Microbiome differential abundance methods produce different results across 38 datasets

Alterations in mucosa branched N-glycans lead to dysbiosis and downregulation of ILC3: a key driver of intestinal inflammation

The perturbation of the symbiotic relationship between microbes and intestinal immune system contributes to gut inflammation and Inflammatory Bowel Disease (IBD) development. The host mucosa glycans (glycocalyx) creates a major biological interface between gut microorganisms and host immunity that remains ill-defined. Glycans are essential players in IBD immunopathogenesis, even years before disease onset. However, how changes in mucosa glycosylation shape microbiome and how this impact gut immune response and inflammation remains to be clarified. Here, we revealed that alterations in the expression of complex branched N-glycans at gut mucosa surface, modeled in glycoengineered mice, resulted in dysbiosis, with a deficiency in Firmicutes bacteria. Concomitantly, this mucosa N-glycan switch was associated with a downregulation of type 3 innate lymphoid cells (ILC3)-mediated immune response, leading to the transition of ILC3 toward an ILC1 proinflammatory phenotype and increased TNFα production. In addition, we demonstrated that the mucosa glycosylation remodeling through prophylactic supplementation with glycans at steady state was able to restore microbial-derived short-chain fatty acids and microbial sensing (by NOD2 expression) alongside the rescue of the expression of ILC3 module, suppressing intestinal inflammation and controlling disease onset. In a complementary approach, we further showed that IBD patients, often displaying dysbiosis, exhibited a tendency of decreased MGAT5 expression at epithelial cells that was accompanied by reduced ILC3 expression in gut mucosa. Altogether, these results unlock the effects of alterations in mucosa glycome composition in the regulation of the bidirectional crosstalk between microbiota and gut immune response, revealing host branched N-glycans/microbiota/ILC3 axis as an essential pathway in gut homeostasis and in preventing health to intestinal inflammation transition.

Alterations in Gut Microbiome-Host Relationships After Immune Perturbation in Patients With Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Gut microbial symbionts have been shown to influence the development of autoimmunity in multiple sclerosis (MS). Emerging research points to an important relationship between the microbial-IgA interface and MS pathophysiology. IgA-secreting B cells are observed in the MS brain, and shifts in gut bacteria-IgA binding have been described in some patients with MS. However, the relationships between the gut microbiome and the host immune response, particularly regarding B-cell-depleting immunomodulation, remain underexplored. This study aimed to evaluate the composition of the gut microbiome in patients with newly diagnosed MS at baseline and after B-cell depletion, using long-read sequencing for enhanced taxonomic resolution. We further aimed to investigate the host/microbiome interface by evaluating microbe/immunoglobulin A relationships.

METHODS

We collected stool samples from 43 patients with newly diagnosed, untreated MS and 42 matched healthy controls. Nineteen patients with MS initiated anti-CD20 monoclonal antibody treatment and donated additional stool samples after 6 months of treatment. We evaluated the host-microbial interface using bacterial flow cytometry and long-read 16S rRNA gene amplicon sequencing. We used Immune Coating Scores to compare the proportions of bacteria identified in the IgA-coated vs IgA-uncoated bacterial fractions.

RESULTS

Patients with untreated, newly diagnosed MS showed significant reductions in IgA-bound fecal microbiota compared with controls. Using multiple linear regression models adjusted for potential confounders, we observed significant (p < 0.05) changes in the abundance and prevalence of various strain-level gut bacteria amplicon sequence variants (ASVs) within both total and IgA-coated bacterial fractions. Some changes (e.g., decreased relative abundance of a Faecalibacterium prausnitzii variant in MS) were consistent with previous reports, while others (e.g., increased relative abundance and prevalence of Monoglobus pectinyliticus in MS) were novel. Immune Coating Scores identified subsets of organisms for which normal IgA-coating patterns were disrupted at the onset of MS, as well as those (particularly Akkermansia muciniphila) whose IgA-coating became more aligned with controls after therapy.

DISCUSSION

This analysis of gut microbial ASVs reveals shifts in taxonomic strains induced by immune modulation in MS.

Very preterm gut microbiota development from the first week of life to 3.5 years of age: a prospective longitudinal multicenter study

This study analyzed the longitudinal evolution of intestinal microbiota in very preterm neonates (PN) during and after their hospitalization. The bacterial 16S rRNA gene sequencing approach was applied for the analysis of fecal samples (n = 1,307) from 596 PN. Samples were collected at one week after birth, at one month, at the neonatal intensive care unit discharge, and at 3.5 years of age. Over time, the intestinal microbiota of the infants matured progressively, with increasing alpha diversity and decreasing beta diversity. Based on a Dirichlet multinomial mixture clustering approach (DMM), during hospitalization, infants progressed among ten different clusters. At 3.5 years of age, only three clusters were identified. The influence of the gestational age, the neonatal antibiotic administration, and the maternal antibiotic therapy during delivery on the gut microbiota varied over time and depended on the sampling period. Preconceptional maternal body mass index (BMI) was associated with the gut microbiota of infants during the hospitalization period and at 3.5 years of age. Infants with a lower gestational age or those born by Cesarean section shifted between clusters more frequently. Using PICRUSt2, the inferred metabolic pathways revealed a change in the functional capacities of the intestinal microbiota over time. We found that preconceptional maternal BMI was the only consistent perinatal factor influencing the development of the gut microbiota over time. After hospital discharge, infants exhibited a transition toward a microbiota community similar to that of adults by 3.5 years of age, in accordance with the functional metabolic pathways of the gut microbiota.IMPORTANCEThis study is among the very few reports analyzing the gut microbiota development in very preterm infants over time in a large, multicenter population of 596 children from a well-described nationwide birth cohort, with a follow-up until the age of 3.5 years. The maturation of the intestinal microbiota was confirmed to occur over time, with increased alpha diversity and decreased beta diversity. Specifically, 13 microbiota clusters were identified during the hospitalization period, while and only three clusters were observed at 3.5 years. Infants born prematurely or via Cesarean section exhibited a less stable microbiota, frequently shifting clusters. A number of perinatal factors were identified as influencing the development of the microbiota. Among these, the preconceptional maternal BMI emerged as the only consistent factor up to 3.5 years. The metabolic pathways of the microbiota evolved over time, in accordance with the maturation of the gut microbiota.

Discovery of viruses and bacteria associated with swine respiratory disease on farms at a nationwide scale in China using metatranscriptomic and metagenomic sequencing

Respiratory disease (RD) is a worldwide leading threat to the pig industry, but there is still limited understanding of the pathogens associated with swine RD. In this study, we conducted a nationwide genomic surveillance on identifying viruses, bacteria, and antimicrobial resistance genes (ARGs) from the lungs of pigs with RD in China. By performing metatranscriptomic sequencing combined with metagenomic sequencing, we identified 21 viral species belonging to 12 viral families. Among them, porcine reproductive and respiratory syndrome virus, influenza A virus, herpes virus, adenovirus, and parvovirus were commonly identified. However, emerging viruses, such as Getah virus and porcine respiratory coronaviruses, were also characterized. Apart from viruses, a total of 164 bacterial species were identified, with Streptococcus suis, Mycoplasma hyorhinis, Mycoplasma hyopneumoniae, Glaesserella parasuis, and Pasteurella multocida being frequently detected in high abundances. Notably, Escherichia coli, Enterococcus faecalis, Staphylococcus aureus, and Klebsiella pneumoniae were also highly detected. Our further analysis revealed a complex interaction between the identified pathogens in swine RD. We also conducted retrospectively analyses to demonstrate the prevalent viral genotypes or bacterial serotypes associated with swine RD in China. Finally, we identified 48 ARGs, which conferred resistance to 13 predicted antimicrobial classes, and many of these ARGs were significantly associated with a substantial number of mobile genetic elements, including transposons (e.g., tnpAIS1, tnpA1353, int3, and ISCau1) and plasmids (e.g., Col(BS512), Col(YC)]. These findings will contribute to further understanding the etiology, epidemiology, and microbial interactions in swine RD, and may also shed a light on the development of effective vaccines.IMPORTANCEIn this study, we identified viruses and bacteria from the lungs of pigs with RD in China at a nationwide farm scale by performing metatranscriptomic sequencing combined with metagenomic sequencing. We also demonstrated the complex interactions between different viral and/or bacterial species in swine RD. Our work provides a comprehensive knowledge about the etiology, epidemiology, and microbial interactions in swine RD and data reference for the research and development of effective vaccines against the disease.

Challenging the impact of consortium diversity on bioaugmentation efficiency and native bacterial community structure in an acutely PAH-contaminated soil

Polycyclic aromatic hydrocarbons (PAHs) are priority pollutants. We studied the effect of bioaugmentation using three allochthonous bacterial consortia with increasing diversity: SC AMBk, SC1, and SC4, on the structure and functionality of an artificially and acutely PAH-contaminated soil microbiome. The PAH supplementation increased substrate availability, allowing the inocula to efficiently degrade the supplemented PAHs after 15 days of incubation, become temporarily established, and modify the number of total interactions with soil residents. Sphingobium and Burkholderia, both members of the inoculants, were the major contributors to functional KOs (KEGG orthologs) linked to degradation and were differentially abundant genera in inoculated microcosms, indicating their competitiveness in the soil. Hence, bioaugmentation efficiency relied on them, while further degradation could be carried out by native microorganisms. This is one of the first studies to apply three inocula, designed from naturally occurring bacteria, and to study their effect on the soil's native community through ANCOM-BC. We revealed that when a resource that can be used by the inoculant is added to the soil, a high-diversity inoculant is not necessary to interact with the native community and establish itself. This finding is crucial for the design of microbiome engineering in bioremediation processes.

Ultrasound-assisted metabolite detection in different extraction processes of Bletilla striata and bitter metabolite detection

Bletilla striata, a medicinal orchidaceous plant, is recognized for its significant pharmacological value. However, the lack of comparative metabolomic data across different extraction methods for analyzing its bioactive components has significantly undervalued the application potential of B. striata in the traditional Chinese medicine market. Using six ultrasound-assisted extraction methods and UPLC-MS/MS, this study identified 1,945 metabolites in B. striata extracts. The dominant categories were lipids (51.35%), flavonoids (18.00%), and phenolic acids (12.51%). KEGG analysis revealed alterations in flavonoids and isoflavonoids biosynthesis pathways. Thirteen bitter metabolites, including cinnamic acid, were identified in B. striata tubers, underscoring their potential pharmacological applications, such as anti-inflammatory, antioxidant and antibacterial activities. Optimizing different extraction methods can better preserve the bioactive components of B. striata extracts, thereby enhancing its potential applications in the food and pharmaceutical industries.

Cold atmospheric plasma treatment induces oxidative stress and alters microbial community profile in the leaves of sweet basil (Ocimum basilicum var. Kiera) plant

The oxidative species generated by cold atmospheric plasma (CAP) treatment can impact the plant stress response system. We hypothesized that this response is not limited to the site of CAP application and it is transmitted through the plant. The resulting stress response can influence the plant microbiome on the intact plant. These hypotheses were tested by the application of CAP to live sweet basil (Ocimum basilicum var. Kiera). A single upper leaf of the plant underwent a 60 s CAP treatment at three different wattage intensity levels. Reactive oxygen species (ROS) generation in directly treated leaves and leaves in the vicinity of the treatment site (i.e., one, two, or three nodes away) was measured using the fluorescein degradation assay (ex/em: 485/525). Leaves directly exposed to CAP showed a marked increase in ROS production. Interestingly, basil leaves not directly treated by CAP also showed a significant (p < 0.05) increase in ROS generation compared to untreated control, extending to the two nearest nodes from the treatment site in all plants tested. The leaf microbiomes were evaluated using 16S rRNA gene sequencing. CAP appeared to drive restructuring of the leaf microbiota profiles, despite maintaining a similar α-diversity. CAP treatment intensity led to significant differences (p < 0.05) in the relative abundances of a variety of dominant bacterial families (e.g., Psuedomonadaceae and Streptomycetaceae) and phyla, and the effects on certain taxa were dependent on leaf distance from the treatment site. CAP's ability to restructure plant microbiota may have applications to improve produce microbial safety and shelf-life. PRACTICAL APPLICATION: Cold atmospheric plasma induces a stress response in a living plant beyond the site of application. This response includes an increase in the production of reactive oxygen species that can trigger pathways to enhance the production of phytochemicals. CAP treatment also alters the microbial community profile, possibly through plant stress response. Results from this study can be useful in developing CAP treatment of intact plant for improved growth, production of health-benefiting phytochemicals, and managing its microbiota.

Maternal pre- and postnatal stress and maternal and infant gut microbiota features

BACKGROUND

Maternal stress can have short and long term adverse (mental) health effects for the mother and her child. Previous evidence suggests that the gut microbiota may be a potential mediator and moderator for the effects of stress via various pathways. This study explored the maternal microbiota trajectory during pregnancy as well as the association between pre- and postnatal maternal stress and features of the maternal and infant gut microbiota during and after pregnancy. In line with previous research, we hypothesized that maternal stress would be positively related to maternal and infant microbiota volatility and that infants of highly stressed mothers would show a relative increase in Proteobacteria and a relative decrease in Bifidobacterium.

METHODS

We collected maternal stool samples at 18 and 32 weeks of pregnancy and 8 months postpartum. Infant stools samples were obtained at 2, 6 and 12 weeks and 8 months postpartum. All samples were analyzed using shotgun metagenome sequencing. We also collected several measures of maternal stress (self-reported depression, anxiety, and stress, and hair cortisol and cortisone), most at the same time points as the microbiota samples.

RESULTS

Our data indicated that the maternal microbiota does not undergo drastic changes from the second to the third trimester of pregnancy but that the postpartum microbiota differs significantly from the prenatal microbiota. Furthermore, we identified associations between several stress measures and maternal and infant gut microbiota features at different time points including positive and negative associations with alpha diversity, beta diversity and individual microbial phyla and species relative abundances. Also, the maternal stress composite score, the perceived stress score and the log-ratio of hair cortisol and cortisone were all positively associated with infant microbiota volatility.

CONCLUSION

Our study provides evidence that maternal prenatal and postnatal stress is related to both the maternal and the infant microbiota. Collectively, this and previous studies indicate that maternal stress does not uniformly associate with most gut microbial features. Instead, the associations are highly time point specific. Regarding infant microbiota volatility, we have consistently found a positive association between stress and infant microbiota volatility. This warrants future research investigating this link in more depth.

A Pilot Study of the Gut Microbiota in Spine Fusion Surgery Patients

Background

The microbiome has been identified as a contributor to bone quality. As skeletal health is critical to success of orthopedic surgery, the gut microbiome may be a modifiable factor associated with postoperative outcomes. For spine fusion surgery in particular, de novo bone formation and sufficient bone mineral density are essential for successful outcomes. Given the prevalence and complexity of these procedures, the identification of novel factors that may be related to operative success is important.

Questions/purposes

We sought to investigate how the composition of the microbiota related to bone health in a focused spinal fusion surgery cohort.

Methods

We investigated the composition of the microbiome in a cohort of 31 patients prior to spinal fusion surgery, as well as changes in the microbiome over 6 weeks postoperatively. Preoperative areal bone mineral density (BMD) was measured by dual-energy X-ray absorptiometry.

Results

Composition of gut microbiota differed among spinal fusion patients with low bone mass (T-score ≤ -1.0) and those with normal BMD (P = .03). There was no significant change in composition of the gut microbiota between preoperative evaluation and 6 weeks postoperatively.

Conclusions

Our findings in this small sample suggest there may be a relationship between BMD and composition of the gut microbiome in patients who undergo spinal fusion surgery. Further work is needed to investigate these relationships as well as potential interventions to foster a favorable microbial composition in spinal fusion surgery patients.

Uropygial gland microbiota of nearctic-neotropical migrants vary with season and migration distance

Symbiotic microbiota are important drivers of host behaviour, health, and fitness. While most studies focus on humans, model organisms, and domestic or economically important species, research investigating the role of host microbiota in wild populations is rapidly accumulating. Most studies focus on the gut microbiota; however, skin and other glandular microbiota also play an important role in shaping traits that may impact host fitness. The uropygial gland is an important source of chemical cues and harbours diverse microbes that could mediate chemical communication in birds, so determining the factors most important in shaping host microbiota should improve our understanding of microbially-mediated chemical communication. Hypothesizing that temporal, geographic, and taxonomic effects influence host microbiota, we evaluated the effects of season, migration distance, and taxonomy on the uropygial gland microbiota of 18 passerine species from 11 families. By sampling 473 birds at a single stopover location during spring and fall migration and using 16S rRNA sequencing, we demonstrate that season, followed by migration distance, had the strongest influence on uropygial gland microbial community composition. While statistically significant, taxonomic family and species had only weak effects on gland microbiota. Given that temporal effects on gland microbiota were nearly ubiquitous among the species we tested, determining the consequences of and mechanisms driving this seasonal variation are important next steps.

Spinach seed microbiome characteristics linked to suppressiveness against Globisporangium ultimum damping-off

Recently we demonstrated that the seed microbiome of certain spinach (Spinacia oleracea) seed lots can confer disease suppression against Globisporangium ultimum damping-off (previously known as Pythium ultimum). We hypothesized that differences in the microbial community composition of spinach seed lots correlate with the levels of damping-off suppressiveness of each seed lot. Here, we show that a large proportion of variance in seed-associated bacterial (16S) and fungal (Internal Transcribed Spacer 1) amplicon sequences was explained by seed lot identity, while 9.8% of bacterial and 7.1% of fungal community variance correlated with disease suppression. More specifically, a higher relative abundance of basidiomycetous dimorphic yeasts such as Vishniacozyma, Filobasidium, and Papiliotrema and of the bacterial genus Massilia was a key feature of suppressive seed microbiomes. We suggest that the abundance of these genera is indicative of seed lot suppressive potential. Seed processing and treatment can become more targeted with indicator taxa being used to evaluate the presence of beneficial seed-associated microbial functions. This process, in turn, could contribute to the sustainable management of seedling diseases. Finally, this study highlights the ubiquity of yeasts in spinach seed microbiota and their potential beneficial roles for seed health.

Compositional data analysis enables statistical rigor in comparative glycomics

Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious "decreases" of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.

MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis

Background/Objectives: The integration of microbiome and metabolome data could unveil profound insights into biological processes. However, widely used multi-omic data analyses often employ a stepwise mining approach, failing to harness the full potential of multi-omic datasets and leading to reduced detection accuracy. Synergistic analysis incorporating microbiome/metabolome data are essential for deeper understanding. Method: This study introduces a Coupled Matrix and Tensor Factorization (CMTF) framework for the joint analysis of microbiome and metabolome data, overcoming these limitations. Two CMTF frameworks were developed to factorize microbial taxa, functional pathways, and metabolites into latent factors, facilitating dimension reduction and biomarker identification. Validation was conducted using three diverse microbiome/metabolome datasets, including built environments and human gut samples from inflammatory bowel disease (IBD) and COVID-19 studies. Results: Our results revealed biologically meaningful biomarkers, such as Bacteroides vulgatus and acylcarnitines associated with IBD and pyroglutamic acid and p-cresol associated with COVID-19 outcomes, which provide new avenues for research. The CMTF framework consistently outperformed traditional methods in both dimension reduction and biomarker detection, offering a robust tool for uncovering biologically relevant insights. Conclusions: Despite its stringent data requirements, including the reliance on stratified microbial-based pathway abundances and taxa-level contributions, this approach provides a significant step forward in multi-omics integration and analysis, with potential applications across biomedical, environmental, and agricultural research.

Differences in gut microbial diversity and composition between growth phenotypes of farmed juvenile sandfish, Holothuria scabra

BACKGROUND

The observed growth variability of different aquaculture species in captivity hinders its large-scale production. For the sandfish Holothuria scabra, a tropical sea cucumber species, there is a scarcity of information on its intestinal microbiota in relation to host growth, which could provide insights into the processes that affect growth and identify microorganisms with probiotic or biochemical potential that could improve current production strategies. To address this gap, this study used 16 S rRNA amplicon sequencing to characterize differences in gut and fecal microbiota among large and small juveniles reared in floating ocean nurseries.

RESULTS

We recovered 5915 amplicon sequence variants and diversity indices revealed significant differences between large and small juveniles (p < 0.05). Gut microbiota of large juveniles had lower bacterial diversity than its smaller counterparts. The genus cluster Burkholderia-Caballeronia-Paraburkholderia (BCP) is the most common and abundant taxa found in the gut for both size categories but less abundant in fecal samples. Small juveniles had a higher abundance of members from the Roseobacter clade (Rhodobacteriaceae) such as Ruegeria, Shimia, Psuedoruegeria and Marivita among others while the genus Schlegelella (Caldimonas) and Bosea were primarily found in larger juveniles. Predicted physiological functions identified signatures of metabolism, biosynthesis, and biodegradation pathways unique for each size category. Significant differences in diversity and composition were also exhibited between the pooled fecal and gut sample types.

CONCLUSIONS

The bacterial composition in the intestinal tract of the sandfish H. scabra is an important factor in the observed growth variability in aquaculture. The results show differences in diversity, composition and predicted physiological functions between the size groups, despite being from the same cohort and environment. It was also evident that the fecal microbiota differs from the gut and does not correspond to size category, warranting caution in using the fecal matter as a proxy to infer microbial composition and interactions in the gastrointestinal tract. Understanding the roles that these microorganisms play in sandfish growth could support the development of strategies to manage size variation in captive-bred sea cucumbers, or for the promotion and selection for faster-growing individuals.

Integrating 16S rRNA Gene Sequencing and Metabolomics Analysis to Reveal the Mechanism of L-Proline in Preventing Autism-like Behavior in Mice

BACKGROUND/OBJECTIVES

Autism spectrum disorder (ASD) is characterized by impaired social interaction and repetitive stereotyped behavior. Effective interventions for the core autistic symptoms are currently limited.

METHODS

This study employed a valproic acid (VPA)-induced mouse model of ASD to assess the preventative effects of L-proline supplementation on ASD-like behaviors. The method of 16S rRNA sequencing and untargeted metabolomics analyses were conducted to investigate the modulation of gut microbiota and gut metabolites by L-proline.

RESULTS

The results indicated that L-proline supplementation significantly prevented ASD-like behavioral disorders, including alleviating social communication deficits and reducing repetitive behavior in the ASD mice. The 16S rRNA sequencing analysis revealed that L-proline regulated the composition and structure of gut microbiota. L-Proline supplementation enhances the abundance of the Verrucomicrobia at the phylum level and the Akkermansia at the genus level, while concurrently reducing the abundance of the Patescibacteria at the phylum level, as well as the Ileibacterium, Candidatus_Saccharimonas, and Lachnospiraceae_UCG-006 at the genus level in the VPA-induced mouse model for ASD. Additionally, the untargeted metabolomics results indicated that L-proline also modified the gut metabolite profiles. Functional analysis of the gut microbiota and KEGG pathway enrichment analysis of differential metabolites between the L-proline-supplemented and VPA groups corroborated that L-proline decreased pathways related to nucleotide metabolism, taurine and hypotaurine metabolism, and pyruvate metabolism, while increasing pathways involved in alpha-linolenic acid metabolism and phenylalanine, tyrosine, and tryptophan biosynthesis. The integrative metabolomic and microbiome analyses showed strong connections between the gut metabolites and gut microbiota affected by L-proline. These findings suggest that the modulatory effects of L-proline on gut microbiota and its metabolites may play a crucial role in preventing autism in mice.

CONCLUSIONS

These findings suggest that dietary L-proline may represent a viable, effective option for preventing the physiological and behavioral deficits associated with ASD in mice.

Genetic modification of the shikimate pathway to reduce lignin content in switchgrass (Panicum virgatum L.) significantly impacts plant microbiomes

Switchgrass (Panicum virgatum L.) is considered a sustainable biofuel feedstock, given its fast-impact growth, low input requirements, and high biomass yields. Improvements in bioenergy conversion efficiency of switchgrass could be made by reducing its lignin content. Engineered switchgrass that expresses a bacterial 3-dehydroshikimate dehydratase (QsuB) has reduced lignin content and improved biomass saccharification due to the rerouting of the shikimate pathway towards the simple aromatic protocatechuate at the expense of lignin biosynthesis. However, the impacts of this QsuB trait on switchgrass microbiome structure and function remain unclear. To address this, wild-type and QsuB-engineered switchgrass were grown in switchgrass field soils, and samples were collected from inflorescences, leaves, roots, rhizospheres, and bulk soils for microbiome analysis. We investigated how QsuB expression influenced switchgrass-associated fungal and bacterial communities using high-throughput Illumina MiSeq amplicon sequencing of ITS and 16S rDNA. Compared to wild-type, QsuB-engineered switchgrass hosted different microbial communities in roots, rhizosphere, and leaves. Specifically, QsuB-engineered plants had a lower relative abundance of arbuscular mycorrhizal fungi (AMF). Additionally, QsuB-engineered plants had fewer Actinobacteriota in root and rhizosphere samples. These findings may indicate that changes in the plant metabolism impact both AMF and Actinobacteriota similarly or potential interactions between AMF and the bacterial community. This study enhances understanding of plant-microbiome interactions by providing baseline microbial data for developing beneficial bioengineering strategies and by assessing nontarget impacts of engineered plant traits on the plant microbiome.

IMPORTANCE

Bioenergy crops provide an important strategy for mitigating climate change. Reducing the lignin in bioenergy crops could improve fermentable sugar yields for more efficient conversion into bioenergy and bioproducts. In this study, we assessed how switchgrass engineered for low lignin impacted aboveground and belowground switchgrass microbiome. Our results show unexpected reductions in mycorrhizas and actinobacteria in belowground tissues, raising questions on the resilience and function of genetically engineered plants in agricultural systems.

Composition and the predicted functions of fungal communities and the key drivers in acidic soils of Jiaodong Peninsula, China

Introduction

Soil acidification imperils soil health and hinders the agricultural sustainability. As being more tolerant than bacteria to soil acidification, so it would be more meaningful for agricultural management and crop yield to characterize fungal community in acidic soils and manifest its key drivers.

Method

This study investigated the composition and diversity of fungal communities and its key driving factors by collecting 90 soil samples from the acidic region of Jiaodong Peninsula China, spanning 3 × 104 km2.

Results

The results indicated that most soil pH values ranged from 5.01 to 6.42, and the exchangeable acidity (EA) content raised significantly (p < 0.01) along with soil acidic degree increasing. However, no significant differences were found in fungal community diversity and composition among various soil samples, which were all predominantly habited with the phyla of Ascomycota and Basidiomycota. Results of the linear discriminant analysis effect size (LEfSe) analysis revealed that saprophytic fungi were biomarkers of the slightly acidic soil (pH 6.0-6.5), including Nectriaceae, Thielavia, Nectria, Haematonectria, and unclassified Microascaceae, while plant pathogenic fungi, such as Didymellaceae, were biomarkers of the soils pH < 5.5. Similarly, the FUNGuild results also indicated that saprophytic fungi and pathogenic fungi were the dominant functional guilds in the investigated acidic soils, accounting for 66% of the total fungal communities. Redundancy analysis (RDA) revealed that soil pH as well as nitrate nitrogen (NO 3 - -N) and total nitrogen (TN) significantly associated with fungal community at the phylum level, whilst soil pH was the only factor significantly linked to individual fungal classes (p < 0.01 or 0.05). The further Mantel test analysis and structural equation modeling (SEM) suggested that, in contrast to the negative and directive driving of soil pH on fungal communities' variation, the specific plant pathogenic fungi, Gibberella and Didymellaceae, were significantly and positively associated with soil acidic characteristics (p < 0.05).

Discussion

These findings highlighted that, in addition to modulating the variation of soil fungal community, soil acidification might prime some plant pathogens development. So that, more attentions should be focused on impact of soil acidification on fungal ecology, as well as plant pathogenic fungi.

Effects of data transformation and model selection on feature importance in microbiome classification data

BACKGROUND

Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, compositionality, and population-specificity present significant challenges. Microbiome data transformations can alleviate some of the aforementioned challenges, but their usage in machine learning tasks has largely been unexplored.

RESULTS

Our analysis of over 8500 samples from 24 shotgun metagenomic datasets showed that it is possible to classify healthy and diseased individuals using microbiome data with minimal dependence on the choice of algorithm or transformation. Presence-absence transformations performed comparably to abundance-based transformations, and only a small subset of predictors is necessary for accurate classification. However, while different transformations resulted in comparable classification performance, the most important features varied significantly, which highlights the need to reevaluate machine learning-based biomarker detection.

CONCLUSIONS

Microbiome data transformations can significantly influence feature selection but have a limited effect on classification accuracy. Our findings suggest that while classification is robust across different transformations, the variation in feature selection necessitates caution when using machine learning for biomarker identification. This research provides valuable insights for applying machine learning to microbiome data and identifies important directions for future work.

PreLect: Prevalence leveraged consistent feature selection decodes microbial signatures across cohorts

The intricate nature of microbiota sequencing data-high dimensionality and sparsity-presents a challenge in identifying informative and reproducible microbial features for both research and clinical applications. Addressing this, we introduce PreLect, an innovative feature selection framework that harnesses microbes' prevalence to facilitate consistent selection in sparse microbiota data. Upon rigorous benchmarking against established feature selection methodologies across 42 microbiome datasets, PreLect demonstrated superior classification capabilities compared to statistical methods and outperformed machine learning-based methods by selecting features with greater prevalence and abundance. A significant strength of PreLect lies in its ability to reliably identify reproducible microbial features across varied cohorts. Applied to colorectal cancer, PreLect identifies key microbes and highlights crucial pathways, such as lipopolysaccharide and glycerophospholipid biosynthesis, in cancer progression. This case study exemplifies PreLect's utility in discerning clinically relevant microbial signatures. In summary, PreLect's accuracy and robustness make it a significant advancement in the analysis of complex microbiota data.

Advances in multi-omics integrated analysis methods based on the gut microbiome and their applications

The gut microbiota actually shares the host's physical space and affects the host's physiological functions and health indicators through a complex network of interactions with the host. However, its role as a determinant of host health and disease is often underestimated. With the emergence of new technologies including next-generation sequencing (NGS) and advanced techniques such as microbial community sequencing, people have begun to explore the interaction mechanisms between microorganisms and hosts at various omics levels such as genomics, transcriptomics, metabolomics, and proteomics. With the enrichment of multi-omics integrated analysis methods based on the microbiome, an increasing number of complex statistical analysis methods have also been proposed. In this review, we summarized the multi-omics research analysis methods currently used to study the interaction between the microbiome and the host. We analyzed the advantages and limitations of various methods and briefly introduced their application progress.

Computational Study Protocol: Leveraging Synthetic Data to Validate a Benchmark Study for Differential Abundance Tests for 16S Microbiome Sequencing Data

Background

Synthetic data's utility in benchmark studies depends on its ability to closely mimic real-world conditions and reproduce results obtained from experimental data. Building on Nearing et al.'s study (1), who assessed 14 differential abundance tests using 38 experimental 16S rRNA datasets in a case-control design, we are generating synthetic datasets that mimic the experimental data to verify their findings. We will employ statistical tests to rigorously assess the similarity between synthetic and experimental data and to validate the conclusions on the performance of these tests drawn by Nearing et al. (1). This protocol adheres to the SPIRIT guidelines, demonstrating how established reporting frameworks can support robust, transparent, and unbiased study planning.

Methods

We replicate Nearing et al.'s (1) methodology, incorporating synthetic data simulated using two distinct tools, mirroring the 38 experimental datasets. Equivalence tests will be conducted on a non-redundant subset of 46 data characteristics comparing synthetic and experimental data, complemented by principal component analysis for overall similarity assessment. The 14 differential abundance tests will be applied to synthetic and experimental datasets, evaluating the consistency of significant feature identification and the number of significant features per tool. Correlation analysis and multiple regression will explore how differences between synthetic and experimental data characteristics may affect the results.

Conclusions

Synthetic data enables the validation of findings through controlled experiments. We assess how well synthetic data replicates experimental data, try to validate previous findings with the most recent versions of the DA methods and delineate the strengths and limitations of synthetic data in benchmark studies. Moreover, to our knowledge this is the first computational benchmark study to systematically incorporate synthetic data for validating differential abundance methods while strictly adhering to a pre-specified study protocol following SPIRIT guidelines, contributing to transparency, reproducibility, and unbiased research.

Leveraging human microbiomes for disease prediction and treatment

The human microbiome consists of diverse microorganisms that inhabit various body sites. As these microbes are increasingly recognized as key determinants of health, there is significant interest in leveraging individual microbiome profiles for early disease detection, prevention, and drug efficacy prediction. However, the complexity of microbiome data, coupled with conflicting study outcomes, has hindered its integration into clinical practice. This challenge is partially due to demographic and technological biases that impede the development of reliable disease classifiers. Here, we examine recent advances in 16S rRNA and shotgun-metagenomics sequencing, along with bioinformatics tools designed to enhance microbiome data integration for precision diagnostics and personalized treatments. We also highlight progress in microbiome-based therapies and address the challenges of establishing causality to ensure robust diagnostics and effective treatments for complex diseases.

Metagenomic analyses of gut microbiome composition and function with age in a wild bird; little change, except increased transposase gene abundance

Studies on wild animals, mostly undertaken using 16S metabarcoding, have yielded ambiguous evidence regarding changes in the gut microbiome (GM) with age and senescence. Furthermore, variation in GM function has rarely been studied in such wild populations, despite GM metabolic characteristics potentially being associated with host senescent declines. Here, we used 7 years of repeated sampling of individuals and shotgun metagenomic sequencing to investigate taxonomic and functional changes in the GM of Seychelles warblers (Acrocephalus sechellensis) with age. Our results suggest that taxonomic GM species richness declines with age and in the terminal year, with this terminal decline occurring consistently across all ages. Taxonomic and functional GM composition also shifted with host age. However, the changes we identified occurred linearly with age (or even mainly during early years prior to the onset of senescence in this species) with little evidence of accelerated change in later life or during their terminal year. Therefore, the results suggest that changes in the GM with age are not linked to senescence. Interestingly, we found a significant increase in the abundance of a group of transposase genes with age, which may accumulate passively or due to increased transposition induced as a result of stressors that arise with age. These findings reveal taxonomic and functional GM changes with age, but not senescence, in a wild vertebrate and provide a blueprint for future wild functional GM studies linked to age and senescence.

A salt-tolerant growth-promoting phyllosphere microbial combination from mangrove plants and its mechanism for promoting salt tolerance in rice

BACKGROUND

Mangrove plants growing in the high salt environment of coastal intertidal zones colonize a variety of microorganisms in the phyllosphere, which have potential salt-tolerant and growth-promoting effects. However, the characteristics of microbial communities in the phyllosphere of mangrove species with and without salt glands and the differences between them remain unknown, and the exploration and the agricultural utilization of functional microbial resources from the leaves of mangrove plants are insufficient.

RESULTS

In this study, we examined six typical mangrove species to unravel the differences in the diversity and structure of phyllosphere microbial communities between mangrove species with or without salt glands. Our results showed that a combination of salt-tolerant growth-promoting strains of Pantoea stewartii A and Bacillus marisflavi Y25 (A + Y25) was constructed from the phyllosphere of mangrove plants, which demonstrated an ability to modulate osmotic substances in rice and regulate the expression of salt-resistance-associated genes. Further metagenomic analysis revealed that exogenous inoculation with A + Y25 increased the rice rhizosphere's specific microbial taxon Chloroflexi, thereby elevating microbial community quorum sensing and ultimately enhancing ionic balance and overall microbial community function to aid salt resistance in rice.

CONCLUSIONS

This study advances our understanding of the mutualistic and symbiotic relationships between mangrove species and their phyllosphere microbial communities. It offers a paradigm for exploring agricultural beneficial microbial resources from mangrove leaves and providing the potential for applying the salt-tolerant bacterial consortium to enhance crop adaptability in saline-alkaline land. Video Abstract.

Vaginal Bacteria and Proinflammatory Host Immune Mediators as Biomarkers of Human Immunodeficiency Virus Acquisition Risk Among African Women

BACKGROUND

Few investigations have assessed contributions of both vaginal bacteria and proinflammatory immune mediators to human immunodeficiency virus (HIV) acquisition risk in a prospective cohort.

METHODS

We conducted a nested case-control study of African women who participated in a randomized placebo-controlled trial of daily oral versus vaginal tenofovir-based preexposure prophylaxis for HIV infection. Vaginal concentrations of 23 bacterial taxa and 16 immune mediators were measured. Relationships between individual bacterial concentrations or immune mediators and HIV risk were analyzed using generalized estimating equations in a multivariable model. Factor analysis assessed relationships between combinations of bacterial taxa, immune mediators, and HIV acquisition risk.

RESULTS

We identified 177 HIV pre-seroconversion visits from 150 women who acquired HIV and 531 visits from 436 women who remained HIV uninfected. Fourteen bacterial taxa and 6 proinflammatory cytokines and chemokines were individually associated with greater HIV risk after adjusting for confounders. Women with all 14 taxa versus <14 taxa (adjusted odds ratio [aOR], 4.45 [95% confidence interval {CI}, 2.20-8.98]; P < .001) or all 6 immune mediators versus <6 mediators (aOR, 1.77 [95% CI, 1.24-2.52]; P < .001) had greater risk for HIV acquisition. Factor analysis demonstrated that a bacterial factor comprised of 14 high-risk bacterial taxa (aOR, 1.57 [95% CI, 1.27-1.93]; P < 0.001) and the interferon gamma-induced protein 10 (highest quartile: aOR, 3.19 [95% CI, 1.32-7.72]; P = 0.002) contributed to the highest HIV risk.

CONCLUSIONS

Bacterial and host biomarkers for predicting HIV acquisition risk identify women at greatest risk for HIV infection and can focus prevention efforts.

Dietary prevention of antibiotic-induced dysbiosis and mortality upon aging in mice

Oral antibiotic use is both widespread and frequent in older adults and has been linked to dysbiosis of the gut microbiota, enteric infection, and chronic diseases. Diet and nutrients, particularly prebiotics, may modify the susceptibility of the gut microbiome to antibiotic-induced dysbiosis. We fed 12-month-old mice a high glycemic (HG) or low glycemic (LG) diet with or without antibiotics (ampicillin and neomycin) for an additional 11 months. The glycemic index was modulated by the ratio of rapidly digested amylopectin starch to slowly digested amylose, a type-2-resistant starch. We observed a significant decrease in survival of mice fed a HG diet containing antibiotics (HGAbx) relative to those fed a LG diet containing antibiotics (LGAbx). HGAbx mice died with an enlarged and hemorrhagic cecum, which is associated with colonic hyperplasia and goblet cell depletion. Gut microbiome analysis revealed a pronounced expansion of Proteobacteria and a near-complete loss of Bacteroidota and Firmicutes commensal bacteria in HGAbx, whereas the LGAbx group maintained a population of Bacteroides and more closely resembled the LG microbiome. The predicted functional capacity for bile salt hydrolase activity was lost in HGAbx mice but retained in LGAbx mice. An LG diet containing amylose may therefore be a potential therapeutic to prevent antibiotic-induced dysbiosis and morbidity.

MaAsLin 3: Refining and extending generalized multivariable linear models for meta-omic association discovery

A key question in microbial community analysis is determining which microbial features are associated with community properties such as environmental or health phenotypes. This statistical task is impeded by characteristics of typical microbial community profiling technologies, including sparsity (which can be either technical or biological) and the compositionality imposed by most nucleotide sequencing approaches. Many models have been proposed that focus on how the relative abundance of a feature (e.g. taxon or pathway) relates to one or more covariates. Few of these, however, simultaneously control false discovery rates, achieve reasonable power, incorporate complex modeling terms such as random effects, and also permit assessment of prevalence (presence/absence) associations and absolute abundance associations (when appropriate measurements are available, e.g. qPCR or spike-ins). Here, we introduce MaAsLin 3 (Microbiome Multivariable Associations with Linear Models), a modeling framework that simultaneously identifies both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 also newly accounts for compositionality with experimental (spike-ins and total microbial load estimation) or computational techniques, and it expands the space of biological hypotheses that can be tested with inference for new covariate types. On a variety of synthetic and real datasets, MaAsLin 3 outperformed current state-of-the-art differential abundance methods in testing and inferring associations from compositional data. When applied to the Inflammatory Bowel Disease Multi-omics Database, MaAsLin 3 corroborated many previously reported microbial associations with the inflammatory bowel diseases, but notably 77% of associations were with feature prevalence rather than abundance. In summary, MaAsLin 3 enables researchers to identify microbiome associations with higher accuracy and more specific association types, especially in complex datasets with multiple covariates and repeated measures.

How the early life microbiome shapes immune programming in childhood asthma and allergies

Despite advances in our understanding of their diagnosis and treatment, pediatric allergies impose substantial burdens on affected children, families, and healthcare systems. Further, the prevalence of allergic diseases has dramatically increased over the past half-century, leading to additional concerns and concerted efforts to identify the origins, potential predictors and preventions, and therapies of allergic diseases. Together with the increase in allergic diseases, changes in lifestyle and early-life environmental influences have corresponded with changes in colonization patterns of the infant gut microbiome. The gut microbiome plays a key role in developing the immune system, thus greatly influencing the development of allergic disease. In this review, we specifically highlight the importance of the proper maturation and composition of the gut microbiome as an essential step in healthy child development or disease progression. By exploring the intertwined development of the immune system and microbiome across pediatric allergic diseases, we provide insights into potential novel strategies for their prevention and management.

Cefazolin shifts the kidney microbiota to promote a lithogenic environment

Clinical studies of the urinary tract microbiome, termed urobiome, suggest a direct, antibiotic-dependent, impact of the urobiome on kidney physiology. However, evidence for kidney bacteria comes from indirect sources or infected tissue. Further, it is unclear how antibiotics impact kidney bacteria. Here we show direct evidence for the presence of bacteria in the kidneys, with microniches in nephrons. In murine kidneys, administration of cefazolin, a commonly used perioperative antibiotic, led to a loss of uroprotective Lactobacillus spp. and proliferation of Enterobacteriaceae (which includes many known uropathogens). This effect was dependent on treatment duration, with recovery post treatment. Uroprotective L. crispatus and a strain of stone-associated E. coli differentially influenced calcium oxalate (CaOx) crystallization through the incorporation of CaOx inhibitors or promoters, respectively. In humans, microbial signatures were identified in the kidney, with unique niches between the glomeruli and tubules, established through RNA sequencing analysis and direct imaging of two independent populations. Collectively, findings support the hypothesis that the kidneys harbor a stable and antibiotic-responsive microbiota that can influence CaOx lithogenesis. The presence of unique, age-dependent microbial signatures in the glomeruli and tubuli carry implications for non-infectious kidney diseases.

Exploring and exploiting the rice phytobiome to tackle climate change challenges

The future of agriculture is uncertain under the current climate change scenario. Climate change directly and indirectly affects the biotic and abiotic elements that control agroecosystems, jeopardizing the safety of the world's food supply. A new area that focuses on characterizing the phytobiome is emerging. The phytobiome comprises plants and their immediate surroundings, involving numerous interdependent microscopic and macroscopic organisms that affect the health and productivity of plants. Phytobiome studies primarily focus on the microbial communities associated with plants, which are referred to as the plant microbiome. The development of high-throughput sequencing technologies over the past 10 years has dramatically advanced our understanding of the structure, functionality, and dynamics of the phytobiome; however, comprehensive methods for using this knowledge are lacking, particularly for major crops such as rice. Considering the impact of rice production on world food security, gaining fresh perspectives on the interdependent and interrelated components of the rice phytobiome could enhance rice production and crop health, sustain rice ecosystem function, and combat the effects of climate change. Our review re-conceptualizes the complex dynamics of the microscopic and macroscopic components in the rice phytobiome as influenced by human interventions and changing environmental conditions driven by climate change. We also discuss interdisciplinary and systematic approaches to decipher and reprogram the sophisticated interactions in the rice phytobiome using novel strategies and cutting-edge technology. Merging the gigantic datasets and complex information on the rice phytobiome and their application in the context of regenerative agriculture could lead to sustainable rice farming practices that are resilient to the impacts of climate change.

Probiotic Mixture Attenuates Colorectal Tumorigenesis in Murine AOM/DSS Model by Suppressing STAT3, Inducing Apoptotic p53 and Modulating Gut Microbiota

Colorectal cancer (CRC) is one of the most common cancers worldwide. The standard CRC chemo drug, 5-Fluorouracil (5-FU), has a poor response rate and chemoresistance, prompting the need for a more effective and affordable treatment. In this study, we aimed to evaluate whether Prohep, a novel probiotic mixture, would alleviate azoxymethane/dextran sodium sulfate (AOM/DSS)-induced colorectal tumorigenesis and enhance 5-FU efficacy and its mechanism. Our results suggested that Prohep showed stronger anti-tumorigenesis effects than 5-FU alone or when combined in the AOM/DSS model. Prohep significantly reduced the total tumor count, total tumor size, caecum weight, colonic crypt depth, colonic inflammation, and collagen fibrosis. Prohep downregulated pro-inflammatory TNF-α and proliferative p-STAT3 and upregulated apoptotic p53. Metagenomics analysis indicated that Prohep-enriched Helicobacter ganmani, Desulfovibrio porci, Helicobacter hepaticus, and Candidatus Borkfalkia ceftriaxoniphila were inversely correlated to the total tumor count. In addition, Prohep-enriched Prevotella sp. PTAC and Desulfovibrio porci were negatively correlated to AOM/DSS enriched bacteria, while forming a co-existing community with other beneficial bacteria. From KEGG analysis, Prohep downregulated CRC-related pathways and enhanced pathways related to metabolites suppressing CRC like menaquinone, tetrapyrrole, aminolevulinic acid, and tetrahydrofolate. From Metacyc analysis, Prohep downregulated CRC-related peptidoglycan, LPS, and uric acid biosynthesis, and conversion. Prohep elevated the biosynthesis of the beneficial L-lysine, lipoic acid, pyrimidine, and palmitate. Prohep also elevated metabolic pathways related to energy utilization of lactic acid-producing bacteria (LAB) and acetate producers. Similarly, fecal acetate concentration was upregulated by Prohep. To sum up, Prohep demonstrated exceptional anti-tumorigenesis effects in the AOM/DSS model, which revealed its potential to develop into a novel CRC therapeutic in the future.

Pro-inflammatory and hyperinsulinaemic dietary patterns are associated with specific gut microbiome profiles: a TwinsUK cohort study

Metabolic dietary patterns, including the Empirical Dietary Index for Hyperinsulinaemia (EDIH) and Empirical Dietary Inflammatory Pattern (EDIP), are known to impact multiple chronic diseases, but the role of the colonic microbiome in mediating such relationships is poorly understood. Among 1,610 adults with faecal 16S rRNA data in the TwinsUK cohort, we identified the microbiome profiles for EDIH and EDIP (from food frequency questionnaires) cross-sectionally using elastic net regression. We assessed the association of the dietary pattern-related microbiome profile scores with circulating biomarkers in multivariable-adjusted linear regression. In addition, we used PICRUSt2 to predict biological pathways associated with the enriched microbiome profiles, and further screened pathways for associations with the dietary scores in linear regression analyses. Microbiome profile scores developed with 32 (EDIH) and 15 (EDIP) genera were associated with higher insulin and homeostatic model assessment of insulin resistance. Six genera were associated with both dietary scores: Ruminococcaceae_UCG-008, Lachnospiraceae_UCG-008, Defluviitaleaceae_UCG-011 Anaeroplasma, inversely and Negativibacillus, Streptococcus, positively. Further, pathways in fatty acid biosynthesis, sugar acid degradation, and mevalonate metabolism were associated with insulinaemic and inflammatory diets. Dietary patterns that exert metabolic effects on insulin and inflammation may influence chronic disease risk by modulating gut microbial composition and function.

Revisiting microgenderome: detecting and cataloguing sexually unique and enriched species in human microbiomes

BACKGROUND

Microgenderome or arguably more accurately microsexome refers to studies on sexual dimorphism of human microbiomes aimed at investigating bidirectional interactions between human microbiomes, sex hormones, and immune systems. It is important because of its implications to disease susceptibility and therapy, in which men and women demonstrate divergence in many diseases especially autoimmune diseases. In a previous report [1], we presented analyses of several key ecological aspects of microgenderome by leveraging the large datasets of the HMP (human microbiome project) but failed to offer species-level composition differences such as sexually unique species (US) and enriched species (ES). Existing approaches, for such tasks, including differential species relative abundance analysis and differential network analysis, possess certain limitations given that virtually all rely on species abundance alone or are univariate, while ignoring species distribution information across samples. Obviously, it is both species abundance and distribution that shape/drive the structure and dynamics of human microbiomes, and both should be equally responsible for the universal heterogeneity of microbiomes including the sexual dimorphism.

RESULTS

Here, we fill the gap by taking advantages of a recently developed computational algorithm, species specificity, and specificity diversity (SSD) framework (refer to the companion article) to reanalyze the HMP and complementary seminovaginal microbiome datasets. The SSD framework can randomly search and catalogue the sexually specific unique/enriched species with statistical rigor, guided by species specificity (a synthetic metric of abundance and distribution) and specificity diversity (SD). The SSD framework reveals that men seem to have more unique species than women in their gut and reproductive system microbiomes, but women seem to have more unique species than men in the airway, oral, and skin microbiomes, which is likely due to sexual dimorphism in the hormone and immune systems. We further investigate co-dependency and heterogeneity of those sexually unique/enriched species across 15 body sites, with core/periphery network analyses.

CONCLUSIONS

This study not only produced sexually unique/enriched species in the human microbiomes and analyzed their codependency and heterogeneity but also further validated the robustness of the SSD framework presented in the companion article, by performing all negative control tests based on the HMP gut microbiome samples.

Integrative multi-omics analysis uncovers tumor-immune-gut axis influencing immunotherapy outcomes in ovarian cancer

Recurrent ovarian cancer patients, especially those resistant to platinum, lack effective curative treatments. To address this, we conducted a phase 2 clinical trial (NCT02853318) combining pembrolizumab with bevacizumab, to increase T cell infiltration into the tumor, and oral cyclophosphamide, to reduce the number of regulatory T cells. The trial accrued 40 heavily pretreated recurrent ovarian cancer patients. The primary endpoint, progression free survival, was extended to a median of 10.2 months. The secondary endpoints demonstrated an objective response rate of 47.5%, and disease control in 30% of patients for over a year while maintaining a good quality of life. We performed comprehensive molecular, immune, microbiome, and metabolic profiling on samples of trial patients. Here, we show increased T and B cell clusters and distinct microbial patterns with amino acid and lipid metabolism are linked to exceptional clinical responses. This study suggests the immune milieu and host-microbiome can be leveraged to improve antitumor response in future immunotherapy trials.

Species specificity and specificity diversity (SSD) framework: a novel method for detecting the unique and enriched species associated with disease by leveraging the microbiome heterogeneity

BACKGROUND

Differentiating the microbiome changes associated with diseases is challenging but critically important. Majority of existing efforts have been focused on a community level, but the discerning power of community or holistic metrics such as diversity analysis seems limited. This prompts many researchers to believe that the promise should be downward to species or even strain level-effectively and efficiently identifying unique or enriched species in diseased microbiomes with statistical rigor. Nevertheless, virtually, all species-level approaches such as differential abundance and differential network analysis methods exclusively rely on species abundances without considering species distribution information, while it can be said that distribution is equally, if not more, important than abundance in shaping the spatiotemporal heterogeneity of community compositions.

RESULTS

Here, we fill the gap by developing a novel framework-species specificity and specificity diversity (SSD)-that synthesizes both abundance and distribution information to differentiate microbiomes, at both species and community scales, under different environmental gradients such as the healthy and diseased treatments. The proposed SSD framework consists of three essential elements. The first is species specificity (SS), a concept that reincarnates the traditional specialist-generalist continuum and is defined by Mariadassou et al. (Ecol Lett 18:974-82, 2015). The SS synthesizes a species' local prevalence (distribution) and global abundance information and attaches specificity measure to each species in a specific habitat (e.g., healthy or diseased treatment). The second element is a new concept to introduce here, the (species) specificity diversity (SD), which is inspired by traditional species (abundance) diversity in community ecology and measures the diversity of specificity (a proxy for metacommunity heterogeneity, essentially) with Renyi's entropy. The third element is a pair of statistical tests based on the principle of permutation tests.

CONCLUSIONS

The SSD framework can (i) identify and catalogue lists of unique species (US), significantly enriched species (ES) in each treatment based on SS and specificity permutation (SP) test and (ii) measure the holistic differences between assemblages (or treatments) based on SD and specificity diversity permutation (SDP) test. Both capacities can be enabling technologies for general comparative microbiome research including risk assessment, diagnosis, and treatment of microbiome-associated diseases.

Oral microbiome test as an alternative diagnostic tool for gastric alterations: A prospective, bicentric cross-sectional study

The human microbiome plays a pivotal role in influencing various physiological processes and maintaining overall well-being, including the gastric system. Current diagnostic tests for gastric diseases often involve invasive procedures, sampling limitations, and medication effects, leading to potential diagnostic errors and discomfort to patients. Considering the connection between oral and gastric microbiomes, this cross-sectional study aimed to assess the diagnostic potential of the oral bacterial profile in patients undergoing upper digestive endoscopy. Oral samples from 266 participants across two Brazilian sites (Belterra and Sao Paulo) were sequenced and subjected to bioinformatic analysis to identify microbiome composition across endoscopy outcome groups, exploring alpha and beta-diversity, richness, and differential abundance and prevalence. Prevotella, Haemophilus, Fusobacterium, Neisseria, and Streptococcus were the most abundant genera observed. No significant associations were found between alpha diversity profiles and endoscopy outcomes; beta diversity analyses similarly showed no correlations. Overall, the study did not establish the oral microbiome as a reliable marker for gastric health, underscoring the necessity for broader studies in the development of non-invasive diagnostic tests.

Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease

OBJECTIVES

Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing.

METHODS

We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with five-fold cross validation.

RESULTS

Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the β-diversity between groups (P = 0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871.

CONCLUSION

Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

Review and revamp of compositional data transformation: A new framework combining proportion conversion and contrast transformation

Due to the development of next-generation sequencing technology and an increased appreciation of their role in modulating host immunity and their potential as therapeutic agents, the human microbiome has emerged as a key area of interest in various biological investigations of human health and disease. However, microbiome data present a number of statistical challenges not addressed by existing methods, such as the varying sequencing depth, the compositionality, and zero inflation. Solutions like scaling and transformation methods help to mitigate heterogeneity and release constraints, but often introduce biases and yield inconsistent results on the same data. To address these issues, we conduct a systematic review of compositional data transformation, with a particular focus on the connection and distinction of existing techniques. Additionally, we create a new framework that enables the development of new transformations by combining proportion conversion with contrast transformations. This framework includes well-known methods such as Additive Log Ratio (ALR) and Centered Log Ratio (CLR) as special cases. Using this framework, we develop two novel transformations-Centered Arcsine Contrast (CAC) and Additive Arcsine Contrast (AAC)-which show enhanced performance in scenarios with high zero-inflation. Moreover, our findings suggest that ALR and CLR transformations are more effective when zero values are less prevalent. This comprehensive review and the innovative framework provide microbiome researchers with a significant direction to enhance data transformation procedures and improve analytical outcomes.

Influence of reef habitat on coral microbial associations

Corals have complex symbiotic associations that can be influenced by the environment. We compare symbiotic dinoflagellate (family: Symbiodiniaceae) associations and the microbiome of five scleractinian coral species from three different reef habitats in Palau, Micronesia. Although pH and temperature corresponded with specific host-Symbiodiniaceae associations common to the nearshore and offshore habitats, bacterial community dissimilarity analyses indicated minimal influence of these factors on microbial community membership for the corals Coelastrea aspera, Psammocora digitata, and Pachyseris rugosa. However, coral colonies sampled close to human development exhibited greater differences in microbial community diversity compared to the nearshore habitat for the coral species Coelastrea aspera, Montipora foliosa, and Pocillopora acuta, and the offshore habitat for Coelastrea aspera, while also showing less consistency in Symbiodiniaceae associations. These findings indicate the influence that habitat location has on the bacterial and Symbiodiniaceae communities comprising the coral holobiont and provide important considerations for the conservation of coral reef communities, especially for island nations with increasing human populations and development.

Characterising healthy Australian oral microbiomes for 'super donor' selection

OBJECTIVES

Among healthy people, we understand very little about the sociodemographic, lifestyle, and dental hygiene behaviours that shape their oral microbiota. This study investigates how sociodemographic, lifestyle and dental hygiene behaviours shape oral microbiota diversity and composition in an Australian population to better inform healthy oral microbiota donors for Oral Microbiota Transplantation (OMT).

METHODS

The study comprised 93 healthy adults who underwent comprehensive oral examinations and questionnaires to assess their health status. Participants were excluded if they had any active systemic or oral disease. All completed a questionnaire containing information on socio-economic, lifestyle, behavioural, and oral health factors. Supragingival plaque was collected, and 16S ribosomal RNA (rRNA) amplicon sequencing was used to analyse microbial composition. Associations between the core microbiome, alpha- (within-sample), beta-diversity (between-sample) and an individual's co-variates were tested for statistical significance. A redundancy analysis (RDA), multivariate adonis, differential abundance and correlation analysis were performed to characterise which factors drive the variation in the healthy oral microbiome.

RESULTS

Streptococcus and Corynebacterium were the most prevalent and abundant genera among healthy Australians. The alpha and beta diversity were higher among unemployed non-Australian-born students who consumed low carbohydrates, fat, and sugar and had not visited the dentist for over 12 months. Additionally, beta diversity was significantly higher among daily flossers who abstained from fluoride treatment and had high salivary pH, although no single factor explained >4 % of the total variation (R2= 0.042). Alloprevotella, Lachnosporacea, and Parvimonas were significantly abundant among non-Australians who did not visit the dentist within a year. The RDA analysis revealed associations between microbiome composition and factors such as high carbohydrate, sugar, and fat consumption, low fibre intake, and regular dental checks among Australian-born individuals.

CONCLUSION

These findings indicate that alpha and beta diversity of the oral microbiome varied significantly with sociodemographic, lifestyle, and dietary factors, including non-Australian birthplaces, unemployment, diet, and infrequent dental visits.

CLINICAL SIGNIFICANCE

These findings underscore the importance of considering diverse sociodemographic, lifestyle, and dietary factors in oral health management. Before microbiome transplantations, clinicians should account for individual characteristics that may be beneficial for shaping and maintaining optimal oral microbiome diversity and health.

The mind, brain, and body study: A protocol for examining the effects of the gut-brain-immune axis on internalizing symptoms in youth exposed to caregiving-related early adversity

Experiences of caregiving-related adversity are common and one of the strongest predictors of internalizing psychopathology (i.e., anxiety and depression). Specifically, individuals who have been exposed to such early adversities have altered affective neurodevelopment, impaired memory systems, increased risk of developing internalizing disorders, greater inflammation, and differences in gastrointestinal (gut) microbiome composition. Crucially, the gut microbiome undergoes a sensitive period of development that precedes neural and immune sensitive periods, thus making it a potentially fruitful target for intervention. Though previous work has assessed neural, immune, and gut microbiome systems in individuals exposed to early adversity, studies have primarily looked at these biological systems independently. The Mind, Brain, and Body study (MBB) implements multimodal and longitudinal design to assess how changes in the gut microbiome following caregiving-related adversity may underlie altered affective neurodevelopment, memory, and immune functioning in youth and contribute to internalizing symptoms. Across three waves, spread approximately 12-18 months apart, youth with and without previous experiences of caregiving-related adversity completed self-report measures of mental and physical health, provided stool, saliva, hair, and blood samples, and completed an MRI scan. Results of this study will expand our knowledge on how the gut microbiome shapes several biological and cognitive systems and motivate future work investigating the gut microbiome as potential target for intervention.

Host DNA depletion on frozen human respiratory samples enables successful metagenomic sequencing for microbiome studies

Most respiratory microbiome studies use amplicon sequencing due to high host DNA. Metagenomics sequencing offers finer taxonomic resolution, phage assessment, and functional characterization. We evaluated five host DNA depletion methods on frozen nasal swabs from healthy adults, sputum from people with cystic fibrosis (pwCF), and bronchoalveolar lavage (BAL) from critically ill patients. Median sequencing depth was 76.4 million reads per sample. Untreated nasal, sputum, and BAL had 94.1%, 99.2%, and 99.7% host reads, respectively. Host depletion effects varied by sample type, generally increasing microbial reads, species and functional richness; this was mediated by higher effective sequencing depth. Rarefaction curves showed species richness saturation at 0.5-2 million microbial reads. Most methods did not change Morisita-Horn dissimilarity for BAL and nasal samples although the proportion of gram-negative bacteria decreased for sputum from pwCF. Freezing did not affect the viability of Staphylococcus aureus but reduced the viability of Pseudomonas aeruginosa and Enterobacter spp.; this was mitigated by adding a cryoprotectant. QIAamp-based host depletion minimally impacted gram-negative viability even in non-cryoprotected frozen isolates. While some host depletion methods may shift microbial composition, metagenomics sequencing without host depletion severely underestimates microbial diversity of respiratory samples due to shallow effective sequencing depth and is not recommended.

lefser: implementation of metagenomic biomarker discovery tool, LEfSe, in R

SUMMARY

LEfSe is a widely used Python package and Galaxy module for metagenomic biomarker discovery and visualization, utilizing the Kruskal-Wallis test, Wilcoxon Rank-Sum test, and Linear Discriminant Analysis. R/Bioconductor provides a large collection of tools for metagenomic data analysis but has lacked an implementation of this widely used algorithm, hindering benchmarking against other tools and incorporation into R workflows. We present the lefser package to provide comparable functionality within the R/Bioconductor ecosystem of statistical analysis tools, with improvements to the original algorithm for performance, accuracy, and reproducibility. We benchmark the performance of lefser against the original algorithm using human and mouse metagenomic datasets.

AVAILABILITY AND IMPLEMENTATION

Our software, lefser, is distributed through the Bioconductor project (https://www.bioconductor.org/packages/release/bioc/html/lefser.html), and all the source code is available in the GitHub repository https://github.com/waldronlab/lefser.

Ruminococcus gnavus in the gut: driver, contributor, or innocent bystander in steatotic liver disease?

The human gut microbiome plays a crucial role in regulating intestinal and systemic health, impacting host immune response and metabolic function. Dysbiosis of the gut microbiome is linked to various diseases, including steatotic liver diseases. Metabolic dysfunction-associated steatotic liver disease (MASLD), a chronic liver disease characterized by excess hepatic lipid content and impaired metabolism, is the leading cause of liver disease worldwide. Among the gut microbes, Ruminococcus gnavus (R. gnavus) has garnered attention for its association with inflammatory and metabolic diseases. While R. gnavus abundance correlates to liver fat accumulation, further research is needed to identify a causal role or therapeutic intervention in steatotic liver disease. This review surveys our current understanding of R. gnavus in the development and progression of steatotic liver diseases, highlighting its potential mechanisms through metabolite secretion, and emphasizes the need for comprehensive microbiome analyses and longitudinal studies to better understand R. gnavus' impact on liver health. This knowledge could pave the way for targeted interventions aimed at modulating gut microbiota to treat and prevent MASLD and its comorbidities.

PhyImpute and UniFracImpute: two imputation approaches incorporating phylogeny information for microbial count data

Sequencing-based microbial count data analysis is a challenging task due to the presence of numerous non-biological zeros, which can impede downstream analysis. To tackle this issue, we introduce two novel approaches, PhyImpute and UniFracImpute, which leverage similar microbial samples to identify and impute non-biological zeros in microbial count data. Our proposed methods utilize the probability of non-biological zeros and phylogenetic trees to estimate sample-to-sample similarity, thus addressing this challenge. To evaluate the performance of our proposed methods, we conduct experiments using both simulated and real microbial data. The results demonstrate that PhyImpute and UniFracImpute outperform existing methods in recovering the zeros and empowering downstream analyses such as differential abundance analysis, and disease status classification.

A novel computational approach for the mining of signature pathways using species co-occurrence networks in gut microbiomes

BACKGROUND

Advances in metagenome sequencing data continue to enable new methods for analyzing biological systems. When handling microbial profile data, metagenome sequencing has proven to be far more comprehensive than traditional methods such as 16s rRNA data, which rely on partial sequences. Microbial community profiling can be used to obtain key biological insights that pave the way for more accurate understanding of complex systems that are critical for advancing biomedical research and healthcare. However, such attempts have mostly used partial or incomplete data to accurately capture those associations.

METHODS

This study introduces a novel computational approach for the identification of co-occurring microbial communities using the abundance and functional roles of species-level microbiome data. The proposed approach is then used to identify signature pathways associated with inflammatory bowel disease (IBD). Furthermore, we developed a computational pipeline to identify microbial species co-occurrences from metagenome data at various granularity levels.

RESULTS

When comparing the IBD group to a control group, we show that certain co-occurring communities of species are enriched for potential pathways. We also show that the identified co-occurring microbial species operate as a community to facilitate pathway enrichment.

CONCLUSIONS

The obtained findings suggest that the proposed network model, along with the computational pipeline, provide a valuable analytical tool to analyze complex biological systems and extract pathway signatures that can be used to diagnose certain health conditions.

Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy

BACKGROUND/OBJECTIVES

The prevalence of food allergy (FA) in children is increasing. Dysbiosis of the microbiome has been linked to FA but needs to be better understood. We aimed to characterize the gut and skin microbiome of young food-allergic children over time and within different types of immunoglobulin E (IgE)-mediated FA.

METHODS

We studied 23 patients, as a pilot study of an ongoing prospective multicenter cohort study including children < 2y with newly diagnosed IgE-mediated FA. Samples (feces/skin swabs) were collected at enrollment and at 1-year follow-up and sequenced for the bacterial 16S rRNA gene (hypervariable v1-v2 region).

RESULTS

Gut and skin bacterial diversity was significantly higher in patients compared with controls and increased over time (beta test, Shannon diversity, p < 0.01). Within different types of IgE-mediated FA, bacterial diversity was similar. Community composition differed significantly over time and within IgE-mediated FA types (PERMANOVA: p < 0.01). Several significantly different genus abundances were revealed. We observed a positive correlation between high total IgE and a high abundance of the genus Collinsella in patients with a higher number of allergies/sensitizations (≥3), and patients with tree nut and/or peanut allergy.

CONCLUSIONS

This study revealed an increased bacterial diversity in children with FA compared with non-atopic children. Importantly, the gut and skin microbiome differed in their composition over time and within different types of IgE-mediated FA. These findings contribute to the understanding of microbiome changes in children with FA and indicate the potential of the genus Collinsella as a biomarker for tree nut and/or peanut allergy and possibly for allergy persistence.

Coral high molecular weight carbohydrates support opportunistic microbes in bacterioplankton from an algae-dominated reef

High molecular weight (HMW; >1 kDa) carbohydrates are a major component of dissolved organic matter (DOM) released by benthic primary producers. Despite shifts from coral to algae dominance on many reefs, little is known about the effects of exuded carbohydrates on bacterioplankton communities in reef waters. We compared the monosaccharide composition of HMW carbohydrates exuded by hard corals and brown macroalgae and investigated the response of the bacterioplankton community of an algae-dominated Caribbean reef to the respective HMW fractions. HMW coral exudates were compositionally distinct from the ambient, algae-dominated reef waters and similar to coral mucus (high in arabinose). They further selected for opportunistic bacterioplankton taxa commonly associated with coral stress (i.e., Rhodobacteraceae, Phycisphaeraceae, Vibrionaceae, and Flavobacteriales) and significantly increased the predicted energy-, amino acid-, and carbohydrate-metabolism by 28%, 44%, and 111%, respectively. In contrast, HMW carbohydrates exuded by algae were similar to those in algae tissue extracts and reef water (high in fucose) and did not significantly alter the composition and predicted metabolism of the bacterioplankton community. These results confirm earlier findings of coral exudates supporting efficient trophic transfer, while algae exudates may have stimulated microbial respiration instead of biomass production, thereby supporting the microbialization of reefs. In contrast to previous studies, HMW coral and not algal exudates selected for opportunistic microbes, suggesting that a shift in the prevalent DOM composition and not the exudate type (i.e., coral vs algae) per se, may induce the rise of opportunistic microbial taxa.

IMPORTANCE

Dissolved organic matter (DOM) released by benthic primary producers fuels coral reef food webs. Anthropogenic stressors cause shifts from coral to algae dominance on many reefs, and resulting alterations in the DOM pool can promote opportunistic microbes and potential coral pathogens in reef water. To better understand these DOM-induced effects on bacterioplankton communities, we compared the carbohydrate composition of coral- and macroalgae-DOM and analyzed the response of bacterioplankton from an algae-dominated reef to these DOM types. In line with the proposed microbialization of reefs, coral-DOM was efficiently utilized, promoting energy transfer to higher trophic levels, whereas macroalgae-DOM likely stimulated microbial respiration over biomass production. Contrary to earlier findings, coral- and not algal-DOM selected for opportunistic microbial taxa, indicating that a change in the prevalent DOM composition, and not DOM type, may promote the rise of opportunistic microbes. Presented results may also apply to other coastal marine ecosystems undergoing benthic community shifts.

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for enhanced false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches and better sensitivity over popular differential abundance testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.

Assessing functional properties of diet protein hydrolysate and oil from fish waste on canine immune parameters, cardiac biomarkers, and fecal microbiota

Locally produced fish hydrolysate and oil from the agrifood sector comprises a sustainable solution both to the problem of fish waste disposal and to the petfood sector with potential benefits for the animal's health. This study evaluated the effects of the dietary replacement of mainly imported shrimp hydrolysate (5%) and salmon oil (3%; control diet) with locally produced fish hydrolysate (5%) and oil (3.2%) obtained from fish waste (experimental diet) on systemic inflammation markers, adipokines levels, cardiac function and fecal microbiota of adult dogs. Samples and measurements were taken from a feeding trial conducted according to a crossover design with two diets (control and experimental diets), six adult Beagle dogs per diet and two periods of 6 weeks each. The experimental diet, with higher docosahexaenoic (DHA) and eicosapentaenoic (EPA) acids contents, decreased plasmatic triglycerides and the activity of angiotensin converting enzyme, also tending to decrease total cholesterol. No effects of diet were observed on serum levels of the pro-inflammatory cytokines interleukin (IL)-1β, IL-8, and IL-12/IL-23 p40, and of the serum levels of the anti-inflammatory adipokine adiponectin. Blood pressure, heart rate and echocardiographic measurements were similar between diets with the only exception of left atrial to aorta diameter ratio that was higher in dogs fed the experimental diet, but without clinical relevance. Diet did not significantly affect fecal immunoglobulin A concentration. Regarding fecal microbiome, Megasphaera was the most abundant genus, followed by Bifidobacterium, Fusobacterium, and Prevotella, being the relative abundances of Fusobacterium and Ileibacterium genera positively affected by the experimental diet. Overall, results from the performed short term trial suggest that shrimp hydrolysate and salmon oil can be replaced by protein hydrolysate and oil from fish by-products without affecting systemic inflammatory markers, cardiac structure and function, but potentially benefiting bacterial genera associated with healthy microbiome. Considering the high DHA and EPA contents and the antioxidant properties of fish oil and hydrolysate, it would be worthwhile in the future to assess their long-term effects on inflammatory markers and their role in spontaneous canine cardiac diseases and to perform metabolomic and metagenomics analysis to elucidate the relevance of microbiota changes in the gut.