In their article, Hooks et al. propose a critical analysis of microbiota-gut-brain (MGB) research showing that it has many methodological flaws and interpretative issues. Although we think that the authors’ arguments are generally well founded and embraceable, we argue that some of the major issues are typical of an emerging multidisciplinary field of research. As is the case with many other disciplines, microbiome research has committed some sins of youth, mostly attributable to the eagerness of publishing new concepts in a field that is dominated by financial interests. On the one hand, this has attracted new funding. On the other hand, it has generated many expectations both from an industrial point of view and from the social community. For these reasons, in the attempt to be pragmatic, we suggest two corrective measures.

First, we suggest that neurobiologists, immunologists, or microbiologists seek advice from scientists in the multidisciplinary fields related to the research, to limit misinterpretation or generalizations. Today, biomedical sciences are increasingly complex, often requiring an intertwined methodological repertoire and knowledge in several fields. The same revolution has hit other areas of microbiota research. Mucosal immunology, for example, has witnessed an injection of scientists, including microbiologists or non-mucosal immunologists, that had no clue as to what an immunoglobulin A or epithelial barrier was. Still, after careful reconsideration of some misleading concepts or interpretations, the benefit of such an injection has arrived, including the awareness of how the microbiota may shape immune responses also at distant sites. In the last few years, next-generation sequencing and the development of meta-omics approaches (e.g., metagenomics, metatranscriptomics) have allowed us to investigate the composition and the modulation of gut microbiota. This is important as only around 1%–2% of bacteria can be cultured in the laboratory, which has led to the proliferation of correlation studies without any functional relevance of the “putative” correlative strains. Culturomics (i.e., the art of culturing microorganisms) has thus been rejuvenated, and many groups are now trying to isolate strains of interest and to demonstrate a “cause-effect” relationship.

Second, we suggest interpreting some of the major issues identified by the authors as ordinary challenges of standardization. As many sociologists of science have shown, standards are a necessary condition for the very existence of contemporary science, and “standards-setting activities” are legitimate scientific tasks (e.g., Goodrich et al. Reference Goodrich, Di Rienzi, Poole, Koren, Walters, Caporaso, Knight and Ley2014). Hence, we should not overestimate problems that are part of an ongoing process, typical of emerging disciplines. To illustrate this point, let us restate some of the methodological issues of MGB research in terms of standards (see Timmermans & Epstein Reference Timmermans and Epstein2010).

Design standards define the properties and features of experimental systems. Researchers need robust mouse models to perform studies investigating causal roles of the gut microbiota on the host. Several of them are available, such as germ-free (GF), humanized gnotobiotic, and specific pathogen-free (SPF) mice. All of them allow us to evaluate the impact of microbiota on an experimental question. However, each comes with its own drawback. For example, GF mice have an immature immune system and behavioral defects, thus affecting the outcome when the scientific question relies on the involvement of the immune or neurologic system. Gnotobiotic and SPF mice are susceptible to the high variability of microbiota composition attributable to environmental factors (Hugenholtz & de Vos Reference Hugenholtz and de Vos2018). Therefore, it is important that several models are used simultaneously to minimize bias. This strategy would allow us to address more precisely the role of microbiota in a given experimental question (Rescigno Reference Rescigno2017), but it would require a greater financial and cognitive effort.

Terminological standards ensure the stability of meanings across researchers and over time. But scientific terms are constantly evolving, and their use is often a matter of establishing a convention within research communities. Gene, for example, has been extensively revised over the years, and still there is not a unanimous definition (Boem et al. Reference Boem, Ratti, Andreoletti and Boniolo2016). As the authors know (see Hooks & O'Malley Reference Hooks and O'Malley2017), the idea of identifying a wild-type microbiota, one in which gut bacteria exist in an evolutionarily optimized form, has been abandoned already in favor of a functional account (Lloyd-Price et al. Reference Lloyd-Price, Abu-Ali and Huttenhower2016). Nonetheless, recent work on relevant probiotic strains has also led to the isolation and characterization of certain soluble factors secreted by live bacteria called postbiotics. These studies suggest that postbiotics may contribute to the host health by improving specific physiological functions (see Tsilingiri & Rescigno Reference Tsilingiri and Rescigno2012). Likely, an improved understanding of the exact mechanisms of postbiotic activities might contribute to grasping the impact of dysbiosis on disease development, bypassing the complexity of individual variability in microbiota ecology (Levy et al. Reference Levy, Kolodziejczyk, Thaiss and Elinav2017).

Performance standards set outcome specifications. For example, statistical significance specifies the level of acceptability of an experimental result. However, the use of statistics in biomedical sciences has been harshly contested in the last few years. Misuse and misinterpretations of null-hypothesis testing have led to many non-reproducible and inconsistent findings in many disciplines, including biology (e.g., Nosek & Errington Reference Nosek and Errington2017). The challenges seem to be widespread and pervasive enough to warrant immediate and careful attention. Lowering the threshold of statistical significance is just a proposal (see Benjamin et al. Reference Benjamin, Berger, Johannesson, Nosek, Wagenmakers, Berk, Bollen, Brembs, Brown, Camerer, Cesarini, Chambers, Clyde, Cook, De Boeck, Dienes, Dreber, Easwaran, Efferson, Fehr, Fidler, Field, Forster, George, Gonzalez, Goodman, Green, Green, Greenwald, Hadfield, Hedges, Held, Hua Ho, Hoijtink, Hruschka, Imai, Imbens, Ioannidis, Jeon, Jones, Kirchler, Laibson, List, Little, Lupia, Machery, Maxwell, McCarthy, Moore, Morgan, Munafó, Nakagawa, Nyhan, Parker, Pericchi, Perugini, Rouder, Rousseau, Savalei, Schönbrodt, Sellke, Sinclair, Tingley, Van Zandt, Vazire, Watts, Winship, Wolpert, Xie, Young, Zinman and Johnson2018), which also has been largely contested (e.g., Ioannidis Reference Ioannidis2018). As of today, the standard of statistical significance for claiming a scientific finding remains fixed at 0.05, and it is unlikely to change soon. Nonetheless, the development of more accurate performance standards is a need for the entire field of biomedical research.

In conclusion, there is no denying that microbiota research, although still in an “early phase,” can contribute to improving our understanding of biology in all its aspects. We should expect its findings to improve as the standards are settled and collaboration between scientific departments is improved.