquitination of enzymes, which leads to their proteasome dependent inactivationVID30 differ in three non-synonymous SNPs  in PDB1 and VID30. The allelic variants of PDB1 account for variation in the glycerol synthesis flux, which we hypothesize to be caused by redox imbalances as a result of altered pyruvate conversion. The variants of VID30 impact the fluxes of glycolysis, ethanol synthesis and the TCA cycle, which we propose to be caused by differences in the regulation of enzymes catalysing glutamate formation or differences in the catabolite-induced degradation of enzymes involved in sugar uptake, gluconeogenesis and the TCA cycle. Compared to strains of other origins, the parental variants of the evaluated target genes are comparatively close. The characterization of more distant variants and an evaluation of their influence on intracellular flux distributions will disclose genetic resources that bear further potential to shape the metabolic profile of strains.The solidity of our approach was further confirmed by the validation of two target genes within the identified fQTLs, In summary, our findings of fQTLs and allelic variants impacting metabolic fluxes increase our knowledge of the links between genomic variation and yeast metabolic properties and provide a proof-of-concept for the applicability of QTL mapping on modelled metabolic fluxes. This result offers exciting opportunities for uncovering superior allelic variants impacting these traits, which could be used to improve strains for manifold purposes, e.g., the production of biofuels or other bulk and fine chemicals.Yeast strains were cultured at 28 °C in yeast extract peptone dextrose (YPD) media, containing 10 g/L yeast extract, 20 g/L peptone and 20 g/L glucose. Solid YPD media contained 1.5% agar. Selective YPD media contained 200 µg/mL geneticin (G418), 200 µg/mL nourseothricin (clonNAT) or 200 µg/mL hygromycin B.et al. 199074. The medium contains glucose and fructose at a concentration of 100 g/L each, and assimilable nitrogen in the form of ammonium and free amino acids at a concentration of 200 mg/L.Fermentations were carried out in synthetic grape must (SM200) as described by Bely S. cerevisiae during alcoholic fermentation46. The parent strains of this segregant population are strain MTF2621 (haploid spore of 4CAR1 [ΔHO::Neor]) and MTF2622 (haploid spore of T73 [ΔHO::Natr]). Strain T73 belongs to the phylogenetic clade of wine strains, whereas strain 4CAR1 belongs to the group of champagne strains, which originated through crossings between strains of the wine clade and the flor clade75.This study used a set of 130 F2-segregants generated in a former study to map QTLs for main and volatile metabolite production by 46. Parent and segregant strains were genotyped by whole genome sequencing using Illumina technology, and a global set of 18155 biallelic variant positions was obtained. This set was reduced to a dataset of 3727 SNP markers with a minimum spacing of 2.0 kb between SNPs. Four segregant strains with the most ambiguous markers were excluded to increase the meaningfulness of the analysis. One strain was excluded because of its close genomic proximity to another segregant. This exclusion left a population of 125 F2-segregants for statistical analyses. Information available in the Saccharomyces Genome Database (https://www.yeastgenome.org) was used to associate SNPs with annotated protein domains. The effect of detected SNPs on putative transcription factor binding sites was analysed using YEASTRACT (release 2017)76.The segregant strain marker map used for linkage analysis in this study was generated during a previous study in our working group6 cells/mL, weighed and left at 24 °C under stirring (300 rpm). Fermentations were sampled during the exponential phase (when 10 g/L of CO2 was produced), which was determined by weighting the fermenters regularly. For each fermentation, dry biomass was determined in duplicate by filtering 10 mL of cell suspension through a nitrocellulose membrane with a porosity of 0.45 μm  and known dry weight. The membrane was rinsed twice with 10 mL of distilled water, dried for 48 h and weighed to determine the dry biomass of the sample. The concentrations of unfermented sugars (glucose and fructose) and extracellular carbon metabolites  in the fermentation medium were determined by high-performance liquid chromatography (HPLC). The flow rate of the device  was set to 0.6 mL/min (0.005 N H2SO4). Samples were separated by a pre-column and an ion-exclusion column (Phenomenex REZEX™ ROA-Organic Acid H +(8%)), which was thermostatically controlled at 60 °C. Compounds were detected using a refractometer in combination with a UV spectrometer at 210 nm. Chromatograms were processed with Agilent EZChrom software.Segregant strains were phenotyped in duplicate with the parent strains as a control. Sterilized 300-mL glassware mini fermenters were filled with 280 mL of SM200 (synthetic must with 200 mg/L of assimilable nitrogen) and closed with an air lock. The fermenters were inoculated with overnight yeast cultures to a cell density of 1 × 1018. This model has been chosen for several reasons. First, it had been designed specifically to predict fluxes in oenological fermentation. It covers S. cerevisie central carbon metabolism with the specification of anaerobic metabolism, e.g., TCA fluxes are composed of an oxidative and reductive branch and therefore do not form a cycle. Second, the biomass reaction function of the model had been calibrated with wine yeast strain EC1118, which is phylogenetically closer to the parent strains used in this study than the widely used laboratory reference strain18. Third, it had been used and validated in two previous projects in very similar conditions32. Last, the choice of a comparatively small model  is consistent with other studies in literature that use large scale models to examine qualitative phenotypes13, whereas small-scale models are used for quantitative modelling77.To predict intracellular metabolic fluxes, we used DynamoYeast, a previously developed model of yeast central carbon metabolism78), that derive mass data to obtain flux distributions . For computing mass distribution, we follow the assumption that all fluxes - along with the biomass composition - are constant during the exponential phase. The estimation of mass distribution allows for the expression of fluxes as yields instead of reaction rates.The DynamoYeast model covers 68 reactions  with the R/qtl v1.40–8 and R/eqtl v1.1–7 libraries80. Two different phenotype models were tested, the normal model using Haley-Knott regression and non-parametric analysis. The statistical analyses resulted in logarithm of odds (LOD) scores for each marker and pseudo-marker every 2.5 cM. Individual LOD score thresholds for a false discovery rate of 0.05 were determined with 1000 permutations. If the same locus was detected with both phenotype models, the results with the higher LOD score were selected. An interval estimate of the location of each QTL was obtained as the region in which the LOD score is within 1 unit of the peak LOD score . QTL mapping results for single metabolic reactions were considered to be a common fQTL if their peaks were less than 10 cM apart.QTL mapping of modelled metabolic fluxes was carried out by single QTL mapping  using the genotype and phenotype information of segregant strains. Linkage analysis was performed using the programming language R v3.2.3 . The disruption cassettes were amplified by polymerase chain reaction (PCR) of the plasmid pAG32 with the primers del_(GENE)_fw and del_(GENE)_rv (Supplementary Table Molecular dissection of the detected QTLs was performed by RHASupplementary Information.


The nature and magnitude of the earliest transitions, hundreds of million years before photosynthesis evolved, remain poorly understood. Using a novel ecosystem-planetary model, we find that pre-photosynthetic methane-cycling microbial ecosystems are much less productive than previously thought. In spite of their low productivity, the evolution of methanogenic metabolisms strongly modifies the atmospheric composition, leading to a warmer but less resilient climate. As the abiotic carbon cycle responds, further metabolic evolution (anaerobic methanotrophy) may feed back to the atmosphere and destabilize the climate, triggering a transient global glaciation. Although early metabolic evolution may cause strong climatic instability, a low CO:CH Biology can profoundly influence the planet’s climate, but over Earth’s long history these effects are poorly constrained. Here the authors show that on early Earth, the evolution of microbes producing and consuming methane likely controlled warming and glacial events, and thus Earth’s habitability Astrophysical and geophysical data concur in showing that the planet was habitable 400 My earlier at the very least, and possibly as early as ~4.5 Gya, depending on the occurrence, magnitude, and effect of large asteroid impacts during the Hadean3. Early on, Earth’s carbon cycle likely established and maintained temperate climatic conditions5 in spite of a Sun being 20–25% dimmer than it is today6. The earliest microbial ecosystems evolving under these conditions, hundreds of million years before the first anoxygenic phototrophs7 became actors of the Archean climate8, most likely involved chemolithotrophs  producing methane as a metabolic waste. Phylogenetic analyses10 combined with isotopic evidence11 and the then-time predominance13 of the electron donors H2 and CO lend weight to a very early origin of H2-based methanogens (MG), CO-based autotrophic acetogens (AG), and methanogenic acetotrophs (AT). As CH4 built up in the atmosphere, the evolution of anaerobic methanotrophy (MT) may have been favored. Contrary to the modern biosphere, the biomass productivity of such ecosystems was likely low and energy limited, i.e., limited by the availability of electron donors rather than nutrients such as nitrogen, phosphorus, or iron14. Whether and how the evolution of a primitive biosphere formed by these metabolisms influenced the planetary environment globally is unclear.By 3.5 Ga, life had emerged on Earth13 have addressed the productivity of primitive, chemolithotrophic ecosystems and their influence on the young Earth’s equilibrium atmospheric conditions. Such studies relied on equilibrium analyses of the planetary ecosystem; they made strongly simplifying assumptions on the function of chemolithotrophic microbial metabolisms, and did not close the feedback loop linking biological activity, atmospheric composition, and climate. Although these studies showed that primitive biospheres may have had a significant impact on the planet’s early atmosphere and climate, their ability to quantify this impact and estimate the underlying biomass productivity was limited. Furthermore, earlier theory based on equilibrium analyses could not address the coupled dynamics of metabolic evolution and planetary surface conditions, whereby evolutionary changes might trigger significant atmospheric and climatic events and lead to novel steady states. Thus, advancing existing models is needed to generate hypotheses on the history of atmospheric and climatic conditions that metabolic evolutionary innovation may have driven on the early Earth.Previous studies15 and a 1D photochemical model16, combined with a simple temperature-dependent carbon cycle model5. Advancing the previous studies13, this planetary model is coupled dynamically to a biological model of cell population dynamics and evolutionary adaptation, constructed by scaling the intracellular processes of energy acquisition , cell maintenance, and biomass production  up to ecosystem function18. The biological model is grounded in thermodynamics and based on observations of cell size and temperature kinetic dependencies, widely and robustly shared among modern unicellular organisms23. Quantitative validation of similar models was obtained from laboratory experiments on anoxic ecosystems in bioreactors18.Here we ask, how constrained was the habitability of late Hadean/early Archean Earth to methane-cycling ecosystems? How productive were these ecosystems and did they have a significant impact on the atmospheric composition and climate? How did their impact change as different metabolisms evolved? To answer these questions, we lay out a new probabilistic modeling framework for an evolving microbial community coupled to early Earth surface geochemistry and climate , ranging from 5 × 109 to 2 × 1011 molecules cm−2 s−1. By performing a Monte-Carlo exploration of the space of biological parameters, we generate posterior distributions of possible life-atmosphere-climate outcomes  on an initially cool, lifeless Earth. The initial abiotic surface temperature 6 years, of the transition from the initially cool, lifeless state to a planet populated by one of three methanogenic biospheres, MG, AG + AT, or MG + AG + AT. On such a short timescale, the carbon cycle has a negligible influence on the atmospheric CO2. H2 is both a metabolic substrate of MG and involved in the photochemical production of CO, the metabolic substrate of AG . Temperature influences both cell kinetics (metabolisms are slower at lower temperature) and thermodynamics . TGeo also correlates positively with pCO2 and pCO, which are both substrates of methanogenesis. We evaluate the influence of TGeo by examining bio-geo environmental feedbacks  for TGeo ranging from −18 to 57 °C, which corresponds to pCO2 ranging from 5 × 10-4 to 1 bar  has been proposed as a general mechanism of climate regulation25. In the late Archean, pCO2 was low5, favoring organic haze formation that may have prevented hot runaway scenarios. In the Hadean/early Archean, however, our model predicts organic hazes to form in a limited range of conditions, at very high H2 volcanic outgassing rates, low pCO2, and low abiotic temperature close to or below the freezing point  to methanogenesis, and not organic hazes, that is expected to limit Archean climate warming by biological activity.The formation of organic hazes when the int Fig. . Under t2 outgassing, our model predicts biomass production to range from 106 to 109 molecule C cm−2 s−1. This is 1–4 orders of magnitude below previous estimates13 (based on models that assumed a fixed biomass yield per electron donor consumed) and 4–7 orders of magnitude below modern values26.In spite of their strong impact on the planet’s atmosphere and climate, primitive methanogenic ecosystems are characterized by extremely low biomass productivity, AG + AT being the most productive pathway by far Fig. . As micrTGeo. Supplementary Fig. TGeo between −10 and 10 °C depending on the intensity of H2 volcanic outgassing (slightly above 109 molecules C cm−2 s−1 at high rates of H2 volcanic outgassing), and strongly decreases for higher TGeo. The maximum biomass production is of the same order in the AG + AT ecosystem and reached for similar conditions , but its dependence upon TGeo and the rate of H2 volcanic outgassing is much weaker. In the MG + AG + AT ecosystem the two methanogenic pathways interact synergistically, leading to a nonlinear, multiplicative increase in biomass production at low and high temperature. The synergy involves the combination of biogeochemical recycling loops, both locally and globally. Locally, while MG consumes CO2 to produce CH4, AT decomposes CH3COOH and produces CO2. The metabolic waste of AT is, therefore, the metabolic substrate of MG, and the combination of the two metabolic pathways pulls the system further away from its thermodynamic equilibrium, hence an increase in the efficiency of both pathways. Globally, as the MG metabolism releases additional CH4 in the atmosphere, the production of CO through photochemistry is accelerated; CO being the metabolic substrate of AG, its metabolic efficiency is enhanced. The synergistic effect is greatest at low abiotic temperature. In those very specific conditions and for the highest values of H2 outgassing, biomass production can reach about 1010 molecules C cm−2 s−1—about 1000 times less than estimates of modern primary production26.Albeit extremely low, biomass production is very sensitive to the metabolic composition of the ecosystem and temperature, 27. Our evolutionary sequences culminate with the evolution of anaerobic methanotrophy, which uses the oxidation of CH4 as primary source of energy, based on the consumption of H2SO4, the main oxidative species of the globally reduced early Earth28. We, therefore, make the plausible assumption that a full methane-cycling biosphere may have evolved before the advent of photosynthesis.Next, we investigate how the evolutionary process of metabolic diversification of the primitive biosphere shaped the planet atmospheric and climatic history. We consider alternate scenarios of biosphere evolutionary complexification Fig.  consiste5 that on the timescale of 107 to 108 years, the carbon cycle tends to mitigate temperature variations through a negative feedback on the atmospheric CO2 concentration. We adapted the model from ref. 5 to add a full, temperature-dependent carbon cycle to our planetary ecosystem model. The abiotic equilibrium pCO2 is now determined by the balance between CO2 outgassing and sequestration in the oceanic floor; pCH4, by the balance between serpentinization and photodissociation rates; and pH2, by the balance between outgassing and photochemical reactions. Additionally, the H2SO4 oceanic concentration is determined by the balance between rainout and hydrothermal remineralization rates (taken from ref. 29). By drawing values for the abiotic supplies in CO2, CH4, H2, and H2SO4 from reasonable, log-uniform priors , thus unfolding on a timescale over which the geochemical cycles interacted dynamically and reciprocally with biological activity. In particular, it has been shownrs Table  and meanrs Table .Table 1A4 atmospheric ratio of 102–104 to 1, which differs markedly from the ratio predicted with a functional biosphere, regardless of its metabolic composition. As the biosphere complexifies, biological activity increases the atmospheric concentration in CH4, decreases the atmospheric CO, or both, causing the CO:CH4 ratio to fall. By comparing median values between the abiotic state and the most complex biosphere (MG + AG + AT + MT), the CO:CH4 ratio is predicted to be reduced by a factor of ~5000. The earliest evolutionary events, whereby the MG, AG, or AG + AT ecosystem emerges, all cause atmospheric shifts that can be distinguished in the pCO-pH2-pCH4 space , and evolution time of methanotrophy (from 0 to 125 million years after methanogenesis), 50% experience a temperature drop larger than 8.3 °C below TGeo  are also the conditions under which the evolution of methanotrophy has the most dramatic effect on climate and habitability  to be maintained only transitorily. We assume that during the early Archean, prior to the evolution of methanotrophy, the oceanic stock of H2SO4 builds up as volcanoes emit SO2 photochemically transformed into H2SO4, which then deposits on the ocean surface. With a deposition rate corresponding to our lowest value of 107 molecules cm−2 s−1 (ref. 30), and a hydrothermal removal rate of [H2SO4]oc. × 7.2 × 1012 L y−1 (ref. 29), we obtain an abiotic oceanic concentration of 0.4 mM. Such a stock is sufficient for methanotrophs to consume most of the atmospheric CH4, leading to the global cooling described above. Since the rate of CH4 reduction by methanotrophs is faster than the deposition rate, the H2SO4 stock will ultimately be depleted by methanotrophs, thereby driving a new abrupt environmental shift towards the equilibrium described in Fig. pCH4 is high and the H2SO4 oceanic concentration is very low, below the micromolar, which aligns with the sulfur isotopic fractionation records32. This result suggests that anaerobic methanotrophy may have been the main sink of H2SO4 prior to the Neoarchean (2.5–2.7 Gya).For a methane-cycling ecosystem that triggers and eventually survives global glaciation, we expect the resulting equilibrium , acetogenesis (AG), methanogenic acetotrophy (AT), and anaerobic oxidation of methane (MT). The biosphere evolves when a metabolism that was not present appears and adapts. By closing the global feedback loop between biological and planetary surface processes, the model predicts both ecosystem and atmosphere-climate states under the assumption that they reciprocally influence one another.Our initial questions were: How constrained was the habitability of late Hadean/early Archean Earth to methane-cycling ecosystems? How productive were these ecosystems and did they have a significant impact on the atmospheric composition and climate? How did their impact change as different metabolisms evolved? To address these questions, we build on the previous theory2 supply, these biospheres were key factors of the climatic and atmospheric evolution of the planet34. On short time scales (105–106 years) the evolution of methanogenic biospheres may have considerably warmed the climate and influenced its resilience, in spite of a very low ecosystem productivity. On longer timescales, commensurate with the abiotic response of the carbon cycle to temperature variation, all ecosystems converge to new stable equilibria. Under these long-term equilibrium conditions, the mean surface temperature does not differ much from the lifeless state, and the carbon cycle is the predominant mechanism of climate regulation. However, all ecosystem equilibria share a robust atmospheric signature (low CO:CH4 ratio), distinctive from the lifeless state. In addition to influencing planetary characteristics at equilibrium, metabolic evolution generates atmospheric and climatic transients. The pace of evolution thus has a strong influence on the atmosphere and climate history. In particular, fast evolution of methanotrophs has limited or no effect on climate, whereas their delayed evolution may cause strong transients leading to global glaciation.Our results confirm the contention that the late Hadean/early Archean planet was most likely habitable to methane-cycling chemolithotrophic biospheres and that under the assumption of high enough H34, our model is the first to couple models of the Archean atmosphere, climate, and carbon cycle to an explicit eco-evolutionary model of cell population dynamics in order to quantify this effect. Our model differs from previous work primarily by addressing how climate change driven by ecological function feeds back to the biological activity of microbial populations, through the thermal dependence of the thermodynamics and kinetics of cell metabolism, and triggers an abiotic response of the carbon cycle. Closing the global feedback loop between ecological and planetary processes allows us to predict the ecosystem and climate states under the assumption that they reciprocally influence one another on multiple timescales.Although the influence of chemotrophic methane-cycling ecosystems on climate has been discussed in the previous work13 and 3–7 orders of magnitude below modern values26. Maximum global biomass production (reached for a very specific combination of biotic and abiotic conditions) is 1010 molecules C cm−2 s−1, or 3 × 107 ton C year−1. This is ~1000 times less than estimates of modern primary production. Both in terms of stock and fluxes, biomass has therefore very little effect on the biogeological coupling, which is a major difference with modern ecosystems.A general result is that MG and AG + AT ecosystems are characterized by extremely low biomass production relative to their planetary impact on the atmosphere and climate. We predict biomass production to be 1–4 orders of magnitude smaller than previous estimates14, primitive ecosystems with such low productivity should have been limited by the availability in electron donors rather than by N and P nutrients. More specifically, ref. 14 evaluated the most likely limitation to biomass production prior to the evolution of oxygenic photosynthesis. By assuming a fixed yield for biomass production per molecules of electron donor consumed (as in ref. 13), they found that the N and P supplies were most likely sufficient during the Archean for electron donors to be limiting. This is even more likely given our finding of a biomass productivity far lower than the previous estimates14.A key assumption of our model is that nutrients N and P are not limiting; this assumption is backed up by the prediction of very low biomass production. As previously argued35. This suggests that more productive, likely photosynthetic life forms must have evolved more than 3.5 Gya ago.Our prediction of very low biomass productivity may help better constrain the timeline of the evolution of metabolic innovation on Earth. Even in our most productive scenario and under the (extreme) assumption that 100% of the dead organic matter is buried before remineralization, the estimated biomass production is still 4–5 times lower than the level consistent with the C isotopic fractionation estimated from rocks as old as 3.5 Gy7–108 years), on which the carbon cycle responds to ecosystem function and sequential metabolic diversification of the biosphere, we find that the climate regulation of the planet by the abiotic carbon cycle largely buffers the influence of early methanogenic activity on climate. However, depending on the pace of evolution, metabolic transitions such as the evolution of methanotrophy can interact with the abiotic carbon cycle and trigger strong transitory climatic events such as global glaciation over 107–108 years.By performing equilibrium analyses of the planetary system on longer time-scales , by reduction of nitrate, nitrite, or sulfate, is not well resolved, the fact that AOM proceeds enzymatically as a reversal of methanogenesis has been used to suggest that AOM may have evolved relatively soon after methanogens, i.e. before phototrophy2, H2SO4, NO3, NO2) of methanotrophs, or by simply driving the abiotic oxidation of atmospheric CH4 by outgassed O2, oxygenic photosynthesis may have driven a dramatic decline in atmospheric CH4 which, coupled to a delayed response of the carbon cycle, could have caused global cooling, triggering the Proterozoic glaciation ~2.3 Gya41.The mechanism by which the evolution of early methane-cycling ecosystems may have exposed the Earth to high risks of global glaciation—fast removal of atmospheric methane, slow response of the carbon cycle—is general. It is, therefore, tantalizing to speculate about its potential involvement in the major climatic events that paralleled the evolution of oxygenic photosynthesis. By poisoning methanogens, removing the resource limitation  measures the cost of maintenance for a single cell per unit of time. The ∆G terms measure the oxidative power released or consumed by the catabolic and anabolic reactions; they are given by the Nernst relationship:R is the ideal gas constant, T is the temperature (in K) and ∆G0(T) (in kJ) is the Gibbs energy of the reaction. ∆G0(T) is obtained from the Gibbs–Helmholtz relationship:TS is the standard temperature of 298.15 K. Note that equations (E2) and (E3) describes how the thermodynamics of any given metabolism  vary with temperature.The biological model describes the dynamics of one or several biological populations of chemotrophic organisms, driven by the growth, birth (division) and death of individual cells. The individual cell life cycle is controlled by catabolic and anabolic reactions occurring within cellsKS is the half-saturation constant, qmax the maximum metabolic rate of the cell (d−1), and Michaelis–Menten kinetics apply to catabolism:qcat > qm. If the cell does not meet this requirement, the basal mortality rate of the cell, m, (in d−1), is augmented by a decay-related mortality term equal to qcat < qm) no energy is allocated to biomass production. Conversely, when those requirements are met (qcat < qm), then the energy remaining after allocation to cell maintenance can be directed to anabolism. A constant quantity of energy17 we consider the following empirical relationship:NoC is the number of carbons in the carbon source used by the anabolic reaction, and The cell therefore meets its energy requirement only if KS independent of the substrate, as was assumed in ref. 43. As biomass accumulates in the cell at rate qana, cell growth may lead to cell division. The cell division rate, r, is given by:The Michaelis–Menten kinetics of anabolism is given byBStruct, r is of sigmoidal form so that the division rate first increases exponentially with the intracellular biomass content then saturates to rmax when biomass is largely available.This means that a cell cannot divide if its internal biomass is not at least twice its cell structural biomass so that the two daughter cells meet this structural requirement. Above this threshold value of 2MT denotes the ensemble of metabolic types considered, Ni is the number of individual cells in a population of a given metabolic type, Bi is the average cellular biomass of that type, and i in the microbial community. The F(Xj) terms describe the environmental forcing resulting from ocean circulation and atmosphere-ocean exchanges as simulated by a stagnant boundary layer model as in ref. 13. The system of equations (E11) is solved numerically using a forward Euler method.Using the rates of catabolic and anabolic cell activity and resulting cell division and mortality rates, we derive the following system of ordinary differential equations driving the cell population dynamics (dynamics of the number of cells and average cell biomass) and the feedback of the population on its environment :qmax (in d−1), and the rate of energy consumption for maintenance, Em (in kJ d−1). They are both expressed as functions of temperature and cell size of the form ea+bTVc. Default values for parameters a, b and c entering qmax and Em are given in Supplementary Table The flow of energy through the cell is driven by the maximum metabolic rate, BStruct increases with cell size according to BStruct  = aVb. Both metabolic rate and maintenance cost increase with cell size, but not as fast as structural biomass. Consequently, the biomass specific rates of metabolism and energy consumption for maintenance decrease with cell size. Thus, small organisms are better at acquiring energy, but large organisms are more cost efficient due to lower maintenance requirements. A trade-off mediated by cell size thus exists between metabolic and maintenance rates, hence an optimal (intermediate) cell size, which is consistent with previous work conducted on unicellular marine organisms22. Both metabolic and maintenance rates increase with temperature, but the metabolic rate increases slightly faster20, shifting the trade-off toward larger sizes. As a consequence, the optimal cell size increases with temperature.The structural cell biomass 46, populations that are better at exploiting their environment have lower Q* and evolution by natural selection will favor the cell size that minimizes Q*, thus leading to the evolutionarily optimal cell size. Supplementary Fig. Q* as a function of cell size and temperature for the H2-based methanogenesis.To compute the evolutionarily optimal cell size for a given metabolic type at a given temperature, we run simulations across a range of cell size and measure the level of resource use at equilibrium given by SC*, follows a positive relationship with temperature given by SC* = 10a+bT. We consider that over geological time scales, adaptive evolution acting on genetic variation among cells is fast enough so that SC is equal to SC*. As temperature changes, evolutionary adaptation by natural selection tracks the temperature-dependent optimal cell size.The optimal cell size, 2 volcanic outgassing. Although biomass production is sufficient to sustain a population of fermentors, we found no significant effect of their biological activity on the atmospheric composition. Additionally, when we can compare the global atmospheric redox budget of the planet with and without biomass production , and the rest is buried into the ocean floor. Although the most general version of our model includes populations of acetogenic biomass fermentors and acetotrophs, their inclusion considerably increases simulation time (results not shown). All the results presented in the main text have been obtained without fermentors, assuming that the dead biomass accumulates in the ocean’s interior. It appears that biomass productivity is so low that the effect of biomass recycling (or lack thereof) on the atmospheric composition is negligible. We verified this by testing the effect of a recycling pathway in each ecosystem , for an intermediate value of H13) as lower bound.Because we do not model the fate of dead biomass explicitly, we evaluate the consistency of our predictions of biomass production with the geological record (carbon isotopic fractionation data) by taking biomass production as the upper bound for burial (in which case 100% of the produced biomass is ultimately buried) and using the modern value of burial :Our computation of climate state and mean surface temperature is based on 3D simulations with the Generic LMD GCMIn the coupled biological-planetary model, we assume that the climate is always at equilibrium, meaning that the timescale of climate convergence is shorter than biological and geochemical timescales.50. The chemical network includes 30 species (mostly hydrocarbons) and 114 reactions. We use the reactions from ref. 50 for H2-H2O-CO2 and from ref. 51 for hydrocarbons. The photochemistry model also includes a pathway for the formation of hydrocarbon aerosol (C2H + C2H2 → HCAER + H)52.Photochemistry is computed with the 1D version of the Generic LMD GCM, which now includes a photochemistry core from ref. 53 and the solar UV spectrum at 3.8 Ga from ref. 54. Boundary conditions are set by the mixing ratios of CO2, CH4, and H2O at the first atmospheric layer. The atmospheric CO is either consumed biotically by acetogens when they are present in the biosphere, or abiotically by the formation of formate in the ocean (resulting in a deposition velocity of ~108 cm s−1) as in ref. 55. This eliminates the possibility of CO-runaway, which otherwise occurs in our simulations at high level of H2 or CO2. In addition, we assume that H2 undergoes diffusion-limited atmospheric escape (see for instance ref. 13).We use the eddy diffusion vertical profile from ref. pCO2 (104–105 ppm), pCH4 (1–104 ppm), H2 (100–104 ppm). For our range of atmospheric composition, photochemistry is dominated by the photolysis of CO2 and CH4, whose net reactions are:We performed simulations across a range of 4, CO, H2 :u (between 0.5 and 1) is given byFrom the simulation outputs we derived simple parametrizations for the production rates and loss rates of CHpCO2 with time and feedbacks between the microbial community, climate, and the carbon cycle, we use a simple carbon cycle model based on ref. 5. The model computes the evolution of atmospheric CO2, dissolved inorganic carbon  and pH in the ocean and in seafloor pores. The model takes into account outgassing (from volcanoes and mid-oceanic ridges), continental silicate weathering, dissolution of basalts in the seafloor, and oceanic chemistry, as sources and sinks of CO2. For all parameters in the carbon cycle model, we use the mean values given in ref. 5. For the temperature dependences of silicate weathering and oceanic chemistry, we use the mean surface temperature from our climate model. Even though this model only computes global mean quantities and does not take into account the organic matter in carbon sources and sinks of carbon, it is computationally very fast and well suited for studying the major feedbacks between biological populations and the atmosphere and climate.To simulate the evolution of 3–106 years) compared to biological dynamics (days to years). We use a timescale separation approach and assume that the planetary environment is fixed on the biological time scale, and the biological system  is at ecological and evolutionary equilibrium on the geological time scale. This allows us to resolve the biological and geological dynamics separately and couple them at discrete points in time.The model resolves dynamics that take place on extremely different timescales—geological processes are extremely slow , with m9.5, 2 × 1010, 2 × 1010.5, and 2  × 1011 molecules cm−2 s−1, hence a total of 20,000 simulations. Each simulation was run to equilibrium.We explore the parameter range by stochastically varying the parameters that determine the maximum metabolic rate, the rate of energy consumption for maintenance, the metabolic half-saturation constant, the decay and mortality rates, and the maximum division rate. We also vary the values of the parameters that shape the dependencies on size, temperature, and cellular biomass (see Supplementary Table 4 biogenic emission of the subsamples. The results are shown in Supplementary Fig. 10.5 molecules cm−2 s−1.The results can be divided into two subsets of roughly equivalent size, regardless of the ecosystems and volcanic activity considered: those with biological activity and those without (Supplementary Fig. Supplementary InformationPeer Review File


Biomass has incredible potential as an alternative to fossil fuels for energy production that is sustainable for the future of humanity. Hydrogen evolution from photocatalytic biomass conversion not only produces valuable carbon-free energy in the form of molecular hydrogen but also provides an avenue of production for industrially relevant biomass products. This photocatalytic conversion can be realized with efficient, sustainable reaction materials (biomass) and inexhaustible sunlight as the only energy inputs. Reported herein is a general strategy and mechanism for photocatalytic hydrogen evolution from biomass and biomass-derived substrates . Recent advancements in the synthesis and fundamental physical/mechanistic studies of novel photocatalysts for hydrogen evolution from biomass conversion are summarized. Also summarized are recent advancements in hydrogen evolution efficiency regarding biomass and biomass-derived substrates. Special emphasis is given to methods that utilize unprocessed biomass as a substrate or synthetic photocatalyst material, as the development of such will incur greater benefits towards a sustainable route for the evolution of hydrogen and production of chemical feedstocks. Moreover, hydrogen is also attractive as an alternative energy source because of its high energy density , which is greater than that of most fuels  at room temperature . I. I2 semix/TiO2 NP loaded with a graphite carbon layer was reported to produce 270 μmol h−1 g−1cat of hydrogen at room temperature, although this rate increased to 4150 μmol h−1 g−1cat when the reaction temperature was raised to 80 °C, purportedly due to the increased solubility of carbohydrate byproducts [x with a graphitic overlayer was required to improve biomass photoreforming activity. Proton reduction occurs between the carbon layer and NiOx/reduced Ni sites  to solve this fundamental issue of high charge recombination [3+ can be easily reduced to Au0 by the photogenerated electrons from photoexcitation of Au/CdS compared to the reduction of H+ to H2 [2 evolution from oxidized glucose under visible light illumination. This work inspired a pathway to construct heterojunction photocatalysts for the H2 evolution from biomass.Wang and co-workers reported Aubination . 1 D CdSbination . As showH+ to H2 , 47. The2 yield from biomass because this process competes with proton reduction to H2. General strategies employed to avoid recombination include the use of sacrificial electron donors; however, these strategies can reduce the oxidizing capability of the excited holes. Instead of utilizing sacrificial agents as hole scavengers, CdS nanosheets loaded with nickel co-catalyst (CdS/Ni) can suppress charge recombination and drive organic transformation resulting in H2 production and the formation of value-added chemicals from biomass conversion [2 under a photocatalytic Ni/CdS system in aquatic media with a blue LED   Fig. b. Photo- W) Fig. d. As a r W) Fig. e.Fig. 7a2 production when incorporated with TiO2 or g-C3N4 [2 evolution from lignocellulose biomass, lactic acid, and lignin [Nickel sulfide (NiS) as a p-type semiconductor has been used for photocatalytic Hr g-C3N4 –50. Li a2 production is shown in Fig. + in the solution due to the unsaturated sulfide ions of NiS. This strong affinity for H + can boost the photocatalytic reduction of H+ to H2. The photoinduced holes of CdS can be oxidized by lactic acid and/or lignin as the hole scavengers. This consumption of holes in the valence band of CdS facilitates the minimization of charge recombination of the electron and hole; this resulted in the suppression of photocorrosion of CdS in the solution and ultimately improved the photocatalytic H2 production rate.The proposed mechanism of photocatalytic H−1 gcat−1 with optimized carbon content for rapid photoelectron transfer and suppression of charge recombination [2S-Na2SO3 under visible light, in comparison with regular CdS, were proven to have improved radiative absorption in the 250–800 nm wavelength range and demonstrated increased photostability when compared to bare CdS nanoparticles. When loaded with Pt, the hydrogen production amounted to 1.323 mmol h−1 gcat−1 [xCd1-xS to promote S-vacancies resulted in an H2 production rate of 419 μmol h−1 g−1cat, and its photocatalytic activity was explained by long-lived charge carriers and the increased formation of photo-hole pairs from the doping [With renewable seaweed-derived carrageenan as the source of carbon and sulfur for CdS@Carbon Aerogels (CA/CdS), the hydrogen evolution rate was found to be 113.5 μmol hbination . Carrage1 gcat−1 . Interst2 from biomass, a variety of metal-free photocatalysts such as poly(p-phenylene), graphitic carbon nitride, boron carbides, and graphite oxide have been widely developed [3N4) was first explored for hydrogen production under visible light [3N4 has demonstrated limited photocatalytic efficiency on its own, due to low surface area, limited light absorption, and high rate of charge recombination [. realized a high-value application of crude bio-oil that circumvents expensive and industrially-infeasible preparation or upgrading, by doping g-C3N4 with non-purified bio-oil produced from pyrolyzed pine sawdust [3N4 through solvothermal treatment was observed by X-ray photoelectron spectroscopy to lower the conduction band for improved hole-separation and hydrogen reduction. A maximal hydrogen evolution rate of 1654 μmol h−1 gcat−1 was achieved by solvothermal treatment at 180 °C with 1:1 weight ratio of bio-oil to g-C3N4.In pursuit of more green and sustainable chemistry for the generation of Heveloped –58. For le light . Howeverbination . In orde sawdust . InsertiNCNCNx, to H2 generation was optimized through addition of a molecular bis(diphosphine) Ni proton reduction co-catalyst to NCNCNx. Under AM 1.5G irradiation, an H2 production yield of 2.62 μmol was achieved, with alkaline conditions allowing for optimal hydrogen production yields [−1 gcat−1 of H2 was achieved. However, the proton reduction co-catalyst lacks structural stability and was demonstrated to degrade after 24 h, allowing other co-catalysts with slower kinetics to produce much greater amounts of hydrogen, outperforming even after 12 days.Aqueous room-temperature photoreforming of α-cellulose via cyanamide-functionalized carbon nitride, n yields . This wa. doped the carbon-based electrode with nitrogen and demonstrated increased stability and similar resistivity as non-doped carbon-based electrodes [Metal-free electrodes for hydrogen evolution by water splitting can be developed from biomass that would otherwise be utilized inefficiently as fossil fuels or consumed by pyrolysis. For hydrogen production, recent work reports the use of biomass products (e.g. char) as a metal-free carbon-based electrode pellets formed without binders that sacrificially decompose, and whose electrochemical performance can be modified through surface enhancement or exchange of electrolyte identity . For exaectrodes . Researc2 generation [2 production could offer another economic advantage [2 production. This section will review the range of raw biomass and biomass-derived substrates that do represent viable sources for the photocatalytic production of hydrogen gas. The consideration will go in order of molecular size, starting with monomeric substrates such as alcohols, aldehydes, carboxylic acids, and glucose, then moving onto saccharides and ending with raw lignocellulosic biomass itself. Reported efficiencies for the photocatalytic reaction and hydrogen yields will be considered, along with the difficulties or benefits of a given substrate type.Photocatalytic water splitting to obtain hydrogen gas using low-energy ambient reaction conditions and solar radiation as the only energy input will contribute to the realization of future low-cost, environmentally-conscious fuels. As an alternative to water, biomass offers another possible source of electrons and protons for the photochemical formation of hydrogen, with the added possibility of forming value added organic products, depending on the anodic chemistry coupled to the cathodic Hneration , 16, 18.dvantage . These cdvantage . It must2 production, including methanol from syngas production, ethanol from sugar fermentation, and glycerol from biofuel production [A myriad of monomeric waste products from industrial biomass processing could serve as feedstock for Hoduction , 65, 66.2 [Methanol can be formed by the syngas process, but the energy intensive nature of this approach, and its general reliance on fossil fuels as a source of carbon, make this an unattractive source of methanol as part of a green-energy future . Ferment2 . However2 , 84, 85.2 and CO2 observed using Pt/TiO2 or Au/TiO2 surfaces features cyclic adsorption and desorption processes for each reduction and subsequent oxidation step proceeding from methanol → formaldehyde → formic acid → H2 and CO2, as resolved by limiting the irradiation time and sampling the resulting solution [4, with decreasing concentrations when shifting from UV to visible irradiation [2 catalysts [2 photocatalysts induces oxygen vacancies that deactivate the noble metal active sites. Thus, an optimized loading weight of 1 wt% noble metal onto the semiconducting material has been found for methanol photo-reforming [The photo-reformation mechanism of methanol to Hsolution . Howeveradiation . CO can atalysts , especiaeforming . It is g2 as the photocatalyst. However, the expected subsequent oxidation step is hindered by inadequate adsorption of the acetaldehyde onto the photocatalyst surface; instead, side-reactions producing the hemiacetal 1-ethoxyethanol have been observed [Ethanol generation is a well-founded source of biofuels through hydrolysis of lignocellulosic biomass . Studyinobserved . The pre2 in the Pt/TiO2 construct can suppress this reaction [2 surface areas promoting adsorption of substrate intermediates, increasing the likelihood of oxidation reactions as opposed to radical coupling interactions. From these and other observations, Bahruji et al. determined that the ability to photo-reform a variety of alcohols, CxHyOH, using a Pd/TiO2 photocatalyst to hydrogen and hydrocarbon Cx-1 products depended on a set of general rules: (1) the availability of hydrogen on the Cα–OH, (2) the presence of alkyl groups attached to the alcohol, (3) the presence of methylene groups between alcohol functional groups, and (4) the relative strength of the alpha C–C bond [The complete photo-reformation of ethanol into hydrogen and carbon dioxide gas proceeds with hydrogen gas first formed in the reaction sequence following dehydrogenation to give acetaldehyde . Carbon reaction . This waC–C bond .2  containing solution as the source of electron [2 and C3N4 revealed that this substrate was more photoactive on TiO2, suggesting that the photomechanism proceeds via the electron density located on the N atom of the amine group [−1 gcat−1 is achieved [3N4/TiO2 photocatalyst which achieved 1041 μmol h−1 gcat−1 by comparison [Recently, amines have been given attention as a potential biomass-derived substrate, specifically trimethylamine (TMA), triethylamine (TEA), triethanolamine (TEOA), and ethylendiamine tetraaceticacid (EDTA) , 80, 100achieved , which fmparison .2) through hydroxyl and carbonyl functional groups [In addition to the formation of carboxylic acids in the photoreforming of biomass, this moiety can also be produced by the industrial fermentation of saccharides . A majorl groups , 102, wh2 generation, while TiO2 preferred the decarboxylation step; this was realized by the CdS formation of pyruvic acid, and the TiO2 formation of acetaldehyde, acetic acid, ethanol, and primarily CO2 [2 would be preferred for complete photoreformation of carboxylic acid substrates due to its ability to react with subsequently produced photo-substrates. Recently, a new photocatalyst CdS@g-C3N4 was able to convert lactic acid into H2 at a maximum rate of 19.88 mmol h−1 gcat−1 without the use of noble metal catalyst [3N4 photocatalysts, the high hydrogen production rate was demonstrated.Lactic, acetic, and formic acid are the principally studied biomass-derived carboxylic acid substrates due to their formation during biomass-processing such as fermentation . The selrily CO2 . The difcatalyst . It was 4 was demonstrated over Cu/TiO2 catalyst by controlling the oxidation state of Cu islands on the photocatalyst [2 production  improved.Recently, a method of favoring the photoreformation pathway over the decarboxylation pathway into CHcatalyst . Over thion Eqs. , 7. By i2 and H2 by chalcogenides or under UV-rich light, requiring greater judicious efforts when selecting a Pt/CdS photocatalyst for a system in which CO is not readily able to be abstracted or is detrimental to the photoreactive system [2 semiconductors found that, although formic acid outperformed methanol for the production of hydrogen in five of the seven photocatalytic systems (up to 22.8 mmol h−1 gcat−1 over Au@TiO2), that these improved rates were negligible at low concentrations of substrate (1%) [In addition, formic acid as a substrate has been demonstrated to decompose into CO as well as COe system . Moreoveate (1%) . Interesate (1%) .Table 2P2, decreasing hydrogen evolution was found in order of arabinose > glucose > fructose > cellobiose as substrates [2 achieving as much as 286 μmol h−1 gcat−1 [The formation of mono-, di-, and polysaccharides when photocatalyzing raw biomass makes saccharide-based studies widely applicable to other biomass systems of photocatalysis , 108. Phbstrates . Recentlbstrates . Regardlbstrates . By obta1 gcat−1 .2 photocatalysts were used to generate hydrogen gas economically and effectively from glucose [Glucose as a model biomass-derived substrate has been extensively studied for photocatalytic production of hydrogen gas due to its prevalence in biological systems and higher-order biomass structure , 39, 108 glucose .2 can be produced from cellulose by thermochemical gasification and acid hydrolysis [ca. 33 μmol without cellulose hydrolysis, to 170 μmol with cellulose hydrolysis), without affecting the photocatalyst stability. This method was even demonstrated on 500 mg of paper pulp, whereby the hydrogen production increased to 1320 μmol h−1 gcat−1, but the fact remains that this method is hindered by the reaction conditions (i.e. 403 K and 0.6 M sulfuric acid) necessary to stimulate acid hydrolysis of cellulose in the system. Another method to overcome cellulose insolubility is the use of ball-milling and immobilization pretreatment of cellulose to increase surface area exposure between cellulose and the photocatalyst, demonstrated by Zhang et al. By mechanochemically converting the crystalline cellulose into submicron sizes (0.1–3 μm) and immobilizing it onto photocatalytic Pt/TiO2 nanoparticles, they achieved enhanced hydrogen production over non-immobilized controls. A systemic study found that phototransformation of cellulose and water to hydrogen gas could occur in mineralized seawater and pH 7.4 conditions gave the highest yield of hydrogen [in-situ dye-sensitization of the photocatalyst by absorption of HMF oligomers, as its photoreforming is less favored than other carbohydrate-substrates formed during the reaction. This effect increases the absorption in the visible region of the photocatalyst, an additive effect of an already expanded visible-range absorption by Pt on TiO2.The production of hydrogen gas by photocatalyzed consumption of cellulose as a sacrificial electron donator is a popular research topic, due to the fact that cellulose is a major component of lignocellulosic biomass , 42. Curdrolysis , but as drolysis . By hydr2 and CO2 upon phototransformation [ca. 3000 μmol/h/gcat have been demonstrated with sucrose, lactose, and cellobiose [Disaccharides such as cellobiose produced from the hydrolysis of cellulose perform better than their parent species for hydrogen production , but stiormation . Hydrogellobiose .2 being the most popular and effective choice of photocatalyst. It was found that aquatic biomass, such as seaweed, enabled higher efficiencies than land-derived biomass, presumably due to the lack of lignin necessary to maintain their biological rigidity and the limited UV penetration at the depth of water they reside [2. For example, the production rate of 0.061 mmol h−1 gcat−1 was achieved with fescue grass over Pt/TiO2 substrate by elevating the reaction temperature to 60 °C [. were able to demonstrate that hydrogen evolution varies widely over different selected biomass substrates, but that all were enhanced by the alkalinity of the solution and thus the solubility of the substrate [i solution [x photocatalyst in 10 M KOH solution [The focus of this review is on the ultimate use of raw biomass as the source of protons and electrons for the photocatalytic production of hydrogen production. The capacity for humans to take waste byproducts from current biomass industrial utilization and valorize them into hydrogen fuel and low molecular weight chemicals could play a key part in more sustainable economy by reducing reliance on fossil fuels and enabling more complete use of appropriated biomass. The chemical and structural complexity of raw biomass limits its use in photocatalytic applications and requires the implementation of pretreatment methods before lignocellulosic valorization, which in and of itself is an active field of current research . Moreovey reside . Consideto 60 °C . Wakerlyubstrate . A systesolution  in compasolution , demonst2 production without the need for pretreatment of the biomass source. Metal oxide semiconductors and noble metal catalysts have established a starting point in this research, but the use of metal free semiconductor materials such as C3N4-based systems is desirable as these materials can be regenerated more effectively and economically from renewable resources. In the past, this research field has mainly focused on light absorbing semiconductors that depended on the absorption of UV light to drive photocatalytic activity. Recent focus has shifted to designing narrow band-gap materials and/or metal-free photocatalysts for H2 production from biomass to improve photocatalytic performance under visible or natural sunlight and the use of mild conditions to enhance selectivity toward value-added products along with H2 productivity.Although progress has been made in regard to the photocatalytic reforming of biomass into value-added chemical substrates and molecular hydrogen, much research is needed to find active photocatalysts composed of low-cost, non-toxic materials that can sustain high rates of HFurthermore, the efficiency of this process needs to be taken into consideration, as it will be a balance between renewable semiconducting resources and the efficiency that will be sacrificed to make this composite possible. The use of biomass itself, not pre-refined biomass-derived molecules, as the electron-donating substrate in the photocatalytic generation of hydrogen gas is crucial to the realization of an industrially and environmentally relevant process. In regard to foundational mechanistic studies, much is still to be desired, and the results of these future studies will determine the absolute optimal reaction conditions to photo-reform biomass into specific platform molecules or for biofuel applications.2 in a sustainable, climate-neutral way must be developed, and solar-driven photochemical biomass conversion presents an obvious possible solution.Lastly, photocatalysis offers the potential to replace the energy intensive approach of thermally reforming biomass for hydrogen production using abundant and free solar energy. Developing strategies using earth-abundant elements to form the light absorbers and photocatalysts for the capture and conversion of solar energy to electric potential energy, and eventually chemical potential energy in the form of the H–H bond, presents a key challenge in this field. As a carbon-free, energy-dense material, hydrogen will likely play an important role in mankind’s energy economy of the future. A method of supplying H