Myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) (ME/CFS) is a disabling and debilitating disease of unknown aetiology. It is a heterogeneous disease characterized by various inflammatory, immune, viral, neurological and endocrine symptoms. Several microbiome studies have described alterations in the bacterial component of the microbiome (dysbiosis) consistent with a possible role in disease development. However, in focusing on the bacterial components of the microbiome, these studies have neglected the viral constituent known as the virome. Viruses, particularly those infecting bacteria (bacteriophages), have the potential to alter the function and structure of the microbiome via gene transfer and host lysis. Viral-induced microbiome changes can directly and indirectly influence host health and disease. The contribution of viruses towards disease pathogenesis is therefore an important area for research in ME/CFS. Recent advancements in sequencing technology and bioinformatics now allow more comprehensive and inclusive investigations of human microbiomes. However, as the number of microbiome studies increases, the need for greater consistency in study design and analysis also increases. Comparisons between different ME/CFS microbiome studies are difficult because of differences in patient selection and diagnosis criteria, sample processing, genome sequencing and downstream bioinformatics analysis. It is therefore important that microbiome studies adopt robust, reproducible and consistent study design to enable more reliable and valid comparisons and conclusions to be made between studies. This article provides a comprehensive review of the current evidence supporting microbiome alterations in ME/CFS patients. Additionally, the pitfalls and challenges associated with microbiome studies are discussed.

Graphical Abstract
Graphical Abstract

What is the microbiome?

Virtually every surface of the human body is colonized by vast populations of microbes, including prokaryotes, archaea, viruses, fungi and unicellular eukaryotes [1–3]. Bacteria of the phyla Bacteriodetes and Firmicutes dominate the diverse and complex intestinal bacteriome of most animals [4]. Microbial colonization begins rapidly at birth when the infant is first exposed to microbes in its immediate environment. The microbiome increases in diversity during the first 2–4 years of life in response to various hosts (i.e. genetics), and environmental factors including diet, lifestyle and behaviour [5–7]. It is believed that the early colonizers of the infant intestine play a key role in laying the foundations for the development of the complex and diverse adult microbiome and lifelong health [8]. In recent years, the role of microbiome in health of the host and its contribution to disease development has emerged [9–11]. It contributes to various body systems including immunity, metabolism, neurological signalling and homeostasis [12,13]. Describing the microbiome in detail is beyond the scope of this article, however several excellent review papers have been published recently [7,14–17].

The neglected virome

The vast majority of microbiome research has to date focused on its bacterial component, largely neglecting the other organisms. However, the influence of these lesser studied organisms, such as viruses are just beginning to be understood, thanks primarily to recent advancements in sequencing technology and bioinformatics capability (Table 1) [18].

Table 1
Overview of important faecal virome studies in health and disease
Year Study participants Comments Reference 
2003 Healthy adults First virome metagenomics study [168
2006 Healthy adults Plant RNA viruses contribute towards virome [169
2008 Infants Virome establishment begins within 1 week of birth [21
2011 Healthy adults Diversity and abundance of ssDNA viruses [170
2011 Monozygotic twins and mothers Virome is individualized and highly stable [22
2011 Healthy adults Virome is influenced by diet [157
2012 Healthy adults Hypervariation driven by unique reverse transcriptase based mechanism [171
2013 Healthy adult Virome is relatively stable; 80% of virome remained through 2.5-year study [172
2013 Pediatric CD patients CD patients exhibited higher bacteriophage levels than controls [49
2013 CD patients Similar results as above; results depend on interpretation of data [173
2015 Infants Longitudinal study of virome establishment in infant twins [174
2015 Malnourished Malawian twins Virome establishment affected by severe malnourishment [178
2015 IBD patients Virome in IBD patients [51
2015 IBD patients Increase in phage-richness abundance compared with healthy controls [175
2015 CD patients Alterations in virome according to disease status and therapy [58
Year Study participants Comments Reference 
2003 Healthy adults First virome metagenomics study [168
2006 Healthy adults Plant RNA viruses contribute towards virome [169
2008 Infants Virome establishment begins within 1 week of birth [21
2011 Healthy adults Diversity and abundance of ssDNA viruses [170
2011 Monozygotic twins and mothers Virome is individualized and highly stable [22
2011 Healthy adults Virome is influenced by diet [157
2012 Healthy adults Hypervariation driven by unique reverse transcriptase based mechanism [171
2013 Healthy adult Virome is relatively stable; 80% of virome remained through 2.5-year study [172
2013 Pediatric CD patients CD patients exhibited higher bacteriophage levels than controls [49
2013 CD patients Similar results as above; results depend on interpretation of data [173
2015 Infants Longitudinal study of virome establishment in infant twins [174
2015 Malnourished Malawian twins Virome establishment affected by severe malnourishment [178
2015 IBD patients Virome in IBD patients [51
2015 IBD patients Increase in phage-richness abundance compared with healthy controls [175
2015 CD patients Alterations in virome according to disease status and therapy [58

Abbreviations: CD, Crohn’s disease; IBD; inflammatory bowel disease.

It is estimated that there are 1031 different DNA and RNA viruses on the planet; many of which remain undiscovered [19]. This collection of viruses (dsDNA, ssDNA, dsRNA and ssRNA) within an ecosystem is defined as the virome [20]. Similar to the bacteriome, the intestinal virome is established from birth and increases in diversity/complexity with age [21]. A large proportion of this complex environment consists of prokaryotic viruses (bacteriophage); with archaea-, human-, plant- and amoeba-infecting viruses found at lower frequencies [20]. The tailed, dsDNA viruses of the Order Caudovirales (Siphoviridae, Myoviridae, Podoviridae) dominate the bacteriophage portion of the virome (Figure 1) [22].

TEM images of Caudovirales from faecal water

Figure 1
TEM images of Caudovirales from faecal water

(AC) Myoviridae and (D,E) Siphoviridae. Imaging completed by S.-Y.H. and K.C.

Figure 1
TEM images of Caudovirales from faecal water

(AC) Myoviridae and (D,E) Siphoviridae. Imaging completed by S.-Y.H. and K.C.

The microbiota in health and disease

The importance of the intestinal microbiome in maintaining health is an emerging research topic with advances in high-throughput sequencing technology allowing the identification and characterization of microbes that contribute to host health [10,11]. The microbiota has been implicated in immunomodulation, pathogen resistance, maintenance of intestine structure/function and nutrition and host metabolism [12,13]. It provides the host with a physical barrier to pathogen invasion and infection by, for example competitive exclusion and competing for nutrients, occupation of attachment sites and production of antimicrobial proteins [23–26]. Importantly, various microbiome members have been found to contribute to the intestinal metabolome, through for example vitamin synthesis, bile salt metabolism and xenobiotic degradation [27]. There is bidirectional communication between the microbiome and the local host immune system [28]. The immune system influences the composition of the microbiota and gut microbes and their products (e.g. metabolites and microbe associated molecular pattern (MAMPs) molecules) direct immune maturation and the development and possibly maintenance of immune (microbial) tolerance and homoeostasis [29,30].

There is increasing evidence that an imbalance of the intestinal microbiota (dysbiosis) may contribute to the pathogenesis of diseases affecting the gastrointestinal (GI) tract and other organ systems. Dysbiosis is characterized by a detrimental alteration of intestine microbial populations and ecology that can result in the growth and expansion of pathogenic microbes (pathobionts) and the production of factors toxic or harmful to host cells. These alterations are normally held in check by an intact microbiome but dysbiosis can result in the development and/or maintenance of chronic inflammatory infections caused by Clostridium difficile and Helicobacter pylori, metabolic syndrome and obesity, colorectal cancer, irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) [14,31–34]. The stability of the microbiome is largely influenced by age, behaviour and lifestyle [12,35,36]. It has been hypothesized that intestinal microbial dysbiosis can lead to an imbalance in the immune system, resulting in diseases such as IBD, common variable immunodeficiency and rheumatoid arthritis [37,38]. However, to understand the significance of dysbiosis, it is necessary to establish if microbiome alterations cause, follow, precede or simply correlate with disease onset. Dysbiosis can be precipitated by drugs and medications (i.e. antibiotics), immune dysregulation, age-associated reduction in microbiota diversity, colonization by pathogenic microbes, stress and changes in diet [36,38–41]. The precise trigger or cause of dysbiosis in any disease has yet to be established but is likely to be multifactorial.

The virome in health

Viruses utilize a lytic or lysogenic life cycle. In the lytic life cycle, infected host cells are destroyed during viral replication whereas in the lysogenic life cycle the virus integrates into the host chromosome as a prophage. Lytic phages can have both narrow or broad host ranges, and lysogenic phages can be converted into a lytic cycle in response to environmental stressors such as antibiotics [20]. Lytic phages can alter the microbiome by killing bacterial hosts, providing a competitive growth advantage to bacteria resistant to phages. Prophages encode mobile genetic elements which contribute to horizontal gene transfer between bacteria altering antibiotic resistance, virulence or metabolic pathways [42]. This can provide a competitive advantage by allowing bacteria to metabolize new nutrient sources or acquire antibiotic resistance [43–45]. Temperate phages, able to perform lysogenic or lytic cycle, have been shown to influence the dynamics of biofilms and dispersal by a number of important pathogens such as Pseudomonas aeruginosa (opportunistic pathogen), Streptococcus pneumoniae (e.g. pneumonia) and Bacillus anthracis (e.g. anthrax) [46–48]. For example, the presence of lysogenic phages Bcp1, Wip1, Wip4 and Frp2 in B. anthracis results in the formation of a durable, complex and viable biofilm; allowing prolonged survival of the bacterium [48]. There is a constant shift of phages between lytic and lygosenic forms that is presumed to contribute to microbiome homoeostasis and that a differential spatial distribution of phages is correlated with health.

The virome in disease

Alterations in the virome have been implicated as sources of intestinal microbial (prokaryotic) dysbiosis for several different diseases [49–52]. Prophage induction in response to various environmental stressors can induce ‘community shuffling’ which alters the ratio of symbionts to pathobionts creating an imbalance within microbial communities that can lead to occupation of symbiont niches by pathobionts [42]. These events provide an explanation for the raised number of virus-like particles (VLPs, see Figure 2) as well as microbial population shifts in patients with GI-related disorders. Of note, an experimental model of Salmonella typhimurium diarrhoea has shown that inflammation increases lysogenic conversion of prophages [53].

VLP isolation protocol

Figure 2
VLP isolation protocol

Overview of VLP isolation protocol involving filtration (1,2) and centrifugation (3). Following isolation of concentration VLPs, DNA is extracted (4), sequencing and (5) bioinformatic tools applied to determine virome community composition.

Figure 2
VLP isolation protocol

Overview of VLP isolation protocol involving filtration (1,2) and centrifugation (3). Following isolation of concentration VLPs, DNA is extracted (4), sequencing and (5) bioinformatic tools applied to determine virome community composition.

The involvement of lytic phages in disease pathogenesis has been demonstrated through studying prophages that encode virulence factors (e.g. Shiga toxin) [54]. In the healthy intestine, toxin gene expression is silent in lysogenic phages that infect Escherichia coli. However, dysbiosis is accompanied by induction of prophages and activation of Shiga toxin genes resulting in release of the toxin into the intestine [55]. Additionally, in vitro experiments have shown phages can transmigrate across epithelial barrier cells. A recent study suggests that intestinal phages can interact directly with eukaryotic cells outside the GI tract; likely contributing to human health and immunity [56].

Although the virome is suspected to play a role in disease, relatively few studies have been undertaken with most studies focusing on IBD and HIV [49,50,52,57]. In one study, Crohn’s disease (CD) patients were shown to have a higher number of VLPs, which provide a crude estimate of phage numbers, in colonic biopsies compared with healthy controls. Patients with ulcerated mucosa had significantly fewer VLPs than non-ulcerated mucosa. The authors hypothesized that viruses had higher survival rates or a higher frequency of viruses in non-ulcerated areas. Based upon these findings, it was proposed that phages play an indirect role in the immune dysregulation evident in CD patients through microbiome alterations [57]. A later study also described differences in the virome in CD patients according to their disease status (newly diagnosed, active onset, active presurgery) and therapy. Newly diagnosed patients had a higher viral diversity in faecal and biopsy samples than those with active disease. Additionally, there were significant differences in virome diversity between patients on immunosuppressive therapy, steroids, combination therapy or no therapy. The clinical relevance of these results is unknown although more detailed studies of the alteration in virome composition in IBD patients is warranted [58]. Alterations in the enteric virome have also been reported in children susceptible to developing type 1 diabetes (T1D), prior to disease onset. Importantly, a disease-specific sequencing ‘fingerprint’ was identified in children susceptible to T1D that went on to develop the disease, compared with children that did not develop the disease. The present study reported that specific components of the virome were both directly and inversely associated with the development of T1D in these patients [59].

Additionally, a bidirectional communication network between the intestinal and central nervous system (the gut–brain–axis) is gaining research focus [60]. Its’ role is to monitor and integrate intestine functions as well as linking emotional and cognitive centres of the brain with peripheral intestinal functions and mechanisms such as immune system activation, intestinal permeability, enteric reflex, pain perception and enteroendocrine signalling [61]. Both clinical and experimental evidence suggest that the enteric microbiota has an important impact on communication pathways between the intestine and brain, also known as the gut–microbiome–brain axis. The microbiome can interact locally with intestinal cells and enteric nervous system, but also have indirect interactions with the CNS through neuroendocrine and metabolic pathways. Therefore, significant alterations in the resident microbiota or their metabolites might have a direct effect on the host nervous system and lead to neurological pathologies [62]. For example, changes in the microbiome have been associated with autism, depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease [63–67].

Our own research is focused on developing a mechanistic understanding of the intestine–microbiome–brain axis and the GI tract microbiome in the pathogenesis of the neurological disorder, myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) (ME/CFS).

ME/CFS

Historical perspective

The causative factor(s) of ME/CFS remain elusive resulting in a lack of robust diagnostics and effective treatments [68,69]. The disease onset and progression varies from patient to patient with the onset normally associated with an acute flu-like viral infection, which is either gradual or rapid [68]. Approximately 25% of patients become house- or bed-bound with less than 10% returning to predisease levels of function [70]. The socioeconomic burden of ME/CFS is significant and estimated to be between $17 and $24 billion per annum. This considerable cost is due to direct and indirect effects of the illness, such as healthcare and loss of work for patient and/or family carers [71].

The heterogenic nature of ME/CFS suggests a multifactorial and self-sustaining disorder [72]. Several theories have been proposed including mitochondrial dysfunction, viral infection and autoimmunity [68,73,74]. Important clues for the involvement of (viral) infections in the aetiology of ME/CFS can be obtained from historical reports of epidemic or sporadic outbreaks of cases; the first of which was reported in 1934 in a suspected epidemic of poliomyelitis in Los Angeles, California [75,76]. The inconsistent disease pattern observed in patients led doctors to classify this epidemic as atypical; differing from polio cases endemic at the time by the lack of flaccid paralysis, which normally defines poliomyelitis [77]. Additionally, the affected cases were mainly older children and young adults compared with polio which affected infants and children of less than 5 years of age [78]. The disease at the onset consisted of an acute upper respiratory tract infection accompanied by muscle weakness, fever, pain, malaise and photophobia. The patients reported recurrence of fever and other symptoms during recovery, which were at a greater incidence than those in typical epidemic poliomyelitis [76].

A similar apparent epidemic of poliomyelitis appeared in Akureyri, Iceland between 1948 and 1949. There were striking similarities between this outbreak of atypical poliomyelitis and the one recorded in Los Angeles in 1934, including both overlapping symptoms and occurrence of relapse. This disease was named Iceland (or Akureyri) disease [79]. Sixty-one other outbreaks of a similar disease were reported worldwide between 1934 and 1990 [75]. The most significant outbreak was in 1955 at the Royal Free Hospital in London, where 292 hospital staff were affected by the illness. The disease when fully developed showed features of a generalized infection with involvement of the lymphoreticular system, and widespread involvement of the central nervous system. The mysterious polio-like illness (including the disease at the Royal Free Hospital) was renamed ME and later extended to CFS (ME/CFS) to include a seemingly identical disease [80,81].

What is ME/CFS?

In both historical and current cases of ME/CFS persistent fatigue is the dominant and defining symptom, which is accompanied by a range of heterogeneous symptoms that are universally present in all the patients. It is classified by the World Healthy Organization International Classification of Diseases (ICD-10) as a neurological disorder (WHO Reference 93.3). Patients often report delayed exacerbation of symptoms following mental or physical exertion and daily or weekly variations in symptom severity that have a significant impact on day-to-day living [69,82]. A standardized criterion for ME/CFS is urgently needed, with diagnosis relying heavily upon clinical observations and by exclusion of other disorders. This situation is further complicated by the use of different diagnostic criteria within the same country and between different countries. As a result, it is can take several years for sufferers to receive a diagnosis [83–85]. To date, an effective treatment for ME/CFS does not exist, with current treatments aimed at alleviating symptoms [86].

An intestinal origin for ME/CFS

The co-morbidity of ME/CFS and GI symptoms is well documented, with one study reporting 92% of patients exhibiting IBS [87]. Additional studies have reported increased mucosal and systemic levels of pro-inflammatory cytokines such as IL-6, IL-8, IL-1β and TNFα in patients with coexistent IBS [88,89]. The significant GI symptoms often experienced by ME/CFS patients has led researchers, including ourselves, to investigate the microbiome in these patients. Several studies have reported significant changes in microbiota composition of ME/CFS patients compared with controls [90–92]. However, ME/CFS microbiome studies to date have largely focused on alterations in bacterial populations. The advancement in sequencing technology and emerging influence of the virome on human health has enabled studies of the virome of ME/CFS [18].

Intestinal microbiome and ME/CFS

We completed a literature search to determine the extent of microbiome research in ME/CFS using the following search terms: ‘Myalgic encephalomyelitis’, ‘Chronic Fatigue Syndrome’, ‘CFS/ME’, ‘ME/CFS’ in combination with ‘virome’, ‘microbiome’, ‘metabolome’, ‘metagenomics’, ‘viromics’ and ‘metabolomics’. The resulting papers were screened according to abstract contents. Articles were excluded if an intervention was used (e.g. probiotics) and measurements not reported prior to the invention. This resulted in 11 papers that had examined the microbiome and/or intestinal metabolome of ME/CFS patients, dating from 1998 to 2017 (Table 2). Due to inconsistencies in study design including small sample sizes, different sequencing platforms and bioinformatics software analyses, microbial sequencing depth and a single time point‘snapshot’ of sampling and analysis; it was not possible to compare the studies statistically. However, from examining the articles individually there is sufficient evidence to support the claim of an altered intestinal microbiome in ME/CFS patients.

Table 2
ME/CFS microbiome articles selected following literature review
Number Year Author Title Area of study 
2017 Armstrong [93The association of faecal microbiota and faecal, blood, serum and urine metabolites in ME/CFS Microbiome and metabolites 
2017 Nagy-Szakal [90Faecal metagenomic profiles in subgroups of patients with ME/CFS Microbiome 
2016 Giloteaux [91Reduced diversity and altered composition of the gut microbiota in individuals with ME/CFS Microbiome 
2016 Giloteaux [107A pair of identical twins discordant for ME/CFS differ in physiological parameters and gut microbiome composition Microbiome and virome 
2013 Fremont [92High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in ME/CFS patients Microbiome 
2009 Sheedy [94Increased d-lactic acid intestinal bacteria in patients with CFS Microbiome and metabolites 
2009 Fremont [109Detection of herpes virus and parvovirus B19 in gastric and intestinal mucosa of CFS patients Virome 
2008 Chia [108CFS is associated with chronic enterovirus infection of the stomach Virome 
2007 Evengård [176Patients with CFS have higher numbers of anaerobic bacteria in the intestine compared with healthy subjects Microbiome 
10 2001 Butt [177Bacterial colonosis’ in patients with persistent fatigue Microbiome 
11 1998 Butt [98Faecal microbial growth inhibition in chronic fatigue/pain patients Microbiome and metabolites 
Number Year Author Title Area of study 
2017 Armstrong [93The association of faecal microbiota and faecal, blood, serum and urine metabolites in ME/CFS Microbiome and metabolites 
2017 Nagy-Szakal [90Faecal metagenomic profiles in subgroups of patients with ME/CFS Microbiome 
2016 Giloteaux [91Reduced diversity and altered composition of the gut microbiota in individuals with ME/CFS Microbiome 
2016 Giloteaux [107A pair of identical twins discordant for ME/CFS differ in physiological parameters and gut microbiome composition Microbiome and virome 
2013 Fremont [92High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in ME/CFS patients Microbiome 
2009 Sheedy [94Increased d-lactic acid intestinal bacteria in patients with CFS Microbiome and metabolites 
2009 Fremont [109Detection of herpes virus and parvovirus B19 in gastric and intestinal mucosa of CFS patients Virome 
2008 Chia [108CFS is associated with chronic enterovirus infection of the stomach Virome 
2007 Evengård [176Patients with CFS have higher numbers of anaerobic bacteria in the intestine compared with healthy subjects Microbiome 
10 2001 Butt [177Bacterial colonosis’ in patients with persistent fatigue Microbiome 
11 1998 Butt [98Faecal microbial growth inhibition in chronic fatigue/pain patients Microbiome and metabolites 

Abbreviations: CFS, chronic fatigue syndrome; ME, myalgic encephalomyelitis

The literature search revealed nine articles that examined the microbiome in ME/CFS patients, with three articles also examining intestinal metabolites (Table 3); it is challenging to compare these studies because of different diagnostic criteria, patient selection, use or non-use of appropriately matched control subjects and microbial identification techniques. Of the nine articles, four used sequencing technologies, but different platforms, with five using culture-based techniques. One study (2017) performed metagenomic sequencing on 50 patient samples and was able to determine species-level differences compared with samples from control subjects [90]. A simple comparison between studies revealed eight similar results and seven conflicting results (Table 4). For example, while Giloteaux et al. [91] and Armstrong et al. [93] reported a general decrease in bacterial abundance, Sheedy et al. [94] reported an increase. These studies utilized different microbial identification techniques, which might account for the conflicting results. The lack of statistical analysis of the datasets constrains direct cross-study comparisons. From the limited cross-study analysis shown in Table 4, one finding of note was the decrease in Faecalibacterium seen in three studies. A reduction in butyrate-producing genus, which includes Faecalibacteria has been associated with dysbiosis in CD patients [95]. Butyrate has several protective properties, including improving the mucosal barrier, and immunomodulatory and anti-inflammatory effects by down-regulating pro-inflammatory cytokines [96]. However, decrease in the relative abundance of Faecalibacterium are associated with several other disorders and is not therefore specific for ME/CFS [97]. Interestingly, there was an increase in Enterobacteriaceae in two studies [91,98]. However, this may result from ME/CFS symptoms instead of a disease-specific microbial alteration. Enterobacteriaceae are dominant in the upper GI tract and are present in at low levels in the faeces of healthy individuals [99]. These taxa likely become enriched with faster stool transit time (i.e. signature of diarrhoea). The notable increase in this family would be consistent with increased transit time and reported in IBS-like symptoms in patients [100]. A depletion in the butyrate-producing family Ruminococcaceae was recorded in two studies and is also associated with diarrhoea [101]. Interestingly, Bacteroides spp. known for producing butyrate were reduced in several studies [97]. It would be beneficial to perform longitudinal studies of the microbiome in ME/CFS patients throughout the duration of the illness. This may provide more insightful clues as to the significance of any microbiome compositional changes with disease progression and severity within an individual patient. Several studies have been published on the longitudinal evaluation of ME/CFS patients; however, these focus on immune aspects, rehabilitative treatments and employment status [102–105].

Table 3
Overview of articles selected studying the microbiome in ME/CFS
Author Number of patients Number of controls Studying Study design 
Armstrong [9334 25 Microbiome and metabolites Culture + MS 
Nagy-Szakal [9050 50 Microbiome Metagenomics 
Giloteaux [9148 39 Microbiome 16s rRNA gene sequencing 
Giloteaux [107Microbiome and virome 16s rRNA gene sequencing 
Fremont [9243 36 Microbiome 16s rRNA gene sequencing 
Sheedy [94108 177 Microbiome and metabolites Culture 
Evengard [17610 10 Microbiome Culture 
Butt [1771390 Microbiome Culture 
Butt [9827 Microbiome and metabolites Culture 
Author Number of patients Number of controls Studying Study design 
Armstrong [9334 25 Microbiome and metabolites Culture + MS 
Nagy-Szakal [9050 50 Microbiome Metagenomics 
Giloteaux [9148 39 Microbiome 16s rRNA gene sequencing 
Giloteaux [107Microbiome and virome 16s rRNA gene sequencing 
Fremont [9243 36 Microbiome 16s rRNA gene sequencing 
Sheedy [94108 177 Microbiome and metabolites Culture 
Evengard [17610 10 Microbiome Culture 
Butt [1771390 Microbiome Culture 
Butt [9827 Microbiome and metabolites Culture 

Abbreviation: MS, mass spectrometry

Table 4
Basic comparison of microbiome composition alterations noted in articles selected for review
 Article details 
Microbiome comparison Armstrong (2017) [93Nagy-Szakal (2017) [90Giloteaux (2016) [91Giloteaux (2016) [107Fremont (2013) [92Sheedy (2009) [94Evangård (2007) [176Butt (2001) [177Butt (1998) [98
Overall abundance ↓  ↓   ↑    
Phylum Firmicutes   ↓ ↑      
Phylum Proteobacteria   ↑ ↓      
Family Bacteroidaceae   ↓ ↑      
Family Enterobacteriacaeae   ↑      ↑ 
Family Prevotellaceae   ↑ ↓      
Family Rickenellaceae   ↓ ↓      
Family Ruminococcaceae   ↓ ↓      
Genus Bacteroides ↓        ↓ 
Genus Bifidobacterium   ↓ ↓   ↑ ↓ ↓ 
Genus Clostridium  ↑ ↓       
Genus Coprobacillus  ↑ ↑       
Genus Faecalibacterium  ↓ ↓ ↓      
Genus Haemophilus  ↓ ↓       
Genus Ruminococcus   ↓  ↓     
Species Enterococcus faecalis      ↑   ↑ 
Species E. coli        ↓ ↓ 
 Article details 
Microbiome comparison Armstrong (2017) [93Nagy-Szakal (2017) [90Giloteaux (2016) [91Giloteaux (2016) [107Fremont (2013) [92Sheedy (2009) [94Evangård (2007) [176Butt (2001) [177Butt (1998) [98
Overall abundance ↓  ↓   ↑    
Phylum Firmicutes   ↓ ↑      
Phylum Proteobacteria   ↑ ↓      
Family Bacteroidaceae   ↓ ↑      
Family Enterobacteriacaeae   ↑      ↑ 
Family Prevotellaceae   ↑ ↓      
Family Rickenellaceae   ↓ ↓      
Family Ruminococcaceae   ↓ ↓      
Genus Bacteroides ↓        ↓ 
Genus Bifidobacterium   ↓ ↓   ↑ ↓ ↓ 
Genus Clostridium  ↑ ↓       
Genus Coprobacillus  ↑ ↑       
Genus Faecalibacterium  ↓ ↓ ↓      
Genus Haemophilus  ↓ ↓       
Genus Ruminococcus   ↓  ↓     
Species Enterococcus faecalis      ↑   ↑ 
Species E. coli        ↓ ↓ 

Seventeen criteria were either similar or conflicting between studies (microbiome composition). The down arrows represent a decrease in patients and up arrows represent an increase in patients.

As with any microbiome study, it is difficult to determine if the alterations observed cause, precede or correlate with disease. The microbiome of a patient would exhibit disease-specific microbial signatures and general microbial changes due to an unbalanced microbiome [106]. It is important, therefore, to separate microbial alterations associated with an unbalanced microbiome from those associated with a specific disease (microbiome disease biomarkers).

Virome and ME/CFS

Of the 11 articles selected in our literature search, only 3 examined the intestinal virome of CFS/ME patients (Table 5). Of these, two articles used direct virus detection (e.g. PCR or immunostaining) and one article used a high-throughput sequencing technique (Illumina MiSeq). An increase in bacteriophage richness, particularly Siphoviridae and Myoviridae, in patients was noted in the Giloteaux et al. study [107]. However, this study is statistically underpowered due to its small sample size. Chia and Chia [108] and Frémont et al. [109] used virus detection techniques to examine the presence of eukaryotic viruses within the gastric/intestinal mucosa. These studies reported an increase in parvovirus B19, enteroviral RNA and viral capsid protein 1 in patients. Also of note, Nagy-Szakal et al. [90] used metagenomic based approach on a large (n=50) cohort of patients although they did not perform virome analysis on the dataset. The authors reported significant changes in the bacterial components of the microbiome in ME/CFS patients compared with controls [90]. Virome analysis could be performed with the available date to determine if significant changes are observed in the viral components of ME/CFS patients.

Table 5
Overview of articles selected studying the virome in ME/CFS
Author Number of patients Number of controls Study design 
Giloteaux [107Viral metagenomics 
Fremont [10948 35 PCR detection 
Chia [108165 34 PCR detection and immunoperoxidase staning 
Author Number of patients Number of controls Study design 
Giloteaux [107Viral metagenomics 
Fremont [10948 35 PCR detection 
Chia [108165 34 PCR detection and immunoperoxidase staning 

Metabolomics studies

The identification of the bacterial and viral components of the microbiome is an important step forward, as is understanding how the use of nutrients by these microorganisms influences the overall metabolism within the gut. Metabolomics can be used to identify metabolites within the microbiome [110]. Only a handful of studies have attempted to characterize faecal metabolites in ME/CFS patients despite its potential for deciphering microbiome function (Table 6) [93,94,98]. There are significant challenges associated with identifying faecal metabolites due to differing metabolite properties and range of metabolite concentrations in samples [162,163]. A major challenge is not only to identify all metabolites (insufficient reference libraries available) but also to produce metadata (i.e. sample origin, tissue, experimental conditions) in a format that is easily interpreted [166]. The biological interpretation of metabolites as potential disease-associated biomarkers is often challenging as it requires data analysis and integration [167] and targeted and non-targeted metabolomics to dissect the metabolic pathway(s) and origin of metabolite(s) of interest [164]. Currently, 1H NMR is the most used analytical technique for metabolite profiling and is routinely used in clinical or pharmaceutical research and applications [165].

Table 6
Overview of articles selected for studying the metabolome in ME/CFS
Author Number of patients Number of controls Study design 
Armstrong [9334 25 NMR spectroscopy 
Sheedy [94108 177 C13-labelled bacteria/metabolites for HPLC and NMR 
Butt [9827 Specific metabolites 
Author Number of patients Number of controls Study design 
Armstrong [9334 25 NMR spectroscopy 
Sheedy [94108 177 C13-labelled bacteria/metabolites for HPLC and NMR 
Butt [9827 Specific metabolites 

Abbreviations: NMR, nuclear magnetic resonance; HPLC, high performance liquid chromatography

Armstrong et al. [93] quantified metabolites using high-throughput 1H NMR spectroscopy from ME patient faecal filtrates. This technique provides a non-targeted metabolic profile that measures all high concentration metabolites with non-exchangeable protons [111]. In addition to faecal metabolomics, the authors performed urine and blood serum metabolite analysis. The present study presented a robust metabolome workflow and eluded to the relationship of faecal metabolites and microbes with host blood serum and urine metabolites [93]. Two older studies used selective culture based systems to examine the metabolic output of specific bacteria [94,98]. It is difficult to draw conclusions from these older studies because of the culture-based techniques used. It is possible that the isolation of bacteria from the complex intestinal environment alters the excreted/secreted metabolites, resulting in metabolites specific to the artificial in vitro culture environment. To obtain a true picture of the faecal metabolome, samples should be prepared directly from the faecal sample.

Unfortunately, it is not possible to compare these three metabolomics studies directly because different metabolites were studied. However, it is possible to make general comparisons between metabolites and microbes. For example, Sheedy et al. [94] reported an increase in lactic acid and an increase in Enterococcus faecalis, a lactic acid producing bacteria. Interestingly, Armstrong et al. [93] reported a general increase in the short chain fatty acids (SCFAs) butyrate, isovalerate and valerate. This contradicts the microbiome studies as known SCFA-producing bacteria (Faecalibacterium, Eubacteria, Roseburia and Ruminococcus) were consistently decreased across multiple studies [90–92,107]. A decrease in lactate was also reported in this study [93]. Several bacterial members of the microbiota produce lactate, which is the most common short chain hydroxyl-fatty acid in the intestinal lumen [97,112]. It can be converted into other SCFAs by a subgroup of lactate-fermenting bacterial species. Changes in these lactate-fermenting bacterial species were not noted in the current microbiome studies. Future studies will need to examine microbiome and metabolome alterations in tandem and then integrate the data to reveal a truer picture of microbiome metabolism.

Studying the microbiome: techniques and challenges

Within recent years, the increased interest in trying to understand the effect of the microbiome on health and disease has resulted in significant advancements in techniques to characterize it [9–11]. In particular, metagenomics is increasingly popular and favoured over sequencing bacterial 16s rRNA due to increased taxonomic sensitivity and potential for functional interpretation. Additionally, established techniques are being applied to microbiome research, such as metabolomics [11,113]. The research at the Quadram Institute Bioscience is focused on optimizing protocols, standardizing microbiome studies and applying this to ME/CFS. There are several pitfalls and challenges associated with microbiome studies, which need to be addressed prior to patient recruitment and sample collection [114]. The considerations that need to be made in designing microbiome studies in ME/CFS and some recommendations are outlined in Figure 3. Below we describe in some detail the particular constraints on microbiome and virome studies in ME/CFS and the approaches that can be taken to mitigate against or overcome them.

Important considerations in designing ME microbiome studies

Figure 3
Important considerations in designing ME microbiome studies

Recommendations for designing a microbiome study and important questions to consider

Figure 3
Important considerations in designing ME microbiome studies

Recommendations for designing a microbiome study and important questions to consider

Patient recruitment

A standardized criterion for ME/CFS diagnosis is lacking, with diagnosis relying heavily upon clinical observations and exclusion [83,115]. Multiple diagnosis checklists have been created, with each checklist differing slightly on symptom emphasis and severity [69,83,84]. The International ME criteria and Canadian criteria place greater emphasis on the delayed exacerbation of physical and mental symptoms following exertion. However, it does not exclude psychiatric illness such as depression or anxiety [116,117]. Therefore, it is difficult to determine how many patients recruited have an accurate diagnosis of ME/CFS and how many have been misdiagnosed using inadequate criteria. Several studies have attempted to address this by using multiple diagnostic checklists, with the majority using the 1994 Fukuda diagnostic scale. The severity of ME/CFS is ranked according to impact of illness upon daily life and ranges from mild to very severe. The severity grade given to a patient is subjective and generally given by the diagnosing clinician. Several studies have used patient questionnaires to assess the level of illness [90,91,94,107]. However, four different patient questionnaires were used by four different studies (Short Form 36 Healthy Survey, Multidimensional Fatigue Inventory, Bell’s Disability Scale and McGregor 1995 questionnaire). Due to the multifactorial nature of the disease, standardization in diagnosis and disease severity are imperatives. These are a basic requirement to produce robust and reproducible microbiome studies. It is very difficult to determine if any microbiome differences are due to a true ME/CFS signature or complexities of patient recruitment. Future studies should aim to stratify patients according to disease duration and onset (sudden or gradual).

As microbiome research has increased, the need for properly matched controls has become apparent. The complexity of the GI microbiome and potential role within healthy/diseased states produce confounding factors [118]. As many of these confounding factors need to be taken into consideration in a microbiome study, and as many as possible should be accounted for or eliminated. Age, lifestyle, medications and drug use, geography and diet have all been reported to influence microbiome function and composition [5–7]. The effect of antibiotics on the microbiome is well documented [119]. However, other prescription and recreational drugs can affect microbiome analyses [120]. For example, decreasing stomach acidity with proton pump inhibitors allows upper GI microbes to move down into the intestine more readily, altering the composition of the lower GI microbiota and increasing risk of C. difficile infections [121].

Diet also influences the microbiome. Long-term dietary patterns have been linked to faecal microbiomes dominated by certain genera [118,122]. High protein/animal fat diets are associated with the prevalence of Bacteriodes, whereas diets high in carbohydrates are associated with high Prevotella [123]. To account for this, details of food consumption at least 48 h prior to sample collection should be obtained. Moreover, healthy household controls could be used to identify and exclude environmental confounders (e.g. diet, living environment); increasing the likelihood of identifying disease-specific microbiome signatures [118]. Additionally, the microbiome changes during aging and declines in diversity in the elderly [124]. Therefore, the use of age-matched controls would be beneficial to account for this important variable.

Recently, the influence of gender on the microbiome (termed as ‘microgenderome’) has become evident [125–128]. The intestine and its’ microbiome serves as a virtual endocrine organ due to the metabolites and neurotransmitters and hormones it can produce [129]. For example, early microbial exposure increases testosterone levels in male mice, leading to a protective effect against T1D [128]. Additionally, microbiome alterations are observed in pre- and post-menopausal women; highlighting hormonal cross-talk within the microbiome [130]. Certain microbes have also been discovered to be a source of hormones and neurotransmitters. Experimental models have confirmed the bidirectional relationship between the intestinal microbiota, sex hormones and the immune system and provided an explanation for sexual dimorphism in T1D [128,131]. The results of these studies revealed evidence of sex-specific microbial communities, sex-specific responses to the same microbial communities, the role of sexual maturation impacting on changes on microbial communities and that microbial communities can play a protective and therapeutic role by influencing hormonal, metabolic and immune pathways [125–128]. A 2015 study compared the microbiome of male and female patients with ME/CFS revealing significant sex-specific interactions between Firmicutes (Clostridium, Streptococcus, Lactobacillus and Enterococcus) and symptoms, regardless of compositional similarity of microbial levels across the sexes [132]. This study highlights the need for gender-matched controls to account for any gender bias from future microbiome studies.

Although it is often impractical and perhaps impossible to control for all confounding factors within a microbiome study, efforts should be made to account for as many as possible.

Sample collection, storage and processing

As the number of microbiome studies has increased, the need for consistency in sampling techniques and standard operating procedures (SOPs) has also increased. An excellent review of the critical factors for sample collection, storage, transport and ‘gold standard’ techniques for longitudinal microbiome studies in human populations was recently published [114]. The most important considerations for storing microbiome samples are to reduce changes in the original microbiota from sample collection to processing and to keep storage conditions consistent for all samples in a study [133,134]. Sample storage conditions are not always consistent due to study or research group-specific downstream applications and resource limitations. Additionally, considerations are not always taken for preserving anaerobic bacteria within an anaerobic environment. Different studies often store samples at differing temperature (e.g. 4 to −80°C), affecting the long-term preservation of certain bacteria [114]. Additionally, the length of time for which the sample is stored and frequency of freeze/thaw cycles can significantly affect the microbiome composition. For example, Bacteroides is sensitive to freezing and should be processed within 6 weeks of storage (at −80°C) to avoid bacterial degradation [135,136]. The microbiome and ME/CFS studies reviewed here used different sample collection and storage techniques; including storage at <12°C, immediate processing, −20 and −80°C. For logistical reasons, it can be difficult to standardize this across all studies. However, it is important to be aware of these limitations.

The microbe composition changes laterally and longitudinally along the GI tract, therefore it has been suggested that there is significant variation within a single faecal sample. A 2015 study reported a reduction in intrasample variation following homogenization of the whole faecal sample [137]. However, several studies use a random section of the faeces without homogenization.

Additionally, different DNA extraction techniques have been used as a prelude to sequencing (MoBio PowerSoil DNA isolation kit, QIAmp DNA Stool Mini Kit and DNeasy Blood and Tissue kit). These kits differ in protocol, bead size, reagents used and are likely to introduce unnecessary bias [138].

Identification of prokaryotes

The development of sequencing, characterization of the bacterial component of the faecal microbiome relied on culture-based techniques that allow the identification of anaerobic and aerobic bacteria using selective or non-selective culture conditions and media; albeit taxonomic resolution and sensitivity is relatively low [12]. However, this approach does inform the cultured organism’s growth requirements and substrate utilization and other physiological parameters, which cannot be obtained from sequence-based approaches [12]. Next-generation sequencing technology now makes it possible to characterize the bacterial microbiome using the 16S rRNA gene ‘fingerprint’ for identification and as an indicator of genetic diversity [4]. The 16S rRNA gene was chosen because of its relatively small size (~1.5 kb) and harbouring enough variation to distinguish between different species, yet enough similarity to assign members belonging to the same larger phylogenetic group (e.g. order, family or phylum) [5,139]. However, this approach has its limitations. It only detects and analyses a short, specific genomic region and taxonomic resolution or functional inference is therefore limited [11]. For example, this assay cannot recognize the different serovars within Salmonella enterica or detect toxin genes that could distinguish pathogenic C. difficle or distinguish pathogenic Escherichia strains from non-pathogenic strains [140]. This is particularly problematic in comparative studies of the microbiome in healthy and diseased states. It also provides no insight into functionality of the bacteriome [11].

Metagenomic sequencing is increasingly being chosen over 16S rRNA sequencing due to its higher taxonomic resolution and ability to infer functional potential [140]. It provides sequence information from the collective genomes of the microbiota, which in turn can be used to infer or predict functional contributions and biological roles of this complex community in human health and disease [11,139]. However, the absence of whole genome sequences in public databases limits the ability to identify gene function based on known sequence information. In comparison with 16S-based sequencing approaches, whole community metagenomics with an appropriate sequencing depth and coverage can be used to identify other microbes (i.e. archaea and viruses) within the microbiome [140,141]. Although it is possible to infer functional potential from metagenomic analysis through gene presence/abundance, the presence of a gene does not necessarily infer function; it is possible for the gene to be present but not transcribed [113]. Therefore, careful consideration needs to be taken when inferring functional potential from metagenomic sequences and, if possible, the predicted function should be examined using laboratory based techniques (e.g. antibiotic resistance), assuming the candidate microbe(s) can be cultured in isolation.

To date, only one study has utilized metagenomics in ME/CFS microbiome studies [90]. However, the analysis was incomplete and did not fully exploit the data produced. Whenever possible, metagenomics should be applied to microbiome studies in ME/CFS in order to achieve the required taxonomic resolution to fully examine the bacteriome and virome.

Identification of viruses

Virus genomes do not encode universally conserved genes such as the 16S or 18S genes of prokaryotes and eukaryotes respectively, and they are genetically highly diverse [142]. Consequently, it is not possible to use metataxonomic approaches such as 16S rRNA gene sequencing to characterize VLPs within an ecosystem [20]. Traditionally, classical approaches of microscopy and cultivation have been used to characterize VLPs isolated from faecal samples originating in the human intestine [143,144]. The only reliable molecular method currently available for surveying the human virome is metagenomics. However, to achieve an adequate sequencing depth, lytic VLPs need to be separated from the faecal material [145,146]. An excellent review describing the human virome and its characterization was recently published [147].

The efficient isolation of VLPs is an essential step in viral metagenomics in order to obtain an accurate picture and profile of the virome [148,149]. The workflow for sequencing the nucleic acid in VLPs (Figure 2) from faecal material begins with homogenization of faeces in buffer, centrifugation to remove cell debris followed by filtration to remove bacteria [150]. Ultracentrifugation can be used to separate the sample into differing densities and a specific density containing VLPs can be selected for downstream processing. Within the intestinal microbiome community, viral genomes represent a small proportion of the total DNA compared with bacterial genomes [149,151]. It is important therefore to use a reliable, robust and efficient VLP isolation protocol with as few manipulations of the sample as possible to minimize loss of VLPs. Various VLP protocols have been published that differ in details such as filter pore size, centrifugation speed and the inclusion/omission of ultracentrifugation [148–150,152,153]. Importantly, these protocols have yet to be directly compared. It is highly desirable therefore that standardized faecal VLP isolation and DNA extraction techniques are adopted to enable direct comparisons of datasets from different virome studies.

Viral metagenomic data are analysed in a manner similar to bacterial metagenomic data [154,155]. High-quality reads are aligned to reference databases, assembly is then attempted with non-aligned reads and functional characteristics inferred [147]. However, the lack of conserved genes, high genetic variation and under-representation of virus genomes within reference databases means a minority of the reads can be taxonomically assigned. It is predicted that less than 0.001% of the predicted phage diversity is represented in global sequence databanks [156]. One virome study has reported that 98% of the generated reads did not significantly match to an identified sequence within a database [157]. Therefore, a majority of sequencing reads are unassigned to any known genomes and are considered ‘viral dark matter’. Additionally, assembly of sequencing reads is made difficult due to their short-read lengths [158,159]. Several research groups are now investigating the possibility of long-read sequencing to characterize the virome. The release of the Oxford Nanopore MinION has drastically reduced the cost of long-read sequencing (compared with PacBio sequencing). These have the potential to provide complete or near complete phage genomes without the need for alignment or representation in databases [160].

Perhaps the biggest challenge in studying the intestinal virome is the lack of bioinformatics tools for the analysis of sequence data [146,147,161]. To date, there is no easy-to-use pipeline that uses raw reads, can remove host DNA, can search for bacterial contaminants and assign taxonomy and functionality to viruses within the sample. However, efforts are being made to generate such tools. In addition to isolation and sequencing of VLPs, it is possible to identify prophages and the bacterial host(s) from metagenomic sequencing. To accurately study the virome, both techniques should be utilized to study the lytic and lysogenic phages [154,155,157]. The Norwich U.K., ME/CFS research group is currently optimizing and standardizing VLP isolation and DNA/RNA sequencing protocols in addition to developing fit-for purpose viromics pipelines to comprehensively analyse the virome in ME/CFS patients.

Concluding remarks

Several microbiome studies have been performed on ME/CFS patients in the hope of identifying disease-specific signatures. This disease should be viewed as multifactorial and that the alteration of one body system (e.g. microbiome) may not be the exclusive cause. The dysregulation of the microbiome may be variously placed in a disease progression pathway interfacing with other systems (immune, neuroendocrine and mitochondrial), tipping the body into persistent imbalance. Although studies to date often report conflicting results, microbiome dysbiosis in ME/CFS patients is evident. However, in order to discover disease-specific microbe alterations, future studies need to adopt standardized techniques and analyses. The recent advancements in sequencing technology allows the characterization of the previously neglected virome. As virome research increases, it is becoming clear that the virome can directly and indirectly affect host health, and may play a role in the pathogenesis of ME/CFS. Confirmation of such a role will be largely dependent on the adoption of robust patient selection, reproducible study design and appropriate data analyses by different research groups investigating the microbiome/virome in complex diseases such as ME/CFS.

Clinical perspectives

  • Several studies have reported alterations in the intestinal microbiome of ME/CFS patients, suggesting the involvement of microbial dysbiosis.

  • Study design needs to be consistent to allow statistical comparison between different microbiome studies.

  • Future microbiome studies should take account of the virome.

We thank Navena Navaneetharaja, Nadine Davies, Daniel Vipond and Lesley Hoyles for their contribution towards this research; additionally, Kathryn Cross for the TEM imaging of intestinal viruses. ME/CFS research was carried out in the laboratories of S.R.C. and T.W.

Competing interests

The authors declare that there are no competing interests associated with the manuscript.

Funding

This work was supported in part by Invest in ME to S.R.C. and T.W.; and the Ph.D. funding [grant number R205102 to F.N.)].

Author contribution

F.N. wrote the manuscript with support from S.R.C. and T.W. T.W. also designed the graphical illustrations. S.-Y.H. prepared samples for TEM imaging.

Abbreviations

     
  • CD

    Crohn’s disease

  •  
  • CFS

    chronic fatigue syndrome

  •  
  • GI

    gastrointestinal

  •  
  • IBD

    inflammatory bowel disease

  •  
  • IBS

    irritable bowel syndrome

  •  
  • IL

    Interleukin

  •  
  • ME

    myalgic encephalomyelitis

  •  
  • ME/CFS

    myalgic encephalomyelitis/chronic fatigue syndrome

  •  
  • SCFA

    short chain fatty acid

  •  
  • T1D

    type 1 diabetes

  •  
  • TNFα

    Tumor necrosis factor alpha

  •  
  • VLP

    virus-like particle

  •  
  • WHO

    World Health Organization

References

References
1
Kunz
C.
,
Kuntz
S.
and
Rudloff
S.
(
2009
)
Intestinal flora
.
Adv. Exp. Med. Biol.
639
,
67
79
[PubMed]
2
Morelli
L.
(
2008
)
Postnatal development of intestinal microflora as influenced by infant nutrition
.
J. Nutr.
138
,
1791S
1795S
[PubMed]
3
Neish
A.S.
(
2009
)
Microbes in gastrointestinal health and disease
.
Gastroenterology
136
,
65
80
[PubMed]
4
Eckburg
P.B.
,
Bik
E.M.
,
Bernstein
C.N.
,
Purdom
E.
,
Dethlefsen
L.
,
Sargent
M.
et al. 
(
2005
)
Diversity of the human intestinal microbial flora
.
Science
308
,
1635
1638
,
[PubMed]
5
Palmer
C.
,
Bik
E.M.
,
DiGiulio
D.B.
,
Relman
D.A.
and
Brown
P.O.
(
2007
)
Development of the human infant intestinal microbiota
.
PLoS Biol.
5
,
1556
1573
6
Koenig
J.E.
,
Spor
A.
,
Scalfone
N.
,
Fricker
A.D.
,
Stombaugh
J.
,
Knight
R.
et al. 
(
2011
)
Succession of microbial consortia in the developing infant gut microbiome
.
Proc. Natl. Acad. Sci. U.S.A.
108
,
4578
4585
7
Tamburini
S.
,
Shen
N.
,
Wu
H.C.
and
Clemente
J.C.
(
2016
)
The microbiome in early life: implications for health outcomes
.
Nat. Med.
22
,
713
717
[PubMed]
8
Mackie
R.I.
,
Sghir
A.
and
Gaskins
H.R.
(
1999
)
Developmental microbial ecology of the neonatal gastrointestinal tract
.
Am. J. Clin. Nutr.
69
,
1035S
1045S
[PubMed]
9
Robinson
C.J.
,
Bohannan
B.J.M.
and
Young
V.B.
(
2010
)
From structure to function: the ecology of host-associated microbial communities
.
Microbiol. Mol. Biol. Rev.
74
,
453
476
,
[PubMed]
10
Bassis
C.
,
Young
V.
and
Schmidt
T.
(
2013
)
Methods for characterizing microbial communities associated with the human body
. In
The Human Microbiota
, pp.
51
74
,
John Wiley & Sons, Inc.
,
Hoboken, NJ, U.S.A.
11
Di Bella
J.M.
,
Bao
Y.
,
Gloor
G.B.
,
Burton
J.P.
and
Reid
G.
(
2013
)
High throughput sequencing methods and analysis for microbiome research
.
J. Microbiol. Methods
95
,
401
414
,
[PubMed]
12
Sekirov
I.
,
Russell
S.
and
Antunes
L.
(
2010
)
Gut microbiota in health and disease
.
Physiol. Rev.
90
,
859
904
[PubMed]
13
O’Hara
A.M.
and
Shanahan
F.
(
2006
)
The gut flora as a forgotten organ
.
EMBO Rep.
7
,
688
693
[PubMed]
14
Shreiner
A.B.
,
Kao
J.Y.
and
Young
V.B.
(
2016
)
The gut microbiome in health and in disease
.
Curr. Opin. Gastroenterol.
31
,
69
75
15
Pfeiffer
J.K.
and
Virgin
H.W.
(
2016
)
Viral immunity. Transkingdom control of viral infection and immunity in the mammalian intestine
.
Science
351
,
aad5872
16
Lim
E.S.
,
Wang
D.
and
Holtz
L.R.
(
2016
)
The bacterial microbiome and virome milestones of infant development
.
Trends Microbiol.
24
,
801
810
,
[PubMed]
17
Columpsi
P.
,
Sacchi
P.
,
Zuccaro
V.
,
Cima
S.
,
Sarda
C.
,
Mariani
M.
et al. 
(
2016
)
Beyond the gut bacterial microbiota: The gut virome
.
J. Med. Virol.
88
,
1467
1472
[PubMed]
18
Haynes
M.
and
Rohwer
F.
(
2011
)
The human virome
. In
Metagenomics of the Human Body
, pp.
63
77
,
Springer New York
,
New York
19
Breitbart
M.
and
Rohwer
F.
(
2005
)
Here a virus, there a virus, everywhere the same virus?
Trends Microbiol.
13
,
278
284
,
[PubMed]
20
Virgin
H.W.
(
2014
)
The virome in mammalian physiology and disease
.
Cell
157
,
142
150
21
Breitbart
M.
,
Haynes
M.
,
Kelley
S.
,
Angly
F.
,
Edwards
R.A.
,
Felts
B.
et al. 
(
2008
)
Viral diversity and dynamics in an infant gut
.
Res. Microbiol.
159
,
367
373
[PubMed]
22
Reyes
A.
,
Haynes
M.
,
Hanson
N.
,
Angly
F.E.
,
Heath
A.C.
,
Rohwer
F.
et al. 
(
2011
)
Viruses in the faecal microbiota of monozygotic twins and their mothers
.
Nature
466
,
334
338
,
[PubMed]
23
Cario
E.
,
Gerken
G.
and
Podolsky
D.K.
(
2007
)
Toll-like receptor 2 controls mucosal inflammation by regulating epithelial barrier function
.
Gastroenterology
132
,
1359
1374
,
[PubMed]
24
Hooper
L.V.
,
Wong
M.H.
,
Thelin
A.
,
Hansson
L.
,
Falk
P.G.
and
Gordon
J.I.
(
2001
)
Molecular analysis of commensal host-microbial relationships in the intestine
.
Science
291
,
881
884
,
25
Lutgendorff
F.
,
Akkermans
L.M.A.
and
Söderholm
J.D.
(
2008
)
The role of microbiota and probiotics in stress-induced gastro-intestinal damage
.
Curr. Mol. Med.
8
,
282
298
,
[PubMed]
26
Rakoff-Nahoum
S.
,
Paglino
J.
,
Eslami-Varzaneh
F.
,
Edberg
S.
and
Medzhitov
R.
(
2004
)
Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis
.
Cell
118
,
229
241
,
[PubMed]
27
Hooper
L.V.
,
Bry
L.
,
Falk
P.G.
and
Gordon
J.I.
(
1998
)
Host-microbial symbiosis in the mammalian intestine: exploring an internal ecosystem
.
Bioessays
20
,
336
343
,
28
Rooks
M.G.
and
Garrett
W.S.
(
2016
)
Gut microbiota, metabolites and host immunity
.
Nat. Rev. Immunol.
16
,
341
352
,
[PubMed]
29
Hooper
L.V.
and
Macpherson
A.J.
(
2010
)
Immune adaptations that maintain homeostasis with the intestinal microbiota
.
Nat. Rev. Immunol.
10
,
159
169
,
[PubMed]
30
Kabat
A.M.
,
Srinivasan
N.
and
Maloy
K.J.
(
2014
)
Modulation of immune development and function by intestinal microbiota
.
Trends Immunol.
35
,
507
517
,
[PubMed]
31
Kostic
A.D.
,
Chun
E.
,
Robertson
L.
,
Glickman
J.N.
,
Gallini
C.A.
,
Michaud
M.
et al. 
(
2013
)
Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment
.
Cell Host Microbe
14
,
207
215
,
[PubMed]
32
Kostic
A.D.
,
Xavier
R.J.
and
Gevers
D.
(
2014
)
The microbiome in inflammatory bowel disease: current status and the future ahead
.
Gastroenterology
146
,
1489
1499
,
[PubMed]
33
Le Chatelier
E.
,
Nielsen
T.
,
Qin
J.
,
Prifti
E.
,
Hildebrand
F.
,
Falony
G.
et al. 
(
2013
)
Richness of human gut microbiome correlates with metabolic markers
.
Nature
500
,
541
546
[PubMed]
34
Distrutti
E.
,
Monaldi
L.
,
Ricci
P.
and
Fiorucci
S.
(
2016
)
Gut microbiota role in irritable bowel syndrome: New therapeutic strategies
.
World J. Gastroenterol.
22
,
2219
2241
,
35
Rajilić-Stojanović
M.
,
Heilig
H.G.H.J.
,
Tims
S.
,
Zoetendal
E.G.
and
de Vos
W.M.
(
2013
)
Long-term monitoring of the human intestinal microbiota composition
.
Environ. Microbiol.
15
,
1146
1159
,
36
David
L.A.
,
Maurice
C.F.
,
Carmody
R.N.
,
Gootenberg
D.B.
,
Button
J.E.
,
Wolfe
B.E.
et al. 
(
2014
)
Diet rapidly and reproducibly alters the human gut microbiome
.
Nature
505
,
559
563
,
[PubMed]
37
Scher
J.U.
,
Littman
D.R.
and
Abramson
S.B.
(
2016
)
Microbiome in inflammatory arthritis and human rheumatic diseases
.
Arthritis Rheumatol.
68
,
35
45
,
38
Berbers
R.M.
,
Nierkens
S.
,
van Laar
J.M.
,
Bogaert
D.
and
Leavis
H.L.
(
2017
)
Microbial dysbiosis in common variable immune deficiencies: evidence, causes, and consequences
.
Trends Immunol.
38
,
206
216
,
[PubMed]
39
Yatsunenko
T.
,
Rey
F.E.
,
Manary
M.J.
,
Trehan
I.
,
Dominguez-Bello
M.G.
,
Contreras
M.
et al. 
(
2012
)
Human gut microbiome viewed across age and geography
.
Nature
486
,
222
227
[PubMed]
40
Foster
J.A.
,
Rinaman
L.
and
Cryan
J.F.
(
2017
)
Stress & the gut-brain axis: regulation by the microbiome
.
Neurobiol. Stress
7
,
124
136
,
41
Dethlefsen
L.
,
Huse
S.
,
Sogin
M.L.
and
Relman
D.A.
(
2008
)
The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16s rRNA sequencing
.
PLoS Biol.
6
,
e280
42
De Paepe
M.
,
Leclerc
M.
,
Tinsley
C.R.
and
Petit
M.-A.
(
2014
)
Bacteriophages: an underestimated role in human and animal health?
Front. Cell Infect. Microbiol.
4
,
39
[PubMed]
43
Modi
S.R.
,
Lee
H.H.
,
Spina
C.S.
and
Collins
J.J.
(
2013
)
Antibiotic treatment expands the resistance reservoir and ecological network of the phage metagenome
.
Nature
499
,
219
222
,
[PubMed]
44
Meessen-Pinard
M.
,
Sekulovic
O.
and
Fortier
L.C.
(
2012
)
Evidence of in vivo prophage induction during Clostridium difficile infection
.
Appl. Environ. Microbiol.
78
,
7662
7670
,
[PubMed]
45
Matos
R.C.
,
Lapaque
N.
,
Rigottier-Gois
L.
,
Debarbieux
L.
,
Meylheuc
T.
,
Gonzalez-Zorn
B.
et al. 
(
2013
)
Enterococcus faecalis prophage dynamics and contributions to pathogenic traits
.
PLoS Genet.
9
,
e1003539
,
46
Webb
J.S.
,
Lau
M.
and
Kjelleberg
S.
(
2004
)
Bacteriophage and phenotypic variation in Pseudomonas aeruginosa biofilm development
.
J. Bacteriol.
186
,
8066
8073
,
[PubMed]
47
Carrolo
M.
,
Frias
M.J.
,
Pinto
F.R.
,
Melo-Cristino
J.
and
Ramirez
M.
(
2010
)
Prophage spontaneous activation promotes DNA release enhancing biofilm formation in Streptococcus pneumoniae
.
PLoS ONE
5
,
e15678
,
48
Schuch
R.
and
Fischetti
V.A.
(
2009
)
The secret life of the anthrax agent Bacillus anthracis: Bacteriophage-mediated ecological adaptations
.
PLoS ONE
4
,
e6532
,
49
Wagner
J.
,
Maksimovic
J.
,
Farries
G.
,
Sim
W.H.
,
Bishop
R.F.
,
Cameron
D.J.
et al. 
(
2013
)
Bacteriophages in gut samples from pediatric Crohn’s disease patients: metagenomic analysis using 454 pyrosequencing
.
Inflamm. Bowel Dis.
19
,
1598
1608
,
[PubMed]
50
Pérez-Brocal
V.
,
García-López
R.
,
Vázquez-Castellanos
J.F.
,
Nos
P.
,
Beltrán
B.
,
Latorre
A.
et al. 
(
2013
)
Study of the viral and microbial communities associated with Crohn’s disease: a metagenomic approach
.
Clin. Transl. Gastroenterol.
4
,
e36
[PubMed]
51
Wang
W.
,
Jovel
J.
,
Halloran
B.
,
Wine
E.
,
Patterson
J.
,
Ford
G.
et al. 
(
2015
)
Metagenomic analysis of microbiome in colon tissue from subjects with inflammatory bowel diseases reveals interplay of viruses and bacteria
.
Inflamm. Bowel Dis.
21
,
1419
1427
,
[PubMed]
52
Monaco
C.L.
,
Gootenberg
D.B.
,
Zhao
G.
,
Handley
S.A.
,
Ghebremichael
M.S.
,
Lim
E.S.
et al. 
(
2016
)
Altered virome and bacterial microbiome in human immunodeficiency virus-associated acquired immunodeficiency syndrome
.
Cell Host Microbe
19
,
311
322
,
[PubMed]
53
Diard
M.
,
Bakkeren
E.
,
Cornuault
J.K.
,
Moor
K.
,
Hausmann
A.
,
Sellin
M.E.
et al. 
(
2017
)
Inflammation boosts bacteriophage transfer between Salmonella spp
.
Science
355
,
1211
1215
,
54
Boyd
E.F.
(
2012
)
Bacteriophage-encoded bacterial virulence factors and phage-pathogenicity island interactions
.
Adv. Virus Res.
82
,
91
118
,
[PubMed]
55
Gamage
S.D.
,
Patton
A.K.
,
Hanson
J.F.
and
Weiss
A.A.
(
2004
)
Diversity and host range of Shiga toxin-encoding phage
.
Infect. Immun.
72
,
7131
7139
,
[PubMed]
56
Nguyen
S.
,
Baker
K.
,
Padman
B.S.
,
Patwa
R.
,
Dunstan
R.A.
,
Weston
T.A.
et al. 
(
2017
)
Bacteriophage transcytosis provides a mechanism to cross epithelial cell layers
.
MBio
8
,
e01874
17
,
[PubMed]
57
Lepage
P.
,
Colombet
J.
,
Marteau
P.
,
Sime-Ngando
T.
,
Doré
J.
and
Leclerc
M.
(
2008
)
Dysbiosis in inflammatory bowel disease: a role for bacteriophages?
Gut.
57
,
424
425
,
[PubMed]
58
Pérez-Brocal
V.
,
García-López
R.
,
Nos
P.
,
Beltrán
B.
,
Moret
I.
and
Moya
A.
(
2015
)
Metagenomic analysis of Crohn’s disease patients identifies changes in the virome and microbiome related to disease status and therapy, and detects potential interactions and biomarkers
.
Inflamm. Bowel Dis.
21
,
2515
2532
,
[PubMed]
59
Kramná
L.
,
Kolářová
K.
,
Oikarinen
S.
,
Pursiheimo
J.P.
,
Ilonen
J.
,
Simell
O.
et al. 
(
2015
)
Gut virome sequencing in children with early islet autoimmunity
.
Diabetes Care
38
,
930
933
[PubMed]
60
Rhee
S.H.
,
Pothoulakis
C.
and
Mayer
E.A.
(
2009
)
Principles and clinical implications of the brain-gut-enteric microbiota axis
.
Nat. Rev. Gastroenterol. Hepatology
6
,
306
314
,
61
Carabotti
M.
,
Scirocco
A.
,
Maselli
M.A.
and
Severi
C.
(
2015
)
The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems
.
Ann. Gastroenterol.
28
,
203
209
,
[PubMed]
62
Grenham
S.
,
Clarke
G.
,
Cryan
J.F.
and
Dinan
T.G.
(
2011
)
Brain-gut-microbe communication in health and disease
.
Front. Physiol.
2
,
94
,
[PubMed]
63
Mulle
J.G.
,
Sharp
W.G.
and
Cubells
J.F.
, and (
2013
)
The gut microbiome: a new frontier in autism research
.
Curr. Psychiatry Rep.
15
,
337
,
64
Evrensel
A.
and
Ceylan
M.E.
(
2015
)
The gut-brain axis: the missing link in depression
.
Clin. Psychopharmacol. Neurosci.
13
,
239
244
,
65
Dickerson
F.
,
Severance
E.
and
Yolken
R.
(
2017
)
The microbiome, immunity, and schizophrenia and bipolar disorder
.
Brain Behav. Immun.
62
,
46
52
,
[PubMed]
66
Vogt
N.M.
,
Kerby
R.L.
,
Dill-McFarland
K.A.
,
Harding
S.J.
,
Merluzzi
A.P.
,
Johnson
S.C.
et al. 
(
2017
)
Gut microbiome alterations in Alzheimer’s disease
.
Sci. Rep.
7
,
13537
,
[PubMed]
67
Sampson
T.R.
,
Debelius
J.W.
,
Thron
T.
,
Janssen
S.
,
Shastri
G.G.
,
Ilhan
Z.E.
et al. 
(
2016
)
Gut microbiota regulate motor deficits and neuroinflammation in a model of Parkinson’s disease
.
Cell
167
,
1469.e12
1480.e12
,
68
Bansal
A.S.
,
Bradley
A.S.
,
Bishop
K.N.
,
Kiani-Alikhan
S.
and
Ford
B.
(
2012
)
Chronic fatigue syndrome, the immune system and viral infection
.
Brain Behav. Immun.
26
,
24
31
[PubMed]
69
Bansal
A.S.
(
2016
)
Investigating unexplained fatigue in general practice with a particular focus on CFS/ME
.
BMC Fam. Pract.
17
,
81
[PubMed]
70
Kerr
J.R.
,
Cunniffe
V.S.
,
Kelleher
P.
,
Bernstein
R.M.
and
Bruce
I.N.
(
2003
)
Successful intravenous immunoglobulin therapy in 3 cases of parvovirus B19-associated chronic fatigue syndrome
.
Clin. Infect. Dis.
36
,
e100
6
,
[PubMed]
71
Jason
L.A.
,
Benton
M.C.
,
Valentine
L.
,
Johnson
A.
and
Torres-Harding
S.
(
2008
)
The economic impact of ME/CFS: individual and societal costs
.
Dyn. Med.
7
,
6
[PubMed]
72
Hickie
I.
,
Lloyd
A.
,
Hadzi-Pavlovic
D.
,
Parker
G.
,
Bird
K.
and
Wakefield
D.
(
1995
)
Can the chronic fatigue syndrome be defined by distinct clinical features?
Psychol. Med.
25
,
925
935
,
[PubMed]
73
Sanders
P.
and
Korf
J.
(
2008
)
Neuroaetiology of chronic fatigue syndrome: an overview
.
World J. Biol. Psychiatry
9
,
165
171
,
74
Morris
G.
,
Berk
M.
,
Galecki
P.
and
Maes
M.
(
2014
)
The emerging role of autoimmunity in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)
.
Mol. Neurobiol.
49
,
741
756
,
[PubMed]
75
Underhill
R.A.
(
2015
)
Myalgic encephalomyelitis, chronic fatigue syndrome: an infectious disease
.
Med. Hypotheses.
85
,
765
773
,
[PubMed]
76
Meals
R.W.
,
Hauser
V.F.
and
Bower
A.G.
(
1935
)
Poliomyelitis-The Los Angeles Epidemic of 1934 : part I
.
Cal. West. Med.
43
,
123
125
,
[PubMed]
77
Mateen
F.J.
and
Black
R.E.
(
2013
)
Expansion of acute flaccid paralysis surveillance: beyond poliomyelitis
.
Trop. Med. Int. Health
18
,
1421
1422
,
78
Hall
W.J.
,
Nathanson
N.
and
Langmuir
A.D.
(
1957
)
The age distribution of poliomyelitis in the United States in 1955
.
Am. J. Hyg.
66
,
214
234
,
[PubMed]
79
Sigurdsson
B.
,
Sigurjónsson
J.
,
Sigurdsson
J.H.J.
,
Thorkelsson
J.
and
Gudmundsson
K.R.
(
1950
)
A disease epidemic in Iceland simulating poliomyelitis
.
Am. J. Epidemiol.
52
,
222
238
,
80
Staff
M.
(
1957
)
An outbreak of encephalomyelitis in the royal free hospital group, London, in 1955
.
Br. Med. J.
2
,
895
904
,
[PubMed]
81
Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome; Board on the Health of Select Populations and Institute of Medicine
(
2015
) In
Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness
,
National Academies Press
,
Washington (DC)
82
Meeus
M.
,
Van Eupen
I.
,
Van Baarle
E.
,
De Boeck
V.
,
Luyckx
A.
,
Kos
D.
et al. 
(
2011
)
Symptom fluctuations and daily physical activity in patients with chronic fatigue syndrome: a case-control study
.
Arch. Phys. Med. Rehabil.
92
,
1820
1826
[PubMed]
83
Twisk
F.N.
(
2015
)
Accurate diagnosis of myalgic encephalomyelitis and chronic fatigue syndrome based upon objective test methods for characteristic symptoms
.
World J. Methodol.
5
,
68
87
[PubMed]
84
Fukuda
K.
,
Straus
S.E.
,
Hickie
I.
,
Sharpe
M.C.
,
Dobbins
J.G.
and
Komaroff
A.
(
1994
)
The chronic fatigue syndrome: a comprehensive approach to its definition and study. International Chronic Fatigue Syndrome Study Group
.
Ann. Intern. Med.
121
,
953
959
85
Capelli
E.
,
Zola
R.
,
Lorusso
L.
,
Venturini
L.
,
Sardi
F.
and
Ricevuti
G.
(
2010
)
Chronic fatigue syndrome/myalgic encephalomyelitis: an update
.
Int. J. Immunopathol. Pharmacol.
23
,
981
989
[PubMed]
86
Whiting
P.
,
Bagnall
A.-M.
,
Sowden
A.J.
,
Cornell
J.E.
,
Mulrow
C.D.
and
Ramírez
G.
(
2001
)
Interventions for the treatment and management of chronic fatigue syndrome
.
JAMA
286
,
1360
,
[PubMed]
87
Aaron
L.A.
,
Burke
M.M.
and
Buchwald
D.
(
2000
)
Overlapping conditions among patients with chronic fatigue syndrome, fibromyalgia, and temporomandibular disorder
.
Arch. Intern. Med.
160
,
221
,
[PubMed]
88
Quigley
E.M.M.
(
2011
)
The enteric microbiota in the pathogenesis and management of constipation
.
Best Pract. Res. Clin. Gastroenterol.
25
,
119
126
,
[PubMed]
89
Quigley
E.M.
(
2011
)
Gut microbiota and the role of probiotics in therapy
.
Curr. Opin. Pharmacol.
11
,
593
603
,
[PubMed]
90
Nagy-Szakal
D.
,
Williams
B.L.
,
Mishra
N.
,
Che
X.
,
Lee
B.
,
Bateman
L.
et al. 
(
2017
)
Fecal metagenomic profiles in subgroups of patients with myalgic encephalomyelitis/ chronic fatigue syndrome
.
Microbiome
5
,
44
,
[PubMed]
91
Giloteaux
L.
,
Goodrich
J.K.
,
Walters
W.A.
,
Levine
S.M.
,
Ley
R.E.
,
Hanson
M.R.
et al. 
(
2016
)
Reduced diversity and altered composition of the gut microbiome in individuals with myalgic encephalomyelitis/chronic fatigue syndrome
.
Microbiome
4
,
953
959
92
Fremont
M.
,
Coomans
D.
,
Massart
S.
and
De Meirleir
K.
(
2013
)
High-throughput 16S rRNA gene sequencing reveals alterations of intestinal microbiota in myalgic encephalomyelitis/chronic fatigue syndrome patients
.
Anaerobe
22
,
50
56
[PubMed]
93
Armstrong
C.W.
,
McGregor
N.R.
,
Lewis
D.P.
,
Butt
H.L.
and
Gooley
P.R.
(
2017
)
The association of fecal microbiota and fecal, blood serum and urine metabolites in myalgic encephalomyelitis/chronic fatigue syndrome
.
Metabolomics
13
,
8
,
94
Sheedy
J.R.
,
Wettenhall
R.E.H.
,
Scanlon
D.
,
Gooley
P.R.
,
Lewis
D.P.
,
Mcgregor
N.
et al. 
(
2009
)
Increased d-lactic acid intestinal bacteria in patients with chronic fatigue syndrome
.
In Vivo
23
,
621
628
95
Wright
E.K.
,
Kamm
M.A.
,
Teo
S.M.
,
Inouye
M.
,
Wagner
J.
and
Kirkwood
C.D.
(
2015
)
Recent advances in characterizing the gastrointestinal microbiome in Crohn’s disease: a systematic review
.
Inflamm. Bowel Dis.
21
,
1219
1228
[PubMed]
96
Machiels
K.
,
Joossens
M.
,
Sabino
J.
,
De Preter
V.
,
Arijs
I.
,
Eeckhaut
V.
et al. 
(
2014
)
A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis
.
Gut
63
,
1275
1283
,
[PubMed]
97
Ríos-Covián
D.
,
Ruas-Madiedo
P.
,
Margolles
A.
,
Gueimonde
M.
,
De los Reyes-Gavilán
C.G.
and
Salazar
N.
(
2016
)
Intestinal short chain fatty acids and their link with diet and human health
.
Front. Microbiol.
7
,
185
98
Butt
H.
,
Dunstan
R.
,
McGregor
N.
and
Roberts
T.
(
1998
)
Faecal microbial growth inhibition in chronic fatigue/pain patients
.
Proceedings of the AHMF International Clinical and Scientific Conference
,
Newcastle, Australia
99
Gall
A.
,
Fero
J.
,
McCoy
C.
,
Claywell
B.C.
,
Sanchez
C.A.
,
Blount
P.L.
et al. 
(
2015
)
Bacterial composition of the human upper gastrointestinal tract microbiome is dynamic and associated with genomic instability in a Barrett’s esophagus cohort
.
PLoS ONE
10
,
e0129055
,
100
Carroll
I.M.
,
Ringel-Kulka
T.
,
Siddle
J.P.
and
Ringel
Y.
(
2012
)
Alterations in composition and diversity of the intestinal microbiota in patients with diarrhea-predominant irritable bowel syndrome
.
Neurogastroenterol. Motil.
24
,
521
530, e248
,
101
Vandeputte
D.
,
Falony
G.
,
Vieira-Silva
S.
,
Tito
R.Y.
,
Joossens
M.
and
Raes
J.
(
2016
)
Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates
.
Gut
65
,
57
62
,
[PubMed]
102
Goldsmith
K.
,
Chalder
T.
,
White
P.
,
Sharpe
M.
and
Pickles
A.
(
2015
)
Longitudinal mediation in the PACE randomised clinical trial of rehabilitative treatments for chronic fatigue syndrome: modelling and design considerations
.
Trials
16
(
Suppl. 2
),
O43
,
103
Hardcastle
S.L.
,
Brenu
E.W.
,
Johnston
S.
,
Nguyen
T.
,
Huth
T.
,
Ramos
S.
et al. 
(
2015
)
Longitudinal analysis of immune abnormalities in varying severities of Chronic Fatigue Syndrome/Myalgic Encephalomyelitis patients
.
J. Transl. Med.
13
,
299
,
[PubMed]
104
Nyland
M.
,
Naess
H.
,
Birkeland
J.S.
and
Nyland
H.
(
2014
)
Longitudinal follow-up of employment status in patients with chronic fatigue syndrome after mononucleosis
.
BMJ Open
4
,
e005798
,
[PubMed]
105
Brenu
E.W.
,
van Driel
M.L.
,
Staines
D.R.
,
Ashton
K.J.
,
Hardcastle
S.L.
,
Keane
J.
et al. 
(
2012
)
Longitudinal investigation of natural killer cells and cytokines in chronic fatigue syndrome/myalgic encephalomyelitis
.
J. Transl. Med.
10
,
88
,
[PubMed]
106
Duvallet
C.
,
Gibbons
S.M.
,
Gurry
T.
,
Irizarry
R.A.
and
Alm
E.J.
(
2017
)
Meta-analysis of gut microbiome studies identifies disease-specific and shared responses
.
Nat. Commun.
8
,
1784
,
[PubMed]
107
Giloteaux
L.
,
Hanson
M.R.
and
Keller
B.A.
(
2016
)
A pair of identical twins discordant for myalgic encephalomyelitis/chronic fatigue syndrome differ in physiological parameters and gut microbiome composition
.
Am. J. Case Rep.
17
,
720
729
[PubMed]
108
Chia
J.K.S.
and
Chia
A.Y.
(
2007
)
Chronic fatigue syndrome is associated with chronic enterovirus infection of the stomach
.
J. Clin. Pathol.
61
,
43
48
,
[PubMed]
109
Frémont
M.
,
Metzger
K.
,
Rady
H.
,
Hulstaert
J.
and
De Meirleir
K.
(
2009
)
Detection of herpesviruses and parvovirus B19 in gastric and intestinal mucosa of chronic fatigue syndrome patients
.
In Vivo
23
,
209
213
[PubMed]
110
Aguiar-Pulido
V.
,
Huang
W.
,
Suarez-Ulloa
V.
,
Cickovski
T.
,
Mathee
K.
and
Narasimhan
G.
(
2016
)
Metagenomics, metatranscriptomics, and metabolomics approaches for microbiome analysis
.
Evol. Bioinform. Online
12
(
Suppl. 1
),
5
16
111
Matysik
S.
,
Le Roy
C.I.
,
Liebisch
G.
and
Claus
S.P.
(
2016
)
Metabolomics of fecal samples: a practical consideration
.
Trends Food Sci. Technol.
57
,
244
255
,
112
Duncan
S.H.
,
Louis
P.
and
Flint
H.J.
(
2004
)
Lactate-utilizing bacteria, isolated from human feces, that produce butyrate as a major fermentation product
.
Appl. Environ. Microbiol.
70
,
5810
5817
,
[PubMed]
113
Morgan
X.C.
and
Huttenhower
C.
(
2014
)
Meta’omic analytic techniques for studying the intestinal microbiome
.
Gastroenterology
146
,
1437.e1
1448.e1
114
Vandeputte
D.
,
Tito
R.Y.
,
Vanleeuwen
R.
,
Falony
G.
and
Raes
J.
(
2017
)
Practical considerations for large-scale gut microbiome studies
.
FEMS Microbiol. Rev.
41
(
Supp_1
),
S154
S167
,
[PubMed]
115
Attree
E.A.
,
Arroll
M.A.
,
Dancey
C.P.
,
Griffith
C.
and
Bansal
A.S.
(
2014
)
Psychosocial factors involved in memory and cognitive failures in people with myalgic encephalomyelitis/chronic fatigue syndrome
.
Psychol. Res. Behav. Manag.
7
,
67
76
,
[PubMed]
116
Carruthers
B.M.
(
2007
)
Definitions and aetiology of myalgic encephalomyelitis: how the Canadian consensus clinical definition of myalgic encephalomyelitis works
.
J. Clin. Pathol.
60
,
117
119
117
Carruthers
B.M.
,
Van de Sande
M.I.
,
De Meirleir
K.L.
,
Klimas
N.G.
,
Broderick
G.
,
Mitchell
T.
et al. 
(
2011
)
Myalgic encephalomyelitis: International Consensus Criteria
.
J. Intern. Med.
270
,
327
338
,
[PubMed]
118
Kim
D.
,
Hofstaedter
C.E.
,
Zhao
C.
,
Mattei
L.
,
Tanes
C.
,
Clarke
E.
et al. 
(
2017
)
Optimizing methods and dodging pitfalls in microbiome research
.
Microbiome
5
,
52
119
Langdon
A.
,
Crook
N.
and
Dantas
G.
(
2016
)
The effects of antibiotics on the microbiome throughout development and alternative approaches for therapeutic modulation
.
Genome Med.
8
,
39
,
120
Wilson
I.D.
and
Nicholson
J.K.
(
2017
)
Gut microbiome interactions with drug metabolism, efficacy, and toxicity
.
Transl. Res.
179
,
204
222
,
[PubMed]
121
Minalyan
A.
,
Gabrielyan
L.
,
Scott
D.
,
Jacobs
J.
and
Pisegna
J.R.
(
2017
)
The gastric and intestinal microbiome: role of proton pump inhibitors
.
Curr. Gastroenterol. Rep.
19
,
42
,
[PubMed]
122
Singh
R.K.
,
Chang
H.-W.
,
Yan
D.
,
Lee
K.M.
,
Ucmak
D.
,
Wong
K.
et al. 
(
2017
)
Influence of diet on the gut microbiome and implications for human health
.
J. Transl. Med.
15
,
73
,
[PubMed]
123
Wu
G.D.
,
Chen
J.
,
Hoffmann
C.
,
Bittinger
K.
,
Chen
Y.Y.
,
Keilbaugh
S.A.
et al. 
(
2011
)
Linking long-term dietary patterns with gut microbial enterotypes
.
Science
334
,
105
108
,
124
Claesson
M.J.
,
Cusack
S.
,
O’Sullivan
O.
,
Greene-Diniz
R.
,
de Weerd
H.
,
Flannery
E.
et al. 
(
2011
)
Composition, variability, and temporal stability of the intestinal microbiota of the elderly
.
Proc. Natl. Acad. Sci. U.S.A.
108
,
4586
4591
,
125
Mueller
S.
,
Saunier
K.
,
Hanisch
C.
,
Norin
E.
,
Alm
L.
,
Midtvedt
T.
et al. 
(
2006
)
Differences in fecal microbiota in different European study populations in relation to age, gender, and country: a cross-sectional study
.
Appl. Environ. Microbiol.
72
,
1027
1033
,
[PubMed]
126
Haro
C.
,
Rangel-Zúñiga
O.A.
,
Alcalá-Díaz
J.F.
,
Gómez-Delgado
F.
,
Pérez-Martínez
P.
,
Delgado-Lista
J.
et al. 
(
2016
)
Intestinal microbiota is influenced by gender and body mass index
.
PLoS ONE
11
,
e0154090
,
127
Yurkovetskiy
L.
,
Burrows
M.
,
Khan
A.A.
,
Graham
L.
,
Volchkov
P.
,
Becker
L.
et al. 
(
2013
)
Gender bias in autoimmunity is influenced by microbiota
.
Immunity
39
,
400
412
,
[PubMed]
128
Markle
J.G.M.
,
Frank
D.N.
,
Mortin-Toth
S.
,
Robertson
C.E.
,
Feazel
L.M.
,
Rolle-Kampczyk
U.
et al. 
(
2013
)
Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity
.
Science
339
,
1084
1088
,
129
Sudo
N.
(
2014
)
Microbiome, HPA axis and production of endocrine hormones in the gut
.
Adv. Exp. Med. Biol.
817
,
177
194
,
[PubMed]
130
Baker
J.M.
,
Al-Nakkash
L.
and
Herbst-Kralovetz
M.M.
(
2017
)
Estrogen-gut microbiome axis: Physiological and clinical implications
.
Maturitas
103
,
45
53
,
[PubMed]
131
Fransen
F.
,
van Beek
A.A.
,
Borghuis
T.
,
Meijer
B.
,
Hugenholtz
F.
,
van der Gaast-de Jongh
C.
et al. 
(
2017
)
The impact of gut microbiota on gender-specific differences in immunity
.
Front. Immunol.
8
,
754
,
[PubMed]
132
Wallis
A.
,
Butt
H.
,
Ball
M.
,
Lewis
D.P.
and
Bruck
D.
(
2016
)
Support for the microgenderome: associations in a human clinical population
.
Sci. Rep.
6
,
19171
[PubMed]
133
Choo
J.M.
,
Leong
L.E.
and
Rogers
G.B.
(
2015
)
Sample storage conditions significantly influence faecal microbiome profiles
.
Sci. Rep.
5
,
16350
,
[PubMed]
134
Tedjo
D.I.
,
Jonkers
D.M.A.E.
,
Savelkoul
P.H.
,
Masclee
A.A.
,
Van Best
N.
,
Pierik
M.J.
et al. 
(
2015
)
The effect of sampling and storage on the fecal microbiota composition in healthy and diseased subjects
.
PLoS ONE
10
,
e0126685
,
135
Rochet
V.
,
Rigottier-Gois
L.
,
Rabot
S.
and
Doré
J.
(
2004
)
Validation of fluorescent in situ hybridization combined with flow cytometry for assessing interindividual variation in the composition of human fecal microflora during long-term storage of samples
.
J. Microbiol. Methods
59
,
263
270
,
[PubMed]
136
Jia
J.
,
Frantz
N.
,
Khoo
C.
,
Gibson
G.R.
,
Rastall
R.A.
and
McCartney
A.L.
(
2010
)
Investigation of the faecal microbiota associated with canine chronic diarrhea
.
FEMS Microbiol. Ecol.
71
,
304
312
,
[PubMed]
137
Gorzelak
M.A.
,
Gill
S.K.
,
Tasnim
N.
,
Ahmadi-Vand
Z.
,
Jay
M.
and
Gibson
D.L.
(
2015
)
Methods for improving human gut microbiome data by reducing variability through sample processing and storage of stool
.
PLoS ONE
10
,
e0134802
,
[PubMed]
138
Bag
S.
,
Saha
B.
,
Mehta
O.
,
Anbumani
D.
,
Kumar
N.
,
Dayal
M.
et al. 
(
2016
)
An improved method for high quality metagenomics DNA extraction from human and environmental samples
.
Sci. Rep.
6
,
26775
,
[PubMed]
139
Peterson
D.A.
,
Frank
D.N.
,
Pace
N.R.
and
Gordon
J.I.
(
2008
)
Metagenomic approaches for defining the pathogenesis of inflammatory bowel diseases
.
Cell Host Microbe
3
,
417
427
,
140
Ranjan
R.
,
Rani
A.
,
Metwally
A.
,
McGee
H.S.
and
Perkins
D.L.
(
2016
)
Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing
.
Biochem. Biophys. Res. Commun.
469
,
967
977
,
[PubMed]
141
Jovel
J.
,
Patterson
J.
,
Wang
W.
,
Hotte
N.
,
O’Keefe
S.
,
Mitchel
T.
et al. 
(
2016
)
Characterization of the gut microbiome using 16S or shotgun metagenomics
.
Front. Microbiol.
7
,
459
,
[PubMed]
142
Woolhouse
ME.
,
Howey
R.
,
Gaunt
E.
,
Reilly
L.
,
Chase-Topping
M.
and
Savill
N.
(
2008
)
Temporal trends in the discovery of human viruses
.
Proc. Biol. Sci.
275
,
2111
2115
,
143
Flewett
T.H.
,
Bryden
A.S.
and
Davies
H.
(
1974
)
Diagnostic electron microscopy of faeces: I The viral flora of the faeces as seen by electron microscopy
.
J. Clin. Pathol.
27
,
603
608
,
[PubMed]
144
Mitgutsch
K.
,
Schirra
S.
and
Verrilli
S.
(
2013
)
Movers and shakers
.
CHI’13 Ext. Abstr. Hum. Factors Comput. Syst. CHI EA’13
976
,
715
145
Lecuit
M.
and
Eloit
M.
(
2013
)
The human virome: new tools and concepts
.
Trends Microbiol.
21
,
510
515
[PubMed]
146
Wylie
K.M.
,
Weinstock
G.M.
and
Storch
G.A.
(
2013
)
Virome genomics: a tool for defining the human virome
.
Curr. Opin. Microbiol.
16
,
479
484
[PubMed]
147
Carding
S.R.
,
Davis
N.
and
Hoyles
L.
(
2017
)
Review article: the human intestinal virome in health and disease
.
Aliment. Pharmacol. Ther.
46
,
800
815
,
[PubMed]
148
Castro-Mejía
J.L.
,
Muhammed
M.K.
,
Kot
W.
,
Neve
H.
,
Franz
C.M.A.P.
,
Hansen
L.H.
et al. 
(
2015
)
Optimizing protocols for extraction of bacteriophages prior to metagenomic analyses of phage communities in the human gut
.
Microbiome
3
,
64
[PubMed]
149
Kleiner
M.
,
Hooper
L.V.
and
Duerkop
B.A.
(
2015
)
Evaluation of methods to purify virus-like particles for metagenomic sequencing of intestinal viromes
.
BMC Genomics
16
,
7
[PubMed]
150
Hoyles
L.
,
McCartney
A.L.
,
Neve
H.
,
Gibson
G.R.
,
Sanderson
J.D.
,
Heller
K.J.
et al. 
(
2014
)
Characterization of virus-like particles associated with the human faecal and caecal microbiota
.
Res. Microbiol.
165
,
803
812
[PubMed]
151
Reyes
A.
,
Semenkovich
N.P.
,
Whiteson
K.
,
Rohwer
F.
and
Gordon
J.I.
(
2012
)
Going viral: next-generation sequencing applied to phage populations in the human gut
.
Nat. Rev. Microbiol.
10
,
607
617
[PubMed]
152
Conceição-Neto
N.
,
Zeller
M.
,
Lefrère
H.
,
De Bruyn
P.
,
Beller
L.
,
Deboutte
W.
et al. 
(
2015
)
Modular approach to customise sample preparation procedures for viral metagenomics: a reproducible protocol for virome analysis
.
Sci. Rep.
5
,
16532
[PubMed]
153
Hall
R.J.
,
Wang
J.
,
Todd
A.K.
,
Bissielo
A.B.
,
Yen
S.
,
Strydom
H.
et al. 
(
2014
)
Evaluation of rapid and simple techniques for the enrichment of viruses prior to metagenomic virus discovery
.
J. Virol. Methods
195
,
194
204
[PubMed]
154
Bikel
S.
,
Valdez-Lara
A.
,
Cornejo-Granados
F.
,
Rico
K.
,
Canizales-Quinteros
S.
,
Soberón
X.
et al. 
(
2015
)
Combining metagenomics, metatranscriptomics and viromics to explore novel microbial interactions: Towards a systems-level understanding of human microbiome
.
Comput. Struct. Biotechnol. J.
13
,
390
401
[PubMed]
155
Hurwitz
B.L.
,
U’Ren
J.M.
and
Youens-Clark
K.
(
2016
)
Computational prospecting the great viral unknown
.
FEMS Microbiol. Lett.
363
,
[PubMed]
156
Ogilvie
L.A.
and
Jones
B.V.
(
2015
)
The human gut virome: a multifaceted majority
.
Front. Microbiol.
6
,
[PubMed]
157
Minot
S.
,
Sinha
R.
,
Chen
J.
,
Li
H.
,
Keilbaugh
S.A.
,
Wu
G.D.
et al. 
(
2011
)
The human gut virome: Inter-individual variation and dynamic response to diet
.
Genome Res.
21
1616
1625
[PubMed]
158
Brum
J.R.
and
Sullivan
M.B.
(
2015
)
Rising to the challenge: accelerated pace of discovery transforms marine virology
.
Nat. Rev. Microbiol.
13
,
147
159
[PubMed]
159
Mizuno
C.M.
,
Rodriguez-Valera
F.
,
Kimes
N.E.
and
Ghai
R.
(
2013
)
Expanding the marine virosphere using metagenomics
.
PLoS Genet.
9
,
e1003987
,
160
Lu
H.
,
Giordano
F.
and
Ning
Z.
(
2016
)
Oxford Nanopore MinION sequencing and genome assembly
.
Genomics Proteomics Bioinformatics
14
,
265
279
,
161
Wylie
K.M.
,
Weinstock
G.M.
and
Storch
G.A.
(
2012
)
Emerging view of the human virome
.
Transl. Res.
160
,
283
290
[PubMed]
162
De Preter
V.
and
Verbeke
K.
(
2013
)
Metabolomics as a diagnostic tool in gastroenterology
.
World J. Gastrointest. Pharmacol. Ther.
4
,
97
,
[PubMed]
163
Smirnov
K.S.
,
Maier
T.V.
,
Walker
A.
,
Heinzmann
S.S.
,
Forcisi
S.
,
Martinez
I.
et al. 
(
2016
)
Challenges of metabolomics in human gut microbiota research
.
Int. J. Med. Microbiol.
306
,
266
279
,
[PubMed]
164
Vernocchi
P.
,
Del Chierico
F.
and
Putignani
L.
(
2016
)
Gut microbiota profiling: metabolomics based approach to unravel compounds affecting human health
.
Front. Microbiol.
7
,
165
Zhang
C.
,
Yin
A.
,
Li
H.
,
Wang
R.
,
Wu
G.
,
Shen
J.
et al. 
(
2015
)
Dietary modulation of gut microbiota contributes to alleviation of both genetic and simple obesity in children
.
EBio Med.
2
,
968
984
166
Weckwerth
W.
and
Morgenthal
K.
(
2005
)
Metabolomics: from pattern recognition to biological interpretation
.
Drug Discov. Today
10
,
1551
1558
,
[PubMed]
167
Nassar
A.-E.F.
and
Talaat
R.E.
(
2004
)
Strategies for dealing with metabolite elucidation in drug discovery and development
.
Drug Discov. Today
9
,
317
327
,
[PubMed]
168
Breitbart
M.
,
Hewson
I.
,
Felts
B.
,
Mahaffy
J.M.
,
Nulton
J.
,
Salamon
P.
et al. 
(
2003
)
Metagenomic analyses of an uncultured viral community from human feces metagenomic analyses of an uncultured viral community from human feces
.
J. Bacteriol.
185
,
6220
6223
[PubMed]
169
Zhang
T.
,
Breitbart
M.
,
Lee
W.H.
,
Run
J.Q.
,
Wei
C.L.
,
Soh
S.W.L.
et al. 
(
2006
)
RNA viral community in human feces: Prevalence of plant pathogenic viruses
.
PLoS Biol.
4
,
0108
0118
, ,
[PubMed]
170
Kim
M.-S.
,
Park
E.-J.
,
Roh
S.W.
and
Bae
J.-W.
(
2011
)
Diversity and abundance of single-stranded DNA viruses in human feces
.
Appl. Environ. Microbiol.
77
,
8062
8070
[PubMed]
171
Minot
S.
,
Grunberg
S.
,
Wu
G.D.
,
Lewis
J.D.
and
Bushman
F.D.
(
2012
)
Hypervariable loci in the human gut virome
.
Proc. Natl. Acad. Sci. U.S.A.
109
,
3962
3966
,
[PubMed]
172
Minot
S.
and
Bryson
A.
(
2013
)
Rapid evolution of the human gut virome
.
Proc. Natl. Acad. Sci. U.S.A.
110
,
12450
12455
,
[PubMed]
173
Pérez-Brocal
V.
,
García-López
R.
,
Vázquez-Castellanos
J.F.
,
Nos
P.
,
Beltrán
B.
,
Latorre
A.
et al. 
(
2013
)
Study of the viral and microbial communities associated with Crohn’s disease: a metagenomic approach
.
Clin. Transl. Gastroenterol.
4
,
e36
,
[PubMed]
174
Lim
E.S.
,
Zhou
Y.
,
Zhao
G.
,
Bauer
I.K.
,
Droit
L.
,
Ndao
I.M.
et al. 
(
2015
)
Early life dynamics of the human gut virome and bacterial microbiome in infants
.
Nat. Med.
21
,
1228
1234
[PubMed]
175
Norman
J.M.
,
Handley
S.A.
,
Baldridge
M.T.
,
Droit
L.
,
Liu
C.Y.
,
Keller
B.C.
et al. 
(
2015
)
Disease-specific alterations in the enteric virome in inflammatory bowel disease
.
Cell
160
,
447
460
[PubMed]
176
Evengård
B.
,
Nord
C.E.
and
Sullivan
Â
(
2007
)
P1239 Patients with chronic fatigue syndrome have higher numbers of anaerobic bacteria in the intestine compared to healthy subjects
.
Int. J. Antimicrob. Agents
29
,
S340
,
177
Butt
H.
,
Dunstan
R.
,
McGregor
N.
and
Roberts
T.
(
2001
)
Bacterial colonosis in patients with persistent fatigue
.
Proceedings of the AHMF International Clinical and Scientific Conference
,
Sydney, Australia
178
Reyes
A.
,
Blanton
L.
,
Cao
S.
,
Zhao
G.
,
Manary
M.
,
Trehan
I.
et al. 
(
2015
)
Gut viromes of Malawian twins discordant for severe acute malnutrition
.
Proc. Natl. Acad. Sci. U.S.A.
112
,
11941
11946
[PubMed]
This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY).