Source: UNIV OF CALIFORNIA (VET-MED) submitted to NRP
INTEGRATING HOST GENOME AND RUMEN MICROBIOME TO IMPROVE MILK PRODUCTION EFFICIENCY IN DAIRY CATTLE
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
COMPLETE
Funding Source
AFRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
1023524
Grant No.
2019-67015-32114
Cumulative Award Amt.
$471,901.64
Proposal No.
2020-06070
Multistate No.
(N/A)
Project Start Date
Feb 1, 2020
Project End Date
May 30, 2024
Grant Year
2020
Program Code
[A1231]- Animal Health and Production and Animal Products: Improved Nutritional Performance, Growth, and Lactation of Animals
Recipient Organization
UNIV OF CALIFORNIA (VET-MED)
(N/A)
DAVIS,CA 95616
Performing Department
VM: Population Hlth & Reprod
Non Technical Summary
The overall objective of this integrated research proposal is to identify genomic markers associated with efficient plant deconstruction by rumen microbiome to improve selection for milk production efficiency and improve current practices on the genetic selection of dairy cattle. The proposed studies will advance the field of nutrient utilization and efficiency addressing the impact of gastrointestinal microbiome and host genetics influence on milk production efficiency. A deeper understanding of the genetic structure associated with the rumen microbiome and its modulation of milk production efficiency will be identified contributing to the next generation of genomic markers to advance milk production efficiency.
Animal Health Component
50%
Research Effort Categories
Basic
40%
Applied
50%
Developmental
10%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
3023410101040%
3033410108040%
3023410110320%
Knowledge Area
303 - Genetic Improvement of Animals; 302 - Nutrient Utilization in Animals;

Subject Of Investigation
3410 - Dairy cattle, live animal;

Field Of Science
1103 - Other microbiology; 1010 - Nutrition and metabolism; 1080 - Genetics;
Goals / Objectives
Our long-term goal is to improve available tools for dairy cattle selection and optimize management towards increased productivity. Without this critical knowledge, advances in genetic selection and engineering of new compounds that can optimize rumen contribution to efficient milk production are largely hindered. The overall objective of this proposal is to identify rumen microbiome signatures linked to milk production efficiency and host's single nucleotide polymorphisms (SNP) associated with the maintenance of such microbial populations. Our central hypothesis is that genetic control of milk production efficiency in dairy cows is mediated by a combination of host genetics and rumen microbiome and single nucleotide polymorphism linked to this interaction can be used to improve selection for milk production efficiency.I.2. Specific objectivesObjective 1: Create a database containing milk production efficiency phenotypes (defined as residual feed intake at the same level of energy corrected milk production), genotypes, rumen microbiome profiles, markers for nutrient digestibility in Holstein cows.Sub-objective 1.A.: Characterize milk production efficiency in a reference population (n = 800) of lactating Holstein cows from university herds in the SE, NE, and MW regions of the US.Sub-objective 1.B.: Genotype the reference population using the Clarifide Ultra Genomic Test, 62,000 SNP (Zoetis Services LLC).Sub-objective 1.C.: Collect rumen fluid during peak lactation (60 ± 3 DIM) for evaluation of volatile fatty acids (VFA) concentrations and rumen microbiome through next-generation sequencing (NGS) of 16S ribosomal RNA (rRNA) gene.Sub-objective 1.D.: Evaluate diet chemical composition and collect fecal samples for measurement of in vitro and in vivo fiber and starch digestibility at peak lactation.Objective 2: Dissect the relationships between host genetics and rumen microbiome and incorporate this information in prediction models for selection and optimized management.Sub-objective 2.A.: Perform a comprehensive association analysis between rumen microbiome and milk production efficiency phenotypes.Sub-objective 2.B.: Estimate the variance components and genetic parameters for different phenotypes related to rumen microbiome structure and function.Sub-objective 2.C.: Perform a GWAS and pathway analysis of different rumen microbiome phenotypes.Sub-objective 2.D.: Detect functional relationships and potential causal effects of the genotype of the cows to the rumen microbiome to the end-point phenotypes.Sub-objective 2.E.: Develop predictive models that explore genetic markers and rumen microbiome phenotypes for predicting milk production efficiency.Objective 3: Assess the interaction of genomic prediction for milk production efficiency and rumen microbiome.Sub-objective 3.A.: Identify cows with high and low milk production efficiency and perform the exchange of entire rumen content to evaluate the effects on performance, rumen metabolism, digestion, and feed efficiency.Sub-objective 3.B.: Compare the rumen microbiome from cows of high vs. low milk production efficiency before and after performing the exchange of entire rumen content.
Project Methods
Objective 1: To create a database with information regarding milk production efficiency phenotypes, nutrient digestibility, host genotypes, and rumen microbiome in lactating Holstein cows.Cows will be housed in free-stall barns equipped with Calan gates in Florida and tie-stall sand barns at Cornell University and the University of California, Davis. Information regarding the specific diets will be monitored daily and weekly composite will be analyzed. The three farms milk cows twice a day. Information including lactation number, sire identification, calving date, the incidence of disease postpartum will be obtained from all farms and consolidated into a single Excel spreadsheet to be used for further analysis. Primiparous and multiparous cows that calve throughout the cool season (October to May) will be eligible for enrollment. Cows will be moved one week before the start of the monitoring period (i.e. 53 ± 3 DIM) in order to adapt to a new feeding system. Feed intake, refusal weights, and feed and refusal samples will be collected daily according to previously established methods for each cow over 14 days. Milk samples will be collected for 14 consecutive days starting 1 day after feed sample collection during morning and evening milking by the researcher teams and stored at 4°C.Blood will be sampled at enrollment from all cows or isolation of DNA from, which will be stored at -80oC until assayed. Isolated DNA will be submitted to a commercial laboratory for interrogation of genomic variation by using Dairy Bovine Clarifide Ultra (Zoetis Services LLC).Rumen samples will be collected three times each other day, starting at 60 ± three days and pooled to evaluate rumen microbiome and metabolite profile. Samples will be collected early in the morning before feeding. Rumen samples will be aliquoted for DNA and metabolic extraction. Samples for DNA will be snap-frozen in liquid nitrogen and stored at -80oC for later analyses. The VFA concentrations will be measured in rumen fluid samples in a central laboratory as described previously.DNA isolation from rumen samples will be performed as previously described by our research group. Final libraries will be sequenced using the MiSeq reagent kit V3 (600 cycles).Bioinformatics: Upstream data analysis will be performed using two high-performance computers at Dr. Bicalho's laboratory. The open-source software pipeline Quantitative Insights Into Microbial Ecology (QIIME) 2 will be used.TMR analysis: Samples of the TMR will be collected daily and weekly composites will be analyzed for nutrient content (Dairyland Laboratories, Inc., Arcadia, WI) for characterization of the diet in each site.Fecal samples will be used to determine starch concentrations and estimation of starch total tract digestibility as described previously. Single point analysis of fecal starch has been validated to estimate total tract starch digestion.Objective 2: The dataset generated in Objective 1 will be carefully mined to better understand the relationships between the biology of the cow and the influences of health. The genetic makeup of the cow, its rumen microbiome, and end-point phenotypes (e.g., milk yield, milk components, RFI) will be considered. Our goal is to dissect the relationships between the cow's genome and its rumen microbiome. The long-term objective of this component is to improve prediction models for selective breeding towards improved nutrient utilization for milk production and optimize the management of dairy cows.A comprehensive investigation of the relationship between rumen microbiome and end-point phenotypes will be performed using microbiome-wide association analysis.Estimation of variance components (e.g. additive genetic variance) and relevant genetic parameters (e.g. heritability and genetic correlations) for different phenotypes closely related to rumen microbiome structure and function will be determined.A GWAS analysis will be performed testing the significance of each SNP at a time using a likelihood ratio test. Associations between each SNP and rumen microbiome phenotypes will be analyzed using mixed models that included a random polygenic effect in order to account for population structure and hence reduce false-positive results.Functional relationships and potential causal effects from the genotype of the cows to the rumen microbiome and the end-point phenotypes will be accomplished using Bayesian Network and Structure Equation Model methods.Development and validation of prediction models that explore not only genetic marker information but also rumen microbiome signals will be performed.Objective 3: Evaluate the interaction between milk production efficiency and rumen microbiome.Primiparous cows enrolled in objective 1 at the University of Florida (~160 cows) will be ranked according to milk production efficiency (MPE), which will be defined as residual feed intake at the same level of energy corrected milk production. All cows at the University of Florida Dairy Research Unit are sired from proved bulls and genotyped. The top and bottom 20th percentiles for milk production efficiency will be screened initially to assure that at least 30 of them are also top or bottom percentiles for milk production efficiency, respectively. Within the top and bottom of primiparous groups, a minimum of 4 unique sires must be represented. Then the 16 selected primiparous, 8 within the top and 8 within the bottom percentiles at ~200 d of gestation will be used for rumen cannulation.Procedures: The experiment will be a randomized complete block design (RCBD). Blocks of 4 cannulated primiparous cows calving within a 2 wk period, 2 of high and 2 of low MPE will be formed. The treatment of interest is exchanging all rumen content between MPE groups (high and low MPE). Treatment will be randomly applied to each cow within block between 90 and 100 DIM of their second lactation such that there will be four treatments with a factorial arrangement within a block: HH: High MPE cow receiving rumen content from another high MPE cow; HL: High MPE cow receiving rumen content from a low MPE cow; LH: Low MPE cow receiving rumen content from a high MPE cow; LL: Low MPE cow receiving rumen content from a low MPE cow. TAll cows in the study will be fed the same lactation diet from 30 to 160 DIM according to NRC. Measurements will start 30 days before and end 60 d after treatment (day of rumen exchange) is applied. Measurements taken before treatments will serve as a baseline to characterize the two genetic groups and their respective microbiomes and also to evaluate the changes as treatments are applied. Measurements will include milk yield, body weight, and DMI daily, and twice-weekly milk composition and body condition. Four days before and again on d 28 after treatment, rumen contents will be evacuated manually through the rumen cannula, at approximately 2 h after feeding, and again at approximately 2 h before feeding. Rumen content mass and volume will be determined. Rumen pool sizes of nutrients will be determined. Sixteen days before treatment and starting again on day 16 after treatment, cows will be dosed via rumen cannula with indigestible markers for 12 days and samples collected to estimate digestibility and omasal flow of nutrients. Rumen fluid will be collected and analyzed for pH and then preserved for analyses of NH3 and VFA. Rumen and total tract apparent digestibility, and fractional rates of nutrient passage from the rumen will be calculated as described previously.Sub-objective 3.B.: Compare the rumen microbiome from cows of high vs. low MPE before and after performing the exchange of entire rumen content. DNA from rumen samples collected in Sub-objective 3A will be sequenced as described in Sub-objective 1.C.

Progress 02/01/20 to 05/30/24

Outputs
Target Audience:The findings of this study were disseminated broadly to diverse stakeholders within the dairy community. Results were presented as five abstracts at the American Dairy Science Association Annual Meeting and discussed during invited presentations at both national and international events, including the Ruminant Nutrition Symposium in Florida, the Amino Acid Summit in Reggio Emilia, Italy, and an invited lecture at the Department of Biosciences at the University of Guelph in Canada. Furthermore, four peer-reviewed articles have been published--one in Animal Microbiome, one in Scientific Reports, and two in the Journal of Dairy Science--with a third Journal of Dairy Science manuscript currently under review. These dissemination efforts engaged a broad array of audiences, from undergraduate and graduate students to postdoctoral researchers, dairy scientists, nutritionists, veterinarians, and farm managers. Changes/Problems:Aim 3 of our project remains outstanding due to unforeseen complications regarding the University of Florida's involvement. The subcontractor designated for the execution of the study faced significant uncertainty concerning the viability of the agricultural site, as there was a potential sale of the farm where the study was to be conducted. This situation created apprehension about the continuity and feasibility of our aim 3. After a lengthy period of deliberation, the decision was ultimately made not to proceed with the sale of the farm. This resolution provides a renewed opportunity to advance our research agenda. Consequently, we are now poised to complete Aim 3 in the forthcoming year. While the challenges presented by the potential sale of the study site posed significant obstacles, the decision to retain the farm paves the way for the eventual completion of Aim 3. We remain optimistic about moving forward and fulfilling our research agenda. What opportunities for training and professional development has the project provided?This project allowed two postdocs to sharpen their analytical and writing skills and become significant contributors in the field. Moreover, the project allowed five graduate students to learn how to perform studies that included routine farm visits to collect feed, rumen, fecal, and blood samples to create complex metadata needed for genotypical and phenotypical characterization and improvethe specific skills linked withconducting a project such as organizational skills, communication skills, teamwork, time management, among others. How have the results been disseminated to communities of interest?Yes. As alluded to in the previous sections, the data has been published in multiple peer-reviewed manuscripts and national conference abstracts. Also, there were some national and international invitations to share the findings for the current project. What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? Aims 1 and 2 have been finalized. The data collected for aims 1 and 2 already generated four significant peer-reviewed manuscripts that advanced our understanding of the rumen microbiome and host genomics relationship modulating feed efficiency in dairy cows. The main takeaways of the three manuscripts published are: 1) Using an artificial intelligence approach, we demonstrated that the rumen microbiome composition explains a significant portion of the variation in residual feed intake (RFI), presenting a promising site of exploration for future improvements in predictive models to decrease the dairy sector's carbon footprint. The associations of RFI, as well as MFE (milk fat efficiency), MPE (milk protein efficiency), and their residuals with the rumen microbiome, unraveled through an ensemble method, further indicate key microbial players that could be targeted to further evaluate their effect on the efficiency of dairy cows. Additionally, the predictability of heritable traits by the rumen microbiome underscores the need for future research to dissect host-microbiome interactions in shaping feed and milk production efficiency. This exploration, and consequently further validation studies with complementary results from digestive parameters (e.g., digestibility) to more detailed microbiome approaches (shotgun metagenomics, metatranscriptomics, and metabolomics), is vital to pioneer advances in ruminant nutrition and fortify sustainable dairy production pathways. 2) Incorporating the rumen microbiome information in addition to genomic data allows for revealing the relative effects of the hostgenome and the microbiome on feed efficiency traits in dairy cattle. Rumen microbiome data can be used to estimate host direct and indirect genetic effects on feed efficiency. Indeed, the differences obtained between the h2 and the hd2 strongly suggest that the microbiome mediates part of the host's genetic effect. The holobiont model, which incorporates the host genome-by-microbiome interaction, provides further insights into the biological mechanisms underlying dairy cow feed efficiency. 3) Structural equation models offer an alternative to disentangle the relationships between the host genome, rumen microbiome, and feed efficiency traits in lactating Holstein cows. We classified rumen microbes into three groups, each of which could have different uses in dairy farming. For example, we found microbes that could be useful for external interventions because they have a causal effect on feed efficiency traits and low heritability. We also found two more groups of microbes that could change the total heritability and response to selection. The total and direct heritability estimates were similar for DMI, NESec, and RFI. Therefore, one group of microbes with moderate host genetic control, significant phenotypic effects, and genetic covariance and phenotypic effect with the same sign improves total heritability and response to selection, and the other group with genetic covariance and phenotypic effects with opposite signs decreases the heritability and response to selection. In summary, structural equation models provide guidance to target microbes that can be manipulated using selective breeding or feeding management to improve dairy cattle feed efficiency. 4)Weinvestigate gene-microbiome networks underlying feed efficiency traits by integrating genotypic, microbial, and phenotypic data from lactating dairy cows. Data consisted of dry matter intake (DMI), net energy secreted in milk, residual feed intake (RFI) records, SNP genotype, and 16S rRNA rumen microbial abundances from 448 mid-lactation Holstein cows. We first assessed marginal associations between genotypes and phenotypic and microbial traits through genomic scans, and then, in regions with multiple significant hits, we assessed gene-microbiome-phenotype networks using causal structural learning algorithms. We found significant regions co-localizing the rumen microbiome and feed efficiency traits. Interestingly, we found three types of network relationships: (1) the cow genome directly affects both rumen microbial abundances and feed efficiency traits; (2) the cow genome (Chr3: 116.5 Mb) indirectly affects RFI, mediated by the abundance of Syntrophococcus, Prevotella, and an unknown genus of Class Bacilli; and (3) the cow genome (Chr7: 52.8 Mb and Chr11: 6.1-6.2 Mb) affects the abundance of Rikenellaceae RC9 gut group mediated by DMI. Our findings shed light on how the host genome acts directly and indirectly on the rumen microbiome and feed efficiency traits and the potential benefits of the inclusion of specific microbes in selection indexes or as correlated traits in breeding programs. Overall, the multistep approach described here, combining whole-genome scans and causal network reconstruction, allows us to reveal the relationship between genome and microbiome underlying dairy cow feed efficiency.

Publications

  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, G Rosa, and F Pe�agaricano. 2024. Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling. J Dairy Sci. 107:8193-8204. https://doi.org/10.3168/jds.2024-24675
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Martinez-Boggio, G., Monteiro, H.F., Lima, F.S. et al. Revealing host genomemicrobiome networks underlying feed efficiency in dairy cows. Sci Rep 14, 26060 (2024). https://doi.org/10.1038/s41598-024-77782-z
  • Type: Peer Reviewed Journal Articles Status: Accepted Year Published: 2024 Citation: Monteiro, H.F., Figueiredo, C.C., Mion, B. et al. An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. anim microbiome 6, 5 (2024). https://doi.org/10.1186/s42523-024-00289-5
  • Type: Peer Reviewed Journal Articles Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, and F Pe�agaricano. 2024. Host and microbiome contributions to feed efficiency traits in Holstein cows. J Dairy Sci. Dairy Sci. 107:30903103 https://doi.org/10.3168/jds.2023-23869
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, R Profeta, M Van Heule, BC Weimer, CT Brown, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagaricano, and FS Lima. 2024. An interplay of viruses, bacteria, and protozoa in the rumen of dairy cows may contribute to improved feed efficiency. J Dairy Sci. 107(1):142.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, CS Schlesener, R Profeta, M Van Heule, BC Weimer, P Dini, CT Brown, and FS Lima. 2024. The impact of genomic database choice on microbiome analysis: Why should the dairy community care about it? J Dairy Sci. 107(1):172.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, CT Brown, M Van Heule, BC Weimer, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagaricano, and FS Lima. 2024. A comprehensive overview of the archaea activity in the rumen of dairy cows and their impact on feed efficiency. J Dairy Sci. 107(1):269.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, R Profeta, M Van Heule, BC Weimer, CT Brown, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagaricano, AP Faciola, MI Marcondes, and FS Lima. 2024. A novel comprehensive analysis of amino acid profile in microbial protein and its impact in feed efficiency. J Dairy Sci. 107(1):386.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, G Rosa, and F Pe�agaricano. 2024. Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling. J Dairy Sci. 107(1):410.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2024 Citation: Coelho Jr., WC, HF Monteiro, RS Bisinotto, JEP Santos, ES Ribeiro, F Schenkel, F Pen�garicano, BC Weimer, P. Vahmani, FS Lima. 2024. Assessing serum fatty acid associations with genomic prediction for feed saved in dairy cows. J Dairy Sci. 107(1):175.


Progress 02/01/23 to 01/31/24

Outputs
Target Audience:The findings of the current study were shared with a wide range of stakeholders in the dairy community. This was done through the presentation of five abstracts at the American Dairy Science Meeting, speaking engagements at national (e.g., Ruminant Nutrition Symposium in Florida) and international (e.g., Amino Acid Summit in Reggio Emilio, Italy; Invited Talk at the Department of Biosciences, University of Guelph, Guelph, Canada) conferences, as well as through the publication of three peer-reviewed papers, one in Animal Microbiome and two in the Journal of Dairy Sciences. The dissemination of the project's findings reached various audiences, including undergraduate students, graduate students, postdoctoral scholars, dairy scientists, dairy nutritionists, dairy veterinarians, and farm managers.rm managers.rm managers. Changes/Problems:Aim 3 was intended to be conducted last year at the University of Florida dairy farm. However, because the Department of Animal Sciences was considering selling the farm, it put Aim 3 on hold. Fortunately, the Department of Animal Sciences decided to keep the farm we will be able accomplish the aim 3 next year. What opportunities for training and professional development has the project provided?This project allowedtwo postdocs to sharpentheir analytical and writing skills and become significant contributors in the field. Moreover, the project allowed five graduate students to learn how to perform studies that included routine farm visits to collect feed, rumen, fecal, and blood samples to create complex metadata needed for genotypical and phenotypical characterization. How have the results been disseminated to communities of interest?Yes. As alluded to in the previous sections,the data has been published in multiple peer-reviewed manuscripts and national conference abstracts. Also, there were some national and international invitations to share the findings for the current project. What do you plan to do during the next reporting period to accomplish the goals?We intend to finish aim 3.

Impacts
What was accomplished under these goals? Aims 1 and 2 have been finalized. The data collected for aims 1 and 2 already generated four significant peer-reviewed manuscripts that advanced our understanding of the rumen microbiome and host genomics relationship modulating feed efficiency in dairy cows. The main takeaways of the three manuscripts published are: 1)Using an artificial intelligence approach, we demonstrated thatthe rumen microbiome composition explains a significant portion of the variation in residual feed intake (RFI), presenting a promising site of exploration for future improvements in predictive models to decrease the dairy sector's carbon footprint. The associations of RFI, as well as MFE (milk fat efficiency), MPE (milk protein efficiency), and their residuals with the rumen microbiome, unraveled through an ensemble method, further indicate key microbial players that could be targeted to further evaluate their effect on the efficiency of dairy cows. Additionally, the predictability of heritable traits by the rumen microbiome underscores the need for future research to dissect host-microbiome interactions in shaping feed and milk production efficiency. This exploration, and consequently further validation studies with complementary results from digestive parameters (e.g., digestibility) to more detailed microbiome approaches (shotgun metagenomics, metatranscriptomics, and metabolomics), is vital to pioneer advances in ruminant nutrition and fortify sustainable dairy production pathways. 2)Incorporating the rumen microbiome information in addition to genomic data allows for revealing the relative effects of the host genome and the microbiome on feed efficiency traits in dairy cattle. Rumen microbiome data can be used to estimate host direct and indirect genetic effects on feed efficiency. Indeed, the differences obtained between the h2 and the hd2 strongly suggest that the microbiome mediates part of the host's genetic effect. The holobiont model, which incorporates the host genome-by-microbiome interaction, provides further insights into the biological mechanisms underlying dairy cow feed efficiency. 3)Structural equation models offer an alternative to disentangle the relationships between the host genome, rumen microbiome, and feed efficiency traits in lactating Holstein cows. We classified rumen microbes into three groups, each of which could have different uses in dairy farming. For example, we found microbes that could be useful for external interventions because they have a causal effect on feed efficiency traits and low heritability. We also found two more groups of microbes that could change the total heritability and response to selection. The total and direct heritability estimates were similar for DMI, NESec, and RFI. Therefore, one group of microbes with moderate host genetic control, significant phenotypic effects, and genetic covariance and phenotypic effect with the same sign improves total heritability and response to selection, and the other group with genetic covariance and phenotypic effects with opposite signs decreases the heritability and response to selection. In summary, structural equation models provide guidance to target microbes that can be manipulated using selective breeding or feeding management to improve dairy cattle feed efficiency.

Publications

  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, G Rosa, and F Pe�agaricano. 2024. Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling. J Dairy Sci. 107:8193-8204. https://doi.org/10.3168/jds.2024-24675
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Monteiro HF, CC Figueiredo, B Mion, JEP Santos, RS Bisinotto, F Pe�agaricano, ES Ribeiro, MN Marinho, R Zimpel, AC Silva, A Oyebade, RR Lobo, WM Coelho Jr, PMG Peixoto, MB Ugarte Marin, SG Uma�a-Sed�, TDG Rojas, M Elvir-Hernandez, FS Schenkel, BC Weimer, CT Brown, E Kebreab, FS Lima. 2024. An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. Anim Microbiome 6:5. https://doi.org/10.1186/s42523-024-00289-5
  • Type: Journal Articles Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, and F Pe�agaricano. 2024. Host and microbiome contributions to feed efficiency traits in Holstein cows. J Dairy Sci. Dairy Sci. 107:30903103 https://doi.org/10.3168/jds.2023-23869
  • Type: Journal Articles Status: Submitted Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, G Rosa, and F Pe�agaricano. 2024. Revealing host genome-microbiome networks underlying feed efficiency in dairy cows. Sci Reports
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, R Profeta, M Van Heule, BC Weimer, CT Brown, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagari-cano, and FS Lima. 2024. An interplay of viruses, bacteria, and protozoa in the rumen of dairy cows may contribute to improved feed efficiency. J Dairy Sci. 107(1):142.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, CS Schlesener, R Profeta, M Van Heule, BC Weimer, P Dini, CT Brown, and FS Lima. 2024. The impact of genomic database choice on microbiome analysis: Why should the dairy community care about it? J Dairy Sci. 107(1):172.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, CT Brown, M Van Heule, BC Weimer, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagaricano, and FS Lima. 2024. A comprehensive overview of the archaea activity in the rumen of dairy cows and their impact on feed efficiency. J Dairy Sci. 107(1):269.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Monteiro, HF, R Profeta, M Van Heule, BC Weimer, CT Brown, JEP Santos, RS Bisinotto, ES Ribeiro, F Penagaricano, AP Faciola, MI Marcondes, and FS Lima. 2024. A novel comprehensive analysis of amino acid profile in microbial protein and its impact in feed efficiency. J Dairy Sci. 107(1):386.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, G Rosa, and F Pe�agaricano. 2024. Investigating relationships between the host genome, rumen microbiome, and dairy cow feed efficiency using mediation analysis with structural equation modeling. J Dairy Sci. 107(1):410.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Coelho Jr., WC, HF Monteiro, RS Bisinotto, JEP Santos, ES Ribeiro, F Schenkel, F Pen�garicano, BC Weimer, P. Vahmani, FS Lima. 2024. Assessing serum fatty acid associations with genomic prediction for feed saved in dairy cows. J Dairy Sci. 107(1):175.


Progress 02/01/22 to 01/31/23

Outputs
Target Audience:The study efforts from February 2022 to January 2023 included the collection of complete phenotypes for feed efficiency, milk production, and rumen samples for a reference population of approximately 200 Holstein genotyped cows. We generated four abstracts submitted, accepted, and presented at American Dairy Science Annual Meeting, 2023, in Ottawa, ON, Canada. We also presented findings in two regional local seminars. We also submit two manuscripts under review at the Proceedings of the National Academy of Sciences and the Journal of Dairy Sciences. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The project has provided opportunities for several graduate students to learn about how to manage cows in a controlled environment to measure feed intake and how to collect data to build robust multi-location phenotype for milk production and feed efficiency and how to collect rumen samples, store it for future use for DNA, RNA, and metabolites assessment. Furthermore, the study allowed postdoctoral scientists to learn and advance their quantitative and analytical skills, including the incorporation of artificial intelligence models to predict traits of interest for the study. How have the results been disseminated to communities of interest?Yes, four abstracts were present at the ADSA meeting of 2023. local symposium in California and invited seminar in other institutions such as the University of Guelph. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, we will finish the analytical components for sub-aims 2C, 2D, and 2E and prepare abstracts, manuscripts, and presentations related to these findings. In the next reporting period we will also conduct the experiment for aim 3.

Impacts
What was accomplished under these goals? Objective 2 (sub-objectives 2A and B) was accomplished in the last year. The findings from the current study clearly suggest that the rumen microbiome is highly associated with feed efficiency and milk production efficiency, and it can be used to improve the predictions of these traits. Our current findings highlight that incorporating information regarding the composition and abundance of the rumen microbiome improves the phenotypic prediction of feed efficiency traits in lactating dairy cows. The holobiont model, which considers the cow genome, the rumen microbiome, and the genome-by-microbiome interaction, showed the best goodness-of-fit and comparable predictive performance, and it is probably the preferred option for analyzing complex traits in ruminants. The differences obtained between the heritability and the direct heritability strongly suggest that the rumen microbiome mediates part of the host genetic effect. Microbiome data could be used to estimate cow direct and indirect genetic effects on feed efficiency traits. Also, our findings highlighted the contribution of the rumen microbial community on a large scale to RFI and gross feed efficiency of dairy cows and the potential for improvements in the selection of low-carbon footprint dairy cows. Feed efficiency, defined as RFI, was shown to be largely modulated by the rumen microbiome and promoted the greatest reductions in feed use while also being a potential CH4 mitigator among the studied selection strategies. Milk production efficiency traits, such as MPE, should also be considered in further exploring genetic selection toward more sustainable dairy production. The rumen microbiome largely modulated milk protein production efficiency and showed potential for future modulation through rumen microbiome changes. Finally, predictability by the rumen microbiome of heritable traits suggests that future studies should evaluate the interplay between host-microbiome interactions in the modulation of feed and milk production efficiency traits, so the long-term sustainability of the dairy sector can be achieved.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 13. Monteiro HF, C Figueiredo, B Mion, WM Coelho Jr., RS Bisinotto, M. Nehme, JEP Santos, F Pe-�agaricano, ES Ribeiro, F Schenkel, BC Weimer, L Guan, A Neves, T Brown, FS Lima. 2023. A mul-ti-omics approach to characterize the role of the rumen microbiome on feed efficiency in dairy cows. J Dairy Sci. 106(1):142.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 1. Coelho Jr., WC, HF Monteiro, CC Figueiredo, B Mion, PMG Peixoto, RS Bisinotto, M Nehme, JEP Santos, F Pen�garicano, ES Ribeiro, F Schenkel, BC Weimer, L Guan, T Brown, FS Lima. 2023. Feed-efficient dairy cows show potentially more significant protozoa activity towards microorganisms in the rumen. J Dairy Sci. 106(1):142.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 6. Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schenkel, ES Ribeiro, KA Weigel, and F Pe�agaricano. 2023. Host and microbiome contributions to feed efficiency traits in Holstein cows. J Dairy Sci. 106 (1):121.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 12. Monteiro HF, C Figueiredo, B Mion, WM Coelho Jr., RS Bisinotto, M. Nehme, JEP Santos, F Pe-�agaricano, ES Ribeiro, F Schenkel, BC Weimer, L Guan, A Neves, T Brown, FS Lima. 2023. Using the rumen microbiome, genomic PTA, and artificial intelligence to predict feed and milk production efficiency in dairy cows. J Dairy Sci. 106(1):141-142.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: 15. Monteiro HF, C Figueiredo, B Mion, WM Coelho Jr., RS Bisinotto, M. Nehme, JEP Santos, F Pe-�agaricano, ES Ribeiro, F Schenkel, BC Weimer, L Guan, A Neves, T Brown, FS Lima. 2023. The gastrointestinal microbiome of dairy cows outweighs genetics in determining dairy cows efficiency to produce milk: A holistic multi-omics study. 8th Annual UC Davis Postdoctoral Research Symposi-um, 39, Davis, CA, USA
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: 2. Monteiro HF, CC Figueiredo, B Mion, JEP Santos, RS Bisinotto, F Penagaricano, ES Ribeiro, MN Mari-nho, R Zimpel, AC da Silva, A Oyebade, RR Lobo, WM Coelho Jr., PMG Peixoto, MB Urgarte Marin, F Schenkel, BC Weimer, E Kebreak, T Brown, FS Lima. 2023. Artificial intelligence-driven analysis of the rumen microbiome unravels a path to identify dairy cows with a low carbon footprint. PNAS
  • Type: Journal Articles Status: Under Review Year Published: 2023 Citation: 6. Martinez Boggio G, HF Monteiro, FS Lima, CC Figueiredo, RS Bisinotto, JEP Santos, B Mion, FS Schen-kel, ES Ribeiro, KA Weigel, and F Pe�agaricano. 2023. Host and microbiome contributions to feed efficiency traits in Holstein cows. J Dairy Sci.


Progress 02/01/21 to 01/31/22

Outputs
Target Audience: The study efforts for the period of February 2021 to January2022included the collection of complete phenotypes for feed efficiency, milk production, and rumen samples for a reference population of approximately 300 Holstein genotyped cows. In 2021, we also sequenced the rumen microbiome of 495 cows. We performed the bioinformatics analysis and statistical analysis to characterize the association of the rumen microbiome with milk production and feed efficiency traits. We generated two abstracts that were submitted and accepted to be present at American Dairy Science Annual Meeting, 2022, in Kansas City, MO. Changes/Problems:Dr. Rodrigo Bicalho left Cornell University to work exclusively in the start-up company Fera Animal Health. The 100 cows that were supposed to be collected at Cornell University are now being at the University of Florida. The other activities related to DNA extraction and rumen sample sequencing and bioinformatics are currently being conducted at the University of California. None of the plans for the work to be conducted as part of the proposed project will be affected by this change. What opportunities for training and professional development has the project provided?In 2021, undergraduate and graduate students continued receiving training on how to collect rumen, fecal, blood, and diet samples due to the increase in animals sampled during the respective time. Students have been trained to handle rumen samples in the laboratory for DNA extraction and sequencing analysis.In bioinformatics, some of these students have been trained to perform 16S rRNA upstream and downstream analysis which requires heavy computational skills. How have the results been disseminated to communities of interest?Preliminary results from this study have been presented locally to the University of California, Davis, which in one of the occasions the postdoctoral scholar in the project was awarded the best oral presentation among postdocs from all departments university-wide. What do you plan to do during the next reporting period to accomplish the goals?In 2022, we will continue collecting data for all variables from our defined metadata in order to achieve our target reference population of 800 lactating Holstein cows sampled. We will continue performing DNA extraction of rumen samples and gather genomic data from the sampled animals. This way inferences about the association of rumen microbiome and cow's genotype and phenotype for milk production can be made.

Impacts
What was accomplished under these goals? In 2021, our reference population reached 495 lactating Holstein cows from university herds in the SE, NE, and MW regions of the US out of a total of 800 cows we plan to have as a final reference population as stated in objective 1. Data for milk production and feed efficiency phenotypes, genotypes, rumen microbiome samples, and diet and fecal samples were collected for all animals.Progress has also been made in objective2, specifically in sub-objectives 2.A and 2.B through the performance ofassociation analyses between the rumen microbiome and productive efficiency andgenetic parameters collected from our current reference population. These preliminary results will be presented at the 2022 American Dairy Science Association Meeting through 2 different abstracts that have been accepted for the conference.

Publications

  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: H. F. Monteiro, R. S. Bisinotto, C. C. Figueiredo, J. E. P. Santos, F. Penagaricano, E. S. Ribeiro, F. Schenkel, M. I. Marcondes, B. C. Weimer, and F. S. Lima. 2022. Characterizing ruminal microbiome contribution to residual feed intake and milk production efficiency in a large cohort of lactating dairy cows. J Dairy Sci 105 Accepted.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2022 Citation: H. F. Monteiro, R. S. Bisinotto, C. C. Figueiredo, J. E. P. Santos, F. Penagaricano, E. S. Ribeiro, F. Schenkel, M. I. Marcondes, B. C. Weimer, and F. S. Lima. 2022. Rumen microbiome contributions to dry matter intake modulation in lactating dairy cows. J Dairy Sci 105 Accepted.


Progress 02/01/20 to 01/31/21

Outputs
Target Audience:Period 02/01/2020 - 12/31/2020: The study efforts for the period of February 2020 to December 2020 included the collection of complete phenotype for feed efficiency, milk production, and rumen samples for a reference population of over 200 Holstein genotyped cows. The target audience for the research being conducted is stakeholders in the dairy industry including dairy farmers, dairy nutritionists, dairy Veterinarians, people working in the related dairy industry, dairy scientists, undergraduate and graduate students, and postdoctoral associates. Period 01/01/2021 - 12/31/2021: The study efforts for the period of January 2021 to December 2021 included the collection of complete phenotypes for feed efficiency, milk production, and rumen samples for a reference population of approximately 300 Holstein genotyped cows. In 2021, we also sequenced the rumen microbiome of 495 cows. We performed the bioinformatics analysis and statistical analysis to characterize the association of rumen microbiome and milk production and feed efficiency traits. We generated two abstracts that were submitted and accepted to be present at American Dairy Science Annual Meeting, 2022, in Kansas City, MO. Changes/Problems:The project director was recruited and accepted a job offer to move to Davis University of California in February of 2020. That is the reason that the project was halted and only started in the middle of June after initial issues with pandemics. What opportunities for training and professional development has the project provided?Throughout the year 2020, undergraduate and graduate students received training on how to collect rumen samples, fecal samples, blood samples, and diet samples. They learned on how to train cows to eat on Calan gates and how to input data into Excel spreadsheets. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?In 2021, we will continue to collect data for milk production and feed efficiency phenotypes, genotypes, rumen microbiome samples, and diet and fecal samples. Additionally, we will start to perform DNA extraction of rumen samples and we will sequence rumen samples to assess associations of rumen microbiome with cows' genotype and phenotype for milk production and feed efficiency.

Impacts
What was accomplished under these goals? In 2020 after moving from the University of Illinois to the University of California, Davis, the process of field collection of samples and data was re-started. Data for milk production and feed efficiency phenotypes, genotypes, rumen microbiome samples, and diet and fecal samples were collected for a subset of over Holstein 200 cows. The activities concluded are part of sub-objectives 1 and 2 were conducted with some delayed starts due to issues with pandemics.

Publications