Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks.

Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.

From pollen to putrid: Comparative metagenomics reveals how microbiomes support dietary specialization in vulture bees.

For most animals, the microbiome is key for nutrition and pathogen defence, and is often shaped by diet. Corbiculate bees, including honey bees, bumble bees, and stingless bees, share a core microbiome that has been shaped, at least in part, by the challenges associated with pollen digestion. However, three species of stingless bees deviate from the general rule of bees obtaining their protein exclusively from pollen (obligate pollinivores) and instead consume carrion as their sole protein source (obligate necrophages) or consume both pollen and carrion (facultative necrophages). These three life histories can provide missing insights into microbiome evolution associated with extreme dietary transitions. Here, we investigate, via shotgun metagenomics, the functionality of the microbiome across three bee diet types: obligate pollinivory, obligate necrophagy, and facultative necrophagy. We find distinct differences in microbiome composition and gene functional profiles between the diet types. Obligate necrophages and pollinivores have more specialized microbes, whereas facultative necrophages have a diversity of environmental microbes associated with several dietary niches. Our study suggests that necrophagous bee microbiomes may have evolved to overcome cellular stress and microbial competition associated with carrion. We hypothesize that the microbiome evolved social phenotypes, such as biofilms, that protect the bees from opportunistic pathogens present on carcasses, allowing them to overcome novel nutritional challenges. Whether specific microbes enabled diet shifts or diet shifts occurred first and microbial evolution followed requires further research to disentangle. Nonetheless, we find that necrophagous microbiomes, vertebrate and invertebrate alike, have functional commonalities regardless of their taxonomy.

Cross study analyses of SEND data: toxicity profile classification.

A SEND toxicology data transformation, harmonization and analysis platform was created to improve the identification of unique findings related to the intended target, species, and duration of dosing using data from multiple studies. The lack of a standardized digital format for data analysis had impeded large-scale analysis of in vivo toxicology studies. The CDISC SEND standard enables the analysis of data from multiple studies performed by different laboratories. This work describes methods to analyze data and automate cross study analysis of toxicology studies. Cross study analysis can be used to understand a single compound's toxicity profile across all studies performed and/or to evaluate on-target versus off-target toxicity for multiple compounds intended for the same pharmacological target. This work involved development of data harmonization/transformation strategies to enable cross-study analysis of both numerical and categorical SEND data. Four de-identified SEND data sets from the BioCelerate database were used for the analyses. Toxicity profiles for key organ systems were developed for liver, kidney, male reproductive tract, endocrine system, and hematopoietic system using SEND domains. A Cross-Study Analysis dashboard with a built-in user-defined scoring system was created for custom analyses, including visualizations to evaluate data at the organ system level and drill down into individual animal data. This data analysis provides the tools for scientists to compare toxicity profiles across multiple studies using SEND. A cross-study analysis of two different compounds intended for the same pharmacological target is described and the analyses indicate potential on-target effects to liver, kidney, and hematopoietic systems.

Symbiotic bacteria and fungi proliferate in diapause and may enhance overwintering survival in a solitary bee.

Host-microbe interactions underlie the development and fitness of many macroorganisms, including bees. Whereas many social bees benefit from vertically transmitted gut bacteria, current data suggests that solitary bees, which comprise the vast majority of species diversity within bees, lack a highly specialized gut microbiome. Here, we examine the composition and abundance of bacteria and fungi throughout the complete life cycle of the ground-nesting solitary bee Anthophora bomboides standfordiana. In contrast to expectations, immature bee stages maintain a distinct core microbiome consisting of Actinobacterial genera (Streptomyces, Nocardiodes) and the fungus Moniliella spathulata. Dormant (diapausing) larval bees hosted the most abundant and distinctive bacteria and fungi, attaining 33 and 52 times their initial copy number, respectively. We tested two adaptive hypotheses regarding microbial functions for diapausing bees. First, using isolated bacteria and fungi, we found that Streptomyces from brood cells inhibited the growth of multiple pathogenic filamentous fungi, suggesting a role in pathogen protection during overwintering, when bees face high pathogen pressure. Second, sugar alcohol composition changed in tandem with major changes in fungal abundance, suggesting links with bee cold tolerance or overwintering biology. We find that A. bomboides hosts a conserved core microbiome that may provide key fitness advantages through larval development and diapause, which raises the question of how this microbiome is maintained and faithfully transmitted between generations. Our results suggest that focus on microbiomes of mature or active insect developmental stages may overlook stage-specific symbionts and microbial fitness contributions during host dormancy.

Overcome the Limitation of Phenome-Wide Association Studies (PheWAS): Extension of PheWAS to Efficient and Robust Large-Scale ICD Codes Analysis.

The Phenome-wide association studies (PheWAS) have become widely used for efficient, high-throughput evaluation of relationship between a genetic factor and a large number of disease phenotypes, typically extracted from a DNA biobank linked with electronic medical records (EMR). Phecodes, billing code-derived disease case-control status, are usually used as outcome variables in PheWAS and logistic regression has been the standard choice of analysis method. Since the clinical diagnoses in EMR are often inaccurate with errors which can lead to biases in the odds ratio estimates, much effort has been put to accurately define the cases and controls to ensure an accurate analysis. Specifically in order to correctly classify controls in the population, an exclusion criteria list for each Phecode was manually compiled to obtain unbiased odds ratios. However, the accuracy of the list cannot be guaranteed without extensive data curation process. The costly curation process limits the efficiency of large-scale analyses that take full advantage of all structured phenotypic information available in EMR. Here, we proposed to estimate relative risks (RR) instead. We first demonstrated the desired nature of RR that overcomes the inaccuracy in the controls via theoretical formula. With simulation and real data application, we further confirmed that RR is unbiased without compiling exclusion criteria lists. With RR as estimates, we are able to efficiently extend PheWAS to a larger-scale, phenome construction agnostic analysis of phenotypes, using ICD 9/10 codes, which preserve much more disease-related clinical information than Phecodes.

Development and Implementation of an Integrated Preclinical Atherosclerosis Database.

BACKGROUND: Basic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. METHODS: Using atherosclerosis as a test case, we extracted data from the 2 most common mouse models of atherosclerosis (ApoE [apolipoprotein E]-knockout and LDLR [low-density lipoprotein receptor]-knockout). We restricted analyses to manuscripts published in 2 well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention, and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. RESULTS: Extracted data include animal sex, diet, intervention type, and distinct plaque pathologies (size, inflammation, and lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes, and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. CONCLUSIONS: This database is provided as a resource for future interrogation of sub-data sets associated with distinct plaque pathologies, cell type, or sex. We also provide the methods and software needed to expand this data set and apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

Impact of Insurance Status and Region on Angiotensin Receptor-Neprilysin Inhibitor Prescription During Heart Failure Hospitalizations.

BACKGROUND: An angiotensin receptor-neprilysin inhibitor (ARNI) is the preferred renin-angiotensin system (RAS) inhibitor for heart failure with reduced ejection fraction (HFrEF). Among eligible patients, insurance status and prescriber concern regarding out-of-pocket costs may constrain early initiation of ARNI and other new therapies. OBJECTIVES: In this study, the authors sought to evaluate the association of insurance and other social determinants of health with ARNI initiation at discharge from HFrEF hospitalization. METHODS: The authors analyzed ARNI initiation from January 2017 to June 2020 among patients with HFrEF eligible to receive RAS inhibitor at discharge from hospitals in the Get With The Guidelines-Heart Failure registry. The primary outcome was the proportion of ARNI prescription at discharge among those prescribed RAS inhibitor who were not on ARNI on admission. A logistic regression model was used to determine the association of insurance status, U.S. region, and their interaction, as well as self-reported race, with ARNI initiation at discharge. RESULTS: From 42,766 admissions, 24,904 were excluded for absolute or relative contraindications to RAS inhibitors. RAS inhibitors were prescribed for 16,817 (94.2%) of remaining discharges, for which ARNI was prescribed in 1,640 (9.8%). Self-reported Black patients were less likely to be initiated on ARNI compared to self-reported White patients (OR: 0.64; 95% CI: 0.50-0.81). Compared to Medicare beneficiaries, patients with third-party insurance, Medicaid, or no insurance were less likely to be initiated on ARNI (OR: 0.47 [95% CI: 0.31-0.72], OR: 0.41 [95% CI: 0.25-0.67], and OR: 0.20 [95% CI: 0.08-0.47], respectively). ARNI therapy varied by hospital region, with lowest utilization in the Mountain region. An interaction was demonstrated between the impact of insurance disparities and hospital region. CONCLUSIONS: Among patients hospitalized between 2017 and 2020 for HFrEF who were prescribed RAS inhibitor therapy at discharge, insurance status, geographic region, and self-reported race were associated with ARNI initiation.

Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administrative purposes raise questions about the consistency and reproducibility of EHR-based multimorbidity research. Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combining data from multiple sources for online multimorbidity analysis. Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies (Kendall's τ = 0.643) and comorbidity strengths (Pearson ρ = 0.79). Consistent network statistics across EHRs suggest similar structures of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network's ability to uncover clinically relevant disease relationships and provide novel insights. Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying phenome-wide multimorbidities. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared biology of diseases. The consistent core-periphery structure offers analytical insights to discover complex disease interactions. This work also sets the stage for advanced disease modeling, with implications for precision medicine. Funding: VUMC Biostatistics Development Award, the National Institutes of Health, and the VA CSRD.

Walking the leadership walk: Integrating inclusivity in a large-scale health care organization.

This article focuses on the roles, actions, and resources developed by a group of leaders from the Office of Mental Health and Suicide Prevention (OMHSP) within the Department of Veterans Affairs during the unprecedented times of spring of 2020, when society was shaken by the fears and challenges of COVID-19 as well as social unrest sparked by the murder of George Floyd. We share a summary of our efforts to move beyond platitudes and statements and bring meaningful and sustainable change in justice, equity, diversity, and inclusion within OMHSP and across mental health services in Department of Veterans Affairs. This article is written through the lens of the founding members of the OMHSP's Diversity, Equity, and Inclusion Steering Committee. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Recurrence After Atrial Fibrillation Ablation and Investigational Biomarkers of Cardiac Remodeling.

BACKGROUND: Recurrence after atrial fibrillation (AF) ablation remains common. We evaluated the association between recurrence and levels of biomarkers of cardiac remodeling, and their ability to improve recurrence prediction when added to a clinical prediction model. METHODS AND RESULTS: Blood samples collected before de novo catheter ablation were analyzed. Levels of bone morphogenetic protein-10, angiopoietin-2, fibroblast growth factor-23, insulin-like growth factor-binding protein-7, myosin-binding protein C3, growth differentiation factor-15, interleukin-6, N-terminal pro-brain natriuretic peptide, and high-sensitivity troponin T were measured. Recurrence was defined as ≥30 seconds of an atrial arrhythmia 3 to 12 months postablation. Multivariable logistic regression was performed using biomarker levels along with clinical covariates: APPLE score (Age >65 years, Persistent AF, imPaired eGFR [<60 ml/min/1.73m2], LA diameter ≥43 mm, EF <50%; which includes age, left atrial diameter, left ventricular ejection fraction, persistent atrial fibrillation, and estimated glomerular filtration rate), preablation rhythm, sex, height, body mass index, presence of an implanted continuous monitor, year of ablation, and additional linear ablation. A total of 1873 participants were included. A multivariable logistic regression showed an association between recurrence and levels of angiopoietin-2 (odds ratio, 1.08 [95% CI, 1.02-1.15], P=0.007) and interleukin-6 (odds ratio, 1.02 [95% CI, 1.003-1.03]; P=0.02). The area under the receiver operating characteristic curve of a model that only contained clinical predictors was 0.711. The addition of any of the 9 studied biomarkers to the predictive model did not result in a statistically significant improvement in the area under the receiver operating characteristic curve. CONCLUSIONS: Higher angiopoietin-2 and interleukin-6 levels were associated with recurrence after atrial fibrillation ablation in multivariable modeling. However, the addition of biomarkers to a clinical prediction model did not significantly improve recurrence prediction.

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia.

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Feasibility of precision smoking treatment in a low-income community setting: results of a pilot randomized controlled trial in The Southern Community Cohort Study.

BACKGROUND: The feasibility of precision smoking treatment in socioeconomically disadvantaged communities has not been studied. METHODS: Participants in the Southern Community Cohort Study who smoked daily were invited to join a pilot randomized controlled trial of three smoking cessation interventions: guideline-based care (GBC), GBC plus nicotine metabolism-informed care (MIC), and GBC plus counseling guided by a polygenic risk score (PRS) for lung cancer. Feasibility was assessed by rates of study enrollment, engagement, and retention, targeting > 70% for each. Using logistic regression, we also assessed whether feasibility varied by age, sex, race, income, education, and attitudes toward precision smoking treatment. RESULTS: Of 92 eligible individuals (79.3% Black; 68.2% with household income < $15,000), 67 (72.8%; 95% CI 63.0-80.9%) enrolled and were randomized. Of these, 58 (86.6%; 95% CI 76.4-92.8%) engaged with the intervention, and of these engaged participants, 43 (74.1%; 95% CI 61.6-83.7%) were retained at 6-month follow-up. Conditional on enrollment, older age was associated with lower engagement (OR 0.83, 95% CI 0.73-0.95, p = 0.008). Conditional on engagement, retention was significantly lower in the PRS arm than in the GBC arm (OR 0.18, 95% CI 0.03-1.00, p = 0.050). No other selection effects were observed. CONCLUSIONS: Genetically informed precision smoking cessation interventions are feasible in socioeconomically disadvantaged communities, exhibiting high enrollment, engagement, and retention irrespective of race, sex, income, education, or attitudes toward precision smoking treatment. Future smoking cessation interventions in this population should take steps to engage older people and to sustain participation in interventions that include genetic risk counseling. TRIAL REGISTRATION: ClinicalTrials.gov No. NCT03521141, Registered 27 April 2018, https://www. CLINICALTRIALS: gov/study/NCT03521141.

Unbiased characterization of atrial fibrillation phenotypic architecture provides insight to genetic liability and clinically relevant outcomes.

Background: Atrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. Methods: We identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. Results: The analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. Conclusion: Patient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

Plant-pollinator network architecture does not impact intraspecific microbiome variability.

Variation in how individuals interact with food resources can directly impact, and be affected by, their microbial interactions due to the potential for transmission. The degree to which this transmission occurs, however, may depend on the structure of forager networks, which determine the community-scale transmission opportunities. In particular, how the community-scale opportunity for transfer balances individual-scale barriers to transmission is unclear. Examining the bee-flower and bee-microbial interactions of over 1000 individual bees, we tested (1) the degree to which individual floral visits predicted microbiome composition and (2) whether plant-bee networks with increased opportunity for microbial transmission homogenized the microbiomes of bees within that network. The pollen community composition carried by bees was associated with microbiome composition at some sites, suggesting that microbial transmission at flowers occurred. Contrary to our predictions, however, microbiome variability did not differ based on transfer opportunity: bee microbiomes in asymmetric networks with high opportunity for microbial transfer were similarly variable compared to microbiomes in networks with more evenly distributed links. These findings suggest that microbial transmission at flowers is frequent enough to be observed at the community level, but that community network structure did not substantially change the dynamics of this transmission, perhaps due to filtering processes in host guts.

LNK/SH2B3 loss of function increases susceptibility to murine and human atrial fibrillation.

AIMS: The lymphocyte adaptor protein (LNK) is a negative regulator of cytokine and growth factor signalling. The rs3184504 variant in SH2B3 reduces LNK function and is linked to cardiovascular, inflammatory, and haematologic disorders, including stroke. In mice, deletion of Lnk causes inflammation and oxidative stress. We hypothesized that Lnk-/- mice are susceptible to atrial fibrillation (AF) and that rs3184504 is associated with AF and AF-related stroke in humans. During inflammation, reactive lipid dicarbonyls are the major components of oxidative injury, and we further hypothesized that these mediators are critical drivers of the AF substrate in Lnk-/- mice. METHODS AND RESULTS: Lnk-/- or wild-type (WT) mice were treated with vehicle or 2-hydroxybenzylamine (2-HOBA), a dicarbonyl scavenger, for 3 months. Compared with WT, Lnk-/- mice displayed increased AF duration that was prevented by 2-HOBA. In the Lnk-/- atria, action potentials were prolonged with reduced transient outward K+ current, increased late Na+ current, and reduced peak Na+ current, pro-arrhythmic effects that were inhibited by 2-HOBA. Mitochondrial dysfunction, especially for Complex I, was evident in Lnk-/- atria, while scavenging lipid dicarbonyls prevented this abnormality. Tumour necrosis factor-α (TNF-α) and interleukin-1 beta (IL-1ß) were elevated in Lnk-/- plasma and atrial tissue, respectively, both of which caused electrical and bioenergetic remodelling in vitro. Inhibition of soluble TNF-α prevented electrical remodelling and AF susceptibility, while IL-1ß inhibition improved mitochondrial respiration but had no effect on AF susceptibility. In a large database of genotyped patients, rs3184504 was associated with AF, as well as AF-related stroke. CONCLUSION: These findings identify a novel role for LNK in the pathophysiology of AF in both experimental mice and humans. Moreover, reactive lipid dicarbonyls are critical to the inflammatory AF substrate in Lnk-/- mice and mediate the pro-arrhythmic effects of pro-inflammatory cytokines, primarily through electrical remodelling.

Pollen Diet Diversity does not Affect Gut Bacterial Communities or Melanization in a Social and Solitary Bee Species.

Pollinators face many stressors, including reduced floral diversity. A low-diversity diet can impair organisms' ability to cope with additional stressors, such as pathogens, by altering the gut microbiome and/or immune function, but these effects are understudied for most pollinators. We investigated the impact of pollen diet diversity on two ecologically and economically important generalist pollinators, the social bumble bee (Bombus impatiens) and the solitary alfalfa leafcutter bee (Megachile rotundata). We experimentally tested the effect of one-, two-, or three-species pollen diets on gut bacterial communities in both species, and the melanization immune response in B. impatiens. Pollen diets included dandelion (Taraxacum officinale), staghorn sumac (Rhus typhina), and hawthorn (Crataegus sp.) alone, each pair-wise combination, or a mix of all three species. We fed bees their diet for 7 days and then dissected out guts and sequenced 16S rRNA gene amplicons to characterize gut bacterial communities. To assess melanization in B. impatiens, we inserted microfilament implants into the bee abdomen and measured melanin deposition on the implant. We found that pollen diet did not influence gut bacterial communities in M. rotundata. In B. impatiens, pollen diet composition, but not diversity, affected gut bacterial richness in older, but not newly-emerged bees. Pollen diet did not affect the melanization response in B. impatiens. Our results suggest that even a monofloral, low-quality pollen diet such as dandelion can support diverse gut bacterial communities in captive-reared adults of these bee species. These findings shed light on the effects of reduced diet diversity on bee health.