Recipient Organization
OKLAHOMA STATE UNIVERSITY
(N/A)
STILLWATER,OK 74078
Performing Department
(N/A)
Non Technical Summary
The concern for farm animal health and welfare has increased in the past few decades. Animal health and welfare are generally assessed by physical health, immune response, behavior, and other physiological indicators, including stress-related biomarkers. The stress the sow experiences during pregnancy can affect the physiology and behavior of her progeny before and after birth which may contribute to long-term health and welfare problems later in life. The gut microbial environment (microbiota) has been reported to influence host phenotypes greatly. However, the gut microbiota, which greatly influences the brain and immune system and ultimately affects behavior and well-being, is often absent from welfare assessments. It has been suggested that the maternal microbiome and metabolites transferred play a vital role in preparing the neonate for optimal host-microbe interactions influencing immune and behavior later in life. However, limited data exist on the effects of prenatal stress on the maternal microbiome-immune axis responsible for influencing the health and welfare outcomes of the progeny. Investigating the effects of maternal stress on the gut-brain-immune axis provides a novel approach to measuring animal welfare; thus, data obtained from these proposed studies can be used to assess and ameliorate poor welfare systematically. We will address the following specific objectives: (a) identify the role of gut microbes-immune axis in stressed sows on the colonization of progeny, (b) characterize the effects of multiple stressors on the microbiota-brain-immune axis in progeny born to stressed sows, and (c) decipher the molecular microbiota signatures and gut-immune and behavioral phenotypes imprinted on the progeny in subsequent generations. These data will begin to advance our understanding of the maternal microbiome and stress responsiveness effects on microbial-brain-immune signatures in the offspring that may indicate better health and well-being.
Animal Health Component
10%
Research Effort Categories
Basic
80%
Applied
10%
Developmental
10%
Goals / Objectives
Effects of prenatal stress on maternal and neonatal microbiome-immune axis primarily drive health and welfare outcomes. Investigating the effects of maternal stress on the gut-immune and gut-brain axes provides a novel approach to measuring animal welfare in the short and long term. The information from the proposed studies can be used to assess and ameliorate poor welfare systematically. Our long-term goals are to delineate the crosstalkbetween the resident microbiomes (gut, vagina, and putative placenta) and hormones of stressed vs. "non-stressed" pregnant sows to define the microbiome, immune, and behavioral imprinting signatures of the progeny and develop novel intervention strategies that can improve health and well-being across generations while promoting positive animal welfare and safeguarding animal agriculture. Our overall objective is to continue to characterize the differential maternal-fetal microbial-immune-brain phenotypes associated with maternal stress, unravel the mediators of the microbiota affecting gut-brain and immune development and responsiveness, and delineate the long-term consequences of the microbial, immune, and behavioral signatures of the future progeny born to stressed sows. We will address the following specific objectives:1. Delineate maternal microbiota role on the gut-immune axis among chronically-stressed sows and its impact on the colonization of progeny.2. Characterize the effects of multiple stressors on the microbiota-brain-immune axis in progeny born to stressed sows.3. Decipher the molecular microbiota signatures and gut-immune and behavioral phenotypes imprinted on the progeny in subsequent generations.Upon completing these objectives, we will advance our understanding of the maternal microbiome and stress responsiveness effects on microbial-brain-immune signatures indicative of health and well-being in swine.
Project Methods
Our overall aims are to:(a) study the effects of maternal stress during pregnancy on host-microbiome interactions on the establishment of the microbiome-gut-brain and gut-immune axes that drive health and welfare in the progeny and (b) to unravel the mechanisms driving the progeny's imprinted microbial signatures on immune and behavioral outcomes.The approaches taken are novel and original. We will use a whole animal system with cutting-edge technologies that integrate the behavioral, immunologic, microbiologic, and reproductive sciences utilizing phenotypic, functional, and -omics tools.Using a maternal stress model previously established (USDA award#2020-67016-31468), in Obj 1, we will delineate maternal microbiota's role on the gut-immune axis among chronically stressed during mid and late gestation (receiving hydrocortisone acetate for 21 days) or non-stressed (placebo). We will establish the maternal microbial, hormonal, and immunological shifts in sows by collecting vaginal, fecal, and blood samples from these sows at time points relevant to treatment and(or) gestational days. Colostrum samples will be collected during farrowing, and umbilical cord blood will be collected from progeny (these piglets will be used in Obj 2). Samples will be processed and analyzed for functional and descriptive aspects of innate and adaptive immunity. Serum, plasma, and colostrum will be used to measure cytokines, immunoglobulins, hormones, cortisol, and metabolites (fecal) using methods routinely used in Salak-Johnson's lab. Vaginal and fecal samples will be frozen until all samples are collected for further analysis using 16S rRNA sequencing and metatranscriptomics.In Obj 2, we will characterize the effects of multiple stressors (prenatal and postnatal) on the gut-brain-immune axis in progeny born to sows in Obj 1. To establish crosstalk between maternal microbiota-fetal gut microbiota and neuroendocrine system on the development of the progeny's gut-brain axis, intestines (and content), and brains from 24-h-old piglets born to stressed and non-stressed sows (Obj 1, n =12) and gilts (Ojb 3, n =12). We use immunohistochemistry, immunophenotyping (microglial cells), 16S rRNA sequencing, and single-cell RNA-seq and ATAC-seq technologies. It should be noted that only the hypothalamus from progeny born to mid-treated and control sows will be used for Single Cell RNA-seq. Brain tissue not used for microglial or single cells will be flash-frozen or fixed for DNA/RNA extraction and immunohistochemistry. During lactation and post-wean fecal, oral, and blood samples will be collected for piglets pre-selected at birth (Obj 1).Piglets born to sows in Obj 1 will be subjected to weaning and mixing stress, and blood samples will be collected before weaning, 24-h post-weaning, and 7, 14, and 21 days post-weaning or post-mixing. At 42 d of age, pigs will be challenged with either ACTH or saline or LPS or saline, and blood samples will be taken at specified time points. Samples will be processed for immune, endocrine, and microbiome phenotypes. The behavior will also be measured.In Obj 3, we will decipher the molecular microbiota signatures and gut-immune and behavioral phenotypes imprinted on the progeny in subsequent generations. Gilts born to sows in Obj 1 and subjective to multiple stressors in Obj 2 will accomplish this goal. Fecal and vaginal samples will be collected monthly from these gilts starting at 6-mo of age and ending at the end of lactation. Before breeding gilts, we will challenge them with ACTH to determine their stress responsiveness. Progeny born to these gilts will be characterized. Gut and Brain will be collected from a subset of piglets born to these gilts and processed as described in Obj 2.More details of some of the methodologies: Immune measures and microglial isolation: All immune assays have been optimized in Salak-Johnson's laboratory. Serum and plasma samples will be analyzed using commercially available ELISA or microarrays.Behavior: Video and live behavioral observations will be registered and analyzed. Sow/gilt recordings will occur during the 21-day treatment period, biweekly through gestation, 2 days before, and 48-h post farrowing (Obj 1 and 3). Piglet behaviors will be measured during lactation and when subjected to stress models. Behaviors associated with stress, nesting, maternal, nursing, social, and exploratory/interactive behaviors will be measured when appropriate. 16S rRNA Sequencing & Metatranscriptome analyses: Following previously established protocols by Dr. Koehler and refined protocols for the pigs in conjunction with the Salak-Johnson lab, the Illumina MiSeq sequencing platform will target 16S rRNA genes of bacteria and archaea. Data will be used to reconstruct genomes using binning protocols, and community composition and diversity will be computed from sequence data using QIIME 2 and additional software packages. Differential abundance analysis will be conducted. Two methods will be used to determine dual RNA-seq from fecal and gut samples. RNA will be used to prepare stranded total RNA-seq library pair-end 100 base pairs reads. Reads will be checked for quality and aligned to the Sus Scrofa genome using Tophat/Bowtie2. Statistical analysis will be conducted on these data using SAS, R, DAVID, and Ingenuity Pathway Analysis. Single Cell RNA-seq and ATAC-seq: Dr. Salak-Johnson will dissect the hypothalamus and prepare the samples for Dr. Moraes's laboratory. He will perform single-cell sequencing samples from piglets born to mid-treated and control sows and gilts. Libraries will be generated using 10x Genomics protocol from nuclei isolated from these samples. Sequencing reads will be processed using the Cellranger pipeline, and results will be analyzed using the R toolkit Seurat. Dimensionality reduction algorithms will be used for unsupervised cluster analysis, enabling us to identify unique cell clusters. Statistical Analysis: The linear mixed-effects model specific for repeated measures in SAS will be used to analyze continuous measures of difference between gestational treatments, days, time, and interactions. Each model will include gestational treatment, day, time, replicate, and all possible interactions. Sow nested in gestational treatment will be used as a random effect. Predictive (inferential) models will be created using factor analysis and multivariate regression to determine the cause-and-effect relationship between the animal's physiological response and environmental exposure. Partial least squares regression (PLSR) will determine the relationships between correlated dependent and independent variables. PLSR provides a robust mathematical framework for collinear data and has been effectively employed in several biostatistical studies.Differential abundances in bacterial taxa will be determined using established parametric/non-parametric approaches available in QIIME 2 and other sources. Analyses will be adjusted depending on the type of metadata (categorical or continuous). A beta-diversity statistic available through the R package and accessible with QIIME 2 will be used to calculate statistical significance between groups. Similarly, alpha diversity and beta diversity analyses (Bray-Curtis dissimilarity, UniFrac distances), PERMANOVA calculations, and further calculations of interindividual variation will be performed using QIIME 2 or specific R packages.