Metabolites as global regulators: A new view of protein regulation

The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects

Kynurenic acid (KYNA) is an antioxidant degradation product of tryptophan that has been shown to have a variety of cytoprotective, neuroprotective and neuronal signalling properties. However, mammalian transporters and receptors display micromolar binding constants; these are consistent with its typically micromolar tissue concentrations but far above its serum/plasma concentration (normally tens of nanomolar), suggesting large gaps in our knowledge of its transport and mechanisms of action, in that the main influx transporters characterized to date are equilibrative, not concentrative. In addition, it is a substrate of a known anion efflux pump (ABCC4), whose in vivo activity is largely unknown. Exogeneous addition of L-tryptophan or L-kynurenine leads to the production of KYNA but also to that of many other co-metabolites (including some such as 3-hydroxy-L-kynurenine and quinolinic acid that may be toxic). With the exception of chestnut honey, KYNA exists at relatively low levels in natural foodstuffs. However, its bioavailability is reasonable, and as the terminal element of an irreversible reaction of most tryptophan degradation pathways, it might be added exogenously without disturbing upstream metabolism significantly. Many examples, which we review, show that it has valuable bioactivity. Given the above, we review its potential utility as a nutraceutical, finding it significantly worthy of further study and development.

Fibrinaloid Microclots and Atrial Fibrillation

Atrial fibrillation (AF) is a comorbidity of a variety of other chronic, inflammatory diseases for which fibrinaloid microclots are a known accompaniment (and in some cases, a cause, with a mechanistic basis). Clots are, of course, a well-known consequence of atrial fibrillation. We here ask the question whether the fibrinaloid microclots seen in plasma or serum may in fact also be a cause of (or contributor to) the development of AF. We consider known 'risk factors' for AF, and in particular, exogenous stimuli such as infection and air pollution by particulates, both of which are known to cause AF. The external accompaniments of both bacterial (lipopolysaccharide and lipoteichoic acids) and viral (SARS-CoV-2 spike protein) infections are known to stimulate fibrinaloid microclots when added in vitro, and fibrinaloid microclots, as with other amyloid proteins, can be cytotoxic, both by inducing hypoxia/reperfusion and by other means. Strokes and thromboembolisms are also common consequences of AF. Consequently, taking a systems approach, we review the considerable evidence in detail, which leads us to suggest that it is likely that microclots may well have an aetiological role in the development of AF. This has significant mechanistic and therapeutic implications.

Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling

Motivation

Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning.

Results

By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, we found that the implementation of SARTRE with random forest classifiers accurately predicts metabolite-protein interactions, supported by an average area under the receiver operating curve of 0.86 and 0.85, respectively. Ranking of features based on their importance for classification demonstrated the key role of shadow prices in predicting metabolite-protein interactions. The quality of predictions is further supported by the excellent agreement of the organism-specific classifiers on unseen interactions shared between the two model organisms. Further, predictions from SARTRE are highly competitive against those obtained from a recent deep-learning approach relying on a variety of protein and metabolite features. Together, these findings show that features extracted from constraint-based analyses of metabolic networks pave the way for understanding the functional roles of the interactions between proteins and small molecules.

Availability and implementation

https://github.com/fayazsoleymani/SARTRE.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes Using a Single-Step Affinity Purification

Cellular protein-metabolite interactions (PMI), for decades relatively overlooked, are seeing a golden age in recent years. To facilitate simultaneous characterization of PMI and protein-protein interactions (PPI) of a given protein ("bait"), we developed a protocol that utilizes antibody-assisted affinity purification (AP) followed by liquid chromatography-mass spectrometry (LC-MS). Aside from its speed, simplicity, and adaptability to a variety of biological systems, its main strength lies in the parallel identification, in a near-physiological environment, of a given protein's protein and small-molecule partners.

Drug discovery inspired by bioactive small molecules from nature

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery.

Rewiring of the protein-protein-metabolite interactome during the diauxic shift in yeast

In budding yeast Saccharomyces cerevisiae, the switch from aerobic fermentation to respiratory growth is separated by a period of growth arrest, known as the diauxic shift, accompanied by a significant metabolic rewiring, including the derepression of gluconeogenesis and the establishment of mitochondrial respiration. Previous studies reported hundreds of proteins and tens of metabolites accumulating differentially across the diauxic shift transition. To assess the differences in the protein-protein (PPIs) and protein-metabolite interactions (PMIs) yeast samples harvested in the glucose-utilizing, fermentative phase, ethanol-utilizing and early stationary respiratory phases were analysed using isothermal shift assay (iTSA) and a co-fractionation mass spectrometry approach, PROMIS. Whereas iTSA monitors changes in protein stability and is informative towards protein interaction status, PROMIS uses co-elution to delineate putative PPIs and PMIs. The resulting dataset comprises 1627 proteins and 247 metabolites, hundreds of proteins and tens of metabolites characterized by differential thermal stability and/or fractionation profile, constituting a novel resource to be mined for the regulatory PPIs and PMIs. The examples discussed here include (i) dissociation of the core and regulatory particle of the proteasome in the early stationary phase, (ii) the differential binding of a co-factor pyridoxal phosphate to the enzymes of amino acid metabolism and (iii) the putative, phase-specific interactions between proline-containing dipeptides and enzymes of central carbon metabolism.

Regulatory Modules of Metabolites and Protein Phosphorylation in Arabidopsis Genotypes With Altered Sucrose Allocation

Multi-omics data sets are increasingly being used for the interpretation of cellular processes in response to environmental cues. Especially, the posttranslational modification of proteins by phosphorylation is an important regulatory process affecting protein activity and/or localization, which, in turn, can have effects on metabolic processes and metabolite levels. Despite this importance, relationships between protein phosphorylation status and metabolite abundance remain largely underexplored. Here, we used a phosphoproteomics-metabolomics data set collected at the end of day and night in shoots and roots of Arabidopsis to propose regulatory relationships between protein phosphorylation and accumulation or allocation of metabolites. For this purpose, we introduced a novel, robust co-expression measure suited to the structure of our data sets, and we used this measure to construct metabolite-phosphopeptide networks. These networks were compared between wild type and plants with perturbations in key processes of sugar metabolism, namely, sucrose export (sweet11/12 mutant) and starch synthesis (pgm mutant). The phosphopeptide-metabolite network turned out to be highly sensitive to perturbations in sugar metabolism. Specifically, KING1, the regulatory subunit of SnRK1, was identified as a primary candidate connecting protein phosphorylation status with metabolism. We additionally identified strong changes in the fatty acid network of the sweet11/12 mutant, potentially resulting from a combination of fatty acid signaling and metabolic overflow reactions in response to high internal sucrose concentrations. Our results further suggest novel protein-metabolite relationships as candidates for future targeted research.

Long-chain fatty acyl coenzyme A inhibits NME1/2 and regulates cancer metastasis

SignificanceThe study provided a long-sought molecular mechanism that could explain the link between fatty acid metabolism and cancer metastasis. Further understanding may lead to new strategies to inhibit cancer metastasis. The chemical proteomic approach developed here will be useful for discovering other regulatory mechanisms of protein function by small molecule metabolites.

Untargeted Defining Protein-Metabolites Interaction Based on Label-Free Kinetic Size Exclusion Chromatography-Mass Spectrometry

The specific interactions between protein and metabolites (PMIs) are closely related to many cellular processes and play a vital role in signal transduction and regulating material and energy metabolism. However, most of the available analytical strategies for PMIs involve chemical modification of metabolites or immobilization of protein, which has restricted current PMIs study mainly to lipid-protein and hydrophobic metabolites. In this work, a label-free online kinetic size exclusion chromatography-mass spectrometry (KSEC-MS) method combined with untargeted metabolomics was developed to define PMIs in a complex system. The metabolite mixture and target protein were injected into the SEC column sequentially without preincubation, and the separation results of KSEC were monitored by global metabolite profiling with mass spectrometry. The potential ligands in the metabolite mixture can be discovered if their migration patterns were affected by the target protein and the variation was positively correlated with the concentration of target protein. To verify this approach, carbonic anhydrase was first selected as a test protein, and acetazolamide as its known inhibitor was successfully defined. Furthermore, human serum albumin (HSA) as the common transport carrier of metabolites was selected as a target protein to demonstrate the usefulness of this approach. Multiple endogenous ligands of HSA were simultaneously defined from the extracted metabolites of human serum; most of them are polar metabolites rather than nonpolar lipids. This approach can provide a novel way for mapping and identifying unknown PMIs in a complex system, especially for polar metabolites-protein interactions.

Who Rules the Cell? An Epi-Tale of Histone, DNA, RNA, and the Metabolic Deep State

Epigenetics refers to the mode of inheritance independent of mutational changes in the DNA. Early evidence has revealed methylation, acetylation, and phosphorylation of histones, as well as methylation of DNA as part of the underlying mechanisms. The recent awareness that many human diseases have in fact an epigenetic basis, due to unbalanced diets, has led to a resurgence of interest in how epigenetics might be connected with, or even controlled by, metabolism. The Next-Generation genomic technologies have now unleashed torrents of results exposing a wondrous array of metabolites that are covalently attached to selective sites on histones, DNA and RNA. Metabolites are often cofactors or targets of chromatin-modifying enzymes. Many metabolites themselves can be acetylated or methylated. This indicates that the acetylome and methylome can actually be deep and pervasive networks to ensure the nuclear activities are coordinated with the metabolic status of the cell. The discovery of novel histone marks also raises the question on the types of pathways by which their corresponding metabolites are replenished, how they are corralled to the specific histone residues and how they are recognized. Further, atypical cytosines and uracil have also been found in eukaryotic genomes. Although these new and extensive connections between metabolism and epigenetics have been established mostly in animal models, parallels must exist in plants, inasmuch as many of the basic components of chromatin and its modifying enzymes are conserved. Plants are chemical factories constantly responding to stress. Plants, therefore, should lend themselves readily for identifying new endogenous metabolites that are also modulators of nuclear activities in adapting to stress.

The human endogenous metabolome as a pharmacology baseline for drug discovery

We have limited understanding of the variation in in vitro affinities of drugs for their targets. An analysis of a highly curated set of 815 interactions between 566 drugs and 129 primary targets reveals that 71% of drug-target affinities have values above that of the corresponding endogenous ligand, 96% of them fitting within a range of two orders of magnitude. Our findings suggest that the evolutionary optimised affinity of endogenous ligands for their native proteins can serve as a baseline for the primary pharmacology of drugs. We show that the degree of off-target selectivity and safety risks of drugs derived from their secondary pharmacology depend very much on that baseline. Thus, we propose a new approach for estimating safety margins.

Cooperative cobinding of synthetic and natural ligands to the nuclear receptor PPARγ

Crystal structures of peroxisome proliferator-activated receptor gamma (PPARγ) have revealed overlapping binding modes for synthetic and natural/endogenous ligands, indicating competition for the orthosteric pocket. Here we show that cobinding of a synthetic ligand to the orthosteric pocket can push natural and endogenous PPARγ ligands (fatty acids) out of the orthosteric pocket towards an alternate ligand-binding site near the functionally important omega (Ω)-loop. X-ray crystallography, NMR spectroscopy, all-atom molecular dynamics simulations, and mutagenesis coupled to quantitative biochemical functional and cellular assays reveal that synthetic ligand and fatty acid cobinding can form a 'ligand link' to the Ω-loop and synergistically affect the structure and function of PPARγ. These findings contribute to a growing body of evidence indicating ligand binding to nuclear receptors can be more complex than the classical one-for-one orthosteric exchange of a natural or endogenous ligand with a synthetic ligand.

2 in 1: One-step Affinity Purification for the Parallel Analysis of Protein-Protein and Protein-Metabolite Complexes

Cellular processes are regulated by interactions between biological molecules such as proteins, metabolites, and nucleic acids. While the investigation of protein-protein interactions (PPI) is no novelty, experimental approaches aiming to characterize endogenous protein-metabolite interactions (PMI) constitute a rather recent development. Herein, we present a protocol that allows simultaneous characterization of the PPI and PMI of a protein of choice, referred to as bait. Our protocol was optimized for Arabidopsis cell cultures and combines affinity purification (AP) with mass spectrometry (MS)-based protein and metabolite detection. In short, transgenic Arabidopsis lines, expressing bait protein fused to an affinity tag, are first lysed to obtain a native cellular extract. Anti-tag antibodies are used to pull down protein and metabolite partners of the bait protein. The affinity-purified complexes are extracted using a one-step methyl tert-butyl ether (MTBE)/methanol/water method. Whilst metabolites separate into either the polar or the hydrophobic phase, proteins can be found in the pellet. Both metabolites and proteins are then analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS or UPLC-MS/MS). Empty-vector (EV) control lines are used to exclude false positives. The major advantage of our protocol is that it enables identification of protein and metabolite partners of a target protein in parallel in near-physiological conditions (cellular lysate). The presented method is straightforward, fast, and can be easily adapted to biological systems other than plant cell cultures.

PROMIS, global analysis of PROtein-metabolite interactions using size separation in Arabidopsis thaliana

Small molecules not only represent cellular building blocks and metabolic intermediates, but also regulatory ligands and signaling molecules that interact with proteins. Although these interactions affect cellular metabolism, growth, and development, they have been largely understudied. Herein, we describe a method, which we named PROtein–Metabolite Interactions using Size separation (PROMIS), that allows simultaneous, global analysis of endogenous protein–small molecule and of protein–protein complexes. To this end, a cell-free native lysate from Arabidopsis thaliana cell cultures was fractionated by size-exclusion chromatography, followed by quantitative metabolomic and proteomic analyses. Proteins and small molecules showing similar elution behavior, across protein-containing fractions, constituted putative interactors. Applying PROMIS to an A. thaliana extract, we ascertained known protein–protein (PPIs) and protein–metabolite (PMIs) interactions and reproduced binding between small-molecule protease inhibitors and their respective proteases. More importantly, we present examples of two experimental strategies that exploit the PROMIS dataset to identify novel PMIs. By looking for similar elution behavior of metabolites and enzymes belonging to the same biochemical pathways, we identified putative feedback and feed-forward regulations in pantothenate biosynthesis and the methionine salvage cycle, respectively. By combining PROMIS with an orthogonal affinity purification approach, we identified an interaction between the dipeptide Tyr–Asp and the glycolytic enzyme glyceraldehyde-3-phosphate dehydrogenase. In summary, we present proof of concept for a powerful experimental tool that enables system-wide analysis of PMIs and PPIs across all biological systems. The dataset obtained here comprises nearly 140 metabolites and 5000 proteins, which can be mined for putative interactors.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Enrichment of Metabolite-Binding Proteins by Affinity Elution in Tandem Hydrophobic Interaction Chromatography (AETHIC) Reveals RKIP Regulating ERK Signaling in an ATP-Dependent Manner

To elucidate the molecular mechanisms underlying the action of bioactive compounds such as metabolites, identification of their binding targets is essential. However, available techniques for enriching metabolite-binding proteins are practically restrained by special equipment requirements and laborious efforts. Here we have developed a novel method, affinity elution in tandem hydrophobic interaction chromatography (AETHIC), which enables enrichment of metabolite-binding proteins from a crude tissue extract. AETHIC constitutes two major steps, protein fractionation and affinity elution. The basic strategy of AETHIC uses a series of HIC matrices encompassing aliphatic chains of different length and thus provides a wide range of hydrophobicity for interactions with most proteins. Thereafter, target proteins are eluted selectively by a given ligand. As our first proof-of-principle, we demonstrated that AETHIC was able to enrich ATP-binding proteins from porcine brain extract. In addition, we have demonstrated that raf kinase inhibitory protein (RKIP) is an ATP-binding protein and ATP attenuates the interaction between RKIP and Raf-1. In parallel, short-term ATP depletion in cultured HEK293 cells augments interaction between RKIP and Raf-1, resulting in decreased activation of the downstream ERK signaling. Therefore, the ATP-binding function renders RKIP's inhibition on Raf-1 modulated by cellular ATP concentrations. These data shed light on how energy levels affect the propagation of cellular signaling. Taken together, the enclosed results advocate the potential of AETHIC in the study of metabolite-protein interactions.

Acetoacetate Accelerates Muscle Regeneration and Ameliorates Muscular Dystrophy in Mice

Acetoacetate (AA) is a ketone body and acts as a fuel to supply energy for cellular activity of various tissues. Here, we uncovered a novel function of AA in promoting muscle cell proliferation. Notably, the functional role of AA in regulating muscle cell function is further evidenced by its capability to accelerate muscle regeneration in normal mice, and it ameliorates muscular dystrophy in mdx mice. Mechanistically, our data from multiparameter analyses consistently support the notion that AA plays a non-metabolic role in regulating muscle cell function. Finally, we show that AA exerts its function through activation of the MEK1-ERK1/2-cyclin D1 pathway, revealing a novel mechanism in which AA serves as a signaling metabolite in mediating muscle cell function. Our findings highlight the profound functions of a small metabolite as signaling molecule in mammalian cells.

Individualization of treatments with drugs metabolized by CES1: combining genetics and metabolomics

CES1 is involved in the hydrolysis of ester group-containing xenobiotic and endobiotic compounds including several essential and commonly used drugs. The individual variation in the efficacy and tolerability of many drugs metabolized by CES1 is considerable. Hence, there is a large interest in individualizing the treatment with these drugs. The present review addresses the issue of individualized treatment with drugs metabolized by CES1. It describes the composition of the gene encoding CES1, reports variants of this gene with focus upon those with a potential effect on drug metabolism and provides an overview of the protein structure of this enzyme bringing notice to mechanisms involved in the regulation of enzyme activity. Subsequently, the review highlights drugs metabolized by CES1 and argues that individual differences in the pharmacokinetics of these drugs play an important role in determining drug response and tolerability suggesting prospects for individualized drug therapies. Our review also discusses endogenous substrates of CES1 and assesses the potential of using metabolomic profiling of blood to identify proxies for the hepatic activity of CES1 that predict the rate of drug metabolism. Finally, the combination of genetics and metabolomics to obtain an accurate prediction of the individual response to CES1-dependent drugs is discussed.

Shared functions of plant and mammalian StAR-related lipid transfer (START) domains in modulating transcription factor activity

BACKGROUND

Steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains were first identified from mammalian proteins that bind lipid/sterol ligands via a hydrophobic pocket. In plants, predicted START domains are predominantly found in homeodomain leucine zipper (HD-Zip) transcription factors that are master regulators of cell-type differentiation in development. Here we utilized studies of Arabidopsis in parallel with heterologous expression of START domains in yeast to investigate the hypothesis that START domains are versatile ligand-binding motifs that can modulate transcription factor activity.

RESULTS

Our results show that deletion of the START domain from Arabidopsis Glabra2 (GL2), a representative HD-Zip transcription factor involved in differentiation of the epidermis, results in a complete loss-of-function phenotype, although the protein is correctly localized to the nucleus. Despite low sequence similarly, the mammalian START domain from StAR can functionally replace the HD-Zip-derived START domain. Embedding the START domain within a synthetic transcription factor in yeast, we found that several mammalian START domains from StAR, MLN64 and PCTP stimulated transcription factor activity, as did START domains from two Arabidopsis HD-Zip transcription factors. Mutation of ligand-binding residues within StAR START reduced this activity, consistent with the yeast assay monitoring ligand-binding. The D182L missense mutation in StAR START was shown to affect GL2 transcription factor activity in maintenance of the leaf trichome cell fate. Analysis of in vivo protein-metabolite interactions by mass spectrometry provided direct evidence for analogous lipid-binding activity in mammalian and plant START domains in the yeast system. Structural modeling predicted similar sized ligand-binding cavities of a subset of plant START domains in comparison to mammalian counterparts.

CONCLUSIONS

The START domain is required for transcription factor activity in HD-Zip proteins from plants, although it is not strictly necessary for the protein's nuclear localization. START domains from both mammals and plants are modular in that they can bind lipid ligands to regulate transcription factor function in a yeast system. The data provide evidence for an evolutionarily conserved mechanism by which lipid metabolites can orchestrate transcription. We propose a model in which the START domain is used by both plants and mammals to regulate transcription factor activity.

Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery

Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

An analysis of a 'community-driven' reconstruction of the human metabolic network

Following a strategy similar to that used in baker's yeast (Herrgård et al. Nat Biotechnol 26:1155-1160, 2008). A consensus yeast metabolic network obtained from a community approach to systems biology (Herrgård et al. 2008; Dobson et al. BMC Syst Biol 4:145, 2010). Further developments towards a genome-scale metabolic model of yeast (Dobson et al. 2010; Heavner et al. BMC Syst Biol 6:55, 2012). Yeast 5-an expanded reconstruction of the Saccharomyces cerevisiae metabolic network (Heavner et al. 2012) and in Salmonella typhimurium (Thiele et al. BMC Syst Biol 5:8, 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonellatyphimurium LT2 (Thiele et al. 2011), a recent paper (Thiele et al. Nat Biotechnol 31:419-425, 2013). A community-driven global reconstruction of human metabolism (Thiele et al. 2013) described a much improved 'community consensus' reconstruction of the human metabolic network, called Recon 2, and the authors (that include the present ones) have made it freely available via a database at http://humanmetabolism.org/ and in SBML format at Biomodels (http://identifiers.org/biomodels.db/MODEL1109130000). This short analysis summarises the main findings, and suggests some approaches that will be able to exploit the availability of this model to advantage.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Analyzing In Vivo Metabolite-Protein Interactions By Large-Scale Systematic Analyses

Metabolites interact with proteins in vivo in various ways other than enzymatic reactions. Profiling of such interactions may help disclose unknown molecular mechanisms that regulate protein functions, and provide potential targets for disease treatment. Here we describe a procedure for systematic analyses of metabolite-protein interactions in vivo. This procedure couples protein affinity purification and mass spectrometry to identify metabolite-protein interactions. The primary effort can be completed within one day and scaled to process hundreds of samples in a batch. Originally developed in yeast, the same principle and protocol can be adapted to other organisms.