Dong Xia

Microbiome studies commonly characterise taxa by their prevalence across samples and their average abundance within samples. However, these descriptors do not capture how abundance is distributed among hosts and therefore cannot distinguish taxa that maintain stable abundance from those that occasionally undergo extreme expansion. Such sporadic dominance events may have disproportionate functional consequences, including transient amplification of antimicrobial resistance gene (ARG) abundance. Here, we introduce bloominess, a metric for quantifying sporadic dominance by measuring the divergence between mean and median abundance across samples in which a taxon is present. Using a genome-resolved chicken caecal microbiome atlas, we applied this framework to thousands of metagenome-assembled genomes (MAGs). Bloominess revealed that sporadic dominance was constrained at both low and high prevalence but varied widely among taxa of intermediate prevalence. Taxonomic groups differed markedly in bloominess distributions, with several clinically relevant taxa exhibiting sporadic dominance. When integrated with antimicrobial resistance (AMR) annotations, bloominess distinguished stable background reservoirs of resistance from taxa capable of transiently amplifying resistance signals. Together, bloominess provides a complementary ecological axis for interpreting microbiome organisation and shows that AMR risk reflects both resistance gene content and the ecological behaviour of the taxa that carry it.

Background: Obesity is associated with insulin resistance and affects glycemic control in diabetic patients. Hypothesis/Objectives: Assess the impact of 12-week caloric restriction on remission and glycemic control in overweight diabetic cats using a prospective, randomized controlled trial. Animals: Seventy-two overweight (body condition score ≥ 6/9) client-owned insulin-treated diabetic cats, randomized either to caloric restriction (intervention, 32; target approximately 2% weekly weight loss) or body weight maintenance (control, 40). Methods: All cats received a novel therapeutic diabetic diet, suitable for weight reduction, for 12 weeks (%metabolizable energy [protein/fat/nitrogen-free extract]: dry [49.4/24/26.6]; wet [63.2/25.3/11.5]). Physical examination, serum biochemistry, home blood glucose curves (BGC), diabetic clinical score, and quality of life questionnaires were performed on weeks: −1, 4, 8, and 12. Insulin dose was recorded and glycemic variability (SD of BGC) was calculated. Induction of diabetic remission was the primary outcome measure. Data were analyzed using regression and linear mixed models. Results: By week 12, intervention had 2.1 times higher probability of remission (16/32) compared with controls (12/40, P = .04). Weight loss was 7.2% (95% confidence interval [CI], 5.7-8.7) for the intervention versus 2.7% (95% CI, 1.3-4.1, P < .001) for controls. For cats not achieving remission, glycemic variability decreased 45% (95% CI, 26-65) and 7% (95% CI, 16-21) for intervention and control cats, respectively (P = .01), insulin-dose decreased by 36% (95% CI, 2-70) for intervention and increased 28% (95% CI, 3-53, P = .004) for controls. Conclusions and clinical importance: Caloric restriction, using a therapeutic diabetic diet suitable for weight reduction increased the probability of remission and improved glycemic control in overweight diabetic cats.

Claw horn disruption lesions (CHDL) are painful non-infectious foot lesions of significant animal welfare and socioeconomic importance to the dairy industry. Lameness caused by CHDL is the main cause of involuntary culling, considerably reduces milk production, constitutes a major animal welfare concern and impacts directly on sustainability through increased costs, veterinary intervention and raising replacement cattle with environmental implications. As a complex heritable disease, better understanding of the genomic architecture of CHDL pathogenesis and development is essential for facilitating genetic improvement of hoof health. We have collected detailed longitudinal phenotypic, genetic data and metadata from 2,300 dairy cattle and performed an in-depth genetic characterisation integrating multi-omics data including: genome-wide association studies (GWAS), whole genome sequencing (WGS), mRNA-sequencing, lncRNA-sequencing, expression quantitative trait loci (eQTL), reduced representation bisulfide sequencing (RRBS) in foot tissue and peripheral blood lymphocytes. According to our results, pathways related to complement cascade, inflammation, neuronal signalling (specifically pain perception) and bone and cartilage development, play a key role in CHDL resistance. These findings highlight novel genetic targets for precise breeding strategies to reduce CHDL prevalence. Implementation could improve animal welfare, decrease involuntary culling, and enhance dairy sector sustainability by enabling smaller herds with reduced methane emissions and resource use.

Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics. Understanding the effect of drugs or disease on the underlying bio-network could facilitate future drug development and improvement of precision medicine. In this article, a novel graph theoretic approach is proposed to infer a co-occurrence network from 16S microbiome data. The approach is specialised to handle datasets containing a small number of samples. Small datasets exacerbate the significant challenges faced by biological data, which exhibit properties such as sparsity, compositionality, and complexity of interactions. Methodologies are also proposed to enrich and statistically filter the inferred networks. The utility of the proposed method lies in that it extracts an informative network from small sampled data that is not only feature-rich, but also biologically meaningful and statistically significant. Although specialised for small data sets, which are abundant, it can be generally applied to any small-sampled dataset, and can also be extended to integrate multi-omics data. The proposed methodology is tested on a data set of chickens vaccinated against and challenged by the protozoan parasite Eimeria tenella. The raw genetic reads are processed, and networks inferred to describe the ecosystems of the chicken intestines under three different stages of disease progression. Analysis of the expression of network features derive biologically intuitive conclusions from purely statistical methods. For example, there is a clear evolution in the distribution of node features in line with the progression of the disease. The distributions also reveal clusters of species interacting mutualistically and parasitically, as expected. Moreover, a specific sub-network is found to persist through all experimental conditions, representative of a ‘persistent microbiome’. A clustering algorithm is implemented on the network to demonstrate its utility for downstream analysis.

Coccidiosis, caused by Eimeria parasites, significantly impacts poultry farm economics and animal welfare. Beyond its direct impact on health, Eimeria infection disrupts enteric microbial populations leading to dysbiosis and increases vulnerability to secondary diseases such as necrotic enteritis, caused by Clostridium perfringens . The impact of Eimeria infection or anticoccidial vaccination on host gastrointestinal phenotypes and enteric microbiota remains understudied. In this study, the metabolomic profiles and microbiota composition of chicken caecal tissue and contents were evaluated concurrently during a controlled experimental vaccination and challenge trial. Cobb500 broilers were vaccinated with a Saccharomyces cerevisiae -vectored anticoccidial vaccine and challenged with 15,000 Eimeria tenella oocysts. Assessment of caecal pathology and quantification of parasite load revealed correlations with alterations to caecal microbiota and caecal metabolome linked to infection and vaccination status. Infection heightened microbiota richness with increases in potentially pathogenic species, while vaccination elevated beneficial Bifidobacterium . Using a multi-omics factor analysis, data on caecal microbiota and metabolome were integrated and distinct profiles for healthy, infected, and recovering chickens were identified. Healthy and recovering chickens exhibited higher vitamin B metabolism linked to short-chain fatty acid-producing bacteria, whereas essential amino acid and cell membrane lipid metabolisms were prominent in infected and vaccinated chickens. Notably, vaccinated chickens showed distinct metabolites related to the enrichment of sphingolipids, important components of nerve cells and cell membranes. Our integrated multi-omics model revealed latent biomarkers indicative of vaccination and infection status, offering potential tools for diagnosing infection, monitoring vaccination efficacy, and guiding the development of novel treatments or controls. IMPORTANCE: Advances in anticoccidial vaccines have garnered significant attention in poultry health management. However, the intricacies of vaccine-induced alterations in the chicken gut microbiome and its subsequent impact on host metabolism remain inadequately explored. This study delves into the metabolic and microbiotic shifts in chickens post-vaccination, employing a multi-omics integration analysis. Our findings highlight a notable synergy between the microbiome composition and host-microbe interacted metabolic pathways in vaccinated chickens, differentiating them from infected or non-vaccinated cohorts. These insights pave the way for more targeted and efficient approaches in poultry disease control, enhancing both the efficacy of vaccines and the overall health of poultry populations.

Methylation profiles of animals are known to differ by age and disease status. Bovine respiratory disease (BRD), a complex infectious disease, primarily affects calves and has significant impact on animal welfare and the cattle industry, due to production losses, increased veterinary costs as well as animal losses. BRD susceptibility is multifactorial, influenced by both environmental and genetic factors. We have performed a pilot study to investigate the epigenetic profile of BRD susceptibility in six calves (three healthy versus three diagnosed with BRD) and age-related methylation differences between healthy calves and adult dairy cows (three calves versus four adult cows) using Reduced Representation Bisulfite Sequencing (RRBS). We identified 2537 genes within differentially methylated regions between calves and adults. Functional analysis revealed enrichment of developmental pathways including cell fate commitment and tissue morphogenesis. Between healthy and BRD affected calves, 964 genes were identified within differentially methylated regions. Immune and vasculature regulatory pathways were enriched and key candidates in BRD susceptibility involved in complement cascade regulation, vasoconstriction and respiratory cilia structure and function were identified. Further studies with a greater sample size are needed to validate these findings and formulate integration into breeding programmes aiming to increase animal longevity and disease resistance.

Egg-laying performance is of great economic importance in poultry, but the underlying genetic mechanisms are still elusive. In this work, we conduct a multi-omics and multi-tissue integrative study in hens with distinct egg production, to detect the hub candidate genes and construct hub molecular networks contributing to egg-laying phenotypic differences. We identifiy three hub candidate genes as egg-laying facilitators: TFPI2, which promotes the GnRH secretion in hypothalamic neuron cells; CAMK2D, which promotes the FSHβ and LHβ secretion in pituitary cells; and OSTN, which promotes granulosa cell proliferation and the synthesis of sex steroid hormones. We reveal key endocrine factors involving egg production by inter-tissue crosstalk analysis, and demonstrate that both a hepatokine, APOA4, and an adipokine, ANGPTL2, could increase egg production by inter-tissue communication with hypothalamic-pituitary-ovarian axis. Together, These results reveal the molecular mechanisms of multi-tissue coordinative regulation of chicken egg-laying performance and provide key insights to avian reproductive regulation.

Maintaining gut health is a persistent and unresolved challenge in the poultry industry. Given the critical role of gut health in chicken performance and welfare, there is a pressing need to identify effective gut health intervention (GHI) strategies to ensure optimal outcomes in poultry farming. In this study, across three broiler production cycles, we compared the metagenomes and performance of broilers provided with ionophores as control against birds subjected to five different GHI combinations involving vaccination, probiotics, prebiotics, essential oils, and reduction of ionophore use. Using a binning strategy, 84 (≥75% completeness, ≤ 5% contamination) metagenome-assembled genomes (MAGs) from 118 caecal samples were recovered and annotated for their metabolic potential. The majority of these (n = 52, 61%) had a differential response across all cohorts and are associated with the performance parameter - European Poultry Efficiency Factor (EPEF). The control group exhibited the highest EPEF, followed closely by the cohort where probiotics are used in conjunction with vaccination. The use of Probiotics B, a commercial Bacillus strain-based formulation, was determined to contribute to the superior performance of birds. GHI supplementation generally affected abundance of microbial enzymes relating to carbohydrate and protein digestion, and metabolic pathways relating to energy, nucleotide synthesis, short-chain fatty acid synthesis, and drug-transport systems. These shifts are hypothesised to differentiate performance among groups and cycles, highlighting the beneficial role of several bacteria including Rikenella microfusus and UBA7160 species. All GHIs are shown to be effective methods for gut microbial modulation, with varying influences on MAG diversity, composition and microbial functions. These metagenomic insights greatly enhance our understanding of microbiota-related metabolic pathways, enabling us to devise strategies against enteric pathogens related to poultry products and presenting new opportunities to improve overall poultry performance and health.

Behavioural states such as walking, sitting and standing are important in indicating welfare, including lameness in broiler chickens. However, manual behavioural observations of individuals are often limited by time constraints and small sample sizes. Three-dimensional accelerometers have the potential to collect information on animal behaviour. We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25 to 49 days old) were used to train and test the algorithm, and unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains, the random forest model classified behaviours with very good accuracy (92%) and specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain, the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%). We therefore successfully used a random forest model to automatically detect three broiler behaviours across four different strains and different ages using accelerometers. These findings demonstrated that accelerometers can be used to automatically record behaviours to supplement biomechanical and behavioural research and support in the reduction principle of the 3Rs.