Longitudinal dynamics of farmer and livestock nasal and faecal microbiomes and resistomes.

Globally, half a billion people are employed in animal agriculture and are directly exposed to the associated microorganisms. However, the extent to which such exposures affect resident human microbiomes is unclear. Here we conducted a longitudinal profiling of the nasal and faecal microbiomes of 66 dairy farmers and 166 dairy cows over a year-long period. We compare farmer microbiomes to those of 60 age-, sex- and ZIP code-matched people with no occupational exposures to farm animals (non-farmers). We show that farming is associated with microbiomes containing livestock-associated microbes; this is most apparent in the nasal bacterial community, with farmers harbouring a richer and more diverse nasal community than non-farmers. Similarly, in the gut microbial communities, we identify more shared microbial lineages between cows and farmers from the same farms. Additionally, we find that shared microbes are associated with antibiotic resistance genes. Overall, our study demonstrates the interconnectedness of human and animal microbiomes.

Assessment of the current status of real-world pharmacogenomic testing: informed consent, patient education, and related practices.

Introduction: The practice of informed consent (IC) for pharmacogenomic testing in clinical settings varies, and there is currently no consensus on which elements of IC to provide to patients. This study aims to assess current IC practices for pharmacogenomic testing. Methods: An online survey was developed and sent to health providers at institutions that offer clinical germline pharmacogenomic testing to assess current IC practices. Results: Forty-six completed surveys representing 43 clinical institutions offering pharmacogenomic testing were received. Thirty-two (74%) respondents obtain IC from patients with variability in elements incorporated. Results revealed that twenty-nine (67%) institutions discuss the benefits, description, and purpose of pharmacogenomic testing with patients. Less commonly discussed elements included methodology and accuracy of testing, and laboratory storage of samples. Discussion: IC practices varied widely among survey respondents. Most respondents desire the establishment of consensus IC recommendations from a trusted pharmacogenomics organization to help address these disparities.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction.

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Genetic risk score in multiple sclerosis is associated with unique gut microbiome.

Multiple sclerosis (MS) is a complex autoimmune disease in which both the roles of genetic susceptibility and environmental/microbial factors have been investigated. More than 200 genetic susceptibility variants have been identified along with the dysbiosis of gut microbiota, both independently have been shown to be associated with MS. We hypothesize that MS patients harboring genetic susceptibility variants along with gut microbiome dysbiosis are at a greater risk of exhibiting the disease. We investigated the genetic risk score for MS in conjunction with gut microbiota in the same cohort of 117 relapsing remitting MS (RRMS) and 26 healthy controls. DNA samples were genotyped using Illumina's Infinium Immuno array-24 v2 chip followed by calculating genetic risk score and the microbiota was determined by sequencing the V4 hypervariable region of the 16S rRNA gene. We identified two clusters of MS patients, Cluster A and B, both having a higher genetic risk score than the control group. However, the MS cases in cluster B not only had a higher genetic risk score but also showed a distinct gut microbiome than that of cluster A. Interestingly, cluster A which included both healthy control and MS cases had similar gut microbiome composition. This could be due to (i) the non-active state of the disease in that group of MS patients at the time of fecal sample collection and/or (ii) the restoration of the gut microbiome post disease modifying therapy to treat the MS. Our study showed that there seems to be an association between genetic risk score and gut microbiome dysbiosis in triggering the disease in a small cohort of MS patients. The MS Cluster A who have a higher genetic risk score but microbiome profile similar to that of healthy controls could be due to the remitting phase of the disease or due to the effect of disease modifying therapies.

Prognostic microRNA signature for estimating survival in patients with hepatocellular carcinoma.

OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the leading cancer types with increasing annual incidence and high mortality in the USA. MicroRNAs (miRNAs) have emerged as valuable prognostic indicators in cancer patients. To identify a miRNA signature predictive of survival in patients with HCC, we developed a machine learning-based HCC survival estimation method, HCCse, using the miRNA expression profiles of 122 patients with HCC. METHODS: The HCCse method was designed using an optimal feature selection algorithm incorporated with support vector regression. RESULTS: HCCse identified a robust miRNA signature consisting of 32 miRNAs and obtained a mean correlation coefficient (R) and mean absolute error (MAE) of 0.87 ±â€…0.02 and 0.73 years between the actual and estimated survival times of patients with HCC; and the jackknife test achieved an R and MAE of 0.73 and 0.97 years between actual and estimated survival times, respectively. The identified signature has seven prognostic miRNAs (hsa-miR-146a-3p, hsa-miR-200a-3p, hsa-miR-652-3p, hsa-miR-34a-3p, hsa-miR-132-5p, hsa-miR-1301-3p and hsa-miR-374b-3p) and four diagnostic miRNAs (hsa-miR-1301-3p, hsa-miR-17-5p, hsa-miR-34a-3p and hsa-miR-200a-3p). Notably, three of these miRNAs, hsa-miR-200a-3p, hsa-miR-1301-3p and hsa-miR-17-5p, also displayed association with tumor stage, further emphasizing their clinical relevance. Furthermore, we performed pathway enrichment analysis and found that the target genes of the identified miRNA signature were significantly enriched in the hepatitis B pathway, suggesting its potential involvement in HCC pathogenesis. CONCLUSIONS: Our study developed HCCse, a machine learning-based method, to predict survival in HCC patients using miRNA expression profiles. We identified a robust miRNA signature of 32 miRNAs with prognostic and diagnostic value, highlighting their clinical relevance in HCC management and potential involvement in HCC pathogenesis.

Suppressed immune and metabolic responses to intestinal damage-associated microbial translocation in myalgic encephalomyelitis/chronic fatigue syndrome.

The etiology and mechanism of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) are poorly understood and no biomarkers have been established. Specifically, the relationship between the immunologic, metabolic, and gastrointestinal abnormalities associated with ME/CFS and their relevance to established symptoms of the condition remain unclear. Relying on data from two independent pairs of ME/CFS and control cohorts, one at rest and one undergoing an exercise challenge, we identify a state of suppressed acute-phase innate immune response to microbial translocation in conjunction with a compromised gut epithelium in ME/CFS. This immunosuppression, along with observed enhancement of compensatory antibody responses to counter the microbial translocation, was associated with and may be mediated by alterations in glucose and citrate metabolism and an IL-10 immunoregulatory response. Our findings provide novel insights into mechanistic pathways, biomarkers, and potential therapeutic targets in ME/CFS, including in the context of exertion, with relevance to both intestinal and extra-intestinal symptoms.

Dairy Farm Work and Protection from Gastrointestinal Illness.

Gastrointestinal (GI) disorders are a major public health burden in the United States. Due to close contact with animals, farmers may be a high risk subgroup for acute GI infections, though some studies suggest farm work is actually protective against GI illness. The purpose of this study was to examine associations between dairy farm work and GI symptoms over 3 years. A prospective, matched cohort study was used that included 70 adult dairy farm workers and 74 matched (age, gender, ZIP code) non-farm participants from central Wisconsin. The outcome was mean GI symptom scores for abdominal pain, diarrhea, constipation, dyspepsia, nausea, and reflux, per the 23-item Gastrointestinal Symptoms Severity Index (GISSI). After adjustment for potential confounding variables, linear regression results indicated dairy farm workers had significantly lower GISSI scores for abdominal pain (mean±SE = 4.3 ± 1.1 dairy vs. 7.6 ± 1.1 non-farm, p = .047), diarrhea (3.2 ± 1.0 dairy vs. 7.0 ± 1.0 non-farm, p = .010), constipation (2.0 ± 0.8 dairy vs. 6.6 ± 0.8 non-farm, p < .001), and dyspepsia (2.0 ± 0.6 dairy vs. 3.9 ± 0.5 non-farm, p = .026). Working on a dairy farm was associated with significantly less frequent and severe GI illness symptoms in adults. Future research should identify underlying causal pathways, including possible farm animal exposures, that influence beneficial gut microbiota that could inform therapeutic remedies to help prevent clinical GI disorders.

Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction.

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection..

Efficacy of Omadacycline or Vancomycin Combined With Germinants for Preventing Clostridioides difficile Relapse in a Murine Model.

BACKGROUND: Clostridioides difficile infections (CDI) and recurrence (rCDI) are major health care burdens. Recurrence is likely caused by spores in the gastrointestinal tract that germinate after antibiotic therapy. This murine study explores germinant-antibiotic combinations for CDI. METHODS: Previously described murine models were evaluated using C. difficile VPI 10463. The severe model compared omadacycline versus vancomycin in survival, weight loss, clinical scoring, and C. difficile toxin production. The nonsevere model compared these antibiotics with and without germinants (solution of sodium taurocholate, taurine, sodium docusate, calcium gluconate). Additionally, colon histopathology, bile acid analysis, environmental/spore shedding, and 16S sequencing was evaluated. RESULTS: In the severe model, omadacycline-treated mice had 60% survival versus 13.3% with vancomycin (hazard ratio [HR], 0.327; 95% confidence interval [CI],.126-.848; P = .015) along with decreased weight loss, and disease severity. In the nonsevere model, all mice survived with antibiotic-germinant treatment versus 60% antibiotics alone (HR, 0.109; 95% CI, .02-.410; P = .001). Omadacycline resulted in less changes in bile acids and microbiota composition. Germinant-treated mice showed no signs of rCDI, spore shedding, or significant toxin production at 15 days. CONCLUSIONS: In murine models of CDI, omadacycline improved survival versus vancomycin. Germinant-antibiotic combinations were more effective at preventing rCDI compared to antibiotics alone without inducing toxin production.

Omadacycline compared to vancomycin when combined with germinants to disrupt the life cycle of Clostridioides difficile.

Clostridioides difficile (C. difficile) infections (CDI) are commonly treated with antibiotics that do not impact the dormant spore form of the pathogen. CDI-directed antibiotics, such as vancomycin and metronidazole, can destroy the vegetative form of C. difficile and protective microbiota. After treatment, spores can germinate into vegetative cells causing clinical disease relapse and further spore shedding. This in vitro study compares the combination of germinants with vancomycin or omadacycline to antibiotics alone in eradicating C. difficile spores and vegetative cells. Among the four strains in this study, omadacycline minimum inhibitory concentrations (0.031-0.125 mg/L) were lower than vancomycin (1-4 mg/L). Omadacycline nor vancomycin in media alone reduced spore counts. In three of the four strains, including the epidemic ribotype 027, spore eradication with germinants was 94.8-97.4% with vancomycin and 99.4-99.8% with omadacycline (p<0.005). In ribotype 012, either antibiotic combined with germinants resulted in 100% spore eradication at 24 hours. The addition of germinants with either antibiotic did not result in significant toxin A or B production, which were below the limit of detection (<1.25 ng/mL) by 48 hours. Limiting the number of spores present in patient GI tracts at the end of therapy may be effective at preventing recurrent CDI and limiting spore shedding in the healthcare environment. These results with germinants warrant safety and efficacy evaluations in animal models.

The gut microbiome molecular mimicry piece in the multiple sclerosis puzzle.

The etiological complexity of multiple sclerosis, an immune-mediated, neurodegenerative disease with multifactorial etiology is still elusive because of an incomplete understanding of the complex synergy between contributing factors such as genetic susceptibility and aberrant immune response. Recently, the disease phenotypes have also been shown to be associated with dysbiosis of the gut microbiome, a dynamic reservoir of billions of microbes, their proteins and metabolites capable of mimicring the autoantigens. Microbial factors could potentially trigger the neuroinflammation and symptoms of MS. In this perspective article, we discussed how microbial molecules resulting from a leaky gut might mimic a host's autoantigen, potentially contributing to the disease disequilibrium. It further highlights the importance of targeting the gut microbiome for alternate therapeutic options for the treatment of MS.

Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles.

Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

SPIKES: Identification of physicochemical properties of spike proteins across diverse host species of SARS-CoV-2.

We describe a protocol to identify physicochemical properties using amino acid sequences of spike (S) proteins of SARS-CoV-2. We present an S protein prediction technique named SPIKES, incorporating an inheritable bi-objective combinatorial genetic algorithm to determine the host species specificity. This protocol addresses the S protein amino acid sequence data collection, preprocessing, methodology, and analysis. For complete details on the use and execution of this protocol, please refer to Yerukala Sathipati et al. (2022).

Expression of Staphylococcal Virulence Genes In Situ in Human Skin and Soft Tissue Infections.

BACKGROUND: Staphylococcus aureus, the most common pathogen in skin and soft tissue infections (SSTI), harbors many well-characterized virulence genes. However, the expression of many of them in SSTIs is unknown. In this study, S. aureus virulence genes expressed in SSTI were investigated. METHODS: Fifty-three subjects presenting to the outpatient's care and emergency departments with a purulent SSTI at two medical centers in Wisconsin, USA, were enrolled in the study. Total mRNA was extracted from the purulent or swab materials, made into cDNA and sequenced on MiSeq platform. The relative cDNA counts to gmk and identifications of the transcripts were carried out with respect to USA300 reference genome and using SAMTOOLS v.1.3 and BWA, respectively. RESULT: A significantly higher cDNA count was observed for many of the virulence and regulatory gene transcripts in the pus samples compared to the swab samples relative to the cDNA counts for gmk, a housekeeping gene. They were for lukS-PV (18.6 vs. 14.2), isaA (13.4 vs. 8.5), ssaA (4.8 vs. 3.1), hlgC (1.4 vs. 1.33), atl (17.7 vs. 8.33), clfA (3.9 vs. 0.83), eno (6.04 vs. 3.16), fnbA (5.93 vs. 0.33), saeS (6.3 vs. 1.33), saeR (5.4 vs. 3.33) and agrC (5.6 vs. 1.5). CONCLUSIONS: A relative increase in the transcripts of several toxins, adhesion and regulatory genes with respect to a gmk in purulent materials suggests their role in situ during SSTIs, perhaps in an orchestrated manner.

MicroRNA signature for estimating the survival time in patients with bladder urothelial carcinoma.

Bladder urothelial carcinoma (BLC) is one of the most common cancers in men, and its heterogeneity challenges the treatment to cure this disease. Recently, microRNAs (miRNAs) gained promising attention as biomarkers due to their potential roles in cancer biology. Identifying survival-associated miRNAs may help identify targets for therapeutic interventions in BLC. This work aims to identify a miRNA signature that could estimate the survival in patients with BLC. We developed a survival estimation method called BLC-SVR based on support vector regression incorporated with an optimal feature selection algorithm to select a robust set of miRNAs as a signature to estimate the survival in patients with BLC. BLC-SVR identified a miRNA signature consisting of 29 miRNAs and obtained a mean squared correlation coefficient and mean absolute error of 0.79 ± 0.02 and 0.52 ± 0.32 year between actual and estimated survival times, respectively. The prediction performance of BLC-SVR had a better estimation capability than other standard regression methods. In the identified miRNA signature, 14 miRNAs, hsa-miR-432-5p, hsa-let-7e-3p, hsa-miR-652-3p, hsa-miR-629-5p, and hsa-miR-203a-3p, hsa-miR-129-5p, hsa-miR-769-3p, hsa-miR-570-3p, hsa-miR-320c, hsa-miR-642a-5p, hsa-miR-496, hsa-miR-5480-3p, hsa-miR-221-5p, and hsa-miR-7-1-3p, were found to be good biomarkers for BLC diagnosis; and the six miRNAs, hsa-miR-652-5p, hsa-miR-193b-5p, hsa-miR-129-5p, hsa-miR-143-5p, hsa-miR-496, and hsa-miR-7-1-3p, were found to be good biomarkers of prognosis. Further bioinformatics analysis of this miRNA signature demonstrated its importance in various biological pathways and gene ontology annotation. The identified miRNA signature would further help in understanding of BLC diagnosis and prognosis in the development of novel miRNA-target based therapeutics in BLC.

Tracking the amino acid changes of spike proteins across diverse host species of severe acute respiratory syndrome coronavirus 2.

Knowledge of the host-specific properties of the spike protein is of crucial importance to understand the adaptability of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) to infect multiple species and alter transmissibility, particularly in humans. Here, we propose a spike protein predictor SPIKES incorporating with an inheritable bi-objective combinatorial genetic algorithm to identify the biochemical properties of spike proteins and determine their specificity to human hosts. SPIKES identified 20 informative physicochemical properties of the spike protein, including information measures for alpha helix and relative mutability, and amino acid and dipeptide compositions, which have shown compositional difference at the amino acid sequence level between human and diverse animal coronaviruses. We suggest that alterations of these amino acids between human and animal coronaviruses may provide insights into the development and transmission of SARS-CoV-2 in human and other species and support the discovery of targeted antiviral therapies.

Proteomics, Lipidomics, Metabolomics, and 16S DNA Sequencing of Dental Plaque From Patients With Diabetes and Periodontal Disease.

Oral microbiome influences human health, specifically prediabetes and type 2 diabetes (Pre-DM/DM) and periodontal diseases (PDs), through complex microbial interactions. To explore these relations, we performed 16S rDNA sequencing, metabolomics, lipidomics, and proteomics analyses on supragingival dental plaque collected from individuals with Pre-DM/DM (n = 39), Pre-DM/DM and PD (n = 37), PD alone (n = 11), or neither (n = 10). We identified on average 2790 operational taxonomic units and 2025 microbial and host proteins per sample and quantified 110 metabolites and 415 lipids. Plaque samples from Pre-DM/DM patients contained higher abundance of Fusobacterium and Tannerella than plaques from metabolically healthy patients. Phosphatidylcholines, plasmenyl phosphatidylcholines, ceramides containing non-OH fatty acids, and host proteins related to actin filament rearrangement were elevated in plaques from PD versus non-PD samples. Cross-omic correlation analysis enabled the detection of a strong association between Lautropia and monomethyl phosphatidylethanolamine (PE-NMe), which is striking because synthesis of PE-NMe is uncommon in oral bacteria. Lipidomics analysis of in vitro cultures of Lautropia mirabilis confirmed the synthesis of PE-NMe by the bacteria. This comprehensive analysis revealed a novel microbial metabolic pathway and significant associations of host-derived proteins with PD.

An Early History of Phage Therapy in the United States: Is it Time to Reconsider?

Frederick William Twort and Felix d'Hérelle independently discovered bacteriophages in 1915 and 1917, respectively. This led to the early trials of using bacteriophages to treat infectious diseases worldwide. The earliest reported use of bacteriophages therapeutically in the United States was in 1922. With the subsequent discovery of antibiotics in the 1940s, and because of disappointing results of phage therapy in the next decade, use of bacteriophages as therapeutic agents declined in western countries. This paper addresses two questions in the field: what is the historical record of the successes and failures of phage therapy in the United States and, what led to abandoning phage therapy in the United States? We examined the literature from 1915 to 1965, and we present a numerical analysis of the papers published during that period. We report key historical factors leading to a decline in the use of phage therapy in the United States by the 1950s. Since bacteriophages were first used therapeutically, several changes have occurred: increased antimicrobial drug resistance and a better knowledge of the biology of bacteriophages are important examples. Early assessments leading to the rejection of phage therapy in the United States were perhaps appropriate. However, it is time to reconsider the role of bacteriophages in treatment of bacterial infections.

Manure Microbial Communities and Resistance Profiles Reconfigure after Transition to Manure Pits and Differ from Those in Fertilized Field Soil.

In agricultural settings, microbes and antimicrobial resistance genes (ARGs) have the potential to be transferred across diverse environments and ecosystems. The consequences of these microbial transfers are unclear and understudied. On dairy farms, the storage of cow manure in manure pits and subsequent application to field soil as a fertilizer may facilitate the spread of the mammalian gut microbiome and its associated ARGs to the environment. To determine the extent of both taxonomic and resistance similarity during these transitions, we collected fresh manure, manure from pits, and field soil across 15 different dairy farms for three consecutive seasons. We used a combination of shotgun metagenomic sequencing and functional metagenomics to quantitatively interrogate taxonomic and ARG compositional variation on farms. We found that as the microbiome transitions from fresh dairy cow manure to manure pits, microbial taxonomic compositions and resistance profiles experience distinct restructuring, including decreases in alpha diversity and shifts in specific ARG abundances that potentially correspond to fresh manure going from a gut-structured community to an environment-structured community. Further, we did not find evidence of shared microbial community or a transfer of ARGs between manure and field soil microbiomes. Our results suggest that fresh manure experiences a compositional change in manure pits during storage and that the storage of manure in manure pits does not result in a depletion of ARGs. We did not find evidence of taxonomic or ARG restructuring of soil microbiota with the application of manure to field soils, as soil communities remained resilient to manure-induced perturbation.IMPORTANCE The addition of dairy cow manure-stored in manure pits-to field soil has the potential to introduce not only organic nutrients but also mammalian microbial communities and antimicrobial resistance genes (ARGs) to soil communities. Using shotgun sequencing paired with functional metagenomics, we showed that microbial community composition changed between fresh manure and manure pit samples with a decrease in gut-associated pathobionts, while ARG abundance and diversity remained high. However, field soil communities were distinct from those in manure in both microbial taxonomic and ARG composition. These results broaden our understanding of the transfer of microbial communities in agricultural settings and suggest that field soil microbial communities are resilient against the deposition of ARGs or microbial communities from manure.