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MitoQ as an antenatal antioxidant treatment improves markers of lung maturation in healthy and hypoxic pregnancy
research-article
Author(s):
Mitchell C. Lock
1 ,
Kimberley J. Botting
2 ,
Beth J. Allison
2 ,
Youguo Niu
2 ,
Sage G. Ford
2 ,
Michael P. Murphy
3 ,
Sandra Orgeig
4 ,
Dino A. Giussani
2 ,
Janna L. Morrison
1
,
Publication date (Electronic):
19 July 2023
Journal:
The Journal of Physiology
Publisher:
John Wiley and Sons Inc.
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
Abstract
Chronic fetal hypoxaemia is a common pregnancy complication that increases the risk of infants experiencing respiratory complications at birth. In turn, chronic fetal hypoxaemia promotes oxidative stress, and maternal antioxidant therapy in animal models of hypoxic pregnancy has proven to be protective with regards to fetal growth and cardiovascular development. However, whether antenatal antioxidant therapy confers any benefit on lung development in complicated pregnancies has not yet been investigated. Here, we tested the hypothesis that maternal antenatal treatment with MitoQ will protect the developing lung in hypoxic pregnancy in sheep, a species with similar fetal lung developmental milestones as humans. Maternal treatment with MitoQ during late gestation promoted fetal pulmonary surfactant maturation and an increase in the expression of lung mitochondrial complexes III and V independent of oxygenation. Maternal treatment with MitoQ in hypoxic pregnancy also increased the expression of genes regulating liquid reabsorption in the fetal lung. These data support the hypothesis tested and suggest that MitoQ as an antenatal targeted antioxidant treatment may improve lung maturation in the late gestation fetus.
Key points
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Chronic fetal hypoxaemia promotes oxidative stress, and maternal antioxidant therapy in hypoxic pregnancy has proven to be protective with regards to fetal growth and cardiovascular development. MitoQ is a targeted antioxidant that uses the cell and the mitochondrial membrane potential to accumulate within the mitochondria.
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Treatment of healthy or hypoxic pregnancy with MitoQ, increases the expression of key molecules involved in surfactant maturation, lung liquid reabsorption and in mitochondrial proteins driving ATP synthesis in the fetal sheep lung.
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There were no detrimental effects of MitoQ treatment alone on the molecular components measured in the present study, suggesting that maternal antioxidant treatment has no effect on other components of normal maturation of the surfactant system.
Abstract
Abstract figure legend Summary of molecular changes within the fetal lung as a result of MitoQ treatment. Changes in expression of pathways are indicated by direction of arrows; no change in expression is represented by purple sideways double‐sided arrows. A main effect of MitoQ is represented by a green arrow. An interaction of hypoxia and MitoQ is indicated by yellow arrows. MitoQ treatment increased the expression of mitochondrial complex III and ATP synthase (Complex V) and expression of surfactant proteins B and C. There was an interaction of MitoQ with hypoxia resulting in increased expression of sodium transporter SCNN1A and surfactant protein D within the fetal lung.
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Most cited references62
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Improving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research
Carol Kilkenny, William J Browne, Innes Cuthill … (2010)
In the last decade the number of bioscience journals has increased enormously, with many filling specialised niches reflecting new disciplines and technologies. The emergence of open-access journals has revolutionised the publication process, maximising the availability of research data. Nevertheless, a wealth of evidence shows that across many areas, the reporting of biomedical research is often inadequate, leading to the view that even if the science is sound, in many cases the publications themselves are not “fit for purpose,” meaning that incomplete reporting of relevant information effectively renders many publications of limited value as instruments to inform policy or clinical and scientific practice [1]–[21]. A recent review of clinical research showed that there is considerable cumulative waste of financial resources at all stages of the research process, including as a result of publications that are unusable due to poor reporting [22]. It is unlikely that this issue is confined to clinical research [2]–[14],[16]–[20]. Failure to describe research methods and to report results appropriately therefore has potential scientific, ethical, and economic implications for the entire research process and the reputation of those involved in it. This is particularly true for animal research, one of the most controversial areas of science. The largest and most comprehensive review of published animal research undertaken to date, to our knowledge, has highlighted serious omissions in the way research using animals is reported [5]. The survey, commissioned by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), a UK Government-sponsored scientific organisation, found that only 59% of the 271 randomly chosen articles assessed stated the hypothesis or objective of the study, and the number and characteristics of the animals used (i.e., species/strain, sex, and age/weight). Most of the papers surveyed did not report using randomisation (87%) or blinding (86%) to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods fully described them and presented the results with a measure of precision or variability [5]. These findings are a cause for concern and are consistent with reviews of many research areas, including clinical studies, published in recent years [2]–[22]. Good Reporting Is Essential for Peer Review and to Inform Future Research Scrutiny by scientific peers has long been the mainstay of “quality control” for the publication process. The way that experiments are reported, in terms of the level of detail of methods and the presentation of key results, is crucial to the peer review process and, indeed, the subsequent utility and validity of the knowledge base that is used to inform future research. The onus is therefore on the research community to ensure that their research articles include all relevant information to allow in-depth critique, and to avoiding duplicating studies and performing redundant experiments. Ideally scientific publications should present sufficient information to allow a knowledgeable reader to understand what was done, why, and how, and to assess the biological relevance of the study and the reliability and validity of the findings. There should also be enough information to allow the experiment to be repeated [23]. The problem therefore is how to ensure that all relevant information is included in research publications. Using Reporting Guidelines Measurably Improves the Quality of Reporting Evidence provided by reviews of published research suggests that many researchers and peer reviewers would benefit from guidance about what information should be provided in a research article. The CONSORT Statement for randomised controlled clinical trials was one of the first guidelines developed in response to this need [24],[25]. Since publication, an increasing number of leading journals have supported CONSORT as part of their instructions to authors [26],[27]. As a result, convincing evidence is emerging that CONSORT improves the quality and transparency of reports of clinical trials [28],[29]. Following CONSORT, many other guidelines have been developed—there are currently more than 90 available for reporting different types of health research, most of which have been published in the last ten years (see http://www.equator-network.org and references [30],[31]). Guidelines have also been developed to improve the reporting of other specific bioscience research areas including metabolomics and gene expression studies [32]–[37]. Several organisations support the case for improved reporting and recommend the use of reporting guidelines, including the International Committee of Medical Journal Editors, the Council of Science Editors, the Committee on Publication Ethics, and the Nuffield Council for Bioethics [38]–[41]. Improving the Reporting of Animal Experiments—The ARRIVE Guidelines Most bioscience journals currently provide little or no guidance on what information to report when describing animal research [42]–[50]. Our review found that 4% of the 271 journal articles assessed did not report the number of animals used anywhere in the methods or the results sections [5]. Reporting animal numbers is essential so that the biological and statistical significance of the experimental results can be assessed or the data reanalysed, and is also necessary if the experimental methods are to be repeated. Improved reporting of these and other details will maximise the availability and utility of the information gained from every animal and every experiment, preventing unnecessary animal use in the future. To address this, we led an initiative to produce guidelines for reporting animal research. The guidelines, referred to as ARRIVE (Animals in Research: Reporting In Vivo Experiments), have been developed using the CONSORT Statement as their foundation [24],[25]. The ARRIVE guidelines consist of a checklist of 20 items describing the minimum information that all scientific publications reporting research using animals should include, such as the number and specific characteristics of animals used (including species, strain, sex, and genetic background); details of housing and husbandry; and the experimental, statistical, and analytical methods (including details of methods used to reduce bias such as randomisation and blinding). All the items in the checklist have been included to promote high-quality, comprehensive reporting to allow an accurate critical review of what was done and what was found. Consensus and consultation are the corner-stones of the guideline development process [51]. To maximise their utility, the ARRIVE guidelines have been prepared in consultation with scientists, statisticians, journal editors, and research funders. We convened an expert working group, comprising researchers and statisticians from a range of disciplines, and journal editors from Nature Cell Biology, Science, Laboratory Animals, and the British Journal of Pharmacology (see Acknowledgments). At a one-day meeting in June 2009, the working group agreed the scope and broad content of a draft set of guidelines that were then used as the basis for a wider consultation with the scientific community, involving researchers, and grant holders and representatives of the major bioscience funding bodies including the Medical Research Council, Wellcome Trust, Biotechnology and Biological Sciences Research Council, and The Royal Society (see Table 1). Feedback on the content and wording of the items was incorporated into the final version of the checklist. Further feedback on the content utility of the guidelines is encouraged and sought. 10.1371/journal.pbio.1000412.t001 Table 1 Funding bodies consulted. Name of Bioscience Research Funding Body Medical Research Council Biotechnology and Biological Sciences Research Council Wellcome Trust The Royal Society Association of Medical Research Charities British Heart Foundation Parkinson's Disease Society The ARRIVE guidelines (see Table 2) can be applied to any area of bioscience research using laboratory animals, and the inherent principles apply not only to reporting comparative experiments but also to other study designs. Laboratory animal refers to any species of animal undergoing an experimental procedure in a research laboratory or formal test setting. The guidelines are not intended to be mandatory or absolutely prescriptive, nor to standardise or formalise the structure of reporting. Rather they provide a checklist that can be used to guide authors preparing manuscripts for publication, and by those involved in peer review for quality assurance, to ensure completeness and transparency. 10.1371/journal.pbio.1000412.t002 Table 2 Animal Research: Reporting In Vivo experiments: The ARRIVE guidelines. ITEM RECOMMENDATION TITLE 1 Provide as accurate and concise a description of the content of the article as possible. ABSTRACT 2 Provide an accurate summary of the background, research objectives (including details of the species or strain of animal used), key methods, principal findings, and conclusions of the study. INTRODUCTION Background 3 a. Include sufficient scientific background (including relevant references to previous work) to understand the motivation and context for the study, and explain the experimental approach and rationale.b. Explain how and why the animal species and model being used can address the scientific objectives and, where appropriate, the study's relevance to human biology. Objectives 4 Clearly describe the primary and any secondary objectives of the study, or specific hypotheses being tested. METHODS Ethical statement 5 Indicate the nature of the ethical review permissions, relevant licences (e.g. Animal [Scientific Procedures] Act 1986), and national or institutional guidelines for the care and use of animals, that cover the research. Study design 6 For each experiment, give brief details of the study design, including:a. The number of experimental and control groups.b. Any steps taken to minimise the effects of subjective bias when allocating animals to treatment (e.g., randomisation procedure) and when assessing results (e.g., if done, describe who was blinded and when).c. The experimental unit (e.g. a single animal, group, or cage of animals).A time-line diagram or flow chart can be useful to illustrate how complex study designs were carried out. Experimental procedures 7 For each experiment and each experimental group, including controls, provide precise details of all procedures carried out. For example:a. How (e.g., drug formulation and dose, site and route of administration, anaesthesia and analgesia used [including monitoring], surgical procedure, method of euthanasia). Provide details of any specialist equipment used, including supplier(s).b. When (e.g., time of day).c. Where (e.g., home cage, laboratory, water maze).d. Why (e.g., rationale for choice of specific anaesthetic, route of administration, drug dose used). Experimental animals 8 a. Provide details of the animals used, including species, strain, sex, developmental stage (e.g., mean or median age plus age range), and weight (e.g., mean or median weight plus weight range).b. Provide further relevant information such as the source of animals, international strain nomenclature, genetic modification status (e.g. knock-out or transgenic), genotype, health/immune status, drug- or test-naïve, previous procedures, etc. Housing and husbandry 9 Provide details of:a. Housing (e.g., type of facility, e.g., specific pathogen free (SPF); type of cage or housing; bedding material; number of cage companions; tank shape and material etc. for fish).b. Husbandry conditions (e.g., breeding programme, light/dark cycle, temperature, quality of water etc. for fish, type of food, access to food and water, environmental enrichment).c. Welfare-related assessments and interventions that were carried out before, during, or after the experiment. Sample size 10 a. Specify the total number of animals used in each experiment and the number of animals in each experimental group.b. Explain how the number of animals was decided. Provide details of any sample size calculation used.c. Indicate the number of independent replications of each experiment, if relevant. Allocating animals to experimental groups 11 a. Give full details of how animals were allocated to experimental groups, including randomisation or matching if done.b. Describe the order in which the animals in the different experimental groups were treated and assessed. Experimental outcomes 12 Clearly define the primary and secondary experimental outcomes assessed (e.g., cell death, molecular markers, behavioural changes). Statistical methods 13 a. Provide details of the statistical methods used for each analysis.b. Specify the unit of analysis for each dataset (e.g. single animal, group of animals, single neuron).c. Describe any methods used to assess whether the data met the assumptions of the statistical approach. RESULTS Baseline data 14 For each experimental group, report relevant characteristics and health status of animals (e.g., weight, microbiological status, and drug- or test-naïve) before treatment or testing (this information can often be tabulated). Numbers analysed 15 a. Report the number of animals in each group included in each analysis. Report absolute numbers (e.g. 10/20, not 50%a).b. If any animals or data were not included in the analysis, explain why. Outcomes and estimation 16 Report the results for each analysis carried out, with a measure of precision (e.g., standard error or confidence interval). Adverse events 17 a. Give details of all important adverse events in each experimental group.b. Describe any modifications to the experimental protocols made to reduce adverse events. DISCUSSION Interpretation/scientific implications 18 a. Interpret the results, taking into account the study objectives and hypotheses, current theory, and other relevant studies in the literature.b. Comment on the study limitations including any potential sources of bias, any limitations of the animal model, and the imprecision associated with the resultsa.c. Describe any implications of your experimental methods or findings for the replacement, refinement, or reduction (the 3Rs) of the use of animals in research. Generalisability/translation 19 Comment on whether, and how, the findings of this study are likely to translate to other species or systems, including any relevance to human biology. Funding 20 List all funding sources (including grant number) and the role of the funder(s) in the study. a Schulz, et al. (2010) [24]. Improved Reporting Will Maximise the Output of Published Research These guidelines were developed to maximise the output from research using animals by optimising the information that is provided in publications on the design, conduct, and analysis of the experiments. The need for such guidelines is further illustrated by the systematic reviews of animal research that have been carried out to assess the efficacy of various drugs and interventions in animal models [8],[9],[13],[52]–[55]. Well-designed and -reported animal studies are the essential building blocks from which such a systematic review is constructed. The reviews have found that, in many cases, reporting omissions, in addition to the limitations of the animal models used in the individual studies assessed in the review, are a barrier to reaching any useful conclusion about the efficacy of the drugs and interventions being compared [2],[3]. Driving improvements in reporting research using animals will require the collective efforts of authors, journal editors, peer reviewers, and funding bodies. There is no single simple or rapid solution, but the ARRIVE guidelines provide a practical resource to aid these improvements. The guidelines will be published in several leading bioscience research journals simultaneously [56]–[60], and publishers have already endorsed the guidelines by including them in their journal Instructions to Authors subsequent to publication. The NC3Rs will continue to work with journal editors to extend the range of journals adopting the guidelines, and with the scientific community to disseminate the guidelines as widely as possible (http://www.nc3rs.org.uk/ARRIVE).
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Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes
Jo Vandesompele, Katleen De Preter, Filip Pattyn … (2002)
Background Gene-expression analysis is increasingly important in many fields of biological research. Understanding patterns of expressed genes is expected to provide insight into complex regulatory networks and will most probably lead to the identification of genes relevant to new biological processes, or implicated in disease. Two recently developed methods to measure transcript abundance have gained much popularity and are frequently applied. Microarrays allow the parallel analysis of thousands of genes in two differentially labeled RNA populations [1], while real-time RT-PCR provides the simultaneous measurement of gene expression in many different samples for a limited number of genes, and is especially suitable when only a small number of cells are available [2,3,4]. Both techniques have the advantage of speed, throughput and a high degree of potential automation compared to conventional quantification methods, such as northern-blot analysis, ribonuclease protection assay, or competitive RT-PCR. Nevertheless, these new approaches require the same kind of normalization as the traditional methods of mRNA quantification. Several variables need to be controlled for in gene-expression analysis, such as the amount of starting material, enzymatic efficiencies, and differences between tissues or cells in overall transcriptional activity. Various strategies have been applied to normalize these variations. Under controlled conditions of reproducible extraction of good-quality RNA, the gene transcript number is ideally standardized to the number of cells, but accurate enumeration of cells is often precluded, for example when starting with solid tissue. Another frequently applied normalization scalar is the RNA mass quantity, especially in northern blot analysis. There are several arguments against the use of mass quantity. The quality of RNA and related efficiency of the enzymatic reactions are not taken into account. Moreover, in some instances it is impossible to quantify this parameter, for example, when only minimal amounts of RNA are available from microdissected tissues. Probably the strongest argument against the use of total RNA mass for normalization is the fact that it consists predominantly of rRNA molecules, and is not always representative of the mRNA fraction. This was recently evidenced by a significant imbalance between rRNA and mRNA content in approximately 7.5% of mammary adenocarcinomas [5]. Also, it has been reported that rRNA transcription is affected by biological factors and drugs [6,7,8]. Further drawbacks to the use of 18S or 28S rRNA molecules as standards are their absence in purified mRNA samples, and their high abundance compared to target mRNA transcripts. The latter makes it difficult to accurately subtract the baseline value in real-time RT-PCR data analysis. To date, internal control genes are most frequently used to normalize the mRNA fraction. This internal control - often referred to as a housekeeping gene - should not vary in the tissues or cells under investigation, or in response to experimental treatment. However, many studies make use of these constitutively expressed control genes without proper validation of their presumed stability of expression. But the literature shows that housekeeping gene expression - although occasionally constant in a given cell type or experimental condition - can vary considerably (reviewed in [9,10,11,12]). With the increased sensitivity, reproducibility and large dynamic range of real-time RT-PCR methods, the requirements for a proper internal control gene have become increasingly stringent. In this study, we carried out an extensive evaluation of 10 commonly used housekeeping genes in 13 different human tissues, and outlined a procedure for calculating a normalization factor based on multiple control genes for more accurate and reliable normalization of gene-expression data. Furthermore, this normalization factor was validated in a comparative study with frequently applied microarray scaling factors using publicly available microarray data. Results Expression profiling of housekeeping genes Primers were designed for ten commonly used housekeeping genes (ACTB, B2M, GAPD, HMBS, HPRT1, RPL13A, SDHA, TBP, UBC and YWHAZ) (see Table 1 for full gene name, accession number, function, chromosomal localization, alias, existence of processed pseudogenes, and indication that primers span an intron; see Table 2 for primer sequences). Special attention was paid to selecting genes that belong to different functional classes, which significantly reduces the chance that genes might be co-regulated. The expression level of these 10 internal control genes was determined in 34 neuroblastoma cell lines (independently prepared in different labs from different patients), 20 short-term cultured normal fibroblast samples from different individuals, 13 normal leukocyte samples, 9 normal bone-marrow samples, and 9 additional normal human tissues from pooled organs (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus). The raw expression values are available as a tab-delimited file (see Additional data files). Single control normalization error To determine the possible errors related to the common practice of using only one housekeeping gene for normalization, we calculated the ratio of the ratios of two control genes in two different samples (from the same tissue panel) and termed it the single control normalization error, E (see Materials and methods). For two ideal internal control genes (constant ratio between the genes in all samples), E equals 1. In practice, observed E values are larger than 1 and constitute the erroneous E-fold expression difference between two samples, depending on the particular housekeeping gene used for normalization. E values were calculated for all 45 two-by-two combinations of control genes and 865 two-by-two sample combinations within the available tissue panels (neuroblastoma, fibroblast, leukocyte, bone marrow and a series of normal tissues from Clontech; that is, a total of 38,925 data points) (Figure 1). In addition, the systematic error distribution was calculated by analysis of repeated runs of the same control gene. The average 75th and 90th percentile E values are 3.0 (range 2.1-3.9), and 6.4 (range 3.0-10.9), respectively. Gene-stability measure and ranking of selected housekeeping genes It is generally accepted that gene-expression levels should be normalized by a carefully selected stable internal control gene. However, to validate the presumed stable expression of a given control gene, prior knowledge of a reliable measure to normalize this gene in order to remove any nonspecific variation is required. To address this circular problem, we developed a gene-stability measure to determine the expression stability of control genes on the basis of non-normalized expression levels. This measure relies on the principle that the expression ratio of two ideal internal control genes is identical in all samples, regardless of the experimental condition or cell type. In this way, variation of the expression ratios of two real-life housekeeping genes reflects the fact that one (or both) of the genes is (are) not constantly expressed, with increasing variation in ratio corresponding to decreasing expression stability. For every control gene we determined the pairwise variation with all other control genes as the standard deviation of the logarithmically transformed expression ratios, and defined the internal control gene-stability measure M as the average pairwise variation of a particular gene with all other control genes. Genes with the lowest M values have the most stable expression. Assuming that the control genes are not co-regulated, stepwise exclusion of the gene with the highest M value results in a combination of two constitutively expressed housekeeping genes that have the most stable expression in the tested samples. To manage the large number of calculations, we have written a Visual Basic Application (VBA) for Microsoft Excel - termed geNorm - that automatically calculates the gene-stability measure M for all control genes in a given set of samples (geNorm is freely available from the authors on request). The program enables elimination of the worst-scoring housekeeping gene (that is, the one with the highest M value) and recalculation of new M values for the remaining genes. Using this VBA applet, we ranked the ten control genes in the five tissue panels tested according to their expression stability (Figure 2, Table 3). In addition, the systematic variation was calculated as the pairwise variation, V, for repeated RT-PCR experiments on the same gene, reflecting the inherent machine, enzymatic and pipet variation. Normalization factor calculation based on the geometric mean of multiple control genes We concluded that in order to measure expression levels accurately, normalization by multiple housekeeping genes instead of one is required. Consequently, a normalization factor based on the expression levels of the best-performing housekeeping genes must be calculated. For accurate averaging of the control genes, we propose to use the geometric mean instead of the arithmetic mean, as the former controls better for possible outlying values and abundance differences between the different genes. The number of genes used for geometric averaging is a trade-off between practical considerations and accuracy. It is obvious that an accurate normalization factor should not include the rather unstable genes that were observed in some tissues. On the other hand, it remains relatively impractical to quantify, for example, eight control genes when only a few target genes need to be studied, or when only minimal amounts of RNA are available. Furthermore, it is a waste of resources to quantify more genes than necessary if all genes are relatively stably expressed and if the normalization factor does not significantly change whether or not more genes are included. Taking all this into consideration, we recommend the minimal use of the three most stable internal control genes for calculation of an RT-PCR normalization factor (NF n , n = 3), and stepwise inclusion of more control genes until the (n + 1)th gene has no significant contribution to the newly calculated normalization factor (NF n + 1). To determine the possible need or utility of including more than three genes for normalization, the pairwise variation V n/n + 1 was calculated between the two sequential normalization factors (NF n and NF n + 1) for all samples within the same tissue panel (with a ij = NF n,i and a ik = NF n + 1,i , n the number of genes used for normalization (3 ≤ n ≤ 9), and i the sample index; see Equations 2 and 3 in Materials and methods). A large variation means that the added gene has a significant effect and should preferably be included for calculation of a reliable normalization factor. For all tissue types, normalization factors were calculated for the three most stable control genes (that is, those with the lowest M value) and for seven additional factors by stepwise inclusion of the most stable remaining control gene. Pairwise variations were subsequently calculated for every series of NF n and NF n + 1 normalization factors, reflecting the effect of adding an (n + 1)th gene (Figure 3a). It is apparent that the inclusion of a fourth gene has no significant effect (that is, low V 3/4 value) for leukocytes, fibroblasts and bone marrow. This is also illustrated by the nearly perfect correlation between NF3 and NF4 values, as shown for fibroblasts in Figure 3b. On the basis of these data, we decided to take 0.15 as a cut-off value, below which the inclusion of an additional control gene is not required. For neuroblastoma and the pool of normal tissues, one and two additional genes, respectively, are necessary for reliable normalization (see also Figure 3b). The high V 8/9 and V 9/10 values for the normal pool, neuroblastoma and leukocytes corroborate very well the findings obtained by stepwise exclusion of the worst-scoring control gene (Figure 2). This analysis showed an initial steep decrease in average M value, pointing at two aberrantly expressed control genes for leukocytes and one unstable gene for neuroblastoma and the pool of normal tissues. Furthermore, the need to include additional control genes for these last two tissue panels is in keeping with the high variation in control-gene expression, as evidenced from Figure 2. Validation of proposed real-time RT-PCR normalization factors To assess the validity of the established gene-stability measure, that is, that genes with the lowest M values have indeed the most stable expression, we determined the gene-specific variation for each control gene as the variation coefficient of the expression levels after normalization. This coefficient should be minimal for proper housekeeping genes. Three different normalization factors were calculated, based on the geometric mean of three genes with, respectively, the lowest (NF3(1-3)), the highest (NF3(8-10)), and intermediate M values (NF3(6-8)) (as determined by geNorm). We subsequently determined the average gene-specific variation of the three genes with the most stable expression (that is, the lowest variation coefficient) for each normalization factor and within each tissue panel (Figure 4a). It is clear that the gene-specific variation in all tissue panels is by far the smallest when the data are normalized to NF3(1-3). This demonstrates that the gene-stability measure effectively identified the control genes with the most stable expression. To verify that a high M value is characteristic of an unstable or differentially expressed gene, we analyzed the expression level of MYCN - a highly differentially expressed protooncogene in neuroblastoma with prognostic value [13] - together with the set of ten housekeeping genes. MYCN was readily identified as the most differentially expressed gene, with an M value of 6.02 compared to 2.17 for the least stable control gene (B2M) in neuroblastoma. It was further observed that normalization with a single control gene consistently resulted in significantly higher gene-specific variations of the other control genes (data not shown), which underscores the improvement in normalization by using multiple housekeeping genes. To show that the associations between the best control genes are independent of cell proliferation, we analyzed the expression level of the proliferation marker PCNA in the neuroblastoma cancer cell lines, and determined the Spearman rank correlation coefficient between the raw expression levels of the four best housekeepers and the marker gene PCNA. From this analysis, it was clear that the control genes were - as expected - significantly correlated (p < 0.001, correlation coefficient between 0.60 and 0.76). In contrast, no correlation was observed between PCNA and three of the four control genes, and only a weak correlation (p = 0.024, coefficient = 0.43) between PCNA and control gene HPRT1. These data firmly demonstrate that the most stable control genes (identified by the geNorm algorithm) are not per se linked to the state of cell proliferation of the samples. To further validate the accuracy of geometric averaging of carefully selected control genes for normalization, the geometric means of housekeeping-gene expression levels obtained from publicly available microarray data were compared with commonly applied microarray normalization factors calculated for the same data. For this purpose, an 8,000-gene array data set [14] was chosen, containing nine of the ten control genes evaluated in this RT-PCR study. Two commonly applied microarray normalization factors (based on median ratio normalization, and total intensity normalization) [15,16,17] were determined for eight randomly selected hybridization sets. Subsequently, for each hybridization set, the background-corrected expression levels of nine housekeeping genes for the two fluorescence channels were imported into geNorm and ranked, as described for the RT-PCR data. As these microarray data originate from hybridizations of cell lines from various histological origin versus a reference pool of multiple cell lines, we have calculated the geometric mean of the five most stable control genes (NF5) for each hybridization set, in accordance to the recommendations for reliable normalization within a heterogeneous tissue panel (see previous paragraph). Alternatively, internal control genes were excluded in a stepwise manner until the M values of the remaining genes were below 0.7 (experimental value shown to eliminate the most variable and outlying genes in this microarray dataset). Depending on the hybridization set, seven to nine genes fitted this criterion, upon which the geometric mean was calculated (NF M < 0.7). Both normalization factors (NF5 and NF M < 0.7) were shown to be similar to the calculated microarray normalization factors (Figure 4b). Tissue-specific housekeeping gene expression To compare the control gene-expression levels within the heterogeneous group of all 13 tested tissues, the same set of control genes should be used for normalization. We therefore calculated the geometric mean of six control genes that were withheld from the set of ten genes after elimination of the two genes with the highest M value within each tissue panel (that is, B2M, RPL13A, ACTB and HMBS) (see Table 3). Given the large variety of tested tissues, this is the optimal strategy to eliminate most variation, and to allow direct comparison between the different samples. Under the assumption of equal PCR threshold cycle values for equal transcript numbers of different genes, an estimation of the transcript abundance of the various control genes can be made. Figure 5 shows that the ten tested genes belong to various abundance classes, with an approximately 400-fold expression difference between the most abundant (ACTB) and the rarest (HMBS) transcript. Although the overall abundance of a given control gene in the different tissues is relatively similar, we clearly observe tissue-specific expression differences, for example, B2M expression level is 112-fold higher in leukocytes compared to fetal brain, and ACTB shows an expression difference of 22-fold between fibroblasts and heart tissue. It is also clear that some genes have a relatively constant expression level (for example, UBC and HPRT1) compared to the differential expression pattern of others (for example, B2M and ACTB). Discussion Accurate normalization of gene-expression levels is an absolute prerequisite for reliable results, especially when the biological significance of subtle gene-expression differences is studied. Still, little attention has been paid to the systematic study of normalization procedures and the impact on the conclusions. For RT-PCR, there is a general consensus on using a single control gene for normalization purposes. A comprehensive literature analysis of expression studies that were published in high-impact journals during 1999 indicated that GAPD, ACTB, 18S and 28S rRNA were used as single control genes for normalization in more than 90% of cases [11]. As numerous studies reported that housekeeping gene expression can vary considerably [6,9,10,11,12], the validity of the conclusions is highly dependent on the applied control. Some laboratories have tried to find the optimal control gene for their experimental system, and often rRNA molecules were proposed as best references. These studies should be approached with some caution, as often only the variation in expression of the tested genes with respect to the mass loading of total RNA was assessed. As rRNA molecules make up the bulk of total RNA, they should indeed correlate very well with the total RNA mass, but that does not necessarily make them good control genes. As outlined in the introduction, total RNA and rRNA levels are not proper references, because of the observed imbalance between rRNA and mRNA fractions. In addition to searching for a stable control gene, we aimed at determining the errors related to the common practice of single control normalization. In this study, we provide evidence that a conventional normalization strategy based on a single housekeeping gene leads to erroneous normalization up to 3.0- and 6.4-fold in 25% and 10% of the cases, respectively, with sporadic cases showing error values above 20. This analysis showed that a few control genes were unstable and significantly differentially expressed in some tissue panels, as evidenced by the decrease from 5.9 to 4.5 for the 90th-percentile single control normalization error value for neuroblastoma when the B2M gene is omitted (data not shown). This finding agrees with the reported differential expression of B2M in neuroblastoma, corresponding to the stage of differentiation of the tumor cells [18]. The error-distribution curves not only reflect the stability of expression of the applied controls, but also the sample heterogeneity within a tissue panel, as noted from the less steep curve for the heterogeneous set of normal pooled tissues compared to the other, relatively homogeneous, tissue panels. In this regard, the issue has been raised that finding proper control genes is even more important when working with tissues of different histological origin [9]. The single control normalization error values point to inherent noisy oscillations in expression levels of the control genes, a finding which has been corroborated in other large-scale studies where several thousand genes were measured in different cells or tissues by microarray analysis. No gene was found on an 8,000-feature array that did not vary by ratios of at least twofold across a panel of 60 cell lines [14], and a set of genes frequently used for normalization (including GAPD and ACTB) was found to vary in expression by 7- to 23-fold [9]. Taken together, our data and these studies clearly show that ideal and universal control genes do not exist. This warrants the search for stably expressed genes in each experimental system, and for the development of an accurate normalization strategy. To validate the expression stability of the tested control genes without any prior assumption of a metric for standardization, we had initially measured the correlation between the raw, non-normalized expression levels of any two control genes, which should be nearly perfect for proper control genes. We observed, however, that the data range between the minimum and maximum expression levels, or any outlying value, could have a profound influence on the slope of the regression line, and consequently on the value of the correlation coefficient. This made Pearson and Spearman correlation coefficients unsuitable for this kind of analysis. We have therefore developed a new stability measure, based on the principle that the expression ratio of two proper control genes should be identical in all samples, regardless of the experimental condition or cell type, with increasing ratio variation corresponding to decreasing expression stability of one (or both) of the tested genes. The proposed standard deviation of log-transformed control gene ratios is a robust measure for the variation between two control genes, as it does not impose any requirements for normality or homoscedasticity of the data points. Furthermore, this measure is independent of the abundance difference between the genes, and is equally affected by any outlying or extreme ratio (that is, outliers for a sample with low or high overall expression, or outliers caused by an upregulated or downregulated gene have an equivalent increase in pairwise variation V). Logarithmic transformation of the ratios is required for symmetrical distribution of the data around zero, resulting in equal absolute values (but opposite signs) for a given ratio and the inverse ratio. As a result, the standard deviation of log-transformed ratios is identical to the standard deviation of log-transformed inverse ratios, which makes this measure characteristic for every combination of two genes. Having established a robust measure to assess the variation in expression of two control genes, we subsequently defined a gene-stability measure M as the average pairwise variation between a particular gene and all other control genes. Using a VBA applet geNorm developed in-house, we ranked ten commonly used housekeeping genes belonging to different functional and abundance classes according to their expression stability in five tested tissue panels. The clear decrease of M of the remaining control genes during stepwise exclusion of the worst-scoring gene points at differences in the stability of gene-specific expression and demonstrates that the remaining genes are more stably expressed than the excluded genes. Some tissue panels show a relatively steep initial decline, which reflects the exclusion of one or more aberrantly expressed control genes (for example, ACTB and HMBS for leukocytes), as also noticed from the single control normalization error analyses (see above). The average gene stability values of the remaining genes during stepwise elimination of the least stable control genes also indicates tissue-specific differences, with bone marrow and the pool of normal tissues having the lowest and highest overall expression variation, respectively. The latter is no surprise, given the larger tissue heterogeneity in this panel. The question of whether the observed high variation for neuroblastoma is a cancer-related phenomenon of deregulated expression is currently under further investigation. From these analyses, it is clear that there is no universal control gene suitable for all cell types. ACTB and B2M appear to be the worst-scoring genes, whereas UBC, GAPD and HPRT1 seem to be the best overall control genes, each belonging to the four most stable genes in four out of five tested tissues. However, these generalizations should be treated with caution. B2M appears to be one of the least stable control genes, but is nevertheless a good choice for normalization of leukocyte expression levels. This clearly demonstrates that a proper choice of housekeeping genes is highly dependent on the tissues or cells under investigation. This is even more important when considering the differences in transcript abundance of some control genes between different tissues. The large expression differences between the tissues tested for B2M and ACTB, for instance, would definitely result in large normalization errors if they were used for standardization. Interestingly, the observed tissue-specific expression of these control genes is in keeping with their known role or function: there is high B2M expression in leukocytes, where it is a major cell-surface marker, and relatively low non-muscle cytoskeletal ACTB expression in heart tissue, which is predominantly of muscular origin. In view of the inherent variation in expression of housekeeping genes, we recommend the use of at least three proper control genes for calculating a normalization factor, and present a procedure to determine whether or not more - and if so, how many - control genes were required for reliable normalization. This analysis clearly showed that three stable control genes sufficed for accurate normalization of samples with relatively low expression variation, whereas other tissue panels required a fourth, or even a fifth control gene to capture the observed variation. The purpose of normalization is to remove the sampling differences (such as RNA quantity and quality) in order to identify real gene-specific variation. For proper internal control genes, this variation should be minimal or none. To validate the gene-stability measure M and the geNorm algorithm to identify the most stable control genes in a set of samples, we have calculated the gene-specific variation for each gene as the coefficient of variation of normalized expression levels. To this end, the raw expression values were standardized to different normalization factors, calculated as the geomean of the most, intermediate, or least stable control genes (as determined by geNorm). The rationale of this analysis is that a normalization factor based on proper internal control genes should remove all nonspecific variation. In contrast, unstable control genes cannot completely remove the nonspecific variation, and even add more variation, resulting in larger so-called gene-specific variations for the tested control genes. This analysis clearly demonstrated that most nonspecific variation was removed when the most stable control genes (as determined by geNorm) were used for normalization, which proves that the novel stability measure and strategy presented here effectively allowed the stability of gene expression in the different tissue panels to be assessed. Further validation demonstrated that the geometric mean of carefully selected control genes is an accurate estimate of the mRNA transcript fraction, as determined by comparison with frequently applied microarray normalization factors. Although both RT-PCR normalization factors based on geometric averaging are relatively similar, the one based on at least seven control genes (that is, NF M < 0.7) is slightly more equivalent to the microarray-scaling factors. Two possible explanations can account for this observation. First, the five most stable control genes as determined by geNorm are based on only two RNA samples (that is, a Cy3-labeled reference pool, and a Cy5-labeled test sample), in contrast to the RT-PCR data, where 9 to 34 samples were used, resulting in more reliable estimation of the expression stability. Second, recent technical reports clearly state that array hybridization analyses experience considerable - often underestimated - variation and uncertainty at several levels. Accurate background fluorescence correction and spot quality assessment, among others, have been described as critical issues for reliable ratio estimation [19,20,21]. The higher variability associated with array hybridization results might thus explain the need for more control genes to normalize the data. Nevertheless, this study clearly showed that normalization based on the geometric mean of carefully selected control genes results in equivalent ratio estimation compared to commonly applied array scale factors, which validates its use for RT-PCR normalization. In addition, the method presented could easily be applied to normalize gene-expression levels resulting from microarray hybridization experiments, where only a limited number of genes are spotted, including some housekeeping genes. In conclusion, we described and validated a procedure to identify the most stable control genes in a given set of tissue samples, and to determine the optimal number of genes required for reliable normalization of RT-PCR data. The strategy presented can be applied to any number or kind of genes or tissues, and should allow more accurate gene-expression profiling. This is of utmost importance for studying the biological significance of subtle expression differences, and for confirmatory and/or extended analyses of microarray results by means of RT-PCR. Materials and methods Sample preparation Thirty-four neuroblastoma cell lines were grown to subconfluency according to standard culture conditions. RNA was isolated using the RNeasy Midi Kit (Qiagen) according to the manufacturer's instructions. Nine RNA samples from pooled normal human tissues (heart, brain, fetal brain, lung, trachea, kidney, mammary gland, small intestine and uterus) were obtained from Clontech. Blood and fibroblast biopsies were obtained from different normal healthy individuals. Thirteen leukocyte samples were isolated from 5 ml fresh blood using Qiagen's erythrocyte lysis buffer. Fibroblast cells from 20 upper-arm skin biopsies were cultured for a short time (3-4 passages) and harvested at subconfluency as described [22]. Bone marrow samples were obtained from nine patients with no hematological malignancy. Total RNA of leukocyte, fibroblast and bone marrow samples was extracted using Trizol (Invitrogen), according to the manufacturer's instructions. Real-time RT-PCR DNase treatment, cDNA synthesis, primer design and SYBR Green I RT-PCR were carried out as described [23]. In brief, 2 μg of each total RNA sample was treated with the RQ1 RNase-free DNase according to the manufacturer's instructions (Promega). Treated RNA samples were desalted (to prevent carry over of magnesium) before cDNA synthesis using Microcon-100 spin columns (Millipore). First-strand cDNA was synthesized using random hexamers and SuperscriptII reverse transcriptase according to the manufacturer's instructions (Invitrogen), and subsequently diluted with nuclease-free water (Sigma) to 12.5 ng/μl cDNA. RT-PCR amplification mixtures (25 μl) contained 25 ng template cDNA, 2x SYBR Green I Master Mix buffer (12.5 μl) (Applied Biosystems) and 300 nM forward and reverse primer. Reactions were run on an ABI PRISM 5700 Sequence Detector (Applied Biosystems). The cycling conditions comprised 10 min polymerase activation at 95°C and 40 cycles at 95°C for 15 sec and 60°C for 60 sec. Each assay included (in duplicate): a standard curve of four serial dilution points of SK-N-SH or IMR-32 cDNA (ranging from 50 ng to 50 pg), a no-template control, and 25 ng of each test cDNA. All PCR efficiencies were above 95%. Sequence Detection Software (version 1.3) (Applied Biosystems) results were exported as tab-delimited text files and imported into Microsoft Excel for further analysis. The median coefficient of variation (based on calculated quantities) of duplicated samples was 6%. Single control normalization error E For any given m tissue samples, real-time RT-PCR gene-expression levels a ij of n internal control genes are measured. For every combination of two tissue samples p and q, and every combination of two internal control genes j and k, the single control normalization error E was calculated (Equation 1). This is the fold expression difference between samples p and q when normalized to housekeeping gene j or k. ( j,k [1,n], p,q [1,m], j ≠ k and p ≠ q): Internal control gene-stability measure M For every combination of two internal control genes j and k, an array A jk of m elements is calculated which consist of log2-transformed expression ratios a ij /a ik (Equation 2). We define the pairwise variation V jk for the control genes j and k as the standard deviation of the A jk elements (Equation 3). The gene-stability measure M j for control gene j is the arithmetic mean of all pairwise variations V jk (Equation 4). ( j,k [1,n] and j ≠ k): V jk = st.dev (A jk ) (3) Normalization of array data Publicly available raw microarray data [14] were downloaded as tab-delimited files. Eight hybridization data sets were randomly selected and imported into Microsoft Excel software for further manipulation (MCF7, DU-145, 786-0, BC2, K562, A549, U251, and SK-OV-3). For each hybridization array, all spots with Cy3 or Cy5 fluorescence intensities below the average overall background level plus one standard deviation were discarded. Subsequently, a local background correction for each spot was applied. Two scale factors were calculated for each slide on the basis of median ratio normalization (median ratio set to 1) and total intensity normalization (equalized sum of fluorescence intensities for both channels). Nine housekeeping genes were identified by BLAST similarity or keyword search against the database of cDNA clones present on the array (see IMAGE clones listed in Table 1). Additional data files The raw expression values are available as a tab-delimited file. Supplementary Material Additional data file 1 Raw expression values Click here for additional data file
- Record: found
- Abstract: found
- Article: not found
Mechanisms underlying acute protection from cardiac ischemia-reperfusion injury.
Elizabeth Murphy, Charles Steenbergen (2008)
Mitochondria play an important role in cell death and cardioprotection. During ischemia, when ATP is progressively depleted, ion pumps cannot function resulting in a rise in calcium (Ca(2+)), which further accelerates ATP depletion. The rise in Ca(2+) during ischemia and reperfusion leads to mitochondrial Ca(2+) accumulation, particularly during reperfusion when oxygen is reintroduced. Reintroduction of oxygen allows generation of ATP; however, damage to the electron transport chain results in increased mitochondrial generation of reactive oxygen species (ROS). Mitochondrial Ca(2+) overload and increased ROS can result in opening of the mitochondrial permeability transition pore, which further compromises cellular energetics. The resultant low ATP and altered ion homeostasis result in rupture of the plasma membrane and cell death. Mitochondria have long been proposed as central players in cell death, since the mitochondria are central to synthesis of both ATP and ROS and since mitochondrial and cytosolic Ca(2+) overload are key components of cell death. Many cardioprotective mechanisms converge on the mitochondria to reduce cell death. Reducing Ca(2+) overload and reducing ROS have both been reported to reduce ischemic injury. Preconditioning activates a number of signaling pathways that reduce Ca(2+) overload and reduce activation of the mitochondrial permeability transition pore. The mitochondrial targets of cardioprotective signals are discussed in detail.
Author and article information
Contributors
Janna L. Morrison:
ORCID: https://orcid.org/0000-0002-8602-8519
Janna.Morrison@unisa.edu.au
Journal
Journal ID (nlm-ta): J Physiol
Journal ID (iso-abbrev): J Physiol
Journal ID (doi): 10.1111/(ISSN)1469-7793
Journal ID (publisher-id): TJP
Journal ID (hwp): jphysiol
Title:
The Journal of Physiology
Publisher:
John Wiley and Sons Inc.
(Hoboken
)
ISSN
(Print):
0022-3751
ISSN
(Electronic):
1469-7793
Publication date
(Electronic):
19
July
2023
Publication date
(Print):
15
August
2023
Publication date PMC-release: 19
July
2023
Volume: 601
Issue: 16
(
doiID:
10.1113/tjp.v601.16
)
Pages: 3647-3665
Affiliations
Author notes
[*]
[*
]
Corresponding author J. L. Morrison: Head, Early Origins of Adult Health Research Group, Health and Biomedical
Innovation, UniSA: Clinical and Health Science, University of South Australia, GPO
Box 2471, Adelaide, South Australia, Australia 5001. Email:
Janna.Morrison@
123456unisa.edu.au
Author information
Mitchell C. Lock
https://orcid.org/0000-0002-3594-1455
Beth J. Allison
https://orcid.org/0000-0002-1060-513X
Youguo Niu
https://orcid.org/0000-0002-8843-9952
Dino A. Giussani
https://orcid.org/0000-0002-1308-1204
Janna L. Morrison
https://orcid.org/0000-0002-8602-8519
Article
Publisher ID: TJP15653DOI: 10.1113/JP284786
PMC ID: 10952154
PubMed ID: 37467062
SO-VID: 960c7b5f-a4a3-47e6-aae3-cee2cdc37ca1
Copyright © © 2023 The Authors.
The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
License:
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
History
Date
received
: 03
April
2023
Date
accepted
: 04
July
2023
Page count
Figures: 9,
Tables: 3,
Pages: 19,
Words: 10475
Funding
Funded by: British Heart Foundation
, doi 10.13039/501100000274;
Award ID: RG/17/8/32924
Funded by: Department of Education and Training | Australian Research Council
Award ID: FT170100431
Funded by: Medical Research Council
, doi 10.13039/501100000265;
Award ID: MC_UU_00028/4
Categories
Subject:
Research Article
Subject:
Placenta, Pregnancy and Perinatal Physiology
Custom metadata
source-schema-version-number 2.0
cover-date 15 August 2023
details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.9 mode:remove_FC converted:20.03.2024
ScienceOpen disciplines: Human biology