Targeting the Human Genome–Microbiome Axis for Drug Discovery

Reviews pubs.acs.org/jpr

Targeting the Human Genome−Microbiome Axis for Drug Discovery: Inspirations from Global Systems Biology and Traditional Chinese Medicine Liping Zhao,*,† Jeremy K. Nicholson,*,‡ Aiping Lu,§ Zhengtao Wang,⊥ Huiru Tang,∥ Elaine Holmes,‡ Jian Shen,† Xu Zhang,† Jia V. Li,‡ and John C. Lindon‡ †

Shanghai Center for Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China ‡ Biomolecular Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, South Kensington, London SW7 2AZ, United Kingdom § Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Dongzhimen, Beijing 100700, China ⊥ The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201210, China ∥ State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Centre for Biospectroscopy and Metabonomics, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China ABSTRACT: Most chronic diseases impairing current human public health involve not only the human genome but also gene−environment interactions, and in the latter case the gut microbiome is an important factor. This makes the classical single drug−receptor target drug discovery paradigm much less applicable. There is widespread and increasing international interest in understanding the properties of traditional Chinese medicines (TCMs) for their potential utilization as a source of new drugs for Western markets as emerging evidence indicates that most TCM drugs are actually targeting both the host and its symbiotic microbes. In this review, we explore the challenges of and opportunities for harmonizing Eastern− Western drug discovery paradigms by focusing on emergent functions at the whole body level of humans as superorganisms. This could lead to new drug candidate compounds for chronic diseases targeting receptors outside the currently accepted “druggable genome” and shed light on current high interest issues in Western medicine such as drug−drug and drug−diet−gut microbial interactions that will be crucial in the development and delivery of future therapeutic regimes optimized for the individual patient. KEYWORDS: drug discovery, global systems biology, gut microbiome, superorganism, traditional Chinese medicine

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INTRODUCTION Human beings have been named as “superorganisms” consisting of 10% human cells and 90% symbiotic microbial cells, most of which reside in the gut.1 The total genetic repertoire in this “ecosystem man” contains two interacting genomes including the structurally fixed and genetically inherited human genome (ca. 23 000 genes) and the environmentally acquired and plastic human microbiome (>3.3 million genes).2−4 The human genome and gut microbiome exchange their respective metabolically active molecules and exert influences on each other via enterohepatic circulation, gut barrier and other anatomic and physiological connections. This human−microbial metabolic axis connects the human genome and microbiome as a hologenome which serves as the genetic/ epigenetic foundation for maintaining human functions in immunity and nutrition.5,6 The human gut microbiome plays a © 2012 American Chemical Society

critical role in regulating immunity and thus in the defense against infectious diseases.7 In recent years, accumulating evidence indicates a pivotal role of the gut microbiota in many forms of chronic diseases. A possible mechanism to explain this gut origin of chronic diseases might be that metabolites with cytotoxicity, genotoxicity, and immunotoxicity produced by some members of the gut microbiota may enter the bloodstream via a partially impaired gut barrier to induce various immune and metabolic deteriorations.8−10 Although epigenetic and transcriptomic changes do occur, the human genome stays throughout life, but the human microbiome is structurally plastic and dynamic because the bacterial composition and the collection of genes vary with Received: February 19, 2012 Published: May 24, 2012 3509

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Figure 1. Parallels between the multivariate strategies in TCM and global systems biology.

foods, drugs, diseases, and age. Moreover, the most healthrelevant environmental factors such as food ingredients and ingested drugs are cotransformed and metabolized by functions encoded in both the human genome and the microbiome. Because of the intimate interactions between the human genome and microbiome, changes of the composition of the gut microbiome inevitably modulate gene expression and the immunity of the host, and thus affect the global metabolism of the human body. To maintain the health of human bodies under various environmental and physiological conditions, these two genomes must coordinate with each other and work in harmony in response to internal or external challenges.11 This superorganism view of the human body provides a complete new systems concept for managing human health at the clinically relevant whole body level. The recently improved understanding of this superorganism concept represents one of the most significant paradigm shifts in modern medicine. Traditionally, the human genome has been the primary target for drug discovery. Western drug development usually aims to find chemicals that inhibit or stimulate a given target receptor in humans with the hypothesis that this will have a beneficial effect on a human disease. Suitable compounds are synthesized and tested until a robust candidate is found, and this is then evaluated for efficacy, suitable pharmacokinetic and metabolism characteristics, and lack of toxicity. If activity is retained and no adverse effects are seen, it is then tested in humans in clinical trials. In all of this process, the effects of the gut microbiome have largely been ignored. This bottom-up, one gene−one disease, one target−one drug approach with the human genome as the only focus is probably largely exhausted and new approaches are needed.12 The discovery of the role of gut microbiota in chronic diseases such as obesity and diabetes has already opened a new territory for the pharmaceutical industry. Pharmacogenomics, despite some well-documented successes such as herceptin, has not yet delivered solutions in many chronic disease areas, because it ignores the function of the gut microbiome and cannot encompass environmental

influences such as nutrition, lifestyle choices, environmental effects, etc. A concept that does indeed take all of these factors into account is metabonomics,13 and in an extension of this, pharmacometabonomics,14,15 the idea that a basal metabolic profile taken from an easily accessible biofluid such as urine or blood, or from a stool sample, can be used to predict how that individual will react to a subsequent drug administration. Another concept that focuses on the long-ignored, other genome of human beings is metagenomics, which can capture variations of genetic potentials encoded in the gut microbiome with next-generation, high throughput sequencing. The integration of metabonomics and metagenomics gives birth to a new approach termed “functional metagenomics”, in which interindividual and intraindividual variations of gut microbiome and urine metabolites are correlated to understand the response of the human−gut microbiome metabolic axis to pathological conditions or therapeutic interventions.16 This whole-body systems approach considers the two genome nature of the human body and can produce a multivariate measure of a relatively complete physiology of the human body, thus, offering a new top-down paradigm in monitoring human health changes to complex interventions such as traditional Chinese medicines (TCMs). There are substantial parallels and compatibility between traditional Chinese medical philosophy and this top-down systems approach (Figure 1). TCM incorporates a 3000-yearold set of empirical observations and multiple therapies (possibly up to 100 000 formulas) that are at least in part congruent with recent concepts in polypharmacy (multiple therapeutic targeting) where herbal decoctions contain many biologically active natural products many of which may act synergistically.17 TCM is also the ultimate exemplification of personalized healthcare using noninvasive whole body diagnostic methods to stratify patients into subclasses, which then receive a specifically tailored therapy often consisting of herbal decoctions, acupuncture, massage, and dietary and lifestyle management. Framed in this way, TCM seems remarkably 3510

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APL model in vivo and in the induction of APL cell differentiation in vitro, and that A was the principal component of the formula, whereas T and I served as adjuvant ingredients. Another example is the TCM Sanshibaitu decoction that has been shown to have anti-inflammatory effects in a rat model of arthritis by modulating the expression of nuclear factor Kappa B and P38 MAP kinase.29 Not all TCMs are efficacious and few are likely to be optimized by “natural selection” through clinical practice. Furthermore, many contain toxins in variable amounts so, there is still much scope for optimization within existing therapeutic frames of reference. In addition, Chinese disease patterns are changing with Westernization, and it is unclear whether “tried and tested” remedies will cope with the deluge of Western disease now engulfing Asian populations. More commonly the benefits of applying TCMs in combination with conventional Western therapies are documented. TCM combination with conventional anticancer therapy for nasopharyngeal carcinoma has been shown to increase survival over 1−5 years and to increase immunostimulation and enhance tumor response.30 However, the molecular foundation for such combinations remains to be elucidated.

modern, yet we still have a poor understanding of the modes of action and relative efficacy of most TCMs compared to Western drugs. In addition, the paucity of well-controlled clinical trials and occasional manifestations of toxicity have impeded its acceptance into mainstream Western medicine. Also, the laudable “personalized and holistic nature” of TCM also makes it more difficult to apply the randomized, doubleblinded, and placebo-controlled trials used in mainstream medicine,18 although developments have been made recently in this area, particularly to guard against placebo effects.19 This has seriously hampered the integration of TCM into modern medical scheme, but with the advent of top-down systems biology tools there may be new ways to understand and assess these therapies. By integration and mapping of multiple input and output parameters, a framework for systematically applying and monitoring TCMs can be developed. Recent evidence suggests that diet and herbal medicines interact strongly with the gut microbiome which in turn also influences human health,20 and we have recently shown that population phenotype variation links to disease risk factors and that a significant part of the human metabolic phenotype may be influenced by microbial activity variation.21 As most Chinese and other herbal medicines are orally administered, the host− microbial−metabolic axis may be an important and as yet little studied part of TCM action, and it is even possible that TCMs may work largely by modulating this axis.20 Hence application of the new palette of metabolic phenotyping and metagenomic screening technologies to look at linked temporal variation of metabolites and microbes post-TCM treatment may provide new molecular evidence to verify or disprove the efficacy/safety of such therapies.22 In this new f unctional metagenomic screening paradigm, urinary metabolites and gut bacterial characterization would provide two noninvasive parameter windows indicative of the global health status of individual patients, and which can then offer systems and quantitative assessment of health responses to TCM or any other types of complementary therapeutics. This concept links closely to evaluation of a patient’s metabolic phenotype over time as they undertake a journey from presentation and diagnosis, through treatment, and hopefully to full recovery.23

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(ii). The Integral Role of the Gut Microbiome in Chronic Diseases and Their Treatment Using TCMs

The possible role of the gut microbiome in initiation and development of chronic diseases such as obesity, diabetes, and cardiovascular diseases has become a most intriguing recent discovery in medical science. It has been long considered that the fundamental cause of obesity is mainly the imbalance between energy intake and expenditure, but recent evidence shows that the gut microbiome may influence the energy equilibrium and participate in the onset and development of obesity. Germ-free mice are resistant to high fat diet (HFD)induced obesity, and the manipulation of the gut microbiome by prebiotics and antibiotics improves the obesity and insulin resistance in diet-induced and genetically obese mice,31,32 indicating that the gut microbiome play an indispensable role in mediating the etiology of obesity and other related metabolic diseases. The gut microbiome can directly control the two sides of the energy equation of the host via several mechanisms. Gut bacteria ferment dietary indigestible polysaccharides into short chain fatty acids (SCFAs), which are absorbed and used as energy resources by the host, so the gut microbiome helps the host extract more calories from food. The colonization of the gut bacteria suppresses the AMP-activated protein kinase (AMPK) activity in the peripheral tissues, resulting in decreased fatty acid oxidation. The gut microbiome also promotes adipocyte incorporation of circulating fatty acids by suppressing the intestinal expression of fasting-induced adipose factor (Fiaf).31,33 These factors might explain why recolonization of germ-free mice by a normal gut microbiota leads to increased body fat storage despite reduced food intake. The gut microbiome also functions as a trigger to induce low-grade, systemic, and chronic inflammation that characterizes obesity, insulin resistance, and related metabolic diseases. The inflammation is an important pathological factor for the onset and progression of insulin resistance and metabolic diseases,34,35 because the elevated levels of inflammatory cytokines such as TNF-α impair the insulin signaling in peripheral cell by phosphorylating the serine of the insulin receptor substrate 1.36 Recent studies with mice showed that

COMPONENTS OF A GLOBAL SYSTEMS BIOLOGY LED DRUG DISCOVERY PARADIGM

(i). The Effectiveness of Chinese Traditional Medicine

TCM practitioners have for thousands of years been able to diagnose, cure, and prevent many diseases through the administration of complex mixtures derived largely from plant, mineral (and sometimes animal) products. Undoubtedly, and despite scientific skepticism in the West, this approach works at least within the social and economic environment in China. There have been well-documented problems in trying to transfer this paradigm to Western cultures. A number of publications have recently shown the efficacy of TCMs and provided details of the molecular mechanisms of actions as would be required for Western medicines.24−28 Wang et al. used modern biomedical approaches to study the working mechanisms of one TCM Realgar-Indigo naturalis formula, which contains tetraarsenic tetrasulfide (A), indirubin (I), and tanshinone IIA (T) as major active ingredients, in treating human acute promyelocytic leukemia (APL).24 They found that this combination yielded synergy in the treatment of a murine 3511

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HFD feeding significantly diminished the gut bifidobacteria that are reported to fortify the gut barrier function, increased gut permeability, raised the plasma concentration of lipopolysaccharides (LPS) that are the endotoxin produced by gut Gramnegative opportunistic pathogens by 2−3 times, and led to lowgrade systemic chronic inflammation comparable to what has been found in obese human subjects. Correlation analysis showed that the increased plasma endotoxin was significantly associated with the decrease of gut bifidobacteria, and the chronic subcutaneous injection of the LPS to mice resulted in inflammation, adiposity, and insulin resistance, indicating that the reason for inflammation in obesity and insulin resistance is that more endotoxin produced by the diet-disrupted gut microbiota enters into blood across a damaged gut barrier and activates the host innate immune system.8 When mice consumed fat-rich foods supplemented with prebiotics which specifically promote the growth of gut bifidobacteria and improve gut barrier function, the endotoxemia and inflammation were reduced, and the obesity and insulin resistance were much improved.32 Low-grade, systemic, and chronic inflammation is also associated with aging-related diseases and cancer.37,38 The bacterial composition of the gut microbiome is stable over years if no dramatic changes in diet pattern or health status take place,39 and the endotoxins released by the microbiome into the circulating system can chronically provoke the immunosystem to maintain an inflammatory state, finally leading to various forms of chronic diseases. A dysregulated human− microbial metabolic axis therefore could serve as the therapeutic target for these chronic diseases. Most TCMs are administered orally and are inevitably exposed to the microbiota in the gastrointestinal tract, so TCMs might work both by modulating gut microbiota to regain ecological balance and by regulating genes within the host to regain metabolic/immune homeostasis. 40,41 Kato et al. evaluated the role of gut microbiota in the effects of one TCM formula, Shi-Quan-Da-Bu-Tang, by comparing the gene expression in the gut and liver of germ-free and conventional mice after the TCM administration. They found the heat shock protein (HSP) gene expression changed only in the conventional mice but not in the germ-free mice, and the TCM decreased unculturable bacteria and increased Lactobacillus johnsoni, indicating the changes in the gut microbiota are necessary for the TCM formula to exert its modulation effects on HSP gene expression.41 The transformation of predrug components of TCMs by the gut microbiota into active and bioavailable compounds is critical for the therapeutical effects of TCMs. Puerarin and daidzin are the main isoflavones in the rhizome of Pueraria thunbergiana that is one common TCM, but their estrogenic effects are weak. Human fecal microbiota have been shown to efficiently hydrolyze puerarin and daidzin to daidzein which has much stronger effects than its precursors, suggesting the gut bacteria can activate a potent estrogenic activity of P. thunbergiana by processing isoflavones of plant origin into more bioactive chemicals.42 Panax notoginseng is one TCM used in cancer therapy, and ginsenoside Rb1 (Rb1), the main saponin in ginseng, is considered to exert the antitumor effects, but Rb1 is poorly absorbed orally and its bioactivity is low. Intestinal bacteria can metabolize Rb1 into 20-O-β-Dglucopyranosyl-20(S)-protopanaxadiol (I) with β-D-glucosidases, the efficacy of which is much greater than Rb1.43

TCMs can also significantly modify the composition of human intestinal microbiota.44−47 Berberine is the main active compound of Rhizoma coptidis (RC), one TCM used to treat diarrhea and diabetes. In HFD-fed mice, Xie et al. found RC and berberine significantly improved obesity, glycemia, and lipidemia, and reduced the proportions of fecal Firmicutes and Bacteroidetes to total bacteria. In in vitro culture of the fecal suspension under aerobic and anaerobic conditions, RC and berberine significantly inhibit the growth of gut bacteria,48 so it is hypothesized that berberine exerts antiobesity and antidiabetes effects via modulating the gut microecology.49 We also found that, along with effectively preventing the development of obesity and insulin resistance, oral administration of berberine markedly altered the gut microbiota structure in HFD-fed rats. Microbiome-wide association study (MiWAS) based on high-throughput pyrosequencing of the 16S rRNA genes identified a total of 268 key species-specific phylotypes, the abundance data of which performed well in predicting the host phenotypes, suggesting the changes of these bacterial phylotypes are associated with health or disease. Short-chain fatty acid (SCFA)-producing bacteria, Allobaculum and Blautia, were selectively enriched by berberine. Accordingly, the intestinal SCFA concentrations were also significantly elevated, and systemic inflammation decreased (unpublished data). Thus, we proposed that the modulation of gut microbiota might participate in the pharmacology of berberine against obesity or insulin resistance through reducing the exogenous antigen load in the host, elevating SCFA levels in the intestine, and eventually alleviating systemic inflammation. Given the complex and multiple effects of TCM, these can realistically only be evaluated and understood within the context of “global or whole-body systems biology” with tools such as functional metagenomics. (iii). The Need for New Regulatory and Quality Control Processes in TCM

There are numerous potential regulatory issues that emerge if TCMs are to be considered for widespread use in Europe and the USA. These concern quality control, analytical chemistry procedures, identification of active and potentially toxic components, mechanisms of action, and testing procedures. Since 2004, the U.S. Food and Drug Administration (FDA) has issued the “Guide for Herbal Products” and “Complementary and Alternative Medicine (CAM) Products and FDA Administration Guide Draft” and approved some botanical drugs such as Veregen and Coartem tablets.50 The European Union has commenced the process of regulation of TCMs and other herbal remedies through the EMEA. They have issued a number of position papers and documents on working practices and many are available on the Internet.51 Integrated analytical and systems biology approaches offer new ways to address these regulatory issues as they enable mechanistic understanding as well as potential novel multivariate metrics for product quality control. (iv). New Directions in the Pharmaceutical Discovery Processes

The pharmaceutical industry enjoyed a sustained period of growth and success in the second half of the 20th century to bring to market a succession of novel drugs initially for infectious diseases, but later for many systemic and chronic disorders. This has transformed the lives of millions of people and strongly improved quality of life and decreased morbidity. Most success was achieved by a careful analysis of the detailed 3512

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receptor pharmacology and intelligent medicinal chemistry and drug design based on the physicochemical properties of the candidate drugs. However, more recently, the need to address more intractable diseases with less well-defined end points, coupled with the increased cost of drug discovery and development, has led to a shift in the whole industry toward high throughput chemical synthesis and high throughput screening of compound libraries in in vitro systems. While some success stories have been evident,52 it is true nevertheless that the number of new chemical entities (NCEs) approved by the U.S. FDA has continued to decline at the same time as the cost of bringing each of these to the market has escalated markedly to ca. $1 billion.53 This drive for additional economies has led to mergers and hostile acquisition of pharmaceutical companies to leave only a few global players. An additional event that spurred pharmaceutical companies was the decoding of the human genome and the promise that this new approach of genomics would unlock many of the mechanisms of human diseases and make drug discovery easier. It soon became apparent that this was a gross oversimplification, and while analysis of gene expression (transcriptomics) has helped in the understanding of interperson differences, the huge investment in such “pharmacogenomics” methods has yet to bear significant fruit, although again some successes can be acknowledged.54 Nevertheless, this has led to the concept of personalized healthcare as a way of maximizing efficacy and minimizing the possibility of adverse effects. The mindset of one drug for one disease (monopharmacy) is embedded in Western medicine, and although many, particularly older, people now have a polypharmacy for the multiple conditions and symptoms that become common in old age (hypertension, atherosclerosis, diabetes, analgesics, inflammatory conditions), there has been little drive to find genuine holistic solutions targeting the roots of these diseases.

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Table 1. The Multivariate Nature of TCM Therapy Inputs and Outputs and Corresponding Sources of Variation formulation or clinical procedure TCM source materials and their quality control

decoction preparation and its quality control

patient presentation administration of decoction to patients medical outcome − individual medical outcome − population wide

variance contributions first-order genetic and agronomic plant metabolome differences second-order chemical variation analytical technology quantitation errors (method dependent) cumulative errors from above combination of ingredients errors analytical technology quantitation errors (method dependent) variation in practitioner diagnosis decisions variation in practitioner choices − dose, timing, etc. efficacy variation toxicity variation human phenotypic variation human genetic variation microbiome differences nutritional differences other environmental differences

agronomic practices.61−63 This is a typical feature of many herbal medicines from different geographical locations.64−70 One successful method of characterizing TCMs and achieving quality control is spectroscopic profiling. However, to date this strategy has been mainly used to characterize variation in single plant species such as feverfew, Ginkgo biloba, and epimedium.62,68 Moreover, many of the articles promoting the use of a holistic metabonomic strategy for characterizing TCMs are published in Chinese journals limiting the accessibility and impact of relevant studies. On the other hand, by taking advantage of the variability in different batches of the same TCM and linking this to an observed variability in efficacy through multivariate statistics, the opportunity is then presented for identification of the active substances in that TCM, and hence new drug candidate molecules.

REDUCTION TO PRACTICE − THE COMBINATION OF TCMS AND GLOBAL SYSTEMS BIOLOGY

(i). Background

The potential of using metabonomics and global systems biology in harnessing the power of TCMs with respect to its integration into Western medicine has been mentioned many times and has been successfully applied in a limited number of case studies, for example, in demonstrating an improvement in type 2 diabetes and in stratifying patients with rheumatoid arthritis with respect to response to a TCM therapy.52,55−60 However, the deep integration of metabolic response profile data to TCMs treatment with the metagenomic signature, together with the bidirectional integration of compositional variation and response variation presented here, represents a paradigm shift in TCM and drug discovery thinking. Also the less challenging but nonetheless important issue of sample characterization and quality control of TCMs can be addressed by metabolic profiling in combination with multivariate statistical modeling. However, the compositional complexity of TCM products and the lack of regulatory bodies for governing their composition can make the systematic evaluation of their effect extremely challenging. Studies have repeatedly shown that there is high variability between manufacturers and even TCM batches and that the chemical composition of plants is modulated by many environmental factors including soil conditions, temperature, climate, seasonal variation, and

(ii). Principal Methodologies for Metabonomic and Metagenomic Profiling

The analysis of biological samples for metabonomic studies has been predominantly carried out by NMR spectroscopy and by mass spectroscopy (MS) coupled to a separating system such as gas chromatography (GC), high-performance liquid-chromatography (HPLC), or ultraperformance liquid-chromatography (UPLC), although this is often a more targeted strategy.13,71 NMR spectroscopy is a nondestructive technique that is highly effective for structural elucidation. Two-dimensional NMR can also enhance information recovery and aid in metabolite discovery by further increasing signal dispersion and revealing the connectivities between different NMR peaks with a molecule.72 Another technique, called high resolution 1H magic angle spinning (MAS) NMR spectroscopy, allows data acquisition on tissue samples.73 This allows for all biological sample types to be analyzed for metabolite analysis, rather than just biofluids. All of these data sets can be combined so that an integrated metabonomic approach across different types of sample is possible. MS is also an effective technique for identification of metabolic biomarkers. It is inherently more sensitive than NMR 3513

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Figure 2. Megavariate nature of the linkage between TCM composition variability, patient phenotype before and during treatment, and the necessity of multivariate statistical extraction of biomarkers of disease and efficacy as a function of time.

spectra, so that correlations from spectral peaks belonging to the same molecule can be identified.79 An extension of STOCSY, statistical heterospectroscopy (SHY) allows for the coanalysis of data sets obtained by both NMR spectroscopy and MS.80 DNA sequencing is the gold standard for comprehensive and quantitative characterization of the structure of microbial communities. The 16S rRNA gene clone library is one of the most widely used techniques in studies on the diversity of microbial communities.81 Here, the 16S rRNA gene of all bacteria in a community is amplified and subsequently cloned to construct a library. Clones are picked randomly for sequencing, and each unique sequence is aligned to those deposited in public databases (such as NCBI and RDP) to identify the microbial species that the sequence originated from and to determine its phylogenetic position. Clones with identical or similar sequences are classified as one operational taxonomic unit (OTU). The number of OTUs represents the number of different bacterial species (or populations) in the community, and the proportion of each OTU represents the richness of the corresponding bacterial species (or population), these being the parameters reflecting the structure of the bacterial community. For rapid, high-throughput, and low cost analyses, the nextgeneration sequencing methods, such as 454 pyrosequencing, Illumina, and SOLiD sequencings, have become the most promising new-generation sequencing approaches for gut microbiota profiling.82−86 It is highly automatic without requirements for clone library construction, fluorescencelabeled nucleotide primers/probes or electrophoresis. Moreover, it is a high-throughput system capable of producing several billion bases of accurate nucleotide sequences per experiment, which reduces the cost significantly compared to conventional Sanger sequencing. Because of the short length of each read (typically 450 bp for 454 sequencing; 50 to 100 bp for Illumina, and SOLiD sequencings), certain hypervariable

spectroscopy but requires a separation technique prior to analysis. MS is generally coupled to HPLC for metabonomic studies on biofluids, and both positive and negative ion mode spectra are usually acquired. Each data point on the HPLC chromatogram has a complementary full mass spectrum. Tandem MS/MS experiments can also be performed which produce fragments ions to provide more structural information.74 UPLC operates at much higher pressures than HPLC and uses smaller reversed-phase column packing material, thus giving much better chromatographic peak resolution than HPLC, with a ∼10-fold increase in speed and 3−5-fold increase in sensitivity. This subsequently reduces the major MS problem of ion suppression from coeluting peaks. Studies combining both MS and NMR techniques provide the optimal means to obtain full molecular characterization, and this is a different sort of integrated study.75,76 Chemometric tools have been developed to analyze the complex data sets that are produced.77,78 Simple methods such as principal components analysis (PCA) allow visualization of clustering of similar samples and determination of aberrant or outlier samples. In what are known as “supervised” methods, multiparametric data sets can be modeled so that the class of separate samples (a “validation set”) can be predicted based on a series of mathematical models derived from the original data or “training set”. Supervised methods allow the prediction of the parameters describing the validation set based on a series of mathematical models derived from the original data or training set. Partial least-squares (PLS) is one such method that relates a data matrix with known outcomes, for example, spectral intensity values, to a matrix containing dependent variables, for example, measurements of a response, for those samples. Statistical correlation methods utilizing the variance in spectroscopic profiles across a cohort of samples have been established. Statistical total correlation spectroscopy (STOCSY) takes advantage of the colinearity of the intensity variables for the multiple peaks of a metabolite in a set of NMR 3514

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fragments of 16S ribosome RNA genes, particularly V3 region, are always selected and sequenced for phylogenetic studies. More than 300 samples can be sequenced together in a sequencing experiment by using a sample-specific bar-coded PCR amplification followed by equal combination, purification, sequencing, and sequence assignment into samples according to the barcodes.87−89 Multivariate statistical methods, including both conventional methods (PCA, PLS discriiminate analysis, PLS regression, redundancy analysis, etc.) and phylogenetic distance-based UniFrac metrics have been used for the structural and functional analysis of the gut microbial community.16,90−93 Another approach in metagenomics is whole genome shotgun sequencing, which sequences the genomic DNA fragments isolated from environmental samples in a random manner.2,94,95 The metagenome represents the genomes of all constituent microbes in one microbial community.96 In multiplex cloning the total genomic DNA is extracted from the microbial communities in environmental samples, and the resultant DNA fragments are inserted into vectors to construct bacterial artificial chromosomes or fosmid libraries. By sequencing clones selected with gene markers reflecting the evolution of bacteria species such as the 16S rRNA gene, microbial species present in the community can be identified even if they are unculturable, and other fragments adjacent to the marker gene in their genomes can be discovered. In addition, new genes encoding new functional enzymes can be discovered by sequencing clones selected with bioactive assays, particularly in the field of gut microbiota studies.2,3,97

capacity to move the treated patients toward the metabolic space of the reference health populations, the responsible biomarker variables can be identified via appropriate statistics and well-tested spectroscopic methods. There is still a need for new and rapid approaches for confirmation of known molecules and identification of new biomarkers.98 The identified urinary metabolites or gut bacterial species can be used as a starting point to formulate hypotheses regarding why a therapy can change these biological parameters. This new temporal functional metagenomic approach may provide a powerful new tool for evaluating TCM therapies in the context of patient monitoring and stratification based on a holistic view of the human body including the metagenome. The integration of TCM with modern noninvasive “omics” platforms not only offers a new lease-on-life to TCM but could also transform this ancient empirical practice into evidence-based science and a new source of therapies for meeting 21st century global health challenges. (iv). Bidirectional Modeling of TCM Composition and Metabolic/Clinical Effects in Patients

The problem of understanding and optimizing TCM action is basically the same as that posed by any multiple drug interaction or polypharmacy. Even this is a simplification as many diseases have a multivariate and conditional set of causes and progressions and the concept of a single disease becomes less meaningful, especially in older people. TCMs are known to selectively alleviate some pathological effects, perhaps without removing the underlying cause (e.g., improvement in life quality of HIV-positive subjects by improving gut health, even though the viral load is not reduced).99 In TCM, unlike in Western medicine, in most cases the important and active molecular species and concentrations are unknown and can be concealed in a complex background of inactive or potentially toxic unknowns. The Western approach in this and other natural product discovery scenarios is to separate, screen, and isolate the “active” components and then possibly chemically modify these for clinical use. However, this is not in keeping with the philosophy of TCM or indeed the notion that the active ingredients may act synergistically, in which case the Western drug discovery strategy will be ineffective. The only tractable solution to this immensely complex problem is to treat it as a two-way multivariate optimization. It is known that there are variations in decoction composition and variations between batches and source materials and it is also known that there are differential responses to the treatments in the patients (which can be measured using global systems biology tools and metabolic phenotyping) that will partly relate to starting compositional variation which can be determined spectroscopically. So by statistically linking the variance on both sides of the equation (as indicated in Table 1), that is, spectrally based compositional profiles of starting materials to metabolic/ metagenomic outcomes of treatments in individual patients it should be possible to determine the major active components directly. Identification of the optimum TCM mixture should lead to more standardized treatments, easier regulatory compliance, and indeed new drug candidate molecules. Moreover this also allows the potential synergistic effects of TCM components to be retained as illustrated in the flow diagram shown in Figure 2. This approach leads naturally to stratified treatments that are better targeted to individuals and small homogeneous groups of patients, and fits well with the concept of monitoring

(iii). Implementation Strategy for Identification of Bioactive Components

To evaluate a TCM therapy with temporal functional metagenomics, a group of healthy people will first need to be selected to establish a population-specific reference data set which will define the boundary of metabolic and metagenomic diversity spaces of healthy local people.6 Then, the metabolic and metagenomic patterns of patients prior to the TCM treatment can also be characterized to divide them into subclasses, and patients of the same subclasses will be given one particular form of TCM therapy. Patients who receive any particular form of TCM therapy will be monitored by collecting their urine and fecal samples before, during, and after the intervention together with all relevant clinical data. The metabolic or metagenomic trajectory of each patient will be calculated by comparing with all the other patients and the reference populations with multivariate statistics to monitor if the distance between a particular patient and the reference healthy population is decreased, increased, or maintained in response to interventions. The reference population will define a target space toward which all the patients should move toward if the intervention they receive improves their health condition. If the intervention has some adverse effect it can be expected that the patient will move in a different metabolic direction within the hyperspace and away from the reference population. In principle, this scheme can be used to test if a therapy has any effects in single individuals. This makes clinical trials for personalized therapy possible using each as their own control. This approach can also be used to evaluate efficacy or toxicity of one particular therapy to many individuals to show that if a particular measure for preventing a disease is actually working in the targeted populations. If a particular therapy showed the 3515

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metabolically the individual patient journey over time for optimized treatment.23

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CONCLUSIONS The concept of the patient as a multivariate phenotype who is treated with a multivariate therapy and considering multivariate end point as figures of merit is a novel and holistic approach that can provide benefits for patient health in both the East and the West. The approach should lead to new drug target opportunities and new drug compound classes by defining the active principles present in TCMs, and thus this presents a novel direction for conventional Western pharmaceutical drug discovery efforts. Moreover, a whole new set of drug receptors/ targets become accessible by attacking the gut microbiome. Finally, the idea of a multivariate metagenomic and metabolic phenotype offers vastly increased chance of finding new biomarkers of disease and of therapy efficacy, and hence new opportunities in diagnostics and theranostics. Using the concept of pharmacometabonomics, one can envisage that patient stratification for treatment becomes possible based on baseline metabolic and metagenomic profiles. Thus it is possible that optimization of TCMs for patients of various ethnic backgrounds, where long time information is not available as for Chinese populations would be achievable. This approach would minimize the potential toxic side effects of such treatments. Moreover, the dynamic response of a patient to therapy could be mapped and evaluated and thus linked to the therapeutic intervention in an iterative fashion. We believe that the successful implementation of TCM optimization via systems biology methods will lead to new approaches for the development of polypharmacies, not only for Western drugs but for novel combinatorial interventions of Western drugs with TCMs.

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AUTHOR INFORMATION

Corresponding Author

*(L.Z.) Tel: 86-21-34204877. Fax: 86-21-34204878. Mobile: 86-13817866559. E-mail: [email protected]; lpzhao@ sjtu.edu.cn. (J.K.N.) E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank a large number of scientists in both the U.K. and China for helpful discussions, and, via the formation of an informal Sino−U.K. consortium, we particularly acknowledge the help of Shengli Yang, Chenhong Zhang, and Menghui Zhang of the Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai; Yulan Wang from the Wuhan Institute of Physics and Mathematics, The Chinese Academy of Sciences, Wuhan; Feifang Zhang and Xinmiao Liang of the Dalian Institute of Chemical Physics, The Chinese Academy of Sciences, Dalian; Guoping Zhao from the Human Genome Sequencing Center, Shanghai; Nigel J. Gooderham and Caroline Sands from Biomolecular Medicine, Imperial College London; and Peter Hylands from the Pharmacy Department, Kings College, London. We also thank the U.K. Foreign and Commonwealth Office/MOST and the Chinese Ministry of Science and Technology (Project No. 2006BAI11B08) for provision of funding for a workshop on systems biology in TCM research in Shanghai in March 2010. 3516

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Journal of Proteome Research

Reviews

(82) Shendure, J.; Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 2008, 26 (10), 1135−1145. (83) Mardis, E. R. Next-generation DNA sequencing methods. Annu. Rev. Genomics Hum. Genet. 2008, 9, 387−402. (84) Margulies, M.; Egholm, M.; Altman, W. E.; Attiya, S.; Bader, J. S.; Bemben, L. A.; Berka, J.; Braverman, M. S.; Chen, Y.-J.; Chen, Z.; Dewell, S. B.; Du, L.; Fierro, J. M.; Gomes, X. V.; Godwin, B. C.; He, W.; Helgesen, S.; Ho, C. H.; Irzyk, G. P.; Jando, S. C.; Alenquer, M. L. I.; Jarvie, T. P.; Jirage, K. B.; Kim, J.-B.; Knight, J. R.; Lanza, J. R.; Leamon, J. H.; Lefkowitz, S. M.; Lei, M.; Li, J.; Lohman, K. L.; Lu, H.; Makhijani, V. B.; McDade, K. E.; McKenna, M. P.; Myers, E. W.; Nickerson, E.; Nobile, J. R.; Plant, R.; Puc, B. P.; Ronan, M. T.; Roth, G. T.; Sarkis, G. J.; Simons, J. F.; Simpson, J. W.; Srinivasan, M.; Tartaro, K. R.; Tomasz, A.; Vogt, K. A.; Volkmer, G. A.; Wang, S. H.; Wang, Y.; Weiner, M. P.; Yu, P.; Begley, R. F.; Rothberg, J. M. Genome sequencing in microfabricated high-density picolitre reactors. Nature 2005, 437 (7057), 376−380. (85) Bentley, D. R.; Balasubramanian, S.; Swerdlow, H. P.; Smith, G. P.; Milton, J.; Brown, C. G.; Hall, K. P.; Evers, D. J.; Barnes, C. L.; Bignell, H. R.; Boutell, J. M.; Bryant, J.; Carter, R. J.; Keira Cheetham, R.; Cox, A. J.; Ellis, D. J.; Flatbush, M. R.; Gormley, N. A.; Humphray, S. J.; Irving, L. J.; Karbelashvili, M. S.; Kirk, S. M.; Li, H.; Liu, X.; Maisinger, K. S.; Murray, L. J.; Obradovic, B.; Ost, T.; Parkinson, M. L.; Pratt, M. R.; Rasolonjatovo, I. M. J.; Reed, M. T.; Rigatti, R.; Rodighiero, C.; Ross, M. T.; Sabot, A.; Sankar, S. V.; Scally, A.; Schroth, G. P.; Smith, M. E.; Smith, V. P.; Spiridou, A.; Torrance, P. E.; Tzonev, S. S.; Vermaas, E. H.; Walter, K.; Wu, X.; Zhang, L.; Alam, M. D.; Anastasi, C.; Aniebo, I. C.; Bailey, D. M. D.; Bancarz, I. R.; Banerjee, S.; Barbour, S. G.; Baybayan, P. A.; Benoit, V. A.; Benson, K. F.; Bevis, C.; Black, P. J.; Boodhun, A.; Brennan, J. S.; Bridgham, J. A.; Brown, R. C.; Brown, A. A.; Buermann, D. H.; Bundu, A. A.; Burrows, J. C.; Carter, N. P.; Castillo, N.; Chiara, E.; Catenazzi, M.; Chang, S.; Neil Cooley, R.; Crake, N. R.; Dada, O. O.; Diakoumakos, K. D.; Dominguez-Fernandez, B.; Earnshaw, D. J.; Egbujor, U. C.; Elmore, D. W.; Etchin, S. S.; Ewan, M. R.; Fedurco, M.; Fraser, L. J.; Fuentes Fajardo, K. V.; Scott Furey, W.; George, D.; Gietzen, K. J.; Goddard, C. P.; Golda, G. S.; Granieri, P. A.; Green, D. E.; Gustafson, D. L.; Hansen, N. F.; Harnish, K.; Haudenschild, C. D.; Heyer, N. I.; Hims, M. M.; Ho, J. T.; Horgan, A. M.; Hoschler, K.; Hurwitz, S.; Ivanov, D. V.; Johnson, M. Q.; James, T.; Huw Jones, T. A.; Kang, G.-D.; Kerelska, T. H.; Kersey, A. D.; Khrebtukova, I.; Kindwall, A. P.; Kingsbury, Z.; Kokko-Gonzales, P. I.; Kumar, A.; Laurent, M. A.; Lawley, C. T.; Lee, S. E.; Lee, X.; Liao, A. K.; Loch, J. A.; Lok, M.; Luo, S.; Mammen, R. M.; Martin, J. W.; McCauley, P. G.; McNitt, P.; Mehta, P.; Moon, K. W.; Mullens, J. W.; Newington, T.; Ning, Z.; Ling Ng, B.; Novo, S. M.; O/’Neill, M. J.; Osborne, M. A.; Osnowski, A.; Ostadan, O.; Paraschos, L. L.; Pickering, L.; Pike, A. C.; Pike, A. C.; Chris Pinkard, D.; Pliskin, D. P.; Podhasky, J.; Quijano, V. J.; Raczy, C.; Rae, V. H.; Rawlings, S. R.; Chiva Rodriguez, A.; Roe, P. M.; Rogers, J.; Rogert Bacigalupo, M. C.; Romanov, N.; Romieu, A.; Roth, R. K.; Rourke, N. J.; Ruediger, S. T.; Rusman, E.; Sanches-Kuiper, R. M.; Schenker, M. R.; Seoane, J. M.; Shaw, R. J.; Shiver, M. K.; Short, S. W.; Sizto, N. L.; Sluis, J. P.; Smith, M. A.; Ernest Sohna Sohna, J.; Spence, E. J.; Stevens, K.; Sutton, N.; Szajkowski, L.; Tregidgo, C. L.; Turcatti, G.; vandeVondele, S.; Verhovsky, Y.; Virk, S. M.; Wakelin, S.; Walcott, G. C.; Wang, J.; Worsley, G. J.; Yan, J.; Yau, L.; Zuerlein, M.; Rogers, J.; Mullikin, J. C.; Hurles, M. E.; McCooke, N. J.; West, J. S.; Oaks, F. L.; Lundberg, P. L.; Klenerman, D.; Durbin, R.; Smith, A. J. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 2008, 456 (7218), 53−59. (86) Shendure, J.; Porreca, G. J.; Reppas, N. B.; Lin, X.; McCutcheon, J. P.; Rosenbaum, A. M.; Wang, M. D.; Zhang, K.; Mitra, R. D.; Church, G. M. Accurate Multiplex Polony Sequencing of an Evolved Bacterial Genome. Science 2005, 309 (5741), 1728−1732. (87) Parameswaran, P.; Jalili, R.; Tao, L.; Shokralla, S.; Gharizadeh, B.; Ronaghi, M.; Fire, A. Z. A pyrosequencing-tailored nucleotide barcode design unveils opportunities for large-scale sample multiplexing. Nucleic Acids Res. 2007, 35 (19), e130.

(88) Andersson, A. F.; Lindberg, M.; Jakobsson, H.; Bäckhed, F.; Nyrén, P.; Engstrand, L. Comparative Analysis of Human Gut Microbiota by Barcoded Pyrosequencing. PLoS One 2008, 3 (7), e2836. (89) Zhang, C.; Zhang, M.; Wang, S.; Han, R.; Cao, Y.; Hua, W.; Mao, Y.; Zhang, X.; Pang, X.; Wei, C.; Zhao, G.; Chen, Y.; Zhao, L. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J. 2009, 4 (2), 232−241. (90) Zhang, M.; Zhang, M.; Zhang, C.; Du, H.; Wei, G.; Pang, X.; Zhou, H.; Liu, B.; Zhao, L. Pattern extraction of structural responses of gut microbiota to rotavirus infection via multivariate statistical analysis of clone library data. FEMS Microbiol. Ecol. 2009, 70 (2), 177−185. (91) Wang, T.; Cai, G.; Qiu, Y.; Fei, N.; Zhang, M.; Pang, X.; Jia, W.; Cai, S.; Zhao, L. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 2012, 6 (2), 320−329. (92) Lozupone, C.; Lladser, M. E.; Knights, D.; Stombaugh, J.; Knight, R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011, 5 (2), 169−172. (93) Lozupone, C.; Knight, R. UniFrac: A new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 2005, 71 (12), 8228−8235. (94) Tringe, S. G.; von Mering, C.; Kobayashi, A.; Salamov, A. A.; Chen, K.; Chang, H. W.; Podar, M.; Short, J. M.; Mathur, E. J.; Detter, J. C.; Bork, P.; Hugenholtz, P.; Rubin, E. M. Comparative metagenomics of microbial communities. Science 2005, 308 (5721), 554−557. (95) Venter, J. C.; Remington, K.; Heidelberg, J. F.; Halpern, A. L.; Rusch, D.; Eisen, J. A.; Wu, D.; Paulsen, I.; Nelson, K. E.; Nelson, W.; Fouts, D. E.; Levy, S.; Knap, A. H.; Lomas, M. W.; Nealson, K.; White, O.; Peterson, J.; Hoffman, J.; Parsons, R.; Baden-Tillson, H.; Pfannkoch, C.; Rogers, Y.-H.; Smith, H. O. Environmental genome shotgun sequencing of the Sargasso Sea. Science 2004, 304 (5667), 66−74. (96) Schloss, P. D.; Handelsman, J. Biotechnological prospects from metagenomics. Curr. Opin. Biotechnol. 2003, 14 (3), 303−310. (97) Turnbaugh, P. J.; Ley, R. E.; Mahowald, M. A.; Magrini, V.; Mardis, E. R.; Gordon, J. I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006, 444 (7122), 1027− 1131. (98) Bradshaw, J.; Butina, D.; Dunn, A. J.; Green, R. H.; Hajek, M.; Jones, M. M.; Lindon, J. C.; Sidebottom, P. J. A rapid and facile method for the dereplication of purified natural products. J. Nat. Prod. 2001, 64 (12), 1541−154. (99) Anastasi, J. K.; Dawes, N. C.; Li, Y. M. Diarrhea and human immunodeficiency virus: Western and Eastern perspectives [corrected]. J. Altern. Complement. Med. 1997, 3 (2), 163−168.

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dx.doi.org/10.1021/pr3001628 | J. Proteome Res. 2012, 11, 3509−3519