publications & talks
Publications by categories (articles, talks and thesis) and in reversed chronological order.
peer-reviewed articles
2024
- Different kinds of data: samples and the relational frameworkA. PotironBiology and Philosophy, 2024
This paper proposes an original definition of samples as a kind of data within the relational framework of data. The distinction between scientific objects (e.g., samples, data, models) often needs to be clarified in the philosophy of science to understand their role in the scientific inquiry. The relational framework places data at the forefront of knowledge construction. Their epistemic status depends on their evaluation as potential evidence in a research situation and their ability to circulate among researchers. While samples are significant in data-generating science, their role has been underexplored in the philosophy of data literature. I draw on a case study from data-centric microbiology, viz. amplicon sequencing, to introduce specifications of the relational framework. These specifications capture the distinctive epistemic role of samples, allowing the discussion of their significance in the inquiry process. I argue that samples are necessarily transformed to be considered as evidence, portable in the limits of a situation, and they act as world anchors for claims about a phenomenon. I compare these specifications with other data and evidence frameworks and suggest they are compatible. The paper concludes by considering the extension of these criteria in the context of biobanking. The specifications proposed here help analyze other life sciences cases and deepen our understanding of samples and their epistemological role in scientific research.
@article{Potiron2024, title = {Different kinds of data: samples and the relational framework}, author = {Potiron, A.}, journal = {Biology and Philosophy}, volume = {39}, year = {2024}, publisher = {Springer Nature}, doi = {https://doi.org/10.1007/s10539-024-09962-0}, keywords = {Samples, Data, Relational framework, Amplicon sequencing} }
2019
- Microbial impact on cholesterol and bile acid metabolism: current status and future prospectsJournal of Lipid Research, 2019
Recently, the gut microbiota has emerged as a crucial factor that influences cholesterol metabolism. Ever since, significant interest has been shown in investigating these host-microbiome interactions to uncover microbiome-mediated functions on cholesterol and bile acid (BA) metabolism. Indeed, changes in gut microbiota composition and, hence, its derived metabolites have been previously reported to subsequently impact the metabolic processes and have been linked to several diseases. In this context, associations between a disrupted gut microbiome, impaired BA metabolism, and cholesterol dysregulation have been highlighted. Extensive advances in metagenomic and metabolomic studies in this field have allowed us to further our understanding of the role of intestinal bacteria in metabolic health and disease. However, only a few have provided mechanistic insights into their impact on cholesterol metabolism. Identifying the myriad functions and interactions of these bacteria to maintain cholesterol homeostasis remain an important challenge in such a field of research. In this review, we discuss the impact of gut microbiota on cholesterol metabolism, its association with disease settings, and the potential of modulating gut microbiota as a promising therapeutic target to lower hypercholesterolemia.
@article{Kriaa2019, title = {Microbial impact on cholesterol and bile acid metabolism: current status and future prospects}, author = {Kriaa, A. and Bourgin, M. and Potiron, A. and Mkaouar, H. and Jablaoui, A. and Gérard, P. and Maguin, E. and Rhimi, M.}, journal = {Journal of Lipid Research}, volume = {60}, issue = {2}, pages = {323--332}, year = {2019}, publisher = {The American Society for Biochemistry and Molecular Biology}, doi = {10.1194/jlr.R088989}, issn = {15397262}, keywords = {Cholesterol metabolism,Gut microbiota,Hypercholesterolemia,Metabolic diseases} }
2015
- Recent Patents on Hypocholesterolemic Therapeutic Strategies: An UpdateRecent Advances in DNA & Gene Sequences (Discontinued), 2015
Worldwide, the cardiovascular diseases constitute a major cause of death with an ever growing incidence. Many medical approaches were developed against this physiopathology and patented; however up to now, no efficient treatment exists. Future developments are not only focusing on the identification of new therapeutic strategies against the cardiovascular diseases but also on a better understanding of the determinants of these multifactorial diseases. In this report, we reviewed the most recent patents that have been reported in this field of research.
@article{Potiron2015, title = {Recent Patents on Hypocholesterolemic Therapeutic Strategies: An Update}, author = {Potiron, A. and Gérard, P. and Le Roy, T. and Lesnik, P. and Maguin, E. and Rhimi, M.}, journal = {Recent Advances in DNA & Gene Sequences (Discontinued)}, volume = {9}, number = {1}, pages = {36--44}, year = {2015}, doi = {10.2174/2352092210666151216143459}, url = {http://www.eurekaselect.com/article/72538}, issn = {2352-0922/2352-0930}, keywords = {Cardiovascular diseases,Atherosclerosis,Cholesterolemia,Therapeutic strategies,Patents, Healyh} }
talks
2024
- Diversity measurement in microbial ecologyA. PotironPSA Biennial Meeting 2024, Nov 2024As part of the Symposium "Data Practices in Microbiology and Microbiome Science: Unpacking Upstream Choices in Experimental Design" with V. Keiser, D. Beiting, and J. Wallis - New Orleans, USA
Taxonomic diversity is a common measure in the scientific literature, yet a philosophical analysis of diversity as measurement remains elusive. Here, I propose such an analysis for the case of microbial ecology. I contend that upstream choices made by researchers before generating measurement data impact the value assigned to diversity. The talk outlines the implications of this analysis for the model-based accounts of measurement and the species concept. First, I analyze DNA-based microbial diversity assessment using model-based accounts of measurement. In these accounts, measurements are related to theoretical and statistical modeling. Measurements have a concrete and representational level (Tal 2013). When measuring a quantity, here diversity, of an object of interest, here microbial community, both need to be idealized before inferring the value for that quantity. For example, researchers can choose richness as an idealization of diversity and a molecular marker as an idealization of the object of interest, the community. An ideal solution is then sought, assumed to be attainable by ideal measuring instruments, i.e., instruments that give traceable results to appropriate standards. Researchers can then choose which instruments to use – the DNA sequencers, the bioinformatics tools, etc. Finally, de-idealization is required to obtain the measured value. Here, the concept of species comes in. The ideal measurement entails that there is an ideal way of distinguishing species. Still, in the actual measurement, researchers make assumptions about this, e.g., similarity thresholds at which two DNA sequences of the same marker are from different species. The measure’s validity depends not only on the empirical qualities of the measurement but also on the adequacy of the idealizations and the degree of correspondence between the ideal and the theoretical measurement process (Giordani and Mari 2012). Next, I assess the consequences for model-based accounts of measurement. Even if these accounts are suitable to analyze this case and highlight some of the choices needed in the production of the diversity measure, it is not easy to see how they can guide researchers in their choices. The choice of the model itself is not straightforward – for example, which instrument to include in the model or which standards to use. These choices will impact the value of the quantity researched – diversity – and what can be said about it. These accounts might need modifications. Finally, I assess the role of the species concept. This concept enters measurement in the modeling activities, thus creating relevant uncertainties to the diversity parameter—the diversity value changes depending on which concrete definition of species researchers choose. I explore if the need for a concrete definition is at odds with the argument for a general definition of species in the context of conservation biology, where diversity measures are compared.
- A new light on causes in human health-associated microbiome studies by unearthing its ecological rootsA. PotironCausality in Epidemiology - CitS conference series, May 2024Linz, Austria
Animals host microorganisms at various body sites like the gastrointestinal tract, skin, lungs, vagina, and urinary tract. The human gut microbiome has been associated with multiple health and disease phenotypes for the past twenty years. This tendency culminates with the integration of microbiomes in epidemiology studies. The research of causes in health-associated microbiome studies is based on the original or variants of Koch’s postulates (e.g., Lynch et al., 2019; Vonaesh et al., 2018). They are helpful for the detection of single and specific causal factors of diseases, e.g., microorganisms. They have been successful for some time, especially in the case of monocausal diseases, and gave rise to the germ theory of diseases. The ideas of specificity in terms of monocausality and homogeneity of causes are pervasive in contemporary medicine (Ross, 2018). They are also ubiquitous in microbiome studies, e.g., researching biomarkers for cancers or researching a curative “silver bullet” bacteria. As a result, the default assumptions in those researches are often a monocausal and specific cause for a disease or an intervention. However, I argue that scientists in health-associated microbiome studies have forgotten their history. Microbiology and microbiome studies are also ecological disciplines, not only medical ones. The father of microbial ecology is Sergei Winogradsky. While younger than Robert Koch, he was one of his contemporaries. In Winogradsky’s work, the research of causes is not focused on entities but on processes and interactions. He was studying the natural phenomena of the soil and the microorganismal community within it. He recognized at face values that those phenomena were complex by nature, i.e., multifactorial and non-entity specific. In this work, I will explore the state of health-associated microbiome studies if the default position would have been “Winogradskian,” i.e., looking for several factors and non-specific entities before looking for specific and unique causes. It might be then that the contemporary attempts at simplicity would be an exception rather than a rule and that more global and integrative approaches, such as the one defended in the OneHealth concept, would have come sooner, impacting epidemiology-related policies.
2023
- Species is a unit of measurementA. PotironEPSA Biennial Meeting 2023, Sep 2023Belgrade, Serbia
Species is a concept that has long been studied by biologists and philosophers, giving rise to several practical, epistemological, and theoretical questions now known as the “species problem”. Species are considered units of classification and evolution, but also units of diversity. In microbial ecology, in particular, diversity can be assessed using DNA sequence data. Yet, this process requires material tools such as DNA sequencers as well as bioinformatic treatments, both of which influence the value of diversity. While species diversity is often associated with a measure in the literature, a philosophical analysis of diversity as measurement remains elusive. This paper proposes to use the species concept in the context of diversity-as-a-measurement and outlines its implications for the practical, epistemological, and theoretical dimensions of the species problem. First, I analyse the DNA-based assessment of microbial diversity using the ideas of model-based accounts of measurement. In these accounts, measurements are related to theoretical and/or statistical modelling. In particular, measurements have a concrete and a representational, abstract level (Tal, 2013). When measuring a quantity, here diversity, of an object of interest, here microbial community, both need to be idealised before inferring the value for that quantity. For example, richness is an idealisation of the quantity diversity, and a molecular marker is an idealisation of the object of interest – the community. An ideal solution is then sought, which is assumed to be attainable by ideal measuring instruments, i.e. instruments that give results that are traceable to appropriate standards. Following my example, the measuring instrument is a combination of DNA sequencers and bioinformatics tools. Finally, de-idealisation is required to obtain the measured value. This is where the concept of species comes in. Indeed, the ideal measurement entails that there is an ideal way of distinguishing species, but the actual measurement makes some assumptions about this, e.g., thresholds at which two DNA sequences of the same marker are from different species. The validity of the measure depends on the empirical qualities of the measurement, but also the adequacy of the idealisations and the degree of correspondence between the ideal and the theoretical measurement process (Giordani and Mari, 2012). In this analysis, the species concept is one of many sources of uncertainty in the diversity measured by a measurement process. Second, I assess the implications of this analysis for the practical dimensions of the species problems. In the practice of microbial ecology, the definition of species is not so important as long as the assumptions in the measurement process are clearly stated and as long as the degree of uncertainty reflects these assumptions. This also makes the measure highly contextual (Shade, 2016). Yet, in the broader context of conservation biology, where diversity measures are compared, the contrary has been argued, i.e., a general definition of species is used to measure diversity across environments (Amitani, 2022). It would therefore be interesting to examine the measurement process in this discipline, where and how the species concept plays a role in this process, and whether the conclusion about the importance of the problem still holds. Third, I explore the epistemic role of the species concept as a unit of measurement and its implications for the homonymy thesis. Species are a source of uncertainty about the measure of the quantity diversity. On the other hand, it has been argued that there is an epistemological disunity between three other epistemic roles of the species concept: classification, generalisation, and evolution (Reydon, 2022, 76). Thus, it remains to be examined whether this new epistemic use is associated with a concept of species that is coextensive with the ones used in the above-mentioned epistemological roles. Moreover, the disunity argument partly grounds the homonymy thesis, according to which the term ‘species’ has several meanings at the same time and these meanings refer to multiple and distinct entities in the world (Reydon, 2022, 74). Therefore, analysing the concept(s) of species used in uncertainty assessment in measurement processes of diversity can strengthen or not the homonymy thesis. Finally, I follow the practice-oriented philosophy project to investigate the theoretical assumptions and the explanatory importance of the species concept in diversity measurement. In the case of measuring the diversity of a microbial community using a molecular marker, DNA sequences encountered in a delineated community and spatial region are compared and then assigned to groups that we call species. The sequences are considered different enough between groups, and the changes in the nucleotide sequence are sufficiently large to qualify for the status of species. This is close to the theoretical idea of ‘evolutionary independence’. This idea has been developed to save the technical use of the term ‘species’ in the ‘species-as-status’ thesis. In this thesis, because of the acceptance of the homonymy thesis, the concept of species lacks conceptual unity and a real entity to which it can be applied, so it has no ontological meaning (Reydon, 2022). In addition, in my example, diversity is one parameter of community ecology that has to be explained (Shade, 2016). Thus, it would be interesting to deepen and diversify the analysis of measurement processes of diversity to understand the explanatory importance of the species concept in these contexts.
- Towards an integrative view of explanation in human gut microbiome researchA. PotironISHPSSB Biennial Meeting 2023, Jul 2023University of Toronto, Canada
Experimental studies associate changing microbiomes with various healthy and diseased human phenotypes. Microbiomes are now integrated into the One Health concept, which considers the interconnections of animals, plants, humans and their shared environment to achieve better health outcomes (Berg et al., 2020). Clear explanations are rendered difficult by such complex and multilevel systems, initiating philosophical discussions about explanations, especially causal explanations. Indeed, Lynch and colleagues use a key explanatory standard inherited from 1880s microbiology, the Koch postulates, and a standard to establish causation in experimental science, the interventionist account, to assess the strength of causal claims about the microbiomes (Lynch et al., 2019). This approach is legitimate but overlooks part of microbiology’s history leading to an incomplete analysis of the assumptions used in this discipline. I propose here an alternative account of explanation in human gut microbiome research that considers this multipath history by recognizing the existence of an important alternative: the mechanistic account of explanation. First, I show that microbiology is not only a medical discipline. Parallel to the studies of the pathologist Koch, microbial ecology founders, Sergei Winogradsky and Martinus Beijerinck, studied microorganisms to learn about their role and behavior in their environment. Contemporary research in human gut microbiomes is considered part of both disciplines (Berg et al., 2020). Second, I argue that explanation in microbial ecology adopts a mechanistic-like strategy, which is not based on Koch’s postulates, but where the activity of the microbial community is explained by the interactions of the components of that community. For example, Winogradsky described how two groups of microorganisms interact with available resources and other microorganisms to explain the natural phenomenon of nitrification in the soil (transformation of ammonium to nitrate) (Winogradsky, 1949, 269). Finally, I develop a more comprehensive and encompassing account of explanation in human gut microbiome research. I propose to apply Craver’s mechanistic account to this discipline as he aims to understand constitutive explanation or “how the behavior of a whole is explained in terms of the behavior of its parts” (Craver, 2007, 160). The advantage of this account is that it can bind both explanatory traditions of microbiology by integrating the interventionist account in the selection of relevant causes. I argue that such an account is needed to accommodate contemporary appeal to a more holistic view (Berg et al., 2020), such as the One Health concept.
- How does the history of microbiology call for a more integrative view of explanation in human gut microbiome research?A. PotironIntegrated History and Philosophy of Science - &HPS9, Mar 2023University of South Carolina, Columbia, USA
Human gut microbiome research is a growing field in microbiology and in philosophy. Experimental studies have associated microbiomes with various healthy and diseased human phenotypes, but use various definitions of “microbiome”. A collective recently attempted to define the microbiome as the microbiota – the microorganisms – and their “theatre of activity”, all their activities and metabolites (Berg et al., 2020). Clear explanations are rendered difficult by such complex and multilevel systems. Scientific claims about causal microbiomes have initiated philosophical discussions about explanations and in particular causal explanations in this domain. So far, the main study assessing this issue is the one developed by Lynch and colleagues (Lynch et al., 2019). They use a key explanatory standard inherited from 1880s microbiology, the Koch’s postulates, and a standard to establish causation in experimental science, the interventionist account to assess the strength of causal claims about the microbiomes. They conclude that few, if any, of these claims are causal. Following these discussions, this work analyzes the history of microbiology to suggest an account of scientific explanation in the context of human gut microbiome research. I build my argument on the realization that there is not a single understanding of explanation in microbiology. Focusing on Koch’s postulates and interventionism is legitimate but overlooks the existence of an important alternative: the mechanistic account of explanation. First, I retain the appeal to history of microbiology used by Lynch et al. to analyze what is an explanation in microbiology, but show that microbiology is not only a medical discipline. Indeed, the first microscopic observations and descriptions of microorganisms were done in an ecological mindset where the aim was to describe how these ‘animalcules’ live in their surrounding environments (Kolter, 2021). More significantly, parallel to Robert Koch’s development of his postulates (first enunciation in 1878, revisions in 1882), other microbiologists such as Sergei Winogradsky and Martinus Beijerinck studied microorganisms with the aim of knowing their role and behavior in their environment (Caumette et al., 2015). Both scientists are considered today as the founders of microbial ecology. Communications between these two sub-disciplines were not very intense (Kolter, 2021) but contemporary research in human gut microbiomes is considered as part of both disciplines (Berg et al., 2020; Caumette et al., 2015; Kolter, 2021). Therefore, second, I analyze how microorganisms alone or in community explain phenomena in microbial ecology. I take this analysis to be complementary to the analysis of explanation in medical microbiology. I claim that explanation in microbial ecology is not based on Koch’s postulates. First, microorganisms are not viewed only as pathogens. On the contrary, they are viewed as a part of the process of life: integrated in their environment and interacting with it. The notion of interaction is crucial in the understanding of (microbial) ecology. Second, ecological approaches look for biochemical pathways or mechanisms that explain some outcomes of environments colonized by microorganisms. For example, Winogradsky worked on the transformation of mineral compounds. In 1889, he discovered that two groups of microorganisms are involved in the nitrification cycle in the soil (transformation of ammonium to nitrate) and was interested in the discovery of chemical mechanisms of life process (Caumette et al., 2015,15-16). This call for a more mechanistic view of explanation in microbiology, where the activity of the microbial community is explained by the interactions of the components of that community. More contemporarily, Berg et al. (2020) advocate a holistic view of the human microbiome, not as separate from the host but as a whole with the host. Therefore, in a third claim, I argue that we need a more comprehensive and encompassing account of explanation in this discipline that considers this multipath history. It was also suggested by some replies to Lynch et al. (Klassen, 2020; Schneider, 2020). Here I do not offer such a detailed account but I suggest the possibility of using the mechanistic account developed by Craver (2007). Craver’s aim is the understanding of constitutive explanation or “how the behavior of a whole is explained in terms of the behavior of its parts” (Craver, 2007, 160). The advantage of this mechanistic account is that it can bind both explanatory traditions of microbiology by integrating the interventionist account in the selection of relevant causes. Such account has a normative target of distinguishing good from bad explanation in human gut microbiome research that can help scientists to make their claims clearer. Moreover, to overlook part of a discipline’s history when analyzing the assumptions used in this discipline is, philosophically speaking, problematic. A first step would be to apply Craver’s mechanistic account to human gut microbiome research. This would require secondly, to develop it forward by deepening our understanding of interlevel relationships and the difference between causal and constitutive explanations.
2022
- Not everything is data: A revision of the relational framework of dataA. PotironSixth European Advanced School in the Philosophy of the Life Sciences - EASPLS, Sep 2022Bordeaux, France
The concept of data is related to the original dichotomy between observations of the world and theories about it. In the early 20 th century, the logical empiricists looked for a legitimization of the authority of scientific claims by appeal to the objectivity of observational reports or experiential data. In the late 1980’s philosophers and sociologists of sciences emphasized the importance of experimentation in the production of these data and therefore in the fact that data might be less objective than expected because of the human and instrumental manipulations needed to obtain them. Data remain representations of the world but a tension exists between their alleged objectivity and the fact that they are human made. Recently, a new framework was introduced, which defines the data according to their role within the scientific inquiry. This relational framework developed by Sabina Leonelli in her book Data centric biology: a philosophical study accounts very well for the contemporary uses of data such as big data and other data centric practices It defines data as any products of the scientific inquiry that have 1) the potential to travel between different situations of scientific inquiry and 2) the potential to be used as evidence to sustain knowledge claims. The aim of this work is twofold First, using a case study from data centric microbiology, viz. amplicon sequencing, I pinpoint some shortcomings of this frame work. Second, I propose a refinement of this framework to meet this challenge. Amplicon Sequencing (AS) is widely used in microbiology and microbial ecology to address the issue of the complexity of a microbial community in a given environment. The principle is the DNA sequencing and analysis of a single gene AS is expected to uncover the underlying characteristics of the microbial community in general. It is a methodological substitution to microbial observation which disrupts the link between microorganisms and their DNA. AS is a data centered method that generates millions of DNA sequences Therefore, it should, in principle, support the relational framework of data. First, I show that all the products of amplicon sequencing inquiry meet the two requi rements of the relational framework. However, scientists do distinguish among these products, which means, at least for practitioners of practice oriented philosophy of science that there is a prima facie problem I argue that there is continuity between these products throughout their production However, they have different roles in the scientific inquiry. Second, I propose refinements of the framework in order to respond to this problem. First, I emphasize the realization of a potentiality rather than the potentiality. Second, I focus on how the products of research activity are reused and for what purpose, rather than on their ability to travel. These working revisions allow me to identify by their role in the scientific inquiry, ’samples’ and a sub category of data that scientists called ’raw’. Because of the ambiguity of this term, I will use ’primary’ data
2021
- How can highly contextual data such as metabarcoding data relate to the world?A. PotironISHPSSB Biennial Meeting 2021, Jul 2021Cold Spring Harbor Laboratory, Online
This paper aims to reconsider the relationship between scientific data and the world. Data are usually considered as direct “screenshot” of the world. They are thus supposed to be fixed, context-independent and objective. In particular, in the context of modern Big Data-based science, data are supposed to “speak for themselves”. Metabarcoding is one method of modern microbiology that generates such data. Its principle is the DNA sequencing and analysis of a single gene, called a barcode. DNA sequences are thus central in those methods and passed from the world (the microorganisms) to the computer. During the transformation, choices are made which impaired the view of the fixed and objective screen shot. A sequenced barcode uniquely identifies the species of microorganisms in the community of a given environment. Thus, metabarcoding data are highly contextual where “context” is supposed to refer to a particular place, time and/or conditions. Thus, in metabarcoding, the path between data to knowledge is less straightforward than expected. This paper uses metabarcoding as a case study to develop a new understanding of the relationship between scientific data and the world. Metabarcoding generates millions of sequences, and is considered a data-centered method. Hence, I use the data journey framework developed by Leonelli in her book Data-centric biology: a philosophical study (2016). First, I assess what constitutes data in metabarcoding. I argue that DNA sequences become data not by acquiring the ability to travel but by acquiring the ability to sustain empirical claims. Sequences do so by increasing their reliability, through the interactions between environmental DNA and scientific technologies and through bioinformatics steps performed on sequences retrieved from sequencing platforms. Second, I evaluate how metabarcoding data can sustain empirical claims. I argue that the biological relevance of these data and conclusions made from them are better understood if we conceive the environment at the same epistemic level as the organism. Biology is usually conceived as gathering data and conclusions about individual organisms or even population. Yet, metabarcoding is about gathering data about a given environment. Projects such as the Earth Microbiome Project, in developing an ontology of studied environments that is more or less inclusive depending on the level of the taxonomy tend to integrate this shift.
- Human gut microbiomes research: what are the benefits of the mechanistic causal account?A. PotironThe Philosophy Exchange Philosophy of Science Graduate Conference PSGC2021: From Theory to Practice, Oct 2021The London School of Economincs, Online
This paper develops this working hypothesis: to focus on the interventionist framework of causal explanation in human gut microbiomes research underestimates the role of mechanistic account of causal explanation and it risks overlooking relevant therapeutics avenues. Human gut microbiomes studies are a recent and growing research field both in microbiology and in philosophy. Recently, causal explanation in this field have been studied using the interventionist framework of causal explanation. Lynch and colleagues advocate a methodological reductionism in order to achieve stronger causal explanation by improving the specificity, stability and proportionality of the causal relationship (Lynch, Parke, & O’Malley, 2019). Attah and colleagues agreed but indicated that in some cases a more coarse-grained, less specified explanation is preferable (Attah, DiMarco, & Plutynski, 2020). However, I argue that in human gut microbiomes studies, mechanistic causal accounts are used to strengthen causal relationship. Looking at the literature on the human gut microbiomes research linked with health outcomes, the published work can be divided into two camps. The first camp investigates single or few microorganisms (often bacteria) isolated from human microbiomes to study their effects both in vitro and in vivo. Researchers there aim to produce a probiotic: a bacterial strain with interesting properties for human health. I argue that despite relying on intervention such as giving the microorganism to mice in order to improve the phenotype of a disease, the causal relationship between the microorganism and the phenotype is strengthen when a mechanism is known or at least hypothesized (e.g., via the decrease of the gut barrier permeability). The second camp explores the role of whole microbiome in healthy and diseased phenotypes. There, the composition of the microbiome is uncovered mainly to be able to infer functions, when functions are not studied first. In her Philosophy of Microbiology (2014), O’Malley argued that functions here are understood in the ‘Cummins causal-role sense’ (p. 147). In this view, the functions of the components contribute to the function of the system containing them. This account can be labelled as mechanistic where functions from components (e.g., bacteria) are transmitted to the function of the system (e.g., whole microbiome). Both camps seem to value mechanistic causation in their explanation of phenotypes. I argue that there are at least two advantages of this account in comparison to the interventionist one. First, steps of the mechanism can be therapeutically targeted. Second, as the second camps analysis seem to show, researchers find relevant to study the whole microbiome and even consider it as causal. Mechanistic account does not suffer from the problem of specificity and proportionality and thus, as long as a mechanism between the whole microbiome and its effect is found, the whole microbiome can be causal. Focusing on the mechanistic account and not only the interventionist account makes more sense of the current state of therapeutic approaches and recent researches in the human gut microbiomes. This paper is a scientific case-study analysed with a philosophical perspective in order to challenge and complete the recent account of causality in microbiomes research.
- Challenging the philosophical definition of data using amplicon sequencingA. PotironMolecular Ecology and Evolution Seminar Autumn/Winter 2021, Nov 2021Bangor University, Online
2017
- Cholesterol conversion into coprostanol by bacterial strains isolated from the human gut microbiota can modify host cholesterol metabolismA. Potiron, A. Kriaa, S. Boudebbouze, A. Bruneau, M. Lhomme, P. Lesnik, E. Maguin, M. Rhimi, and P. Gérard7th Congress of European Microbiologists - FEMS, Jul 2017Valencia, Spain