Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production
Abstract
:Simple Summary
Abstract
1. Introduction
2. Multi-Omics Approaches for Understanding Microbe-Host Interactions: Implications for Animal Health and Nutrition
3. The Complex and Vital Role of Rumen Microbiota in Ruminant Nutrition and Health
4. Factors Affecting Microbiome Establishment in Rumens
4.1. Age-Dependent Changes in Microbial Population
4.2. Stress-Related Changes in the Composition of the Microbiota
5. Role of Lower-Gut Microbiome in Host Gut Health
6. Lower-Gut Microbiome Influence the Immune System of the Host
7. Gut-Microbiota-Generated Metabolites in Animal Health
8. Future Prospective
- Understanding the impact of the microbiome on production traits: Research can focus on understanding how the microbiome of the rumen and other digestive organs influences production traits such as feed efficiency, weight gain, and milk yield. By better understanding these relationships, it may be possible to develop management strategies to optimize microbiome function and improve production efficiency.
- Developing microbial interventions for improved animal health: Similar to human medicine, microbial interventions could be developed to promote health and prevent disease in cattle. For example, probiotics or microbial supplements could be used to promote beneficial microorganisms or microbial therapies could be used to treat infections or other health conditions.
- Exploring the role of the microbiome in animal behavior: Emerging research suggests that the microbiome may play a role in regulating animal behavior, potentially influencing stress response, social behavior, and other traits. Investigating these relationships in cattle could provide new insights into how the microbiome influences animal welfare and production.
- Investigating the impact of management practices on the microbiome: Management practices such as feed composition, housing conditions, and antibiotic use may all influence the composition and function of the cattle microbiome. Understanding these relationships can help identify best practices for managing the microbiome and improving animal health and productivity.
- Developing precision management tools for the microbiome: Advances in technology are enabling increasingly precise analysis of the microbiome which could, in turn, enable more targeted and personalized management strategies. For example, microbial profiling could be used to identify individual animals or herds with particular microbiome profiles, which could be managed in a more targeted manner to optimize production and health outcomes.
9. Limitation of the Study Topic
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Factors | Model | Technology | Results | References |
---|---|---|---|---|
Age | Bovine | 454 tag-encoded amplicon pyrosequencing | Between high residual feed intake groups and low residual feed intake groups, the diversity and within-group similarity increase with age, and each group has its own distinct microbiota | [30] |
Calf | 16S rRNA gene sequencing, whole genome shotgun approach | Rumen microbiota of preruminant calves displays compositional heterogeneity, but functional classes between the 2 age groups (14-day-old calves and 42-day-old calves) are similar | [31] | |
Calf | 16S rRNA gene amplicon sequencing and functional composition prediction. | The fecal microbial community has a great alteration within the time after weaning in both cattle and yak calves, but cattle showed a larger change. | [32] | |
Bovine | 16S rRNA gene amplicon sequencing and microbial diversity analysis | Prevotella was strongly correlated with methanobrevibacter in heifers; however, in older cows (96–120 months) this association was replaced by a correlation between Succinivibrio and Methanobrevibacter | [33] | |
Diet | Bovine | Metabolomics | Roughage type can significantly influence the ruminal microbial metabolome, especially organic acids, amines, and amino acids | [34] |
Feed efficiency | Bovine | 16S rRNA gene sequencing, shotgun DNA sequencing | Megasphaera elsdenii is enriched in the rumen of the feed-efficient group; Methanobrevibacter was diminished | [35] |
Bovine | Metatranscriptomics | Lachnospiraceae, Lactobacillaceae, and Veillonellaceae are more abundant in low-feed efficiency animals; Methanomassiliicoccales was diminished | [36] | |
Bovine | metagenomics, metatranscriptomics, and metabolomics | Rumen of HiEf animals, Selenomonas and some species of Succinivibrionaceae might interact positively with each other and play an important role as keystone bacteria. | [37] | |
Bovine | 16SrRNA Gene Sequencing | Host-associated microbial interactions differed within each breed depending on the feeding system, which indicated that breed-specific feeding systems should be taken into account for farm management | [38] | |
Genetics | Bovine | SNP-based heritability estimates and 16S rRNA gene sequencing | Host genetic variation is associated with specific microbes | [39] |
Bovine | Metagenomics | Host genetics shapes the microbiome Inoculation with bison rumen contents alters the cattle rumen microbiome and metabolism | [40,41] | |
Bovine | High throughput sequencing | Rumen microbial features are heritable and could be influenced by host genetics, highlighting a potential to manipulate and obtain a desirable and efficient rumen microbiota using genetic selection and breeding. | [42] | |
Bovine | 16S rRNA gene sequencing | Single-nucleotide polymorphisms found in cattle genomes and corresponding rumen bacterial community composition, annotated genes associated suggest the associations observed are directed toward selective absorption of volatile fatty acids from the rumen to increase energy availability to the host | [43] |
Bacterial Species | End Product | Function | Reference |
---|---|---|---|
Fibrobacter succinogens, Clostridium longisporum, Clostridium cellobioparum | Acetate, Formate, Ethanol, propionate | Degrade the cellulose into smaller oligo/disaccharides | [84] |
Butyrivibrio fibrisolvens | Saturated fatty acids and conjugated linoleic acids | Lipolysis and biodehydrogenation | [85] |
Prevotella ruminicola, Ruminobacter amylophilus, Streptococcus Bovis | Hydrolysis of starch | [86] | |
Anaerovibrio lipolytica | Acetate and propionate | Lipolytic activity of rumen contents of cattle | [87] |
Lachnospira multiparus, Treponema saccharophilum | Acetate and formate | Fermentation of pectin and glucose | [88] |
Methanobrevibacter spp. | Methane | Methane emissions | [89] |
Succinivibrio sp. Lactobacillus sp. | Lactate, Acetate, Fumarate, Succinate | Lactate, Acetate, Fumarate, Succinate | [84,90] |
Prevotella ruminicola, Eubacterium uniformis, Eubacterium xylanophilum | Acetate, Formate, Ethanol, propionate | Xylan consumption | [88,91] |
Ruminococcaceae, Clostridium, Turicibacter and unclassified Peptostreptococcaceae | Volatile fatty acid | Degradation of starch and fiber | [45] |
Ruminobacter amylophilus, Prevotella sp., Clostridium bifermentans | Amino acids, nitrogen | Starch hydrolysis | [91] |
Entodiniomorphs, Eudiplodinium, Dasytricha, Diplodinium, Metadinium, Ophryoscolex, and Ostracodinium | Methane | Support methanogenesis, Engulf and digest a wide range of bacteria | [92,93] |
Isotricha Dasytricha Charonina | Glucose, Fructose, Galcturonic acid | Fermentation of Starch, pectin, soluble sugars, proteins | [94] |
Megasphaera elsdenii | Ammonia and CO2 | Acts upon lactate (end product of bacterial fermentation) | [84,90] |
Entodinium, Diplodinium, Eudiplodinium, Ostracodinium, Metadinium, Polyplastron, Ophryoscolex, Epidinium | Glucose, Xylose | Hydrolyze structural polysaccharides | [95] |
Eubacterium oxidoreducens, Streptococcus Caprinus | Lactate, Acetate, Fumarate, Succinate | Degradation of tannin | [83] |
Metabolites | Model Organism | Diseases/Infection Control | Mode of Action | Reference |
---|---|---|---|---|
SCFA | Cow | Mastitis | Improve condition by conserving mucosal barrier integrity, restoring blood-milk barrier and prevent pathogens and their metabolites intrusion, maintain immune homeostasis | [130] |
Ursodeoxycholic acid | Neonatal dairy calves | ESBL-EAEC-induced clinical symptoms and colitis | Show antibacterial action, inhibit proinflammatory effect, and decreased cell integrity loss | [131] |
Deoxycholic acid | Chicken | Campylobacter jejuni colonization | Prevent infection by modulating microbiota composition | [132] |
Butyrate | Cattle | Subacute ruminal acidosis (SARA) | Prevent SARA by increasing ruminal pH | [133] |
SCFA | Pig | Intestinal Barrier Function | Improve gut health by regulating IL-22 production | [134] |
Indole derivatives (3-indoleacrylic acid, kynurenic acid) | Simmental cattle | Daily weight gain | Enhance body weight by regulating intestinal homeostasis, gut barrier function and immune system | [135] |
Putrescine | Pig | Diarrhea | Alleviate diarrhea by enhancing anti-inflammatory function and suppressing inflammatory response | [136] |
volatile fatty acids (VFAs) | Dairy Cow | Subacute ruminal acidosis (SARA) | Cause acidosis by decreasing rumen pH below physiological range | [137] |
Butyrate | Pig | lipopolysaccharide (LPS)-induced colitis | Protect against colitis by diminishing secretion of pro-inflammatory cytokines (IL)-1β, IL-6, tumor necrosis factor (TNF)α, IL-8, and IL-12 | [136] |
Leukotriene B4 | Cow | Clinical mastitis | Cause mastitis by increasing inflammatory response | [138] |
Histamine | Dairy Cow | Subacute ruminal acidosis (SARA) | Cause SARA diseases by activating NF-kb and mTOR signaling followed by mammary inflammtion | [139] |
LPS, lactate | Dairy Cow | Laminitis | Disturb rumen environment by decreasing rumen pH | [140] |
5-Hydroxymethyl-2-furancarboxaldehyde | Lactating Holstein dairy cows | Clinical Mastitis | Cause mastitis by enhancing production of pro-inflammatory factors, such as TNF-α and IL-1β | [138] |
δ-aminolevulinic acid | Dairy Calves | Diarrhea | Cause diarrhea by gut dysbiosis | [141] |
Non-esterified fatty acid (NEFA), and β-hydroxybutyric acid (BHBA) | Dairy Cow | Left Displaced Abomasum | Cause LDA by increased Ketosis and negative energy balance (NEB) prepartum | [142] |
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Kaur, H.; Kaur, G.; Gupta, T.; Mittal, D.; Ali, S.A. Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production. Biology 2023, 12, 1200. https://doi.org/10.3390/biology12091200
Kaur H, Kaur G, Gupta T, Mittal D, Ali SA. Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production. Biology. 2023; 12(9):1200. https://doi.org/10.3390/biology12091200
Chicago/Turabian StyleKaur, Harpreet, Gurjeet Kaur, Taruna Gupta, Deepti Mittal, and Syed Azmal Ali. 2023. "Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production" Biology 12, no. 9: 1200. https://doi.org/10.3390/biology12091200
APA StyleKaur, H., Kaur, G., Gupta, T., Mittal, D., & Ali, S. A. (2023). Integrating Omics Technologies for a Comprehensive Understanding of the Microbiome and Its Impact on Cattle Production. Biology, 12(9), 1200. https://doi.org/10.3390/biology12091200