Samodha Charaka Fernando

Background/Objectives: The high prevalence of obesity and obesity-associated diseases in the United States and worldwide places a tremendous burden on public health. Although lifestyle interventions, such as calorie-restricted diets and increased exercise, are generically recommended to individuals with overweight and obesity, it is well acknowledged that individual responses to the same lifestyle intervention vary significantly, underscoring the importance of individualized or precision nutrition-based approaches in obesity management. Methods: In a recent randomized, parallel-arm pilot study, manuka honey (21 g) was given daily to overweight and obese participants aged 40–75 years for four weeks. The levels of plasma interleukin (IL)-6 at the baseline and after honey treatment were measured. The 14 female participants were stratified into responder (R) and non-responder (NR) groups, based on their circulating IL-6 level changes. Plasma and fecal samples from the R and NR groups were subjected to untargeted metabolomics analysis and 16S rRNA analysis, respectively. Results: Among the female participants with overweight and obesity, 50% had reduced plasma levels of IL-6 after honey intake, and other 50% showed no such responses. Untargeted metabolomics analysis demonstrated that 22 metabolites markedly increased and eight decreased in the R group, relative to the NR group. A decrease in circulating glutamic acid could potentially predict the responsiveness to honey intake. 16S rRNA analysis showed that 23 and 14 genera were uniquely enriched in the R and NR groups, respectively. Enriched Bacteroides and Akkermansia in the R group are capable of metabolizing glutamic acid and thus may contribute to the decreased level of circulating glutamic acid. Conclusions: In this pilot study, participants with overweight and obesity showed different responses to honey intake. Circulating glutamic acid may have the potential to predict the responsiveness of patients to honey and provide guidance for precision nutrition-based dietary intervention.

Introduction: Studies have investigated the rumen microbiome composition and functions to improve ruminant agriculture and its environmental impacts. Yet, sample collection for rumen microbiome analysis can be difficult and invasive, hindering the ability to sample large animal populations. Studies have proposed using oral swabs as an alternative to rumen sample collection. Here, we investigated the potential of using the oral bacterial community as a proxy for the rumen bacterial community during the cattle production cycle. Methods: We investigated the development of the bovine rumen and oral bacterial communities using longitudinal sampling and the applicability of using the oral to predict host phenotypes. To this end, we utilized 16S rRNA gene sequencing to characterize and compare the rumen and oral bacterial community composition over multiple time points using amplicon sequence variants (ASVs) in a beef cattle population of 166 animals. Additionally, host phenotype of weaning weight was predicted using the Bayesian ridge regression model to evaluate the applicability of using the oral bacterial community for phenotype prediction. Results: Our results identified the rumen and oral bacterial communities to have different trajectories of assembly. The proportion of Proteobacteria and Actinobacteriota was higher ( p < 0.0001) in the oral samples. Whereas rumen samples had greater abundance of members of the phyla Bacteroidota , Firmicutes , Verrucomicrobiota , Fibrobacterota , and Spirochaetota . The investigation of the oral and rumen bacterial community establishment demonstrated considerable dynamism, where diet and age-related factors to contribute toward bacterial colonization through introduction of new species and the proliferation of early colonizers. Finally, a Bayesian ridge regression model was developed to estimate weaning weight using the centered and scaled log-transformed relative abundance of ASVs. The proportion of variation explained in weaning weight by the oral and rumen bacterial communities were 30 and 37%, respectively. Discussion: Results from this study suggest that oral and rumen bacterial communities are distinctive, and the oral bacterial community may not serve as a good proxy for the rumen bacterial community even in adult animals with a well-established microbiome. However, the oral bacterial community may serve as a proxy for phenotypic traits of interest in beef cattle.

At the heart of degradation of complex OM within the rumen are its microbes. These microbes can convert unusable OM into usable protein and energy sources and therefore can directly influence an animal's health, performance, and utilization of feed. As such, understanding stochastic and deterministic factors that contribute toward rumen microbial assembly and persistence can provide valuable information for developing methods to improve animal health, performance, and nutrient utilization through microbiome modulation. In this review, we describe how ecological concepts such as colonization history, priority effects, and historical contingency may influence rumen microbial community assembly and in turn affect community composition and function. Additionally, we discuss how such ecological concepts can be used to develop novel microbial community assembly strategies to develop rumen microbiomes with long-term benefits to the host ruminant. Finally, we discuss current knowledge gaps in rumen microbiome research associated with host-microbe interactions and microbial assembly and propose potential opportunities for future microbiome studies to improve understanding of the rumen microbiome to promote animal health and productivity.

Some forages require significant amounts of water to grow, causing the dairy industry to be dependent on a limited resource. Feeding crop residues and feed coproducts in dairy rations may represent opportunities when alfalfa is not readily available, and may reduce the industry's use of water. A study using indirect calorimetry and 12 multiparous lactating Jersey cows (BW = 447.5 ± 43.7 kg; DIM = 71 ± 11 d, mean ± SD) was conducted to determine the effect of feeding dried distillers grains and solubles (DDGS) and straw in replacement of alfalfa hay on milk production and energy utilization. A triplicated 4 × 4 Latin square design was used to evaluate the replacement of alfalfa hay with a coproduct mixture (COP) of wheat straw and DDGS. Animals were blocked by milk yield and randomly assigned to 1 of 4 experimental treatments as follows (proportions on a DM basis): a control diet (CON) containing 18.2% of alfalfa hay, a low-coproduct diet (LCOP) that contained 8.1% COP, a medium-coproduct diet (MCOP) that contained 16.3% COP, and a high-coproduct diet (HCOP) that contained 24.3% COP. No differences were observed for daily DMI or milk yield (mean ± SEM), 19.5 kg ± 0.60 and 29.6 kg ± 0.91, respectively. A quadratic tendency was observed, where increasing inclusion of COP up to 16.3% maintained ECM and milk fat yield but decreased these when animals were fed 24.3% COP. Total methane production decreased linearly from 429.4 to 345.0 ± 22.8 L/d from CON to HCOP diets, respectively. The digestibility of CP increased linearly from 64.0% to 70.4% ± 0.95%, and N balance increased linearly from 43.3 to 90.7 ± 15.0 g/d in animals consuming CON to HCOP diets. Total time spent ruminating was lowest in animals consuming the HCOP diet. A linear increasing tendency in digestible and metabolizable energy of 2.92 to 3.02 ± 0.041 Mcal/kg and 2.58 to 2.70 ± 0.047 Mcal/kg was observed in animals consuming CON to HCOP. The proportion of metabolizable energy from digestible energy tended to linearly increase from 88.3 to 89.4 ± 0.454 when COP was added to the diet. Results of this study indicate that alfalfa hay with a mixture of straw and DDGS can maintain milk production and DMI, but the partial or full replacement of alfalfa with the COP mixture may result in differences in energy utilization, in part driven by effects on CH₄ reduction.

Introduction: The full extent of interactions between human immunodeficiency virus (HIV) infection, injection drug use, and the human microbiome is unclear. In this study, we examined the microbiomes of HIV-positive and HIV-negative individuals, both drug-injecting and non-injecting, to identify bacterial community changes in response to HIV and drug use. We utilized a well-established cohort of people who inject drugs in Puerto Rico, a region with historically high levels of injection drug use and an HIV incidence rate disproportionately associated with drug use.Methods: Using amplicon-based 16S rDNA sequencing, we identified amplicon sequence variants (ASVs) that demonstrated significant variations in the composition of microbial communities based on HIV status and drug use.Results and discussion: Our findings indicate that the HIV-positive group exhibited a higher abundance of ASVs belonging to the genera Prevotella, Alloprevotella, Sutterella, Megasphaera, Fusobacterium, and Mitsuokella. However, Bifidobacteria and Lactobacillus ASVs were more abundant in injectors than in non-injectors. We examined the effect of drug use on the gut microbiome in both HIV-infected and non-infected patients, and found that multiple drug use significantly affected the microbial community composition. Analysis of differential of bacterial taxa revealed an enrichment of Bifidobacterium spp., Faecalibacterium spp., and Lactobacillus spp. in the multiple drug-injecting group. However, in the non-injecting group, Parabacteroides spp., Prevotella spp., Paraprevotella spp., Sutterella spp., and Lachnoclostridium spp. The presence of multiple drug-injecting groups was observed to be more prevalent. Our findings provide detailed insight into ASV-level changes in the microbiome in response to HIV and drug use, suggesting that the effect of HIV status and drug injection may have different effects on microbiome composition and in modulating gut bacterial populations.

Microbes play an important role in human and animal health as well as animal productivity. The host microbial interactions within ruminants play a critical role in animal health and productivity and provide up to 70% of the animal's energy need in the form of fermentation products. As such, many studies have investigated microbial community composition to understand microbial community changes and factors that affect microbial colonization and persistence. The advances in next generation sequencing (NGS) technologies and low cost of sequencing have gravitated many studies to utilize 16S rDNA-based analysis tools for interrogation of microbiomes at a much finer scale than traditional culturing. However, such methods that rely on single base pair differences for bacterial taxa clustering may inflate or underestimate diversity leading to inaccurate identification of bacterial diversity. Therefore, in this study, we sequenced mock communities of known membership and abundance to establish filtration parameters to reduce inflation of microbial diversity due to PCR and sequencing errors. Additionally, we evaluated the effect of the resulting filtering parameters proposed using established bioinformatic pipelines on a study consisting of Holstein and Jersey cattle to identify bread and treatment effects on the bacterial community composition and the impact of the filtering on global microbial community structure analysis and results. Filtration resulted in a sharp reduction in bacterial taxa identified, yet retain most sequencing data (retaining > 79% of sequencing reads) when analyzed using 3 different microbial analysis pipelines (DADA2, Mothur, USEARCH). After filtration, conclusions from α and β-diversity tests show very similar results across all analysis methods. The mock community-based filtering parameters proposed in this study help provide a more realistic estimation of bacterial diversity. Additionally, the filtration reduced the variation between microbiome analysis methods and help identify microbial community differences that could have been missed due to large animal to animal variation observed in the unfiltered data. As such, we believe, the new filtering parameters described in this study will help obtain diversity estimates closer to realistic values and will improve the ability of detecting microbial community differences and help better understand microbial community changes in 16S rDNA-based studies.

The gut microbiome is a highly intricate ecosystem that exerts a pivotal influence on the host’s physiology. Characterizing fish microbiomes is critical to understanding fish physiology and health, but little is known about the ecology and colonization dynamics of microorganisms inhabiting fish species. In this study, we investigated the bacterial communities of two small-bodied fish species, Cyprinella lutrensis (red shiner) and Notropis stramineus (sand shiner), two fish species where gut microbiomes have not been investigated previously and surrounding waters, collected from rivers in Nebraska, USA. Our study focused on evaluating microbial diversity in small-bodied fish and identifying autochthonous microbes present within these species irrespective of location to better understand bacterial community composition and possible roles of such bacterial species. Our results revealed that both red shiner and sand shiner exhibited gut bacterial communities dominated by typical bacterial phyla found in freshwater fish. The phylum Bacteroidota was minimally abundant in both species and significantly lower in relative abundance compared to the surrounding water microbial community. Furthermore, we found that the gut microbiomes of red shiner and sand shiner differed from the microbial community in the surrounding water, suggesting that these fish species contain host-associated bacterial species that may provide benefits to the host such as nutrient digestion and colonization resistance of environmental pathogens. The fish gut bacterial communities were sensitive to environmental conditions such as turbidity, dissolved oxygen, temperature, and total nitrogen. Our findings also show bacterial community differences between fish species; although they shared notable similarities in bacterial taxa at phyla level composition, ASV level analysis of bacterial taxa displayed compositional differences. These findings contribute to a better understanding of the gut bacterial composition of wild, freshwater, small-bodied fish and highlight the influence of intrinsic (host) and environmental factors on shaping the bacterial composition.

Introduction: An increasing emergence of novel animal pathogens has been observed over the last decade. Viruses are a major contributor to the increased emergence and therefore, veterinary surveillance and testing procedures are greatly needed to rapidly and accurately detect high-consequence animal diseases such as Foot and Mouth Disease, Highly Pathogenic Avian Influenza, Classical Swine Fever, and African Swine Fever. The major detection methods for such diseases include real-time PCR assays and pathogen-specific antibodies among others. However, due to genetic drift or -shift in virus genomes, failure to detect such pathogens is a risk with devastating consequences. Additionally, the emergence of novel pathogens with no prior knowledge requires non-biased detection methods for discovery.Methods: Utilizing enrichment techniques coupled with Oxford Nanopore Technologies MinION™ sequencing platform, we developed a sample processing and analysis pipeline to identify DNA and RNA viruses and bacterial pathogens from clinical samples.Results and discussion: The sample processing and analysis pipeline developed allows the identification of both DNA and RNA viruses and bacterial pathogens simultaneously from a single tissue sample and provides results in less than 12 h. Preliminary evaluation of this method using surrogate viruses in different matrices and using clinical samples from animals with unknown disease causality, we demonstrate that this method can be used to simultaneously detect pathogens from multiple domains of life simultaneously with high confidence.

The bovine ocular microbiome is of interest because of its potential role in ocular disease, such as infectious bovine keratoconjunctivitis. The ocular bacterial community of 223 pre-weaned beef calves from a single cohort were sampled four times; day 0, day 21, day 41, and day 139 (mean calf ages 65, 86, 99, and 204 days, respectively). The bacterial community was phenotyped using the V4 region of the 16S rRNA gene and were grouped into amplicon sequence variants (ASV) which were taxonomically classified at the phylum and family level. Heritability was estimated for the log transformed relative abundance of each phylum, family, and ASV at each time point using ASReml v. 4.2. The model included calf age at time of sampling, calf sex, pinkeye vaccination treatment group, breed fractions, and retained heterosis as fixed effects. The random effect of animal had a (co)variance structure given by an H matrix comprised of 3,025 animals of which 1,207 were genotyped including all of the sampled animals. Heritability estimates ranged from 0.16 to 0.95 at the ASV level, 0.18 to 0.91 at the family level, and 0.18 to 0.81 at the phylum level. Standard errors were approximately 0.20 on average. The number of features (ASV, family, or phylum) with estimates greater than their standard error changed across time points (Table 1). Overall, time point 1 had the largest number of features with estimates greater than their standard error, followed by time point 4. Time point 3 had a greater number ASV with estimates greater than their standard error than time point 2; however, time point 2 had greater numbers of families and phyla with estimates greater than their standard errors. This trend likely stems from the fact that time points 1 and 4 had the greatest diversity of features while time point 2 had the least diversity. Average Chao1 index diversity estimates of ASV were 144.6, 28.1, 64.7, and 194.6, for time points 1 through 4, respectively. We hypothesize this is due to the perturbation of the ocular microbiome at sampling and the fact that overall diversity in the microbiome recovered by the final sampling, although, species composition differed compared with time point 1. While no ASV, family, or phylum had a heritability estimate greater than its standard error across all time points, certain features had heritabilities greater than their standard errors at multiple time points. The phylum Armatimonadota had heritability estimates greater than their standard error at time points 1, 2, and 4 (0.52, 0.18, and 0.37, respectively). Additionally, an ASV of the Streptococcus genus had estimates of 0.26, 0.45, and 0.26 at time points 1, 2, and 3, respectively. These estimates demonstrate that certain features of the ocular microbiome are influenced by host genetics.