The gastrointestinal tracts of ruminants harbor a diverse microbial community that coevolved symbiotically with the host, influencing its nutrition, health, and performance. While the influence of environmental factors on rumen microbes is well documented, the process by which host genetics influences the establishment and colonization of the rumen microbiota still needs to be elucidated. This knowledge gap is due largely to our inability to easily sample the rumen microbiota. There are three common methods for rumen sampling but all of them present at least one disadvantage, including animal welfare, sample quality, labor, and scalability. The development and validation of noninvasive methods, such as buccal swabbing, for large-scale rumen sampling is needed to support studies that require large sample sizes to generate reliable results. The validation of buccal swabbing will also support the development of molecular tools for the early diagnosis of metabolic disorders associated with microbial changes in large herds.

The composition of the nasal microbiota in surgical patients in the context of general anesthesia and nasal povidone-iodine decolonization is unknown. The purpose of this quality improvement study was to determine: (i) if general anesthesia is associated with changes in the nasal microbiota of surgery patients and (ii) if preoperative intranasal povidone-iodine decolonization is associated with changes in the nasal microbiota of surgery patients. One hundred and fifty-one ambulatory patients presenting for surgery were enrolled in a quality improvement study by convenience sampling. Pre- and post-surgery nasal samples were collected from patients in the no intranasal decolonization group (control group, n = 54). Pre-decolonization nasal samples were collected from the preoperative intranasal povidone-iodine decolonization group (povidone-iodine group, n = 97). Intranasal povidone-iodine was administered immediately prior to surgery and continued for 20 minutes before patients proceeded for surgery. Post-nasal samples were then collected. General anesthesia was administered to both groups. DNA from the samples was extracted for 16S rRNA sequencing on an Illumina MiSeq. In the control group, there was no evidence of change in bacterial diversity between pre- and post-surgery samples. In the povidone-iodine group, nasal bacterial diversity was greater in post-surgery, relative to pre-surgery (Shannon’s Diversity Index (P = 0.038), Chao’s richness estimate (P = 0.02) and Inverse Simpson index (P = 0.027). Among all the genera, only the relative abundance of the genus Staphylococcus trended towards a decrease in patients after application (FDR adjusted P = 0.06). Abundant genera common to both povidone-iodine and control groups included Staphylococcus, Bradyrhizobium, Corynebacterium, Dolosigranulum, Lactobacillus, and Moraxella. We found general anesthesia was not associated with changes in the nasal microbiota. Povidone-iodine treatment was associated with nasal microbial diversity and decreased abundance of Staphylococcus. Future studies should examine the nasal microbiota structure and function longitudinally in surgical patients receiving intranasal povidone-iodine.

A major goal in dairy research is to improve milk production efficiency (MPE). With the advent of next-generation sequencing, efforts are underway to improve MPE by manipulating the rumen microbiota. MPE correlates to ruminal bacterial community composition (BCC), but the adult rumen microbiota is highly stable and returns to a baseline BCC after heavy perturbation. We seek to influence rumen BCC by early intervention in pre-weaning calves. Two cannulated Holstein donors of disparate MPE were selected. Three cohorts of 6 bull calves were established and dosed by gavage with a rumen inoculum sourced from the high-efficiency donor (HE), the low-efficiency donor (LE), or an autoclaved 50:50 mix as a microbe-free control (C). Dosing occurred at birth, then biweekly through 6 weeks. Feces were collected at each dosing. Daily preweaning intake of calf starter, which correlates to downstream feed efficiency, was greatest in HE calves and lowest in C calves (P < 0.05), though preweaning average daily gain did not differ between cohorts (P = 0.210). Calves were sacrificed at 8 weeks to access rumen contents and rumen wall sections were collected to assess papillation. Fecal and rumen samples were subjected to 16S rRNA amplicon sequencing. BCC differed by cohort in fecal and rumen samples (P < 0.05), with HE calf samples most similar to adult rumen samples and C calves least similar. Additionally, HE calves tended to have elongated papillae (P =0.062), the development of which is dependent on byproducts of microbial metabolism in the rumen and which points to differences in potential absorptive capacity of the epithelium. These data demonstrate that the rumen BCC can be influenced by early intervention. Future work includes expansion of this dosing protocol to a cohort of 60 female calves, following rumen BCC development and the impact on MPE in through the first lactation.

Ruminants harbor a ruminal microbiome that converts their host-indigestible diet into nutrients. This microbiome contains three distinct communities: liquid, solid, and epithelial (or epimural). Currently, there is limited research examining the diet-dependent responses of all three microbiomes within the same animal. Here, we used next-generation 16S rRNA sequencing to characterize the ruminal solid (RS), liquid (RL), and epimural (RE) microbiotas of 13 lactating and cannulated Holstein dairy cows fed either a high- or low-starch diet in a crossover experimental design. Independent of diet, we found that all sample types were dominated by the phyla Firmicutes and Bacteroidetes. Proteobacteria and Epsilonbacteraeota were also highly abundant but only in the RE. Although the total VFA molar abundance did not differ between diets, propionate and valerate were found to increase with the high-starch diet, while acetate was increased with the low-starch diet. Overall, we found that the RE microbiota was more diverse than the RS and RL communities, with diet impacting community diversity in the RS. We found that sample type, diet treatment, and their interaction significantly impacted community structure and composition. Notably, Prevotella was most abundant in the RL and RS, particularly on the low-starch diet. We also found that Lachnospiraceae were significantly enriched in the RS and RL with a high-starch diet, whereas Succiniclasticum was highly abundant in the RE with a low-starch diet. These data suggest that diet influences all three ruminal microbiomes and provides a useful framework for understanding the role of these microbiomes in mediating host production.

We introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling of RNA-seq including covariates, weights, random effects, covariance matrices, and fit metrics. In simulated datasets, kimma detects differentially expressed genes (DEGs) with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like AIC. Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity.

Understanding the mechanisms of early clearance of Mycobacterium tuberculosis (Mtb) may illuminate new therapeutic strategies for tuberculosis (TB). We previously found genetic, epigenetic, and transcriptomic signatures associated with resistance (resister, RSTR) to tuberculin skin test (TST)/interferon gamma release assay (IGRA) conversion among highly exposed TB contacts. We hypothesized that integration of these datasets with multi-omic latent factor methods would detect pathways differentiating RSTR patients from those with latent infection (LTBI) which were not differentiated by individual dataset analyses. We pre-filtered and scaled features with the largest change between LTBI and RSTR groups for 126 patients with data in at least two of five data modalities: single nucleotide polymorphisms (SNP), monocyte RNAseq (baseline and Mtb-stimulated conditions), and monocyte epigenetics (methylation and ATAC-seq). Using multiomic latent factor analysis (MOFA), we generated ten latent factors on the subset of 33 patients with all five datasets available, four of which were different between LTBI and RSTR (FDR < 0.1). Factor 4, which was the most integrated of the significant factors, showed the greatest difference between RSTR and LTBI groups (FDR < 0.001). Three additional latent factor data integration methods also distinguished the RSTR and LTBI groups and identified overlapping features with MOFA. Using pathway analysis and a cluster-based enrichment method, we identified biologic functions associated with latent factors and found that MOFA Factors 2-4 include functions related to cell-cell adhesion, cell shape, and development of multicellular structures. In summary, latent variable integration methods uncovered signatures associated with resistance to TST/IGRA conversion that were not detected within individual dataset analyses and included pathways associated with cellular interactions and multicellular structures. Author Summary: Tuberculosis (TB) is a leading cause of preventable death worldwide. Previous research has identified some genetic and molecular patterns that are linked to resistance to TB in people who are highly exposed to the bacterium that causes the disease but do not develop an infection. We took genetic, epigenetic, and gene expression data from people who were not infected after exposure to TB within in their household and compared them with people who did develop a latent form of TB disease after similar exposure. We used a special method to analyze these datasets in an integrated way, rather than separately. This approach revealed four key factors that clearly distinguished between the resistant individuals and those with latent infection. One of these factors, in particular, showed a strong difference. We found that these factors were related to important biological processes such as how cells stick together, their shape, and how they form tissues. This method allowed us to see new patterns in the biological pathways linked to TB resistance that were not evident when looking at each type of data on its own.