Obesity as Inducer of Cognitive Function Decline via Dysbiosis of Gut Microbiota in Rats

Diet-induced obesity is a global phenomenon that affects the population worldwide with manifestations at both the phenotypic and genotypic levels. Cognitive function decline is a major global health challenge. The relation between obesity and cognitive function is a debatable issue. The main goal of the current research was to study the implications of obesity on cognitive function and gut microbiota diversity and its impact on plasma and brain metabolic parameters in rats. Obesity was induced in rats by feeding on a high-fat (HF) or a high-fat/high-sucrose (HFHS) diet. The results reveal that both the HF (0.683) and HFHS (0.688) diets were effective as obesity inducers, which was confirmed by a significant increase in the body mass index (BMI). Both diet groups showed dyslipidemia and elevation of oxidative stress, insulin resistance (IR), and inflammatory markers with alterations in liver and kidney functions. Obesity led to a reduction in cognitive function through a reduction in short-term memory by 23.8% and 30.7% in the rats fed HF and HFHS diets, respectively, and learning capacity and visuo-spatial memory reduced by 8.9 and 9.7 s in the rats fed an HF or HFHS diet, respectively. Bacteroidetes, Firmicutes, Proteobacteria, Fusobacteria, and Spirochaetes phyla were detected. The Firmicutes/Bacteroidetes ratio (F/B) significantly decreased in the HF group, while it increased in the HFHS group compared to the normal control. The two species, Bacteroides acidifaciens and Bacteroides ovatus, which are associated with IR, were drastically compromised by the high-fat/high-sucrose diet. Some species that have been linked to reduced inflammation showed a sharp decrease in the HFHS group, while Prevotella copri, which is linked to carbohydrate metabolism, was highly enriched. In conclusion: Obesity led to cognitive impairment through changes in short-term and visuo-spatial memory. A metagenomic analysis revealed alterations in the abundance of some microbial taxa associated with obesity, inflammation, and insulin resistance in the HF and HFHS groups.

Scrutinizing microbiome determinism: why deterministic hypotheses about the microbiome are conceptually ungrounded

This paper addresses the topic of determinism in contemporary microbiome research. I distinguish two types of deterministic claims about the microbiome, and I show evidence that both types of claims are present in the contemporary literature. First, the idea that the host genetics determines the composition of the microbiome which I call "host-microbiome determinism". Second, the idea that the genetics of the holobiont (the individual unit composed by a host plus its microbiome) determines the expression of certain phenotypic traits, which I call "microbiome-phenotype determinism". Drawing on the stability of traits conception of individuality (Suárez in Hist Philos Life Sci 42:11, 2020) I argue that none of these deterministic hypotheses is grounded on our current knowledge of how the holobiont is transgenerationally assembled, nor how it expresses its phenotypic traits.

Effects of Gut Microbiota Alterations on Motor, Gastrointestinal, and Behavioral Phenotype in a Mouse Model of Parkinson's Disease

BACKGROUND

Parkinson's disease (PD) is a multi-system disorder consisting of not only classic motor symptoms but also a variety of non-motor symptoms including gastrointestinal (GI) dysfunction and mood disorders. The gut microbiota has been suggested to play a role in modulating PD motor and non-motor features, although the causality and mechanisms behind these proposed interactions remains largely understudied.

OBJECTIVE

In this study, we aimed to provide in-depth characterization of an established mouse model of PD (transgenic (TG) SNCA A53T) and experimentally address how changes to the gut microbiota impact the PD-like phenotype.

METHODS

We profiled the PD-like phenotype of transgenic mice through a panel of motor, GI, and behavioral tests. We then investigated how antibiotic treatment or gut microbial community transfer (via cohousing with wild-type mice) impacted the PD-like phenotype.

RESULTS

We found that this mouse model demonstrated early (6 weeks of age) motor symptoms when compared to a wild-type control mouse strain. Transgenic mice also exhibited early GI dysfunction, as well as behavioral alterations, including reduced anxiety-like behavior, and increased depression-like and apathy-like behavior. Compared to wild-type mice, the transgenic fecal microbiota was less diverse and compositionally distinct. Interestingly, drastic alterations to the gut microbiota, through antibiotic treatment or cohousing with wild-type mice, had a minimal effect on the motor, GI, and behavioral phenotype of transgenic mice.

CONCLUSION

We concluded that this mouse model effectively recapitulates motor and non-motor features of PD; however, the gut microbiota appears to exhibit a minor impact on the pathophysiology of this PD model.

'Gut health' and the microbiome in the popular press: a content analysis

OBJECTIVE

Extensive research and important discoveries on the microbiome have led to a growth in media coverage. This study explores how the microbiome has been portrayed in press sources popular among American and Canadian audiences.

DESIGN

Content analysis.

METHODS

Using the FACTIVA Database, we compiled a finalised data set of (N=830) articles from press sources popular among American and Canadian audiences which were published between 1 January 2018 and 11 October 2019 and which contained at least one of the following search terms: 'microbiome', 'microbiota', 'gut health', 'healthy gut', 'unhealthy gut', 'gut bacteria', 'probiotic' or 'probiotics.' We performed content analysis on the articles to determine how often ideas of the microbiome were presented as beneficial, in which health contexts, and whether actions could be taken to reap stated benefits. We compared this portrayal of benefits with critical portrayals of the microbiome.

RESULTS

Almost all of the articles (94%) described health benefits associated with the microbiome with many (79%) describing actions which could be taken to reap stated benefits. Articles most often described health benefits in more broad, general context (34%) and most commonly outlined actions related to food/drug (45%) as well as probiotic (27%) intake. Only some articles (19%) provided microbiome-related critiques or limitations. Some of the articles (22%) were focused on highlighting specific research developments, and in these articles, critiques or limitations were more common.

CONCLUSIONS

Articles discussing the microbiome published for American and Canadian audiences typically hype the microbiome's impact and popularise gut health trends while only offering a little in the way of communicating microbiome science. Lifestyle choices including nutrition, taking probiotics, stress management and exercise are often promoted as means of reaping the microbiome-related health benefits. The trend of actionable 'gut health' is foregrounded over more evidence-based descriptions of microbiome science.

Measuring Causal Invariance Formally

Invariance is one of several dimensions of causal relationships within the interventionist account. The more invariant a relationship between two variables, the more the relationship should be considered paradigmatically causal. In this paper, I propose two formal measures to estimate invariance, illustrated by a simple example. I then discuss the notion of invariance for causal relationships between non-nominal (i.e., ordinal and quantitative) variables, for which Information theory, and hence the formalism proposed here, is not well suited. Finally, I propose how invariance could be qualified for such variables.

Causal effects in microbiomes using interventional calculus

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or "disease" variable, and then computing the causal network, referred to as a "disease network", with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

Do seasonal microbiome changes affect infection susceptibility, contributing to seasonal disease outbreaks?

The aim of the present paper is to explore whether seasonal outbreaks of infectious diseases may be linked to changes in host microbiomes. This is a very important issue, because one way to have more control over seasonal outbreaks is to understand the factors that underlie them. In this paper, I will evaluate the relevance of the microbiome as one of such factors. The paper is based on two pillars of reasoning. Firstly, on the idea that microbiomes play an important role in their hosts' defence against infectious diseases. Secondly, on the idea that microbiomes are not stable, but change seasonally. These two ideas are combined in order to argue that seasonal changes in a given microbiome may influence the functionality of the host's immune system and consequently make it easier for infectious agents to infect the host at certain times of year. I will argue that, while this is only a theoretical possibility, certain studies may back up such claims. Furthermore, I will show that this does not necessarily contradict other hypotheses aimed at explaining seasonal outbreaks; in fact, it may even enhance them.

Establishing or Exaggerating Causality for the Gut Microbiome: Lessons from Human Microbiota-Associated Rodents

Human diseases are increasingly linked with an altered or "dysbiotic" gut microbiota, but whether such changes are causal, consequential, or bystanders to disease is, for the most part, unresolved. Human microbiota-associated (HMA) rodents have become a cornerstone of microbiome science for addressing causal relationships between altered microbiomes and host pathology. In a systematic review, we found that 95% of published studies (36/38) on HMA rodents reported a transfer of pathological phenotypes to recipient animals, and many extrapolated the findings to make causal inferences to human diseases. We posit that this exceedingly high rate of inter-species transferable pathologies is implausible and overstates the role of the gut microbiome in human disease. We advocate for a more rigorous and critical approach for inferring causality to avoid false concepts and prevent unrealistic expectations that may undermine the credibility of microbiome science and delay its translation.