Research Article | Open Access
Adaptive Molecular Evolution of AKT3 Gene for Positive Diversifying Selection in Mammals
The V-Akt Murine Thymoma Viral Oncogene Homolog 3 (AKT3) gene is of the serine/threonine-protein kinase family and influences the production of milk fats and cholesterol by acting on the sterol administrative area restricting protein (SREBP). The AKT3 gene is highly preserved in animals, and during lactation in cattle, its expression increases. The AKT3 gene is expressed in the digestive system, mammary gland, and immune cells. A phylogenetic investigation was performed to clarify the evolutionary role of AKT3, by maximum probability. The AKT3 gene sequence data of various mammalian species was evident even with animals undergoing breeding selection. From 39 mammalian species studied, there was a signal of positive diversifying selection with Hominidae at 13Q, 16G, 23R, 24P, 121P, 294K, 327V, 376L, 397K, 445T, and 471F among other codon sites of the AKT3 gene. These sites were codes for amino acids such as arginine, proline, lysine, and leucine indicating major roles for the function of immunological proteins, and in particular, the study highlighted the importance of changes in gene expression of AKT3 on immunity.
An evolutionary study provides an understanding of the genetic development occurring across species. Often a solitary ancestor is responsible for the initiation genetic variation within a population. Speciation, common descent, and natural selection are the main features of evolutionary process. This is understood and explained by various branches of biological sciences including genetics, paleontology, and ecology. Recently, research has become more focused on understanding the evolutionary process of life during the various phases of evolution. In particular, the research is concerned with evaluating genetic diversity, understanding the heritability of important traits, reasons for the molecular evolution, and the ability of genes to contribute through breeding selection, biogeography, and genetic drift.
The field of evolutionary research stimulates research into the evolution of cooperation, ageing evolvability , speciation , and sexual reproduction . Evolutionary biology helps us to understand gene function and the processes of genetic variation and gene transfer including the study of point mutations, gene and genomic duplication, heritability rates, probability, and genome-wide associations . The focus of molecular evolution is to consider those genes that are associated with advantageous traits and their ability to be disseminated through a population via selective breeding .
The AKT family of genes has a role in mammary gland growth, lactation, and mammary degradation, and their isoforms are candidate genes for milk production . The AKT gene family is involved in a diversity of genetic processes including cell propagation, differentiation, angiogenesis, apoptosis, tumor genesis, metabolism, cell endurance, development, glycogen synthesis, and glucose uptake [7, 8].
Mammalian cells contain three genes that encode for three isoforms of AKT, namely, AKT1 (PKBα), AKT2 (PKBβ), and AKT3 (PKBγ). All Akt isoforms contain a N-terminal administrative pleckstrin homology (PH) area, a focal kinase domain with serine/threonine explicitness, and a C-terminal hydrophobic space . The AKT3 gene is a constituent of the serine/threonine protein kinase family and has a function in controlling fat and cholesterol composition in the milk by modifying the action of the sterol administrative component restricting protein. The expression of AKT3 is highly variable in mammals. The AKT3 is highly expressed in the digestive system followed by the mammary organ and is also expressed in immune cells. It is associated with the TLR pathways as adequately as proinflammatory cytokines . AKT3 is highly expressed in immune cells and contributes to immunity processes . During lactation in cattle, the expression levels of AKT3 were increased .
The expression of AKT3 is found in low levels all through the human body , but AKT3 is the least measured isoform. However, the AKT3 gene has a putative oncogenic function given that is overexpressed when there is high enzymatic action in the endoplasmic reticulum of malignant breast cells . The ongoing recognizable proof of somatic mutations of AKT3 including MAGI3-Akt3 and Akt3E17K in various malignancies likewise focuses on the significant role of this isoform in tumorigenesis . AKT3 was the most enhanced isoform in numerous malignant growths, cancer, including GBM, ovarian, melanoma, endometrial, and breast cancer (O’Hurley et al., 2014; ).
Positive selection produces variation in phenotypes among animals and is a mechanism for disseminating favorable genes in a population. The goal of this study was to investigate and determine selection markers utilizing a maximum likelihood probability approach for the distinction of molecular genetics of AKT3 among mammalian species and provide information regarding the applicability of marker assist selection in the diverse species.
2. Material and Methods
2.1. Dataset Preparation and Sequence Analysis
Publically available gene banks such as Ensembl (http://useast.ensembl.org/index.html), NCBI (http://www.ncbi.nlm.nih.gov/genbank), and UniProt (http://www.uniprot.org) were considered, but the NCBI database was used for coding the nucleotide and amino acid sequence of AKT3 for recovery and data analysis. The alignment of the sequences was performed with the help of Clustal Omega, in the MEGA 6.0 program . Maximum likelihood methods were used to devise the phylogenetic tree within MEGA 6.0 for the AKT3 gene. Bootstrapping provided 1000 replicates for the clustering of taxa. The log likelihood of the topology and branch length indicated the number of substitutions per site [16, 17]. The species were identified by their accession number and their mRNA and protein accession numbers as listed in Table 1. The NCBI gene bank accession numbers for the mammalian gene AKT3 datasets were used for testing our hypothesis to construct various datasets.
2.2. Analysis on the Bases of Codon Core Positive Selection
The present study was designed to study and investigate the molecular basis of evolution and the effect of positive selection of AKT3 by analyzing the codon and sequence of AKT3 and comparing the dN/dS ratio of for two maximum likelihood approaches [18, 19]. Software tools utilised were DATAMONKEY (http://www.datamonkey.org/) in conjunction with the HyPhy package .
The analyses are completed in two steps. The 1st step was to find out the maximum likelihood ratio test for positive selection, where indicates the sites of expression. Two models were represented in each analysis by comparing its different sites with are (null), while the other is discrete general [16, 21] where the distribution of X2 compared with is the likelihood log (2Δ1). When the M7 model (null) is used, the interval is assumed to be between 0 and 1 with restricted and a β distribution. The alternative was a M8 model where the value is greater than 1 and obtained from the dataset. The rates of synonymous and nonsynonymous variation were used to calculate and identify positive selection of the AKT3 gene. Using the fixed effect tests of different likelihood of sequences aligned for each site, Ahmad et al.  reported the various likelihood programs as random effect likelihood (REL), single likelihood ancestor counting methods (SLAC), and FEL likelihood, to approximately investigate the values globally of . Only those sites common in all tools were selected. The REL used 95% confidence interval for positive site selections perceived and used Bayes factor . The other analysis measured values of significance was values < 0.05. Many sites were identified at in different genes by the various software platforms.
The second step was to use likelihood tests to confirm the amino acid availability. Bielawski and Yang  reported that for different classes were used to estimate and investigate for each site of the posterior probabilities inferred by using Bayes theorem. The amino acid residues considered as being under selective pressure had a higher value and probabilities of . Kelley and Sternberg  reported the Phyre and Swiss models (http://www.sbg.bio.ic.ac.uk/phyre2/html and http://swissmodel.expasy.org), and positive selection was assessed by amino acid location using crystalline structure. Glaser et al.  reported the bioinformatics tools that were used to predict and find out the protein in which the conserved evolutionary amino acid/nucleic acid level and position are found and on which the sequence phylogenetic relationships were based. Also, the ConSurf server link (http://consurftest.tau.ac.il) was used to predict and find the proteins in which amino acids and nucleic acids had been conserved through evolution (reference). The selection pressure was used to identify important codon sites. The Selection version 2.2 (http://selecton.tau.ac.il/) was used for sequence codon alignment of AKT3. Yang et al.  reported that Bayesian inference methods supported by maximum likelihood test were used accurately to measure the ratios of various codon-aligned sequence shifting.
2.3. Analysis of Protein-Protein Interaction Network
Analysis of the protein-protein interaction network is also crucial for further understanding of the AKT3 molecular function. Gene interactions with AKT3 were predicted using the special linkage analysis of STRING (version 9.1, http://www.string-db.org/) . The web server data bank of biological interactions was used for the identification and identification of interactions of proteins. The cutoff standard value was used as . The highly connected and essential biological function proteins were indicated in the middle nodes. These were identified, documented, and estimated by the number of line connections between proteins of each node and using the resemblance value. Various software and tools were used for protein- protein interaction. The STRING and Cytoscape software tools were used for network construction and visualization of proteins and interactions .
2.4. Phylogenetic Tree of AKT3
The phylogenetic tree for the AKT3 gene was constructed for thirty-nine species. The nucleotides of these various species were downloaded from the public database of NCBI for construction of phylogenetic tree. We have used MEGA 6 for phylogenetic tree construction after aligning the sequence of these species in Clustal w.
2.5. Domain Sites for AKT3
The candidate gene for mastitis-associated AKT3 is a family member of the serine/threonine protein kinase family. Milk fat synthesis and cholesterol is the main function, regulated by sterol regulatory element binding protein (SREBP). We have searched the domains with InterPro Scan in EBI . In our present integrated study of the evolution of the AKT3 gene, a further approach is to search and find out the domains in mammals with InterPro Scan  using the search tool.
3. Results and Discussion
The present advances in the crucial record of genetic contrast have anticipated account recommendations for investigation of the positive selection objectives, which in the end would be significant to illuminate the hereditary suggest and choice work in evolutionary components. In addition, positive choice marks hinder the genomic areas that assume noteworthy jobs. Therefore, investigating such areas will give extensive help to recognizable proof of hereditary deviations, which would encourage the interruption of these utilitarian districts and movement in phenotypic combinations. The enlightenments of the hereditary bases of various characteristics in many species have been premeditated by competitor gene methodology. The recognizable proof of these candidate genes assumes a significant role in phenotypic variety in domesticated animals’ populace and gives new development in the evolutionary procedure and positive choice (Brown et al., 2013).
The AKT3 lineage was <1 for the average across the site ratio (dN/dS). This indicates that, based on the similarities between sequences on the phylogenic relationship, there was many conserved amino acid even though positive selection had occurred. The indicators of selection were masked by the large number of conserved amino acids; however, many amino acids were found to be positively selected. Selection results are shown with color scales in Figure 1. From the likelihood approaches used in this study, there were 14 codon AKT3 sites amenable to positive selection. Codon position of the positive selection sites for AKT3 was detected using REL which discovered four sites, FUBAR identified fourteen sites, and MEME exposed ten sites (Table 2). The number of positive selection sites for AKT3, using REL, FUBAR, MEME, and IFEL, was 33, 418, 20, and 1, respectively.
3.1. Position of Amino Acid and Positive Selection
The structure and function of protein is important for its continuity. Consequently, the sites that were detected as being positively selected may instruct and clarify the AKT3 gene function. Using the crystalline structure of bovine AKT3 as a reference, the positively selected sites were elucidated. The high-probability sites were expected to be important for positive selection with . The location, positive selection sites, and amino acid position were shown in Figure 2. The collective performance of identified codon locations was plotted (Figure 3), and the collective performance of ambiguous, synonymous, and nonsynonymous codon deviations with evolutionary time unit was represented. For the number of starting codons, the collective performance of the synonym mutation is decreasing and then amplified with the evolution of codons, while the performance of nonsynonymous codons is increasing with the passage of evolutionary time unit according to codon position, but it is lower in the position initially and then gradually increases. The ambiguous codons’ performance increases by starting codons’ position and then becomes constant.
3.2. Codon Model Selection
We further analyzed the selection pattern derived by the evolutionary selection forces on amino acid sites in AKT3 proteins. We used different codon models available in DATAMONKEY web server. We found that there was adaptive evolution in basic amino acid sites in these proteins with different substitution ratios during evolution. The maximum substitution rate was 0.17 for the different ratio classes, and the minimum was 0.04 among various amino acid sites in AKT3 genes (Figure 4).
The nucleotide and amino acid substitution in the codon model was used to identify the synonymous and nonsynonymous substitutions , and the substitution model was used to confirm the significant change rate in nucleotides over amino acid position [30, 31]. The codon model of evolution using the genetic algorithm was used to identify the evolutionary fingerprinting in the coding sites of AKT3 genes. The codon model of evolution [20, 32, 33] used phylogenetic Markov model that includes substitution rates, character frequencies , amino acid substitution rate clustering [35, 36], and branch lengths through maximum likelihood estimation method. This resulted in 8245 models that were used in codon model selection based on the likelihood log and modified Bayesian Information Criterion (mBIC).
The selective effects associated with an exchangeable preference for particular amino acids were found in the AKT3 genes with a model that used the combined empirical codon and transition/transversion-related physicochemical parameters [37, 38]. The model with log (L) value -12865.8 for AKT3 was considered the best for amino acid substitution analysis. We have observed the mBIC values 27550.74 two class rates for the distribution of amino acids in different classes (Figure 4) with an estimation of single rate dN/dS substitution (Table 3). The genetic algorithm multirate model was used to analyze the class rates to calculate the substitution rate at the amino acid level during the evolutionary time scale (Figure 4). The substitution rate in each class was calculated through genetic algorithm models by using the Stanfel class parameters . The substitution rate distributed the amino acids into three classes through the evolutionary rate cluster and the substitution pair FWY and HKR have <50% substitution, DENQ has 50% substitution, and ACGILMPSTV has substitution rate 90%.
: number of rate classes included in models; Models: genetic algorithm models; Credible: all the models evaluated by genetic algorithm within 9.21 mBIC unit (the best model has credible values 0.01 or >1); mBIC: modified Bayesian Information Criterion; ΔmBIC: mBIC for rate classes compared to rate classes; dN/dS: maximum likelihood estimates for each rate class.
3.3. Network of Protein-Protein Interaction
We have used the STRING data bank to search the encoded protein of AKT3 and found numerous PPI pairs. The PPI predicted network had 31 nodes (denoted by AKT3 encoded proteins) and 308 edges (the line networks between nodes) as shown in Figure 5. The value of the average local clustering coefficient is 0.887. The value of PPI enrichment is 5.33-11. Ten genes that are coexpressed genes in the PPI network and showing an interaction with AKT3 are as follows: RICTOR, TSC2, GSK3B, PDPK1, PPP2CA, HSP90AA1, PHLPP2, PHLPP1, FOXO3, and PIK3CA (Figure 5). The Human AKT3 sequence was used as a reference for the PPI network analysis. These genes may be involved in biological signaling pathways due to the upregulation of AKT3 .
3.4. Phylogenetic Tree of AKT3
The NCBI database was used to download the coding sequence and protein sequences for construction of the phylogenetic tree. Using the MEGA 6 Clustal W  for aligned sequences, a phylogenetic tree was constructed as shown in Figure 6. In the tree, it is shown that the genes which are evolutionally close form groups with various species with less significant relationships making up different groups.
We have also applied the Ramachandran plot to predict for the AKT3 using http://vadar.wishartlab.com/. A Ramachandran plot is used to visualize energetically allowed regions for a polypeptide backbone torsion angle psi () against phi () of amino acid residues present in a protein structure. The main chain N-Calpha and Calpha-C bond of the polypeptide of the Ramachandran plot has free rotation. The relative rotational angle of torsion was represented by phi and psi, respectively. In nature, the peptide bond is rigid and planar. To understand the Ramachandran, it is important to have knowledge of peptide bond structure. The analysis of the protein structure and the key role played by some amino acids and close contacts of the atoms in protein is shown in Figure 7.
3.5. Domains for the AKT3
AKT3 inadequacy did not influence macrophage apoptosis; however, it advanced macrophage cholesterol collection, lipoprotein uptake, and foam cell development in vitro, through balancing out Acetyl-Coenzyme A acetyltransferase 1 (ACAT1) . Another investigation found that knockdown of AKT3 prompted diminished bad phosphorylation and expanded caspase-9 and caspase-3 action, indicating that these isoforms are significant for cell suitability through guideline of mitochondrial layer potential . The AKT3 gene showed an interesting phenotype of providing anchorage haven independent development and invasion . The AKT3 signaling pathway is constitutively dynamic in ~70% of advanced-phase organize melanomas, and segments of this pathway correspond to potential therapeutic targets since it assumes a significant function in melanoma development, to a limited extent, by suppressing the cell bond particle E-cadherin . While AKT3 substrates associated with mediating its effect on proliferation, apoptosis, and chemoresistance in melanoma have been distinguished, their observation is the first to uncover an immediate substrate concerned with AKT3-induced melanoma relocation .
AKT3 oversees glioma succession and restorative resistance by means of initiating DNA twofold strand break fixation. AKT3 is dominating in the core of glioma cells, and the mark genes related with AKT3-driven tumors are concerned with the DNA fix pathway (HR: homologous recombination; NHEJ: nonhomologous end joining) . The AKT3 signaling pathway assumes a basic function in melanoma arrangement and invasion, and segments of this signaling cascade are along these lines deserving of focus for the treatment of harmful melanoma . They have distinguished the AKT3 target site at serine residue 720 in the TBX3 protein and show that this site is phosphorylated in vivo. This discovery involves AKT3 as a positive controller of TBX3 protein security. AKT3 phosphorylates TBX3 at serine 720 (S720) and improves TBX3 protein solidity (Jade Peres et al., 2014). Phosphorylation by AKT3 advances TBX3 atomic confinement and transcriptional restraint of E-cadherin . Susceptibility to experimentally induced autoimmune system encephalomyelitis was found to be controlled and regulated by AKT3 via the central sensory nervous system and insusceptibility framework. This is because the appropriate function of CNS cells and the management and regulation of T cell capacity require the presence of AKT3.
The past investigations demonstrate that the isoform AKT3 of the AKT family is associated with assorted functionality. She  reported the gene evolution, phylogenetical branch length, and positive selection analysis study on AKT3, and they used the 39 nucleotide coding sequences of various species of mammalian species to investigate the selection pressure experienced. To perform these evaluations using the proregion from AKT3, mature and complete sequences were used. In these mammalian clades, we have found the positive selection codon sites, by study of the phylogenetic tree. We identified 32 positive selection sites with REL, 414+4 sites with FUBAR, 20 sites with MEME, and 1 site with IFEL and selected those which are common to all analyses. We have measured the adaptive selection pressure at codons of the AKT3 sequence and used the mechanistic empirical combination (MEC) model in selection serving to promote positive selection. Axelsson et al.  reported that the dN/dS ratio might increase through conversion of genes with GC nucleotide pairings. Analyses using Empirical Bayes investigated the position of amino acids for the AKT3 gene. The positive selection signals were found at multiple codon positions, and it showed that the selection that will take place on these positions across decades on these selected sites will play key role in signaling. Auclair et al.  reported and detected selection positive signals among 24 mammalian species in the human BMP15. Hence, under positive selection, amino acids sites are important for protein specialty function . They further  reported that BMP15 evolved and allowed positive selection faster in TGF family members in mammalian clade. In this study, we found the positive selection was in AKT3 gene with . The result showed synonymous and nonsynonymous (dN) sites and indicated the quicker and more evolved sites of nonsynonymous and new variants favored by and following in the balancing/purifying selection influenced by positive selection . The protein structure validation and alteration allow identification of changes that affect the signaling pathway . The common lineage divergence discrete result might be the species across substitution amino acid changes and settles with anterior submission. Scannell et al.  reported that the evolutionary routes from common ancestors differ for recent orthologs, which on homologous sites in selected lines may have resulted in genetic deviation. Therefore, for understanding mammalian genomes, the study of selection might stimulate potential exploration areas in the future.
In summary, we have found the selection pressure in mammalian species clade that AKT3 has evolved swiftly. We have used a series of analysis for evolution study in AKT3 using 39 various species coding sequences. We have found various positive selections sites in the gene under study. These positive sites of selection will be important for further study for their role in protein function. This AKT3 gene selection analyses will help and assist in the development of breeding strategies to emphasize advantageous traits.
The data used to support the findings of this study are included within the article.
Conflicts of Interest
All authors do not have any potential conflict of interest related to this research work.
F.U and S.M.H wrote the original draft of the manuscript. L.Y, A.L, and G.H were the major supervisors. F.U, Z.U.R, H.S.T, and M.S conceived and designed the experiment. S.A, A.A, M.K, and M.S helped in data analysis. Z.A and Y.T helped in data analysis. M.Z.A and I.B helped in the first revision of the manuscript and helped in critical analysis. I.U.K helped in the last revision of the manuscript for data management and figures. N.M.S helped in manuscript revision English editing, proofreading, and overall critical review of the manuscript. F.U and S.M.H equally authored this manuscript.
This study was funded by the Earmarked Fund for Modern Agro-industry Technology Research System (CARS-36).
- J. L. Hendrikse, T. E. Parsons, and B. Hallgrímsson, “Evolvability as the proper focus of evolutionary developmental biology,” Evolution & Development, vol. 9, no. 4, pp. 393–401, 2007.
- J. J. Wiens, “What is speciation and how should we study it?” The American Naturalist, vol. 163, no. 6, pp. 914–923, 2004.
- S. P. Otto, “The evolutionary enigma of sex,” The American Naturalist, vol. 174, no. S1, pp. S1–S14, 2009.
- T. A. Manolio, F. S. Collins, N. J. Cox et al., “Finding the missing heritability of complex diseases,” Nature, vol. 461, no. 7265, pp. 747–753, 2009.
- P. C. Sabeti, D. E. Reich, J. M. Higgins et al., “Detecting recent positive selection in the human genome from haplotype structure,” Nature, vol. 419, no. 6909, pp. 832–837, 2002.
- M. Bionaz and J. J. Loor, “Gene networks driving bovine mammary protein synthesis during the lactation cycle,” Bioinformatics and biology insights, vol. 5, p. BBI.S7003, 2011.
- J. A. Engelman, J. Luo, and L. C. Cantley, “The evolution of phosphatidylinositol 3-kinases as regulators of growth and metabolism,” Nature Reviews Genetics, vol. 7, no. 8, pp. 606–619, 2006.
- B. D. Manning and L. C. Cantley, “AKT/PKB signaling: navigating downstream,” Cell, vol. 129, no. 7, pp. 1261–1274, 2007.
- M. Hanada, J. Feng, and B. A. Hemmings, “Structure, regulation and function of PKB/AKT—a major therapeutic target,” Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, vol. 1697, no. 1-2, pp. 3–16, 2004.
- F. Ullah, D. Bhattarai, Z. Cheng et al., “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, vol. 2018, Article ID 1463732, 13 pages, 2018.
- V. Tsiperson, R. C. Gruber, M. F. Goldberg et al., “Suppression of inflammatory responses during Myelin Oligodendrocyte Glycoprotein–Induced experimental autoimmune encephalomyelitis is regulated by AKT3 signaling,” The Journal of Immunology, vol. 190, no. 4, pp. 1528–1539, 2013.
- Z.-Z. Yang, O. Tschopp, N. di-Poï et al., “Dosage-Dependent Effects of Akt1/Protein Kinase Bα (PKBα) and Akt3/PKBγ on Thymus, Skin, and Cardiovascular and Nervous System Development in Mice,” Molecular and Cellular Biology, vol. 25, no. 23, pp. 10407–10418, 2005.
- K. Nakatani, D. A. Thompson, A. Barthel et al., “Up-regulation of Akt3 in estrogen receptor-deficient breast cancers and androgen-independent prostate cancer lines,” Journal of Biological Chemistry, vol. 274, no. 31, pp. 21528–21532, 1999.
- S. Banerji, K. Cibulskis, C. Rangel-Escareno et al., “Sequence analysis of mutations and translocations across breast cancer subtypes,” Nature, vol. 486, no. 7403, pp. 405–409, 2012.
- K. Tamura, G. Stecher, D. Peterson, A. Filipski, and S. Kumar, “MEGA6: molecular evolutionary genetics analysis version 6.0,” Molecular Biology and Evolution, vol. 30, no. 12, pp. 2725–2729, 2013.
- H. I. Ahmad, G. Liu, X. Jiang, C. Liu, Y. Chong, and H. Huarong, “Adaptive molecular evolution of MC1R gene reveals the evidence for positive diversifying selection in indigenous goat populations,” Ecology and Evolution, vol. 7, no. 14, pp. 5170–5180, 2017.
- A. R. Asif, M. Awais, S. Qadri, H. I. Ahmad, and X. du, “Positive selection of IL-33 in adaptive immunity of domestic Chinese goats,” Ecology and Evolution, vol. 7, no. 6, pp. 1954–1963, 2017.
- H. I. Ahmad, M. J. Ahmad, M. M. Adeel, A. R. Asif, and X. du, “Positive selection drives the evolution of endocrine regulatory bone morphogenetic protein system in mammals,” Oncotarget, vol. 9, no. 26, pp. 18435–18445, 2018.
- M. J. Ahmad, H. I. Ahmad, M. M. Adeel et al., “Evolutionary analysis of makorin ring finger protein 3 reveals positive selection in mammals,” Evolutionary Bioinformatics, vol. 15, p. 117693431983461, 2019.
- A. F. Y. Poon, S. D. W. Frost, and S. L. Kosakovsky, “Detecting signatures of selection from DNA sequences using Datamonkey,” in Bioinformatics for DNA Sequence Analysis, pp. 163–183, Springer, 2009.
- H. I. Ahmad, G. Liu, X. Jiang et al., “Maximum-likelihood approaches reveal signatures of positive selection in BMP15 and GDF9 genes modulating ovarian function in mammalian female fertility,” Ecology and Evolution, vol. 7, no. 21, pp. 8895–8902, 2017.
- J. P. Bielawski and Z. Yang, “Maximum Likelihood Methods for Detecting Adaptive Evolution after Gene Duplication,” in Genome Evolution, Springer, Dordrecht, 2003.
- L. A. Kelley and M. J. E. Sternberg, “Protein structure prediction on the web: a case study using the Phyre server,” Nature Protocols, vol. 4, no. 3, pp. 363–371, 2009.
- F. Glaser, T. Pupko, I. Paz et al., “ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information,” Bioinformatics, vol. 19, no. 1, pp. 163-164, 2003.
- J.-R. Yang, B.-Y. Liao, S.-M. Zhuang, and J. Zhang, “Protein misinteraction avoidance causes highly expressed proteins to evolve slowly,” Proceedings of the National Academy of Sciences, vol. 109, no. 14, pp. E831–E840, 2012.
- A. Franceschini, D. Szklarczyk, S. Frankild et al., “STRING v9. 1: protein-protein interaction networks, with increased coverage and integration,” Nucleic Acids Research, vol. 41, no. D1, pp. D808–D815, 2012.
- H. Li, X. Zhao, J. Wang, M. Zong, and H. Yang, “Bioinformatics analysis of gene expression profile data to screen key genes involved in pulmonary sarcoidosis,” Gene, vol. 596, pp. 98–104, 2017.
- E. Quevillon, V. Silventoinen, S. Pillai et al., “InterProScan: protein domains identifier,” Nucleic Acids Research, vol. 33, no. Web Server, pp. W116–W120, 2005.
- S. Miyazawa, “Advantages of a mechanistic codon substitution model for evolutionary analysis of protein-coding sequences,” PLoS One, vol. 6, no. 12, p. e28892, 2011.
- R. Nielsen and Z. Yang, “Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene,” Genetics, vol. 148, no. 3, pp. 929–936, 1998.
- Z. Yang, R. Nielsen, and M. Hasegawa, “Models of amino acid substitution and applications to mitochondrial protein evolution,” Molecular Biology and Evolution, vol. 15, no. 12, pp. 1600–1611, 1998.
- W. Delport, A. F. Y. Poon, S. D. W. Frost, and S. L. Kosakovsky Pond, “Datamonkey 2010: a suite of phylogenetic analysis tools for evolutionary biology,” Bioinformatics, vol. 26, no. 19, pp. 2455–2457, 2010.
- S. L. K. Pond and S. V. Muse, “HyPhy: hypothesis testing using phylogenies,” in Statistical Methods in Molecular Evolution, pp. 125–181, Springer, 2005.
- A. Siepel and D. Haussler, “Combining phylogenetic and hidden Markov models in biosequence analysis,” Journal of Computational Biology, vol. 11, no. 2-3, pp. 413–428, 2004.
- W. Delport, K. Scheffler, G. Botha, M. B. Gravenor, S. V. Muse, and S. L. Kosakovsky Pond, “CodonTest: modeling amino acid substitution preferences in coding sequences,” PLoS Computational Biology, vol. 6, no. 8, p. e1000885, 2010.
- L. E. Stanfel, “A New Approach to Clustering the Amino Acid,” Journal of Theoretical Biology, vol. 183, no. 2, pp. 195–205, 1996.
- C. Kosiol, I. Holmes, and N. Goldman, “An empirical codon model for protein sequence evolution,” Molecular Biology and Evolution, vol. 24, no. 7, pp. 1464–1479, 2007.
- D. Posada and K. A. Crandall, “Modeltest: testing the model of DNA substitution,” Bioinformatics, vol. 14, no. 9, pp. 817-818, 1998.
- K. Masuko-Hongo, F. Berenbaum, L. Humbert, C. Salvat, M. B. GOLDRING, and S. Thirion, “Up-regulation of microsomal prostaglandin E synthase 1 in osteoarthritic human cartilage: critical roles of the ERK-1/2 and p38 signaling pathways,” Arthritis & Rheumatism, vol. 50, no. 9, pp. 2829–2838, 2004.
- H. Yu, T. Littlewood, and M. Bennett, “Akt isoforms in vascular disease,” Vascular Pharmacology, vol. 71, pp. 57–64, 2015.
- H. Mure, S. Hirano, C. C. Tang et al., “Parkinson's disease tremor-related metabolic network: Characterization, progression, and treatment effects,” NeuroImage, vol. 54, no. 2, pp. 1244–1253, 2011.
- R. Endersby, X. Zhu, N. Hay, D. W. Ellison, and S. J. Baker, “Nonredundant functions for Akt isoforms in astrocyte growth and gliomagenesis in an orthotopic transplantation model,” Cancer Research, vol. 71, no. 12, pp. 4106–4116, 2011.
- G. P. Robertson, “Functional and therapeutic significance of Akt deregulation in malignant melanoma,” Cancer and Metastasis Reviews, vol. 24, no. 2, pp. 273–285, 2005.
- S. Wansleben, J. Peres, S. Hare, C. R. Goding, and S. Prince, “T-box transcription factors in cancer biology,” Biochimica et Biophysica Acta (BBA)-Reviews on Cancer, vol. 1846, no. 2, pp. 380–391, 2014.
- P. Ji, X. Ma, and G. Li, “Developing green purchasing relationships for the manufacturing industry: an evolutionary game theory perspective,” International Journal of Production Economics, vol. 166, pp. 155–162, 2015.
- B. She, “Dynamic modeling of the PI3K/Akt signal transduction pathway to dissect the distinct cell fate decisions triggered by PI3K/Akt signaling in hematopoietic system,” 2011.
- E. Axelsson, M. T. Webster, N. G. Smith, D. W. Burt, and H. Ellegren, “Comparison of the chicken and turkey genomes reveals a higher rate of nucleotide divergence on microchromosomes than macrochromosomes,” Genome Research, vol. 15, no. 1, pp. 120–125, 2005.
- S. Auclair, R. Rossetti, C. Meslin et al., “Positive selection in bone morphogenetic protein 15 targets a natural mutation associated with primary ovarian insufficiency in human,” PLoS One, vol. 8, no. 10, p. e78199, 2013.
- L. Persani, R. Rossetti, E. Di Pasquale, C. Cacciatore, and S. Fabre, “The fundamental role of bone morphogenetic protein 15 in ovarian function and its involvement in female fertility disorders,” Human Reproduction Update, vol. 20, no. 6, pp. 869–883, 2014.
- T. Bergström and U. Gyllensten, “Evolution of Mhc class II polymorphism: the rise and fall of class II gene function in primates,” Immunological Reviews, vol. 143, no. 1, pp. 13–31, 1995.
- Z. Kan, B. S. Jaiswal, J. Stinson et al., “Diverse somatic mutation patterns and pathway alterations in human cancers,” Nature, vol. 466, no. 7308, pp. 869–873, 2010.
- D. R. Scannell, O. A. Zill, A. Rokas et al., “The awesome power of yeast evolutionary genetics: new genome sequences and strain resources for theSaccharomyces sensu strictoGenus,” G3: Genes, Genomes, Genetics, vol. 1, no. 1, pp. 11–25, 2011.
- Y. R. Chin, T. Yoshida, A. Marusyk, A. H. Beck, K. Polyak, and A. Toker, “Targeting Akt3 signaling in triple-negative breast cancer,” Cancer Research, vol. 74, no. 3, pp. 964–973, 2014.
- D. G. Corum, P. N. Tsichlis, and R. C. Muise-Helmericks, “AKT3 controls mitochondrial biogenesis and autophagy via regulation of the major nuclear export protein CRM-1,” The FASEB Journal, vol. 28, no. 1, pp. 395–407, 2013.
- L. Ding, T. J. Ley, D. E. Larson et al., “Clonal evolution in relapsed acute myeloid leukaemia revealed by whole- genome sequencing,” Nature, vol. 481, no. 7382, pp. 506–510, 2012.
- B. Dümmler, “Physiological roles of PKB isoforms in development, growth and glucose metabolism,” Tech. Rep., University of Basel, 2007.
- R. M. Easton, H. Cho, K. Roovers et al., “Role for Akt3/protein kinase Bgamma in attainment of normal brain size,” Molecular and Cellular Biology, vol. 25, no. 5, pp. 1869–1878, 2005.
- M. Khan, Effects of Prepartum Dietary Energy and Lipid Supplementation on Hepatic Transcriptome Profiles in Dairy Cows during the Transition Period, University of Illinois at Urbana-Champaign, 2013.
- S. V. Madhunapantula and G. P. Robertson, “The PTEN–AKT3 signaling cascade as a therapeutic target in melanoma,” Pigment Cell & Melanoma Research, vol. 22, no. 4, pp. 400–419, 2009.
- K. M. Turner, Y. Sun, P. Ji et al., “Genomically amplified Akt3 activates DNA repair pathway and promotes glioma progression,” Proceedings of the National Academy of Sciences, vol. 112, no. 11, pp. 3421–3426, 2015.
Copyright © 2020 Farmanullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.