Skip to main content
Journal of Genetic Engineering and Biotechnology
Download PDF

The variation in promoter sequences of the Akt3 gene between cow and buffalo revealed different responses against mastitis

Journal of Genetic Engineering and Biotechnology volume 19, Article number: 164 (2021) Cite this article

Abstract

Background

Serine/threonine kinase 3 (AKT3) is a protein-coding gene that is associated with several cattle immune diseases including different tumors and cancers. The objective of this study was to investigate the differences in structures and functions of AKT3 of cow and buffalo cattle.

Methods

The sequence differences of gene-coding sequence (CDS) and core promoter region of AKT3 in cow and buffalo were analyzed by using bioinformatics tools and PCR sequencing. Also, the functional analysis of promoter regulating gene expression by RT-PCR was performed using 500 Holstein cows and buffalos. And, evaluation of AKT3 inflammatory response to the lipopolysaccharide (LPS)-induced mastitis was performed between both species.

Results

The results revealed the variation in 6 exons out of 13 exons of the two species of CDS. Also, 4 different regions in 3-kb promoters of the AKT3 gene were significantly different between cow and buffalo species, in which cow’s AKT3 promoter sequence region was started from − 371 to − 1247, while in buffalo, the sequence was started from − 371 to − 969 of the promoter crucial region. Thus, the promoter was overexpressed in cows compared to buffaloes. As a result, significant differences (P < 0.05) between the two species in the AKT3 gene expression level related to the LPS stimulation in their mammary epithelial cell line.

Conclusions

This study emphasized the great importance of the structural differences of AKT3 between the animal species on their different responses against immune diseases like mastitis.

Background

The livestock genetic diseases’ impact on its global production and security showed exceptional importance during the last few decades, which significantly affects human civilization, development, and world essential protein consumptions [1].

Most of the diseases that affect cattle production have a relationship with their immune genes, in which Xie and Weiskirchen [2] reported that AKT3 stands for serine/threonine-protein kinase-encoded oncogene. The importance of these genes in the diagnosis and treatment of different cattle diseases lets scientists try to deeply investigate these genes and their functions. For instance, mastitis disease is one of the AKT3-related infections in dairy cattle which caused a massive problem and a significant negative impact on the global dairy economy. The researchers have done a lot of work to prevent, control, and treat mastitis disease and reported mastitis and dairy farming; however, it is still a remarkable issue in dairy farm management. The somatic cell count and somatic cell score play role in association with mastitis and the phenotypic data is required for the association study [3] and mastitis association with somatic cell count study is still under exploration, and further, genetic risk factors and other essentials of mastitis are needed to be addressed [4].

Beecher et al. [5] reported that the relationship of mastitis with SNPs and genes is important based on the vital role of genes in mammary gland immune response for regulating mastitis. Several diverse research tools are using for advanced research in the field of investigation and association with disease, such as the gene mapping in genetics with genomic chips, and recent bioinformatics tools for candidate genes were approached. The important economic traits in dairy animals and the sequence of genes on quantitative trait locus association study and analysis are very important. It has been established as an effective tool for assisting in marker selection against these diseases of economically significant traits. Pimentel et al. [6] reported that for vital traits such as reproduction, immunity, and milk production, single nucleotide polymorphisms were used for candidate gene approach and selection. The association and gene expression for AKT3 with mastitis and its role in immune response as well as the sequence differences between cow and buffalo has not been reported yet. The transcription initiation and gene regulation are very important for gene expression and are controlled by the promoter area. Promoters in upstream of a gene with transcription start site (TSS) is very important for various functions and binding of RNA polymerase [7] and the promoter identification, as well as important consensus sequences, core promoters, and their analysis for the promoter.

YJ Mao et al. [8] reported that the CpG island is also one of the important factors which occur adjacent to the core promoter in humans with 50% probability, in which many model algorithms and computational analyses have been used for promoter analysis and prediction, while the TSS and CpG island and core promoter study for the promoter are usually understood. L Zhang et al. [9] reported that gene expression and physiology are also correlated with various components, which play a very crucial role in gene expressions such as genetic change cis-regulatory and coding sequence if it is compared to various species of specific phenotypes. The new insight in molecular level between cow and buffalo naturally for promoter prediction, differences in genetic sequences, in the upstream and 5′ UTR for the candidate gene could be imperative for gene expression, binding sites, and alteration in transcription. Besides, the cow and buffalo are different but belong to the same origin and phylogeny, but cows are more susceptible to disease as compared to buffalo with mastitis. Khanal and Pandit [10] reported that the prevalence of mastitis in cows is highly predominant than in buffaloes; although they are of the same origin, the cow is more susceptible to mastitis disease. Understanding this mechanism and the fact of genetic analysis show the differences of cow and buffalo sequences for mastitis and ensure the limits as previously no such study is performed for cow and buffalo. Therefore, it is noteworthy to study this candidate gene of AKT3, as they are pro-inflammatory interplays of signaling pathways, and it would be vital for the future as well currently for promoter differences and variation inclusive study in cow and buffalo sequences.

Driessche et al. [11] described that transcription measurement can identify, characterize, and classify the core promoter in the gene sequence, in which RNA polymerase II and III have the potential to cluster both infected and healthy cells. Additionally, the epigenetic study is important for correlating the genes’ functionality with physiological phenotyping [12]. Furthermore, promoter, chromatin remodeling, methylation, and histone modification studies are considered the most important epigenetic studies of the DNA, in which the importance of the plasmid-basic-luciferase vector 3 (pGL3) is for the ligation and cloning of the specific target gene like AKT3 through the use of specific restriction enzymes capable with specific restriction sites like Kpnl and BgIII [13]. That can amplify and detect the upstream region of the gene concerned with the promoter, core promoter, and other important consensus sequences which play an important role in the modification of a gene, transcription binding sites, and its post-translation and expression. Therefore, the proportional, variation, and comparison of promoter studies in cow and buffalo sequence differences could affect their responses to mastitis disease performance.

Thus, the objective of this study was to find out the differences, association, and expression for AKT3 in cows and buffaloes. The bioinformatics tools and PCR sequencing were used and correlated to verify the functionality of promoter gene expression performance. This knowledge might help to provide a target for intervening against the different responses of mastitis disease in cows and buffalos.

Methods

Animals

A total number of 500 Holstein cows (244) and buffalos (256) were used in this study from Hubei province of the dairy herd, Hubei, China. Also, this study sampling was carried out in the Hubei province of the dairy herd, China. The weather was sub-humid tropical climates with annual rainfalls of 1100–1200 mm. The average temperature was 16–17 °C; relative humidity was 75%, in which mainly cows and buffalos were practiced free stall barns with total mixed ration (TMR) three times daily.

Animal blood sampling and extraction of genomic DNA

Blood samples were collected in a tube with EDTA (pH = 8.0) anticoagulants from Holstein cows and buffalos. Also, blood samples with good Danhong injection (DHI) were collected from animals; no further treatments or uses of the animals under the study were made. And the animals were still alive and in good health after the study.

Before genomic DNA extraction, the samples were kept at − 20 °C. Wang et al.’s [14] protocols were used for DNA extraction. A NanoDropTM spectrophotometer (ND-2000c, Thermo Scientific, Huston, USA) was used to measure the quantity and quality (260/280 nm) of the isolated genomic DNA. A flow chart for the entire experimental design is presented in Fig. 1.

Fig. 1
figure 1

Flow chart of the experimental design

Full size image

Bioinformatics data structure

The various gene banks Ensemble, NCBI, and Uniprot of public databases were used for the data sequence of the AKT3 gene for cow and buffalo (http://useast.ensembl.org/index.html), (www.ncbi.nlm.nih.gov/genbank), (http://www.uniprot.org). The AKT3 gene sequence of buffalo and cattle were (NW-005783781.1) and (AC-000173.1) that were selected from NCBI and Ensemble. The bioinformatics approach was used to target the Akt3 gene differences between cow and buffalo for various paramount regions [15].

Primer design and sequencing

The AKT3 gene sequences of buffalo (NW_005783781.1) and cattle (AC_000173.1) were selected from the database of the National Centre for Biotechnology Information (NCBI https://www.ncbi.nlm.nih.gov/gene/?term=Akt3) [15]. The NCBI database was used for the annotation of the AKT3 gene of cows and buffaloes. The protocol was used by the reported for mutation of SNPs region as DNA pooling methods [16]. We have design primers for various regions of the target fragment to amplify that exon and adjacent intronic region of cow and buffalo. The Primer Premier 5.0 software (Premier Biosoft International, Palo Alto, CA, USA) was used to design primers for the specific region and fragment for amplification of the AKT3 in cow and buffalo, and the primers were synthesized by Sangon Biological Technology (Shanghai, China). The 25 μl total volume mixture contained genomic DNA, reaction mix, each primer, and taq DNA polymerase (including 500 μM dNTP each; 20 mM Tris-HCL; pH 9; 100 mM KCl; 3 Mm MgCl2), with the ratio 50 ng, 12.5 μl 2 X, 0.5 μM, and 0.5 units, respectively. The protocol for cycling was followed as 95 °C for 5 min, denaturing cycles as 94 °C for 30 s, annealing as 30-s temperature (Tm), extending at 30 s for 72 °C, with a final extension of 10 min at 72 °C.

Differences in cow and buffalo for CDS/mRNA region in AKT3

The main focus and objective of this study were to find out the differences and variations of the AKT3 for cows and buffaloes. There are thirteen exons in cow and buffalo AKT3; we were interested to find out the variation, differences in the cow and buffalo for coding sequence, mRNA, and confirmation experimentally. The susceptibility of cows to mastitis and other diseases is higher than buffalo. Therefore, the recent project planned to carry out the differences, we blasting exon wise, one by one comparing and checking the similarity and differences through NCBI blasting tool the exon sequences, mRNA of cow and buffalo, and further experimentally work and confirmation. For the sequences similarity, differences and mRNA analysis performed the highly similar sequence (mega blast). NCBI blast tools were used (http://blast.ncbi.nlm.nih.gov/Blast.cgi).

Approach to promoter analysis and bioinformatics

For predicting the transcription start sites, in buffalo and cow, and the promoter sequence prediction, a bioinformatics tool was performed by using http://www.fruitfly.org/seq tools/promoter.html [17]. The CpG islands in buffalo and cow were predicted using MethPrimer (http://www.urogene.org/methprimer/) [18] and the tools provided at http://www.ebi.ac.uk/Tools/emboss/cpgplot/ [19, 20].

Primers construction for cow and buffalo promoters

Various primers for the upstream gene promoters region were prepared and characterized for the different regions of the upstream through the Primer Premier 5.0 software (PREMIER Biosoft Int., CA, USA).

Culture and LPS treatment of the bovine epithelium cell

The HEK 293T (human embryonic kidney 293T) cell line was used to measure the mRNA expression level of the AKT3. The standard protocol of HEK 293T following cell culture used a Roswell Park Memorial Institute (RPMI) 1640 medium (Thermo Fisher, Massachusetts, USA) which contains bovine fetal serum, non-essential amino acids, penicillin, and streptomycin with the ratio of 10%, 1%, 100 U/ml, and 100 mg/ml, respectively. And that is useful for high-efficiency transfection and expression of genes [21]. The cell culture was maintained at 37 °C and 5% CO2. GIBCO reagents (Gibco, Grand Island, NY, USA) were used for this experiment. The LPS dose in the treated cells was standardized in our lab already, which is used as 10.0 ng/ml. The cells were plated with a good density of 1.0 × 105 cells/well in 24-well plates, and after 24 h, 10 ng/ml of LPS was added into the cells. Culture media were used to seed cells at 85–90% confluence and without antibiotics; culture media used 10% FBS and cultured overnight at 37 °C. Three replicates were made after the LPS treatment at various times (3 h, 6 h, 12 h, and 24 h, respectively) for RNA extraction.

RNA extraction, cDNA, and QPCR

Different kits are available for various procedures and processing; Trizol kit was for total RNA extraction (Invitrogen, Carlsbad, CA) following the instruction and steps, while 1× HBSS was used twice to rinse the culture cells. Table 1 presents the QPCR experiment steps. The Nanodrop spectrophotometer absorbance was used for the purity and quantity of RNA, with 260/280 nm of optical density. The cDNA strand was synthesized using a cDNA kit (Toyobo Co., Japan). The Primer Premier 5.0 software was used for the respective gene primer design and preparation and confirmed through the bioinformatics UCSC software. QPCR implication was performed (Bio-Rad iQ5 real-time PCR system, CA, USA); master mixes, RNA-free water, and primers were the ingredients of the reaction master mixture. In addition, there was a control group in the experiment after normalization of mRNA level and a control group of actin and RNAse free water. Also, the experiment samples were conducted in three replicates.

Table 1 presents the QPCR experiment steps
Full size table

Cloning of promoter and generation of luciferase reporter constructs

Specific primers were designed to amplify that specific upstream region of the 2.0-kb genomic region with other substantial sequences. KpnI and BgIII (TaKaRa, Dalian, China) were used for the excision and digestion of the 2.0-kb fragment of the promoter in bovine AKT3. It was used for the generation of the luciferase construct, as the PGL3 basic vector was used for ligation and digestion with the same restriction enzymes. This plasmid is known as PGL3-1933, while other plasmids were characterized according to their size and length, which includes unidirectional deletions of the promoter, and by using specific primers, it is produced by PCR. The PGL3-1933 was used as a template using specific primers with restriction of incorporated Kpnl and BgIII restriction sites. Quick Change Site-Directed Mutagenesis Kit (Stratagene, La Jolla, CA, USA) was used for the construction of substitution mutation and regulation; it was sequence made and constructed in both directions.

Transient transfection and luciferase reporter assay

The 48-well plates with a density of 1.2 × 105 cells/wells were used for the transfection. After 24 h of reaching the density, they were transfected with the vector plasmids. With the help of the Lipofectamine 2000 kit (Invitrogen), 450 ng was added to each well of the cell plate for the transfection process. A PGL3 basic vector was used as a negative control. We have also used the same recombinant plasmid value of 450 ng for the 50-ng pRL-TK as co-transfected. The transfected plates were inoculated overnight at 37 °C, 85–90% humidity, and 10% FBS. The time and duration of the protocol for each step are very important and should be cared for properly. The phosphate-buffered saline was used for cell washing after the transfection phase. For the preparation of both Renilla and Firefly cell lysates, a passive lysis buffer was used for preparing the total lysates according to the Promega kit instruction (Promega, NC, USA). Also, the independent experiments and ratio for both Firefly and Renilla luciferase light units were analyzed.

The assessment, association, evaluation, and comparison of the cow and buffalo AKT3 for functional minimal promoter

The 293T cells were used for testing and checking the level of strength of promoter fragment construct vectors in cow and buffalo. The 293T cells were used for the transfection of the luciferase gene after 48 h, and for the luciferase assay, the cells were harvested. The activities of the relative luciferase of the promoter fragments were represented by bars. Primers were used for mRNA of the AKT3 gene. The list of the primers used is listed in Table 2.

Table 2 List of primers used for mRNA expression
Full size table

Statistical analysis

Different research and statistical model were used for various types of group data. We used it for the detection and to find out the significant result of the group data. One-way ANOVA supporting unpaired t-test was used for the group data analysis to detect significant differences (p < 0.05) of the group data which presented as a whole the experimental data. The values were described as mean ± SD, and various statistical significance levels of the experimental group data three times independently were represented as *p < 0.05, **p < 0.01, and ***p < 0.001.

Results

Differences and functions of CDs in the AKT3 gene between cow and buffalo using bioinformatics analysis

The main objectives of this part were to investigate the differences and their function of the CD region between cow and buffalo and their association study with mastitis. The targeted regions were CDs and important core promoter regions of cow and buffalo.

The detailed gene structure data of various parts of the AKT3 gene reported in cows and buffalo are presented in Table 3. There was a total of 13 exons in cows and buffaloes for the AKT3 gene. The high similarity sequence (mega blast) analysis of the exon was performed between cow and buffalo using the NCBI blast tool (http://blast.ncbi.nlm.nih.gov/Blast.cgi). After comparing, blasting of 13 exon sequences was found with substantial differences in 6 out of 13 exons, 4, 6, 7, 9, 12, and 13; each of the first 5 exons has one nucleotide difference while exon 13 has four nucleotide differences in its sequences between cow and buffalo (Tables 3 and 4).

Table 3 Features of parts of AKT3 length by base pair (bp) for cow and buffalo
Full size table
Table 4 List of nucleotides differences between cow and buffalo in 6 exons
Full size table

The AKT3 characterization of 5′ UTR regulatory region in cow and buffalo based on bioinformatics

The AKT3 characterization of the 5′ UTR regulatory region was used for prediction of promoter and confirmation of the transcription start site (TSS) position in cow and buffalo AKT3. We characterized the 3.0-kb upstream of the AKT3 in cow and buffalo which are related to mastitis inflammation (Table 5). Core promoters (TSS/TATA) was 2/7 for cows compared to 2/4 for buffalos which could be one of the substantial elements that play a central role in the regulatory function of gene and that could be the reason for the higher susceptibility of cows than buffalos to mastitis disease.

Table 5 List of predicted promoters and other important sequences in upstream of the AKT3 In cow and buffalo
Full size table

Further, the expression level of these various promoter regions in cow and buffalo was studied. For this experiment, we have used the − 3000-bp upstream of the AKT3 sequences in cow and buffalo considering ATG as +1, and to predict various regions of the possible promoters, TSS, and other important sequences in the upstream of cow and buffalo. The predicted possible promoter regions were reported as with the distance from starting ATG of the AKT3 in cow were − 225 to − 275, − 826 to − 876, − 2256 to − 2306, and − 2486 to − 2536. The CpG island methylation and possible transcription activity sites are correlated with each other, as the CpG island is necessary for the conceivable activity sites of transcription. However, no CpG island was found in the upstream region of cow and buffalo for the gene under study (Fig. 2).

Fig. 2
figure 2

a Cow sequence length 3.0 kb, CpG island prediction result, no CpG island were found, criteria used: island size > 100, GC percent greater > 50.0, Obs/Exp > 0.6. b Buffalo sequence length 3.0 kb, CpG island prediction result, no CpG island were found, criteria used: island size > 100, GC percent greater > 50.0, Obs/Exp > 0.6

Full size image

For the buffalo upstream from the starting ATG, we have also taken a 3.0-kb sequence to find out the CpG island, but not found in the sequence which is shown in Fig. 3. For further confirmation, we have used some other software tools for CpG island prediction and confirmation in cow and buffalo AKT3 sequences, which show the same result with the previous software with no CpG island found in both species, as shown in Fig. 3.

Fig. 3
figure 3

a Cow sequence length 3.0kb, CpG island prediction result, no CpG island was found, criteria used: island size > 100, GC percent greater > 50.0, Obs/Exp > 0.6. b Buffalo sequence length 3.0kb, CpG island prediction result, no CpG island were found, criteria used: island size > 100, GC percent greater > 50.0, Obs/Exp > 0.6

Full size image

The predicted promoters of AKT3 in cow and buffalo through bioinformatics

We have completely analyzed the upstream region from starting ATG of the cow and buffalo sequence of AKT3 to design and prepare various primers and construct plasmid vectors based on upstream characterization in cow and buffalo. We diagrammatically described in cow and buffalo how these important consensuses occur as well promoters in the sequences. These are the basis to prepare and design various primers for these various regions of the predicted promoters and other sequences upstream of the gene in cows and buffalo. Based on the predicted location and position of the promoters and availability of the suitable primers, we have prepared four different reporter vectors to check and test the core promoter activity and expression in the cow and buffalo AKT3 gene. Figure 4 describes the location and position of the predicted promoters, TSS, and TATA box in cow and buffalo sequences for AKT3 (Fig. 4c). It is interesting to discuss that the ATG in cow sequence occurs in exon 1, while in buffalo sequence, it is situated in exon 2. Meanwhile, in 3000 bp of buffalo upstream sequence of ATG, just 5 promoter regions were predicted on various positions and locations, while in cow, there were four predicted promoter regions (Fig. 4d).

Fig. 4
figure 4

a The AKT3 of cow predicted promoter position and distance from the starting ATG, with TSS and TATAA box. b The AKT3 of buffalo predicted promoter position and distance from the starting ATG, with TSS. c Describes the amplified promoter regions from starting ATG in cow sequence. d Describes the amplified promoter regions from starting ATG in buffalo sequence

Full size image

Validation of AKT3 promoters in cow and buffalo

The primers used in this experimental study are presented in Table 6. The KnpI and BgIII restriction sites and protective base were introduced to the 5’ end of the forward and reverse primers respectively. And, KpnI restriction sites and protective bases were introduced to the 5′ end of the forward primer, and BgIII restriction sites and protective bases were introduced to the 5′ end of the reverse primer.

Table 6 List of primers used in the experiment
Full size table

The functional proximal minimal promoter of AKT3 gene

To know and understand which predicted region is the minimum promoter sequence in the cow and buffalo of the AKT3 gene, we have to compare the different predicted regions of the area to construct various regions between cow and buffalo. The expression and possessions of the reported construct alternate were investigated and estimated into the 293T cells after the transfection and the results are shown in Fig. 5. The different promoter construct reporters were tested and the results showing their activity are reported in the 293T cells in Fig. 5. The PGL3-AKT3-cow-p2 fragment promoter activity was significantly increased among the other different fragment promoters in cow AKT3 in 293T cells, whereas in the buffalo AKT3 in 293T cells, the promoter activity of the PGL3-AKT3-buffalo-P2 activity was higher as compared with the other adjacent corresponding promoters’ sequences in buffalo; these are shown in Fig. 5. The results of cow data indicate that the region upstream from − 371 to − 1247 bp was essential for AKT3 in the cow sequence to maintain the promoter activity of the cow sequence, whereas in the buffalo data, results show that the fragment of the region from − 371 to − 969 was very essential and crucial for buffalo data sequence (Fig. 5b).

Fig. 5
figure 5

a The documentation and identification of AKT3 core promoters in cow. These bars in 293T cells represent the comparative luciferase activities of the promoter fragments. b The certification, identification, and documentation of AKT3 core promoters in buffalo. These bars in 293T cells represent the comparative luciferase activities of the promoter fragments. The values are represented as mean ± standard deviation. The standard deviations signified with the error bars respectively. To detect and clarify by using unpaired student’s t-test for significant differences as *P < 0.05, **P < 0.01, and ***P < 0.001. (n = 3)

Full size image

In Fig. 5a, the significant differences of the cow core promoter in 293T cells were presented. The four various fragments of the upstream reported construct vectors were compared with that of the control group and showed significant results while the buffalo data results are reported in Fig. 5a. For the buffalo data upstream of the promoter-reported construct vectors, four various fragments were prepared and compared with the control group.

AKT3 immune response of LPS-induced mastitis

The immune response of AKT3 was observed for lipopolysaccharide (LPS) with various times intervals and duration [22]. LPS can disrupt the permeability of the blood-milk barrier by altering the expression of tight junction proteins [23]. For more information, LPS is a chemical component isolated from the cell wall of Gram-negative bacteria and is one of the important virulence factors for diseases caused by bacteria on bovine mammary glands [22]. Therefore, it was mined to observe and investigate the immunity response of AKT3 with a time interval relationship with LPS. It will be helpful in future research plans directly concerned with mastitis. A list of the AKT3 constructed primers which were used for the experiment is presented in Table 2.

Moreover, the relative expression level of mRNA was checked for AKT3 by using the bovine epithelium cell line (BEC). The bovine mammary epithelium cell line was used for the investigation of the relative expression of AKT3 mRNA. Also, the mRNA relative expression was evaluated for AKT3 with the time interval of 0, 1, 3, 6, 12, and 24 h. The higher levels of expression considerably were found in the mRNA of AKT3 (P < 0.01) in response to 6 h of LPS (10 ng/ml) challenge. Meanwhile, the level of mRNA expression of AKT3 with a dependent manner of immune response proved the significant difference with time of mRNA expression (Fig. 6b).

Fig. 6
figure 6

a The result of PCR product for mRNA of various primers. b The AKT3 mRNA relative expression in bovine epithelium cell line was observed. The significantly different level of expression with time-dependent manner of LPS (10 ng/ml) treatment was found. The standard deviation (SD) is represented with error bars (n = 3). To detect and clarify by using unpaired student’s t-test for significant differences as *P < 0.05, **P < 0.01, and ***P < 0.001. (n = 3)

Full size image

Discussion

It has been reported that milk quality and quantity of cows and buffalos are influenced by various factors such as genetic profile, nutrition, infections, and environment. The molecular functions related to cow and buffalo milk production could be highly influenced by AKT3 genes that are involved in kinase and ATP binding function [24]. Y Maetani et al. [25] reported that the ATP levels of fibroblast cells are significantly influenced by the AKT genes.

In this study, we focused on the promoter area of AKT3 of cow and buffalo mammary glands. Mammary tissue is necessary for high milk production [15]. Comprehensive bioinformatic analyses for AKT3 promoter activity sequence differences of the cow and buffalo could better highlight the immunity mechanisms, responses, and physiological differences of these two species. The results showed that in cows’ AKT3 promoter region, sequences started from − 371 to − 876, while in buffaloes, it started from − 370 to − 969 crucial region of the promoter. Thus, the promoter activity is sustained by two core function promoter regions. This could play a key role in the inflammatory response and variation for susceptibility in buffalo and cows for mastitis or any other inflammatory disease [26]. Also, Jin et al. [27] mentioned that the differences in promoter region length could significantly affect the numbers of cell metabolic pathways that could make the differences in responses to the infections. Moreover, TSS/TATA was 2/7 for cows compared to 2/4 for buffalos in the core promoters. Zhang et al. [28] mentioned that TSS to TATA are playing an important role in RNA polymerase II transcription for the core promoter region. The core promoter region may contain various elements such as TATA. The core promoters exhibit significant diversity regarding the sequence and function of TATA. And the higher number of TATA in cows is increasing the transcription and therefore the sensitivity against pathogens compared to buffaloes. This could be from the increase in the TATA-binding protein subunit of the basal transcription factor that initiates transcription in conjunction with the initiator [29].

The AKT3 mRNA expression results showed significant differences in the expression levels with a dependent manner of LPS stimulation in the bovine mammary epithelial cells. Yang et al. [30] reported that Akt3 is expressed during mammary gland development, and its expression is upregulated during lactation and is downregulated at the onset of involution. Moreover, the role of Akt promoters in regulating the lactation process was examined. And, its overexpression in the mammary glands resulted in the delay of involution and onset of apoptosis [30].

Despite functions specific differences in AKT isoforms, all the 3 subtypes are imminent entrant genes linked with milk production. Bionaz and Loor [31] reported that during lactation periods in cattle, the expression level of AKT1 and AKT3 was significantly increased. The synthesis of breast milk fat and cholesterol was affected by the AKT family.

The genetic differences among the promoters of the AKT family between cows and buffalos have significantly influenced not only their different biological processes like the produced milk composition but also their responses against infectious diseases like mastitis [15, 32]. The AKT family contributes to numerous disorders such as inflammation and ischemia [33]. Besides, the experimental autoimmune encephalomyelitis susceptibility is regulated by AKT3. In this study, results showed that the promoter region PGL3-AKT3-cow-P2 (− 371 to − 1247) in cow and PGL3-AKT3-buffalo-P2 (− 371 to 969) in buffalo was highly active as compared with the other regions in cow and buffalo (Fig. 5 and Fig. 7). The previous studies show that the isotope AKT3 was involved in the different functions of the mammary gland cells. The prediction of promoter regions and consequent construction of reporter gene to check the activity of promoter region and analysis study were performed to understand the different functional phenomena of the promoter region. The activity of these different promoters was tested in 293 T cells and these regions showed that promoters’ activity was significantly increased in cows than buffaloes. This conserved region could be responsible for the pro-inflammatory signaling mechanism, and positive regulation of the gene.

Fig. 7
figure 7

The different responses to mastitis between cow and buffalo based on the differences in AKT3 gene promoters regions

Full size image

Furthermore, the expression level of the various AKT3 promoter regions reported that the distances from starting ATG of the AKT3 in cow were − 225 to − 275, − 826 to − 876, − 2256 to − 2306, and − 2486 to − 2536, while for the buffalo, upstream CpG islands were not found in the sequence which is shown in Fig. 3. CpG island methylation was correlated with the possible transcription activity sites. Boulougouris et al. [34] mentioned that the CpG island is necessary for the conceivable activity sites for the transcription that is related to mammary gland lactation and mastitis disease.

The constructed AKT3 promoters’ expressions were investigated using 293T cells. The results showed that buffaloes’ PGL3-AKT3-cow-p2 fragment promoters’ activity in 293T cells was significantly higher compared to cows. Also, the akt3 promoter upstream region of cows was started from − 371 to − 1247 bp compared to buffaloes that started from − 371 to − 969 bp. Spangle et al. [35] mentioned that the differences in T cell functional responses could be from the differences in the AKT3 promoter transcription that could lead to different pathways that correlated with a promoter-associated allele in cells.

For the immune response of AKT3 and LPS, the conjugation between LPS and AKT3 which is related to the immune response is important for predicting mastitis in its early stage of Gram-negative infections [36]. Therefore, the mastitis model based on the relationship between LPS and AKT3 can provide a good platform for dairy cow mastitis research. Mastitis is a disease of inflammation that is concerned with immunity response and the immune response genes like AKT3. The higher levels of expression considerably were found in the mRNA of AKT3 (P < 0.01) in response to 6 h of LPS (10 ng/ml) challenge. These results indicate that AKT3 showed immunity response to LPS, and it could play an important role in mastitis, and mastitis is associated with inflammation of the udder [22].

This study’s limitations might be from the concerns of using RNA and DNA binding motifs, which are defined for a subset of some co-expressed genes only, and sometimes are great predictors of transcription factor binding [37]. Another limitation is that even when transcription factor binding is observed through experimental assays, this does not necessarily mean actual binding of the factor at the identified genomic location, nor automatically the regulation of the closest gene [38]. Finally, reproducibility still often remains a challenge for these approaches and the integration of the different online data from the databases like NCBI are not easy because of some more informative data from some sources than others. Therefore, in this study, we integrated both the animal and cell line experiments with the computational bioinformatics studies to face these limitations.

Conclusion

The identification of sequences differences of AKT3 and core promoter confirmation play an important key role in gene expression in cow and buffalo milk production and immune response against mastitis. The higher AKT3 gene expression and transcription in cows than buffalos could have significant prevailing results between the two species. This could be one of the pro-inflammatory mechanisms responsible for the higher signaling phenomenon during pathogenic infection. This could be the reason that buffaloes are more resistant to mastitis disease than cows, while cow is more vulnerable as compared to buffalo. Continuing progress in genomics and systems biology technologies will aid in the understanding of the molecular and genetic bases of mastitis and other tumor heterogeneity and will enable us to identify more reliable diagnostic and prognostic biomarkers for metastatic and recurrent cattle diseases.

Availability of data and materials

All the data and their availability are included in the manuscript text, figures, tables, and supplementary materials. Also, all relevant raw data, will be freely available to any scientist wishing to use them for non-commercial purposes, without breaching.

Abbreviations

AKT3:

Serine/threonine kinase 3

BEC:

Bovine epithelium cell line

cDNA:

Complementary deoxyribonucleic acid

CDS:

Gene-coding sequence

CDs:

Coding regions

CNV:

Copy number variants

DHI:

Danhong injection

EDTA:

Ethylenediaminetetraacetic acid

HBSS:

Hanks’ balanced salt solution

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LPS:

Lipopolysaccharide

NCBI:

National Center for Biotechnology Information

PCR:

Polymerase chain reaction

pGL3:

Plasmid-basic-luciferase vector 3

SNPs:

Single nucleotide polymorphisms

SREBP:

Sterol regulatory element-binding protein

TLR:

Toll-like receptor

TMR:

Total mixed ration

TSS:

Transcription start site

References

  1. Herrero M, Grace D, Njuki J, Johnson N, Enahoro D, Silvestri S, Rufino MC (2013) The roles of livestock in developing countries. Animal: an international journal of animal bioscience 7(Suppl 1):3–18. https://doi.org/10.1017/S1751731112001954

    Article  Google Scholar 

  2. Xie J, Weiskirchen R (2020) What does the “AKT” stand for in the name “AKT kinase”? Some historical comments. Frontiers in oncology 10:1329. https://doi.org/10.3389/fonc.2020.01329

    Article  Google Scholar 

  3. Miles AM, Huson HJ (2021) Graduate student literature review: understanding the genetic mechanisms underlying mastitis. Journal of dairy science 104(1):1183–1191. https://doi.org/10.3168/jds.2020-18297

    Article  Google Scholar 

  4. Oner Y, Serrano M, Sarto P et al. (2021) Genome-wide association studies of somatic cell count in the Assaf breed. Animals: an open access journal from MDPI 11

  5. Beecher C, Daly M, Childs S, Berry DP, Magee DA, McCarthy TV, Giblin L (2010) Polymorphisms in bovine immune genes and their associations with somatic cell count and milk production in dairy cattle. BMC genetics 11(1):99. https://doi.org/10.1186/1471-2156-11-99

    Article  Google Scholar 

  6. Pimentel E, Bauersachs S, Tietze M et al (2011) Exploration of relationships between production and fertility traits in dairy cattle via association studies of SNPs within candidate genes derived by expression profiling. Animal genetics 42(3):251–262. https://doi.org/10.1111/j.1365-2052.2010.02148.x

    Article  Google Scholar 

  7. Sandelin A, Carninci P, Lenhard B, Ponjavic J, Hayashizaki Y, Hume DA (2007) Mammalian RNA polymerase II core promoters: insights from genome-wide studies. Nature Reviews Genetics 8(6):424–436. https://doi.org/10.1038/nrg2026

    Article  Google Scholar 

  8. Mao YJ, Zhu XR, Li R et al. (2015) Methylation analysis of CXCR1 in mammary gland tissue of cows with mastitis induced by Staphylococcus aureus. Genetics and molecular research: GMR 14:12606-12615

  9. Zhang L, Yang M, Li C, Xu Y, Sun J, Lei C, Lan X, Zhang C, Chen H (2014) Identification and genetic effect of a variable duplication in the promoter region of the cattle ADIPOQ gene. Animal genetics 45(2):171–179. https://doi.org/10.1111/age.12112

    Article  Google Scholar 

  10. Khanal T, Pandit A (2013) Assessment of sub-clinical mastitis and its associated risk factors in dairy livestock of Lamjung, Nepal. International Journal of Infection and Microbiology 2(2):49–54. https://doi.org/10.3126/ijim.v2i2.8322

    Article  Google Scholar 

  11. Van Driessche B, Rodari A, Delacourt N et al (2016) Characterization of new RNA polymerase III and RNA polymerase II transcriptional promoters in the Bovine Leukemia Virus genome. Scientific reports 6(1):31125. https://doi.org/10.1038/srep31125

    Article  Google Scholar 

  12. Xu TL, Seyfert HM, Shen XZ (2018) Epigenetic mechanisms contribute to decrease stearoyl-CoA desaturase 1 expression in the liver of dairy cows after prolonged feeding of high-concentrate diet. Journal of dairy science 101(3):2506–2518. https://doi.org/10.3168/jds.2017-12878

    Article  Google Scholar 

  13. Han LQ, Gao TY, Yang GY, Loor JJ (2018) Overexpression of SREBF chaperone (SCAP) enhances nuclear SREBP1 translocation to upregulate fatty acid synthase (FASN) gene expression in bovine mammary epithelial cells. Journal of dairy science 101(7):6523–6531. https://doi.org/10.3168/jds.2018-14382

    Article  Google Scholar 

  14. Wang C, Liu M, Li Q, Ju Z, Huang J, Li J, Wang H, Zhong J (2011) Three novel single-nucleotide polymorphisms of MBL1 gene in Chinese native cattle and their associations with milk performance traits. Veterinary immunology and immunopathology 139(2-4):229–236. https://doi.org/10.1016/j.vetimm.2010.10.023

    Article  Google Scholar 

  15. Ullah F, Bhattarai D, Cheng Z et al (2018, 2018) Comparative analysis of V-Akt murine thymoma viral oncogene homolog 3 (AKT3) gene between cow and buffalo reveals substantial differences for mastitis. BioMed Research International

  16. Bansal AK, Meyer TE (2002) Evolutionary analysis by whole-genome comparisons. Journal of Bacteriology 184(8):2260–2272. https://doi.org/10.1128/JB.184.8.2260-2272.2002

    Article  Google Scholar 

  17. Li S, Wang L, He X, Xie Y, Zhang Z (2015) Identification and analysis of the promoter region of the STGC3 gene. Archives of medical science: AMS 11:1095. https://doi.org/10.5114/aoms.2015.49213

    Article  Google Scholar 

  18. Zhao Z-D, Zan L-S, Li A-N, Cheng G, Li SJ, Zhang YR, Wang XY, Zhang YY (2016) Characterization of the promoter region of the bovine long-chain acyl-CoA synthetase 1 gene: roles of E2F1, Sp1, KLF15, and E2F4. Scientific reports 6(1):19661. https://doi.org/10.1038/srep19661

    Article  Google Scholar 

  19. Yang Y, Adelstein SJ, Kassis AI (2012) Target discovery from data mining approaches. Drug discovery today 17:S16–S23. https://doi.org/10.1016/j.drudis.2011.12.006

    Article  Google Scholar 

  20. Li L, Huang J, Ju Z, Li Q, Wang C, Qi C, Zhang Y, Hou Q, Hang S, Zhong J (2013) Multiple promoters and targeted microRNAs direct the expressions of HMGB3 gene transcripts in dairy cattle. Animal genetics 44(3):241–250. https://doi.org/10.1111/age.12007

    Article  Google Scholar 

  21. Bae DH, Marino M, Iaffaldano B, Fenstermaker S, Afione S, Argaw T, McCright J, Kwilas A, Chiorini JA, Timmons AE, Reiser J (2020) Design and testing of vector-producing HEK293T cells bearing a genomic deletion of the SV40 T antigen coding region. Molecular therapy. Methods & clinical development 18:631–638. https://doi.org/10.1016/j.omtm.2020.07.006

    Article  Google Scholar 

  22. Jiang A, Zhang Y, Zhang X, Wu D, Liu Z, Li S, Liu X, Han Z, Wang C, Wang J, Wei Z, Guo C, Yang Z (2020) Morin alleviates LPS-induced mastitis by inhibiting the PI3K/AKT, MAPK, NF-kappaB and NLRP3 signaling pathway and protecting the integrity of blood-milk barrier. International immunopharmacology 78:105972. https://doi.org/10.1016/j.intimp.2019.105972

    Article  Google Scholar 

  23. Wang JJ, Wei ZK, Zhang X, Wang YN, Fu YH, Yang ZT (2017) Butyrate protects against disruption of the blood-milk barrier and moderates inflammatory responses in a model of mastitis induced by lipopolysaccharide. British journal of pharmacology 174(21):3811–3822. https://doi.org/10.1111/bph.13976

    Article  Google Scholar 

  24. Jaiswal S, Jagannadham J, Kumari J, Iquebal MA, Gurjar AKS, Nayan V, Angadi UB, Kumar S, Kumar R, Datta TK, Rai A, Kumar D (2021) Genome wide prediction, mapping and development of genomic resources of mastitis associated genes in water buffalo. Frontiers in veterinary science 8:593871. https://doi.org/10.3389/fvets.2021.593871

    Article  Google Scholar 

  25. Maetani Y, Asano S, Mizokami A, Yamawaki Y, Sano T, Hirata M, Irifune M, Kanematsu T (2021) Expression of PRIP, a phosphatidylinositol 4,5-bisphosphate binding protein, attenuates PI3K/AKT signaling and suppresses tumor growth in a xenograft mouse model. Biochemical and biophysical research communications 552:106–113. https://doi.org/10.1016/j.bbrc.2021.03.045

    Article  Google Scholar 

  26. Mcdougald DS, Papp TE, Zezulin AU et al (2019) AKT3 gene transfer promotes anabolic reprogramming and photoreceptor neuroprotection in a pre-clinical model of retinitis pigmentosa. Molecular therapy: the j Am Soc Gene Therapy 27:1313–1326

    Article  Google Scholar 

  27. Jin Y, Eser U, Struhl K et al (2017) The ground state and evolution of promoter region directionality. Cell 170:889–898.e810

    Article  Google Scholar 

  28. Zhang M, Chaney HL, Current JZ, Yao J (2021) Identification of the core promoter of ZNFO, an oocyte-specific maternal effect gene in cattle. Gene 791:145717. https://doi.org/10.1016/j.gene.2021.145717

    Article  Google Scholar 

  29. Vo Ngoc L, Wang YL, Kassavetis GA et al (2017) The punctilious RNA polymerase II core promoter. Genes & development 31(13):1289–1301. https://doi.org/10.1101/gad.303149.117

    Article  Google Scholar 

  30. Yang D, Huynh H, Wan Y (2018) Milk lipid regulation at the maternal-offspring interface. Seminars in cell & developmental biology 81:141–148. https://doi.org/10.1016/j.semcdb.2017.10.012

    Article  Google Scholar 

  31. Bionaz M, Loor JJ (2011) Gene networks driving bovine mammary protein synthesis during the lactation cycle. Bioinformatics and biology insights 5 BBI:S7003

    Article  Google Scholar 

  32. Bhattarai D, Dad R, Worku T, Xu S, Ullah F, Zhang M, Liang X, den T, Fan M, Zhang S (2020) The functions and mechanisms of sequence differences of DGAT1 gene on milk fat synthesis between dairy cow and buffalo. The Journal of dairy research 87(2):170–174. https://doi.org/10.1017/S0022029920000126

    Article  Google Scholar 

  33. Zheng Y, Xu Z (2014) MicroRNA-22 induces endothelial progenitor cell senescence by targeting AKT3. Cellular Physiology and Biochemistry 34(5):1547–1555. https://doi.org/10.1159/000366358

    Article  Google Scholar 

  34. Boulougouris X, Rogiers C, Van Poucke M et al (2019) Methylation of selected CpG islands involved in the transcription of myeloperoxidase and superoxide dismutase 2 in neutrophils of periparturient and mid-lactation cows. Journal of dairy science 102(8):7421–7434. https://doi.org/10.3168/jds.2018-16027

    Article  Google Scholar 

  35. Spangle JM, Dreijerink KM, Groner AC, Cheng H, Ohlson CE, Reyes J, Lin CY, Bradner J, Zhao JJ, Roberts TM, Brown M (2016) PI3K/AKT signaling regulates H3K4 methylation in breast cancer. Cell reports 15(12):2692–2704. https://doi.org/10.1016/j.celrep.2016.05.046

    Article  Google Scholar 

  36. Jiang K, Ma X, Guo S, Zhang T, Zhao G, Wu H, Wang X, Deng G (2018) Anti-inflammatory effects of rosmarinic acid in lipopolysaccharide-induced mastitis in mice. Inflammation 41(2):437–448. https://doi.org/10.1007/s10753-017-0700-8

    Article  Google Scholar 

  37. Saint-André V (2021) Computational biology approaches for mapping transcriptional regulatory networks. In: Computational biology approaches for mapping transcriptional regulatory networks. Computational and structural biotechnology journal

    Google Scholar 

  38. Monteiro PT, Pedreira T, Galocha M, Teixeira MC, Chaouiya C (2020) Assessing regulatory features of the current transcriptional network of Saccharomyces cerevisiae. Scientific reports 10(1):17744. https://doi.org/10.1038/s41598-020-74043-7

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful for the financial assistance by the project Research Platform Construction of the Ministry of Agriculture International Exchange and Cooperation in Science and Technology and the Opening Project of Guangxi Key Laboratory of Buffalo Genetics, Reproduction and Breeding (SNKF-2016-01).

Funding

This study was funded by the Project Research Platform Construction of the Ministry of Agriculture International Exchange and Cooperation in Science and Technology and the Opening Project of Guangxi Key Laboratory of Buffalo Genetics, Reproduction and Breeding (SNKF-2016-01). The funder supported the experiment data analyses and software fees.

Author information

Authors and Affiliations

  1. Faculty of Veterinary and Animal Sciences, National Center for Livestock Breeding Genetics and Genomics LUAWMS, Uthal, Balochistan, Pakistan

    Farmanullah Farmanullah

  2. Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, 430070, Hubei, China

    Farmanullah Farmanullah, Zhang Min, Xu Sutong, Zia ur Rehman, Hira Sajjad Talpur, Faheem Ahmed Khan, Nuruliarizki Shinta Pandupuspitasari & Zhang Shujun

  3. College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China

    Mostafa Gouda

  4. Department of Nutrition & Food Science, National Research Centre, Dokki, Giza, 12622, Egypt

    Mostafa Gouda

  5. Faculty of Marine Sciences, Lasbela University of Agriculture Water and Marine Sciences (LUAWMS), Uthal, Balochistan, 90150, Pakistan

    Mohib Ullah KaKar

  6. Department of Internal Medicine, Faculty of Veterinary Sciences, University of Gadjah Mada, Yogyakarta, Indonesia

    Sami Ullah Khan

  7. Department of Forestry and Wildlife Management, The University of Haripur, Khyber Pakhtunkhwa, Pakistan

    Muhammad Salim

  8. Directorate General (Extension) Livestock and Dairy Development Department, Bacha Khan Chowk, Peshawar, Khyber Pakhtunkhwa, Pakistan

    Momen Khan

  9. Department of Animal Health, Faculty of Animal Husbandry and Veterinary Sciences, The University of Agriculture Peshawar, Peshawar, Pakistan

    Zia ur Rehman

  10. Department of Animal Breeding and Genetics, Sindh Agriculture University Tandojam, Hyderabad, Pakistan

    Hira Sajjad Talpur

  11. Laboratory of Molecular Biology and Genomics, Department of Zoology University of Central Punjab, Lahore, Pakistan

    Faheem Ahmed Khan

  12. Laboratory of Food Biotechnology, Faculty of Animal and Plant Science, Diponegoro University, Semarang, Indonesia

    Faheem Ahmed Khan & Nuruliarizki Shinta Pandupuspitasari

Authors
  1. Farmanullah Farmanullah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mostafa Gouda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhang Min
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xu Sutong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohib Ullah KaKar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sami Ullah Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Muhammad Salim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Momen Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zia ur Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hira Sajjad Talpur
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Faheem Ahmed Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Nuruliarizki Shinta Pandupuspitasari
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhang Shujun
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FF and ZS conceived and designed the experiments FF, MG, MS, and MK wrote the original draft of the manuscript ZS was the major supervisor FF, ZM, MG, ZUR, HST, XS, SUK, NSP, FAK, MUK, and NSP helped in data analysis FF, MG, HST, and ZUR helped in manuscript reviewing and editing, and overall critical review of the manuscript The authors read and approved the final manuscript.

Corresponding authors

Correspondence to Farmanullah Farmanullah or Zhang Shujun.

Ethics declarations

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the ethical committee for animal experiments of Huazhong Agricultural University and approved by the Animal Care and Use Committee from the Dairy Cattle Research Center, Hubei Academy of Agricultural Sciences, Hubei, Peoples’ Republic of China (Approval ID: SCXK Hubei 20080005).

Consent for publication

The authors are declare that the work described was an original manuscript that followed all the ethical regulations and that has not been published previously. Also, no conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors to be published.

Competing interests

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farmanullah, F., Gouda, M., Min, Z. et al. The variation in promoter sequences of the Akt3 gene between cow and buffalo revealed different responses against mastitis. J Genet Eng Biotechnol 19, 164 (2021). https://doi.org/10.1186/s43141-021-00258-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43141-021-00258-4

Keywords