Normalization of Gene Expression for Acute Leukemia Studies: Selecting a Set of Endogenous Genes Control

doi:10.21203/rs.3.rs-3117826/v1

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Normalization of Gene Expression for Acute Leukemia Studies: Selecting a Set of Endogenous Genes Control

https://doi.org/10.21203/rs.3.rs-3117826/v1

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Reference genes are used as internal reaction control for gene expression analysis and for this they are considered reliable and must meet several important criteria. In view of the absence of studies regarding the best reference gene for the analysis of acute leukemia patients, a panel of genes commonly used as endogenous was selected from the literature for analysis of stability: GAPDH, ABL, HPRT1, RPLP0, ACTB and TBP. The stability of candidate reference genes was analyzed according to four statistical methods of assessment, namely, NormFinder, GeNorm and R software. From this study’s analysis it was possible to identify that the endogenous set composed by ACTB, ABL, TBP and RPLPO demonstrated good performances and stable expressions between the analyzed groups. Besides that, the GAPDH and HPRT genes could not be classified as good reference genes, considering that they presented a high standard deviation and great variability between groups, indicating low stability. Given these findings, this study suggests the main endogenous set for use as control/reference for the analysis of gene expression in peripheral blood and bone marrow samples from patients with acute leukemias, is composed by the ACTB, ABL, TBP and RPLPO genes.

Leukemia

Reference Genes

RT-qPCR

Gene Expression

Real time-quantitative polymerase chain reaction (RT-qPCR) technology has revolutionized the detection landscape in every area of molecular biology. This technique is considered as the most reliable and most accurate method of diagnosis ^1–9. However, numerous failures can occur during the qPCR process, which can lead to misinterpretation of results and incorrect conclusions. That is why the application of an appropriate normalization method is an absolute necessity to achieve reliable results ^10–13.

The use of reference genes is considered the most effective method for the normalization step of RT-qPCR technique ⁵. Reference genes may be used as an internal reaction control for gene expression analysis and for it they are considered reliable and must meet several important criteria. A good reference gene is unaffected by experimental factors and shows minimal variability between tissues and physiological states of the organism. Therefore, it is beneficial to choose a reference gene that shows a similar threshold cycle in studies within different genes of interest ^{9,10,22, 14–21}.

Basic metabolism genes often present the perfect fulfillment of these conditions since they are expressed at a stable and constant level and are involved in essential cell processes. Because of that they can be called Housekeeping Genes (HKGs). In the past, researchers often avoided this kind of validation process and the HKGs were used due to the fact that they had constant expression levels, supposedly. Nowadays, it is known that these expression regulation networks are really complexes and the experimental confirmation of stability of reference genes began to be a standard requirement ^{6,7, 23–30}.

A relevant number of traits can have different impacts on gene expression, such as tissue type, developmental stage, related species, abiotic stress, diseases, infections, alternative splicing, and tumors. Therefore, it is observed that the need for validation of reference genes has been underlined several times in different articles involving different types of diseases, especially in the oncological area, where types of cancer have different physiologies and the involvement of several genetic and external factors. Since cancer encompasses so many variables, molecular studies are required to determine comparative genes for expression analyses, which is imperative for adequate normalization, whose task is to compensate for PCR variations resulting from basic difficulties inherent to the method. Therefore, the ideal is to find genes with undoubted stable expression for each model of disease/condition ^31–40.

Several studies have already been carried out aiming at the validation of the reference genes for different types of cancer, allowing the normalization of gene expression analyses of future studies. McNeil et al. ³⁷ validated the MRPL19 and PPIA genes as endogenous controls for analysis of breast cancer patients. For the analysis of endometrial cancer, the most stable reference genes were RPL30, MT-ATP6 and ACTB according to research conducted by Ayakannu et al., ⁴¹. In its turn, let-7a, miR-16, were elected the reference genes most stably expressed in patients with colorectal cancer in Chang et al ⁴² study.

Considering the importance of using the reference gene as a control for gene expression analysis and the lack of research in the literature that validates the best reference genes to be used for the analysis of patients with acute leukemias, this study aimed to perform this validation and normalize expression assays with peripheral blood and bone marrow samples from these patients.

Biological Samples

The present study has a total number of 25 pediatric patients diagnosed with Acute Lymphoblastic Leukemia at Octávio Lobo Children’s Hospital (Belém, PA, Brazil), 25 adult patients diagnosed with Acute Lymphoblastic Leukemia and 25 adult patients diagnosed with Acute Myeloid Leukemia both at General Hospital of Fortaleza (Fortaleza, CE, Brazil). Patients’ blood samples were collected at the time of diagnosis, and samples from 15 healthy volunteers were used as control. The study was approved by both the Ethics Committee of the Ophir Loyola Hospital (approval number: 2,798,615) and the Ethics Committee of the General Hospital of Fortaleza (approval number 4,798,575), informed written assent was obtained from the patients or the patients’ legal guardians and all methods were carried out in accordance with Helsinki guidelines and regulations.

RNA extraction and Reverse Transcription of RNA to cDNA

RNA from samples was extracted with TRIzol Reagent® (Invitrogen, Walthan, MA, USA) according to the manufacturer’s instructions. From 20 µL of RNA, the cDNA was synthesized using High-Capacity cDNA Reverse Transcriptase kit (Life Technologies, Carlsbad, CA, USA) to convert the extracted and purified RNA to cDNA. The conversion step was performed on a Veriti® Thermal Cycler (Applied Biosystems, Foster City, CA, USA). After this step, the samples were stored in a freezer at − 20 ◦C until use for analysis.

Identification of Gene Expression by Real-Time Quantitative Polymerase Chain Reaction (qPCR)

Polymerase Chain Reactions were utilized to identify different endogenous expressions that are commonly used in studies of general gene expression. Information about the genes and probes chosen for the study are shown in Table 1. About the protocol, for each sample, the following were used: 1 µL of cDNA, 0.5 µL of each primer/probe, 5 µL of TaqMan® Gene Expression Master Mix (Life Technologies, Carlsbad, CA, USA), and 3.5 µL of ultra-pure water. The gene-expression levels were based on absolute and relative analyses and calculated using the 2−∆∆CQ (delta delta cycle quantification) method. Using the heath samples as the calibrator/control ^36,43.

Fold change data are represented as mean ± standard deviation of three independent experiments. Each sample was analyzed in triplicate for experimental and technique validation (Supplementary File S1), according to the international standards for evaluation of gene expression by real-time PCR ^36,43.

Table 1

Gene symbols, gene names, assay numbers and functions of the six reference genes investigated by RT–qPCR.
Gene symbol	Gene name	Function	Amplicon size	Assay number
ABL1	ABL proto-oncogene 1, non-receptor tyrosine kinase	Protein tyrosine kinase involved in a variety of cellular processes	60	Hs01104728_m1
ACTB	beta-actin	Cytoskeletal structural protein	63	Hs01060665_g1
GAPDH	Glyceraldehyde-3-phosphate dehydrogenase	Oxidoreductase in glycolysis and gluconeogenesis	157	Hs02786624_g1
HPRT1	Hypoxanthine phosphoribosyl-transferase 1	Purine synthesis in salvage pathway	82	Hs2800695_m1
RPLPO	Ribosomal Protein Lateral Stalk Subunit P0	Ribosomal protein translation	76	Hs00420895_gH
TBP	TATA box binding protein	Regulation of transcription DNA	91	Hs00427620_m1

Statistical Analysis

In studies of normalization and validation of endogenous to disease models, the statistical test must be done between endogenous Ct-case and endogenous Ct-control. If there is a difference, the use of the endogenous drug is not feasible, since it is being influenced by the condition. Furthermore, it is necessary to remove assays who’s endogenous presents an outlier behavior (endogenous Ct > 2xSD of the average of endogenous Cts). It is of great importance to verify whether there is a correlation between endogenous Ct and target Ct, considering that it may characterize experimental bias.

The stability of candidate reference genes was analyzed according to four statistical methods of assessment, namely, the Delta Ct method, the estimation of the intra- and intergroup variation (NormFinder) ⁴⁴, and pairwise comparison (GeNorm) ³². In addition, a deeper and more individual analysis was carried out using the R software ⁴⁵ and comprehensive ranking orders of these candidate genes were available from the four methods.

GeNorm uses a pairwise comparison-based model to select from a panel of candidate EC genes, the gene-pair showing least variation in expression ratio across samples. It calculates a measure of gene stability (M) of each gene based on the average pairwise variation between all tested genes. Genes with the lowest M values are those demonstrating the most stable expression ³². This calculation is based on the principle that it stepwise excludes the gene with the highest M value. In addition, GeNorm involves a cut-off value of 0.15, below which the inclusion of an additional reference gene is not required.

NormFinder uses a model-based approach to estimate not only the overall expression variation of the candidate normalization genes but also the variation between the sample subgroups of the sample set. The candidates with the lowest intergroup and intragroup variations give the lowest S stability value and are therefore ranked higher as more stable ⁴⁴. NormFinder involves a cut-off value of 0.15.

To evaluate the potential endogenous genes, three analyses were performed: i) using all samples, ii) using only peripheral blood (PB) samples, and iii) using only bone marrow (BM) samples.

The Kruskal-Wallis (KKW) test was performed for each gene to identify significant differences among the four groups (control, AML, ALL, and ALL_Ped.). Additionally, the sum of the square differences between each condition mean (Ct) and standard deviation and the gene mean (Ct) and standard deviation were calculated. These values indicate the mean and standard deviation variation among different groups. Greater values indicate more variance among groups.

To select the best endogenous gene set, an interactive method was applied: i) the endogenous mean was calculated for each sample; ii) mean outliers samples were removed; iii) Kruskal-Wallis/Wilcoxon-Mann-Whitney test was applied to the endogenous mean among conditions/groups; iv) if there is significantly differences among conditions/groups, the less stable endogenous gene was removed from the analysis, the removed samples were returned to the analysis and the process is restart at step i); v) if there is no significantly differences among groups, the survival endogenous gene set are selected (Fig. 1).

To identify the less stable endogenous gene the follow criteria were applied: i) gene significantly differences among conditions; ii) standard deviation values; iii) sum of the square differences of the mean and standard deviation between conditions/groups and the gene iv) NormFinder and GeNorm stability values.

To evaluate the variation of potential endogenous genes in ALL and AML groups in different origin samples (PB and BM), Two-way ANOVA rank based was used. The statistical significance threshold was set at p. value < 0.05 for all analyses.

In order to identify the best reference genes for gene expression studies in peripheral blood or bone marrow samples of acute leukemia patients, it was designed a qPCR assay based on TaqMan detection for the expression analysis of the six selected genes (ACTB, ABL, GAPDH, HPRT, TBP and RPLPO).

The ranking of genes according to GeNorm indicates that TBP is the gene with the most stable expression for adult patients with ALL (0.098) and AML (0.073), and HPRT is the gene with the most stable expression for both pediatric ALL patients (0.059) and normal samples (0.040) (Table 2).

NormFinder identified a different result from GeNorm, probably because it was possible to identify the different sample groups during the analysis. The ACTB gene was the most stable gene (0.47) followed by the RPLPO gene (0.52). The least stable reference gene candidate was GAPDH (1.78) (Table 2)

Table 2

Endogenous Stability Value Ranking.
Ranking	NormFinder
	Gene	Stability Value(p)
Overall*
1	ACTB	0.47
2	RPLPO	0.52
3	HPRT	0.72
4	ABL	0.86
5	TBP	0.91
6	GAPDH	1.78
	GeNorm
	Gene	Stability Value(M)
ALL
1	TBP	0.098
2	HPRT	0.100
3	ACTB	0.104
4	ABL	0.113
5	RPLPO	0.121
6	GAPDH	0.128
ALL_Ped
1	HPRT	0.059
2	ABL	0.059
3	TBP	0.060
4	ACTB	0.066
5	RPLPO	0.071
6	GAPDH	0.082
AML
1	TBP	0.073
2	ACTB	0.083
3	ABL	0.087
4	GAPDH	0.090
5	RPLPO	0.091
6	HPRT	0.098
Control
1	HPRT	0.040
2	RPLPO	0.044
3	TBP	0.044
4	ABL	0.045
5	ACTB	0.052
6	GAPDH	0.067

*The NormFinder normalizer program allowed a single analysis of all study samples subdivided by groups (AML, ALL, ALL-Ped and Control), identifying the reference genes with the best overall performance. The GeNorm program did not allow the identification of the different groups in its analyses, therefore, each group was analyzed separately.

The R software allowed a deep analysis, considering that this tool is capable of performing intergroup and intragroup analyses, ensuring a more adequate evaluation of the endogenous. It was possible to observe that the ACTB gene presented a very good performance, with low mean and standard deviation values (1.52 and 1.69, respectively), as well as little difference in the standard deviation between the groups (Table 3).

Table 3

Software R analysis of endogenous.
	ABL	GAPDH	ACTB	HPRT	TBP	RPLPO
KKW p-value	0.1768068	0.000000	0.0464394	0.0009511	0.9148049	0.3680534
Gene SD	2.4214183	3.352885	1.6982539	2.1508281	2.4722953	2.3626427
Sum of Mean Square Diff.	0.8932928	22.500085	0.4876446	3.2564599	0.2864836	0.3854516
Sum of SD Square Diff.	2.4489159	7.396640	0.6265889	0.7155819	1.2233513	3.1700468

KKW p-value: kruskal-wallis p-value; Gene.sd: gene standard deviation value; Sum of Mean Square Diff.: sum of mean square difference; Sum of SD Square Diff.: sum of standard deviation difference.

The analysis made in this study showed that the GAPDH and HPRT genes could not be classified as good reference genes, considering that they presented a high standard deviation and great variability between groups, indicating low stability. From these results, it can be considered that GAPDH and HPRT do not behave as a good endogenous for the expression analysis of acute leukemia samples, therefore, they should not be used.

In addition, it was possible to identify that the endogenous set composed by ACTB, ABL, TBP and RPLPO demonstrated good performances and stable expressions between the analyzed groups. This endogenous set is indicated for the expression analysis of acute leukemia samples.

The expression levels for many of these genes fluctuates dramatically both within and across datasets (Fig. 2) and the origin of the sample (peripheral blood or bone marrow), revealing that it may have an influence on the expression of the reference genes (Fig. 3).

The normalization of gene expression in a group of samples is necessary to validate the stability of the expression of a reference gene under experimental conditions before its use for studies. In the literature, it is possible to observe that most works use the standardization programs GeNorm and NormFinder to search for the most appropriate reference genes for their studies. However, although these tools are capable of analyzing endogenous genes in different groups to try to identify specific behaviors for each of them, they do not do it in a completely satisfactory way ^{41, 46–49}.

Since this work had four distinct groups: pediatric ALL, adult ALL, adult AML, and control, checking the data through these tools, it was possible to perceive that when they were analyzed all together, there was a loss of intra-group variability; on the other hand, when the analyzes were performed separately by group, there was a loss of intergroup variability. Thus, it was not possible to actually elect the most appropriate endogenous using these tools for the proposed study. For this reason, additional analyzes were performed using the R software ^32,44,45,50.

Currently, the most frequently used reference genes for general expression studies are B-actin (ACTB), glyceraldehyde-3- phosphate dehydrogenase (GAPDH), and hypoxanthine-guanine phosphoribosyl transferase 1 (HPRT1) ^{5, 51–55}.

The GAPDH gene is involved in many cell processes such as membrane transport and membrane fusion, microtubule assembly, nuclear RNA export, protein phosphotransferase/kinase reactions, DNA replication and DNA repair. With this in mind, the GAPDH expression would be expected to vary as it has a diverse range of functions unrelated to its glycolytic activity ⁵⁶.

This study’s’ analysis determined that the GAPDH gene presented the most unstable behavior between the analyzed endogenous. Our findings agree with several other studies that have scrutinized the stability of commonly known reference gene GAPDH and have demonstrated that it should be used with caution as its expression varied considerably, and it was consequently unsuitable as reference gene in some cases ^{4, 56–59}. However, some studies have showed different results regarding the expression stability of GAPDH, as it was identified as one of the best housekeeping gene in the analysis of a great variety of tissues type ^60–62.

The HPRT gene is also widely used as an endogenous control in many studies of gene expression in different types of cancer. This gene is found in all cells as a soluble cytoplasmic enzyme. Although HPRT is found in all types of somatic cells, significantly higher levels are found in the central nervous system ^63,64. Many studies have shown that the HPRT gene presents a behavior of a good reference gene, both used alone and associated with other genes such as TBP, GAPDH, among others ^{3,57, 65–68}.

However, our study also identified that the HPRT gene presents low stability in samples of patients with acute leukemias and is not indicated as a suitable endogenous. Some other studies have also reported that the HPRT gene exhibits high expression variability and have classified it as an inadequate reference gene, corroborating the data found in this work ^69,70.

By using the previously described method, the endogenous GAPDH and HPRT were removed from the analysis due to poor performance. Both genes presented high standard deviation and high variability between the analyzed groups, characterizing a bad behavior for reference genes. Therefore, the study proposes the set of endogenous ACTB, ABL, TBP and RPLPO as the most appropriate for the analysis of expression assays of acute leukemia samples.

The ACTB gene is an abundant and highly conserved cytoskeleton structural protein that is widely distributed in all eukaryotic cells and that plays critical roles in multiple cell processes. It is usually regarded as a constitutive housekeeping gene, assuming that its expression is normally unaffected by most experimental or physiological conditions. Therefore, ACTB has been widely used as a reference gene for expression analysis in many types of tissues ^2,24,71,72.

In this study, the ACTB gene was reported as one of the most stable endogenous analyzed, presenting low mean and standard deviation values intragroup and between all four groups. This gene has also been reported as a good reference gene in other studies of different types of cancers, but especially in breast cancer expression analysis ^{37, 73–75}.

However, the ACTB gene was found to be differentially expressed in many different types of cancer such as liver, melanoma, renal, colorectal, gastric, pancreatic, esophageal, lung, breast, prostate, ovarian cancers, leukemia, and lymphoma under certain conditions. This suggests that it might be an unsuitable endogenous for expression analysis ^1,72,76,77. Some studies reported ACTB as an unsuitable reference gene ^{53,57,59,78,79}.

Two other genes that showed stable gene expression were two protein-coding genes, ABL and TBP, respectively, according to mean values and standard deviation. The ABL gene is an oncogene likely associated with many roles of cell-cycle regulation, stress responses, integrin signaling and neural development ^80–82. The TATA-binding protein (TBP), in its turn, has been considered a universal transcription factor that is required for initiation by all three nuclear RNA polymerases. This gene is associated with a variety of factors that play important roles in regulation of gene expression ^2,83.

The ABL gene was constantly expressed in the peripheral blood of healthy individuals at levels comparable to other analyzed reference genes in different studies, including studies with chronic myelogenous leukemia (CML) expression analysis ^84–86. Furthermore, a study published by Weisser et al., in 2004 reported that the ABL gene was a suitable endogenous for monitoring minimal residual disease in acute myeloid leukemia patients ⁸⁷.

Altogether, the data from many different studies show the relevance of TBP gene expression stability, indicating that it is a suitable reference gene to be used as control in studies of various kinds of diseases, including some types of cancer such as bladder cancer and glioblastoma. However, the majority of this studies also showed that the use of TBP associated with other reference gene presented an even better performance ^{77,78, 88–92}.

RPLPO is a ribosomal protein that is responsible for recruiting both translation factors and other ribosomal proteins to the ribosomal complexes, facilitating protein synthesis. Usually, it is strongly expressed in normal lymph nodes, skin, spleen, and fetal brain tissue, expressed at lower levels in normal lung, bladder, and placenta, and not expressed in normal colon, kidney, and bone marrow ^93–95.

What was observed in this study in relation to the RPLPO gene is what is usually demonstrated in the other endogenous validation studies for gene expression techniques. The RPLPO gene presents a relatively good expression stability in several studies, but it is not the most suitable reference gene ^{34, 96–100}.

Given these findings, this study suggests the main endogenous set for use as control/reference for the analysis of gene expression in peripheral blood and bone marrow samples from patients with acute leukemias, is composed by the ACTB, ABL, TBP and RPLPO genes. In addition, the statistical analysis in this type of study is indispensable. It is important to verify the variation of endogenous expression between groups. Once the calculation of delta Ct is made, the variability of endogenous expression is transferred to the target. Then, if there is a high endogenous variability between groups, potential differences found on the target may not really indicate target variation, but actually the influence of the endogenous variation. Therefore, it is extremely necessary to perform the validation of reference genes for any gene expression study, considering that the endogenous used influences the reliability and accuracy of these studies.

Author Contributions: Conceptualization, F.M.C.d.P.P, A.S.K, F.C.M and C.A.M.-N.; Provision of data and subsequent analysis and interpretation, F.M.C.d.P.P, V.B.J.V, M.B.O, D.S.O, R.C.M, R.M.R, G.S.L, M.O.d.M.F, M.E.A.d.M, F.M.C, A.S.K and C.A.M.-N.; Bioinformatics analysis, F.M.C.d.P.P, F.M.C, and C.A.M.-N.; Writing—original draft preparation, F.M.C.d.P.P, V.B.J.V, M.B.O, F.M.C and C.A.M.-N; Writing—review and editing, F.M.C.d.P.P, and C.A.M.-N.; Funding acquisition, A.S.K and C.A.M.-N. All authors have read and agreed to the published version of the manuscript.

Funding: This study was supported by Brazilian funding agencies: Coordination for the Improvement of Higher Education Personnel (CAPES; to V.B.J.V and M.B.O), National Council of Technological and Scientific Development (CNPq PQ Scholarships to M.O.d.M.F, M.E.A.d.M, R.C.M, A.S.K. and C.A.M.-N.), Cearense Foundation of Scientific and Technological Support (FUNCAP; to F.M.C.d.P.P and M.O.d.M.F) and, we thank PROPESP/UFPA for publication payment.

Conflicts of interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or data interpretation; in the writing of the manuscript, or in the decision to publish the results.

Data Availability: RT-qPCR raw data and analysis are available as Supplementary File 1.

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No competing interests reported.

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Normalization of Gene Expression for Acute Leukemia Studies: Selecting a Set of Endogenous Genes Control

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INTRODUCTION

MATERIALS AND METHODS

Biological Samples

RNA extraction and Reverse Transcription of RNA to cDNA

Identification of Gene Expression by Real-Time Quantitative Polymerase Chain Reaction (qPCR)

Statistical Analysis

RESULTS

DISCUSSION

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References

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