Unpublished

This section is devoted to (hopefully not too many) papers that I could not publish at all.

Very high abundance of Burkholderia (Betaproteobacteria) in stool samples from patients with Irritable Bowel Syndrome: contamination, bad luck, or new beginning? 

In collaboration with Dr. Remes and his team in the Universidad Veracruzana

Background: That is the title of the last submitted version of the manuscript. Dr. Remes is a very smart Gastroenterologist from the port of Veracruz that is heavily involved with biomedical research. As part of the Newton Fund that he managed from the Mexican side (long story), he and his team enrolled 40 patients with Irritable Bowel Syndrome and collected stool samples at baseline and after one week of daily consumption of dextrose (controls) or three doses (10 g, 20 g and 30 g) of fiber from nopal. Unexpectedly, the average relative abundance of Proteobacteria was 97.8%, with the genus Burkholderia (Betaproteobacteria) being the most abundant taxon (average: 90.9%, min: 37.7%, max: 99.6%; 56 out of 80 samples had >90% of Burkholderia. Neither I nor any of my colleagues have seen anything like this. Subsequent sequencing of the same DNA samples yielded the same results; however, other samples that were processed within the same time frame did not show that much of Burkholderia. Despite having ruled out methodological errors in our analysis, we remained skeptical about the very high abundances of Burkholderia in study 1. Therefore, we analyzed additional stool samples from patients with IBS (n=4) and healthy subjects (n=4) using the original DNA extraction reagents, as well as the reagents after sterilization with UV light. In this study 2, we detected very low abundances of Burkholderia and other members of Proteobacteria in all samples, with and without sterile reagents. 

The reason(s) for the finding of very high abundances of Burkholderia in study 1 remain unexplained. In the discussion, we placed particular emphasis on melioidosis and a little bit on tropical sprue. The last version of the paper was submitted and rejected in BMC Microbiology but is in my opinion worth reading and is available upon request.

A word of caution on the use of the gut dysbiosis index to assess microbial changes in fecal samples of dogs and cats with gastrointestinal disease 

Background: On Tuesday, October 3rd, 2023, Dr. Kawas decided that I was no longer useful for his company, and I took this opportunity to abandon science for good. However, there were some pending projects that for some reason, I think they may be useful for some scientists in the future. And since I am not involved in research matters anymore, and I will of course not pay the open access fee now imposed by FEMS Microbiology Ecology, I decided to include the final version of the manuscript here.

A word of caution on the use of the gut dysbiosis index to assess microbial changes in fecal samples of dogs and cats with gastrointestinal disease 

Jose F. Garcia-Mazcorro

General Escobedo, Nuevo Leon, Mexico 

Published here on Saturday, December 16, 2023.

*Corresponding author. Email: josegarcia_mex@hotmail.com

ABSTRACT

The digestive tract of animals is inhabited by many different types of highly competitive bacteria and other microorganisms (i.e., the gut microbiota) that play a vital role in maintaining health. The gut microbiota experience fluctuations over time and this is due to different environmental (e.g., diet and exercise) and host-associated (e.g., age and breed) factors as well as their interactions. To assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathies that could be useful in clinical practice, a dysbiosis index (DI) was published in 2017 based on data from quantitative real-time PCR (qPCR) targeting the 16S rRNA gene. This DI included a panel of seven bacterial groups as well as total bacteria. Later, a feline DI was also developed including a panel of seven bacterial groups, five of which were also included in the canine DI, as well as total bacteria. Based on current knowledge about microbes and microbial ecology, here a word of caution is raised regarding some of the main weakness associated with these DIs. The last section of this paper is devoted to several characteristics that can be considered for developing an index with increased utility in veterinary and human clinical practice.

INTRODUCTION

The digestive tract of humans and other animals evolved in close synchrony with microbial life, now known as the gut microbiota. The gut microbiota is composed by millions of microorganisms from all three domains of life that inhabit the digestive tract of humans and other animals. Just as in the case of other mammals, the digestive tract of dogs and cats is inhabited by many different microorganisms (particularly bacteria) that play a fundamental role in health and disease (Minamoto et al. 2012; Garcia-Mazcorro and Minamoto 2013; Pilla and Suchodolski 2020). A growing number of studies have shown that the gut microbiota of dogs and cats is often in a complex state of balance with the host and that the disturbance of this stability may bear an association with different disease processes (Hooda et al. 2012; Forster et al. 2018). 

Dogs and cats are important for many reasons. They are the number one pets in many countries, in the USA alone there were over 89 million pet dogs in 2017 (https://www.statista.com/). Also, dogs (and likely cats too, Du et al. 2021) share their microbiota with the owners (Song et al. 2013) and may transmit important diseases to human beings (Baneth et al. 2016). The many different breeds of dogs and mixes of breeds also represent a unique opportunity to learn about physiology and genetics (King 2017 and Watson 2017), and dogs offer unique models for comparative medicine to better understand disease processes, for example inflammatory bowel disorders (Jergens and Simpson 2012; Vázquez-Baeza et al. 2016; Peiravan et al. 2018; Chandra et al. 2019). Finally, the various benefits of sharing a life with a pet dog or a cat have been documented (Straede and Gates 2015; Gupta 2017).

Dogs and cats suffer from a variety of gastrointestinal (GI) disorders that can be challenging to diagnose and to treat. In this regard, there are several parameters that one can measure in a patient to investigate the presence and severity of clinical or subclinical disease. A good example of this is pancreatitis, where clinicians measure the concentration of blood enzymes to detect this disorder (Xenoulis 2015). Other examples include blood glucose levels to detect diabetes mellitus (Hoenig 2014), levels of antibodies to study exposure to certain antigens, as well as electrolytes and all other parameters commonly measured in blood biochemistry panels. 

The first microbiological use of the term dysbiosis appeared in 1920 and today the term may refer to three overlapping, yet distinct, categories of biological phenomena: a general change in microbiota composition, an imbalance in composition (this the most common definition), and/or changes of specific taxa in that composition (Hooks and O’Malley 2017). The diagnosis and interpretation of intestinal dysbiosis in dogs and cats have been discussed (Suchodolski 2016). To assess microbial changes in fecal samples of dogs with chronic inflammatory enteropathies, AlShawaqfeh et al. (2017) developed a dysbiosis index (DI) using 95 clinically healthy dogs and 106 dogs with histologically confirmed chronic inflammatory enteropathy, where a DI below 0 indicates a “normal” fecal microbiota, and a DI of 0 or above indicates fecal dysbiosis. For this, the authors used one machine learning model (the nearest centroid classifier algorithm) with a training set that included samples from 36 healthy dogs and 55 dogs with chronic enteropathy. The DI consists of a panel of seven bacterial groups (Clostridium hiranonis, Blautia, Escherichia coli, Faecalibacterium, Fusobacterium, Streptococcus, and Turicibacter, chosen from a panel of 13 taxa at different phylogenetic level) plus total bacteria, and showed a sensitivity (i.e., ability of a test to correctly detect individuals affected with a disease or condition) of 74% (i.e., the DI would fail to detect dysbiosis in 26% of dogs with chronic enteropathy). On the other hand, the DI showed a high specificity (i.e., ability of a test to correctly identify those individuals without the disease) of 95%, meaning a high likelihood of correctly identifying dogs without chronic enteropathy, and also a small percentage of healthy dogs that would falsely be diagnosed with dysbiosis but may also have an imbalance in their gut microbiota due to other factors such as diet or therapeutic interventions. 

This is not the first time that the clinical utility of the DI in the diagnosis and management of chronic inflammatory enteropathies has been questioned (Heilmann and Steiner 2018) but for different reasons. The paper published by AlShawaqfeh et al. (2017) has been cited several times in peer-reviewed articles (summarized in Table 1, see at the very bottom of this page) even by authors who have not investigated chronic enteropathies (Karl et al. 2018; Gavazza et al. 2017), and it was also referenced in a thesis published in 2016 from the University of Helsinki (Žiga 2016). This thesis work is interesting because it involved fecal microbial transplantation (FMT) via a nasoduodenal tube to three dogs with similar history of chronic inflammatory enteropathy and tylosin treatment. In this work, the author mentioned that all but one of the nine clinically healthy dogs were positive for at least one bacterial pathogen or parasite (i.e., Giardia duodenalis, Cestoda, Trematoda, Salmonella, Yersinia enterocolitica, Campylobacter, Clostridium perfringens, C. difficile), while all three dogs with persistent GI illnesses (FMT-recipient dogs) were negative using the same tests. Based on these findings, the authors rightfully stated that “the suitability of such fecal tests for assessing GI health has been put in question, since many of these bacterial pathogens and parasites are routinely found in healthy dogs”. 

AlShawaqfeh et al. (2017) are not the only ones who have developed a microbial DI with the hope of being useful in clinical practice. Gevers et al. (2014) developed another index to assess intestinal dysbiosis in a context of Crohn’s disease in a pediatric (3-17 years of age) human population. This study involved a total of 1,321 samples, including ileal (n=630) and rectal (n=387) tissue biopsies as well as 304 stool samples, that were subjected to 16S rRNA gene sequencing on the Illumina MiSeq platform (it is interesting that the authors of this paper claimed that “the performance gain driven by the microbiome is a direct and unbiased demonstration of the utility of microbiome features for predicting clinical outcomes”). Also, Casén et al. (2015) developed a “diagnostic test” for determining intestinal dysbiosis in patients with irritable bowel syndrome (IBS) or inflammatory bowel disease (IBD). This study involved a total of 165 stool samples from healthy controls for developing the DI, and 330 stool samples (from healthy volunteers and IBS and IBD patients) for testing the model, that were subject to the probe-based GA-map Dysbiosis test (Genetic Analysis AS, Oslo, Norway) and 16S sequencing using the Illumina MiSeq platform for validation. Other interesting approaches to develop similar indicators or indexes of gut microbiome include the Gut Microbiome Health Index (GMHI, Gupta et al. 2020) and the qPCR-based, 16S gene copies adjusted, by Jian et al. (2020). Key differences between the AlShawaqfeh’s index (2017) for use in dogs and the Gevers’ (2014) and Casén’ (2015) indexes for use in people, include the origin of the data used to develop the respective index (qPCR vs. 16S probes and sequencing), the scope (chronic inflammatory enteropathy vs. Crohn’s disease or other specific manifestations of intestinal disease), as well as the approach used (e.g., Gevers et al. and Casén et al. do not mention anything about the sensitivity and/or specificity of the DI, neither do Gupta et al. with the GMHI). Recently, another DI was developed to evaluate the fecal microbiota in client-owned healthy cats (n=80) and cats with chronic enteropathies (n=68, Sung et al. 2022) that claimed 77% sensitivity and 96% specificity, also using qPCR for seven bacterial taxa (Bacteroides, Bifidobacterium, Clostridium hiranois, E. coli, Faecalibacterium, Streptococcus and Turicibacter, five of which are also included in the canine DI) plus total bacteria. The same nearest centroid classifier algorithm was used for developing this DI and the training set included 47 healthy cats and 32 cats with chronic enteropathy (these numbers are similar to the 36 healthy dogs and 55 dogs with chronic enteropathy used as the training set for the canine DI). The purpose of this paper is to discuss the utility of the DIs in assessing biologically and clinically relevant microbial changes in fecal samples of dogs and cats with GI disease, with a focus on several key aspects which are listed below. 

What is dysbiosis?

The DIs were developed within a high level of uncertainty around the term dysbiosis. The well-known inter-individual differences that we know today were not readily appreciated in the late 1970’s (Savage 1977), although some researchers pointed this out decades ago (Moore et al. 1974). The first studies on the gut microbiota suggested that each individual host carries a microbiota so unique that resembled a fingerprint. Today it is clear and important to highlight that each individual is highly unique in terms of the microbiome and the response of this microbiome to diet and other environmental factors and challenges including antibiotic administration (Dethlefsen and Relman 2011, Johnson et al. 2019). There is also a very high variation in the individual gut microbial composition that is often not considered. One extreme case of this was published over a decade ago, where 1 out of 6 healthy dogs had >70% Lactobacillus in feces (all other dogs had <1% of this taxon) and this finding was consistent within a 2-week sampling time (Garcia-Mazcorro et al. 2012). 

The term dysbiosis was introduced with a microbiological use early in the 20th century and has been used frequently ever since (Hooks and O’Malley 2017). Helmut Haenel used the term repeatedly and is considered to be the first to give it its contemporary interpretation of change and imbalance (Hooks and O’Malley 2017). For instance, he and colleagues (Haenel et al. 1959) used the term to refer to a deviation of normality in the composition of microbes in the stools of infants. Although many years have passed and much research has been performed on this subject, the definition have remained largely the same, although it now appears to be more accurate because it is suggested to include a relationship between the dysbiotic state and the etiology, diagnosis, or treatment of the disease (Levy et al. 2017). Regardless, the mechanisms and consequences of intestinal dysbiosis often remain unclear (Weiss and Hennet 2017; Brüssow 2019). Importantly, people often assume that dysbiosis only involves bacteria, but other organisms such as fungi and protista are also implicated in the definition of dysbiosis (Arumugam et al. 2011; Iliev and Leonardi 2017). In general, the term dysbiosis is often not appropriately used, for instance there are more than 16,000 papers listed in PubMed for that term, including papers on dermatologic conditions and other host environments not related to the gut. Olesen and Alm (2016) also stated in their paper that dysbiosis is not a simple answer to questions related to gut health and microbiome research, and Hooks and O´Malley (2017) also discussed this challenge thoroughly and proposed that researchers should reflect carefully on the ways in which the term dysbiosis is used. The term dysbiosis should be reserved to conditions in which a normal state can be easily and accurately defined, but this is not the case for host-associated complex microbial ecosystems.

The 16S rRNA marker gene

The DIs were based on qPCR data of 16S rRNA gene (16S gene) copies. The ribosome is almost as ancient as life itself (Wittmann 1982) and is composed of RNA (ribosomal RNA, rRNA) and proteins. The gene(s) coding for rRNA have conserved regions (i.e., nucleotide sequences that are the same among most microbes) as well as variable and hyper-variables regions (i.e., sequences that vary among different taxa) and were suggested to be useful to classify life in three domains (Woese et al. 1990). Since then, the 16S gene has been the preferred marker gene to study bacteria. 

The use of the 16S gene has, however, drawbacks. Bacteria have several copies of this gene (Lee et al. 2008), which unlike other studies (Jian et al. 2020), was not considered in the development of the DIs for dogs and cats. The nucleotide sequences of these genes also vary substantially within a genome (Sun et al. 2013), a phenomenon that may reflect adaptations to environmental conditions (López-López et al. 2007), and identical 16S gene sequences can be found in bacteria with highly divergent genomes and ecophysiologies (Jaspers and Overmann 2004), thus underestimating the extent of microbial diversity. Moreover, the classification of Bacteria only uses the nucleotide sequence of the coding strand of the 16S gene, but the non-coding strand may help dictate the variations in the coding strand (Garcia-Mazcorro and Barcenas-Walls 2016). Aside the complex nature of the 16S gene, there are methodological issues related to the use of this gene to study microbial populations. For instance, variations in DNA extraction procedures can introduce substantial differences in the composition of bacterial communities of any given sample that do not necessarily reflect variations in the natural environment, for example in the relative proportions of the different taxa (Lim et al. 2018; Teng et al. 2018; Fiedorová et al. 2019). There are also different sequencing technologies (Loman et al. 2012) and computational and statistical approaches (Olson et al. 2020; Nearing et al. 2022) to analyze 16S sequencing data which may also produce varying results. Finally, the biostructure of microbial communities in feces is important but impossible to take into account with watery feces or when feces are homogenized (Swidsinski et al. 2008), something that has barely been discussed in the veterinary literature. In summary, the 16S gene, while useful to study microbial life, has drawbacks that must be considered to make better use of it.

A bacterial “species” is not homogenous

The DIs consists of a panel of seven bacterial groups plus total bacteria; however, each bacterial species is not homogeneous at all. In fact, the term species has spurred a lot of controversy in microbiology (Cohan 2002; Doolittle and Zhaxybayeva 2009; Ereshefsky 2010). While it is not the objective of this paper to discuss all of these seven groups in depth, it is nonetheless necessary to comment on key aspects of some of these groups to better illustrate this concern.

One of the bacterial species included to obtain the DIs in both dogs and cats is E. coli (family Enterobacteriaceae, phylum Proteobacteria), and it was higher in patients with chronic enteropathies. Proteobacteria contains a wide diversity of Bacteria and it is believed that some of their members contribute to homeostasis of the anaerobic environment of the GI tract (Moon et al. 2018). The 16S gene is highly conserved among the many members of the family Enterobacteriaceae (Roggenkamp 2007; Devanga Ragupathi et al. 2018), which makes it very difficult to differentiate among the groups within this family. Interestingly, one member of Enterobacteriaceae, Providencia spp., a group that is not frequently observed in metagenomic studies of the gut microbiome of dogs and cats (Moon et al. 2018), has been shown to have increased abundance in dogs with acute hemorrhagic diarrhea syndrome compared to healthy dogs (Herstad et al. 2021). Also, some strains of E. coli are beneficial for gut health and used as probiotics (Pradhan and Weiss 2020). Moreover, Lukjancenko et al. (2010) compared sequences of E. coli genomes from 61 strains and demonstrated that the variable or accessory genes make up more than 90% of the pan-genome (i.e., the entire gene set of all strains of a species). It is also known that selective translation occurs during stress in E. coli (Moll and Engelberg-Kulka 2012) and other bacteria, which question the relevance of including this and other microbial groups in the DIs. Overall, these observations indicate that the data obtained from 16S gene copies (e.g., by qPCR) for E. coli and other microbes, may represent 16S gene copies from a variety of highly divergent bacteria.

Evolution of bacteria and bacterial communities

The DIs were developed without considering the evolution of bacteria and their communities. For instance, bacterial doubling times range from a few minutes to many hours, but in vitro estimations may be far from what happens in vivo (Gibson et al. 2018). More importantly, bacterial communities are not static, with environments inhabited by many different taxa (e.g., the digestive tract) coexisting by competitive, rather that cooperative, interactions (Coyte et al. 2015), a context-dependent phenomenon (de Muinck et al. 2013; Kriss et al. 2018). There are also different ways in which bacteria and bacterial communities evolve over the lifetime of the host. For instance, horizontal gene transfer is known to occur extensively in the human gut (Lerner et al. 2017) and this is likely true for the gut microbiota of other mammals. Other aspects of microbial evolution include adaptation, colonization, and persistence, which have recently been extensively discussed (Scanlan 2019), phenomena that likely vary widely among different taxa (Walter et al. 2011; Turroni et al. 2017). Other less explored aspects of microbial evolution include the impact of gut volume (Godon et al. 2016, a topic of great interest considering the differences in size among dog’s breeds), microbial interactions at the transcriptional level (Plichta et al. 2016), the role of bacteriophages (Shkoporov and Hill 2019), the impact of microbial shape (Yang et al. 2016) and density (Contijoch et al. 2019), and the impermanence of bacterial clones (Bobay et al. 2015). In summary, variations in the DIs are likely associated with the complexity of the gut microbiota and the evolution of its microbial constituents. 

Behavior of bacteria 

The DIs were developed without considering the fact that Bacteria behave differently depending on the specific environmental conditions, a topic that has been discussed for over a century (Macfadyen 1887). Indeed, “microorganisms are inherently variable and dynamic systems with considerable genetic dexterity and phenotypic plasticity, able to both respond and adapt” (Cray et al. 2013). For instance, the same type of microbe may behave different between health and disease stages (Popat et al. 2008), and bacteria from soil and other environments have been demonstrated to proliferate in the murine gut (Seedorf et al. 2014). Even the consumption of beneficial bacteria (i.e., probiotics) has the potential to be associated with bacteremia, abscesses, and other, unintended, adverse effects (Lerner et al. 2019). The phenomenon of cross-feeding among microbes also seems to be crucial for gut health (Ze et al. 2012).

Bacteria can further behave differently depending on the population density, a phenomenon called quorum sensing (Abisado et al. 2018), and engage in diverse active and competitive strategies, including accumulation and storage of specific nutrients, blockage of access to favorable habitats, and interference with microbial competitors’ signaling (Hibbing et al. 2009). These survival strategies of some bacterial pathogens have been studied (Álvarez-Ordóñez et al. 2011). The interpretation of the DIs is hampered by the variations in the behavior of Bacteria under different conditions.

The complexity and heterogeneity of GI diseases

The DIs were developed in a context of high heterogeneity of digestive disorders. There are currently over 300 different canine breeds in the world, each one with their own characteristics. More importantly, some GI disorders are often easy to diagnose, and therefore the use of the DI may be of limited clinical utility (e.g., to determine the presence of diarrhea). The gut microbiota is an evolving fingerprint that varies widely among individual healthy dogs and cats (Handl et al. 2011; You and Kim 2021) and also differs between different breeds due to the well-known effect of host genetics (Reddy et al. 2019). Moreover, dogs and cats with chronic enteropathies and other GI disorders usually present at tertiary veterinary centers after being presented at other veterinary offices or clinics. Therefore, the gut microbiota in those patients may have already been modified by changes in dietary regimens and therapeutic interventions (not limited to antimicrobials, see Maier et al. 2018). Finally, the laboratory that currently performs this analysis guarantees that results are typically reported within 2 days, a period of time in which the microbiota may have already changed drastically. 

Why the DI works?

The arguments presented above are in contrast with some of the published literature listed in Table 1 that have shown significant differences in the DI accordingly to different treatment groups (Ziese et al. 2018; Whittemore et al. 2021), clinical conditions (Gavazza et al. 2017), or diets (Schmidt et al. 2018). If the meaning of the term dysbiosis remains obscure, the use of the 16S gene is not well informative, bacterial species are not homogeneous, Bacteria and bacterial communities are always evolving and behaving differently depending on the surrounding conditions, and GI diseases are highly heterogenous, why then the DI seems to work? AlShawaqfeh et al. (2017) defended a biological reason for this, when saying that the taxa included in their DI have all been shown to be altered between healthy dogs and dogs with intestinal inflammation. Interestingly, a recent article about the clinical effects of FMT in dogs affected by chronic enteropathies, used 16S sequencing and showed that five taxa in the canine DI (C. hiranois, Blautia, Faecalibacterium, Fusobacterium, and Turicibacter) were also selected as key features by two different supervised machine learning approaches, a random forest, and a sparse partial least squares-discriminant analysis (Innocente et al. 2022). The authors of this paper also discussed that this is due to a true biological reason by saying that the deficiency or absence of key species disrupts microbiome balance, leading to the proliferation of other species. The possible existence of keystone species or “enterotypes” in the human gut microbiota has been discussed extensively (Wu et al. 2011; Gorvitovskaia et al. 2016) but it is a controversial topic (Fisher and Mehta 2014; Sharon et al. 2022), particularly in a context of low abundant microbes (Claussen et al. 2017). For instance, Trosvik and de Muinck (2015) explained the difference between foundation species (a single species that defines much of the structure of a community by creating locally stable conditions for other species) and keystone species (also critical for the organization and diversity of their ecological communities but per definition of relatively low abundance), but other authors believe that that their role is irrespective of their abundance (Banerjee et al. 2018; Tudela et al. 2021). While it is premature to provide a conclusive answer as to why the DIs seem to work, possible biological reasons behind the detection of a few microbial taxa explaining a departure of normality may be related to their importance for the health and survival of the host (Ze et al. 2012), which is constantly selecting microbes through mechanisms such as immunoglobulin coating (Palm et al. 2014; León and Francino 2022). However, some of those “vital” microbes may not be necessarily from the luminal contents (Garcia-Mazcorro et al. 2020), may possibly depend on many others to exert a positive influence (Ze et al. 2012), or have the ability to increase their abundances based on the levels of molecules originated from the host (Fung et al. 2019). Perhaps more importantly, choosing seven “items” out of thousands, and using those items to numerically explain departure from “normal” may indeed show some patterns, but these patterns are likely to be biologically irrelevant, irrespective of statistical significance. This line of thinking is particularly relevant into the context of randomness in gut microbial ecology (Vega and Gore 2017).   

Concluding remarks and future perspectives

Dogs and cats can be affected by many different GI conditions that deserve to be managed by a veterinary care team, which includes a close collaboration between veterinarians and the biomedical science community, especially microbial ecologists. The DIs developed by AlShawaqfeh et al. (2017) and Sung et al. (2022) may or may not be useful to assess microbial changes in fecal samples of dogs and cats with chronic enteropathies or other GI diseases. Although not necessarily more feasible or simpler, features that may improve such an approach could include finding and targeting true keystone species (Ze et al. 2012; Tudela et al. 2021) or their opposites (Jia et al. 2012), the inclusion of more samples from many breeds and more geographical areas, a more comprehensive usage of the 16S gene (e.g. correction of copy numbers, use of appropriate reference microbes for qPCR standard curves), a narrower and well-defined set of GI diseases in single breeds (Peiravan et al. 2018), the use of more than one molecular technique to analyze the microorganisms (e.g., 16S gene sequencing), the concurrent analysis of chemical compounds such as short-chain fatty acids, inflammatory proteins, and antimicrobial peptides in intestinal contents (Pang et al. 2014; Rossi et al. 2018) and/or in plasma (Tamura et al. 2019), modifications of the nearest-centroid method (Tibshirani et al. 2002) or the use of different mathematical and statistical approaches aside the nearest centroid classifier algorithm (Marcos-Zambrano et al. 2021; Innocente et al. 2022; Choy et al. 2023). Some of these suggestions may also be considered for developing more useful indicators of microbial imbalances in human clinical practice.

ACKNOWLEDGMENTS

The author thanks the critical review provided by Prof. Romy M. Heilmann from the University of Leipzig, and Emma Bermingham from Ilume, on a previous version of this manuscript.

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