{"paper_id":"39d0a786-bfc1-40d8-9488-2909c1ffacd8","body_text":"Rhesus macaques (Macaca mulatta) have served as a model\nfor studying various human diseases for decades. Their use\nas a model is primarily explained by the phylogenetic and\nphysiological similarity to humans, and, consequently, the\npotential for transferring the results obtained. To date, genetic\nmodels of cancer (Brammer et al., 2018; Deycmar et al., 2023),\ncardiovascular diseases (Patterson et al., 2002; Ueda et al.,\n2019), ophthalmologic diseases (Singh et al., 2009; Liu et al.,\n2015; Moshiri et al., 2019; Peterson et al., 2019, 2023), skeletal\ndiseases (Colman, 2018; Paschalis et al., 2019), diseases of\nthe reproductive system (Lomniczi et al., 2012; Nair et al.,\n2016; Abbott et al., 2019), as well as a wide range of neurological\ndiseases (McBride et al., 2018; Sherman et al., 2021)\nare known in rhesus macaques. In addition, rhesus macaques\nare used for research as model objects of toxicity (Kaya et\nal., 2023), radiation (Li et al., 2021; Majewski et al., 2021),\nhormones (Noriega et al., 2010; Eghlidi, Urbanski, 2015), etc.\nIn addition to studying diseases, this model can be used to test\nvarious pharmacological drugs, which is especially important\nfor applied research.\nIt is now known that a wide range of biochemical changes\noccur under various physiological conditions, including at the\ntranscriptome level. Relative transcript levels of individual\ngenes can be accurately and reproducibly measured using\nreal-time polymerase chain reaction (RT-PCR). This method\nis a widely used and versatile tool for analyzing the expression\nof a small number of genes. RT-PCR is also frequently\nused to confirm results obtained using whole-transcriptome\nexpression analysis (Ramsköld et al., 2009). However, this\ntype of study is always complicated by variations in the copy\nnumber of the target mRNA due to differences in the amount\nof total RNA between samples, therefore requiring the preliminary\nselection of control (reference) genes, or “housekeeping\ngenes” (HKGs).\nThe term HKG most often refers to genes stably expressed\nin various cell types and under various conditions and required\nfor basic cellular functions. They are often used as reference\ngenes in gene expression studies to normalize mRNA levels\nbetween different samples.\nIn rhesus macaques, there is currently very little systematic\ndata on the use of HKGs (Ahn et al., 2008; Noriega et al.,\n2010). Noriega et al. (2010) conducted a study only on the\nbrain, while Ahn et al. (2008) worked with both brain tissue\nand some other tissues (intestine, liver, kidney, lung, and\nstomach).\nHowever, neither of these studies examined the\nanimals’ peripheral blood, which is widely used for various expression\nstudies. In this regard, this review conducted a search\nand systematization of data on HKGs in rhesus macaques for\ntheir further use in studying gene expression changes under\nvarious conditions.\n\nCurrently, the selection of HKGs is based on the following\nmain principles. First, the absence of pseudogenes, copies of\ngenes that contain certain defects in the coding region (loss of\nintrons and exons, frameshifts, or premature stop codons, as\nwell as pseudogenes formed as a result of retrotransposition),\nis an important criterion for selecting HKGs (Tutar, 2012).\nPseudogenes are not involved in protein processing but can\nbe expressed at the RNA level. Furthermore, the number of\npseudogenes is known to be unstable in the genomes of different\nindividuals. From a practical standpoint, the presence of\npseudogenes may require additional treatment of the analyzed\nRNA samples with DNases, which is critical for samples with\nlow RNA amounts. Therefore, the presence of pseudogenes\nis highly undesirable when selecting HKGs.\nSecond, expression stability is considered to be another\nimportant criterion for selecting HKGs, i. e., they should have\nrelatively constant expression levels across different cell types,\ntissues, and experimental conditions (Tu et al., 2006). However,\nit is known that HKGs can be expressed differentially\nin different tissues. For example, well-known HKGs such as\nbeta-actin and GAPDH have been shown to vary significantly\nin expression levels across tissues (Cai J. et al., 2014). Therefore,\na high level of HKGs’ expression in the specific tissue\nunder study is an important criterion.\nThird, there is increasing support for the idea that HKGs\nshould be tailored to specific experimental conditions (Silver\net al., 2008). For example, the human HSPA8 gene is a HKG,\nbut it cannot be used as such in the study of age-related or\nneurodegenerative diseases, as there is evidence of a decrease\nin HSPA8 gene expression with age, as well as an association\nbetween this gene and the development of neurodegenerative\ndiseases (Loeffler et al., 2016; Tanaka et al., 2024). Expression\nprofile variability has also been demonstrated for HKGs\nused in the study of cancer (de Kok et al., 2005; Dheda et al.,\n2005). To date, no studies have identified all-purpose HKGs,\nmeaning that HKGs’ selection for the specific pathology being\nstudied is necessary\nThus, an ideal HKG should have no pseudogenes, no association\nwith the disease or condition being studied, and\nit should be stably expressed under specific experimental\nconditions and tissues (Fig. 1). The optimal HKG should be carefully selected for each specific experiment. Using multiple\nHKGs also improves the reliability of the expression data\nobtained (Vandesompele et al., 2002; Dheda et al., 2005).\n\nWe screened scientific publications in the PubMed database\nto find papers focused on the analysis of HKGs in rhesus macaques.\nAn initial search using the keywords (gene expression)\nAND (rhesus macaque) identified 3,017 publications. Since\n“rhesus macaque” and “Macaca mulatta” are synonymous,\nboth terms were used in the analysis of search queries. Due\nto the relatively large number of publications returned, the\nsearch query was specified using the synonymous terms\n“housekeeping genes” and “reference genes”, which yielded\n126 and 97 search results, respectively. Further narrowing the\nsearch by refining it using the keyword “rt-pcr” revealed 16\nand 7 publications (Table 1).\nNotе. Accessed on April 28, 2025.\nA detailed analysis of these seven studies identified two\nmost relevant systematic studies to date on the selection of\nHKGs in rhesus macaques (Ahn et al., 2008; Noriega et al.,\n2010). Five of the seven remaining publications analyzed\ndid not mention HKGs and were therefore not included in\nthe analysis\nNext, a block of 126 open-access publications found in\nPubMed using the keywords (housekeeping genes) AND\n(rhesus macaque) was manually analyzed. It was found that\n107 publications, for one reason or another, did not mention\nany HKGs, while 16 publications used genes recommended by\nthe authors of the two main studies on the selection of HKGs\nin rhesus macaques (Ahn et al., 2008; Noriega et al., 2010).\nThese two types of publications were excluded from further\nanalysis. Our search yielded only one additional publication\n(Robinson et al., 2018). Supplementary Table S11 summarizes\nthe data from these three key studies and describes\n115 genes expressed in the rhesus macaque brain that could\nbe considered as HKGs. These genes were selected for further\nanalysis.\nSupplementary Materials are available in the online version of the paper:\n https://vavilov.elpub.ru/jour/manager/files/Suppl_Shulskaya_Engl_29_8.pdf\nSupplementary Materials are available in the online version of the paper:\n https://vavilov.elpub.ru/jour/manager/files/Suppl_Shulskaya_Engl_29_8.pdf\nDue to periodic database updates, some gene names were\nupdated and given with names different from those used in\n(Ahn et al., 2008; Noriega et al., 2010) when compiling this\nlist. Four sequences that were homologous to human sequences\nbut were absent in the Ensembl database for rhesus macaques\n(Genome assembly: Mmul_10 (GCA_003339765.3))\n(Table S1) and five M. mulatta genes currently identified as\nhaving pseudogenes (LDHB, RPL37, RPS27A, SNRPA, and\nSUI1) were also excluded.\nThis procedure allows us to identify all-purpose HKGs for\nboth humans and macaques, while also avoiding problems\nassociated with the low level of annotation of the rhesus\nmacaque genome assembly. For example, the RPL19 gene,\ncurrently the most widely used HKG in rhesus macaques, is\nnot recommended for use as an all-purpose HKG because it\nhas pseudogenes in human genome\nThe genes selected after the previous screening steps can\nbe used for studies on brain tissue. However, peripheral\nblood, widely used in human studies, is of particular interest.\nPeripheral blood is promising for expression studies due to its\navailability and low invasiveness. Therefore, we considered\nit necessary to select candidate HKGs for peripheral blood,\nfor the purpose of which the selected genes were further\nanalyzed for acceptable expression levels in peripheral blood\n(Table S2).\nSince peripheral blood expression data are currently completely\nlacking for M. mulatta, and due to the similarity\nbetween the macaque and human transcriptomes, publicly\navailable mRNA expression data were analyzed in human\nwhole blood and lymphoblasts. We also included expression\ndata in mice, as these animals are a well-studied model object\n(due to the lack of peripheral blood data, tissues with similar\nexpression patterns, such as bone marrow, lymph nodes, and\nspleen, were used). Expression data in the brain and spleen\nof rhesus macaques were added from the Ensembl database\n(Table S2).\nThis analysis was conducted using the BioGPS database\n(http://biogps.org/), where we selected genes with expression\nabove the median in the tissues of interest. “Median expression”\nrepresents the 50th percentile of the expression data,\nmeaning that half of the tissues have expression levels below\nthe median, and the other half have expression levels above\nthe median. BioGPS uses this metric to provide a summary\nof how a gene is expressed in different tissues, conditions,\nor data sets.\nAs a result of the analysis, the list of genes was divided into\nthree groups: genes with expression levels above the median in\nboth humans and mice, genes with expression levels above the\nmedian in only one of the two species, and genes with expression\nlevels below the median in both humans and mice (Fig. 2,\nTable S2). Genes from all three groups can be considered as\ncandidate HKGs. However, their use will limit the number of\nmodel objects compared based on their expression profiles.\nGenes from the first group are the most promising. It should\nalso be noted that the expression data presented in BioGPS\nrequire experimental verification in the laboratory.\nGenes expressed predominantly in humans are shown in pink, and genes\nexpressed predominantly in mice are shown in purple. The overlapping area\nindicates genes expressed in specific tissues of both species.\nHowever, it is important to note that the median value is not\nalways a good indicator for selecting candidate genes, since\nthe mRNA abundance in the tissue under study may be higher\nthan the median, but the absolute expression levels are quite\nlow. Therefore, all analyzed genes were ranked according to\ntheir relative expression levels in the analyzed tissues. The\nresults of this analysis are presented as a heat map (Fig. 3).\nUltimately, we formed a group of 25 most promising candidate\nHKGs (genes with high or moderate expression levels\nin humans, mice, and rhesus macaques).\nMedian-normalized values for each gene in the BioGPS resource (http://biogps.org) were used as the basis.\nSince HKGs can be used to study changes in the expression\nof various genes in various diseases, potential HKGs should\nnot be implicated in the development of the disease under\nstudy. A selected group of 25 genes was analyzed using the\nMalaCards database (www.malacards.org). MalaCards is a\nsearchable, integrated knowledge base containing comprehensive\ninformation on human diseases, medical conditions, and\ndisorders. We searched for associations between the gene and\ncurrently known disease models in rhesus macaques (Table 2).\nSix genes associated with oncological diseases (AHSA1,\nB4GALT3, HPCAL1, TBP, TMED9, and SSR2), six genes\nassociated with neurological diseases (CSNK2B, DIAPH1,\nMAPKAPK2, NDUFA1, RAD23A, and UBB), as well as genes\nassociated with eye diseases (ARL2 and PRPF8) and some\nother diseases (GPX4 and LDHA) were excluded.\nPubmed (https://pubmed.ncbi.nlm.nih.gov/) has no published data for 2000–2025.\nAs a result, at this final stage of the selection of candidate\nHKGs, we selected eight genes (ARHGDIA, CYB5R1,\nNDUFA7, RRAGA, TTC1, UBA6, VPS72, and YWHAH –\nhighlighted bold in Table 2), characterized by the absence\nof pseudogenes, the absence of data on the involvement of\nthese genes in the development of diseases modeled in rhesus\nmacaques, as well as stable and high expression in the analyzed\ntissues (brain, peripheral blood, spleen, lymph nodes,\nbone marrow).\n\nThus, two panels of promising HKGs for M. mulatta were\nformed: an extended panel consisting of 56 genes (Table S2)\nand a small panel consisting of 8 genes (Table 2). Both panels\nof potential HKGs have no pseudogenes either in macaques or\nin humans, and they are characterized by stable and sufficient\nexpression in the rhesus macaque brain. However, the specialized\npanel is more all-purpose, as it is suitable for selecting\nHKGs for parallel studies on several model organisms (mouse,\nmacaque, and human) or for studying several different diseases\nsimultaneously by a single research group. The small panel is\nof interest for further development of a working HKGs panel\nto study changes in the expression of various genes in various\ndiseases in M. mulatta. At the same time, the extended panel\nof potential HKGs is also quite promising.\n\nThe authors declare no conflict of interest.\n\nAbbott D.H., Rogers J., Dumesic D.A., Levine J.E. Naturally occurring\nand experimentally induced rhesus macaque models for polycystic\novary syndrome: translational gateways to clinical application. 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