Comparison of canine colostrum and milk using a multi-omics approach.

BACKGROUND: A mother's milk is considered the gold standard of nutrition in neonates and is a source of cytokines, immunoglobulins, growth factors, and other important components, yet little is known about the components of canine milk, specifically colostrum, and the knowledge related to its microbial and metabolic profiles is particularly underwhelming. In this study, we characterized canine colostrum and milk microbiota and metabolome for several breeds of dogs and examined profile shifts as milk matures in the first 8 days post-whelping. RESULTS: Through untargeted metabolomics, we identified 63 named metabolites that were significantly differentially abundant between days 1 and 8 of lactation. Surprisingly, the microbial compositions of the colostrum and milk, characterized using 16S rRNA gene sequencing, were largely similar, with only two differentiating genera. The shifts observed, mainly increases in several sugars and amino sugars over time and shifts in amino acid metabolites, align with shifts observed in human milk samples and track with puppy development. CONCLUSION: Like human milk, canine milk composition is dynamic, and shifts are well correlated with developing puppies' needs. Such a study of the metabolic profile of canine milk, and its relation to the microbial community, provides insights into the changing needs of the neonate, as well as the ideal nutrition profile for optimal functionality. This information will add to the existing knowledge base of canine milk composition with the prospect of creating a quality, tailored milk substitute or supplement for puppies.

The Cross-talk Between Intestinal Microbiota and MDSCs Fuels Colitis-associated Cancer Development.

Intestinal chronic inflammation is associated with microbial dysbiosis and accumulation of various immune cells including myeloid-derived suppressor cells (MDSC), which profoundly impact the immune microenvironment, perturb homeostasis and increase the risk to develop colitis-associated colorectal cancer (CAC). However, the specific MDSCs-dysbiotic microbiota interactions and their collective impact on CAC development remain poorly understood. In this study, using a murine model of CAC, we demonstrate that CAC-bearing mice exhibit significantly elevated levels of highly immunosuppressive MDSCs, accompanied by microbiota alterations. Both MDSCs and bacteria that infiltrate the colon tissue and developing tumors can be found in close proximity, suggesting intricate MDSC-microbiota cross-talk within the tumor microenvironment. To investigate this phenomenon, we employed antibiotic treatment to disrupt MDSC-microbiota interactions. This intervention yielded a remarkable reduction in intestinal inflammation, decreased MDSC levels, and alleviated immunosuppression, all of which were associated with a significant reduction in tumor burden. Furthermore, we underscore the causative role of dysbiotic microbiota in the predisposition toward tumor development, highlighting their potential as biomarkers for predicting tumor load. We shed light on the intimate MDSCs-microbiota cross-talk, revealing how bacteria enhance MDSC suppressive features and activities, inhibit their differentiation into mature beneficial myeloid cells, and redirect some toward M2 macrophage phenotype. Collectively, this study uncovers the role of MDSC-bacteria cross-talk in impairing immune responses and promoting tumor growth, providing new insights into potential therapeutic strategies for CAC. SIGNIFICANCE: MDSCs-dysbiotic bacteria interactions in the intestine play a crucial role in intensifying immunosuppression within the CAC microenvironment, ultimately facilitating tumor growth, highlighting potential therapeutic targets for improving the treatment outcomes of CAC.

Children with idiopathic short stature have significantly different gut microbiota than their normal height siblings: a case-control study.

Objectives: To investigate the role of gut microbiota (GM) in pathogenesis of idiopathic short stature (ISS) by comparing GM of ISS children to their normal-height siblings. Methods: This case-control study, conducted at the Schneider Children's Medical Center's Institute for Endocrinology and Diabetes between 4/2018-11/2020, involved 30 pairs of healthy pre-pubertal siblings aged 3-10 years, each comprising one sibling with ISS and one with normal height. Outcome measures from fecal analysis of both siblings included GM composition analyzed by 16S rRNA sequencing, fecal metabolomics, and monitoring the growth of germ-free (GF) mice after fecal transplantation. Results: Fecal analysis of ISS children identified higher predicted levels of genes encoding enzymes for pyrimidine, purine, flavin, coenzyme B, and thiamine biosynthesis, lower levels of several amino acids, and a significantly higher prevalence of the phylum Euryarchaeota compared to their normal-height siblings (p<0.001). ISS children with higher levels of Methanobrevibacter, the dominant species in the archaeal gut community, were significantly shorter in stature than those with lower levels (p=0.022). Mice receiving fecal transplants from ISS children did not experience stunted growth, probably due to the eradication of Methanobrevibacter caused by exposure to oxygen during fecal collection. Discussion: Our findings suggest that different characteristics in the GM may explain variations in linear growth. The varying levels of Methanobrevibacter demonstrated within the ISS group reflect the multifactorial nature of ISS and the potential ability of the GM to partially explain growth variations. The targeting of specific microbiota could provide personalized therapies to improve growth in children with ISS.

Gut microbiome-metabolome interactions predict host condition.

BACKGROUND: The effect of microbes on their human host is often mediated through changes in metabolite concentrations. As such, multiple tools have been proposed to predict metabolite concentrations from microbial taxa frequencies. Such tools typically fail to capture the dependence of the microbiome-metabolite relation on the environment. RESULTS: We propose to treat the microbiome-metabolome relation as the equilibrium of a complex interaction and to relate the host condition to a latent representation of the interaction between the log concentration of the metabolome and the log frequencies of the microbiome. We develop LOCATE (Latent variables Of miCrobiome And meTabolites rElations), a machine learning tool to predict the metabolite concentration from the microbiome composition and produce a latent representation of the interaction. This representation is then used to predict the host condition. LOCATE's accuracy in predicting the metabolome is higher than all current predictors. The metabolite concentration prediction accuracy significantly decreases cross datasets, and cross conditions, especially in 16S data. LOCATE's latent representation predicts the host condition better than either the microbiome or the metabolome. This representation is strongly correlated with host demographics. A significant improvement in accuracy (0.793 vs. 0.724 average accuracy) is obtained even with a small number of metabolite samples ([Formula: see text]). CONCLUSION: These results suggest that a latent representation of the microbiome-metabolome interaction leads to a better association with the host condition than any of the two separated or the simple combination of the two. Video Abstract.

Fecal microbiota transplantation in capsules for the treatment of steroid refractory and steroid dependent acute graft vs. host disease: a pilot study.

Acute graft-versus-host disease (aGvHD) is a serious complication of allogeneic hematopoietic stem-cell transplantation with limited treatment options. The gut microbiome plays a critical role in aGvHD pathogenesis. Fecal microbiota transplantation (FMT) has emerged as a potential therapeutic approach to restore gut microbial diversity. In this prospective pilot study, 21 patients with steroid-resistant or steroid-dependent lower gastrointestinal aGvHD received FMT in capsule form. At 28 days after the first FMT, the overall response rate was 52.4%, with 23.8% complete and 28.6% partial responses. However, sustained responses were infrequent, with only one patient remaining aGvHD-free long-term. FMT was generally well-tolerated. Microbiome analysis revealed dysbiosis in pre-FMT patient stool samples, with distinct microbial characteristics compared to donors. Following FMT, there was an increase in beneficial Clostridiales and a decrease in pathogenic Enterobacteriales. These findings highlight the potential of FMT as a treatment option for steroid-resistant aGvHD. Trial registration number NCT #03214289.

Salmonella manipulates the host to drive pathogenicity via induction of interleukin 1ß.

Acute gastrointestinal infection with intracellular pathogens like Salmonella Typhimurium triggers the release of the proinflammatory cytokine interleukin 1ß (IL-1ß). However, the role of IL-1ß in intestinal defense against Salmonella remains unclear. Here, we show that IL-1ß production is detrimental during Salmonella infection. Mice lacking IL-1ß (IL-1ß -/-) failed to recruit neutrophils to the gut during infection, which reduced tissue damage and prevented depletion of short-chain fatty acid (SCFA)-producing commensals. Changes in epithelial cell metabolism that typically support pathogen expansion, such as switching energy production from fatty acid oxidation to fermentation, were absent in infected IL-1ß -/- mice which inhibited Salmonella expansion. Additionally, we found that IL-1ß induces expression of complement anaphylatoxins and suppresses the complement-inactivator carboxypeptidase N (CPN1). Disrupting this process via IL-1ß loss prevented mortality in Salmonella-infected IL-1ß -/- mice. Finally, we found that IL-1ß expression correlates with expression of the complement receptor in patients suffering from sepsis, but not uninfected patients and healthy individuals. Thus, Salmonella exploits IL-1ß signaling to outcompete commensal microbes and establish gut colonization. Moreover, our findings identify the intersection of IL-1ß signaling and the complement system as key host factors involved in controlling mortality during invasive Salmonellosis.

Signs of aging in midlife: physical function and sex differences in microbiota.

Microbiota composition has been linked to physical activity, health measures, and biological age, but a shared profile has yet to be shown. The aim of this study was to examine the associations between microbiota composition and measures of function, such as a composite measure of physical capacity, and biological age in midlife, prior to onset of age-related diseases. Seventy healthy midlife individuals (age 44.58 ± 0.18) were examined cross-sectionally, and their gut-microbiota profile was characterized from stool samples using 16SrRNA gene sequencing. Biological age was measured using the Klemera-Doubal method and a composition of blood and physiological biomarkers. Physical capacity was calculated based on sex-standardized functional tests. We demonstrate that the women had significantly richer microbiota, p = 0.025; however, microbiota diversity was not linked with chronological age, biological age, or physical capacity for either women or men. Men had slightly greater ß-diversity; however, ß-diversity was positively associated with biological age and with physical capacity for women only (p = 0.01 and p = 0.04; respectively). For women, an increase in abundance of Roseburia faecis and Collinsella aerofaciens, as well as genus Ruminococcus and Dorea, was significantly associated with higher biological age and lower physical capacity; an increase in abundance of Akkermansia muciniphila and genera Bacteroides and Alistipes was associated with younger biological age and increased physical capacity. Differentially abundant taxa were also associated with non-communicable diseases. These findings suggest that microbiota composition is a potential mechanism linking physical capacity and health status; personalized probiotics may serve as a new means to support health-promoting interventions in midlife. Investigating additional factors underlying this link may facilitate the development of a more accurate method to estimate the rate of aging.

Giardiasis and diarrhea in dogs: Does the microbiome matter?

BACKGROUND: Giardia duodenalis (Gd) causes intestinal parasitosis. The involvement of the intestinal microbiome in determining the infection's clinical phenotype is unknown. OBJECTIVE: Investigate the fecal microbiome features in dogs with giardiasis. ANIMALS AND METHODS: Cross-sectional study, including fecal samples of kenneled dogs with Gd diagnosed by fecal Giardia antigen dot ELISA. The fecal microbial compositional characteristics and dysbiosis index (DI) were compared between diarrheic and nondiarrheic dogs. RESULTS: Fecal samples of 38 Gd-infected dogs (diarrheic, 21; nondiarrheic, 17) were included. No differences were found in Faith's phylogenic diversity and beta diversity (weighted UniFrac distances) and in specific taxa abundances at the phylum, genus, and species levels, as well as in alpha and beta diversities between diarrheic and nondiarrheic dogs, and also when divided by sex or age. Among diarrheic dogs, alpha diversity was higher in males than in females (pairwise Kruskal-Wallis, q = 0.01). Among males, fecal abundances of the genus Clostridium (W = 19) and Clostridium spiroforme species (W = 33) were higher in diarrheic compared to nondiarrheic dogs. In diarrheic dog fecal samples, Proteobacteria were more prevalent (W = 1), whereas Verrucomicrobia were less prevalent in dogs <1 year of age than in older dogs. The fecal sample DI of 19 diarrheic and 19 nondiarrheic dogs was similar (median, -0.2; range, -4.3 to 4.5 and median, -1.0; range, -4.3 to 5.8, respectively). CONCLUSIONS: The fecal microbial composition of symptomatic and asymptomatic dogs with giardiasis is similar. Based on fecal DI, giardiasis is not characterized by prominent dysbiosis. Other host and parasite characteristics might determine the severity of giardiasis in dogs.

The maternal gut microbiome in pregnancy: implications for the developing immune system.

The gut microbiome has important roles in host metabolism and immunity, and microbial dysbiosis affects human physiology and health. Maternal immunity and microbial metabolites during pregnancy, microbial transfer during birth, and transfer of immune factors, microorganisms and metabolites via breastfeeding provide critical sources of early-life microbial and immune training, with important consequences for human health. Only a few studies have directly examined the interactions between the gut microbiome and the immune system during pregnancy, and the subsequent effect on offspring development. In this Review, we aim to describe how the maternal microbiome shapes overall pregnancy-associated maternal, fetal and early neonatal immune systems, focusing on the existing evidence and highlighting current gaps to promote further research.

Bacteroides is increased in an autism cohort and induces autism-relevant behavioral changes in mice in a sex-dependent manner.

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which is defined by decreased social communication and the presence of repetitive or stereotypic behaviors. Recent evidence has suggested that the gut-brain axis may be important in neurodevelopment in general and may play a role in ASD in particular. Here, we present a study of the gut microbiome in 96 individuals diagnosed with ASD in Israel, compared to 42 neurotypical individuals. We determined differences in alpha and beta diversity in the microbiome of individuals with ASD and demonstrated that the phylum Bacteroidetes and genus Bacteroides were the most significantly over-represented in individuals with ASD. To understand the possible functional significance of these changes, we treated newborn mice with Bacteroides fragilis at birth. B. fragilis-treated male mice displayed social behavior dysfunction, increased repetitive behaviors, and gene expression dysregulation in the prefrontal cortex, while female mice did not display behavioral deficits. These findings suggest that overabundance of Bacteroides, particularly in early life, may have functional consequences for individuals with ASD.

Successful weight regain attenuation by autologous fecal microbiota transplantation is associated with non-core gut microbiota changes during weight loss; randomized controlled trial.

We previously reported that autologous-fecal-microbiota-transplantation (aFMT), following 6 m of lifestyle intervention, attenuated subsequent weight regain and insulin rebound for participants consuming a high-polyphenol green-Mediterranean diet. Here, we explored whether specific changes in the core (abundant) vs. non-core (low-abundance) gut microbiome taxa fractions during the weight-loss phase (0-6 m) were differentially associated with weight maintenance following aFMT. Eighty-two abdominally obese/dyslipidemic participants (age = 52 years; 6 m weightloss = -8.3 kg) who provided fecal samples (0 m, 6 m) were included. Frozen 6 m's fecal samples were processed into 1 g, opaque and odorless aFMT capsules. Participants were randomly assigned to receive 100 capsules containing their own fecal microbiota or placebo over 8 m-14 m in ten administrations (adherence rate > 90%). Gut microbiome composition was evaluated using shotgun metagenomic sequencing. Non-core taxa were defined as ≤ 66% prevalence across participants. Overall, 450 species were analyzed. At baseline, 13.3% were classified as core, and Firmicutes presented the highest core proportion by phylum. During 6 m weight-loss phase, abundance of non-core species changed more than core species (P < .0001). Subject-specific changes in core and non-core taxa fractions were strongly correlated (Jaccard Index; r = 0.54; P < .001). Following aFMT treatment, only participants with a low 6 m change in core taxa, and a high change in non-core taxa, avoided 8-14 m weight regain (aFMT = -0.58 ± 2.4 kg, corresponding placebo group = 3.18 ± 3.5 kg; P = .02). In a linear regression model, low core/high non-core 6 m change was the only combination that was significantly associated with attenuated 8-14 m weight regain (P = .038; P = .002 for taxa patterns/treatment intervention interaction). High change in non-core, low-abundance taxa during weight-loss might mediate aFMT treatment success for weight loss maintenance.ClinicalTrials.gov: NCT03020186.

Metabolic inputs in the probiotic bacterium Lacticaseibacillus rhamnosus contribute to cell-wall remodeling and increased fitness.

Lacticaseibacillus rhamnosus GG (LGG) is a Gram-positive beneficial bacterium that resides in the human intestinal tract and belongs to the family of lactic acid bacteria (LAB). This bacterium is a widely used probiotic and was suggested to provide numerous benefits for human health. However, as in most LAB strains, the molecular mechanisms that mediate the competitiveness of probiotics under different diets remain unknown. Fermentation is a fundamental process in LAB, allowing the oxidation of simple carbohydrates (e.g., glucose, mannose) for energy production under oxygen limitation, as in the human gut. Our results indicate that fermentation reshapes the metabolome, volatilome, and proteome architecture of LGG. Furthermore, fermentation alters cell envelope remodeling and peptidoglycan biosynthesis, which leads to altered cell wall thickness, aggregation properties, and cell wall composition. In addition, fermentable sugars induced the secretion of known and novel metabolites and proteins targeting the enteric pathogens Enterococcus faecalis and Salmonella enterica Serovar Typhimurium. Overall, our results link simple carbohydrates with cell wall remodeling, aggregation to host tissues, and biofilm formation in probiotic strains and connect them with the production of broad-spectrum antimicrobial effectors.

Celiac-the lone horse? An autoimmune condition without signals of microbiota dysbiosis.

Accumulating evidence supports the role of microbiota in autoimmune processes, but research regarding the role of the gut microbiota in celiac disease (CD) is still emerging, and a consistent CD-associated dysbiosis pattern has not yet been defined. Here, we characterized the microbiota of children newly diagnosed with CD, with their unaffected family members as a healthy control group to reduce confounding factors including genetic background, hygiene, dietary habits, and environment, and followed children with CD over 1 year of dietary intervention (exclusion of gluten) to understand if the microbiota is associated with CD and its mediation. We did not find differences in the microbiota of siblings with and without CD, despite a wealth of evidence in the literature supporting CD-specific microbiota. CD is common among first-degree relatives, so this could suggest that unaffected family members in this study may be living in a pre-CD state, currently below clinical detection. Interestingly, despite the effectiveness of diet in CD control, we did not observe diet-mediated microbiota changes, except for short-term increase in Akkermansia muciniphila. This lack of effect could suggest a very strong CD microbial signature even when controlled or could be a technical shortcoming. Expanded future studies with both related and unrelated controls and diet interventions in both the CD and control arms can provide further context to our findings. IMPORTANCE The microbiota is the community of microbes that live in and on us. These microbes are essential to our health and everyday function. Disruption of the community is associated with diseases ranging from metabolic syndrome to autoimmune diseases to mental disorders. In the case of celiac disease (CD), research remains inconclusive regarding implications of the microbiota in etiology. Here, we compared microbiota of children with CD to those of their unaffected family members and found very few differences in microbiota profiles. We next examined how gluten elimination in CD patients affects the microbiota. Surprisingly, despite diet adherence, microbiota shifts were minimal, with only a short-term increase in Akkermansia muciniphila. Previous studies suggest that family members of CD patients may be living in a pre-CD state, which could explain their microbial similarity. A larger study with unrelated controls and increased microbiota monitoring during diet intervention should give our findings more perspective.

Recipient-independent, high-accuracy FMT-response prediction and optimization in mice and humans.

BACKGROUND: Some microbiota compositions are associated with negative outcomes, including among others, obesity, allergies, and the failure to respond to treatment. Microbiota manipulation or supplementation can restore a community associated with a healthy condition. Such interventions are typically probiotics or fecal microbiota transplantation (FMT). FMT donor selection is currently based on donor phenotype, rather than the anticipated microbiota composition in the recipient and associated health benefits. However, the donor and post-transplant recipient conditions differ drastically. We here propose an algorithm to identify ideal donors and predict the expected outcome of FMT based on donor microbiome alone. We also demonstrate how to optimize FMT for different required outcomes. RESULTS: We show, using multiple microbiome properties, that donor and post-transplant recipient microbiota differ widely and propose a tool to predict the recipient post-transplant condition (engraftment success and clinical outcome), using only the donors' microbiome and, when available, demographics for transplantations from humans to either mice or other humans (with or without antibiotic pre-treatment). We validated the predictor using a de novo FMT experiment highlighting the possibility of choosing transplants that optimize an array of required goals. We then extend the method to characterize a best-planned transplant (bacterial cocktail) by combining the predictor and a generative genetic algorithm (GA). We further show that a limited number of taxa is enough for an FMT to produce a desired microbiome or phenotype. CONCLUSIONS: Off-the-shelf FMT requires recipient-independent optimized FMT selection. Such a transplant can be from an optimal donor or from a cultured set of microbes. We have here shown the feasibility of both types of manipulations in mouse and human recipients. Video Abstract.

"Residential greenness, gestational diabetes mellitus (GDM) and microbiome diversity during pregnancy".

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with reduced gut microbiota richness that was also reported to differ significantly between those living in rural compared to urban environments. Therefore, our aim was to examine the associations between greenness and maternal blood glucose levels and GDM, with microbiome diversity as a possible mediator in these associations. METHODS: Pregnant women were recruited between January 2016 and October 2017. Residential greenness was evaluated as mean Normalized Difference Vegetation Index (NDVI) within 100, 300 and 500 m buffers surrounding each maternal residential address. Maternal glucose levels were measured at 24-28 weeks of gestation and GDM was diagnosed. We estimated the associations between greenness and glucose levels and GDM using generalized linear models, adjusting for socioeconomic status and season at last menstrual period. Using causal mediation analysis, the mediation effects of four different indices of microbiome alpha diversity in first trimester stool and saliva samples were assessed. RESULTS: Of 269 pregnant women, 27 participants (10.04%) were diagnosed with GDM. Although not statistically significant, adjusted exposure to medium tertile levels of mean NDVI at 300 m buffer had lower odds of GDM (OR = 0.45, 95% CI: 0.16, 1.26, p = 0.13) and decreased change in mean glucose levels (ß = -6.28, 95% CI: 14.91, 2.24, p = 0.15) compared to the lowest tertile levels of mean NDVI. Mixed results were observed at 100 and 500 m buffers, and when comparing highest tertile levels to lowest. No mediation effect of first trimester microbiome on the association between residential greenness and GDM was observed, and a small, possibly incidental, mediation effect on glucose levels was observed. CONCLUSION: Our study suggests possible associations between residential greenness and glucose intolerance and risk of GDM, though without sufficient evidence. Microbiome in the first trimester, while involved in GDM etiology, is not a mediator in these associations. Future studies in larger populations should further examine these associations.

The intersection of undernutrition, microbiome, and child development in the first years of life.

Undernutrition affects about one out of five children worldwide. It is associated with impaired growth, neurodevelopment deficits, and increased infectious morbidity and mortality. Undernutrition, however, cannot be solely attributed to a lack of food or nutrient deficiency but rather results from a complex mix of biological and environmental factors. Recent research has shown that the gut microbiome is intimately involved in the metabolism of dietary components, in growth, in the training of the immune system, and in healthy development. In this review, we look at these features in the first three years of life, which is a critical window for both microbiome establishment and maturation and child development. We also discuss the potential of the microbiome in undernutrition interventions, which could increase efficacy and improve child health outcomes.

Multi-level analysis of the gut-brain axis shows autism spectrum disorder-associated molecular and microbial profiles.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.

Ordering taxa in image convolution networks improves microbiome-based machine learning accuracy.

The human gut microbiome is associated with a large number of disease etiologies. As such, it is a natural candidate for machine-learning-based biomarker development for multiple diseases and conditions. The microbiome is often analyzed using 16S rRNA gene sequencing or shotgun metagenomics. However, several properties of microbial sequence-based studies hinder machine learning (ML), including non-uniform representation, a small number of samples compared with the dimension of each sample, and sparsity of the data, with the majority of taxa present in a small subset of samples. We show here using a graph representation that the cladogram structure is as informative as the taxa frequency. We then suggest a novel method to combine information from different taxa and improve data representation for ML using microbial taxonomy. iMic (image microbiome) translates the microbiome to images through an iterative ordering scheme, and applies convolutional neural networks to the resulting image. We show that iMic has a higher precision in static microbiome gene sequence-based ML than state-of-the-art methods. iMic also facilitates the interpretation of the classifiers through an explainable artificial intelligence (AI) algorithm to iMic to detect taxa relevant to each condition. iMic is then extended to dynamic microbiome samples by translating them to movies.