Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
LG ANML CLIN SCI
Non Technical Summary
Concerns regarding the health and welfare of farm animals have grown significantly over the last few decades. Animal health and welfare are typically evaluated through various indicators such as physical health, immune response, behavior, and other physiological markers, including those related to stress. The stress experienced by sows during pregnancy can have a lasting impact on the physiology and behavior of their offspring, both before and after birth, potentially leading to long-term health and welfare issues later in life. The gutmicrobial environment, or microbiota, has been shown to have a significant impact on host phenotypes. Nevertheless, the gut microbiota, which plays a crucial role in influencing the brain and immune system and ultimately affects behavior and overall well-being, is frequently overlooked in welfare evaluations. It has been proposed that the maternal microbiome and the metabolites it transfers are essential in preparing the neonate for optimal interactions between host and microbes, which in turn influence immune function and behavior later in life. However, data are scarce regarding the impact of prenatal stress on the maternal microbiome-immune axis, which is critical for determining the health and welfare outcomes of the offspring. Exploring the effects of maternal stress on the gut-brain-immune axis presents a novel method for assessing animal welfare; therefore, the data gathered from these proposed studies can be utilized to evaluate and improve poor welfare conditions systematically. We will focus on the following specific objectives: (a) to identify the role of the gut microbes-immune axis in stressed sows on the colonization of their progeny, (b) to characterize the effects of various stressors on the microbiota-brain-immune axis in offspring born to stressed sows, and (c) to decipher the molecular microbiota signatures along with gut-immune and behavioral phenotypes that are imprinted on the progeny in subsequent generations. This data will contribute to enhancing our understanding of the maternal microbiome and the effects of stress responsiveness on microbial-brain-immune signatures in the offspring, which may indicate improved health and well-being.
Animal Health Component
10%
Research Effort Categories
Basic
75%
Applied
10%
Developmental
15%
Goals / Objectives
The impact of prenatal stress on the maternal and neonatal microbiome-immune axis has a significant influence on health and welfare outcomes. Examining the effects of maternal stress on the gut-immune and gut-brain axes provides a novel approach for evaluating animal welfare in both the short and long term. The insights gained from the proposed studies can be used to assess and improve poor welfare conditions systematically. Our long-term goals are to delineate the crosstalk between the resident microbiomes (gut, vagina, and putative placenta) and the hormones of stressed versus "non-stressed" pregnant sows. This will facilitate our ability to define better the microbiome, immune, and behavioral imprinting signatures of the offspring, as well as develop innovative intervention strategies aimed at enhancing health and well-being across generations, while promoting positive animal welfare and protecting animal agriculture. Ultimately, our goal is to characterize further the distinct maternal-fetal microbial-immune-brain phenotypes linked to maternal stress, identify the mediators of the microbiota that influence gut-brain and immune development and responsiveness, and outline the long-term effects of the microbial, immune, and behavioral signatures on future offspring born to stressed sows. We are utilizing a pharmacological induced maternal stress model, which involves feeding hydrocortisone acetate (HCA) or placebo (CON) capsules twice daily for 21 days during gestational days 51 to 72 (mid-gestation) or from 81-1-2 (late-gestation) to address these specific objectives:Delineate maternal microbiota role on the gut-immune axis among chronically-stressed sows and its impact on the colonization of progeny. (In progress)Characterize the effects of multiple stressors (prenatal and postnatal) on the microbiota-brain-immune axis in progeny born to stressed sows. (In progress)Decipher the molecular microbiota signatures and gut-immune and behavioral phenotypes imprinted on the progeny in subsequent generations.Data from these studies will enhance our understanding of how maternal stress affects the gut-brain-immune connection, offering a new perspective on assessing animal welfare while advancing our understanding of the maternal microbiome and stress responsivity on microbial-brain-immune signatures 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 utilize a whole-animal system with cutting-edge technologies that integrate the behavioral, immunological, microbiological, and reproductive sciences, employing phenotypic, functional, and -omics tools. Using a pharmacological induced maternal stress model refined and established (USDA award#2020-67016-31468; original USDA 2023 GRANT13691573), in Obj 1, we began to delineate the maternal microbiota's role in the gut-immune axis among chronically stressed during mid and late gestation (receiving hydrocortisone acetate for 21 days) or non-stressed (placebo) and characterize the immune and behavioral phenotype of prenatally-stressed progeny during lactation and post-weaning. This was and will continue to be achieved by collecting vaginal, fecal, and blood samples from sows at time points relevant to treatment and(or) gestational days--colostrum samples during farrowing and umbilical cord blood from progeny. Samples are processed and analyzed to examine the functional and descriptive aspects of innate and adaptive immunity. Cytokines, immunoglobulins, hormones, cortisol, and metabolites (fecal) are measured in serum, plasma and colostrum using methods routinely used in Salak-Johnson's lab. DNA and RNA isolated from vaginal and fecal samples and then processed and analyzed using 16S rRNA sequencing and metatranscriptomics. After achieving a new cohort of breeding animals at Michigan State University (MSU), we plan to complete Obj 2 and accomplish Obj 3. Fecal and vaginal samples will be collected and processed monthly from gilts starting at 6 to 7 months of age until the end of the lactation period to further characterize the gut-immune axis. We will further characterize their microbial-immune and behavioral phenotypes. The progeny born to these gilts will be further characterized in terms of their immune phenotype and stress responsiveness during lactation and in response to stressors, as well as the immune phenotype and stress responsiveness of the progeny born to maternally stressed sows. 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 piglets born to prenatally and maternally stress and non-stressed sows. We utilize immunohistochemistry, immunophenotyping (for microglial cells), 16S rRNA sequencing, single-cell RNA-seq (with a focus on the hypothalamus), and ATAC-seq technologies. Throughout the lactation and post-weaning periods, fecal and oral swabs, as well as blood samples, will be collected from female progeny selected to achieve Objective 3. Pigs will be subjected to weaning and mixing stress, and blood samples will be collected before weaning, 24 hours post-weaning, and at 7-, 14-, and 21-day post-weaning or post-mixing intervals. At 42 days of age, pigs will be challenged with either ACTH 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. Obj 3entails the in-depth characterization of the brain-gut-immune axis. Neonates will be euthanized within 24 h of birth, and ½ brain will be used for microglial isolation, and the other will be flash frozen. Sections of gut and brain samples will be fixed for histological examination. Specifically, we will decipher the molecular microbiota signatures and gut-immune and behavioral phenotypes imprinted on the progeny in subsequent generations. Behavior will be registered throughout these studies.More details of some of the methodologies:Immune measures and microglial isolation: All immune assays have been optimized in Salak-Johnson's laboratory. Serum andplasma 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 2 and 3). Piglet behaviors will bemeasured 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: DNA and RNA will be isolated using established protocols developed by Dr. Koehler at OSU, which Dr. Salak-Johnson's laboratory hasrefined. Samples will be sent to the MSU core facility forIllumina MiSeq sequencing, targeting the 16S rRNAgenes of bacteria and archaea. Data will be used to reconstruct genomes using binning protocols, and community compositionand diversity will be computed from sequence data using QIIME 2 and additional software packages. Differential abundanceanalysis will be conducted. Two methods will be used to determine dual RNA-seq from fecal and gut samples. RNA will be usedto prepare a stranded total RNA-seq library with 100-base-pair reads. Reads will be checked for quality and aligned to theSus Scrofa genome using Tophat/Bowtie2. Statistical analysis will be conducted on these data using SAS, R, DAVID, andIngenuity Pathway Analysis.Single Cell RNA-seq and ATAC-seq: Dr. Salak-Johnson will dissect the hypothalamus and prepare the samples for the MSU core facility. They will perform single-cell sequencing on samples from piglets born to mid- and late-treated and control sows.Libraries will be generated using the 10x Genomics protocol from nuclei isolated from these samples. Sequencing reads will beprocessed using the Cellranger pipeline, and results will be analyzed using the R toolkit Seurat. Dimensionality reductionalgorithms will be used for unsupervised cluster analysis, enabling us to identify unique cell clusters. Statistical Analysis: A linear mixed-effects model specific to repeated measures in SAS will be used to analyze continuous measures of differences between gestational treatments, days, time, and theirinteractions. Each model will include gestationaltreatment, day, time, replicate, and all possible interactions. Sow nested in gestational treatment will be used as a randomeffect. 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 squaresregression (PLSR) will determine the relationships between correlated dependent and independent variables. PLSR offers a robust mathematical framework for handling collinear data and has been effectively applied in several biostatistical studies. Differential abundances in bacterial taxa will be determined using established parametric/non-parametric approaches availablein QIIME 2 and other sources. Analyses will be adjusted depending on the type of metadata (categorical or continuous). A beta diversitystatistic available through the R package and accessible with QIIME 2 will be used to calculate statistical significancebetween 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 Rpackages.