Medicine, Faculty of
Other UBC
Non UBC
Medical Genetics, Department of
Pathology and Laboratory Medicine, Department of
Pediatrics, Department of
Population and Public Health (SPPH), School of
DSpace
Viruses 10 (2): 77 (2018)
University of British Columbia. Centre for Blood Research
Budd, Matthew A.
Calli, Kristina
Samson, Lindy
Bowes, Jennifer
Hsieh, Anthony Y. Y.
Forbes, John C.
Bitnun, Ari
Singer, Joel
Kakkar, Fatima
Alimenti, Ariane
Maan, Evelyn J.
Lewis, M. E. Suzanne
Gentile, Carole
Côté, Hélène C. F.
Brophy, Jason C.
Long-term outcomes of perinatal exposure to maternal antiretroviral therapy in HIV-exposed uninfected (HEU) children are unknown. However, both HIV antiretroviral therapy and autism spectrum disorder (ASD) have been associated with mitochondrial alterations. Leukocyte mitochondrial DNA (mtDNA) content can serve as a marker for mitochondrial dysfunction. In this cross-sectional, nested case-control study, HEU children with ASD were matched approximately 1:3 on age, sex, and ethnicity to HEU children without ASD, HIV-unexposed uninfected (HUU) controls, and HUU children with ASD. Leukocyte mtDNA content was measured using quantitative PCR. Among 299 HEU in this study, 14 (4.7%) were diagnosed with ASD, which is higher than the general population prevalence estimates. HEU children without ASD and HUU children with ASD had higher mtDNA content than HUU controls. HEU children with ASD had significantly higher mtDNA content than all other study groups. Our results suggest a clear association between elevated leukocyte mtDNA content and both HEU and ASD status. This may implicate mitochondrial dysfunction as a contributor to the high ASD prevalence observed in our cohort.
virusesArticleBlood Mitochondrial DNA Content in HIV-ExposedUninfected Children with Autism Spectrum DisorderMatthew A. Budd 1,2, Kristina Calli 3, Lindy Samson 4,5, Jennifer Bowes 5,Anthony Y. Y. Hsieh 1,2, John C. Forbes 6, Ari Bitnun 7, Joel Singer 8,9, Fatima Kakkar 10,Ariane Alimenti 6,11, Evelyn J. Maan 11,12, M. E. Suzanne Lewis 3, Carole Gentile 13,Hélène C.F. Côté 1,2,12,* and Jason C. Brophy 4,5 ID for the CIHR Team on Cellular Aging and HIVComorbidities in Women and Children (CARMA)1 Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 2B5,Canada; m.budd@alumni.ubc.ca (M.A.B.); anthony_y_hsieh@hotmail.com (A.Y.Y.H.)2 Centre for Blood Research, University of British Columbia, Vancouver, BC V6T 1Z3, Canada3 Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada;kcalli@mail.ubc.ca (K.C.); suzanne.lewis@ubc.ca (M.E.S.L.)4 Department of Pediatrics, University of Ottawa, Ottawa, ON K1H 8L1, Canada; Samson@cheo.on.ca (L.S.);JBrophy@cheo.on.ca (J.C.B.)5 Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON K1H 8L1, Canada; jbowes@cheo.on.ca6 Department of Pediatrics, University of British Columbia, Vancouver, BC V6H 3V4, Canada;jcforbes@shaw.ca (J.C.F.); aalimenti@cw.bc.ca (A.A.)7 Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8,Canada; ari.bitnun@sickkids.ca8 CIHR Canadian HIV Trials Network, Vancouver, BC V6Z 1Y6, Canada; jsinger@hivnet.ubc.ca9 School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada10 Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montréal, QC H3T 1C5, Canada;fatima.kakkar@umontreal.ca11 Oak Tree Clinic, BC Women’s Hospital and Health Centre, Vancouver, BC V6H 3N1, Canada;emaan@cw.bc.ca12 Women’s Health Research Institute, Vancouver, BC V6H 3N1, Canada13 Department of Psychology, Children’s Hospital of Eastern Ontario, Ottawa, ON K1H 8L1, Canada;cgentile@cheo.on.ca* Correspondence: helene.cote@ubc.ca; Tel.: +1-604-822-9777Received: 23 November 2017; Accepted: 8 February 2018; Published: 11 February 2018Abstract: Long-term outcomes of perinatal exposure to maternal antiretroviral therapy in HIV-exposeduninfected (HEU) children are unknown. However, both HIV antiretroviral therapy and autismspectrum disorder (ASD) have been associated with mitochondrial alterations. Leukocyte mitochondrialDNA (mtDNA) content can serve as a marker for mitochondrial dysfunction. In this cross-sectional,nested case-control study, HEU children with ASD were matched approximately 1:3 on age, sex,and ethnicity to HEU children without ASD, HIV-unexposed uninfected (HUU) controls, and HUUchildren with ASD. Leukocyte mtDNA content was measured using quantitative PCR. Among 299HEU in this study, 14 (4.7%) were diagnosed with ASD, which is higher than the general populationprevalence estimates. HEU children without ASD and HUU children with ASD had higher mtDNAcontent than HUU controls. HEU children with ASD had significantly higher mtDNA content than allother study groups. Our results suggest a clear association between elevated leukocyte mtDNA contentand both HEU and ASD status. This may implicate mitochondrial dysfunction as a contributor to thehigh ASD prevalence observed in our cohort.Keywords: antiretroviral therapy; genetics; neurodevelopment; prophylaxis; mitochondria;HIV; pediatricsViruses 2018, 10, 77; doi:10.3390/v10020077 www.mdpi.com/journal/virusesViruses 2018, 10, 77 2 of 161. IntroductionThe use of combination antiretroviral therapy (ART) during pregnancy along withpostnatal antiretroviral (ARV) prophylaxis has effectively reduced vertical transmission of humanimmunodeficiency virus (HIV) [1,2]. Accordingly, the number of HIV-exposed, uninfected (HEU)children born worldwide has increased significantly and will continue to rise. Although its preventativebenefits are evident, concerns have been raised regarding potential long-term effects of perinatal ARVexposure in HEU infants [2–4]. A recent study noted no adverse neurodevelopmental outcomes inHEUs before 12 months of age, but stressed a need for assessment in older cohorts [5]. Nucleosidereverse transcriptase inhibitors have been associated with mitochondrial toxicity in adults [6] andchildren [7–9], and several can cross the placental barrier [10] and/or the blood brain barrier [11],prompting concern that in utero exposure to maternal ARVs could be toxic to mitochondria in thedeveloping fetus, and may be associated with mitochondrial dysfunction (i.e., loss of mitochondrialfunction in some capacity) [12]. In addition to potentially increased risk of developing variousmetabolic abnormalities and immune irregularities [13], mitochondrial dysfunction in young childrenhas been linked to autism spectrum disorder (ASD) [14–16]. There is also abundant evidence of anincreased risk of adverse neurodevelopmental problems associated with maternal immune activationand infection during pregnancy [17–20].Elevated levels of mitochondrial DNA (mtDNA) per cell in both brain tissue and blood have beenassociated with advanced age and/or markers of cellular senescence, suggestive of a physiologicalresponse to oxidative stress [21,22]. Excess endogenous buildup of reactive oxygen species mayinduce mitochondrial dysfunction directly through a number of mechanisms, such as damage tomitochondrial proteins, enzymes, lipids, mtDNA, and nuclear DNA (nDNA) [23].As of 2010, the prevalence of ASD in U.S. children (aged eight years) was estimated at 1.47% [24].However, in a study of 158 HEU children followed at one Canadian pediatric HIV clinic [25], a higherprevalence of ASD (9/158 or 5.7%, 95% confidence interval (CI): 2.6% to 10.8%) was recently observed.Many of these children were also enrolled in the prospective, pan-Canadian Children & WomenAntiRetrovirals & Markers of Aging (CARMA) cohort study. Our aim was to identify children withASD in this cohort of HEUs, and to compare blood mitochondrial DNA mtDNA content (the ratioof mtDNA to nDNA), a marker for mitochondrial dysfunction, between HEU and HIV-unexposeduninfected (HUU) children with and without a diagnosis of ASD. As we previously observed higherblood mtDNA in HEU neonates compared to HUUs [21], we hypothesized that HEU would have ahigher level of mtDNA than HUU, and that within the HEU and HUU groups, children with ASDwould have higher levels of mtDNA than corresponding non-ASD controls. Potential relationshipsbetween HEUs’ exposure to ARVs (type and duration) and mtDNA content were also explored.2. Materials and Methods2.1. Study DesignA nested case-control study design was used, whereby HEU children with a diagnosis of ASD(detailed in the next section) were matched 1:3 to each of the following groups: (a) HEU childrenwithout ASD, matched on sex, ethnicity, and age; (b) HUU anonymous controls and HUU siblings ofHUU children with ASD, each matched on sex and age; (c) HUU children with ASD matched on sex,age, and whenever possible, ethnicity.2.2. Study ParticipantsPaediatric HEU participants in this study were participants of the CARMA cohort study, whichprospectively enrolls both children living with HIV and HEU children at four sites across Canadaand investigates the effects of HIV and ARVs on markers of cellular aging and HIV comorbidities inwomen and children [26].Viruses 2018, 10, 77 3 of 16HUU children with ASD were participants in the British Columbia Autism SpectrumInterdisciplinary Research (ASPIRE) Program of ASD-CARC (the ASD-Canadian-American ResearchConsortium, http://www.autismresearch.ca/). HUU anonymous controls were children who hadroutine blood work during a visit to the emergency department of the British Columbia Children’sHospital. For these controls, only sex and age were known and they were assumed to not have ASD.Finally, a small number of non-ASD HUU were siblings of ASPIRE participants, included to allow foradequate numbers of matched case-controls.Clinical and demographic information was retrieved from the CARMA and ASPIRE databases.Demographic and anthropometric data collected by CARMA included the child’s date and country ofbirth, estimated gestational age at birth, sex, ethnicity (as reported by self or parents), and paternal andmaternal dates of birth. For these analyses, children of mixed background were categorized accordingto their non-Caucasian ancestry. Clinical data collected included platelet count, type and duration ofany perinatal ART exposure, and any maternal substance use the child was exposed to in pregnancy(e.g., tobacco, alcohol, illicit drugs) if available. In addition, health issues in the child related to suchfactors as gastrointestinal health, psychological conditions, haematology, or other conditions requiringhospitalization were recorded. Data captured by ASPIRE includes in-depth birth and medical histories,original ASD diagnostic assessments, and family histories followed by comprehensive clinical geneticsassessment in order to rule out known mitochondrial or nuclear genomic disorders.Approval for the study was obtained from the Research Ethics Boards of all participatinginstitutions (University of British Columbia research ethics board #H08-02018; approved24 October 2008).2.3. Diagnosis and Severity of Autism Spectrum DisorderFor the purposes of this study, determination of ASD required a formal diagnosis by an accreditedhealth provider, using the criteria of the fourth or fifth edition of the American Psychiatric Association’sDiagnostic and Statistical Manual of Mental Disorders (typically including assessment with the AutismDiagnostic Observation Schedule and the Autism Diagnostic Interview). All children were screenedand diagnosed with ASD as part of routine clinical care and practice (i.e., routine yearly surveillanceof children for general health and neurodevelopment post-HIV/ARV exposure), which includedgeneral pediatric screening for neurodevelopmental milestones. Diagnoses of ASD for children in bothCARMA and ASPIRE were made between 2004 and 2015, between the ages of three and ten years.Formal diagnostic ASD assessments (clinical assessments by developmental pediatricians and/orpsychologists’ psychometric assessments) and quantifiable information on ASD severity (such asscores from the Childhood Autism Rating Scale [CARS]) were reviewed when available. However,these scores were not available for all study participants, in particular when these assessmentswere not available from the hospital health record. Ascertainment of intellectual disabilities and/ordevelopmental delays in HEUs with ASD were based either on standardized psychometric tests(Stanford-Binet, Bayley, Mullen, Vineland, Adaptive Behaviour Assessment Scale), or on clinicalassessments by developmental paediatricians and/or psychologists for children who were nottestable. For the purposes of analysis, intellectual disability also encompassed assessments of globaldevelopmental delay and children with cognitive test scores in the first or second percentile, as well ascases given a formal diagnosis of intellectual disability. To address the severity of ASD, a qualitativedescriptor (mild, moderate, severe) was assigned to each child whenever possible. This descriptorwas primarily based on CARS scores when available, complemented with clinical assessments byexperienced pediatricians and/or mental health experts, informed by all test results available in thehealth records.2.4. Specimen Collection and PreparationCARMA HEU whole blood (WB) was collected between January 2010 and April 2015; ASPIREWB was collected between 2002 and 2014; anonymous HUU WB was collected between April 2010Viruses 2018, 10, 77 4 of 16and December 2011. For HEUs with ASD, WB specimens drawn closest to the date of ASD diagnosiswere used; if the date of ASD diagnosis was unknown, the most recent blood specimen was used.Collection, processing, and DNA extraction from CARMA WB specimens have been previouslydescribed [26,27]; anonymous controls’ DNA were extracted in the same manner as the CARMA WB.Extracts of WB DNA from HUU ASD children and non-ASD siblings were obtained from the ASPIREbiobank. All blood specimens were processed within 48 h of collection; we have previously ascertainedthat blood mtDNA content is stable over this period of time.2.5. MtDNA Content AssayWB DNA extracts were diluted 1:10 with elution buffer AE (Qiagen, Hilden, Germany) at pH 9.0,containing 10 mM Tris-Cl and 0.5 mM ethylenediaminetetraacetic acid (EDTA). MtDNA content wasmeasured using a monochrome, multiplex quantitative polymerase chain reaction (qPCR) assay similarto a method published for relative telomere length determination, allowing for the quantification oftwo genes in the same well [28]. MtDNA content was expressed as the ratio between mtDNA copynumber (D-loop region) and the copy number of a single-copy nuclear gene (albumin), previouslyshown by members of our group to be suitable for use in a multiplex assay [27]. The primer sequencesused are shown in Supplementary Materials Table S1.DNA extracts were assayed on the LightCycler® 480 platform (Roche, Basel, Switzerland).For each reaction, 2 µL of DNA was added to 8 µL of master mix for final concentrationsof 1× FastStart SYBR Green Master (Roche), 1.2 mM EDTA, and 4 primers at 0.9 µM(Supplementary Materials Table S1). All extracts were assayed in duplicate, and each plate includeda standard curve, a negative control, and two internal controls. The standard curve was generatedby 1:5 serial dilutions of two cloned plasmids containing either the albumin or D-loop amplicon,and mixed in a 1:50 ratio. Matched children were assayed on the same plate to reduce inter-assaybias when performing between-group comparisons, and well positions were randomized. All DNAextracts used were kept blind to the research team until final analysis. The thermal cycling profile isshown in Supplementary Materials Table S2, and was similar to that previously used for telomerelength determination [28].2.6. Statistical AnalysesTwo-tailed Student’s t-tests or Mann-Whitney U-tests were used to compare mtDNA contentbetween groups, after ascertaining the normality of data within each group via the Shapiro-Wilktest. One group’s data (HEU with ASD) was not normally distributed even after log-transformation.To account for performing multiple two-group comparisons, we re-performed analyses of mtDNAcontent using the Kruskal-Wallis one-way analysis of variance, including data from all four studygroups. This was followed by post-hoc testing using Dunn’s method to correct for multiplecomparisons. Confidence intervals for ASD prevalence were estimated using a Poisson distribution.All analyses were conducted using the R computing environment, v3.4.3 (R Foundation for StatisticalComputing, Vienna, Austria). A p-value <0.05 was considered statistically significant.2.7. Sensitivity AnalysesDue to an ethnicity mismatch between CARMA and ASPIRE children, and to account forthe unknown ethnicities of many HUU controls, a sensitivity analysis was performed to examinewhether mtDNA content differed significantly between sex and age-matched Asian/South Asian,Caucasian, and African-Canadian ethnic groups. CARMA HEU children without ASD of Asian orSouth Asian ethnicity were matched 1:2 with non-ASD HEU children of Caucasian (n = 20) andAfrican-Canadian (n = 20) ethnicity. None of the children included in this analysis were includedin the main study. All samples were blinded and randomized in the same manner as in the mainarm of the study, with matched samples assayed on the same plate. Between-group comparisonsof mtDNA content were done using two-tailed Student’s t-test. We performed a sample sizeViruses 2018, 10, 77 5 of 16calculation to estimate the number of participants needed in each group to detect a difference with80% power.We also performed a sensitivity analysis for potential bias due to differential dilution factors ofour DNA extracts. For those extracts with both an albumin copy number and mtDNA content valuewithin acceptable quality control ranges (n = 147), we examined the correlation between albumincopy number and mtDNA content with Spearman’s rank correlation coefficient and modeled therelationship with simple linear regression. We then diluted a separate aliquot of each DNA extractto achieve a common concentration of albumin across all extracts and repeated the assay. We usedPearson’s product moment correlation and linear regression to determine whether our resultswere affected by dilution factor by comparing mtDNA content of our extracts before and afterthis dilution.3. ResultsAmong HEU children enrolled in the CARMA cohort as of December 2015, 14/299 (4.7%, 95% CI:2.6% to 7.7%) had a confirmed diagnosis of ASD. Of note, ASD prevalence among CARMA childrenliving with HIV was 2/144 (1.4%, 95% CI: 0.2% to 5.0%).Basic demographic characteristics of HEU children with and without ASD, and HUU childrenwith and without ASD are shown in Table 1. Among all HEUs, there were three pairs of siblings andone pair of half-siblings (same mother) included in the study; of these, one sibling pair and one of thehalf-siblings received a diagnosis of ASD. Maternal age at birth was available for 41/42 (98%) of HEUswithout ASD, 9/51 (18%) of HUUs without ASD, and all children in other groups. Paternal age at birthwas available for 11/14 (79%) of HEUs with ASD, 38/42 (90%) of HEUs without ASD, 41/42 (98%) ofHUUs with ASD, and 9/51 (18%) of HUUs without ASD. Maternal and paternal age did not differsignificantly between groups.Table 1. Demographic characteristics of the matched study participants (cases and controls).Characteristic HEU with ASDn = 14HEU withoutASD n = 42HUU with ASDn = 42HUU withoutASD n = 51Male sex 10/14 (71) 30/42 (71) 30/42 (71) 36/51 (71)Age at blood collection (years) 6 (4–8) [2–16] 6 (4–8) [2–16] 6 (4–8) [2–16] 6 (4–9) [2–16]EthnicityBlack/African-Canadian 11/14 (79) 33/42 (79) 6/42 (14) 0/51White/Caucasian 3/14 (21) 9/42 (21) 22/42 (52) 5/51 (10)Asian/South Asian 0/14 (0) 0/42 (0) 10/42 (24) 4/51 (8)Indigenous 0/14 (0) 0/42 (0) 4/42 (10) 0/51 (0)Unknown (anonymous) 0/14 (0) 0/42 (0) 0/42 (0) 42/51 (82)Maternal age at birth (years) 30 (24–32) [18–42] 33 (31–36) [25–42] 31 (28–35) [18–42] 27 (26–36) [21–39]Paternal age at birth (years) 35 (29–42) [25–44] 37 (34–41) [24–63] 35 (30–38) [20–51] 30 (28–38) [26–44]Data are reported as median (interquartile range) [range] or n/N (%); HEU—HIV-exposed uninfected;HUU—HIV-unexposed uninfected; ASD—autism spectrum disorder.Maternal ART information was available for 54/56 (96%) of HEU children. Of those childrenwith maternal ART information available, 49/54 (90%) were exposed to ARVs in utero. A summary ofmaternal regimens is shown in Table 2. The majority of mothers who received ART during pregnancywere treated with a combination regimen of two nucleoside reverse transcriptase inhibitors and either(a) a protease inhibitor (37/49, 76%), or (b) a non-nucleoside reverse transcriptase inhibitor (3/49, 6%).If the mother switched regimens during pregnancy, the regimen taken for the majority of pregnancywas reported.The most commonly used individual ARVs were lamivudine (44/54, 81%), zidovudine(33/54, 61%), nelfinavir (21/54, 39%), ritonavir-boosted lopinavir (17/54, 31%), and abacavir(14/54, 26%). Notably, 3/14 (21%) HEUs with ASD received no exposure to ARVs in utero, comparedViruses 2018, 10, 77 6 of 16to 2/42 (5%) HEUs without ASD (Fisher’s exact p = 0.094). However, HEU children with ASDhad a shorter duration of exposure to maternal ART than HEUs without ASD (11 vs. 33 weeks,Table 2) and consequently may have experienced greater exposure to maternal HIV “milieu”. A visualrepresentation relating mtDNA content, maternal ART parameters, and ASD severity is shown inFigure 1.Table 2. Clinical characteristics of matched study participants (cases and controls).Characteristic HEU with ASDn = 14HEU withoutASD n = 42HUU with ASDn = 42HUU withoutASD n = 51Developmental disorders/delaysIntellectual disabilities 9/14 (64) 0/42 (0) 22/42 (52) 0/9 (0)Language delay 2/14 (14) 2/42 (5) 6/42 (14) 0/9 (0)Unknown or unable to assess 2/14 (14) 0/42 (0) 4/42 (10) 42/51 (82)Severity of ASD symptomsMild or mild/moderate 5/14 (36) - 7/42 (17) -Moderate 1/14 (7) - 4/42 (10) -Moderate/severe or severe 7/14 (50) - 5/42 (12) -Unable to assess 0/14 (0) - 13/42 (31) -Not specified 1/14 (7) - 13/42 (31) -History of seizures/epilepsy (ever) 2/14 (14) 0/42 (0) 5/42 (12) 0/9 (0)Low muscle tone 3/14 (21) 2/42 (5) 14/42 (33) 1/9 (11)Chronic gastrointestinal disorders 10/14 (71) 9/42 (21) 7/42 (17) 0/9 (0)Maternal ARV regimen duringpregnancy 1Dual NRTIs+PIZDV + 3TC + PI 5/14 (36) 19/42 (45) - -ABC + 3TC + PI 0/14 8/42 (19) - -TDF + FTC + PI 2/14 (14) 3/42 (7) - -Other combinations + PI 1/14 (7) 4/42 (10)Dual NRTIs + NNRTI 1/14 (7) 2/42 (5) - -NRTI + NNRTI + PI 0/14 (0) 2/42 (5)Other 1/14 (7) 1/42 (2) - -None 3/14 (21) 2/42 (5) - -Unknown 1/14 (7) 1/42 (2) - -Infant received ZDV prophylaxis 13/14 (93) 42/42 (100) - -Length of in utero ARVexposure (weeks) 2 11 (6–35) [0–38] 33 (20–39) [0–41] - -Length of neonatal ZDVprophylaxis (weeks) 6 (4–6) [0–7] 6 (6–6) [4–7] - -Data are reported as median (interquartile range) [range] or n/N (%); ARV—antiretroviral; PI—protease inhibitor;(N)NRTI—(non-)nucleoside reverse transcriptase inhibitor; ZDV—zidovudine; 3TC—lamivudine; TDF—tenofovirdisoproxil fumarate; FTC—emtricitabine; ABC—abacavir; HEU—HIV-exposed uninfected; HUU—HIV-unexposeduninfected; ASD—autism spectrum disorder; 1 Also see Figure 1; 2 Maternal ARV exposure time during pregnancywas available for 12/14 HEUs with ASD and 40/42 HEUs without ASD; -: Not applicable.Viruses 2018, 10, 77 7 of 16Viruses 2018, 10, x FOR PEER REVIEW 7 of 16 Figure 1. Graphical representation of mitochondrial DNA (mtDNA) content, maternal antiretroviral therapy (ART) parameters, and severity of autism spectrum disorder (ASD) in 98 study participants. White numbers reflect ASD severity: 1 = mild or mild/moderate; 2 = moderate; 3 = moderate/severe or severe. Satellite shapes (square, circle, triangle, diamond) indicate non-nucleoside reverse transcriptase inhibitor (NNRTI) drug exposure, and color indicates exposure to NRTI backbone (one per month, clockwise arrangement). 3.1. MtDNA Content Analyses Blood mtDNA content (mtDNA/nDNA ratio) was obtained for 149 study participants (Figure 2). For two DNA extracts (both HUU controls), the measures were outside the known range of linearity of the assay over two independent runs; these were therefore excluded, as per a priori quality control criteria. To evaluate the association between ASD and mtDNA content, we compared measurements between ASD and non-ASD children within each of the HEU and HUU groups. HEU children with ASD had higher mtDNA content (median [interquartile range (IQR)]: 163 [150 to 180], n = 14) than HEU children without ASD (116 [92 to 153], n = 42; p = 0.021). Similarly, HUU children with ASD had higher mtDNA content (110 [100 to 132], n = 42) than HUU children without ASD (100 [73 to 121], n = 49; p = 0.034). To evaluate the association between HIV/ARV exposure status and mtDNA content, we compared measurements between HEU and HUU children in each of the ASD and non-ASD groups. HEU children with ASD had higher mtDNA content than HUU children without ASD (p < 0.001), and HEU children without ASD had higher mtDNA content than HUU children without ASD (p = 0.005). Figure 1. Graphical representation of mitochondrial DNA (mtDNA) content, maternal antiretroviraltherapy (ART) parameters, and severity of autism spectrum disorder (ASD) in 98 study participants.White numbers reflect ASD severity: 1 = mild or mild/moderate; 2 = moderate; 3 = moderate/severe orsevere. Satellite shapes (square, circle, triangle, diamond) indicate non-nucleoside reverse transcriptaseinhibitor (NNRTI) drug exposure, and color indicates exposure to NRTI backbone (one per month,clockwise arrangement).3.1. MtDNA Content AnalysesBlood mtDNA content (mtDNA/nDNA ratio) was obtained for 149 study participants (Figure 2).For two DNA extracts (both HUU controls), the measures were outside the known range of linearity ofthe assay over two independent runs; these were therefore excluded, as per a priori quality controlcriteria. To evaluate the association between ASD and mtDNA content, we compared measurementsbetween ASD and non-ASD children within each of the HEU and HUU groups. HEU children withASD had higher mtDNA content (median [interquartile range (IQR)]: 163 [150 to 180], n = 14) thanHEU children without ASD (116 [92 to 153], n = 42; p = 0.021). Similarly, HUU children with ASD hadhigher mtDNA content (110 [100 to 132], n = 42) than HUU children without ASD (100 [73 to 121],n = 49; p = 0.034).To evaluate the association between HIV/ARV exposure status and mtDNA content, we comparedmeasurements between HEU and HUU children in each of the ASD and non-ASD groups. HEUViruses 2018, 10, 77 8 of 16children with ASD had higher mtDNA content than HUU children without ASD (p < 0.001), and HEUchildren without ASD had higher mtDNA content than HUU children without ASD (p = 0.005).MtDNA content of the HUU anonymous controls (n = 40) and non-ASD HUU siblings of ASDchildren (n = 9) were similar (p = 0.70, Student’s t-test; difference between means [95% CI] = 5 [−19 to 29]).Overall results were unchanged if HUU non-ASD siblings were omitted from the analysis. For thoseHEU participants for whom platelet counts were available in the CARMA database (n = 54/56), there wasno correlation between platelet count and mtDNA content (Pearson’s r = 0.08, p = 0.55). A summary ofall between-group comparisons of mtDNA content is shown in Figure 2.Viruses 2018, 10, x FOR PEER REVIEW 8 of 16 MtDNA content of the HUU anonymous controls (n = 40) and non-ASD HUU siblings of ASD children (n = 9) were similar (p = 0.70, Student’s t-test; difference between means [95% CI] = 5 [−19 to 29]). Overall results were unchanged if HUU non-ASD siblings were omitted from the analysis. For those HEU participants for whom platelet counts were available in the CARMA database (n = 54/56), there was no correlation between platelet count and mtDNA content (Pearson’s r = 0.08, p = 0.55). A summary of all between-group comparisons of mtDNA content is shown in Figure 2. Figure 2. Between-group comparisons of blood mtDNA content in HIV-exposed uninfected (HEU) and HIV-unexposed uninfected (HUU) children with and without ASD. p-Values are reported for Student’s t-test (solid lines) or Mann-Whitney U-test (dashed lines). 3.2. Multiple Group Comparisons The Kruskal-Wallis omnibus p-value was <0.001; p-values for pairwise comparisons calculated via Dunn’s post-hoc test are shown in Table 3. Note that the Bonferroni correction was not applied to the significance level for this analysis in order to reduce the risk of type II error in this exploratory study. p-values for all two-group comparisons were comparable to those from the Mann Whitney U-test (Table 3; Figure 2). Table 3. p-Values for pairwise comparisons of mtDNA content between groups (calculated via Dunn’s post-hoc procedure following Kruskal-Wallis analysis of variance). HEU with ASD HEU without ASD HUU with ASD HEU without ASD 0.011 (0.021) - - HUU with ASD 0.002 (<0.001) 0.38 (0.21) - HUU without ASD <0.001 (<0.001) 0.006 (0.005) 0.063 (0.034) Numbers in brackets signify p-values from Mann-Whitney U-test or Student’s t-test. Statistically significant p values are bold. -: not applicable. 3.3. Sensitivity Analyses 3.3.1. Ethnicity A summary of the groups used in this sensitivity analysis is shown in Table 4. Overall, the children included in this sensitivity analysis were younger than those in the main study due to the constraints of our matching coupled with the relatively small remainder of HEU participants who were not in the main arm. Figure 2. Between-group comparisons of blood mtDNA content in HIV-exposed uninfected (HEU) andHIV-unexposed uninfected (HUU) children with and without ASD. p-Values are reported for Student’st-test (solid lines) or Mann-Whitney U-test (dashed lines).3.2. Multiple Group ComparisonsThe Kruskal-Wallis omnibus p-value was <0.001; p-values for pairwise comparisons calculatedvia Dunn’s post-hoc test are shown in Table 3. Note that the Bonferroni correction was not applied tothe significance level for this analysis in order to reduce the risk of type II error in this exploratorystudy. p-values for all two-group comparisons were comparable to those from the Mann WhitneyU-test (Table 3; Figure 2).Table 3. p-Values for pairwise comparisons of mtDNA content between groups (calculated via Dunn’spost-hoc procedure following Kruskal-Wallis analysis of variance).HEU with ASD HEU without ASD HUU with ASDHEU without ASD 0.011 (0.021) - -HUU with ASD 0.002 (<0. 01) .38 ( .21) -HUU without ASD <0.001 (<0.001) 0.006 (0.005) 0.063 (0.034)Numbers in brackets signify p-values fr m Mann-Whitney U-test or Student’s t-test. Statistically significant p valuesare bold. -: not applicable.Viruses 2018, 10, 77 9 of 163.3. Sensitivity Analyses3.3.1. EthnicityA summary of the groups used in this sensitivity analysis is shown in Table 4. Overall, the childrenincluded in this sensitivity analysis were younger than those in the main study due to the constraintsof our matching coupled with the relatively small remainder of HEU participants who were not in themain arm.There were no significant mtDNA content differences observed between any of the threegroups (p = 0.26 to 0.76, Figure 3). We used Cohen’s d as an estimate of effect size between groups(power = 0.80, α = 0.05). We found that between Caucasian and Asian/South Asian children (d = 0.12),a sample size of n = 1177 in each group would provide 80% power to detect a difference in mtDNAcontent. Similarly, between African-Canadian and Caucasian children (d = 0.27), group sizes of n = 216each would be required, and between Asian/South Asian and African-Canadian children (d = 0.44),group sizes of n = 83 each would be required. Notably, the African-Canadian and Caucasian groupshad median values similar to those of HEU children with ASD (median [IQR]: 163 [150 to 180]).Table 4. Distributions of age, sex, and mtDNA content for three matched groups of HEUs without ASDused in a sensitivity analysis of possible relationship between mtDNA content and ethnicity.Asian/South Asiann = 10White/Caucasiann = 20Black/African-Canadiann = 20Male sex 5 (50) 10 (50) 10 (50)Age at bloodcollection (years) 1.3 (0.6–2.5) [0.3–15.1] 1.4 (0.5–2.6) [0.2–15.7] 1.3 (0.5–2.6) [0.3–15.6]MtDNA content 128 (115–196) [90–203] 160 (99–191) [54–259] 169 (133–192) [62–268]Data reported as median (interquartile range) [range] or n (%).Viruses 2018, 10, x FOR PEER REVIEW 9 of 16 There were no significant mtDNA content differences observed between any of the three groups (p = 0.26 to 0.76, Figure 3). We used Cohen’s d as an estimate of effect size between groups (power = 0.80, α = 0.05). We found that between Caucasian and Asian/South Asian children (d = 0.12), a sample size of n = 1177 in each group would provide 80% power to detect a difference in mtDNA content. Similarly, between African-Canadian and Caucasian children (d = 0.27), group sizes of n = 216 each would be required, and between Asian/South Asian and African-Canadian children (d = 0.44), group sizes of n = 83 each would be required. Notably, the African-Canadian and Caucasian groups had median values similar to those of HEU children with ASD (median [IQR]: 163 [150 to 180]). Table 4. Distributions of age, sex, and mtDNA content for three matched groups of HEUs without ASD used in a sensitivity analysis of possible relationship between mtDNA content and ethnicity. Asian/South Asiann = 10 White/Caucasiann = 20 Black/African-Canadiann = 20 Male sex 5 (50) 10 (50) 10 (5 ) Age at blood collection (years) 1.3 (0.6–2.5) [0.3–15.1] 1.4 (0.5–2.6) [0.2–15.7] 1.3 (0.5–2.6) [0.3–15.6] MtDNA content 128 (115–196) [90–203] 160 (99–191) [54–259] 169 (133–192) [62–268] Data reported as median (interquartile range) [range] or n (%). Figure 3. Univariate between-group comparisons for three sex- and age-matched (1:2) groups of HEU children without ASD (p-values calculated via Student’s t-test). Lines indicate medians. 3.3.2. DNA Extract Dilution Factor The results of this analysis are presented in Figure 4. Extracts were diluted to approximately 5000 to 7000 copies of albumin per 2 μL (mean (standard deviation) [range] = 5906 (806) [3872–9551]). The correlation coefficient, regression equation, and R2 of this model suggest that results were reproducible irrespective of dilution factor and were not affected by systematic shift (Figure 4C). Figure 3. Univariate between-group comparisons for three sex- and age-matched (1:2) groups of HEUchildren without ASD (p-values calculated via Student’s t-test). Lines indicate medians.Viruses 2018, 10, 77 10 of 163.3.2. DNA Extract Dilution FactorThe results of this analysis are presented in Figure 4. Extracts were diluted to approximately 5000to 7000 copies of albumin per 2 µL (mean (standard deviation) [range] = 5906 (806) [3872–9551]).The correlation coefficient, regression equation, and R2 of this model suggest that results werereproducible irrespective of dilution factor and were not affected by systematic shift (Figure 4C).Viruses 2018, 10, x FOR PEER REVIEW 10 of 16 Figure 4. Correlation of mtDNA content and albumin copy number before (panel A) and after dilution to a common albumin concentration (panel B). Linear correlation of mtDNA content measurements before and after systematic dilution (panel C). 3.4. Clinical Outcomes Our study was not designed nor powered to investigate clinical outcomes. Although not statistically significant, the data may suggest a pattern whereby the severity of ASD symptoms appeared to be greater in HEUs than HUUs. Among children for whom psychometric test scores and formal assessments of development were available (n = 56 HEU, n = 47 HUU), 9/12 (75%) of HEU with ASD and 22/38 (58%) of HUUs with ASD were identified as intellectually disabled or showing signs of global developmental delay. Assessments of mild, moderate, or severe ASD as categorized by the Childhood Autism Rating Scale were available for 9/14 HEUs with ASD and 8/42 HUUs with ASD. Delays in language development not otherwise accompanied by cognitive deficits were also noted (Table 2). In total, 11/12 (92%) of HEUs with ASD and 28/38 (74%) of HUUs with ASD showed signs of substantial language and/or cognitive delay. Rates of intellectual disabilities in both HEU and HUU children with ASD were similar to those of the entire ASPIRE cohort (200/337, 59%). Among the HEU children with ASD for whom cognitive and developmental test scores (n = 10) from clinical diagnostic assessments by developmental pediatricians and/or psychologists were available, nine had scores in the first or second percentile for cognitive skills (Bayley Scale of Infant Development, Mullen Scale of Early Learning, or Stanford-Binet Intelligence Scale) and/or adaptive intelligence (Vineland Adaptive Behaviour Scale, Adaptive Behaviour Assessment System), suggesting that these children met criteria for intellectual disability. Only one child was noted as being of normal cognitive ability, but was assessed as having specific areas of weakness in verbal skills and short-term memory. Among HUUs with ASD, 26/42 (62%) were unable to be assessed or did not have a specified severity. Common reasons for these children to not be assessed were that they were not yet developmentally advanced enough for testing or evaluation (7/13, 54%), had a mixed cognitive profile (3/13, 23%), or were uncooperative and/or inattentive during testing (3/13, 23%). HUU children who did not have a severity rating specified generally did not meet criteria for intellectual disability and thus did not undergo evaluation (11/13, 85%), although 2/13 (15%) cases were noted by Figure 4. Correlation of mtD A content and albu in copy nu ber before (panel A) and after dilutionto a co on albu in concentration (panel B). Linear correlation of t content easure entsbefore and after syste atic dilution (panel ).3.4. Clinical OutcomesOur study was not designed nor powered to investigate clinical outcomes. Although notstatistically significant, the data may suggest a pattern whereby the severity of ASD symptomsappeared to be greater in HEUs than HUUs. Among children for whom psychometric test scores andformal assessments of development were available (n = 56 HEU, n = 47 HUU), 9/12 (75%) of HEUwith ASD and 22/38 (58%) of HUUs with ASD were identified as intellectually disabled or showingsigns of global developmental delay. Assessments of mild, moderate, or severe ASD as categorized bythe Childhood Autism Rating Scale were available for 9/14 HEUs with ASD and 8/42 HUUs withASD. Delays in language development not otherwise accompanied by cognitive deficits were alsonoted (Table 2). In total, 11/12 (92%) of HEUs with ASD and 28/38 (74%) of HUUs with ASD showedsigns of substantial language and/or cognitive delay. Rates of intellectual disabilities in both HEU andHUU children with ASD were similar to those of the entire ASPIRE cohort (200/337, 59%). Amongthe HEU children with ASD for whom cognitive and developmental test scores (n = 10) from clinicaldiagnostic assessments by developmental pediatricians and/or psychologists were available, ninehad scores in the first or second percentile for cognitive skills (Bayley Scale of Infant Development,Mullen Scale of Early Learning, or Stanford-Binet Intelligence Scale) and/or adaptive intelligence(Vineland Adaptive Behaviour Scale, Adaptive Behaviour Assessment System), suggesting that theseViruses 2018, 10, 77 11 of 16children met criteria for intellectual disability. Only one child was noted as being of normal cognitiveability, but was assessed as having specific areas of weakness in verbal skills and short-term memory.Among HUUs with ASD, 26/42 (62%) were unable to be assessed or did not have a specifiedseverity. Common reasons for these children to not be assessed were that they were not yetdevelopmentally advanced enough for testing or evaluation (7/13, 54%), had a mixed cognitiveprofile (3/13, 23%), or were uncooperative and/or inattentive during testing (3/13, 23%). HUUchildren who did not have a severity rating specified generally did not meet criteria for intellectualdisability and thus did not undergo evaluation (11/13, 85%), although 2/13 (15%) cases were noted byexperts as being suspected of functioning in the intellectual disability range but had not been evaluated.4. DiscussionIn this study, HIV/ARV exposure, irrespective of ASD diagnosis, was associated with elevatedblood mtDNA content compared to HUU controls. We have ascertained that this finding is likelynot confounded by platelet count, as has been previously described in certain studies [29–31], nor bydifferences in the concentrations of the DNA extracts themselves (Figure 4). This observation isconsistent with previous studies, by both our group [21] and others [32,33], but is in contrast with somereports of decreased mtDNA content in HEU children [34,35]. However, there is heterogeneity amongthese studies with regard to the ARVs to which children were exposed, the timing of mtDNA measuresreported, and whether measurements were made before, during, or after infant prophylaxis. To explainthe elevated mtDNA content in HEUs, some have suggested that there may be a physiologicalrebound effect in mtDNA content after removal of the pressure exerted by HIV and/or ARV exposure.For example, one study reported low mtDNA content in HEU children early in life, that by twoto five years of age had rebounded to values close to those of controls [34]. This effect was alsoobserved by our group, whereby HIV patients treated with certain ARVs experienced mtDNAdepletion that was reversed upon treatment discontinuation or change to less toxic regimens [6,36].Our group has previously reported increases in mtDNA content in ARV-exposed HEU children at birth(consistent with findings from other groups [32]), which persisted months after the ARV prophylacticperiod (first six weeks of life), and is also consistent with our current findings [21]. This would alsopartially explain why two of the three groups in our sensitivity analysis for ethnicity showed mtDNAcontent values close to those for HEU children with ASD, as the children in the sensitivity analysiswere generally much younger (approximately 1–2 years of age (Table 4) in the sensitivity analysisversus approximately 6 years of age in the main study (Table 1)). Another possible explanation forthis finding is that mtDNA haplogroup variation and genetic ancestry may play a contributory role,along with environmental factors, in the development of ASD according to recent findings [37]. It hasbeen shown that self-identified ethnicity may not fully encapsulate the complex nature of haplogroupheterogeneity and it may not be a good predictor of one’s genetic make-up [38–41], pointing to a morenuanced relationship between mtDNA, ASD progression, and ethnicity than the results presented inthis exploratory analysis.ASD diagnosis in both the HEU and HUU groups was also associated with elevated bloodmtDNA content, a finding that is consistent with previous literature [14,42]. It may be that a geneticpredisposition for ASD, mediated by mitochondrial dysfunction, is exacerbated by HIV/ARV exposureleading to a higher level of mitochondrial dysfunction and thus greater penetrance of ASD symptoms.These findings may also indicate that ASD, which has been associated with signs of chronic oxidativestress [43] and mitochondrial dysfunction [15,44], is associated with a persistent deleterious effect onmitochondria that may induce mitochondria biogenesis as a compensatory mechanism. This would beconsistent with the apparent cumulative effect we report in HEU with ASD.Although group sizes for individual ART regimens were small (see Table 2) and precludedquantitative analysis, it is noteworthy that HEU children with ASD tended to have shorter exposureto maternal ART than HEU children without ASD. This could implicate prolonged perinatal HIV“milieu” exposure as a possible predictor for ASD among HEU children. However, this would not beViruses 2018, 10, 77 12 of 16consistent with the finding that only 2/144 (1.4%) of CARMA’s HIV-infected children were diagnosedwith ASD. Most of these children were born to mothers with either untreated or poorly controlledHIV, therefore exposed to elevated viral load. It may be that certain maternal comorbidities and/orsubstance exposures led to a compounded risk of ASD in their child when combined with ARV orHIV exposure in pregnancy. Alternatively, this could suggest that initiating ART later in pregnancymay increase the risk of ASD. At least one previous study suggested increased risk of mitochondrialdysfunction in HEU when ART was started late in pregnancy [45]. More work is needed to elucidateany potential specific associations with different maternal ARV combinations, initiation times, andadherence parameters.LimitationsOur study is subject to the limitations of observational studies. An important caveat of thisstudy is that we cannot compare the prevalence of ASD within our cohort with that reported in thegeneral population because of possible bias during recruitment of participants to the CARMA cohort.More specifically, there was a possible bias toward enrolling HEU children with ASD from 2013 topresent, once it was reported that ASD prevalence among HEUs appeared elevated [25]. At facevalue, this could artificially inflate the prevalence of ASD in HEU children reported here. However,there was also evidence of a clear bias against recruiting HEU children with ASD before this time, as thechildren typically become distressed during blood draws, such that families of ASD children were lesslikely to be invited to participate and/or deemed more likely to decline participation. It should benoted that half (7/14) of the HEU children with ASD in this study were recruited during each period.Overall, we ascertain that enrollment bias in recent years was likely counter-balanced by a reversebias during the early years of the CARMA study, but this remains a limitation that prevents us fromreaching conclusions with respect to the prevalence result. Another factor limiting our interpretationof this prevalence observation is the convenience sampling of participants who may be undergoingcloser clinical observation than the general population due to the risks of perinatal HIV exposure.We acknowledge that identification, screening, and presentation of ASD among these patients is likelyto be variable and future studies should apply a more standardized methodology for case selection.Nevertheless, it provides a strong impetus for other studies to investigate the population-basedprevalence of ASD in the HEU population, as well as to evaluate ASD severity in this group.One of the key limitations of our study is the lack of consistently available information relevantto the diagnosis, evaluation, and monitoring of variables relevant to ASD. Many HUU childrenwith ASD did not receive an assessment of severity for ASD or intellectual disability, or were notassessed due to a lack of intellectual impairment. Among HEUs, we were limited to the informationavailable to us in clinical charts, which commonly did not include information regarding psychometricand neurodevelopmental assessment. This prevents us from commenting on or comparing severitybetween groups; this is something that should be evaluated in future studies involving HEU children.The assumption that children in the anonymous HUU control group do not have ASD cannotbe assured as only sex and age data were available. However, given our observations that ASD andHEU children had elevated mtDNA content compared to controls, any ASD or HEU cases among thisanonymous group would likely present a conservative bias. Ethnicities were unevenly distributedamongst groups, and ethnicity data was unavailable for much of the non-ASD HUU group. However,the sensitivity analysis we performed suggests that potential differences in ethnicity were likely notmeaningful confounders of our results.While we demonstrated an association between HEU status and elevated mtDNA content, it isunclear whether this is modulated by the effects of HIV or ARV exposure. Information about themothers, including prior to or during pregnancy, was often limited when children were enrolledafter infancy. Thus, we lack detailed measures of maternal comorbidities, non-HIV medications andsubstance use in pregnancy, as well as detailed family mental health histories. This lack of informationprevents us from commenting on between-group maternal differences that may exist on such factors asViruses 2018, 10, 77 13 of 16maternal psychotropic mediation use, psychiatric conditions, or smoking during pregnancy, and mayconfound our results. Similarly, information about maternal HIV viral load or CD4 count during theperinatal period was often unavailable. We were also not powered to explore differential associationswith individual ARV agents.The prevalence of ASD among HEU children in the CARMA cohort is approximately three timesthat seen in the general population. If this finding were to be replicated in other cohorts, it wouldnecessitate the conduct of large prospective cohort studies to determine the etiology of the association.5. ConclusionsHIV exposure and ASD diagnosis were both independently associated with elevated bloodmtDNA content in children and adolescents between the ages of two and 16 years. In children withboth HIV exposure and ASD, mtDNA content was further elevated. These results clearly warrantfurther investigation into the possible effects of maternal, clinical, demographic, environmental,and other factors, and to validate whether these findings are reproduced in other cohorts. Furthermore,the prevalence of ASD within our HEU cohort is concerning and stresses the need for longer follow-upand further research on the long-term neurodevelopmental outcomes in the rapidly growing HEUpopulation worldwide [46].Supplementary Materials: The following are available online at www.mdpi.com/1999-4915/10/2/77/s1, TableS1: Forward and reverse primer sequences used to measure mtDNA content via monochrome multiplex qPCR,Table S2: Thermal cycler settings for monochrome, multiplex qPCR of nuclear (albumin) and mitochondrial(D-loop) sequences.Acknowledgments: We extend our thanks to all study participants, as well as the research staff at Oak TreeClinic. The authors wish to acknowledge Beheroze Sattha and Izabelle Gadawski for their assistance with DNAextractions, specimen preparation, and validation assays. We thank David Deng for his significant contributionsto development of the multiplex methodology. We also wish to thank Annie Qiu for her assistance throughoutthe ethical approval process for this collaborative study. These members were supported through a CIHR teamgrant (TCO-125269). We thank Steve Kalloger for his guidance and discussions regarding statistical analyses andmodeling. We also wish to thank members of the ASPIRE team for their efforts, as well as all ASPIRE participants.We acknowledge Ying Qiao for assisting with ASPIRE DNA preparation. Finally, we thank the members ofthe Côté lab and the CARMA team for their support and input on this project. Study data were collected andmanaged using REDCap electronic data capture tools hosted at the University of British Columbia [47]. REDCap(Research Electronic Data Capture) is a secure, web-based application designed to support data capture forresearch studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking datamanipulation and export procedures; (3) automated export procedures for seamless data downloads to commonstatistical packages; and (4) procedures for importing data from external sources. This work was supported by theCanadian Institutes of Health Research (CIHR) Team Grant on Cellular Aging and HIV Comorbidities in Womenand Children (CARMA), to Hélène C.F. Côté, Ari Bitnun, Fatima Kakkar and Jason C. Brophy [TCO-125269] andCIHR grant funding to M.E. Suzanne Lewis [64217]. CIHR Team in Cellular Aging and HIV Comorbidities inWomen and Children (CARMA) investigators: Hugo Soudeyns, PhD, Université de Montréal; Deborah M Money,MD, University of British Columbia; Neora Pick, MD, University of British Columbia; Melanie Murray, MD,University of British Columbia, Patricia Janssen, PhD, University of British Columbia; Joel Singer, PhD, Universityof British Columbia; Normand Lapointe, MD, Université de Montréal; Jerilynn Prior, MD, University of BritishColumbia; Michael Silverman, MD, University of Toronto; Mary Lou Smith, PhD, University of Toronto.Author Contributions: Matthew A. Budd conducted the matching, did all assays and analyses, aided in studydesign, and drafted the manuscript. Kristina Calli aided in collection of ASPIRE subject data and DNA samplesas well as design of phenotype comparison in study. Lindy Samson contributed to initial study concept anddesign, and enrolled and collected data on participants as a site investigator. Jennifer Bowes enrolled andcollected data on participants. Anthony Y.Y. Hsieh developed and adapted the qPCR assay methodology used.John C. Forbes enrolled and collected data on participants, and contributed to the original concept and designof the study. Ari Bitnun enrolled and collected data on participants. Joel Singer aided in the analysis, reporting,and interpretation of quantitative data. Fatima Kakkar assisted in the chart review & data collection process.Ariane Alimenti enrolled and collected data on participants. Evelyn J. Maan contributed to the design of thestudy, managed the project, and enrolled and collected data on participants. M.E. Suzanne Lewis contributedto the design of the study, and led the collection and analysis of ASPIRE subject deep phenotype data andDNA study samples. Carole Gentile aided in the interpretation of psychological and developmental outcomedata. Hélène C.F. Côté contributed to the design of the study and linked with the ASPIRE researchers; all assayswere carried out in her laboratory by her trainee. Jason C. Brophy contributed to the idea for and design of thestudy, and enrolled and collected data on participants. All listed authors have reviewed and approved the finalViruses 2018, 10, 77 14 of 16manuscript as submitted, meet all applicable criteria for authorship, and agree to be held accountable for allaspects of the work.Conflicts of Interest: The authors declare no conflict of interest.References1. Forbes, J.C.; Alimenti, A.M.; Singer, J.; Brophy, J.C.; Bitnun, A.; Samson, L.M.; Money, D.M.; Lee, T.C.K.;Lapointe, N.D.; Read, S.E. A national review of vertical HIV transmission. AIDS 2012, 26, 757–763. [CrossRef][PubMed]2. Thorne, C.; Newell, M.-L. Safety of Agents Used to Prevent Mother-to-Child Transmission of HIV. Drug Saf.2007, 30, 203–213. [CrossRef] [PubMed]3. Thorne, C.; Newell, M.-L. The safety of antiretroviral drugs in pregnancy. Expert Opin. Drug Saf. 2005, 4,323–335. [CrossRef] [PubMed]4. Lewis, W.; Day, B.J.; Copeland, W.C. Mitochondrial toxicity of NRTI antiviral drugs: An integrated cellularperspective. Nat. Rev. Drug Discov. 2003, 2, 812–822. [CrossRef] [PubMed]5. Springer, P.E.; Slogrove, A.L.; Laughton, B.; Bettinger, J.A.; Saunders, H.H.; Molteno, C.D.; Kruger, M.Neurodevelopmental Outcome of HIV-exposed but uninfected infants in the Mother and Infants HealthStudy, Cape Town, South Africa. Trop. Med. Int. Health 2017, 23, 69–78. [CrossRef] [PubMed]6. Côté, H.C.F.; Brumme, Z.L.; Craib, K.J.P.; Alexander, C.S.; Wynhoven, B.; Ting, L.; Wong, H.; Harris, M.;Harrigan, P.R.; O’Shaughnessy, M.V.; et al. Changes in mitochondrial DNA as a marker of nucleoside toxicityin HIV-infected patients. N. Engl. J. Med. 2002, 346, 811–820. [CrossRef]7. Hernàndez, S.; Morén, C.; López, M.; Coll, O.; Cardellach, F.; Gratacós, E.; Miró, O.; Garrabou, G. Perinataloutcomes, mitochondrial toxicity and apoptosis in HIV-treated pregnant women and in-utero-exposednewborn. AIDS 2012, 26, 419–428. [CrossRef] [PubMed]8. Santini-Oliveira, M.; Grinsztejn, B. Adverse drug reactions associated with antiretroviral therapy duringpregnancy. Expert Opin. Drug Saf. 2014, 13, 1623–1652. [CrossRef] [PubMed]9. Newell, M.-L.; Bunders, M.J. Safety of antiretroviral drugs in pregnancy and breastfeeding for mother andchild. Curr. Opin. HIV AIDS 2013, 8, 504–510. [CrossRef] [PubMed]10. Shiramizu, B.; Shikuma, K.M.; Kamemoto, L.; Gerschenson, M.; Erdem, G.; Pinti, M.; Cossarizza, A.;Shikuma, C. Placenta and cord blood mitochondrial DNA toxicity in HIV-infected women receivingnucleoside reverse transcriptase inhibitors during pregnancy. J. Acquir. Immune Defic. Syndr. 2003, 32,370–374. [CrossRef] [PubMed]11. Decloedt, E.H.; Rosenkranz, B.; Maartens, G.; Joska, J. Central nervous system penetration of antiretroviraldrugs: Pharmacokinetic, pharmacodynamic and pharmacogenomic considerations. Clin. Pharmacokinet.2015, 54, 581–598. [CrossRef] [PubMed]12. Sibiude, J.; Warszawski, J.; Blanche, S. Tolerance of the newborn to antiretroviral drug exposure in utero.Expert Opin. Drug Saf. 2015, 14, 643–654. [CrossRef] [PubMed]13. Morris, G.; Berk, M.; Walder, K.; Maes, M. Central pathways causing fatigue in neuro-inflammatory andautoimmune illnesses. BMC Med. 2015, 13, 28. [CrossRef]14. Giulivi, C.; Zhang, Y.-F.; Omanska-Klusek, A.; Ross-Inta, C.; Wong, S.; Hertz-Picciotto, I.; Tassone, F.;Pessah, I.N. Mitochondrial dysfunction in autism. JAMA 2010, 304, 2389–2396. [CrossRef] [PubMed]15. Frye, R.E.; Rossignol, D.A. Mitochondrial dysfunction can connect the diverse medical symptoms associatedwith autism spectrum disorders. Pediatr. Res. 2011, 69, 41R–47R. [CrossRef] [PubMed]16. Oliveira, G.; Diogo, L.; Grazina, M.; Garcia, P.; Ataíde, A.; Marques, C.; Miguel, T.; Borges, L.; Vicente, A.;Oliveira, C. Mitochondrial dysfunction in autism spectrum disorders: A population-based study. Dev. Med.Child Neurol. 2005, 47, 185–189. [CrossRef] [PubMed]17. Shi, L.; Fatemi, S.H.; Sidwell, R.W.; Patterson, P.H. Maternal influenza infection causes marked behavioraland pharmacological changes in the offspring. J. Neurosci. 2003, 23, 297–302. [PubMed]18. Meyer, U.; Feldon, J.; Fatemi, S.H. In Vivo rodent models for the experimental investigation of prenatalimmune activation effects in neurodevelopmental brain disorders. Neurosci. Biobehav. Rev. 2009, 33,1061–1079. [CrossRef] [PubMed]Viruses 2018, 10, 77 15 of 1619. Atladóttir, H.O.; Thorsen, P.; Østergaard, L.; Schendel, D.E.; Lemcke, S.; Abdallah, M.; Parner, E.T. Maternalinfection requiring hospitalization during pregnancy and autism spectrum disorders. J. Autism Dev. Disord.2010, 40, 1423–1430. [CrossRef] [PubMed]20. Matelski, L.; Van de Water, J. Risk factors in autism: Thinking outside the brain. J. Autoimmun. 2016, 67, 1–7.[CrossRef] [PubMed]21. Côté, H.C.F.; Raboud, J.; Bitnun, A.; Alimenti, A.; Money, D.M.; Maan, E.; Costei, A.; Gadawski, I.; Diong, C.;Read, S.; et al. Perinatal exposure to antiretroviral therapy is associated with increased blood mitochondrialDNA levels and decreased mitochondrial gene expression in infants. J. Infect. Dis. 2008, 198, 851–859.[CrossRef]22. Barrientos, A.; Casademont, J.; Cardellach, F.; Estivill, X.; Urbano-Marquez, A.; Nunes, V. Reducedsteady-state levels of mitochondrial RNA and increased mitochondrial DNA amount in human brainwith aging. Brain Res. Mol. Brain Res. 1997, 52, 284–289. [CrossRef]23. Lagouge, M.; Larsson, N.-G. The role of mitochondrial DNA mutations and free radicals in disease andageing. J. Intern. Med. 2013, 273, 529–543. [CrossRef] [PubMed]24. Autism and Developmental Disabilities Monitoring Network Surveillance Year 2010 Principal Investigators.Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and DevelopmentalDisabilities Monitoring Network, 11 Sites, United States, 2010. Morb. Mortal. Wkly. Rep. 2014, 63, 1–21.25. Bowes, J.; Brophy, J.C.; Samson, L.; Gentile, C. Pervasive Developmental Disorder in Antiretroviral- andHIV-Exposed, Uninfected Children [Abstract]. Can. J. Infect. Dis. Med. Microbiol. 2011, 22, 12B.26. Côté, H.C.F.; Soudeyns, H.; Thorne, A.; Alimenti, A.; Lamarre, V.; Maan, E.J.; Sattha, B.; Singer, J.; Lapointe, N.;Money, D.M.; et al. Leukocyte telomere length in HIV-infected and HIV-exposed uninfected children: Shortertelomeres with uncontrolled HIV viremia. PLoS ONE 2012, 7, e39266. [CrossRef]27. Zanet, D.L.; Saberi, S.; Oliveira, L.; Sattha, B.; Gadawski, I.; Côté, H.C.F. Blood and dried blood spot telomerelength measurement by qPCR: Assay considerations. PLoS ONE 2013, 8, e57787. [CrossRef]28. Hsieh, A.Y.Y.; Saberi, S.; Ajaykumar, A.; Hukezalie, K.; Gadawski, I.; Sattha, B.; Côté, H.C.F. Optimizationof a Relative Telomere Length Assay by Monochromatic Multiplex Real-Time Quantitative PCR on theLightCycler 480: Sources of Variability and Quality Control Considerations. J. Mol. Diagn. 2016, 18, 425–437.[CrossRef] [PubMed]29. Hurtado-Roca, Y.; Ledesma, M.; Gonzalez-Lazaro, M.; Moreno-Loshuertos, R.; Fernandez-Silva, P.;Enriquez, J.A.; Laclaustra, M. Adjusting MtDNA Quantification in Whole Blood for Peripheral BloodPlatelet and Leukocyte Counts. PLoS ONE 2016, 11, e0163770. [CrossRef] [PubMed]30. Urata, M.; Koga-Wada, Y.; Kayamori, Y.; Kang, D. Platelet contamination causes large variation as well asoverestimation of mitochondrial DNA content of peripheral blood mononuclear cells. Ann. Clin. Biochem.2008, 45, 513–514. [CrossRef] [PubMed]31. Banas, B.; Kost, B.P.; Goebel, F.D. Platelets, a typical source of error in real-time PCR quantification ofmitochondrial DNA content in human peripheral blood cells. Eur. J. Med. Res. 2004, 9, 371–377. [PubMed]32. McComsey, G.A.; Kang, M.; Ross, A.C.; Lebrecht, D.; Livingston, E.; Melvin, A.; Hitti, J.; Cohn, S.E.;Walker, U.A. Increased mtDNA Levels Without Change in Mitochondrial Enzymes in Peripheral BloodMononuclear Cells of Infants Born to HIV-Infected Mothers on Antiretroviral Therapy. HIV Clin. Trials 2015,9, 126–136. [CrossRef] [PubMed]33. Ross, A.C.; Leong, T.; Avery, A.; Castillo-Duran, M.; Bonilla, H.; Lebrecht, D.; Walker, U.A.; Storer, N.;Labbato, D.; Khaitan, A.; et al. Effects of in utero antiretroviral exposure on mitochondrial DNA levels,mitochondrial function and oxidative stress. HIV Med. 2012, 13, 98–106. [CrossRef] [PubMed]34. Aldrovandi, G.M.; Chu, C.; Shearer, W.T.; Li, D.; Walter, J.; Thompson, B.; McIntosh, K.; Foca, M.; Meyer, W.A.;Ha, B.F.; et al. Antiretroviral exposure and lymphocyte mtDNA content among uninfected infants ofHIV-1-infected women. Pediatrics 2009, 124, e1189–e1197. [CrossRef] [PubMed]35. Poirier, M.C.; Divi, R.L.; Al-Harthi, L.; Olivero, O.A.; Nguyen, V.; Walker, B.; Landay, A.L.; Walker, V.E.;Charurat, M.; Blattner, W.A. Long-Term Mitochondrial Toxicity in HIV-Uninfected Infants Born toHIV-Infected Mothers. JAIDS J. Acquir. Immune Defic. Syndr. 2003, 33, 175–183. [CrossRef] [PubMed]36. Montaner, J.S.G.; Côté, H.C.F.; Harris, M.; Hogg, R.S.; Yip, B.; Chan, J.W.; Harrigan, P.R.; O’Shaughnessy, M.V.Mitochondrial toxicity in the era of HAART: Evaluating venous lactate and peripheral blood mitochondrialDNA in HIV-infected patients taking antiretroviral therapy. J. Acquir. Immune Defic. Syndr. 2003, 34 (Suppl. 1),S85–S90. [CrossRef] [PubMed]Viruses 2018, 10, 77 16 of 1637. Chalkia, D.; Singh, L.N.; Leipzig, J.; Lvova, M.; Derbeneva, O.; Lakatos, A.; Hadley, D.; Hakonarson, H.;Wallace, D.C. Association Between Mitochondrial DNA Haplogroup Variation and Autism SpectrumDisorders. JAMA Psychiatry 2017, 74, 1161. [CrossRef] [PubMed]38. Rao, R.; Rivers, A.; Rahimi, A.; Wooldridge, R.; Rao, M.; Leitch, M.; Euhus, D.; Haley, B.B. Genetic Ancestryusing Mitochondrial DNA in patients with Triple-negative breast cancer (GAMiT study). Cancer 2017, 123,107–113. [CrossRef] [PubMed]39. Cardena, M.M.; Ribeiro-Dos-Santos, A.; Santos, S.; Mansur, A.J.; Pereira, A.C.; Fridman, C. Assessment of therelationship between self-declared ethnicity, mitochondrial haplogroups and genomic ancestry in Brazilianindividuals. PLoS ONE 2013, 8, e62005. [CrossRef] [PubMed]40. Salas, A.; Fachal, L.; Marcos-Alonso, S.; Vega, A.; Martinón-Torres, F.; Grupo de investigación ESIGEM(Estudio Sobre la Influencia Genética en la Enfermedad Meningocócica). Investigating the role ofmitochondrial haplogroups in genetic predisposition to meningococcal disease. PLoS ONE 2009, 4, e8347.[CrossRef] [PubMed]41. Salas, A.; Acosta, A.; Álvarez-Iglesias, V.; Cerezo, M.; Phillips, C.; Lareu, M.V.; Carracedo, Á. The mtDNAancestry of admixed Colombian populations. Am. J. Hum. Biol. 2008, 20, 584–591. [CrossRef] [PubMed]42. Chen, S.; Li, Z.; He, Y.; Zhang, F.; Li, H.; Liao, Y.; Wei, Z.; Wan, G.; Xiang, X.; Hu, M.; et al.Elevated mitochondrial DNA copy number in peripheral blood cells is associated with childhood autism.BMC Psychiatry 2015, 15, 50. [CrossRef] [PubMed]43. James, S.J.; Cutler, P.; Melnyk, S.; Jernigan, S.; Janak, L.; Gaylor, D.W.; Neubrander, J.A. Metabolic biomarkersof increased oxidative stress and impaired methylation capacity in children with autism. Am. J. Clin. Nutr.2004, 80, 1611–1617. [CrossRef] [PubMed]44. Rossignol, D.A.; Frye, R.E. Mitochondrial dysfunction in autism spectrum disorders: A systematic reviewand meta-analysis. Mol. Psychiatry 2012, 17, 290–314. [CrossRef] [PubMed]45. Brogly, S.B.; Ylitalo, N.; Mofenson, L.M.; Oleske, J.; van Dyke, R.; Crain, M.J.; Abzug, M.J.; Brady, M.;Jean-Philippe, P.; Hughes, M.D.; et al. In utero nucleoside reverse transcriptase inhibitor exposure andsigns of possible mitochondrial dysfunction in HIV-uninfected children. AIDS 2007, 21, 929–938. [CrossRef][PubMed]46. Mofenson, L.M. Editorial commentary: New challenges in the elimination of pediatric HIV infection: Theexpanding population of HIV-exposed but uninfected children. Clin. Infect. Dis. 2015, 60, 1357–1360.[CrossRef] [PubMed]47. Harris, P.A.; Taylor, R.; Thielke, R.; Payne, J.; Gonzalez, N.; Conde, J.G. Research electronic data capture(REDCap)—A metadata-driven methodology and workflow process for providing translational researchinformatics support. J. Biomed. Inform. 2009, 42, 377–381. [CrossRef] [PubMed]© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Article
eng
Reviewed
Vancouver : University of British Columbia Library
Multidisciplinary Digital Publishing Institute
CC BY 4.0
Faculty
Antiretroviral therapy
Genetics
Neurodevelopment
Prophylaxis
Mitochondria
HIV
Pediatrics
Blood Mitochondrial DNA Content in HIV-Exposed Uninfected Children with Autism Spectrum Disorder
Text