A microbial workout

In a recent report in Nature, Dohnalová et al., describe a gut-brain axis mechanism in highlighting the importance of the gut microbiota and its derived metabolites in long-term exercise engagement and performance.

CAR-T Cell Therapy and the Gut Microbiota

Chimeric antigen receptor (CAR) - T cell cancer therapy has yielded promising results in treating hematologic malignancies in clinical studies, and a growing number of CAR-T regimens are approved for clinical usage. While the therapy is considered of great potential in expanding the cancer immunotherapy arsenal, more than half of patients receiving CAR-T infusions do not respond, while others develop significant adverse effects, collectively indicating a need for optimization of CAR-T treatment to the individual. The microbiota is increasingly suggested as a major modulator of immunotherapy responsiveness. Studying causal microbiota roles possibly contributing to CAR-T therapy efficacy, adverse effects reduction, and prediction of patient responsiveness constitutes an exciting area of active research. Herein, we discuss the latest developments implicating human microbiota involvement in CAR-T therapy, while highlighting challenges and promises in harnessing the microbiota as a predictor and modifier of CAR-T treatment towards optimized efficacy and minimization of treatment-related adverse effects.

Small Intestinal Microbiota Oscillations, Host Effects and Regulation-A Zoom into Three Key Effector Molecules

The gut microbiota features a unique diurnal rhythmicity which contributes to modulation of host physiology and homeostasis. The composition and activity of the microbiota and its secreted molecules influence the intestinal milieu and neighboring organs, such as the liver. Multiple immune-related molecules have been linked to the diurnal microbiota-host interaction, including Reg3γ, IgA, and MHCII, which are secreted or expressed on the gut surface and directly interact with intestinal bacteria. These molecules are also strongly influenced by dietary patterns, such as high-fat diet and time-restricted feeding, which are already known to modulate microbial rhythms and peripheral clocks. Herein, we use Reg3γ, IgA, and MHCII as test cases to highlight the divergent effects mediated by the diurnal activity of the gut microbiota and their downstream host effects. We further highlight current challenges and conflicts, remaining questions, and perspectives toward a holistic understanding of the microbiome's impacts on circadian human behavior.

Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance

Non-nutritive sweeteners (NNS) are commonly integrated into human diet and presumed to be inert; however, animal studies suggest that they may impact the microbiome and downstream glycemic responses. We causally assessed NNS impacts in humans and their microbiomes in a randomized-controlled trial encompassing 120 healthy adults, administered saccharin, sucralose, aspartame, and stevia sachets for 2 weeks in doses lower than the acceptable daily intake, compared with controls receiving sachet-contained vehicle glucose or no supplement. As groups, each administered NNS distinctly altered stool and oral microbiome and plasma metabolome, whereas saccharin and sucralose significantly impaired glycemic responses. Importantly, gnotobiotic mice conventionalized with microbiomes from multiple top and bottom responders of each of the four NNS-supplemented groups featured glycemic responses largely reflecting those noted in respective human donors, which were preempted by distinct microbial signals, as exemplified by sucralose. Collectively, human NNS consumption may induce person-specific, microbiome-dependent glycemic alterations, necessitating future assessment of clinical implications.

Local and systemic effects of microbiome‐derived metabolites

Commensal microbes form distinct ecosystems within their mammalian hosts, collectively termed microbiomes. These indigenous microbial communities broadly expand the genomic and functional repertoire of their host and contribute to the formation of a “meta‐organism.” Importantly, microbiomes exert numerous biochemical reactions synthesizing or modifying multiple bioactive small molecules termed metabolites, which impact their host's physiology in a variety of contexts. Identifying and understanding molecular mechanisms of metabolite–host interactions, and how their disrupted signaling can contribute to diseases, may enable their therapeutic application, a modality termed “postbiotic” therapy. In this review, we highlight key examples of effects of bioactive microbe‐associated metabolites on local, systemic, and immune environments, and discuss how these may impact mammalian physiology and associated disorders. We outline the challenges and perspectives in understanding the potential activity and function of this plethora of microbially associated small molecules as well as possibilities to harness them toward the promotion of personalized precision therapeutic interventions.

Time-limited diets and the gut microbiota in cardiometabolic disease

In recent years, intermittent fasting (IF), including periodic fasting and time-restricted feeding (TRF), has been increasingly suggested to constitute a promising treatment for cardiometabolic diseases (CMD). A deliberate daily pause in food consumption influences the gut microbiome and the host circadian clock, resulting in improved cardiometabolic health. Understanding the molecular mechanisms by which circadian host-microbiome interactions affect host metabolism and immunity may add a potentially important dimension to effective implementation of IF diets. In this review, we discuss emerging evidence potentially linking compositional and functional alterations of the gut microbiome with IF impacts on mammalian metabolism and risk of development of hypertension, type 2 diabetes (T2D), obesity, and their long-term micro- and macrovascular complications. We highlight the challenges and unknowns in causally linking diurnal bacterial signals with dietary cues and downstream metabolic consequences and means of harnessing these signals toward future microbiome integration into precision medicine.

Utilization of Host and Microbiome Features in Determination of Biological Aging

The term ‘old age’ generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person’s life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person’s temporal physiological status and associated disease susceptibility state. As such, differentiating ‘chronological aging’ from ‘biological aging’ holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person’s physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.

Effects of personalized diets by prediction of glycemic responses on glycemic control and metabolic health in newly diagnosed T2DM: a randomized dietary intervention pilot trial

BACKGROUND

Dietary modifications are crucial for managing newly diagnosed type 2 diabetes mellitus (T2DM) and preventing its health complications, but many patients fail to achieve clinical goals with diet alone. We sought to evaluate the clinical effects of a personalized postprandial-targeting (PPT) diet on glycemic control and metabolic health in individuals with newly diagnosed T2DM as compared to the commonly recommended Mediterranean-style (MED) diet.

METHODS

We enrolled 23 adults with newly diagnosed T2DM (aged 53.5 ± 8.9 years, 48% males) for a randomized crossover trial of two 2-week-long dietary interventions. Participants were blinded to their assignment to one of the two sequence groups: either PPT-MED or MED-PPT diets. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses (PPGR). We further evaluated the long-term effects of PPT diet on glycemic control and metabolic health by an additional 6-month PPT intervention (n = 16). Participants were connected to continuous glucose monitoring (CGM) throughout the study and self-recorded dietary intake using a smartphone application.

RESULTS

In the crossover intervention, the PPT diet lead to significant lower levels of CGM-based measures as compared to the MED diet, including average PPGR (mean difference between diets, - 19.8 ± 16.3 mg/dl × h, p < 0.001), mean glucose (mean difference between diets, - 7.8 ± 5.5 mg/dl, p < 0.001), and daily time of glucose levels > 140 mg/dl (mean difference between diets, - 2.42 ± 1.7 h/day, p < 0.001). Blood fructosamine also decreased significantly more during PPT compared to MED intervention (mean change difference between diets, - 16.4 ± 37 μmol/dl, p < 0.0001). At the end of 6 months, the PPT intervention leads to significant improvements in multiple metabolic health parameters, among them HbA1c (mean ± SD, - 0.39 ± 0.48%, p < 0.001), fasting glucose (- 16.4 ± 24.2 mg/dl, p = 0.02) and triglycerides (- 49 ± 46 mg/dl, p < 0.001). Importantly, 61% of the participants exhibited diabetes remission, as measured by HbA1c < 6.5%. Finally, some clinical improvements were significantly associated with gut microbiome changes per person.

CONCLUSION

In this crossover trial in subjects with newly diagnosed T2DM, a PPT diet improved CGM-based glycemic measures significantly more than a Mediterranean-style MED diet. Additional 6-month PPT intervention further improved glycemic control and metabolic health parameters, supporting the clinical efficacy of this approach.

TRIAL REGISTRATION

ClinicalTrials.gov number, NCT01892956.

Gut microbiota modulates weight gain in mice after discontinued smoke exposure

Cigarette smoking constitutes a leading global cause of morbidity and preventable death¹, and most active smokers report a desire or recent attempt to quit². Smoking-cessation-induced weight gain (SCWG; 4.5 kg reported to be gained on average per 6-12 months, >10 kg year^-1 in 13% of those who stopped smoking³) constitutes a major obstacle to smoking abstinence⁴, even under stable^5,6 or restricted⁷ caloric intake. Here we use a mouse model to demonstrate that smoking and cessation induce a dysbiotic state that is driven by an intestinal influx of cigarette-smoke-related metabolites. Microbiome depletion induced by treatment with antibiotics prevents SCWG. Conversely, fecal microbiome transplantation from mice previously exposed to cigarette smoke into germ-free mice naive to smoke exposure induces excessive weight gain across diets and mouse strains. Metabolically, microbiome-induced SCWG involves a concerted host and microbiome shunting of dietary choline to dimethylglycine driving increased gut energy harvest, coupled with the depletion of a cross-regulated weight-lowering metabolite, N-acetylglycine, and possibly by the effects of other differentially abundant cigarette-smoke-related metabolites. Dimethylglycine and N-acetylglycine may also modulate weight and associated adipose-tissue immunity under non-smoking conditions. Preliminary observations in a small cross-sectional human cohort support these findings, which calls for larger human trials to establish the relevance of this mechanism in active smokers. Collectively, we uncover a microbiome-dependent orchestration of SCWG that may be exploitable to improve smoking-cessation success and to correct metabolic perturbations even in non-smoking settings.

Dietary suppression of MHC class II expression in intestinal epithelial cells enhances intestinal tumorigenesis

Little is known about how interactions of diet, intestinal stem cells (ISCs), and immune cells affect early-stage intestinal tumorigenesis. We show that a high-fat diet (HFD) reduces the expression of the major histocompatibility complex class II (MHC class II) genes in intestinal epithelial cells, including ISCs. This decline in epithelial MHC class II expression in a HFD correlates with reduced intestinal microbiome diversity. Microbial community transfer experiments suggest that epithelial MHC class II expression is regulated by intestinal flora. Mechanistically, pattern recognition receptor (PRR) and interferon-gamma (IFNγ) signaling regulates epithelial MHC class II expression. MHC class II-negative (MHC-II-) ISCs exhibit greater tumor-initiating capacity than their MHC class II-positive (MHC-II+) counterparts upon loss of the tumor suppressor Apc coupled with a HFD, suggesting a role for epithelial MHC class II-mediated immune surveillance in suppressing tumorigenesis. ISC-specific genetic ablation of MHC class II increases tumor burden cell autonomously. Thus, HFD perturbs a microbiome-stem cell-immune cell interaction that contributes to tumor initiation in the intestine.

Severe Dysbiosis and Specific Haemophilus and Neisseria Signatures as Hallmarks of the Oropharyngeal Microbiome in Critically Ill Coronavirus Disease 2019 (COVID-19) Patients

BACKGROUND

At the entry site of respiratory virus infections, the oropharyngeal microbiome has been proposed as a major hub integrating viral and host immune signals. Early studies suggested that infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are associated with changes of the upper and lower airway microbiome, and that specific microbial signatures may predict coronavirus disease 2019 (COVID-19) illness. However, the results are not conclusive, as critical illness can drastically alter a patient's microbiome through multiple confounders.

METHODS

To study oropharyngeal microbiome profiles in SARS-CoV-2 infection, clinical confounders, and prediction models in COVID-19, we performed a multicenter, cross-sectional clinical study analyzing oropharyngeal microbial metagenomes in healthy adults, patients with non-SARS-CoV-2 infections, or with mild, moderate, and severe COVID-19 (n = 322 participants).

RESULTS

In contrast to mild infections, patients admitted to a hospital with moderate or severe COVID-19 showed dysbiotic microbial configurations, which were significantly pronounced in patients treated with broad-spectrum antibiotics, receiving invasive mechanical ventilation, or when sampling was performed during prolonged hospitalization. In contrast, specimens collected early after admission allowed us to segregate microbiome features predictive of hospital COVID-19 mortality utilizing machine learning models. Taxonomic signatures were found to perform better than models utilizing clinical variables with Neisseria and Haemophilus species abundances as most important features.

CONCLUSIONS

In addition to the infection per se, several factors shape the oropharyngeal microbiome of severely affected COVID-19 patients and deserve consideration in the interpretation of the role of the microbiome in severe COVID-19. Nevertheless, we were able to extract microbial features that can help to predict clinical outcomes.

Microbiome and cancer

The human microbiome constitutes a complex multikingdom community that symbiotically interacts with the host across multiple body sites. Host-microbiome interactions impact multiple physiological processes and a variety of multifactorial disease conditions. In the past decade, microbiome communities have been suggested to influence the development, progression, metastasis formation, and treatment response of multiple cancer types. While causal evidence of microbial impacts on cancer biology is only beginning to be unraveled, enhanced molecular understanding of such cancer-modulating interactions and impacts on cancer treatment are considered of major scientific importance and clinical relevance. In this review, we describe the molecular pathogenic mechanisms shared throughout microbial niches that contribute to the initiation and progression of cancer. We highlight advances, limitations, challenges, and prospects in understanding how the microbiome may causally impact cancer and its treatment responsiveness, and how microorganisms or their secreted bioactive metabolites may be potentially harnessed and targeted as precision cancer therapeutics.

Personalized Postprandial Glucose Response-Targeting Diet Versus Mediterranean Diet for Glycemic Control in Prediabetes

OBJECTIVE

To compare the clinical effects of a personalized postprandial-targeting (PPT) diet versus a Mediterranean (MED) diet on glycemic control and metabolic health in prediabetes.

RESEARCH DESIGN AND METHODS

We randomly assigned adults with prediabetes (n = 225) to follow a MED diet or a PPT diet for a 6-month dietary intervention and additional 6-month follow-up. The PPT diet relies on a machine learning algorithm that integrates clinical and microbiome features to predict personal postprandial glucose responses. During the intervention, all participants were connected to continuous glucose monitoring (CGM) and self-reported dietary intake using a smartphone application.

RESULTS

Among 225 participants randomized (58.7% women, mean ± SD age 50 ± 7 years, BMI 31.3 ± 5.8 kg/m², HbA_1c, 5.9 ± 0.2% [41 ± 2.4 mmol/mol], fasting plasma glucose 114 ± 12 mg/dL [6.33 ± 0.67 mmol/L]), 200 (89%) completed the 6-month intervention. A total of 177 participants also contributed 12-month follow-up data. Both interventions reduced the daily time with glucose levels >140 mg/dL (7.8 mmol/L) and HbA_1c levels, but reductions were significantly greater in PPT compared with MED. The mean 6-month change in "time above 140" was -0.3 ± 0.8 h/day and -1.3 ± 1.5 h/day for MED and PPT, respectively (95% CI between-group difference -1.29 to -0.66, P < 0.001). The mean 6-month change in HbA_1c was -0.08 ± 0.19% (-0.9 ± 2.1 mmol/mol) and -0.16 ± 0.24% (-1.7 ± 2.6 mmol/mol) for MED and PPT, respectively (95% CI between-group difference -0.14 to -0.02, P = 0.007). The significant between-group differences were maintained at 12-month follow-up. No significant differences were noted between the groups in a CGM-measured oral glucose tolerance test.

CONCLUSIONS

In this clinical trial in prediabetes, a PPT diet improved glycemic control significantly more than a MED diet as measured by daily time of glucose levels >140 mg/dL (7.8 mmol/L) and HbA_1c. These findings may have implications for dietary advice in clinical practice.

Commensal inter-bacterial interactions shaping the microbiota

The gut microbiota, a complex ecosystem of microorganisms of different kingdoms, impacts host physiology and disease. Within this ecosystem, inter-bacterial interactions and their impacts on microbiota community structure and the eukaryotic host remain insufficiently explored. Microbiota-related inter-bacterial interactions range from symbiotic interactions, involving exchange of nutrients, enzymes, and genetic material; competition for nutrients and space, mediated by biophysical alterations and secretion of toxins and anti-microbials; to predation of overpopulating bacteria. Collectively, these understudied interactions hold important clues as to forces shaping microbiota diversity, niche formation, and responses to signals perceived from the host, incoming pathogens and the environment. In this review, we highlight the roles and mechanisms of selected inter-bacterial interactions in the microbiota, and their potential impacts on the host and pathogenic infection. We discuss challenges in mechanistically decoding these complex interactions, and prospects of harnessing them as future targets for rational microbiota modification in a variety of diseases.

XCR1+ type 1 conventional dendritic cells drive liver pathology in non-alcoholic steatohepatitis

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) are prevalent liver conditions that underlie the development of life-threatening cirrhosis, liver failure and liver cancer. Chronic necro-inflammation is a critical factor in development of NASH, yet the cellular and molecular mechanisms of immune dysregulation in this disease are poorly understood. Here, using single-cell transcriptomic analysis, we comprehensively profiled the immune composition of the mouse liver during NASH. We identified a significant pathology-associated increase in hepatic conventional dendritic cells (cDCs) and further defined their source as NASH-induced boost in cycling of cDC progenitors in the bone marrow. Analysis of blood and liver from patients on the NAFLD/NASH spectrum showed that type 1 cDCs (cDC1) were more abundant and activated in disease. Sequencing of physically interacting cDC-T cell pairs from liver-draining lymph nodes revealed that cDCs in NASH promote inflammatory T cell reprogramming, previously associated with NASH worsening. Finally, depletion of cDC1 in XCR1^DTA mice or using anti-XCL1-blocking antibody attenuated liver pathology in NASH mouse models. Overall, our study provides a comprehensive characterization of cDC biology in NASH and identifies XCR1⁺ cDC1 as an important driver of liver pathology.

The hygiene hypothesis, the COVID pandemic, and consequences for the human microbiome

The COVID-19 pandemic has the potential to affect the human microbiome in infected and uninfected individuals, having a substantial impact on human health over the long term. This pandemic intersects with a decades-long decline in microbial diversity and ancestral microbes due to hygiene, antibiotics, and urban living (the hygiene hypothesis). High-risk groups succumbing to COVID-19 include those with preexisting conditions, such as diabetes and obesity, which are also associated with microbiome abnormalities. Current pandemic control measures and practices will have broad, uneven, and potentially long-term effects for the human microbiome across the planet, given the implementation of physical separation, extensive hygiene, travel barriers, and other measures that influence overall microbial loss and inability for reinoculation. Although much remains uncertain or unknown about the virus and its consequences, implementing pandemic control practices could significantly affect the microbiome. In this Perspective, we explore many facets of COVID-19-induced societal changes and their possible effects on the microbiome, and discuss current and future challenges regarding the interplay between this pandemic and the microbiome. Recent recognition of the microbiome's influence on human health makes it critical to consider both how the microbiome, shaped by biosocial processes, affects susceptibility to the coronavirus and, conversely, how COVID-19 disease and prevention measures may affect the microbiome. This knowledge may prove key in prevention and treatment, and long-term biological and social outcomes of this pandemic.

Breakthroughs and Bottlenecks in Microbiome Research

Over the past 15 years, the research community has witnessed unprecedented progress in microbiome research. We review this increasing knowledge and first attempts of its clinical application, and also limitations and challenges faced by the research community, in mechanistically understanding host-microbiome interactions and integrating these insights into clinical practice.