Acartia tonsa, in future global change conditions (high temperature and high CO2). We found parallel adaptation to global change conditions in genes related to stress response, gene expression regulation, actin regulation, developmental processes, and energy production. However, reciprocal transplantation showed that adaptation resulted in a loss of transcriptional plasticity, reduced fecundity, and reduced population growth when global change-adapted animals were returned to ambient conditions or reared in low food conditions. However, after three successive transplant generations, global change-adapted animals were able to match the ambient-adaptive transcriptional profile. Concurrent changes in allele frequencies and erosion of nucleotide diversity suggest that this recovery occurred via adaptation back to ancestral conditions. These results demonstrate that while plasticity facilitated initial survival in global change conditions, it eroded after 20 generations as populations adapted, limiting resilience to new stressors and previously benign environments.Adaptive evolution and phenotypic plasticity will fuel resilience in the geologically unprecedented warming and acidification of the earth’s oceans, however, we have much to learn about the interactions and costs of these mechanisms of resilience. Here, using 20 generations of experimental evolution followed by three generations of reciprocal transplants, we investigated the relationship between adaptation and plasticity in the marine copepod,  Rapid adaptation will facilitate species resilience under global climate change, but its effects on plasticity are less commonly investigated. This study shows that 20 generations of experimental adaptation in a marine copepod drives a rapid loss of plasticity that carries costs and might have impacts on future resilience to environmental change. However, species can use genetic adaptation, physiological plasticity, or both to persist4. Genetic adaptation, heritable genetic change that improves the mean fitness of a population in an environment5, enables resilience by shifting the mean phenotype of the population to tolerate different conditions. Selection can shift population genetic variation over short time-scales, as few as one to four generations, in response to an extreme change in the environment9. Conversely, plasticity allows a single genotype to generate different phenotypes in response to the environment10. Considering rapid changes in environmental conditions, adaptive plasticity enables organisms to maintain fitness across environments11 through underlying phenotypic changes, such as shifts in metabolism or gene regulation12. However, the interaction and relative contributions of adaptation and plasticity to population persistence under rapid environmental change remains a critical area of investigation16.Global conditions are changing at a geologically unprecedented rate, and the frequency of extreme events is increasing due to human activities resulting in chronic and novel environmental changes17. This reduction is expected when the previous trigger for plasticity is no longer present, the environment is static, or the plasticity is costly enough to be selected against18. Indeed, a reduction of plasticity appears to be common in natural and experimental empirical systems23. This experimental work has also revealed faster losses of plasticity during rapid adaptation than theory would predict24. For example, Waddington showed that a high temperature-induced plastic phenotype, crossveins in Drosophila wings, can become fixed, or assimilated, within 20 generations of artificial selection, reducing plasticity and the environmental responsiveness of a trait25. Under climate change conditions, populations will experience strong selection, and it is essential to understand how selection in these stressful conditions may alter or reduce the plasticity of natural populations.Theoretical models predict that plasticity may increase when a population is exposed to a novel environment, but should decline across longer timeframes26. From this, one can identify both the adaptive potential and the mechanisms of adaptation of a population, directly providing insight into how a population may cope with changing climates27. Experimental evolution in fruit flies29, copepods21, marine algae30, nematodes22, mice31, and others32 have identified phenotypic or genomic responses to selection and have demonstrated that strong selection can rapidly shift or drive a loss of plasticity. The majority of these studies have leveraged model systems and found that when selection regimes are static, plasticity tends to be lost (but see refs. 33). While the static conditions in the lab are a limitation of these studies as they do not necessarily represent the variable conditions found in the wild, experimental losses of plasticity under static conditions raise the concern that the strong selection driving rapid adaptation to global change may also purge phenotypic plasticity from populations. Indeed, a loss of plasticity appears to be present in natural systems rapidly adapting, for example, as seen in marine to freshwater transitions in copepods19 and numerous fish species35, populations of plants and frogs as they adapt to reduced habitat heterogeneity38, fishes in different predation environments39, and adaptive radiations of cichlids with different prey items40.Experimental evolution is a particularly powerful approach to understand resilience as organisms can be exposed to specific selective pressures over multiple generations41. Under global change, it is expected that conditions such as prolonged heat waves1, decreasing salinity42, and warmer winter conditions43 will become more common. For short-lived species, these extreme conditions may be longer than their generation time and therefore selection may drive rapid adaptation that reduces plasticity, resulting in populations that are less resilient when environments fluctuate in the future, which is expected under global change1. However, there have been limited experimental evolution studies investigating the link between adaptive evolution and plasticity in non-model metazoans, particularly in multiple stressor scenarios. Further, work focused on understanding the phenotypic plasticity of experimentally evolved populations across multiple generations of reciprocal transplant in common gardens is lacking. Such experiments could test the effects of long-term adaptation and its impacts on plasticity. Incorporating this multigenerational reciprocal common garden perspective after adaptation is essential to better understand if and how organisms can shift phenotypes following rapid environmental change.The evolution of phenotypic plasticity is dictated by both the degree and predictability of environmental variability relative to generation time; variability must be experienced within the lifespan of an individual to favor plasticity45. Species that live in dynamic environments, have distributions spanning a wide range of environmental conditions, and have high dispersal capacity are predicted to have the greatest capacity to respond to rapid environmental change due to their high levels of standing genetic variation and physiological plasticity46. Many marine species in particular live in environments that vary across time and space and have life histories that promote dispersal49. This is true for copepods, the most abundant marine metazoans50, which have plasticity and genetic variation for adaptation to global change conditions54. However, few studies have measured the genetic response during adaptive evolution to a multiple stressor environment across multiple generations in a marine metazoan and none, to our knowledge, across any taxa, have measured allelic and gene expression responses across multiple generations of reciprocal transplant following adaptation. Further, the relationship between plasticity and genetic adaptation is only beginning to be understood in marine systems56. Given their ecological importance and high levels of genetic variation and plasticity, copepods are ideal models to disentangle the interplay of genetic and plastic responses of natural populations of metazoans to global change conditions52.The use of outbred natural populations in evolution experiments is a powerful approach for revealing the potential contributions of standing genetic variation and plasticity, particularly in context of future global change conditionsAcartia tonsa, to ambient and global change conditions to determine the effects of long-term adaptation on plasticity. This coupling of many generations of experimental evolution to a multi-stressor environment with multiple generations of reciprocal transplant is a first to our knowledge. As a dominant prey item for forage fish, A. tonsa serves as a critical link in the marine food web, supporting economically important fisheries57 and as a major phytoplankton grazer contributes to the storage of atmospheric CO2 and thus mediates marine biogeochemical cycles58. Replicate cultures  of A. tonsa were subjected to 20 generations of selection in ambient (AM: 400 ppm pCO2 and 18 °C) and combined high CO2 × temperature  conditions followed by three generations of reciprocal transplantation of both lines to opposite conditions . RNA was collected from pools of 20 adults from each of four replicate cultures at the first, second, and third generation after transplant for both experimentally evolved and transplanted lines  What are the allelic and gene expression mechanisms underlying adaptation to OWA conditions? (2) To what extent is genetic adaptation vs. transcriptional plasticity required to respond to environmental change? Specifically, does adaptation result in a loss of plasticity in a marine metazoan? (3) Does adaptation result in trade-offs among life-history traits revealed through reciprocal transplant and in low-food stress conditions? We predicted that this coastal copepod would have genetic and plastic mechanisms to respond to warming and acidification conditions and that long-term adaptation would reduce physiological plasticity. Our results support these predictions, revealing the molecular underpinnings of the adaptive mechanisms, and demonstrate a loss of transcriptional plasticity with a capacity for recovery at the expense of adaptive genetic variation.We have previously shown that the OWA lines used here rapidly adapted to OWA conditions by recovering egg production and hatching success63. A total of 17,720 loci  showed consistent allele frequency divergence . Given the likely polygenic nature of adaptation to warming and acidification conditions and that polygenic traits tend to be genetically redundant66, the identified adaptive genetic variation would be prone to false negatives due to the requirement that adaptation be parallel in all replicates. Conversely, linkage disequilibrium contributes some number of false positives when neutral loci are located near the selected loci67. Of potential candidate genes driving adaptation , was an extreme outlier for divergence in allele frequencies, 77% change, which was 72% greater than the global average  that has been shown to be inhibited by heat stress69. In addition, mitochondrial function has been linked to variation in thermal tolerance among populations of the intertidal copepod Tigriopus californicus70. The three substitutions identified here were all non-synonymous, in linkage disequilibrium, and in a single alpha helix of the ETC subunit, including an Isoleucine, non-reactive hydrophobic amino acid, to Asparagine, polar, often active amino acid , suggesting that adaptive divergence in protein function and gene expression targeted different, but functionally related genes75. Similarly, there was no correlation between the degree of expression and allelic responses , suggesting no impact of potential allele-biased expression on estimates of allele frequencies from pools of sequenced RNA. We verified the lack of impact of allele-biased expression on allele frequency estimates by comparing to allele frequency estimates from genomic DNA from the non-transplanted lines at F2563 were differentially expressed between control and experimentally evolved replicate lines in their home environment, differences that could be driven by plastic and evolved mechanisms , a 12.7-fold greater response than for OWA lines . However, at the end of the second and third generation in transplant, this difference diminished due to the relative increase of OWA gene expression changes  and decrease of AM changes .Selection in a stressful environment could increase or decrease plasticity2 fold change: AMOWA vs. AMAM and OWAOWA vs. OWAAM; Fig. 29, indicating a muted transcriptional response by the OWA line. After one generation in transplant conditions, there was a slight negative relationship, slope of −0.25 , but by F3 there was a positive relationship where each transplanted line converged on the gene expression profile of its new environment (slope = 0.37). However, the slope was less than 1, indicating that AM lines had a greater change in expression by F3 than OWA lines and had higher plasticity to change gene expression profiles in response to their new environment. While expression changes at F1 were likely due to plasticity, differential gene expression at F2 and F3 could be due to plasticity or evolution post-transplant. For example, transgenerational plasticity may cause the gene expression at F1 and F2 to be influenced by the environment of the previous generations78, but environmental effects decay after 3 generations of common garden79. Therefore, we would expect F1 and F2 to reflect plastic responses influenced by the previous environment, which could explain the inverse relationship at these generations. By F3, the environmental effects of the original environment are no longer present and the plastic response remains, which was reduced in the OWA lines relative to AM  if they were differentially expressed between AMAM vs. AMOWA and not differentially expressed between AMOWA vs. OWAOWA. Genes were considered to require genetic change (GC) if they were differentially expressed between AMOWA vs OWAOWA. PO and GC genes were mutually exclusive but did not include all of the genes differentially expressed between AMAM and OWAOWA . Last, we similarly categorized the differentially expressed genes as PO or GC for the reverse adaptation from warming and acidification to ancestral ambient conditions: PO: differentially expressed between OWAOWA vs. OWAAM and not differentially expressed between OWAAM and AMAM; GC: differentially expressed between OWAAM vs. AMAM.Gene expression patterns after long-term experimental evolution and reciprocal transplantation also allow us to test the hypothesis that transcriptional plasticity can be retained and serve as a mechanism to facilitate re-adaptation or transgenerational acclimation to ancestral conditions in the context of adaptation to global change conditions and extreme events. Using the approach described in Ho et al.82 found in high-altitude adapted chickens, natural and introduced guppy populations, and experimental evolution in E. coli where genetic-change genes were more common when moving to the new environment and plasticity-only genes were more common when going back to the original environment. After two and three generations of transplant, however, our results match Ho et al.’s patterns: reverse adaptation, OWA to AM, had more plasticity-only genes than genetic-change-needed genes, while forward adaptation, AM to OWA, had more genetic-change-needed than plasticity only genes . Transplanted lines were fit to discriminant function space to determine if and how expression changed to match the adaptive expression profile where the adaptive plasticity for each line is summarized by the length of the arrow in Fig. PMCMC F1AM < 4e-04; F2AM = 0.0007; F3AM < 4e-04; F1OWA = 0.001; F2OWA = 0.004; F3OWA < 4e-04). However, matching the results above, we found lower overall plasticity in OWA transplanted copepods relative to AM transplanted copepods at F1; expression changed to a greater extent for AM as compared to OWA lines at F1 . Discriminant function space was generated from AMent Fig. . We assuPMCMC F1 = 0.001; F2 = 0.001; F3 = 0.0007) while AM shifts were somewhat less significant . At generations F1 and F2, we found allele frequency changes were similar between AM and OWA lines . These shifts in allele frequencies were again mirrored by an independent analysis based on Cochran–Mantel–Haenszel tests . Similarly, ambient lines, both transplanted and not, maintained high levels of genetic diversity (PTukey > 0.05). However, when transplanted back to ambient conditions, OWAAM showed a significant drop in diversity by F3 , while there was no loss of diversity in AMOWA or OWAOWA lines as control contrasts . In addition, the loss of nucleotide diversity for OWAAM lines was concentrated in genes with functions related to sequestration of actin monomers, cytokinesis, and response to stress  for each treatment following 20 generations of selection and after transplant. We found no significant loss of genetic diversity after long-term adaptation to warming and acidification conditions . We found that fecundity was equally high for both lines in their home environments but declined for OWA lines after transplant to AM conditions  plasticity largely buffered populations from environmental change allowing AM lines to tolerate warming and acidification conditions. Conversely, the data suggest that over the longer-term (20 generations) transplanted OWA animals required larger shifts in allele frequency and experienced targeted loss of adaptive genetic diversity to survive in what would be predicted to be relaxed selection in a previously benign environment. This indicates that plasticity to acclimate was lost as adaptation to warming and acidification conditions proceeded, indicating costs to maintain plasticity in warming and acidification conditionsThese results advance our understanding of the relationship between plasticity and adaptive evolution by providing evidence that plasticity did not impede adaptation but, over time, adaptation eroded plasticity. Importantly, by including multiple generations of transplantation following 20 generations of selection, we show that, even with a loss of plasticity, animals can re-evolve to some degree back to their ancestral conditions. However, this re-adaptation carries the cost of losing adaptive genetic variation.A. tonsa to a temporary extreme may result in a population that is maladapted when conditions return to the previous state. Thus, while plasticity may enable persistence initially, adaptation can drive a loss of plasticity that can lead to reduced resiliency as environmental conditions fluctuate. These results demonstrate the utility of experimental evolution in understanding complex adaptation in natural systems and to reveal the mechanisms, time-scales of responses, consequences, and reversibility of this process. As we begin to incorporate adaptive potential and plasticity into species persistence models102, these results caution that, even for species predicted to be resilient to rapid global change, there may be unforeseen costs to adaptive evolution.This interplay between plasticity and adaptation has important implications for our understanding of mechanisms of species persistence to global change conditions. We demonstrate that copepods have the capacity to adapt over 20 generations, approximately one year, using the standing genetic variation that exists in natural populations. However, reciprocal transplant and food challenges revealed significant costs of this adaptation; populations lost physiological plasticity, the ability to tolerate food limitation, and ultimately adaptive genetic variation for warming and acidification conditions. Given the increasing frequency of extreme environmental conditions, rapid adaptation of n = 1000) were collected in June of 2016 from Esker Point Beach in Groton, CT, USA  and raised for at least three generations prior to the start of selection to limit maternal effects78. Parental individuals were acclimated to one of two experimental conditions: (1) AM—Ambient temperature (18 ˚C), Ambient CO2  or (2) OWA—High temperature (22 ˚C), high CO2 . This allowed nearly all surviving adults to procreate while minimizing the chance that remaining adults would cannibalize or mate with their offspring.Copepods n = 000 were OWA and OWAAM (as well as sham transfers: AMAM and OWAOWA). Transplant treatments were housed in incubators that corresponded to the new environments along with the original treatments: AMOWA and OWAOWA were housed in the OWA incubator while OWAAM and AMAM cultures were housed in the AM incubator. Copepods were raised for three subsequent generations and were fed every 48–72 h as described above. This design led to 48 total samples for genomics: 2 treatments × 2 transplant/non-transplant × 4 replicates × 3 generations.At the 21st generation, we performed a reciprocal transplant between copepods from AM and OWA. The lineage x environment interaction yielded four unique treatments. Each of the four replicates from each treatment was split to yield four additional replicates for each of two new transplant treatments: AM104 where leading and trailing quality was set at 2, sliding window length was 4 with a quality of 2, and minimum length was 31.For each sample, RNA from pools of 20 individuals was extracted using TRIzol reagent  and purified with Qiagen RNeasy spin columns . RNAseq libraries were prepared by Novogene  and sequenced with 150 bp paired end reads on an Illumina NovaSeq 600, generating 1.29 billion reads. Raw reads were trimmed for quality and adapter contamination using Trimmomatic V0.36105 using the Trinity_gene_splice_modeler.py command in the Trinity pipeline106. SuperTranscripts act as a reference by collapsing isoforms into a single transcript, which enables variant detection without a reference genome107. Trimmed reads were aligned to the SuperTranscripts using bwa mem108 and duplicate reads were marked with Samblaster v 0.1.22109.To quantify genetic variation, SuperTranscripts were generated from the reference transcriptome110 . Following this, variants were stringently filtered to include no missing data, only sites where at least four samples were called as variable and each with a minor allele frequency of at least 0.025 , any sites with depth greater than 3x the median (median = 132x), and sites with a minimum coverage of 50x per sample  were removed from the dataset. This left 23,324, 24,882, and 24,132 genes for F1, F2, and F3, respectively. Counts were normalized and log transformed using the rlog command in DESeq2114.Gene expression was quantified at the gene level as recommended63. We also assume that the gene expression profiles for each line in their home environment is indicative of an adaptive expression profile for that environment20. Given that we know the populations adapted to their home environments63 and that other studies have found an association between genes that show changes in expression towards the adaptive state and fitness related phenotypes116, this assumption is reasonable to make. However, it should be noted that we cannot be positive, which transcriptional changes are directly linked to fitness with the current data and divergence in protein function likely also explains some fitness differences between lines. Differentially expressed genes were identified using the model ~Line + Treatment + Line:Treatment and any contrasts were considered significant when adjusted P-values were <0.05. We identified the plastic genes for each line as the genes changing expression between environments within a line . We compare the plasticity between AM and OWA lines by plotting the log2 fold change between OWAOWA/OWAAM and AMOWA/AMAM, similar to approaches taken in other plasticity research22. To assess the mechanisms driving gene expression divergence between lines in their home environments after 20 generations, we partitioned gene expression changes into Plastic Only and Genetic Change genes82. For genes that were significantly different in expression between OWAOWA and AMAM , we assessed the mechanisms enabling the transition from AM to OWA (forward evolution) and OWA to AM (reverse). For AM to OWA, when a gene was differentially expressed between the transplanted and home line (AMOWA vs. OWAOWA), the gene expression shift required genetic change. Alternatively, if the gene was significantly plastic (significant AMAM vs. AMOWA) with no genetic change, this was a plasticity only gene. These two categories were mutually exclusive but did not include all genes differentially expressed between AMAM and OWAOWA due to the cases where genes were not differentially expressed between AMAM vs. AMOWA or AMOWA vs. OWAOWA, even though gene expression was different between AMAM vs OWAOWA. We conduct the opposite analysis for OWA to AM. With this analysis we can partition the changes in expression after 20 generations of selection into those that were achieved through plastic vs. evolved mechanisms.DESeq2 was used to identify differentially expressed genes. We assume that each line has adapted to their respective environment after 20 generations, as we have demonstrated for these lines previously117. Discriminant functions for each generation were first generated using the non-transplanted lines to identify genes consistently differentially expressed. Four principal components that represented 81, 78, and 80% of the variation at F1, F2, and F3 were retained and two clusters that represented the two non-transplanted lines were used. Transplanted lines were fit to this discriminant function space and used MCMCglmm models in R118 to model the effect of line origin and transplant on movement in discriminant function space. In all, 2500 posterior estimates were generated and the difference in the transplant effect for each line was quantified by calculating the absolute difference between these estimates. This can be viewed as the difference between the lengths of the lines representing average shifts in Fig. P-value for the difference in magnitude of the effect of transplant on each line in discriminant function space.Discriminant analysis of principal components (DAPC) was used to quantify the degree to which gene expression and allele frequencies converged on the adaptive state within each environment. We know, as above, that lines have adapted to their home environment and made the same assumptions regarding adaptive transcriptional profiles. Using the filtered, normalized, and transformed expression data from DeSeq2, shifts in gene expression in transplanted lines across generations were quantified using DAPC in the adegenet package in RDAPC was also used to identify divergence in allele frequency between the AM and OWA lines. The same approach as for gene expression (above) was taken where we generated the DAPC with lines in their home environment and then fit transplanted lines to discriminant function space. Here, we retained three principal components that represented 56, 59, and 63% of the total variation at each generation and again used two clusters.119. We calculated CMH P-values for AMAM vs. OWAOWA lines  for each of the three sampled generations. Significance thresholds (P < 5.17e-08) were defined with Bonferroni corrections using the total number of SNPs  multiplied by three, representing the total number of tests conducted. The SNPs that were identified in all three generations were considered adaptive  tests were used to identify specific SNPs that were consistently diverged between the AM and OWA lines and represent the most likely targets of adaptation. This approach looks for consistent changes in allele frequencies across all replicates and is a common and powerful technique in experimental evolutionP-values < 0.05.DESeq2 was used to identify the specific genes that were adaptively differentially expressed between non-transplanted AM and OWA lines at each generation. Using the same gene set and model from DeSeq2, above, we identified differentially expressed genes between AM and OWA in their home environment with an adjusted significance threshold of 120 with 100 bp non-overlapping sliding windows. To quantify if π was differentially lost in any treatment, median π values were compared using an Anova with a Tukey post hoc test. We next tested if regions containing adaptively divergent loci between non-transplanted OWA and AM lines lost π at a different rate than regions containing only neutral variants. The change in π was calculated for each replicate and the change in π for OWAAM and AMOWA were compared using Wilcoxon Rank Sum tests with bonferroni corrections. Gene ontology enrichment was performed with GO Mann–Whitney U121, which requires no significance threshold, but takes the change in π for each group and asks if any functional category falls towards the tails of this distribution . We use this same approach for gene expression and allele frequencies divergence between lines in their home environments. For allele frequencies, the minimum P-value was chosen for each gene.We estimate genetic diversity (π) for each replicate using Popoolation10 of the CMH P-values for the allele frequencies and the differential gene expression P-values between OWAOWA and AMAM. This analysis showed the two sets were distinct and the variation explained by each was less than 1% at each generation, indicating that there was minimal bias in estimating allele frequencies due to differentially expressed genes.Finally, we tested for overlap between the allelic and gene expression results to determine if the genes in each were significantly correlated. We correlated the relationship between the –log2 concentration prior to replacement. Survivorship was assessed among twelve 250-mL beakers per treatment (3 food concentrations × 4 replicates = 12 beakers per treatment) containing 25 individual N1 nauplii in the same plexiglass enclosures as described above and monitored until sexual maturity (adulthood). Life-history results are only presented for food replete and food-limited conditions because no starved individuals survived. Log rank analysis of survivorship was assessed using the survival123 and survminer124 packages in R.Day-specific survivorship was measured every 48–72 h, with food media replaced on monitoring days. Food media was provided at the appropriate food concentration  divided in equal carbon proportions between the three afore-mentioned prey species in 0.2 μm filtered seawater collected from Long Island Sound. Food media was acclimated to the appropriate temperature and CO125. Lids of petri dishes were left off-center to allow for full contact with the atmosphere and diffusion of CO2. Plates with dead males were still evaluated for EPR, but not HS. Plates with dead females were not evaluated because we cannot estimate egg production or hatching when no females were alive; mortality was estimated separately and we do not want to confound these two measures. The maximum number of plates excluded was six in the OWAAM treatment at food-limited conditions. No other treatment experienced more than three plates excluded from the assay. After the hatching period, plates were preserved with non-acid Lugol’s solution and eggs counted and nauplii counted. Per capita EPR was calculated as (Eu + Eh)/t where Eu represents unhatched eggs, Eh represents hatched eggs (nauplii), and t represents egg laying time. Hatching success was calculated as Eh/(Eu + Eh).Egg production rate (EPR) and hatching success (HS) were assessed with 36 individual mate pairs of newly matured adults per treatment (3 pairs per food concentration × 3 food concentrations × 4 replicates = 36 pairs per treatment). Adults were incubated in 25 mL petri dishes  over three days in the same temperature-controlled incubators and plexiglass enclosures described above. After the initial three-day incubation, adults were assessed for survival and removed to avoid cannibalism of eggs. Eggs were allowed to hatch over a subsequent 3-day incubation. Food media was prepared as described for survivorship and replaced daily during egg laying to ensure accurate food concentrations were near saturation and not reduced due to daily grazingλ, was calculated as the dominant eigenvalue of an assembled projected age-structured Leslie Matrix constructed from survivorship and fecundity data83. Briefly, day-specific probabilities of survivorship are calculated from day-specific survivorship as lx represents the number of individuals on day x and lx–1 represents the number of individuals on day x–1. Probabilities of survivorship on day 1 are assumed to be 100%. Per capita EPR and HS are calculated as described above, with fecundity rates equaling the product of EPR and HS. As only females produce offspring, total fecundity rates must be scaled to the sex ratio  observed in survivorship experiments. To account for differences in individual development time for each treatment, fecundity rates are assigned to all days after the first matured adult is observed. We assume that survivorship in each beaker is equally as likely to experience the fecundity values observed in EPR experiments. Therefore, each mate-pair fecundity rate was paired with each survivorship beaker to construct a matrix. This yields a maximum of 48 matrices per treatment per food concentration (4 beakers z-score (1.960), SD represents the standard deviation of the mean, and n represents the number of observations.The population net reproductive rate, t-test comparisons were used to conduct pairwise comparisons of the interactions for each life-history trait.We constructed linear mixed models with line, environment, and food concentration as fixed effects with all interactions, and culture replicate as a random effect. The results for net reproductive rate fall within a normal distribution with an inflation of zero values. Thus, we constructed zero-inflated generalized linear mixed models to account for the additional zeroes. Two post hoc tests were conducted. First, to quantify differences in plasticity between the lines, we evaluated genotype Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3Supplementary Dataset 4Reporting Summary


BoLA-DRB3 exon 2 of these native breeds. The results of several PCA were in agreement with clustering patterns observed on the neighbor joining (NJ) trees. These results provide insight into their high survival rate for different tropical diseases and their reproductive capacity in Sudan's harsh environment.Autochthonous Sudanese cattle breeds, namely Baggara for beef and Butana and Kenana for dairy, are characterized by their adaptive characteristics and high performance in hot and dry agro-ecosystems. They are thus used largely by nomadic and semi-nomadic pastoralists. We analyzed the diversity and genetic structure of the BoLA-DRB3 gene, a genetic locus linked to the immune response, for the indigenous cattle of Sudan and in the context of the global cattle repository. Blood samples (n = 225) were taken from three indigenous breeds  distributed across six regions of Sudan. Nucleotide sequences were genotyped using the sequence-based typing method. We describe 53 alleles, including seven novel alleles. Principal component analysis (PCA) of the protein pockets implicated in the antigen-binding function of the MHC complex revealed that pockets 4 and 9 (respectively) differentiate Kenana-Baggara and Kenana-Butana breeds from other breeds. Venn analysis of Sudanese, Southeast Asian, European and American cattle breeds with 115 alleles showed 14 were unique to Sudanese breeds. Gene frequency distributions of Baggara cattle showed an even distribution suggesting balancing selection, while the selection index (ω) revealed the presence of diversifying selection in several amino acid sites along the  The Kenana and Butana breeds are the best-known milk-producing northern Sudan Zebu breeds5 with milk yield of over 1500 kg per lactation8.There is a consensus among population geneticists that the Sudanese cattle populations belong to the humped Zebu cattle breed and are classified into two principal varieties: northern Sudan and Nilotic1. A third breed, the Baggara, is raised by Baggara Bedouin pastoralists. It is the major fattening Zebu cattle breed of northern Sudan, found mostly in west Sudan (Darfur and Kordofan regions), Niger, Chad, Cameroon and Nigeria. They have short horns and a large hump, with a red or dark red coat in Daeinawi Aka Messairi/Rezaigi population or white markings or black markings in the Nyalawi population9.The Kenana breed, predominantly found in the Blue Nile state, is distinguished by a light blue-gray coat color with darker hooves and head. The Butana breed of the Batahin and Shukria tribes inhabits the desert area between the Blue Nile and the River Atbara, and has a red-coat10 and thus it is not surprising that genetic factors are implicated in disease susceptibility in cattle. The major histocompatibility complex (MHC) is a major component of the adaptive immune system, with MHC genes encoding the cell-surface glycoproteins that bind small peptide fragments derived from host- and pathogen-expressed proteins via proteolysis11. Animal breeders are becoming more interested in the MHC due to its association with genetic resistance and susceptibility to a wide variety of diseases12. Genetic characterization of MHC polymorphism can help reduce the occurrence and severity of infectious diseases in domestic animal and cattle breeding programs13. The association of MHC with diseases in ruminants is well documented19. The MHC genes are assigned to Bos taurus autosome chromosome 23 (BTA 23)15, and is known as the Bovine Leukocyte Antigen (BoLA). Recently Kim et al.16, investigated five African breeds for the identification of common and unique African genome-specific selection signatures and compared them with commercial breeds. They identified six BoLA haplotype blocks, and that the major African cattle haplotypes correspond to minor haplotypes in commercial cattle. The BoLA molecules' extensive structural polymorphism is responsible for the large differences in cattle’s immune response to infectious agents. For example, BoLA-DRB3 polymorphisms had been associated with tick infestation resistance16. MHC research may also assist in the formation and design of synthetic peptide-based vaccines containing one or more pathogen T-cell epitope.The immune system in vertebrates evolved to defend against invasive pathogensBoLA-DRB3 gene has been done with only a few breeds and cross-breeds from Europe, Asia and the Americas17–30. Target next generation sequencing (Target-NGS)31, the most powerful tools used to identify diversity of BoLA-DRB3 alleles in cattle breeds, has also not been extensively applied. This is despite the central role of the BoLA-DRB3 alleles in the immune response of cattle. Until now, private African BoLA-DRB3 alleles have been reported by authors using indirect techniques, such as polymerase chain reaction follow by restriction fragment length polymorphism (PCR–RFLP), followed by cloning and sequencing34. These studies focused mainly on screening and analysis of only a few animals from a small selection of African breeds .Polymerase chain reaction-sequence based typing (PCR-SBT) to assess the genetic diversity of the 23.Previous work showed the presence of a high number of private alleles in native breeds. Consequently, there are still a number of breeds that remain uncharacterized, and this number only increases when local native bovine breeds are consideredHere we examine patterns of genetic variation of BoLA-DRB3 alleles in Baggara, Butana and Kenana native cattle breeds of Sudan and compare these with commercial breeds to both identify any unique alleles in Sudanese native cattle breeds and to provide information on these alleles to enable further studies of disease susceptibility and resistance, particularly for designing improved breeding schemes.BoLA-DRB3 alleles  was 46 in Baggara cattle (40 previously reported and six new), 33 in Kenana cattle (28 previously reported and five new), and 33 in Butana cattle (28 previously reported and five new)  under accession numbers LC569724-LC569739. Nucleotide and predicted amino acid sequences of the seven new allele variants are shown in Fig. BoLA-DRB3 reported so far. All seven new BoLA-DRB3 allele variants shared about 89.7–92.6% and 80.52–85.71% nucleotide and amino acid similarity with the BoLA-DRB3 cDNA clone NR1, respectively .PCR-SBT genotyping allowed us to identify 53 ) Tables  and 2. T29. Data were grouped in terms of the breed’s geographical origin as follows: native Sudanese; Southeast Asian; Zebu; European; and American Creole cattle breeds   Fig. .Figure 2BoLA-DRB3*003:02:01, *021:01 *022:01 and *024:01) were common in at least two out of the three Sudanese breeds  when using PCR-SBT genotyping methods30. Regarding amino acid diversity, the average dN and dS substitutions in Sudanese cattle breeds were calculated across the entire BoLA-DRB3 exon 2 and ABS. As expected, the dN/dS ratio was higher when only the ABS was analyzed .Genetic diversity at the DNA and amino acid levels was evaluated using four methods that compare the average amino acid and nucleotide substitutions for every pair of alleles within the breeds. The nucleotide diversity (π) exceeded 0.074 and the mean number of pairwise differences values exceeded 17.99 within Sudanese native breeds Table . Comparia and gene diversity (ho and he). We also performed HWE and Slatkin´s neutrality tests on BoLA-DRB3 to evaluate the possible effect of selection, inbreeding, and population structure on allelic diversity at this locus. The high na values and even gene frequencies observed in the Butana, Kenana and Baggara breeds resulted in he and ho values higher than 0.93 , consistent with the theoretical proportion expected under balancing selection pressures. A similarly even BoLA-DRB3 gene frequency was observed in other cattle breeds, including Japanese Black, Yacumeño Creole, Bolivian Gir, Pyer Sein and Shwe Ni. Conversely, we did not detect balancing selection in the Butana and Kenana cattle (p = 0.225 and p = 0.138) despite these breeds having a large number of alleles with similar frequency. Comparable results were obtained for the majority of the cattle breeds analyzed to date ; p < 0.001) , and slightly lower than those reported for Holstein populations from different countries (FST = 0.009)37  Table . This lo)37 Fig. . When brFST value was 0.0074 (p = 0.164), while the pairwise FST ranged from 0.0002 (p = 0.450) between both Baggara populations and 0.0118 (p < 0.0001) between Baggara Daiwani and Butana Qadarif. Significant differences were observed in nine out of the ten native population comparisons , the average BoLA-DRB3 allele frequencies from Sudanese cattle populations and for each breed included in the dataset were used to generate Nei’s DA and DS genetic distance matrices. Then, dendrograms were constructed from these distance matrices using NJ algorithm. All trees revealed congruent topologies, which were consistent with the historical and geographical origin of the breeds analyzed. As expected, these trees revealed two main clusters, which included the Taurine and Zebuine breeds  and Taurine  breeds, while native breeds from Southeast Asia and Sudan were located in an intermediate position near the axis origin of the plot  and Japanese Jersey  located at opposite ends. Furthermore, this component discriminated between native Sudanese and native Southeast Asian cattle breeds. Finally, the third PC accounted for 11.01% of the variance and allowed for the differentiation of Chilean Hereford, and Japanese Jersey and Japanese Holstein cattle from other Taurine breeds. In summary, the native Sudanese cattle breeds were located within a narrow cloud in an intermediate position between the Zebu and Taurine breeds and close to other Southeast Asian breeds, in agreement with the composite origin of these native breeds. This is also supported by the presence of African and Zebu unique BoLA-DRB3 alleles within these populations. These PCA results agree with the overall clustering observed after NJ tree construction.The results of the PCA showed that the first three components accounted for 47.30% of the data variability. The first principal component (PC) accounted for 24.31% of the total variance and, as shown in a previous study24. To assess whether observed differences in allelic frequency are reflected within amino acid motifs in each pocket, we analyzed frequency of the protein pockets implicated in the antigen-binding function of the MHC complex by PCA. As shown in Fig. The BoLA class II molecule binds peptides derived from antigens via five antigen binding pockets named pocket 1, pocket 4, pocket 6, pocket 7 and pocket 9Finally, PCA was performed at the Sudanese population level to evaluate the degree of genetic structure among the sampling sites . This analysis showed that the first three components accounted for 90.95% of the data variability. The first PC accounted for 30.65% of the total variance and clearly exhibited a differentiation pattern between the Baggara population  and the Butana Bu Qadarif  population, while Kenana, Butana Bu Atbara were located in intermediate positions Fig. . These r38. Over the next decades, several private alleles were identified in taurine, zebu and taurindicus native African breeds, like N´Dama, Boran, and Sanga34; https://www.ebi.ac.uk/ipd/mhc/group/BoLA/). However, in the present study, we carried out the first genetic characterization of the BoLA-DRB3 gene at population level in native Sudanese breeds using PCR-SBT. This analysis allowed us to detect 53 alleles, including seven new variants. The high number of private alleles agrees with data obtained by16, who analyzed the BoLA region in depth using a genome-wide sequencing approach, identifying six major African BoLA haplotype blocks.Since the first pioneering studies based on serotype analysis, a number of striking differences between the BoLA profiles of African and European cattle have been reported due to difference in the antigen’s frequency of occurrence and the presence of unique antigens in African cattleBos primigenius), the ancestor of domestic cattle, inhabited a large geographical area throughout Eurasia and North Africa. According to the trans-species theory of MHC alleles39, it is expected that the extremely high genetic variability present in the BoLA-DRB3 gene  was present in the wide geographical distribution of the aurochs. On the basis of archeological and genetic studies, it has been proposed that modern bovines were domesticated in two geographical sites, one located in the West Asia (Near east), and the other in Indian subcontinent (India and Pakistan)46. Each of these domestication centers would have retained only a fraction of the total diversity as a result of bottleneck and genetic drift effects47. This is clearly seen in the distribution of mitochondrial haplogroups among cattle breeds44. In Africa, taurine cattle originated from the Near east domestication center, and introgressed through the North part of the continent and from there they would have dispersed east, west and south. Then, indicine cattle were introduced to Africa and Bos indicus genes were introgressed into native populations through absorbent crosses48. Currently, an east–west gradient of Zebu influence in African native genes is observed.Wild cattle or ‘aurochs’  and natural and artificial selection would have shaped the BoLA-DRB3 alleles were only detected in the Sudanese cattle breeds. Seven of these alleles corresponded to new variants described in this study  in native African breeds with humped phenotype suggest that current global diversity of this gene could have been retained in the founder group that originate African taurine native breeds45.Two 50; https://www.ebi.ac.uk/ipd/mhc/group/BoLA/)  Table . The intBoLA-DRB3 gene in the native cattle of Sudan would not only have been molded by stochastic forces, such as the formation of the founder group, gene drift and recent or historical gene introgression as described above, but also by processes of natural and artificial selection. In Sudan, as in other African regions, cattle are subjected to strong environmental pressures, such as tropical diseases, heat stress, drought and poor nutritional and forage deficits. Furthermore, animals are affected by diverse infectious diseases, including parasites  and viruses . For this reason, it is to be expected that native Sudanese cattle will be under strong selection pressure, which would contribute to maintaining and shaping the genetic diversity of the BoLA-DRB3 gene. In this sense, a wide repertoire of alleles allows the population to identify and respond to a greater range of antigens. Furthermore, heterozygous animals trigger an immune response to a greater variety of antigens. For these reasons, it has been proposed that this allelic diversity is maintained by balancing or over-dominant selection66. Different indices at the population, nucleotide and amino acid levels showed high levels of genetic diversity in the bovine breeds of Sudan for the BoLA-DRB3 gene. This is clearly reflected in the presence of a homogeneous distribution of gene frequencies . This is particularly extreme in the Baggara breed in which Slatkin’s neutrality test showed evidence that the BoLA-DRB3 gene frequency profile showed an even distribution consistent with the theoretical proportion expected under balancing selection pressures. Similar results have been reported for other cattle breeds, including Japanese Black, Yacumeño Creole, Bolivian Gir, Pyer Sein and Shwe Ni30. Furthermore, the selection index (ω) revealed the presence of diversifying selection in several amino acid sites (mainly in the ABS) in BoLA-DRB3 exon 2 of the Sudan native breeds. In contrast, the HWE test did not detect the effect of over-dominant selection67. As discussed previously21, this effect has been observed only in some of the breeds studied so far and the most common explanation for the absence of heterozygote excess in the studied bovine breeds is the magnitude of the overdominance selection coefficient at MHC loci . Such selection would only be enough to increase the number of heterozygotes in large populations and in the absence of high rates of stochastic forces . For this reason, and because the HWE method may suffer from low resolving power, such effects were not observed.The current repertoire of alleles of the BoLA-DRB3 gene present in the native cattle of Sudan allows these breeds to be clearly differentiated from the rest, forming a cluster in the NJ trees and a narrow cloud in the PCA. This pattern is confirmed when PCAs are performed based on the pocket 4 and pocket 9 gene frequencies. It has previously been proposed that pocket 4 plays an important role in the binding of peptides due to this pocket being located in the center of the PBC 6470. In addition, it has been reported in cattle that immune responses against vaccine and disease resistance is significantly related to differences in the pocket 4 motif50. A particular amino acid  or amino acid motifs , in sites that affect the conformation of pocket 4, have been associated with immune response or resistance to infectious diseases, such as mastitis, persistent lymphocytosis, dermatophilosis, and tick-borne diseases73. Many of these diseases, as well as others mentioned above, are present in Sudan and could have contributed to shaping the current repertoire of BoLA-DRB3 alleles present in native Sudanese cattle. However, these results were obtained in breeds that have different genetic backgrounds and that are raised in different environments and production systems, so further association studies are necessary to determine the effect (resistance or susceptibility) of the alleles present in the native cattle breeds of Sudan against different infectious diseases.The repertoire of alleles of the BoLA-DRB3 alleles in cattle not only in Sudan but in the entire African continent. In addition to the clear genetic clustering of cattle based on ancestral origin and phylogeography, we identify seven novel alleles in the three native Sudanese cattle breeds. Two evolutionary forces appear to contribute to the preservation and shaping of the genetic diversity of the BoLA-DRB3 gene in native Sudanese cattle; diversifying selection mainly affects the ABS of the native breeds and balancing selection. The results demonstrate that the background variation between two cattle groups, taurine and indicine, is primarily due to events of origin, selection, and adaptation, which explains the variations found in the diversity of the BoLA-DRB3 genes, not only between the two major groups but also with the indicine cattle group. This variation may explain how cattle from Sudan are resistant to various diseases. We presume that this genetic information provides a basis for better design of suitable breeding schemes. This variation may contribute to resistance in Sudanese cattle to various diseases.To the best of our knowledge, this is the first study to document in detail the genetic diversity (taurine vs indicine) of The ODK (Open Data Kit) system was used to record the sampling information: breed name, sex, estimated age, sampling location GPS coordinates, photo of the animal and owner’s information. All methods were carried out in accordance with relevant guidelines and regulations of the Faculty of Veterinary Medicine, University of Khartoum (Vet. Med. U of K), and all experimental protocols were approved by the Vet. Med. U of K research board committee. Before animals were sampled, written informed consents were obtained from all animal owners. Three cattle breeds were examined: (1) Butana breed: collected from the Atbara Butana Station and surrounding villages and from El-Gadarif city and Butana plain; (2) Kenana breed: samples were collected from Rabak city and surrounding villages and from UmBanein Kenana Station; (3) Baggara breed populations (i) Nyalawi population, which is a western Baggara breed sampled from calves from Nyala city, South Darfur; (ii) Daeinawi population, from Ed daein city. Whereas Nyalawi are large white cattle, some with black splashes, the Daeinawi are smaller and red with black along the neck and lateral sides of the head, hind quarters and shoulder sides Fig. .A total of 225 native breed cattle were sampled: Baggara N = 113, Butana N = 60 and Kenana N = 52 Table . Seven mBoLA-DRB3 was amplified by PCR as described by26. Using DRB3FRW 5-CGCTCCTGTGA(C/T)CAGATCTATCC-3 and DRB3REV 5-CACCCCCGCGCTCACC-3, PCR reactions were performed in a 25 μl-reaction mixture containing 12.5 μl of 2× Gflex PCR Buffer  , and 0.5 μl of Tks Gflex DNA polymerase (1.25 units/μl) (TaKaRa Bio Inc.), 200 nM of each primer, and 1.0 μl of template. The reaction conditions consisted of an initial denaturation step at 95 °C for 3 min, followed by 35 cycles of 95 °C for 1 min, 58 °C for 30 s and 68 °C for 90 s and a final extension step at 68 °C for 5 min. PCR products were purified using a NucleoSpin Gel and PCR Clean Up Kit (Takara Bio Inc.). Cycle sequencing reactions were performed directly using the two PCR primers using the BigDye Terminator version 3.1 Cycle Sequencing Kit  and analyzed on an ABI Prism 3130 × genetic analyzer (Applied Biosystems) according to the manufacturer’s instructions.Exon 2 of the 74, and were subsequently joined to reconstruct a fragment of 280 bp spanning the entire exon 2.Prior to analysis, all the chromatograms were visualized and sequence fragments were edited manually using ATGC software version 9.1  correcting base calling errors. Multiple sequence alignments were performed using the MUSCLE algorithm implemented in MEGA XBoLA-DRB3 genotypes, we used the method implemented by26: First, we downloaded a MHC_nuc.txt file from the IPD-MHC in order to update the allele database. This file contains all reported BoLA-DRB3 alleles. Then DNA sequences from the cattle for both strands (forward and reverse ab1 files) were imported together into the Assign 400ATF ver. 1.0.2.45 software , which automatically aligned the sampled cattle sequences with those of previously reported BoLA-DRB3 sequences, building a consensus. The most likely genotype is shown in the same window as the chromatograms so that they can be crosschecked. When we found a clear mismatch from several samples, we assigned these samples containing new alleles and revised the BoLA-DRB3 database containing new allele sequences. The accuracy of the in silico genotyping method was demonstrated in Takeshima et al.  where the new detected alleles were confirmed by cloning and sequencing, and the used method was developed and validated for only the BoLA-DRB3 gene. If the sample could not genotype using these criteria, we discarded the sample result from this analysis.For typing a) were obtained by direct counting. The distribution of alleles across breeds was analyzed by a Venn plot created using the R package ‘VennDiagram’ (http://cran.r-project.org/). The observed (ho) and unbiased expected (he) heterozygosity of the BoLA-DRB3 locus were estimated according to73 using the Arlequin 3.5 software for population genetic analyses76 . FIS statistics77 for each breed were calculated using the Exact Test included in Genepop 4.7 software78 to evaluate deviation from Hardy–Weinberg equilibrium (HWE). The Ewens–Watterson–Slatkin Exact Test of neutrality was carried out using the method described by79 and implemented in the Arlequin 3.5 program.Allele frequencies and the number of alleles  algorithm84. Confidence intervals for the groupings were estimated by bootstrap resampling of the data using 1000 replicates. Genetic distances and trees were computed using the Populations 1.2.28 software 84. The trees were then visualized using TreeView85.To condense the genetic variation at the N), and synonymous (dS) nucleotide substitutions per site from averaging over all sequence pairs were estimated within each group using the modified Nei-Gojobori model83 and Jukes–Cantor’s formula implemented in the software MEGA X72. The possibility that certain codon sites are under diversifying selection within each native Sudan breed was investigated using the Bayesian method implemented using OmegaMap86. This method incorporates intragenic recombination and does not assume a known fixed genealogy, so that recombination does not inflate the false detection rate of positive sites87. The BoLA-DRB3 allele tree was constructed from a distance matrix that was based on the NJ method using the MEGA X software. Furthermore, a tree based only on ABS amino acid motifs was inferred using Maximum Parsimony method implemented in MEGA X. To test the significance of the branches of both trees, 1000 bootstrap replicate calculations were performed.Nucleotide diversity (π) and pairwise differences in nucleotide substitutions between alleles within each breed were calculated using Arlequin 3.5. The mean number of nonsynonymous (dSupplementary Information.


Mastitis is a disease that causes significant economic losses, since resistance to mastitis is a difficult trait to be improved due to its multifactorial occurrence. Therefore, our objective was to characterize a Mexican Braunvieh cattle population for genetic resistance and susceptibility to mastitis. We used 66 SNP markers for 45 candidate genes in 150 animals. The average heterozygosity was 0.445 ± 0.076, a value higher than those reported for some European breeds. The inbreeding coefficient was slightly negative for resistance to subclinical (−0.058 ± 0.055) and clinical (−0.034 ± 0.076) mastitis, possibly due to low selection for the immunological candidate genes that influence these traits. The genotypic profiles for the candidate loci per K-means group were obtained, as well as the group distribution through the graphics of the principal component analysis. The genotypic profiles showed high genetic diversity among groups. Resistance to clinical mastitis had the lowest presence of the heterozygous genotypes. Although the percentage of highly inbred animals (>50%) is up to 13.3%, there are highly heterozygous groups in terms of the studied traits, a favorable indicator of the presence of genetic diversity. The results of this study constitute evidence of the genetic potential of the Mexican Braunvieh population to improve mastitis-related traits. One of the major problems in dairy units is mastitis . TropicaSome of the obstacles that traditional genetic selection has faced in bovines are as follows : (1) comResistance to mastitis is a complex trait that is multifactorial in nature and, thus, difficult to be improved. The genomic prediction ability for clinical mastitis is low, 0.19 , compareIn addition to the control of environmental factors to prevent mastitis at the farm level, genomic methods have been used as an alternative solution to the problem of increasing resistance to mastitis. The aim is to obtain animals that are immune to the main mastitis-causing pathogens, and that have qualities that allow them to adapt to environmental factors that cause mastitis .Recently, the genotyping of animals has become easy and relatively cheap. A sample of hair or blood will provide thousands of markers . The marBos taurus and some Bos indicus breeds [Manipulation of genetic variation is key to the development and use of any genetic improvement program for dairy and beef cattle breeds . Genetics breeds . Animalss breeds .Genetic diversity is determined by population parameters and visual dimension reduction tools. The Hardy–Weinberg equilibrium, the coefficient of consanguinity and heterozygosity are the parameters most used in genetic diversity studies. The Hardy–Weinberg equilibrium states that the allele and genotype frequencies in a population will remain constant from generation to generation in the absence of assortative mating, natural selection, gene flow, mutation, and inbreeding. On the other hand, principal component analysis (PCA) has been proven to be useful to visualize the genotypic distribution of animals ,13.The objective of this study was to determine the genetic diversity and population structure of a Mexican Braunvieh population in terms of mastitis resistance and susceptibility, using (1) basic population statistics: inbreeding coefficient, expected and observed heterozygosity, and (2) multivariate statistics, namely K-means and principal components analysis.http://neogene.com, accessed on 28 May 2021). The chip used was the Genomic Profile Bovine Low-Density with 50,000 SNP markers.Genomic information was obtained using hair samples from 150 animals born between 2001 and 2016 on five farms belonging to the Mexican Association of Braunvieh Purebred Breeders in Eastern, Central, and Western Mexico. The samples were genotyped at GeneSeek  and ip is the corresponding allelic frequency. This index was calculated for each of the loci. Empirical distribution was obtained, and the 10% most informative markers were selected. The highest values for H were associated with the highest diversity for the marker. At the end of filtering, 66 SNP markers of 45 candidate genes were left for further analyses. For better handling of the markers in the graphs, the names of the loci were replaced by consecutive numbers, assigned by alphabetical order to the corresponding candidate gene.All of the analyses were carried out using R software, version 4.0.4 . Accordit-test to determine the differences between He and Ho. The null hypothesis was that there were non-significant differences between them. A p-value of 0.01 was used to declare significance and was corrected with the Bonferroni method considering the total number of loci by trait.The expected (He) and observed (Ho) heterozygosity were estimated for all the loci by trait in the study using the adegenet package  availablp-values were higher than 0.01/n with the Bonferroni correction; n is the number of loci per trait.The pegas package  through IT) were calculated and plotted with adegenet [IT per trait was obtained, along with its respective standard deviation. Finally, a histogram including a nonparametric estimate of the density function by trait was obtained. This was done to visualize the animals with an inbreeding coefficient above 0.5 .The inbreeding coefficients by individual with respect to the population  to be formed through the hierarchical clustering and K-means was obtained with Silhouette and Elbow of factoextra [k that allowed for an equilibrium between a value of the silhouette close to one [The optimum number of groups  are above those obtained with breeds derived from the original Braunvieh, but below those reported for e cattle  reportedThis suggests that the Mexican Braunvieh population has increased its genetic versatility for the candidate genes studied, with a higher number of heterozygous individuals. Exposure to climates different from their native climate for over a century in the tropical systems of Mexico could have caused a change in Ho, with respect to original Braunvieh populations ,45. On tAs a graphic resource, IT obtained coincides with those observed in semi-specialized systems of production. The overall average for FIT was 0.017 ± 0.043 for all the traits associated with mastitis. This value is similar to those found for Sahiwal (0.013 ± 0.109), Gyr (0.013 ± 0.106), and Guernsey (0.02 ± 0.217) [IT for the tropical breeds Landim, Angole, and Tete [The estimate for the F± 0.217) . In contand Tete  was 70% and Tete . The firand Tete , while LIT. In contrast, the loci for RSM and RCM were mostly negative; that is, there were no traces of inbreeding. This could be due to the specificity of the last two traits, where the candidate genes involve DNA segments that have undergone null direct selection.IT values for the trait, which is because current improvement programs for the Braunvieh population under study have no selection criteria based on resistance to mastitis.The genes whose symbol starts with CXCL, chemokine (C-X-C motif) ligand, are responsible for the immune response . An in vFor RSM, 40% of the markers belong to EDN2 and HIVEP3, candidate genes associated with immune processes, such as intracellular and sequential signaling of immunoglobulin receptors. Because of their immunologic nature, these genes have not been directly selected for; however, at the farm level there is a rising concern for improving their animals for genes associated with diseases that could be prevented, such as paratuberculosis and clinic mastitis ,52.IT value can be observed in IT. FIT = 50% is equivalent to the value of an inbred animal produced by two progenitors with 100% genetic relationship. Our animals varied from 1.3% to 13.3%. The trait with the highest number of highly inbred animals was SM, while RSM had the lowest.The distribution of animals by their FIT values, the estimate obtained in our study (0.017 ± 0.043) is similar to that reported for other cattle breeds in specialized and semi-specialized systems of production. In those systems, although some animals had high inbreeding coefficients, the average FIT is lower than those reported by [Bos taurus breeds (0.071), Brown Swiss (0.071), Braunvieh (0.059), Original Braunvieh (0.023), Holstein (0.057), and Simmental (0.028).Although up to 13.3% of animals have non-desirable Forted by  for Bos k to equilibrate the silhouette width and the means of squared errors for both algorithms converged at the same number of clusters for each trait. However, better values for silhouette were found by grouping with the K-means algorithm: RM, 0.08; SM, 0.17; RBM, 0.17; RSM, 0.12; and RCM, 0.39. For the first four traits, the silhouette values were low, but in all cases, they were negative, a good indicator of the suitability of the method. The RCM silhouette was the widest.The optimum number of The average proportion of variability among groups relative to the total variability was obtained. The estimates found were RM, 34.9%; SM, 65.5%; RBM, 66.9%; RSM, 54.7%; and RCM, 77.3%. It should be noted that the RCM values for variability and silhouette were the best of all the traits. This could be due to the small number of markers of the trait (6), which were highly informative as well.k number, the number of individuals per group, and the representative animals together with their genotypic profile can be seen in The circular dendrograms of IT are not related because, unlike inbreeding, heterozygosity is not correlated with the markers [IT present high numbers of homozygous animals. This could be explained by the low genetic relationship among the animals for these loci, that is, genotypes AA or BB for the same locus, as seen in Ho and F markers , even thThe trait with the greatest number of heterozygous animals was RM. Groups III, VII, VIII, and XI possess most of their loci with genotype AB. RM is multifactorial, and therefore, it includes candidate genes associated with productive, reproductive, adaptative, and immunological traits. This will cause high genetic diversity, in contrast with the traits RSM and RCM, which, in our study, are only influenced by immunological candidate genes of a specific order whose diversity is limited. According to , these gFor SM, there are well defined groups of animals whose loci are mostly heterozygous. Groups II, IV, V, XV, and XVII have at least 55% of their loci with genotype AB. The groups with fewest heterozygous individuals were III, XVIII, XIX, and XX, with up to 18% AB genotypes. There is high genetic diversity in the population for the trait as was observed for RM; the reason could be the presence of genes influencing traits that are indirectly related with mastitis, and not just loci affecting the immune system ,55.The loci of the groups formed by the algorithm K-means for RBM are from 29%  to 57%  heterozygous. Diversity was high because only 33% of the genes were directly related with immune functions in the animal, genes IL1A and IL1RN, while the rest were related to metabolic processes ,21,22.The PCA was useful to observe the genetic diversity in the groups of animals generated with K-means. The first two dimensions of the PCA explained 22.21% (RM), 42.1% (SM), 40.55% (RBM), 29.68% (RSM), and 58.9% (RCM) of the variation found in the markers . These rFor traits RM and RSM, which possess a larger number of the loci under study, the number of eigenvalues considered to reach a minimum of 80% of variability is higher than that for the rest of the traits. However, the first two dimensions possess a high percentage of variation, evident in the graphic representation of the PCA, where the K-means groups are clearly differentiated, particularly for the RCM graph.For RM, the genes ARHGAP10 and BDH2  contributed the most to the analysis; however, the relationship between them is negative . This meOddly enough, for RM, the highly inbred animal 16 is among the top 10% of the most contributory animals . This caFor SM, the markers for genes ANKRD33B, CTNND2, GRIA3, and SIDT1 contributed the most to the PCA, The markers of genes IL1A and ITGB3 for RBM were in a relationship similar to that of the genes ARHGAP10 and BDH2 for RM . They haThe marker BovineHD0300029925 (10) of HIVEP3  contribuThe population studied presented a high genetic diversity for resistance and susceptibility to mastitis, even higher than that of the original Braunvieh and Brown Swiss. Although 13.3% were highly inbred animals (>50%), there were highly heterozygous groups in terms of the traits studied, a favorable indicator of the presence of diversity; even though the homozygous genotypes for immunologic genes (group CXCL) might be favorable for traits such as resistance to subclinical mastitis and resistance to clinical mastitis.The K-means algorithm and the principal components analysis are good statistical tools to visualize groups of animals with different genotypic profiles. The principal components analysis was useful to detect relationships and contributions of markers for each trait. The results of our research give evidence of the genetic potential of the Mexican Braunvieh population for the improvement of mastitis-related traits in the tropic.


Isoflurane anesthesia is commonly employed in preclinical MRS, but it affects cerebral hemodynamics and functional connectivity. A combination of low doses of isoflurane and medetomidine is routinely used in rodent functional magnetic resonance imaging (fMRI) and shows similar functional connectivity, as in awake animals. As glucose metabolism is tightly linked to neuronal activity, our second aim was to assess the impact of these two anesthetic conditions on the cerebral metabolism of glucose. Brain metabolism of hyperpolarized 13C-glucose and non-hyperpolaized 2H-glucose was monitored in two groups of mice in a 9.4 T MRI system. We found that the very different duration and temporal resolution of the two techniques enable highlighting the different aspects in glucose metabolism. We demonstrate  that hyperpolarized 13C-glucose reports on de novo lactate synthesis and is sensitive to cerebral metabolic rate of glucose (CMRGlc). We show that variations in cerebral glucose metabolism, under different anesthesia, are reflected differently in hyperpolarized and non-hyperpolarized X-nuclei glucose MRS.Glucose is the primary fuel for the brain; its metabolism is linked with cerebral function. Different magnetic resonance spectroscopy (MRS) techniques are available to assess glucose metabolism, providing complementary information. Our first aim was to investigate the difference between hyperpolarized  Moreover, it was hypothesized that due to the very different acquisition time between X-nuclei MRS of HP 13C Glc and non-HP 13C Glc , one should anticipate a difference in the labeling pattern between the two approaches  Glc and non-HP  Glc detected by X-nuclei MRS.Brain consciousness influences cerebral metabolism, in particular, cerebral Glc metabolism, is closely linked to neuronal activity ,30,31. A1H spectra enable to evaluate the effect of the two anesthetic conditions on the endogenous concentration of brain metabolites. A significantly higher lactate pool-size  was found in animals anesthetized by isoflurane (ISO group) compared to those anesthetized by the combination of medetomidine and isoflurane (MED–ISO group). Creatine concentration was higher in the ISO group, without reaching significance, consequently, the calculated creatine-to-phosphocreatine ratio (Cr/PCr) was found to be significantly higher in the ISO group . All other metabolites in the neurochemical profile did not show any significant difference  labeling was observed in the MED–ISO group without reaching significance, while the kinetics of infused Glc was comparable in the two anesthetic conditions and did not reach any significant difference  of the metabolite ratio showed a significantly higher HDO labeling in the ISO group compared to the MED-ISO group . Moreover, fference . Blood ge groups .2H7, U-13C6] Glc presented in 13C] Lac turnover can be readily detected. The Glc bolus and the produced Lac were detectable for 70 s post-injection. The [1-13C] Lac signal was detected for a longer period of time in the MED–ISO group  was found significantly higher in the MED–ISO group compared to the ISO group +128%, c,f,i. Noferences . Overall2H7, U-13C6] Glc metabolism was simulated with a typical Glc input function reaching 70% fractional enrichment in 14 s and with a nominal brain Lac concentration of 4 μmol/g and cerebral metabolic rate of glucose (CMRGlc) of 0.5 μmol/g/min. The impact of the metabolic rate of Glc on the Lac 13C concentration was assessed by varying the CMRGlc by steps of −50%, −20%, 0%, +20% and +100%, i.e., CMRGlc = 0.25, 0.4, 0.5, 0.6, and 1 µmol/g/min  is metabolized into its downstream metabolites, the very different duration and temporal resolution of the two detection schemes highlight different aspects in Glc metabolism.In this study, we compared two recent MR-based methodologies to interrogate cerebral Glc metabolism, namely HP  Glc injection could be followed over a duration of about 60 s, comparable to most in vivo HP studies. After the HP  Glc bolus, the probability of Glc 13C to be transformed into Lac 13C is given by the fractional enrichment of its precursors. Using the small pool approximation for the glycolytic intermediates  Glc (the time when the acquisition of the HP 13C MRS is completed) and for a constant Lac pool size of 4 mmol/kg the fractional enrichment of 13C Lac ranges between 6 and 25%  13C MRS experiments, where labeling steady-state with substantial 13C fractional enrichment is often reached  Glc bolus, Lac labeling was observed in the ISO group and not in the MED–ISO group, consistent with the typical higher Lac pool-size in animals anesthetized by isoflurane . This estimated value is higher than the accepted limit of detection with minimal SNR of 3  Pyr at different brain consciousness states demonstrated that the conversion rate of Pyr to bicarbonate is consistent with the TCA cycle rate determined from non-HP experiments and corresponds to what is expected under different anesthetic conditions [13C] Pyr, the conversion rates of Pyr to Lac did not change significantly under different anesthetic conditions  lactate is a consequence of 12 biochemical steps, including Glc transport, 10 enzymatic steps of glycolysis and Pyr conversion to Lac by lactate dehydrogenase, and should therefore be more sensitive to changes in the Glc metabolic demand.A recent HP nditions . Howevernditions . HP Pyr 13C] Lac compared to the use of isoflurane only. The production of HP [1-13C] Lac depends on both the HP  Glc uptake and the metabolic demands (CMRGlc). The change for a combination of medetomidine and isoflurane can induce several cerebral and systemic effects that may contribute to the observed differences. Firstly, isoflurane at a clinical concentration (1.4–2.5%) has been associated with a significantly reduced facilitated Glc transport in cells  Glc indicates that in our setting the tracer signal was not different under the two anesthetic conditions  and the HDO signal as well. In our experiments the  Glc signal in the sum spectra was not significantly different between the two anesthetic drugs . All experiments were conducted according to federal and local ethical laws and complied with the ARRIVE guidelines. Mice were housed with a 12 h light-dark cycle in a temperature- and humidity-controlled animal facility, with free access to food and water. Twelve hours before the experiment, animals were placed in another cage without food.2H7, U-13C6] Glc, single voxel 1H MRS measurements were carried out in each mouse 10 min before the bolus and the 13C MRS acquisition. An outline of the experimental design is summarized in 2H7, U-13C6] Glc or  Glc. One animal was excluded from this study, as it did not reach any increase in blood glycemia post bolus. Baseline information for the different groups is summarized in Brain metabolism of HP Glc or non-HP Glc was monitored in 12 hour-fasted male C57BL6/J mice. To monitor the effect of brain consciousness on Glc cerebral metabolism two different anesthetic condition were tested. In the first group, mice were anesthetized by isoflurane solely during the entire duration of the experiments (ISO group). In the second group, mice anesthetic induction was performed with isoflurane for the surgical procedure of placing the catheter for the bolus injection, and then the anesthesia was switched to a combination of medetomidine  and low dose isoflurane (MED–ISO group). The duration between the switch in anesthetics and the bolus injection of glucose was chosen to be 60 ± 5 min, to reach a new cerebral metabolic steady-state . In micev/v) containing 2 M D-Glc , 25 mM trityl radical OX63 -bisdithiole-4-yl-methyl sodium salt) as a polarizing agent  doped with 1 mM of Gd3+ as previously described  Glc achieved under these conditions was measured in a separate set of experiments in the separator/infusion pump using a 13C RF coil implemented around the pump upon dissolution with D2O. Polarization of  was calculated from the ratio between the HP signal of the HP  Glc and its thermal equilibrium signal recorded 15 min after dissolution. For the in vivo measurements, once dissolution completed, a bolus of the solution was automatically infused through a vein catheter (see below) as previously described  Glc or ~60 min post bolus of non-HP  Glc) using a PocketChemTM BA analyzer. Animal physiology was monitored during the entire duration of the experiment. Body temperature was monitored by a nonmagnetic rectal probe and maintained at 37.0 ± 0.5 °C by warming the animal with temperature-controlled water circulation . The respiration rate was monitored using a pneumatic pillow sensor .Mice were anesthetized using 1.5 ± 0.5% isoflurane  in 60% oxygen using a nose cone. The anesthetized animals were cannulated to place a femoral vein catheter to deliver the Glc bolus. In the ISO group  interfaced with a 31-cm horizontal-bore actively shielded 9.4 T magnet .1H MRS measurements were performed in the hippocampus in the mice that were injected with HP  Glc bolus. Spectra were acquired using a home-built 1H-quadrature/13C-single loop surface coil that was placed on top of the mouse head. B0 inhomogeneity was corrected using the FASTESTMAP algorithm  Glc concentration of the injected bolus being measured in a high-resolution DRX-400 spectrometer  using [2-13C] acetate 80 mM as reference. For the in vivo acquired spectra, the area under the curve (AUC) of [1-13C] Lac (183.5 ppm), [1-13C]α-Glc (92.9 ppm), and [1-13C]β-Glc (96.8 ppm) were quantified using VNMRJ software by integrating the sum of the 13C MRS spectra series, after phase and baseline correction. Overall, 14 mice were employed .Carbon-13 MR measurements were performed using the lgorithm  in a 125protocol . Note thprotocol . A serieproposed . At the 2H2] D Glc in a physiological buffer  was loaded into the separator/ infusion pump  Glc solution was injected through a vein catheter in a similar automated fashion as for the hyperpolarized infusion (245 μL/s)  Glc reports on de novo Lac synthesis and is sensitive to CMRGlc. The level of brain consciousness reached under different anesthesia resulted in variations in cerebral glucose metabolism, but manifested differently in HP and non-HP X-nuclei Glc MRS.HP [