Molecular Aspects of Medicine 69 (2019) 93–106 Contents lists available at ScienceDirect Molecular Aspects of Medicine journal homepage: www.elsevier.com/locate/mam Microbiome and colorectal cancer: Roles in carcinogenesis and clinical T potential Ester Sausa,b, Susana Iraola-Guzmána,b, Jesse R. Willisa,b, Anna Brunet-Vegac, Toni Gabaldóna,b,d,∗ a Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain b Universitat Pompeu Fabra (UPF), 08003, Barcelona, Spain c Oncology Service, Parc Taulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Universitat Autònoma de Barcelona, Sabadell, Spain d ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain ARTICLE INFO ABSTRACT Keywords: The gastrointestinal tract harbors most of the microbiota associated with humans. In recent years, there has been Gut microbiome a surge of interest in assessing the relationships between the gut microbiota and several gut alterations, including Colorectal cancer colorectal cancer. Changes in the gut microbiota in patients suffering colorectal cancer suggest a possible role of Colon host-microbe interactions in the origin and development of this malignancy and, at the same time, open the door Microbiota for novel ways of preventing, diagnosing, or treating this disease. In this review we survey current knowledge on the healthy microbiome of the gut and how it is altered in colorectal cancer and other related disease conditions. In describing past studies we will critically assess technical limitations of different approaches and point to existing challenges in microbiome research. We will have a special focus on host-microbiome interaction me- chanisms that may be important to explain how dysbiosis can lead to chronic inflammation and drive processes that influence carcinogenesis and tumor progression in colon cancer. Finally, we will discuss the potential of recent developments of novel microbiota-based therapeutics and diagnostic tools for colorectal cancer. 1. Introduction dysbiosis-related inflammation and the biosynthesis of chemical carci- nogens (e.g. acetaldehyde, N-nitroso compounds) by microbes are The gut comprises an abundant and diverse community of micro- among several possible mechanisms through which the microbiota may organisms (archaea, fungi, protozoa and viruses), which is collectively have a role in carcinogenesis (Arthur et al., 2012; Kostic et al., 2013; referred to as the “gut microbiome” (Costello et al., 2012). A typical gut Rubinstein et al., 2013; Sears et al., 2008). The colon is the most heavily microbiome may comprise trillions of microbial cells from over several colonized section of the digestive tract, and it has been estimated that hundreds of different species, which genomes can entail, globally, over this organ contains approximately 70% of the estimated human mi- three million genes (Sender et al., 2016). This complex ecosystem is not crobiome (Sekirov et al., 2010). The colon is also the section of the a passive colonizer of our gut, but rather interacts with the host in many digestive tract that is more prone to develop cancer, with cancer in- ways, contributing to various processes such as nutritional absorption, cidence being 12-fold higher in the colon as compared to the small metabolism, immunity, tissue development, and carcinogenesis (Bosch intestine (Gagnière et al., 2016). In addition, known risk factors in and McFall-Ngai, 2011; Dzutsev et al., 2015). Alterations in the com- colorectal cancer (CRC), such as dietary habits and life-style (Moskal position of the gut microbiome - a state known as dysbiosis - have been et al., 2016; Torre et al., 2015), are known to modulate the gut mi- associated with a growing number of prevalent human diseases, in- crobiota. Thus, a plausible hypothesis is that certain colonic microbes cluding cancer (Schwabe and Jobin, 2013). Indeed, it has been esti- or alterations of the typical resident colonic flora may create a micro- mated that more than 20% of the cancer burden worldwide is attribu- environment that is more favorable to tumor development. This hy- table to known intestinal infectious agents that are often normal pothesis is supported by findings from several studies that explored the residents of the intestinal microbiota (Zur Hausen, 2009). In particular, gut microbiota associated with individuals with CRC (Gagnière et al., Abbreviations: T, tissue; F, feces; NA, not available; SCFA, short-chain fatty acids; 16S, 16S ribosomal RNA sequencing and/or pyrosequencing; WGS, whole-genome shotgun sequencing; qPCR, quantitative PCR ∗ Corresponding author. Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain. E-mail addresses: ester.saus@crg.eu (E. Saus), susana.iraola@crg.eu (S. Iraola-Guzmán), jesseR.Willis@crg.eu (J.R. Willis), ABrunetV@tauli.cat (A. Brunet-Vega), toni.gabaldon.bcn@gmail.com (T. Gabaldón). https://doi.org/10.1016/j.mam.2019.05.001 Received 7 March 2019; Accepted 8 May 2019 Available online 24 May 2019 0098-2997/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 2016; Kostic et al., 2013; Wang et al., 2012; Weir et al., 2013). In this hundred base pairs versus differences across the entire genome can regard, a growing number of studies report specific alterations in the often limit identification to only the genus level. In contrast, WGS al- gut microbiome associated with CRC and explored its value for CRC lows for more accurate detection of species, or even strains, and di- screening (Wong et al., 2017a). This is particularly important for CRC, versity within samples, as well as identification of the coding potential as an early diagnosis of the tumor (in stages 0, I or II) is associated to of the genome, which can only be indirectly inferred in the case of 16S 80% survival rate over five years, which is reduced to only 10% in later sequencing, by extrapolation from known genomes (Ranjan et al., diagnosis (stage IV). In this context, the detection of specific micro- 2016). Further details on the methodologies used in metagenomics biome alterations has emerged as a promising strategy for CRC diag- analyses can be found elsewhere (Mallick et al., 2017; Song et al., nostics (Wong et al., 2017a; Zeller et al., 2014a). Finally, some bacterial 2018). genera have been hypothesized to be protective against CRC (Appleyard Both 16S and WGS approaches have been extensively used to study et al., 2011). This protective phenotype may be mediated through the human gut microbiome, perhaps the most intensively studied niche metabolite production, induction of immunological tolerance, or an so far, with two main large international consortia playing a major role ability to outcompete pathogenic bacteria or fungi (Coker et al., 2018a; in driving the field: the Metagenomics of the Human Intestinal Tract Zhu et al., 2011). In the long run, a better knowledge of the relation- (MetaHIT) (Li et al., 2014), and the Human Microbiome Project (HMP) ships between the microbiota and the origin and progression of CRC (Human Microbiome Project Consortium, 2012; Schmidt et al., 2018). may open novel opportunities for the development of therapies tar- One of the earliest large-scale analyses used WGS to provide a catalogue geting the microbiome. In this regard the development and use of of bacterial genes present in the gut microbiome based on an analysis of prebiotics, probiotics, specific antibiotics, phage therapies, or the 124 European individuals (Qin et al., 2010). They also catalogued be- transplantation of whole microbiomes may bring new tools for the tween 1000 and 1150 prevalent bacterial species, of which each in- prevention and treatment of CRC (Kelly, 2013; Lynch and Pedersen, dividual carried at least 160. In this early work, clustering analyses 2016; Schmidt et al., 2018). In this review we provide an overview of already showed distinct microbial compositions for individuals having how the gut microbiome is studied and survey recent research directed specific gut alterations such as Crohn's disease or ulcerative colitis. towards unveiling possible relationships between gut microbiota and Later analyses showed that healthy individuals also clustered into dif- the origin and development of CRC. We next provide an overview of ferent microbiome composition profiles, which were dubbed “en- potential applications related to microbiome-based diagnostic and terotypes” driven by the abundances of one or a few different organisms therapeutic tools, which are still in very early-development stages. Fi- (Arumugam et al., 2011). The enterotype concept has been somewhat nally, we discuss the current limitations and future potential of this controversial in the field, with some authors suggesting that instead of field of research. placing samples into discrete clusters, a gradient of abundances should be recognized to better compare samples (Jeffery et al., 2012), while 2. The gut microbiome and approaches to assess it others prefer the cluster-centric approach, with the use of carefully defined boundaries (Costea et al., 2018). Moreover, it is as yet not Metagenomic studies have most commonly used one of two main entirely clear whether enterotypes are determined by extrinsic factors approaches to assess the composition of microbiomes: whole-genome such as diet, or immune state, represent intrinsically different ecolo- shotgun (WGS) sequencing, and 16S ribosomal RNA amplicon sequen- gical optima, or both (Costea et al., 2018). Therefore, whatever the cing. In both cases, DNA sequences of microbes present in a given enterotype philosophy, samples may also be stratified, either instead by sample are read using next generation sequencing technologies, and or alongside, other factors like medication (Forslund et al., 2015), age compared to a database of known sequences to determine the presence (Jeffery et al., 2016), lifestyle (Barton et al., 2018) or diet (Zhernakova and abundance of particular taxa. Depending on the sequencing and et al., 2016). Similarly, there has been a major interest in finding what analysis approach, a resolution at the strain, species, genus, or higher microbiome differences may associate with the presence (or the risk of taxonomic levels can be achieved, often depending on the specific developing it in the future) of a particular disease. Such studies com- clades. In addition, WGS can provide information on the genes encoded pare microbiome compositions between donors having a particular by the strains present in the sample, at least the most abundant ones. condition, and a control group, to then find significant differences. Such This information can be used to reconstruct potential metabolic capa- studies have been dubbed Metagenome-Wide (or Microbiome-Wide) cities of a given microbial ecosystem. Association Studies (MWAS) in analogy to Genome-Wide Association WGS is performed by randomly fragmenting all the DNA into small Studies (GWAS) (Gilbert et al., 2016; Wang and Jia, 2016). Finding segments multiple times, so as to determine the sequences of millions of such associations is the first step not only to discover potential new fragments in parallel. Then they are assembled into longer fragments mechanisms underlying the origin, progression, or the effects of the (contigs) corresponding to chromosomal regions (perhaps full genomes disease, but also for the development of future diagnostic and ther- for the most abundant species) by piecing together the overlapping ends apeutic tools based on the monitoring or modulation, respectively, of (Anderson, 1981). Finally, these contigs are annotated and analyzed in key elements of the microbiota (Schmidt et al., 2018). Particular dif- terms of their taxonomic origin and functional capabilities. However, ferences found between CRC and control samples, and their relevance the most commonly used technique in metagenomic studies these days for understanding, detecting, and treating the disease are discussed in is the targeted sequencing of the 16S rRNA gene, which is present in all subsequent sections of this review. bacterial and archaeal genomes. This gene has highly conserved regions The deep understanding of the functional roles of the gut microbiota that allow for the use of universal primer sequences to bind to and and its interactions with the human host is a further step, which is specifically amplify them. Between the highly conserved regions of this needed to enable the application of microbiome knowledge to the 16S gene, there are nine highly variable regions (named V1 through clinics. In this line, the use of metatranscriptomics and metabolomics V9), so mapping the read sequences to a database of known 16S rRNA approaches can be complementary to metagenomics approaches. gene sequences allows for taxonomic identification of the bacteria Metatranscriptomics is the sequencing-based analysis of expressed present in a sample (Weisburg et al., 1991). Focusing on just one or two transcripts in a sample, which provides information on what genes are of these segments of this particular gene highlights the advantage of the active at the time of the experiment. Metatranscriptomics can help to 16S sequencing technique: the vast reduction in cost and data man- elucidate biological functions underlying microbial dysbiosis associated agement which enables cost-effective, large-scale studies. The primary to multiple diseases. Some metatranscriptomic studies have been con- disadvantage of 16S sequencing however is the lack of taxonomic re- ducted to explore the functional role of the human gut microbiota solution; while the variable regions of the gene are particular to dif- (Franzosa et al., 2014; Ranjan et al., 2018) and its relationship with ferent organisms, finding differences within this section of a few disorders such as inflammatory bowel disease (IBD) (Schirmer et al., 94 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 Fig. 1. Microbiome dysbiosis in CRC. This figure summarizes factors influencing the gut microbiome, a model for dysbiosis in carcinogenesis process of CRC, and a description of the bacteria species accounting for dysbiosis in CRC. Upper part: factors shaping the gut microbiome: host (genetics and non-genetics), lifestyle (diet, exercise, sleeping time, etc), and environmental (pool of colonizing strains) factors. All those factors may have an influence in the microbiome, which in turn may influence the state of health/disease of individuals. Other factors influencing the state of gut microbiome include stochastic effects, presence of driver/passenger species, its compositional state and metabolomics. Middle part: The multi-step process of carcinogenesis and the influence of the microbiome. As suggested by several authors, imbalances in the normal content of the gut microbiome (gut dysbiosis) lead to colonization of driver bacteria that induce a chronic inflammation of the gut epithelia. This inflammation changes the microenvironment and biofilm and allow a new colonization by passenger bacteria, which may contribute to carcinogenesis process from adenomatosis to tumor formation. Bottom: Bacterial species enriched/depleted in CRC both tumor and feces samples as compared to control samples. 2018). However, in CRC there is an obvious gap, with, to the best of our 3. Relationships between gut microbiome and colorectal cancer knowledge, no exhaustive metatranscriptomics studies available in CRC patients. The scarcity of metatranscriptomic studies is likely related to CRC is a complex malignant disease whose multi-stage development specific limitations that complicate its use, such as the low stability of involves numerous factors, including genetic and environmental risk RNA, the need to deplete ribosomal RNA, and the complexity of factors. It has been shown that an accumulation of genetic and epige- downstream data analyses (Aguiar-Pulido et al., 2016). netic alterations in proto-oncogenes, tumor suppressor genes, and/or Metabolomics is the study of small molecules or metabolites present DNA repair genes, leads to transformation of the normal colonic epi- in biological specimens. Metabolites and other chemical compounds are thelium into tumoral cells (Fleming et al., 2012). The majority of CRC the ultimate link mediating host-pathogen interactions, and therefore cases occur sporadically and less than 20% of CRC cases are hereditary the metabolome may provide important mechanistic insights into (Carethers and Jung, 2015). Smoking, alcohol consumption, obesity cancer-related processes (Armitage and Barbas, 2014). Metabolites of and diabetes are all known factors involved in this disease. Dietary microbial or host origin are released in the gut and affect the tumor factors such as a diet rich in processed foods, animal fat and red meat microenvironment. The main final aim of metabolomics research in coupled with a low intake of fibre and fruits have also been validated as CRC is to apply this knowledge to i) improving understanding of CRC an important risk factor for the development of sporadic CRC (Moskal etiology – for individual/population risk stratification, and effective et al., 2016). More recently, an increasing number of studies report cancer prevention policies, ii) describing metabolite profiles capable of alterations of the gut microbiota in CRC samples, suggesting that gut predicting patients with CRC at early stages of tumorigenesis, even microbiota may be an essential contributing factor to the initiation and distinguishing solitary adenoma from disease-free controls (diagnosis development of this cancer. biomarkers) (Farshidfar et al., 2016; Tan et al., 2013; Uchiyama et al., The gut microbiota is involved in the maintenance of mucosal 2017), and iii) finding prognosis, survival and recurrence biomarkers to homeostasis and epithelial barrier function. In a healthy situation, the improve intervention and treatment strategies in patients with different intestinal barrier is efficiently compartmentalizing bacteria to the molecular CRC subtypes (Liesenfeld et al., 2015; Qiu et al., 2014). In lumen, but perturbations in gut barrier function can lead to increased addition, metabolomic analyses can serve as orthogonal lines of evi- “intestinal permeability”, which has been shown to be associated with a dence to confirm mechanistic hypotheses generated in MWASs. variety of gastrointestinal disorders and diseases, including in- flammatory bowel disease (IBD), irritable bowel syndrome (IBS), celiac 95 Table 1 Gut microbiota identified in CRC patients and healthy individuals, classified in bacteria enriched, depleted or being controversial in CRC. Columns indicate, in this order: phyla of the bacteria, genus of the bacteria, species of the bacteria identified if having this level of resolution, type of sample studied (feces or tissue), description of the mechanism through which the described bacteria exert their function if known, and E. Saus, et al. methodology used to assess the microbiome. Phyla Genus Species Sample Mechanism/Function Method References Enriched in CRC ACTINOBACTERIA Collinsella — T NA 16S Marchesi et al. (2011) Slackia — T Anti-oxidant potential 16S Marchesi et al. (2011) BACTEROIDETES Alistipes A. finegoldii* F Inflammatory 16S, WGS (Baxter et al., 2014; Dai et al., 2018a; Feng et al., 2015) Bacteroides B. fragilis* F, T Inflammatory, enterotoxigenic (fragilisin) 16S, WGS (Dai et al., 2018a; Drewes et al., 2017; Hale et al., 2018; Wang et al., 2012) Porphyromonas P. asaccharolytica* F, T Inflammatory 16S, WGS, (Ahn et al., 2013; Chen et al., 2012; Dai et al., 2018a; Wang et al., 2012) qPCR Prevotella P.intermedia* F, T Inflammatory 16S, WGS, (Dai et al., 2018a; Flemer et al., 2017; Sobhani et al., 2011) qPCR EURYARCHAEOTA Methanobrevibacter – F, T NA/methane producer 16S, qPCR Mira-Pascual et al. (2015) FIRMICUITES Enterococcus E. faecalis F, T Inflammatory, oxidative stress 16S, qPCR (Wang et al., 2012; Zhou et al., 2016) Gemella — F, T NA 16S (Allali et al., 2015; Chen et al., 2012) Mogibacterium – F, T NA 16S Chen et al. (2012) Parvimonas P. micra* F, T Inflammatory, Immune response 16S, WGS, (Allali et al., 2015; Dai et al., 2018a; Drewes et al., 2017; Flemer et al., 2018; Yu et al., qPCR 2017) Peptostreptococcus P. stomatis*, P. anaerobious F, T Oxidative stress 16S, WGS, (Chen et al., 2012; Drewes et al., 2017; Tsoi et al., 2017; Wang et al., 2012) qPCR Staphylococcus — F NA 16S Wu et al. (2013) Solobacterium S. moorei F NA WGS, qPCR Yu et al. (2017) 96 Streptococcus S. gallolyticus* (previously S.bovis F, T Inflammatory 16S, qPCR (Allali et al., 2015; Boleij and Tjalsma, 2013; Klein et al., 1977; Shen et al., 2010; Wang biotypeI) et al., 2012) FUSOBACTERIA Fusobacterium F. nucleatum* F, T Inflammatory, butyrate producer 16S, WGS, (Dai et al., 2018a; Drewes et al., 2017; Hamada et al., 2018; Kostic et al., 2013; Liang et al., qPCR 2017; Marchesi et al., 2011; Sinha et al., 2016; Wong et al., 2017a; Zhernakova et al., 2016) PROTEOBACTERIA Escherichia — F, T Genotoxin (colibactin), DNA mismatch repair, DNA 16S (Nougayrède et al., 2006; Swidsinski et al., 1998; Taieb et al., 2016; Wang et al., 2012) damage checkpoint Helicobacter H. pylori T Inflammatory 16S (Grahn et al., 2005; Kamada et al., 2013) Klebsiella – F, T Alter the cell biology of the host 16S (Chen et al., 2012; Wang et al., 2012) SYNERGISTETES Thermanaerovibrio T. acidaminovorans* F NA WGS Dai et al. (2018a) VERRUCOMICROBIA Akkermansia A. muciniphila F, T Immune modulatory (involved in PD-1 blockade 16S (Mira-Pascual et al., 2015; Routy et al., 2018; Weir et al., 2013) efficacy) Depleted in CRC ACTINOBACTERIA Bifidobacterium Several F, T Immune modulatory, anti-inflammatory, butyrate 16S, WGS, (Candela et al., 2011; Chen et al., 2012; Feng et al., 2015; Gueimonde et al., 2007) production qPCR BACTEROIDETES Bacteroides B. vulgatus, B. uniformin F Inflammatory 16S Wang et al. (2012) FIRMICUITES Anaerostipes – T Butyrate producer 16S Chen et al. (2012) Clostridium C. butyricum F Secondary bile acids producer, apoptosis of CRC WGS Dai et al. (2018a) cells, inhibition of tumorigenesis (mice) Eubacterium E. ventriosum F, T Inflammatory, butyrate producer, DNA damage 16S, WGS, (Chen et al., 2012; Wu et al., 2013; Yu et al., 2017) Faecalibacterium F. prausnitzii F, T Anti-inflammatory, butyrate producer 16S, qPCR (Balamurugan et al., 2008; Chen et al., 2012; Marchesi et al., 2011; Shah et al., 2018) (continued on next page) Molecular Aspects of Medicine 69 (2019) 93–106 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 disease, and CRC development (Arrieta et al., 2006; Bischoff et al., 2014; Owyang and Wu, 2014; Sattar et al., 1985). In addition, the collective activities of gut microbes and particularly their metabolic products can have a major influence on the immune response, and be a source of chronic inflammation. Microbiota and their metabolites, such as short chain fatty acids (e.g. butyrate) might induce dysfunction in the gut epithelial barrier, thereby activating proinflammatory mediators (Marchesi et al., 2011; Meehan and Beiko, 2014; Wang et al., 2012) such as cytokines, interleukin-6 (IL-6), and tumor necrosis factor-α (Marchesi et al., 2011; Tjalsma et al., 2012; Wang et al., 2012) (TNF-α), that damage epithelial cells and their junctions (Wu et al., 2009; Yoshioka et al., 2009). Some specific bacterial species can trigger inflammatory responses or produce toxins that directly damage gut cells. For instance, Bacteroides fragilis and Enterococcus faecalis produce enterotoxins (i.e. fragylisin) and reactive oxygen species that cause oxidative DNA damage, induce inflammation, and damage the epithe- (Chen et al., 2012; Richard et al., 2018) lial barrier (Goodwin et al., 2011; Wu et al., 2009). In addition, B. fragilis activates β-catenin nuclear signalling and induces cellular pro- liferation (Wu et al., 2003). Similarly Fusobacterium nucleatum can in- duce inflammatory changes by directly adhering to and invading co- lonic epithelial cells via the FadA surface protein, which interacts with (2011) (2011) (2011) (2011) (2011) E-cadherin to mediate changes in β-catenin and Wnt signalling (Kostic Chen et al. (2012) et al., 2013; Rubinstein et al., 2013). As we will see below, some of Zhu et al. (2014) al. al. al. al. al. these bacterial species have been shown to have significantly different et et et et et References abundances in CRC samples as compared to healthy controls. Marchesi Marchesi Marchesi Marchesi Marchesi A growing body of evidence indicates that CRC arises from a step- wise disturbance of the composition of the gut microbiota, induced by food components or diet, plus genetic alterations in oncogenes and tumor-suppressor genes (Fig. 1). Several microbes have been found to be differentially enriched in tumor versus normal tissues or in fecal Method samples from patients with CRC versus healthy control subjects. Altered 16S 16S 16S 16S 16S 16S 16S 16S 16S 16S taxa in CRC are different depending on whether the samples are ob- tained directly from mucosal or from fecal samples (Flemer et al., 2017; Gao et al., 2017). However, despite the fact that the fecal microbiome is Immune modulatory (activation T-cells), mucus SCFA producer, secondary bile acid producer only a proxy for the gut microbiome, this non-invasive approach has provided crucial information of the changes in the gut ecosystem as- Anti-inflammatory, butyrate producer sociated with CRC, and is the most commonly used sampling method in gut microbiome studies. Numerous studies comparing fecal microbiota from CRC patients versus controls have shown that despite having overall similar microbiomes, sets of significantly enriched and depleted microorganisms can be identified that differentiate CRC and control Mechanism/Function barrier maintenance populations (Table 1, Fig. 1). Microbiome in CRC patients is often en- riched in pro-inflammatory opportunistic pathogens and microbes as- Inflammatory Inflammatory Inflammatory Inflammatory Inflammatory Inflammatory sociated with metabolic disorders and depleted in butyrate-producing bacteria, which have been shown to be pivotal for the preservation of intestinal homeostasis (Gao et al., 2015; Marchesi et al., 2011). Some NA bacteria such as Streptococcus gallolyticus (in the past Streptococcus Sample bovis), F. nucleatum, Escherichia coli, B. fragilis and E. faecalis, have high F, T F, T F, T prevalence in CRC patients as compared to the normal population, T T T T T T F whereas genera such as Roseburia, Clostridium, Faecalibacterium and Bifidobacterium are generally depleted in CRC patients (Feng et al., 2015; Gagnière et al., 2016; Gao et al., 2015; Shang and Liu, 2018; Enriched in CRC feces/Depleted in CRC tumor tissue Wang et al., 2012; Yu et al., 2017; Zhang et al., 2019). Although there is significant interest in identifying specific oncomicrobes, no single spe- cies has been found to be universally present among all individuals with CRC and there is significant variation in microbial composition between R. gnavus individuals (Sears and Garrett, 2014). This suggests that different Species combinations of microorganisms - also known as co-abundance groups - — — — may act synergistically. In addition, changes in both harmful and pro- – – – – – – tective bacterial populations are probably responsible for colon tumor initiation and/or progression. Ruminococcus Lactobacillus Different hypotheses have been put forward to explain the role of Cronobacter PROTEOBACTERIA PROTEOBACTERIA Treponema Citrobacter Salmonella Roseburia Kluyvera Table 1 (continued) microbial unbalance in carcinogenesis. Some authors propose that some Serratia SPIROCHAETES Shigella Genus types of dysbiotic gut microbiota originate a functional imbalance that triggers sustained pro-inflammatory responses and epithelial cell Phyla transformation, leading to cancer (Hajishengallis et al., 2012; Lamont and Hajishengallis, 2015; Maloy and Powrie, 2011). A related hy- pothesis is the ‘driver-passenger theory’ (Tjalsma et al., 2012). 97 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 According to this model indigenous intestinal bacteria (defined as tests for the presence and abundance of those indicative species, driver bacteria) trigger the DNA damage in epithelial cells, which in without the need to assess the whole microbiome. For instance, as turn contributes to cancer initiation. In a second step, ongoing tumor- mentioned above, F. nucleatum has consistently been found associated igenesis alters the surrounding microenvironment, favoring the pro- with CRC development and progression, being enriched both in feces of liferation of opportunistic bacteria (termed bacterial passengers). Thus, patients with adenoma and CRC as compared to control individuals, as this model proposes that disease progression causes changes in the well as in tumoral tissue in comparison with surrounding normal tissue microenvironment as a result of the growing tumor, and bacteria are (Castellarin et al., 2012; Flanagan et al., 2014; Kostic et al., 2012; Yu replaced by others which show a competitive advantage in the tumor et al., 2017). Consistently, a recent meta-analysis concluded that this microenvironment and are capable of nurturing tumor progression. sole species could be used as a biomarker for a non-invasive screening This scenario suggests that bacterial drivers and passengers have dis- in CRC and colorectal adenoma (Zhang et al., 2019). Additional efforts tinct temporal associations with CRC and may have separate roles in have been conducted to identify bacterial markers to predict the risk of pathogenesis. developing CRC in a more efficient way (Ai et al., 2017; Rezasoltani et al., 2018; Zeller et al., 2014), with a claim of similar accuracy to the 4. Microbiome-enabled early diagnostics of CRC standard FOBT (Zeller et al., 2014). However, it is still unclear whether such studies are extrapolable to other populations, as host genetics, and The transition from normal mucosa to CRC is a multi-step process, environmental and lifestyle factors are known to play an important role from the development of pre-neoplastic lesions to the adenomatous in the composition of the gut microbiota. Additionally, differences be- polyps. The adenoma-carcinoma sequence may take at least 10 years to tween studies, including the quality of samples, or the experimental develop, which makes CRC an ideal malignancy to be screened. protocols and bioinformatics tools used can have an influence. To Screening can prevent CRC by the detection and removal of pre- overcome this potential lack of reproducibility and to obtain more cancerous growths, or by enabling the detection of cancer at an early consistent outcomes, two recent meta-analyses have been performed, stage, when treatment has a higher success rate. Guided by these pro- which gathered 16S sequencing (Shah et al., 2018) or shotgun meta- mising facts, several countries have adopted population-wide screening genomic data (Dai et al., 2018a) from different populations. In both of and prevention programs aimed at improving CRC survival. The main them, the authors identified microbial markers consistently enriched or screening strategies used worldwide are the fecal occult blood test depleted in CRC, regardless of differences in population and technolo- (FOBT) and the fecal immunochemical test (FIT), which are generally gies applied between studies. Specifically, one of these studies (Dai coupled to a subsequent colonoscopy when the test is positive and other et al., 2018a) found seven bacterial markers that were enriched in CRC parameters recommend it (Navarro et al., 2017). A broad meta-analysis patients and had a good a performance in differentially classifying CRC showed that FOBT and FIT reduced the mortality of CRC by 14% and from controls across four ethnic cohorts from three different countries. 59%, respectively, as compared with no screening in average-risk po- A broader meta-analysis collecting 28 gut microbiome datasets showed pulations (Zhang et al., 2017a). This study also concluded that colo- that some studies claiming to present disease links actually shared some noscopy is the most effective examination to reduce CRC mortality. microbiome signatures, suggesting that some reported connections may However, colonoscopy requires previous bowel preparation and seda- actually be non-specific (Duvallet et al., 2017). Nevertheless, these tion, and it is an expensive, time-consuming, and invasive procedure authors did show that CRC patients could be clustered separately from that exposes patients to serious medical complications (Rutter et al., controls based on the microbiome in all four CRC studies included, and 2014). These limitations prevent the application of colonoscopy on that there was significant agreement in the observed microbial shifts in population-wide screenings and highlight the need for efficient pre- three out of the four. In any case, as in any other disease association colonoscopy tests. FOBT and FIT share the advantage of being non-in- study, the potential for overlap and uncertainty in markers identified by vasive, stool-based techniques but they have a moderate sensitivity different studies requires further exploration. Hence, despite the great (69–86%) to detect CRC (Lee et al., 2014; Robertson et al., 2017). Thus, advances disentangling the bacterial signatures underlying CRC, the use despite the undeniable advances for CRC prevention brought about by of microbiome biomarkers for early detection of CRC is still not being the current screening techniques, research for more accurate diagnostic used in clinical routine. and prognostic biomarkers is still needed. Most current research is di- In addition to the use of the microbiome, some studies have ex- rected towards finding additional criteria, such as risk factors and other plored the metabolome, mostly in feces, to find CRC-associated sig- biomarkers to be considered by the decision algorithms used to derive natures [reviewed in (Zhang et al., 2017b)]. Some fecal metabolites persons with a positive FOBT or FIT test to colonoscopy. In this sense, (short-chain fatty acids such as acetate and butyrate, fructose, linoleic some molecular biomarkers related to the processes underlying carci- acid and nicotinic acid among others) have been suggested as potential nogenesis in CRC are being considered. These include circulating tumor diagnostic markers of CRC since they have been found at lower levels in cells, cell-free DNA, microRNAs as well as metabolites from plasma fecal metabolome of CRC patients in comparison to healthy individuals samples (Coghlin and Murray, 2015). (Monleón et al., 2009; Phua et al., 2014). Some other studies claim the Given the growing evidence of the existence of microbiome altera- possibility of discriminating between CRC patients and tumor-free tions associated to CRC, and the likely involvement of the microbiota in controls using a panel of urinary metabolites (Cheng et al., 2012) or the origin and progression of cancer discussed above, microbial markers differentially expressed serum metabolites with a sensitivity of 0.981 have recently emerged as a promising additional factor to be considered and a specificity of 1.000 (Li et al., 2013). However, a prospective in early screenings. In this context, over the last years, many studies cohort study of patients with CRC could distinguish early-stage patients have shown the possibility of using different microbiome signatures from more advanced stages of the disease but not intermediate stages (especially in stool) to differentiate between patients with CRC and based on the urinary metabolome (Liesenfeld et al., 2015). As in the control individuals, and to some extent also from advanced adenomas case of the microbiome, extrinsic and genetic factors may affect the (Konstantinov et al., 2013). A general observation from studies com- metabolome, and this technique also suffers from similar reproduci- paring CRC and control microbiomes is that the overall microbial bility and standardization problems (Villéger et al., 2018). Thus me- composition does not seem to differ extensively, but differences rather tabolic profiling has the potential of being used as a biomarker for CRC involve abundances of some key organisms (Zeller et al., 2014). Ac- diagnosis, but still more extended and comparable studies are needed to cordingly, an effort in many such studies is to narrow down whole obtain more clear and robust conclusions. Overall, the final purpose of microbiome signatures to differences in specific species or sets of spe- all this emerging research is integrating information on genomics, cies that could act as indicators. This could significantly reduce the cost epigenetics together with microbiome, metabolomics and lifestyle and and time of diagnostic efforts by enabling the development of specific environmental factors to construct new risk models which are more 98 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 robust and precise, thereby enabling a better prediction on CRC in- obesity to a patient (Alang and Kelly, 2015). Among future desirable itiation, progression as well as therapeutic strategies. developments it is the combination of FMT with fecal DNA testing for accuracy in CRC screening, as well as a transition from whole micro- 5. The gut microbiome as a therapeutic target in CRC biome transplant to more precise combinations of microbes (Dai et al., 2018b). Apart from the effects of human host genetics (Hall et al., 2017; A more direct strategy is to specifically target bacteria associated to Kurilshikov et al., 2017), the composition of the gut microbiome is CRC development (i.e. F. nucleatum, B. fragilis, or E. coli). Antibiotics influenced by many host extrinsic factors, including diet (Singh et al., have been successfully used to modulate the microbiome and indirectly 2017), medications (Becattini et al., 2016; Le Bastard et al., 2018; Maier affect CRC progression. For instance, one study used antibiotics to treat et al., 2018), and other lifestyle components, such as exercise, smoking, mice xenografted with CRC (Bullman et al., 2017), and showed that the and sleep cycles (Biedermann et al., 2013; Claus et al., 2016; Das et al., treatment reduced both the load of F. nucleatum and the overall growth 2018; Monda et al., 2017). In addition, research leading to the design of of the tumor. However, the use of antibiotics generally have broad ef- pre- and probiotics able to modulate the gut microbiome, the trans- fects on the gut microbiota, often leading to dysbiosis and facilitating plantation of fecal microbiota from healthy donors, or the use of phage the acquisition of drug resistance. Therefore, there is a need for new therapy, among other strategies, has intensified in recent years products that more specifically target bacteria associated to CRC. In this (Zitvogel et al., 2018). This modulability of the gut microbiota opens context, some molecules have shown promising effects in CRC and the possibility of novel therapeutic approaches to prevent or treat dis- other diseases. For instance, a glycopolymer, antagonist of the E. coli eases that are originated or influenced by alterations in the microbiota. virulence factor FimH, showed a reduction in the adherence of E. coli to Most clinical and pre-clinical studies assessing microbiome-modulation the intestinal epithelia, and therefore its invasiveness in a mouse model treatments suggest that these therapies may have an advantage over of Crohn's disease (Khan et al., 2017). Also, the exposure to a low dose synthetic drugs, at least in terms of their potential side-effects (Juul of a recombinant BFT-2 enterotoxin (a major virulence factor of B. et al., 2018; Lynch and Pedersen, 2016). Given the potential relation- fragilis) decreased the formation of tumors in a CRC mouse model (Lv ships of the gut microbiota with the origin and development of CRC et al., 2017). Some authors have highlighted the importance of ex- discussed above, there is an increasing interest in exploring micro- ploring the interaction between gut bacteria and the viral component, biome-related therapies for aiding in the prevention and treatment of and its potential modulation via phage therapy. Phage therapy is a this cancer. So far, strategies under development for CRC include fecal matter of current interest for its potential advantages in fighting anti- microbiota-transplantation (FMT), use of pre-/probiotics and diet, and biotic-resistant bacteria and its potential application the clinics (Mirzaei phage therapy. and Maurice, 2017). FMT was first described in 1958 (Eiseman et al., 1958), when it was Diet has been proposed as one of the most influential factors shaping used to treat Clostridium difficile infection (CDI), and it was shown to the human gut microbiome (Singh et al., 2017). In particular, current help restore a beneficial microbial composition. Later on, several stu- data shows that gut microbiota changes rapidly in response to dietary dies showed that FMT was an effective treatment in more than 80% of changes. The observation that diet can modulate host-microbe inter- patients with CDI (Bakken et al., 2011; Lee et al., 2016; van Nood et al., actions, suggests a promising therapeutic approach, with effect on im- 2013). More recently, FMT has been re-evaluated as a promising ther- mune response and metabolic pathways. Studies on mouse models have apeutic method to treat other disorders involving gut dysbiosis, such as shown that alterations of the microbial community induced by deoxy- CRC, ulcerative colitis, IBD, IBS, metabolic syndrome, obesity, types 1 cholic acid (a secondary bile acid increased by western-type diet) and 2 diabetes, atopy, multiple sclerosis, autism (Filip et al., 2018). promoted intestinal carcinogenesis (Cao et al., 2017), and that sup- Some case reports have shown favorable outcomes, yet demonstrating plementation with high dietary fiber and butyrate-producing bacteria these effects on a larger scale has proved difficult. In a single-arm open- can significantly reduce colon tumor growth (Donohoe et al., 2014). label study of patients with IBS, FMT proved to be safe and relatively Two further studies performed in pre-clinical tumor models showed effective (Mizuno et al., 2017), however further studies are needed to that gut microbiome may influence the response of two different cancer further develop this approach. immunotherapy agents (anti-CTLA4, and anti-PD-L1), by augmenting To date there are no conclusive data from clinical trials in humans activation of dendritic and anti-tumor T cell responses (West and using FMT to treat CRC, but two recent studies using mouse models Powrie, 2015). In particular, the authors found that immunologic sti- have shown its potential. The first one demonstrated that the gut mi- mulation by Bacteroides spp. and Bifidobacterium spp. respectively, have crobiome of wild-type mice, when transplanted, conferred traits that a profound effect on therapy efficacy. Moreover, it has been reported promote host fitness and limit inflammation of induced neoplastic de- that imbalance of dietary sphingolipids can have a major impact on the velopment (Rosshart et al., 2017). A second study showed that fecal therapeutic efficacy of chemotherapy and radiation (Camp et al., 2017). microbiota from patients with CRC promoted tumorigenesis in germ- In this context, several authors have suggested the need of developing free mice and mice supplied with a carcinogen (Wong et al., 2017b). more personalized methods to study dietary interventions which might Overall, FMT stands out as a promising strategy in the treatment of CRC increase the efficiency of CRC treatments, as for other diseases dietary patients, although there are still open questions to be discussed and interventions are already under clinical trials (Zeevi et al., 2015). potential risks that need a deeper revision. For the use of FMT in this Another line of current research includes the use of probiotics. and other diseases, the debate is still open with respect to factors such Probiotics help maintain healthy microbiota states by regulating pa- as optimal storage conditions of stool, standardization of processes, thogenic bacteria and immune system response, which in turn may selection of donors and recipients, as well as establishment of a proper reduce blood cholesterol, colitis, and prevent CRC (Raman et al., 2013). cycle of FMT administration, dose, and frequency. There is at least one Different probiotics can inhibit CRC by different mechanisms: releasing clinical study showing no significant difference in clinical resolution of detoxifying agents, anti-inflammatory factors, anti-cancer compounds patients with CDI comparing both frozen and fresh FMT (Lee et al., (anti-angiogenesis, promoting anti-PDL1 drugs), and short-chain fatty 2016). Safety and efficacy of FMT in CDI has been proven, although acids (SCFA) that improve the intestinal barrier function (Lin et al., larger long-term follow-up studies are needed to identify potential long- 2018). So far, several lines of evidence support a protective role of term adverse effects. Potential risks of FMT include transmission of probiotics against CRC. For instance, it has been shown that the buty- pathogens, particularly to immunocompromised patients, transmission rate producing species Clostridium butyricum and Bacillus subtilis may of recessive elements silent in healthy donors (Sbahi and Di Palma, have an anti-tumor effect in a CRC mouse model (Chen et al., 2015). 2016), and transmission of other factors accounting for chronic diseases Interestingly, another probiotic, Lactobacillus casei (strain BL23) not i.e. although controversial, a case report suggested transmission of only inhibited CRC in a mouse model, but also, re-established the 99 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 disrupted gut dysbiosis in CRC (Lenoir et al., 2016), whereas L. casei or the technical difficulties in targeting them. In this regard, some (strain ATCC 334) has been reported to produce a molecule: ferri- promising fields which are growing rapidly are the study of the gut chrome, which has been shown to inhibit progression of colon cancer virome and mycobiome (i.e. the viral, and fungal components of the by means of apoptosis mediated through the c-Jun N-terminal kinase microbiome, respectively) in relation to CRC pathology. For example, a pathway (Konishi et al., 2016). Another L. casei strain (variety rham- recent MWAS explored the virome community in fecal samples from nosus, Lcr35) prevented induced intestinal mucositis in CRC-bearing CRC patients and control individuals using a WGS approach (Nakatsu mice (Chang et al., 2018). Recent clinical studies found out that Bifi- et al., 2018). The authors reported a significant increase in the diversity dobacterium probiotics could restore the equilibrium of gut dysbiosis of the bacteriophage virome in CRC patients and identified a set of viral and reduce small intestinal bacterial overgrowth induced in patients markers (e.g. Orthobunyavirus, Inovirus and Tunalikevirus) that dis- with CRC (Liang et al., 2016). Other authors suggest to focus on stra- criminated between CRC patients and healthy subjects and improved tegies that promote growth of positive bacteria to increase antitumor the performance of FOBT or FIT tests for detection of CRC. Gut virome immunity, for instance, by use of prebiotics highly associated with diet. dysbiosis was associated with CRC stage (early-vs late) and the prog- Also, the synergistic combination of prebiotics and probiotics in the nosis of the disease, but it could not differentiate patients with color- form of dietary supplements or ingredients has been formulated, being ectal adenomas from control individuals. called synbiotics. These include for instance OAT fiber/L. Plantarum and Similarly, the mycobiome has been the target of very few studies in FOS/L. sporogens (Pandey et al., 2015). More recently, some authors CRC. Whereas it has been estimated that fungi are orders of magnitude highlighted the benefits of creating new generation probiotics for the less abundant than bacteria, they are ubiquitous and play key roles in treatment and prevention of CRC using genetic engineering techniques. the maintenance of equilibrium at the microbiome (Huffnagle and For instance, increasing the potency of a well-documented probiotic Noverr, 2013). A recent fecal metagenomics study compared the my- species such as Bifidobacterium animalis subsp. Lactis (main source of cobiome in CRC and control individuals (Coker et al., 2018a). Principal butyrate: an anti-inflammatory and anti-tumor molecule), with the component analyses showed different clusters of fungal components for ability to produce mycosporin-like amino acids which exert prebiotic controls, early-stage CRC and late-stage CRC patients, indicating that effects and is able to modulate host immunity by regulating cytokine mycobiome profiles were stage-specific. In addition, the Basidiomyco- production and the proliferation and differentiation of intestinal epi- ta:Ascomycota ratio, which is an indicator of fungal dysbiosis, was thelial cells, macrophages and lymphocytes (Bozkurt et al., 2019). To higher in CRC patients, which were enriched in Malasseziomycetes and date, 24 clinical trials of pro-/synbiotics therapies have been published depleted in Saccharomycetes and Pneumocystidomycetes. At the spe- and shown a favorable benefit for CRC patients (Ding et al., 2018). As cies level, a combination of 14 fungal biomarkers were able to distin- previously mentioned, gut microbiome has a natural anticancer effect guish CRC patients from controls, and controls from early-stage CRC, through the stimulation of the immune system. Therefore, it has been both findings being validated in two independent cohorts. However, suggested that regulation of patients’ immune systems through the there is still very limited information about the involvement of the microbiota is one of the key mechanisms of tumor immunotherapy. It fungal community in carcinogenic processes, particularly in CRC. Im- has been suggested that gut microbiota activate natural anticancer provement of standardization and methodologies to study the myco- immune responses through three main mechanisms: microbial or tu- biome would contribute to shedding new light on the etiopathology of moral antigens activate T-cell response which in turn may activate the disease and to refining of diagnosis and prognosis biomarkers. tumor-specific immune responses (Zitvogel et al., 2018). Among the Related to the limitation of under-studied taxonomic groups, there species identified with known anti-tumoral response through activation is the limited taxonomic resolution achieved in the studied ones, which of immune system (T-cells) there are: Bifidobacteria spp. (Matson et al., currently stays at the genus or higher levels in most studies. This lack of 2018; Sivan et al., 2015), Akkermansia muciniphilia (Routy et al., 2018), resolution limits our understanding of the functional impact of the Enterococcus hirae (Daillère et al., 2016), Bacteroides spp (Vétizou et al., microbiome, as the coding and functional potential can vary greatly 2015). Alternative mechanisms include the activation of pattern re- among species of the same microbial genus, and even among strains of cognition receptors that mediate pro-immune or anti-inflammatory ef- the same species. Connected to this is the currently limited use of fects, and, release of small metabolites that mediate systemic effects in complementary techniques such as metatranscriptomics, metabolomics the host (such as polyamines, vitamin and desaminotyrosine, among and metaproteomics (Mallick et al., 2017), which have the potential to others (Ding et al., 2018)). Indeed, several studies showed a relation help us provide a mechanistic understanding of the roles of the mi- between the gut microbiota and the efficacy and toxicity of che- crobiome. motherapies and immunotherapies (Ding et al., 2018; Iida et al., 2013; Sample collection is another challenging step in microbiome stu- Pouncey et al., 2018). Taken altogether, these data highlight the in- dies. Many studies on the gut microbiome, including those related to terest of investigating the effect of new therapeutic strategies for the CRC disease, are conducted using fecal samples, since it is an easy and treatment of different types of cancer, including CRC, that benefit from non-invasive procedure. While fecal samples represent a powerful the combined strategy of chemotherapy and/or immunotherapy with strategy for finding biomarkers with diagnosis and prognosis purposes, adjuvant treatments targeting the gut microbiota based on more per- tissue samples from colonic mucosa are more valuable to disentangle sonalized medicine, such as diet, probiotics and prebiotics. In this re- the physiopathology of CRC disease. In fact, there is strong evidence spect, recent studies have shown that oral therapy with bifidobacteria pointing to different microbiome profiles between mucosal and fecal combined with PD-L1 inhibitors had a synergistic effect inhibiting al- samples (reviewed in (Yu et al., 2018)). A recent prospective study most completely the tumor growth, as compared with the individual using paired fecal and mucosal samples from patients with CRC con- effect of the drugs (Ding et al., 2018; Lee and Le, 2016; Routy et al., firmed not only that the fecal microbiota did not totally reflect the 2018). underlying mucosal microbiota in CRC, but also that the colonic mi- crobiota was different between patients with distal (including rectal) 6. Current challenges tumors and proximal tumors (Flemer et al., 2017). Accordingly, further studies should consider the most relevant sample collection procedure As discussed in the previous sections, many studies have demon- according to the objectives, and a better definition of subphenotypes strated the association between the microbiome composition and and categorization of CRC tumors. Beyond sample collection issues, cancer, particularly CRC. Still the field faces many serious limitations there are serious challenges regarding heterogeneity and limitations in that need to be considered. A recurrent limitation is that the non-bac- study design and analysis. As mentioned above, broadening the scope of terial components of the microbiome (i.e. fungi, protozoans, viruses) a study by including additional metadata can reveal potentially specific have been barely studied, mainly due to either their lower abundances links to be confounding factors, but often few such data is collected or 100 E. Saus, et al. Molecular Aspects of Medicine 69 (2019) 93–106 stored alongside the sequences. Additionally, new statistical methods In this respect, it is also reasonable to predict a trend from gen- for analyzing microbiome compositional data are indispensable (Dai eralized studies to those focusing on more personalized patterns. The et al., 2018b). For instance, current analyses treat every taxon in- analysis of longitudinal studies compiling multi-dimensional data about dependently, while we know of ecological correlations between taxa. microbiome, diet, metabolic parameters and considering potential un- Technical variation must also be considered in any omics study and derlying disorders, will help to develop personalized diets and lifestyle batch effect correction is often necessary for comparisons between or recommendations to fine-tune the microbiota composition towards a even within studies (Costea et al., 2017; Duvallet et al., 2017). Fur- healthy state. Also, we could hypothesize that once the composition of thermore, compositional data are generally analyzed with a focus on the microbiome of a given patient is characterized, he or she could relative abundances of taxa within each sample. However, this ap- benefit from personalized pre- and probiotics, or from a tailored phage proach does not account for the effects of changes in total abundances therapy to restore a desired bacterial equilibrium (Paule et al., 2018). of taxa, which can be linked to various physiological factors or meta- Furthermore, development of new culturing technologies may help to bolite concentrations. A recent technique called quantitative micro- study for instance difficult-to-culture gut microorganisms in tissue en- biome profiling (QMP) addresses this issue by essentially sampling an gineering organoids (Williamson et al., 2018). This will constitute an equivalent number of sequences from each sample, and correcting the ideal manipulable and fully-controlled platform to study mechanisms of counts of taxa by both the number of copies of the 16S rRNA gene host-pathogen interactions (Arnold et al., 2016). Moreover, it would known to be in that organism and the number of cells from that sample, pave the way for the testing and development of personalized therapies, producing the total microbial load (Vandeputte et al., 2017). QMP has drug screening, or pre-clinical studies for newly developed drugs. shown that alterations in taxa that were previously associated with Crohn's disease are mostly driven by differences in microbial load. This Funding technique can also reveal potential artefacts, as in the same dataset they showed that Bacteroides was found at higher relative abundance in This work was supported by grants from COST (European Crohn's samples, but there was no significant difference in microbial Cooperation in Science and Technology), COST Action CA17118; from load. The reverse was true for Prevotella, wherein relative abundances the Spanish Ministry of Economy, Industry, and Competitiveness were not significantly different between controls and Crohn's samples, (MEIC) for the EMBL partnership, and grants ‘Centro de Excelencia but QMP revealed a significantly lower microbial load of Prevotella in Severo Ochoa’ SEV-2012-0208, and BFU2015-67107 cofounded by Crohn's. QMP is an example of a straightforward technique that can European Regional Development Fund; from the CERCA Programme/ help to mitigate some of the technical variation that occurs in micro- Generalitat de Catalunya; from the Catalan Research Agency (AGAUR) biome studies. SGR857, and grants from the European Union’s Horizon 2020 research Another limitation, shared by many GWASs and MWASs is the po- and innovation programme under the grant agreement ERC-2016- tential lack of representativity of existing datasets, which are mostly 724173, and the Marie Sklodowska-Curie grant agreement No H2020- focused on population of the “westernized” world. A recent study re- MSCA-ITN-2014-642095; and from Instituto Nacional de vealed the limited scope of existing data (Pasolli et al., 2019). By Bioinformatica (INB, grant PT17/0009/0023 - ISCIII-SGEFI/ERDF). combining 50 publicly available datasets collectively representing 9500 WGS samples from various body sites, they more than doubled the Declarations of interest current public repository of microbial genomes associated with the human microbiome. Many of these new genomes were found in “non- None. westernized” populations, hinting at our lack of a global understanding of the human microbiome. 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