Multiple-trait approaches dissected the pleiotropic architecture of wheat yield and correlated traits more as a function of pleiotropy rather than close linkage. Factors affecting yield were investigated using simulations. Triticum aestivum L.) is quantitatively inherited. Correlated GY-syndrome traits such as plant height (PH), heading date (HD), thousand grain weight (TGW), test weight (TW), grains per ear (GPE), and ear weight (EW) influence GY. Most quantitative genetics studies assessed the multiple-trait (MT) complex of GY-syndrome using single-trait approaches, and little is known about its underlying pleiotropic architecture. We investigated the pleiotropic architecture of wheat GY-syndrome through MT association mapping (MT-GWAS) using 372 varieties phenotyped in up to eight environments and genotyped with 18 832 single nucleotide polymorphisms plus 24 polymorphic functional markers. MT-GWAS revealed a total of 345 significant markers spread genome wide, representing 8, 40, 11, 40, 34, and 35 effective GY–PH, GY–HD, GY–TGW, GY–TW, GY–GPE, and GY–EW associations, respectively. Among them, pleiotropic roles of Rht-B1 and TaGW2-6B loci were corroborated. Only one marker presented simultaneous associations for three traits (i.e. GY–TGW–TW). Close linkage was difficult to differentiate from pleiotropy; thus, the pleiotropic architecture of GY-syndrome was dissected more as a cause of pleiotropy rather than close linkage. Simulations showed that minor allele frequencies, along with sizes and distances between quantitative trait loci for two traits, influenced the ability to distinguish close linkage from pleiotropy.Grain yield (GY) of bread wheat ( Triticum aestivum L.) has been continuously increased due to the interactions between technological advances in agronomical practices and genetic improvement (Grain yield (GY) of bread wheat (rovement .Most quantitative genetic studies of the GY-syndrome in wheat have so far relied on single-trait (ST) methods . SimulatMT methods can formally test if the co-location of QTLs for more than one trait is due to pleiotropy  or because of close linkage between more than one genetic factor influencing each trait separately , and theThe main goal of our study was to dissect the pleiotropic architecture of GY-syndrome by means of GWAS using MT statistical approaches in a bread wheat population of varieties adapted to European environments. The specific objectives were: first, to find genomic regions simultaneously associated with GY and GY-syndrome traits using MT-GWAS; secondly, to elucidate if the existence of these regions is a consequence of pleiotropy or due to closely linked non-pleiotropic QTLs; and, thirdly, to understand by means of simulations some factors that could have driven the ability to distinguish between these two phenomena in the current study. Our findings bring a better understanding of the pleiotropic architecture of GY-syndrome and its implications in applied MT wheat breeding.2 to 6.8 m2. Sowing rates and dates were according to local agricultural practices. PH (cm), HD (days since 1 January), and TGW (g) data from these experiments were already available from past studies . Missing values for functional markers with <10% missing data were imputed according to allele frequencies.In a previous study (Best linear unbiased estimators (BLUEs) and variance components were computed based on an unweighted two-stage univariate mixed model analysis approach . First, where µ (i.e. the common mean), and genotypes were considered as fixed factors, whereas replicates, blocks nested within replicates, and error effects were considered as random. After this stage, BLUEs of all environments and unreplicated data were combined together. During the second stage, BLUEs of genotypes across environments for each single trait were computed using the following model:2G) was estimated during the second step, whereas the error (σ2e) and genotype×environment interaction (σ2G×E) variance components are mixed at this stage. Therefore, an error variance (σ2Error), namely the average of σ2e in single environments with replications, was derived during the first step and subsequently subtracted from σ2e during the second step for σ2G×E estimation. Estimates of variance components were later considered for the computation of heritability (h2) on a plot basis as: hPlot2=σG2σG2+σG×E2+σError2. In addition, heritabilities were estimated on an entry-mean basis as presented by hEntry2=σG2σG2+σG×E2No.   Env.+σError2No.Rep.×No.Env., where No.Env. and No.Rep. denote the number of environments and the number of replications, respectively. Phenotypic correlations among the different traits considered in this study were calculated as the Pearson product–moment correlation coefficient between genotypic BLUEs of different traits across environments. Significance of phenotypic correlations was assessed by a t-test.where µ and genotype effects were assumed as fixed factors, whereas environments and error terms were considered as random. In parallel, a model considering genotypes as random factor was fitted for estimation of variance components during the first and second stages. The genotypic variance , μk corresponds to the overall mean of the kth trait, lkj denotes the effect of the jth environment on the kth trait, gki represents the breeding value of the ith genotype for the kth trait, and ekji is the error term for the ykji observation according to the model. Terms μk and lkj were designated as fixed, while gki and ekji were considered random. In matrix nomenclature, the random terms gki and ekji are assumed to be normally distributed in the way g~N(0t×n)×1 and e~N, respectively, where 0t×n)×1, where J denotes an n×n matrix whose every element is 1 and RD is a matrix containing the Rogers’ distances  test comparing a model in which skk'=0 with a model without this restriction. The LR under H0 (skk'=0) follows a χ21 distribution and its significance was interpreted as evidence for a significant genetic correlation.where matrices , A repreistances  among n k traits. Then, the MT-GWAS model corresponds to:We firstly considered a bivariate GWAS approach for each of the six GY plus one GY-syndrome trait combinations following the suggestion of k represents the effect of a particular marker on the kth trait, mi is a scalar taking values 0, 1, or 2 for individuals homozygous for the most frequent allele, heterozygous and homozygous for the second allele at the tested marker, respectively, whereas (αl)kj denotes the interaction of this marker with the jth environment for the kth trait. Both, αk and (αl)kj were assumed as fixed effects within the linear mixed model. In the case of multi-allelic functional markers, we treated each allele as a bi-allelic marker for simplicity. The genetic diversity of the GABI-WHEAT population has been extensively studied using different molecular marker platforms (k and (αl)kj. A general Wald statistic is defined as θ^'Var(θ^)−1θ^, where θ^is a vector containing the estimates for the p fixed effects being tested and Var(Var(θ^)−1)–1 corresponds to the inverse of the variance–covariance matrix among tested effects estimators. The Var(Var(θ^)) matrix is a submatrix obtained by extracting the p corresponding elements from the upper-left quadrant (C11) of the generalized inverse for the left-hand-side coefficient matrix in the mixed model equations , followed by an individual test for each αk in the bivariate model. Markers were declared as potentially pleiotropic for two traits only when the αk effects were significant at the global and at the two post-hoc individual tests. R2 values were computed by fitting all significant markers for each trait combination using a multiple-regression model with one of the traits as dependent variable. Markers with the lowest global Wald test P-values entered first in the model. Subsequently, R2 values were divided by the respective entry mean-based heritability; thus, they measure the proportion of genetic variance explained by a marker. This last procedure allowed direct comparisons among R2 values for different traits. A global Wald test for the (αl)kj effects of both traits together and one test for each trait separately were performed to study marker×environment interactions. In addition to false discovery rate  approach proposed by Meff when correcting the nominal level of significance of tests. For this purpose, pairwise linkage disequilibrium (LD) matrices in the form of r2 were computed for each of the 21 wheat chromosomes using SNPs with unique genetic map positions =p(2), stating that the QTLs for two different traits are located at the same m locus, with H1: p(1)≠p(2), in which these two QTLs have different positions, i.e. m1 and m2. In principle, a pleiotropic model and a linkage model are compared in terms of their likelihoods. Since BLUEs across environments were considered as phenotypic data for the bivariate two-dimensional scan, Equation 4 is reduced to:A bivariate two-dimensional scan based on the method originally proposed by in the case of the pleiotropic model, whereas:k=1 or 2 for the first and second traits included in the model, respectively. Provided that Y=T, the likelihood functions of pleiotropy and linkage models [i.e. f0(Y) and f1(Y)] are multivariate normal density functions with means (1nμ1+mα11nμ2+mα2)and (1nμ1+m1α11nμ2+m2α2), respectively, where 1n is an n-size vector containing only ones, whereas m, m1, and m2 denote n-size vectors of marker profiles. In both f0(Y) and f1(Y), the covariance matrix is G0⊗A+R0⊗In. The LR test is denoted as LR=−2 ln(L0L1), where L1 is the maximum of the likelihoods for a linkage model in the two-dimensional space (H1), and L0 is the maximum of the likelihoods on the diagonal of the two-dimensional space that corresponds to H0. Then, the test statistic under H0 follows a χ21 distribution for which a nominal significance level of 0.05 was applied  along with balanced and unbalanced percentages of genetic variance explained by QTLs (QTL size) on the power and Type I error of close linkage detection in the GABI-WHEAT population. In balanced scenarios, QTL sizes were the same for both simulated traits, whereas QTL sizes were larger  for one of the traits under the unbalanced scenario. The influence of r2 values between QTL positions was considered as an additional factor in linkage scenarios. Marker information , while most GY-syndrome traits, except EW, had higher heritabilities than GY  with GPE and EW but was negatively associated  with PH and TW , except between EW and the two traits PH and HD. At the genetic level, GY–PH and GY–TW correlations shifted towards zero .On a plot basis, GY was moderately heritable  bivariate associations with GY and at least one of the GY-syndrome traits . With thomosomes . Genome omosomes . NonetheL-shaped  and, in L-shaped .R2 for PH attributed to marker RAC875_rep_c105718_304. This marker accounted for 15.3% and 1.2% of PH and GY variation, respectively  were jointly associated with GY and PH . These mectively .wsnp_Ku_c16432_25320146 explained 9.2% of variation in GY but only 0.6% of HD variation.For GY and HD, 93 SNP markers were jointly associated with both traits at FDR <0.05 , which dR2 attributed to SNP BS00028033_51. This marker explained 4.6% of variation in GY but accounted for 0.1% of TGW variation (Significant (FDR <0.05) bivariate associations for GY and TGW were observed for 28 SNP markers , represeariation .R2 values, markers explained on average a comparable proportion of GY and TW variation . Nonetheless, R2 values for GY and TW were again often asymmetric at the single SNP level  for 70 markers , which rR2 values were comparable for GY and EW , R2 values for each trait were often unequal at a single marker level (TaGW2-6B locus (Hap-6B-1 haplotype having positive effects on both traits.The number of markers with significant (FDR <0.05) GY–EW joint associations amounted to 114 , which ser level . The unir2 ~0.55 to 0.70 at MAF ~0.13 with QTLs explaining 10% of genetic variation for both traits, could be attributed to interactions among these factors along with other variables ignored during simulations. Regarding Type I error; that is, wrongly rejecting pleiotropy, no clear trends related to the different levels of MAF and QTL sizes were observed . Thus, 224 markers were considered for the tests of close linkage versus pleiotropy . In contrast, for marker IACX8108 jointly associated with GY and HD, the maximum bivariate likelihood was found on the diagonal; therefore, the hypothesis of pleiotropy cannot be rejected . This marker has negative minor allele effects on each trait, and could explain 0.02, 0.4, and 1.8% of genetic variation in GY, TGW, and TW, respectively, when included alone within a regression model for each trait. Nonetheless, the lack of surrounding linked markers (r2>0.5) for IACX6214 precluded us from disentangling the pleiotropic nature of its simultaneous effects on GY, TGW, and TW.A total of 78 markers presented significant bivariate associations in more than one GY plus GY-syndrome trait combination , suggestr2 levels between QTL positions, have a positive effect on the power to differentiate linkage from pleiotropy  were found for Rht-B1 and TaGW2-6B, respectively . A bivariate association was found for Rht-B1, with the dwarfing allele (Rht-B1b) simultaneously increasing GY and GPE (Rht-B1b can produce more GPE, its positive association with GY would highly depend on effects of Rht-B1b on other GY-syndrome traits, along with counterbalancing effects of higher GPE values on grain weight per se and Rht-B1b interactions with environments (Rht-B1b in the GABI-WHEAT population were strong enough to compensate for any negative feedback on GY, resulting in a positive net effect on these two traits.Dwarfing loci  and GPE . As joinHap-6B-1 to Hap-6B-4, for locus TaGW2-6B. In their study Hap-6B-1 was associated with higher average TGW, this superiority being attributed to increased grain length, width, and/or thickness. The GABI-WHEAT population was characterized in Hap-6B-1 from the other three haplotypes. As in the present study, TaGW2-6B with TGW. Recently, TaGW2-6B in a biparental doubled haploid population, but Hap-6B-2 presented a higher TGW compared with Hap-6B-1. Moreover, in the current study, Hap-6B-1 simultaneously increased GY and EW (TaGW2-6B to influence GY and EW without causing significant changes in TGW in the GABI-WHEAT population. Nevertheless, although a GY component different from TGW was involved, our findings confirm the positive role of Hap-6B-1 on GY-syndrome.Based on 11 SNPs, Y and EW . Future Rht8 are highly stable on PH, this was not the case on GY. In contrast, In past studies, GY QTLs have been often reported as environmentally unstable (e.g. Tdurum_contig10194_765, BS00003586_51, and Tdurum_contig30930_184, presenting bivariate GY–GPE, GY–TGW, and GY–EW associations, respectively, were disentangled as cases of close linkage (Tdurum_contig10194_765 were displaced to markers having an r2 of 0.12, whereas repositioning of GY and EW QTLs of BS00003586_51 and Tdurum_contig30930_184, respectively, resulted in close linkage cases of markers having r2 values of 0.54 and 0.58, respectively. We further investigated the frequencies of the four potential haplotypes generated by combining different alleles at these loci (BS00003586_51 and Tdurum_contig30930_184 would allow the selection of undesired haplotypes, corresponding to 11.6% and 7.7% of the selected fraction, respectively. Although these last magnitudes are relatively small, they illustrate the potential problems caused by blindly using spurious pleiotropic associations and the importance of disentangling close linkage from pleiotropy in MT-MAS. Moreover, most bivariate associations remained as pleiotropic in our study (Markers  linkage . GY and ese loci . For GY–ur study  and, prour study  agrees wur study . ModelinWe assessed the GY-syndrome of bread wheat through MT-GWAS approaches in a population of 372 varieties adapted to European environments and genotyped with 18 832 SNPs plus 24 polymorphic functional markers. We conclude that distinguishing pleiotropy from close linkage underlying the pleiotropic architecture of GY-syndrome is very challenging, especially because of the small sizes of QTLs influencing this complex trait. Our results should be considered as a starting point for further complementary biological validations.JXB online. Supplementary data are available at Simulation methods. Details for simulations on power and Type I error of close linkage detection.Table S1. Details of functional molecular markers used to characterize the GABI-WHEAT population.Table S2. Details of markers used for pleiotropy and close linkage simulations.Table S3. General statistics for BLUEs across environments.Table S4. Details for markers with significant GY–PH associations and test of pleiotropy versus close linkage.Table S5. Details for markers with significant GY–HD associations and test of pleiotropy versus close linkage.Table S6. Details for markers with significant GY–TGW associations and test of pleiotropy versus close linkage.Table S7. Details for markers with significant GY–TW associations and test of pleiotropy versus close linkage.Table S8. Details for markers with significant GY–GPE associations and test of pleiotropy versus close linkage.Table S9. Details for markers with significant GY–EW associations and test of pleiotropy versus close linkage.Table S10. Percentage of phenotypic and genetic variation explained in total by associated markersTable S11. Power to differentiate close linkage from pleiotropy in the case of unequal QTL sizes for two simulated traits.Table S12. Type I error of pleiotropy rejection.Fig. S1. Number of effective bivariate associations.R2 values.Fig. S2. Frequency distributions of r2.Fig. S3. Decay of intrachromosomal pairwise IACX8108.Fig. S4. Two-dimensional scan in the surroundings of marker Fig. S5. Haplotype frequencies for cases disentangled as close linkage.Supplementary_Tables_S1_S3_S10_S12_Figures_S1_S5Click here for additional data file.Supplementary_Tables_S4_S9Click here for additional data file.


SGLT2) inhibitors are able to provoke diabetic ketoacidosis (DKA) with absence or low levels of ketone bodies in urine and slightly elevated blood glucose levels, which could delay the diagnosis; however, the presence of high urine output, due to the excretion of glucose, can help to identify the true cause.Sodium‐glucose cotransporter 2 ( Although the most frequent side effects of these drugs are minor, such as genital mycotic infections and lower urinary tract infections, several cases of diabetic ketoacidosis (DKA) have been reported in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) patients who were taking SGLT2i, with or without other predisposing factors. We present the case of a male patient who had been switched to a SGLT2i several weeks prior to admission to our hospital because of poor control with metformin monotherapy, who developed DKA associated with an acute coronary syndrome and without other potentially precipitating causes.Sodium‐glucose cotransporter 2 inhibitors (SGLT2i) are one of the most recently discovered classes of oral antidiabetics. They act by inhibiting the reabsorption of glucose in the luminal membrane of the proximal tubule. Evidence suggests that SGLT2i promote body weight loss, produce a mild reduction in both systolic and diastolic blood pressure, and could decrease disease progression by improving 2. On physical examination, somnolence, dry skin and mucous membranes, a Kussmaul breathing pattern, and a capillary refill of 3 sec were observed. Blood tests revealed hemoglobin 17.1 g/dL (13.5–18), leukocytes 19.5 × 103 (4–10 × 103), platelets 296 × 103 (150–450 × 103), glucose 248 mg/dL (60–100), creatinine 0.97 mg/dL (0.67–1.17), sodium 136 mmol/L (135–145), potassium 4.7 mmol/L (3.5–5.5), chloride 101 mmol/L (95–112), phosphate 4.9 mg/dL (2.5–4.5), amylase 70 U/L (10–115), lipase 28 U/L (1–67), pH 6.95 (7.35–7.45), pCO2 23 mmHg (35–45), HCO3 5 mmol/L (22–26), lactate 1.8 mmol/L (0–1.5), urine ketone bodies >150 mg/dL (0–0), CK 112 U/L (1–190), CK‐MB 7.3 ng/mL (0.1–5), and troponin I 0.07 ng/mL (0.001–0.05). The electrocardiogram (EKG) showed sinus rhythm with right bundle branch block, and nonspecific repolarization abnormalities. Because of the right bundle branch block was not previously known, a new troponin test was performed six hours later with a peak value of 4.28 ng/mL. Treatment with crystalloids, continuous infusion of intravenous insulin, and administration of potassium and sodium bicarbonate were begun in the emergency room (ER). Due to a poor response over the first two hours, with the persistence of lactic acidosis, the patient was transferred to the intensive care unit (ICU), where more aggressive rehydration with crystalloids was started, without further modifications of the original therapeutic plan.A 58‐year‐old male with dyslipidemia, an eight‐year history of T2DM, a family history, his mother, of T2DM, with no known micro‐ or macrovascular complications, was admitted to the emergency department for malaise, epigastric pain, polyuria, and progressive dyspnea which had begun 10 h ago. He had experienced a 2‐kg weight loss over the last few days. His usual medications included aspirin 100 mg q24 h, atorvastatin 40 mg q24 h, and metformin 850 mg q8 h, which had been switched to dapagliflozin 20 days before, due to poor glycemic control, with HbA1c 12% (108 mmol/mol). His vital signs included a heart rate of 122 bpm, respiratory rate 33 rpm, blood pressure 142/70 mmHg, temperature 36.1°C, and body mass index 22.5 kg/mTwo days later, the patient was discharged from the ICU to the endocrinology ward. Because of his coronary risk factors and the elevated troponin on admission, a coronary angiography was performed, showing triple‐vessel disease. Successful bypass surgery without extracorporeal circulation was performed a few days later, with internal mammary artery grafts to the anterior descendent and marginal obtuse arteries and a saphenous vein graft to the right coronary artery. He was discharged 3 days later on Lantus™ Sanofi‐aventis S.p.a Valcanello,03012 Anagni (FR), Italia (insulin glargine) 20 IU and Insulina Novorapid™: Novo Nordisk A/S. Hallas Allé, DK‐4400. Kalundborg, Dinamarca (insulin aspart) 6‐4‐4‐0 IU subcutaneous insulin, aspirin, clopidogrel, enalapril, bisoprolol, atorvastatin, and furosemide. The oral antidiabetic treatment with dapagliflozin was not restarted. During follow‐up by endocrinology, C‐peptide, anti‐GAD, and IA‐2 antibodies were required due to normal BMI and lack of family history suggested T1DM or a latent autoimmune diabetes of adults (LADA) rather than T2DM as etiology of his diabetes. C‐peptide was 1 mg/dL (0.9–7.1) with glycemia 214 mg/dL (60–100), anti‐GAD <5 U/mL (0–12.5), and anti‐IA‐2 <7.5 U/mL (0–7.5).Inhibition of the reabsorption of glucose in the proximal tubule by SGLT2i leads to urinary excretion of 50–60% of filtered glucose + reabsorption, increasing the tubular Na+ concentration and thus the electronegative gradient, in turn leading to reabsorption of negatively charged ketone bodies.Diabetic ketoacidosis develops in diabetic patients, who suffer an increase in blood ketone bodies as a consequence of both increased production in the liver and a reduced urinary clearance of ketones. This severe acute complication is associated with a 30% rise in mortality Ketoacidosis produced by SGLT2i has some particularities, as it can be associated with normal or slightly increased blood glucose levels due to high kidney glucose excretion. Furthermore, increased renal reabsorption may be the mechanism underlying low urine concentrations of ketone bodies, which should preferably be measured in plasma Diabetic ketoacidosis is typically seen in T1DM patients, who have a total deficit of insulin production, but in T2DM patients, it may occur under stressful situations such as trauma, surgery, or infection , JJGA, SDM, and FMS: reviewed the manuscript.JMGP: wrote the manuscript. TFFG: reviewed the manuscript and wrote the cover letter. BJANone declared.


The purpose of the study was to investigate the effects of short and long term High-Intensity Interval Training (HIIT) on anaerobic and aerobic performance, creatinine, uric acid, urea, creatine kinase, lactate dehydrogenase, catalase, superoxide dismutase, testosterone, corticosterone, and glycogen concentration . The Wistar rats were separated in two groups: HIIT and sedentary/control (CT). The lactate minimum (LM) was used to evaluate the aerobic and anaerobic performance (AP) . The lactate peak determination consisted of two swim bouts at 13% of body weight (bw): (1) 30 s of effort; (2) 30 s of passive recovery; (3) exercise until exhaustion (AP). Tethered loads equivalent to 3.5, 4.0, 4.5, 5.0, 5.5, and 6.5% bw were performed in incremental phase. The aerobic capacity in HIIT group increased after 12 weeks (5.2 ± 0.2% bw) in relation to baseline (4.4 ± 0.2% bw), but not after 6 weeks (4.5 ± 0.3% bw). The exhaustion time in HIIT group showed higher values than CT after 6  and 12 weeks . Glycogen (mg/100 mg) increased in gastrocnemius for HIIT group after 6 weeks (0.757 ± 0.076) and 12 weeks (1.014 ± 0.157) in comparison to baseline (0.358 ± 0.024). In soleus, the HIIT increased glycogen after 6 weeks (0.738 ± 0.057) and 12 weeks (0.709 ± 0.085) in comparison to baseline (0.417 ± 0.035). The glycogen in liver increased after HIIT 12 weeks (4.079 ± 0.319) in relation to baseline (2.400 ± 0.416). The corticosterone (ng/mL) in HIIT increased after 6 weeks (529.0 ± 30.5) and reduced after 12 weeks (153.6 ± 14.5) in comparison to baseline (370.0 ± 18.3). In conclusion, long term HIIT enhanced the aerobic capacity, but short term was not enough to cause aerobic adaptations. The anaerobic performance increased in HIIT short and long term compared with CT, without differences between HIIT short and long term. Furthermore, the glycogen super-compensation increased after short and long term HIIT in comparison to baseline and CT group. The corticosterone increased after 6 weeks, but reduces after 12 weeks. No significant alterations were observed in urea, uric acid, testosterone, catalase, superoxide dismutase, sulfhydryl groups, and creatine kinase in HIIT group in relation to baseline and CT. The High-intensity interval training (HIIT) is characterized by short bouts of exercise, with intensities equal or superior to anaerobic threshold, separated by periods of recovery Billat, . HIIT isStudies have showed that HIIT can improve the aerobic performance in a short term beyond those found by endurance training with low intensity and high volume of exercise  and 12 weeks (long-term) of HIIT in rats. Specifically, to analyze metabolites , muscle injury markers , antioxidant enzymes , hormones (testosterone and corticosterone), glycogen concentrations , and immune system (white blood cells) at baseline, after 6 and 12 weeks. It was hypothesized that HIIT short term: (1) increases aerobic and anaerobic performance similarly HIIT long term and (2) causes lower physiological stress in comparison to HIIT long term.All experiments involving animals were performed in accordance to the principles of laboratory animal care . The experimental protocol was approved by specific resolutions on Bioethics in Experiments with Animals (no. 93/08)Rattus norvegicus albinus (Wistar) rats, 60 days old, were selected for this study. Rats were maintained in collective cages (5 rats/cage). The animals received water and commercial chow  ad libitum and were housed at 22 ± 2°C with an inverted 12:12-h light–dark cycle (18:00–06:00 lights on).Fifty male The purpose of adaptation to the water was to reduce water stress without promoting physiological adaptations to physical training. The adaptation to the water environment consisted of a 5-min (31 ± 1°C) exposure daily, in tanks (80 × 80 × 80 cm) subdivided into four cylindrical compartments of 30 cm diameter × 60 cm depth for individual jumps. For the first week (weeks) of the adaptation period, individual rats were placed in water that had a depth of 20 cm; for the second week the water depth was approximately 30 cm (same depth of training protocol) with workload equivalent to 30% of body weight (bw).The animals were separated into three groups:n = 10) → Training protocol lasted 6 weeks (short term), with exercise sessions of 6 days/weeks. The training workload was equivalent to 50% of body weight (% of bw) calculated every weeks individually. The training session was subdivided in 4 series of 10 jumps in water (depth of approximately 30 cm) with 30 s of passive recovery  → Training protocol lasted 12 weeks (long term), with exercise sessions of 6 days/weeks. The training workload was equivalent to 50% of body weight (% of bw) calculated every weeks individually. The training session was subdivided in 4 series of 10 jumps in water (depth of approximately 30 cm) with 30 s of passive recovery  → Adaptation to the deep water  during the experimental period. Ten rats were euthanized before the experimental period (baseline), after 6 and 12 weeks.Control Group  in accordance with the methods of de Araujo et al. . The LM Blood and tissues were collected after adaptation to the water (baseline) and 24 h after the last session in weeks 6 and 12 of the experimental period. The antioxidants enzymes levels, hormones concentrations, metabolites levels and glycogen concentrations analyses are described below.−1 animal weight) for blood collection and tissue excision (soleus and white gastrocnemius). Blood was collected via cardiac puncture after thoracotomy into EDTA tubes (plasma) or dry tubes (serum) in accordance with the analyses. After this, the soleus, liver and the white gastrocnemius muscles were carefully dissected and then placed on filter paper to remove excess fat and connective tissues. The glycogen concentration of the tissue samples was immediately analyzed according to the methods of Dubois et al. , the animals were euthanized with 20% chloralhydrate , lactate dehydrogenase and β-nicotinamide adenine dinucleotide. The homogenized sample and reagent were incubated at 37°C for 20 min, and absorbance was determined at 340 nm.For the lactate concentration measurement, the blood samples (25 μL) were collected from the animals (tail) and placed in microtubes (1.5 mL) containing 400 μL of 4% Trichloroacetic acid, which were then stored at 8°C. The samples were centrifuged for 3 min, and 100 μL of plasma was placed into fresh tubes containing 500 μL of the following reagent: glycine/EDTA, hydrazine hydrate 88% (*3333) 4, 5, and 6 min after the sample and reagent were mixed.To determine Creatine Kinase-NAC (N-acetyl-L-cysteine) activity, 50 μL of serum (stored at 2–8°C during 1 weeks) was mixed with the kit reagent mix: imidazol (100 mmol/L), creatine kinase (30 mmol/L), ADP (2 mmol/L), glucose (20 mmol/L), NADP (2 mmol/L), hexokinase (2.500 U/L), glucose 6P (2.000 U/L), Mg (10 mmol/L), and AMP (5 mmol/L). The CK-NAC absorbance was determined at 340 nm , sodium hydroxide and sodium hypochlorite. The homogenized sample (10 μL) and reagent (1 mL) were incubated at 37°C for 5 min, and absorbance was determined at 600 nm .Lactate dehydrogenase (LD) concentration was determined by a kinetic method using 100 μL of sample (stored at 2–8° C during 12 h), that was mixed with kit reagent: buffer  and NADH. The LD absorbance was determined after 1, 2, and 3 min after mixing (sample and reagent) at 340 nm : U/L = Δ absorbance/min × 3333.Uric acid concentration was determined using an enzymatic method containing liquid stable mono-reagent: buffer (pH 7.0), DHBS (1 mmol/L), ascorbate oxidase (1 > kU/L), 4-aminoantipyrine (0.3 mmol/L), POD (>2 kU/L), uricase (>30 U/L). The absorbance was determined at 505 nm  after mixing the sample (20 μL) and mono-reagent (1 mL): mg/dL = Δ sample (sample - mono-reagent absorbance)/Δ standard (sample standard - mono-reagent absorbance) × 10. The uric acid sample was analyzed 2 h after euthanasia of rats.* standard factor (standard sample/Δ absorbance of standard sample).Creatinine concentration was analyzed with a kinetic method, using 100 μL of sample (stored at 2–8°C for 1 week) mixed with 1 mL of the following kit reagent: picric acid (60 mmol/L) and buffer. The creatinine absorbance was determined at 340 nm after 30 and 90 s : mg/dL = Δ absorbance 125I (105 ml). The radioactivity was measured in a gamma counter (1 min) following the instructions provided with the commercial Kit-Coat-A-Count from Diagnostic Products Corporation®.For corticosterone and testosterone concentrations (ng/dL) measurement, 50 μL of the serum sample was added to polypropylene tubes containing −1 M−1 (SS – FS – B × 1.57 mM).To measure the sulfhydryl groups, plasma (50 μL) was mixed in 1 mL of Tris–EDTA buffer for the first spectrophotometric (FS) analysis (412 nm). Then, 20 μL of 5.5- diotibis-2-nitrobenzoic (DTNB) 10 mM was added and diluted in methanol, and a second spectrophotometric (SS) analysis was performed after 15 min. A sample containing DTNB and buffer Tris–EDTA was analyzed in a spectrophotometer as a blank. The total amount of sulfhydryl groups was calculated using molar absorbance = 13.600 cmCatalase (CAT) and superoxide dismutase (SOD) activity were measured using commercial kits. The plasma aliquots for the CAT and SOD analyses were stored for 20 days at −20°C. SOD activity was determined using an EIA Cayman Chemical Assay Kit® by the xanthin and xanthin oxidase method (absorbance was 460 nm). CAT activity was measured using an EIA Cayman Chemical Assay Kit®. The sample was mixed in the kit reagents , and the absorbance was measured at 540 nm.The sample was obtained using a specific pipette and diluted in Turk solution (3 min). After mix, the solution was pipette in Chamber Neubauer for leukocytes count in microscope.All of the dependent variables were subjected to the normality test using the Shapiro-Wilk W-test. All analyses were conducted with a statistical software package  and data are presented as mean ± standard error (SE). The analysis of variance (ANOVA two-way) with Ducan's multiple range test was used to examine changes over time  within aerobic and anaerobic performances, glycogen stores  and biochemical analyses  between the groups (CT and HIIT). For non-parametric samples, the Kruskal-Wallis method was used, followed by Dunn's method. The significance level was set a priori α ≤ 0.05.p = 0.05) in relation to HIIT short term. HIIT long term (12 weeks) increased the aerobic performance 27% in comparison to CT 12 weeks (p = 0.01) and 17% when compared to baseline (p = 0.03). The aerobic performance in HIIT short term did not enhance in comparison to baseline and CT after 6 weeks. CT 12 weeks reduced the aerobic capacity in relation to baseline  enhanced 14% , but increased in HIIT group after 6 weeks (6.89 ± 0.40 mmol/L) in comparison to baseline (0 weeks = 5.53 ± 0.38 mmol/L). No differences were found between short and long term of HIIT.p = 0.02) and long term  in comparison to baseline values  for this muscle  and a tendency was found in relation to short term (p = 0.06). No differences was found in HIIT short term in comparison to baseline . The cortiscosterone concentration in HIIT short term was higher than CT 6 weeks and baseline. HIIT long term (12 weeks) reduced the corticosterone concentration in relation to CT 12 weeks and baseline (Table The corticosterone concentration enhanced after HIIT short term in comparison to HIIT long term p = 0.000. The corp = 0.05).No differences were found in testosterone, catalase and sulfhydryl groups concentration during experimental period in both HIIT groups Table . The SODNo differences were found in uric acid, creatinine and urea concentrations during experimental period, but after 6 weeks the creatinine and urea had a higher variation (%) in relation to CT when compared to 12 weeks Figure .p = 0.07) and HIIT short term (p = 0.08)  than HIIT short term. The hepatic glycogen is important fuel for aerobic metabolism and aerobic performance after HIIT long term can be associated with supercompesantion in this tissue.The glycogen synthesis in gastrocnemius increased after 6 weeks and enhanced further after 12 weeks when compared to baseline. On the other hand, our results showed that soleus muscle had a limited glycogen synthesis during HIIT and the synthesis did not exceed ~70% of supercompensation independently of HIIT duration in relation to baseline. The feature of gastrocnemius muscle (fast-twitch fibers) was more HIIT sensible to glycogen synthesis due to higher glycogen synthase activity after exercise and capacity of glycogen repletion during the physical stress in relation to oxidative fibers  in tissues and blood due to large increases in oxygen uptake Ji, . While tThe inflammation enhances during stress periods, as for example during high intensity periods, inappropriate recovery and overtraining state (Lehmann et al., In summary, the duration of HIIT induces different physiological adaptations and performances responses. The HIIT long term enhances the aerobic capacity and glycogen stores beyond HIIT short term without significant biomarkers stress alterations. Taken together, the corticosterone, glycogen stores, white blood cells and aerobic performance indicated a positive adaptation induced by HIIT long term. HIIT long term enables an anabolic period due to the corticosterone and white blood cells reduced in relation to HIIT short term. This anabolic state increases the glycogen synthesis and as consequence the aerobic performance, but not anaerobic performance. However, the high values of corticosterone after HIIT short term shows that 6 weeks of high intensitsy exercise induces a period of transitory stress. Furthermore, no significant interferences of HIIT duration in antioxidant system, metabolites, creatine kinase, and lactate dehydrogenase were found.GD and CG participated in the elaboration of the experimental design, data collection, tabulation, discussion and writing of the manuscript. MP and IM participated in data collection, data discussion and writing of the manuscript. MD participated in the preparation of the research project and data collection.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PINK1 deficiency causes the autosomal recessive PARK6 variant of Parkinson’s disease. PINK1 activates ubiquitin by phosphorylation and cooperates with the downstream ubiquitin ligase PARKIN, to exert quality control and control autophagic degradation of mitochondria and of misfolded proteins in all cell types.Global transcriptome profiling of mouse brain and neuron cultures were assessed in protein-protein interaction diagrams and by pathway enrichment algorithms. Validation by quantitative reverse transcriptase polymerase chain reaction and immunoblots was performed, including human neuroblastoma cells and patient primary skin fibroblasts.Pink1-deleted mice across the lifespan regarding brain mRNAs. The expression changes were always subtle, consistently affecting “intracellular membrane-bounded organelles”. Significant anomalies involved about 250 factors at age 6 weeks, 1300 at 6 months, and more than 3500 at age 18 months in the cerebellar tissue, including Srsf10, Ube3a, Mapk8, Creb3, and Nfkbia. Initially, mildly significant pathway enrichment for the spliceosome was apparent. Later, highly significant networks of ubiquitin-mediated proteolysis and endoplasmic reticulum protein processing occurred. Finally, an enrichment of neuroinflammation factors appeared, together with profiles of bacterial invasion and MAPK signaling changes—while mitophagy had minor significance. Immunohistochemistry showed pronounced cellular response of Iba1-positive microglia and GFAP-positive astrocytes; brain lipidomics observed increases of ceramides as neuroinflammatory signs at old age.In a first approach, we documented Ifit3 and Rsad2 were consistently observed upon five analyses: (1) Pink1−/− primary neurons in the first weeks after brain dissociation, (2) aged Pink1−/− midbrain with transgenic A53T-alpha-synuclein overexpression, (3) human neuroblastoma cells with PINK1-knockdown and murine Pink1−/− embryonal fibroblasts undergoing acute starvation, (4) triggering mitophagy in these cells with trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP), and (5) subjecting them to pathogenic RNA-analogue poly(I:C). The stress regulation of MAVS, RSAD2, DDX58, IFIT3, IFIT1, and LRRK2 was PINK1 dependent. Dysregulation of some innate immunity genes was also found in skin fibroblast cells from PARK6 patients.In a second approach, we assessed PINK1 deficiency in the presence of a stressor. Marked dysregulations of microbial defense factors Thus, an individual biomarker with expression correlating to progression was not identified. Instead, more advanced disease stages involved additional pathways. Hence, our results identify PINK1 deficiency as an early modulator of innate immunity in neurons, which precedes late stages of neuroinflammation during alpha-synuclein spreading.The online version of this article (doi:10.1186/s12974-017-0928-0) contains supplementary material, which is available to authorized users. Parkinson’s disease (PD) is the second most prevalent neurodegenerative disease after Alzheimer’s disease (AD). An autosomal recessive variant of PD, PARK6, is caused by loss-of-function mutations in PINK1 (PTEN-induced kinase 1) –4 and exIndependent from their role for mitochondria, PINK1 and PARKIN are transcriptionally induced by trophic and nutrient deprivation stress , 14, andThus, in spite of the deep knowledge about the roles of PINK1 and PARKIN for selective mitophagy, there is an important need to conduct unbiased surveys to elucidate additional functions of PINK1 and PARKIN in stress responses. Our previous OMICS work observed only mild changes in mRNA, protein abundance and posttranslational modifications in response to PINK1 loss-of-function, failing to identify individual biomarkers that correlate with disease progression –22.Transcriptome dysregulations below 1.5-fold are usually regarded with skepticism and are difficult to validate with other experimental techniques. However, in age-associated neurodegenerative disorders, they cannot be disregarded. It is known that the 1.5-fold increase of beta-amyloid precursor protein (APP) dosage on chromosome 21 leads to typical AD symptoms and neuropathology already by the age of 40 years in most Down syndrome cases , so the The relevance of subtle expression changes has been taken into account by modern analysis tools based on “gene set enrichment analyses” . TherefoPink1−/− mouse brain tissue at three ages, (2) surveyed Pink1-dependent regulations of the global transcriptome in neuron-rich primary cultures from postnatal mouse brain at 12 days after the acute brain dissection stress, (3) validated the results in aged Pink1−/− brain where the transgenic overexpression of A53T-alpha-synuclein (gene symbol SNCA) exerted chronic neurotoxic stress, (4) tested the cellular response of microglia and astrocytes in Pink1−/− brain by immunohistochemistry, (5) used lipidomics to study pro-inflammatory signals, (6) performed a systematic assessment of the expression of key factors of antiviral state in human neuroblastoma cells with lentiviral PINK1-Knock-Down (KD), studying the time course after acute starvation stress, (7) re-assessed the same key factors of antiviral state in human PINK1-(KD) neuroblastoma versus Pink1−/− murine embryonal fibroblasts after mitophagy via FCCP drug treatment, and (8) tested the same factors after stress with the pathogenic poly(I:C) RNA regarding PINK1-dependent expression regulation. Primary skin fibroblasts from three patients at advanced age with manifest PD due to G309D-PINK1 mutations were employed to assess the relevance of these data for the human disease.For this aim, we (1) analyzed the global transcriptome profile of Pink1−/− and wildtype (WT) control mice, which were derived from common ancestors and share the strain 129/SvEv genetic background, were bred and genotyped as previously reported [Pink1−/−+A53T-SNCA double mutant mice was obtained as published [ublished .Pink1−/− versus 3 age and sex-matched WT control mice. Hybridization occurred on Affymetrix Genechip mouse genome 430 2.0 arrays, which represent 39,000 transcripts, of which more than half are anonymous or poorly understood, according to PubMed and GeneCards database searches. The biomathematical analysis was performed in the institute for medical genetics at Tuebingen University.Affymetrix oligonucleotide microarray profiling was performed with Genechip mouse genome 430 2.0 arrays as previously , using chttps://string-db.org/) with standard settings has been employed to visualize networks of significant dysregulations [Mus musculus option, the matching of the input with the correct factors was accepted, and the graphic interaction diagram was generated and archived. The Analysis button was used to generate automated network statistics; significant functional enrichments of GO (Gene Ontology) terms and KEGG pathways were exported into EXCEL files.For protein-protein interaction (PPI) network analysis, the software tool String v.10 (ulations . As recohttp://software.broadinstitute.org/gsea/index.jsp) [p value and the according log2 transformed ratio was taken. GSEA default settings and Reactome v5.2 and KEGG v5.2 gene set database were used. Pathways with p value ≤0.05 and FDR q ≤ 0.25 were regarded as significant. Heat maps were produced with the Perseus software.For an additional comprehensive transcriptome analysis, gene set enrichment analysis   was applw/v) poly-d-lysine coated 6-well plate. In order to limit the growth of dividing cells, cytosine ß-D-Arabinoside was added on the second day of culture as before. The 3 plate pairs (mutant versus WT), where many singular neurons in homogeneous density with a dense network of processes contrasted with very few astrocytes and microglia present, were chosen out of a total of 12 plate pairs on culture day 12 for RNA extraction.Neuron-rich primary cultures from the dissociated brain cerebral cortex of postnatal mice were prepared as previously described . In shorPink1−/− at three ages (10 mutants versus 10 WT) and midbrain from adult double mutant Pink1−/−+A53T-SNCA mice at age 18 months (5 mutants versus 5 WT) for the extraction of global RNA and cDNA synthesis [Mouse aging and dissection of mice was carried out as before, employing cerebellum from single mutant ynthesis .Immunohistochemistry was performed using an automated staining system . The following antibodies were used: anti-GFAP, rabbit polyclonal antibody, dilution 1:14,000 ; anti-Iba1, rabbit antibody, dilution 1:1000 .Pink1−/− (n = 1) and wild-type (n = 1) animals were analyzed using ImageJ software . We quantified positive cells in relation to all cells.Representative areas of striatum, substantia nigra, neocortex, and brainstem of Pink1−/− and Pink1+/+ mice were used for analysis of bioactive lipids in brain tissue. Mice were 9–13 months (four each), 17 months (three each), and 21 months (four each) old at the time of tissue preparation (mean age 15.5 −/− and 17 +/+). Mice were sacrificed by carbon dioxide. Blood was drawn into K+ EDTA microvettes (Sarstedt) for plasma analysis by cardiac puncture. Subsequent intracardial perfusion with saline removed rests of blood. The lumbar spinal cord, olfactory bulb, and hippocampus were dissected, and tissue pieces of 3–5 mg were excised, rapidly frozen in liquid nitrogen, and stored at −80 °C until analysis. The precise tissue weight was determined on precision scales directly before tissue homogenization.Matched pairs of male and female Sphingolipids were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) in different regions of the nervous system  at three different ages . LC-MS/MS analyses were done on an API4000 triple quadrupole mass spectrometer equipped with an APCI  ion source for the analysis of ceramides and with an ESI (Electrospray Ionization) ion source for the analysis of sphingosines, Glu-Cer/Lac-Cer  , 35. Allv/v) (A) and acetonitrile-tetrahydrofuran-formic acid  (B). For separation, a gradient program was used at a flow rate of 0.3 ml/min. The initial buffer composition was 60% (A)/40% (B). It was maintained for 0.6 min, then linearly changed to 0% (A)/100% (B) over 3.9 min, and held for 6.5 min. Subsequently, the ratio was linearly changed back within 0.5 min to 60% (A)/40% (B) and then held for another 4.5 min. The running time for every sample was 16 min. The injection volumes were 15 μl for ceramides and 10 μl for sphingosines. The analyses were done in multiple reaction monitoring (MRM) mode. For every analyte, two transitions were recorded, one for quantification and the other for identification. The peak area of the analyte was normalized for the peak area of the internal standard. Precursor to product ion transitions for quantification were: m/z 539 → 264 for C16:0-Cer, m/z 567 → 264 for C18:0-Cer, m/z 595 → 264 for C20:0-Cer, m/z 651 → 264 for C24:0-Cer, m/z 649 → 264 for C24:1-Cer, m/z 700 → 264 for C16:0-GluCer, m/z 729 → 264 for C18:0-GluCer, m/z 862 → 264 for C16:0-LacCer, m/z 891 → 264 for C18:0-LacCer, m/z 973 → 264 for C24:1-LacCer, m/z 553 → 264 for C17:0-Cer, m/z 300 → 282 for Sph and m/z 380 → 264 for S1P. The dwell times were 15 or 50 ms.Tissue pieces of approximately 4 mg were homogenized in 200-μl extraction buffer (50 μl per mg). For analysis of sphingolipids, 20 μl plasma or homogenized tissue samples consisting in 0.4 mg tissue were extracted with methanol:chloroform:HCl (15:83:2) after spiking with the respective internal standards, which was Cer17:0 for ceramides and sphingosine-D7 and sphingosine-1-phosphate-D7 for sphingosines. A Luna C18 column  was used for chromatographic separation. The HPLC mobile phases consisted of water-formic acid  selection in RPMI medium containing 10% Fetal Calf Serum (FCS), as published already [PINK1-KD and NT control cell lines had the stability of their KD controlled repeatedly over many months. They were switched to HBSS medium without FCS, to subject them to a starvation time course as previously described [The human SH-SY5Y neuroblastoma cell line with dopaminergic properties was stably transduced by lentivirus either with a control (NT for Non-Target knock-down) shRNA or a shRNA directed against  already . These Pescribed .20 and random primers (Invitrogen). cDNA from 20 to 25 ng RNA were utilized in a 20 μl reaction volume using the StepOnePlus Real-Time PCR System and the appropriate murine (lowercase) or human (uppercase) TaqMan gene expression assays (Applied Biosystems): for mouse Pink1 (Mm00550827_m1), Creb3 (Mm00501607_m1), Ddx58 (Mm01216853_m1), Hebp1 (Mm00469161_m1), Ifit1 (Mm00515153_m1), Ifit3 (Mm01704846_s1), Irf3 (Mm00516784_m1), Mapk8 , Mapk9 (Mm00444239_m1), Mapk14 (Mm01301009_m1), Mavs (Mm00523170_m1), Mfn1 (Mm00612599_m1), Nfkbia (Mm00477798_m1), Rsad2 (Mm00491265_m1), Srsf10 (Mm01193320_m1), Tbk1 (Mm00451150_m1), Tnf (Mm00443258_m1), for human PINK1 (Hs00260868_m1), DDX58 (Hs01061436_m1), HEBP1 (Hs00211123_m1), IFIT1 (Hs03027069_s1), IFIT3 (Hs00155468_m1), IRF3 (Hs01547283_m1), LRRK2 (Hs00411197_m1), MAVS (Hs00920075_m1), MFN1 (Hs00966851_m1), RSAD2 (Hs00369813_m1), SQSTM1 (Hs00177654_m1), TBK1 (Hs00179410_m1). mRNA expression was normalized to the TATA binding protein gene expression or the Hypoxanthine Phosphoribosyltransferase 1 gene expression . Relative expression changes were calculated with the 2−ΔΔCt method [RNA was isolated with the RNeasy mini kit (Qiagen) and then treated with DNase I. cDNA was synthesized with SuperScript III reverse transcriptase using oligo(dT) Hs010614_m1, HEBP6 cells) in T25 flasks as previously described [The drug FCCP, which is known to uncouple the mitochondrial membrane gradient and trigger mitophagy , was admescribed ; then, tThe synthetic dsRNA polymer poly(I:C), which induces the RNA sensors that activate innate immunity , 40, wasPreviously established primary skin fibroblast cultures from 3 homozygous PARK6 patients passages 12–14) were employed as published –14 were , 41–43, The isolation of total proteins from the primary skin fibroblasts was carried out as described . Samplest test with Welch’s correction in the GraphPad Prism 5 software.Statistical significance was assessed using ANOVA or unpaired Pink1−/− brain, which were consistent (1) across the lifespan and (2) across diverse brain areas  (to 63% on average), a stress response factor which regulates the levels of the inflammasome component Caspase 1 [Clta (to 32%), which controls antibody isotype switching [Srrm1 (to 187%), which is responsible for splicing of the lymphocyte surface protein CD44 [Hnrnpr, which is known to regulate c-Fos and thus neuronal excitability, but also the expression of classical and non-classical MHC class I proteins (probeset 2610528B01Rik showing transcript elevation to 172% and probeset Gm17388 to 131%) [Previously, we documented the effects of PINK1 deficiency on brain regions such as midbrain, striatum, and cerebellum at different ages in the absence of stress or in the presence of A53T-alpha-synuclein overexpression as stressor, identifying marked effects of PINK1-deficiency on mitochondrial biology and excitability via global transcriptome profiles , 29, 44.aspase 1 . A downrwitching . A conveein CD44 . Anotherto 131%) , 49. Curto 131%)  or in ento 131%) . AgainstTo further elucidate the expression effects of PINK1-deficiency in nervous tissue, we now considered the enrichment of functional pathways and of protein interaction networks, employing automated bioinformatics tools. This approach was focused on the cerebellum, in view of several advantages: (1) The cerebellum is relatively big and easy to dissect with negligible anatomical variance; (2) its expression changes will not get diluted as in other brain regions where substantial neuron population heterogeneity renders many effects minimal upon mixed tissue analysis; and (3) it does not suffer from the neuron loss and astrogliosis, which are expected to occur in the vulnerable brain regions of PD models and which would distort expression profiles.To illustrate the impact of PINK1 deficiency on specific gene networks within the global cerebellar transcriptome and to visualize the progression from age 6 weeks via 6 months until age 18 months, we produced a STRING diagram of protein-protein interaction clusters for each age  in Affymetrix oligonucleotide microarray studies     and “Protein processing in endoplasmic reticulum” (q = 3.34e−04) were notable. The corresponding STRING protein interaction diagram  was detected , 165 factors in “cellular response to stress” , and 23 factors in “regulation of mitophagy” . These data are in agreement with the notion that the well-documented role of PINK1 in mitophagy is a small part of a much broader role of PINK1 in stimulus-dependent signaling and in stress responses. Significant enrichment was also observed in multiple KEGG pathways , for 32 factors in the "ubiquitin-mediated proteolysis pathway" , for 34 factors in the "Protein processing in endoplasmic reticulum" pathway , and for 20 factors in the “Bacterial invasion of epithelial cells” pathway . Mildly significant was the enrichment of 21 factors of the “Dopaminergic synapse” pathway , 16 factors of the “GABAergic synapse” pathway , and 18 factors of the “Glutamatergic pathway” pathway . These data are in excellent consistency with previously established roles of PINK1 as a component of MAPK signaling [In mice at age 18 months, about 3500 factors showed expression changes at this significance threshold, too many to be processed by STRING bioinformatics (its limit is at 2000 nodes). Thus, only the about 1600 dysregulated factors with an adjusted ignaling , 52, as ignaling , 53–56. Creb3, Irf3, Nfkbia, Mapk8, Mapk9, and Stat1 is shown in Fig. In the STRING interaction diagram of cerebellar transcriptome changes at age 18 months, the appearance of neuroinflammatory factors was highlighted, manually placing the components of the significantly enriched KEGG pathways “HTLV-I infection”, “Toll-like receptor signaling”, “TNF signaling pathway”, “Fc epsilon RI signaling”, “T cell receptor signaling pathway”, “RIG-1-like receptor signaling”, “Hepatitis C”, and “Influenza A” in the lower left corner and highlighting the Toll-like receptor signaling pathway factors in red color  with previously defined effects on PINK1-dependent mitochondrial fission [Slc6a1 as a GABA transporter known to be induced by neuroinflammation [Keap1, Mbtps1, Rad23a, Sec61a1, Hif1a, Pi4k2a, the autophagy factors Map1lc3b and Sqstm1. Further opposing regulations occurred for the ubiquitin-binding factor Tollip, which regulates Toll-like receptor signaling as well as the autophagic clearance of ubiquitin conjugates and protein aggregates [Heat maps were then used to focus on prominent examples among the significantly altered pathways, representing each dysregulated component at the three ages, not only for the Parkinson-resistant cerebellar tissue but also for comparing the Parkinson-vulnerable midbrain/brainstem and striatum Table . This apynuclein –59, for immunity , for Dnm fission , 61, as ammation . The reggregates . This suIf the STRING analysis is conducted with high stringency only for those cerebellar factors, which are significantly downregulated with >1.5-fold change, then much information is lost and the KEGG pathway of MAPK signaling becomes prominent at the different ages  software thus performs a comprehensive assessment of the complete transcriptome data and is useful as an independent complement of the STRING approach. In the cerebellum, the most salient result was a significant effect for the KEGG pathway “Parkinson’s disease”, but this biomathematical finding was mainly due to the loss of Srsf10 [Creb3 , which influences endoplasmic reticulum protein processing [Nfkbia , which cooperates with Creb3 to modulate the nuclear control of stress and inflammation responses [Mapk (MAP kinase) gene family  expression, a crucial component of the stress-dependent phosphorylation cascades that control endoplasmic reticulum protein processing, neuroinflammation, and apoptotic or autophagic cell death [Pink1−/− cerebellum at age 18 months in the absence of additional stressors demonstrated a significant but mild upregulation, 1.15-fold ±0.04 SEM with p = 0.01  = 7297; P = 0.0137) and for the interaction “ceramide by genotype” (F (10:200) = 4.558; P < 0.0001). Subsequent post-hoc analyses for each ceramide and sphingolipid separately were significant for Cer18:0 (P < 0.0001), LacCer24:1 (P = 0.0285), and GluCer18:0 (P = 0.0042). For individual ceramides, there was no site-specific significant progression with increasing age, but analysis of log-2 transformed total ceramides revealed age-dependent differences. In wildtype animals, log-2 Cers linearly increased over time, but in Pink1−/−, levels were already increased at 9 months and remained at this elevated level, so that genotype-dependent differences were strongest in young mice  = 16.98, P = 0.0001; for the interaction F  = 2.531, P = 0.089)  Fig. . Hence, Pink1−/− brain slices [Pink1−/− neuron-rich primary culture in vitro—where the previous mechanical dissection, the lack of glial support, and the presence of the toxin cytosine arabinoside (cytarabine) exert combined stress [Nipal3, was encoded in the vicinity of Pink1.Given that PINK1 is important for stress responses , 6, 13, n slices , we decid stress , 86. Hend stress , it was Dram1 and Ret mRNA levels. Ordered by significance, the Dram1 (the abbreviation stands for DNA-damage regulated autophagy modulator 1) transcript 1.7-fold upregulation was the strongest early change after Pink1 loss . Similar to PINK1, DRAM1 is connected to the pathway of selective autophagy, since it promotes the targeting of mycobacteria to degradation [DRAM1 mRNA is selectively downregulated in brain tissue of PD patients [Ret (proto-oncogene RET receptor tyrosine kinase) . RET trophic receptor signaling was already shown to rescue histological and biochemical phenotypes of Pink1 deletion in flies [PINK1-deficient cells without affecting mitophagy. Furthermore, GDNF stimulation rescued mitochondrial defects in PARKIN-deficient cells, while the loss of dopaminergic midbrain neurons in aged RET-deficient animals was rescued by PARKIN overexpression and exacerbated by PARKIN deficiency [Ret mRNA upregulation in Pink1-deficient primary neuron cultures after dissociation stress may also form part of the neuroinflammatory response pathway.Two findings had obvious credibility, but limited novelty, namely the dysregulation of radation , and is patients . A similin flies . In a huficiency . Intriguficiency . Thus, tGin1 (gypsy retrotransposon integrase 1) [Hmox1 (Heme oxygenase 1) [Hebp1 (Heme-binding protein 1) [Snx16 (Sorting nexin 16) [Timp1  [Dnmt2 (DNA methyltransferase 2) [Rsad2  [Adamts4  [Csde1  [Ifit3 (interferon-induced protein with tetratricopeptide repeats 3) [In addition to the known candidates, we observed a remarkable and novel enrichment of additional factors involved in antiviral state, innate immunity, and neuroinflammation  , Hmox1 (enase 1) , 93, Hebotein 1) , Snx16 (exin 16) , Timp1 (inase 1) , 97, Dnmerase 2) –101, RsaViperin) –106, Adaotif, 4) –109, Csdg = UNR) , and Ifipeats 3) , 112.Zfp148, Cdk7, Tia1, Mbd1, Mus81, Mat2a, and Hipk2 [Srxn1, Hlf, Steap1, Tsga10, and Hipk2 [Further enrichments in the transcriptome profile were apparent for DNA/RNA-quality control and for antioxidant/hypoxia factors. The DNA/RNA quality control factors included nd Hipk2 –127. Thend Hipk2 , 41, incnd Hipk2 –132.Foxp1 (Forkhead Box P1) showed a 1.7-fold downregulation . This differentiation factor of midbrain dopaminergic neurons was previously found downregulated also in mice with knock-out of alpha-synuclein, while its upregulation was documented in mice with transgenic overexpression of A53T-alpha-synuclein, which model the PARK1 and PARK4 variants of PD [Foxp1 transcripts levels appears to respond both to acute stress and to constant mutations of at least two PD genes.Intriguingly, ts of PD , 31, 133Pink1−/− primary neurons became apparent under in vitro stress conditions.Overall, already at neonatal stages, a significant expression dysregulation of innate immunity factors in Pink1−/− neurons were not evident in global transcriptome screenings of single-mutant Pink1−/− mouse brains even at old age [Pink1−/− is combined with 1.5-fold overexpression of A53T-alpha-synuclein (SNCA) selectively in neurons as a trigger of Parkinsonian pathology and as a genetic interactor of PINK1 [Pink1−/− mice, and the data are publically available in a database [For independent validation of these observations, and to exclude that a microbial infection of the cultures had produced artifacts, we next studied the transcript levels of candidate genes by qPCR in brain homogenate extracts from independent animals. Given that the stress-evoked observations in primary  old age , we reasof PINK1 , 20. ThePink1−/−+A53T-SNCA mice at the age of 18 months, a significant 1.16-fold upregulation for Mapk9 , a significant 2.15-fold upregulation for Rsad2 , a trend towards significance  for the 0.87-fold downregulation  of Hebp1, and a significant 1.92-fold upregulation for Tnf alpha were observed   control sequence were subjected to acute starvation by changing the culture conditions from RPMI medium with 10% Fetal Calf Serum (FCS) to HBSS medium  without FCS. The transcriptional regulation was documented over 2 days for several key factors of the antiviral defense by qPCR. The overview on this systematic survey of the mitochondria-associated antiviral defense pathway is represented in Fig. PINK1 mRNA in this cell line was reduced to 30% before stress and to 9% after stress at the 16-h time point  similar observations indeed occur in neural cells or have to be attributed to microglia, (II) the mouse data can be translated to human, (III) transient changes can be defined after acute stress, and (IV) trophic and nutrient deprivation stress with concomitant autophagy can trigger these stress response changes, always in dependence on PINK1 deficiency. The starvation stress was initially chosen rather than established Parkinsonian toxins like MPTP or CCCP , 134, si4 h Fig. . The up lls Fig. .Fig. 3SyRSAD2 but also for TBK1 and IRF3. TBK1 was induced up to 1.9-fold in control cells, and this induction was further increased to 2.2-fold in PINK1-KD cells, with strong significance at 4, 8, 24, and 48 h  as an endoplasmic reticulum-associated antiviral factor (data not shown). Given that experimental testing of multiple mRNAs at multiple time-points followed by statistical evaluation via Student’s t tests will exaggerate the significance, an adjustment of the significance threshold according to Bonferroni is useful, by dividing the nominal p values indicated in each panel of Fig. PINK1, MFN1, MAVS, IFIT1, and IFIT3 and the upregulation of RSAD2 and TBK1 remain significant as particularly robust findings.No significant dysregulation was documented for the transcript levels of LRRK2 and SQSTM1 are shown as well  was studied as an adaptor between the autophagy machinery and ubiquitinated cargo, whose mutation cause neurodegenerative disorders such as motor neuron disease. A significant indirect correlation was observed again between the PINK1-deficiency and an exacerbated induction of LRRK2 transcript levels, as previously reported for fibroblasts and neuronal cells derived from PARK6 patients [In order to provide a time-correlation and comparison with autophagy factors that are also regulated by starvation, the curves for ell Fig. –m, givenpatients .Overall, the data confirm that key factors of the anti-microbial response are modulated in their transcriptional regulation by PINK1 in human neural cells after acute starvation stress.PINK1-knockdown and control SH-SY5Y neuroblastoma cells. Mitophagy needs at least 12 h to occur and subsequent transcriptional responses require additional time. Therefore, the RNA extraction and qPCR analyses were performed later than in the previous experiment, at 24 h after drug administration. As shown in Additional file PINK1, DDX58, and MAVS and of TBK1, accompanied by a downregulation of the RNA sensor IFIT1, in control cells. A significant difference between control cells and PINK1 knockdown cells after FCCP treatment was only observed for MAVS, which failed to be induced in the absence of PINK1. These data confirm that key inflammatory factors respond to mitochondrial dysfunction and that particularly the levels of the mitochondrial antiviral signaling factor MAVS depend on PINK1 levels.To further assess if indeed mitochondrial dysfunction underlies changes in innate immunity, we used the uncoupling drug FCCP to trigger mitophagy and then study the transcriptional response of the above key factors in Pink1−/− murine embryonal fibroblasts (MEFs) are a useful tool to test if the presence of lentiviral knock-down double-strand RNA in the human neuroblastoma cells distorts the results and to test whether these effects apply only to neuroinflammatory processes, or represent responses of the innate immunity system of any cell also outside the nervous system. Therefore, MEFs were subjected to FCCP treatment and the transcriptional response of the key antiviral factors was studied in the presence or absence of PINK1. As shown in Additional file Mavs, accompanied by downregulation of Rsad2, Ddx58, Tbk1, Irf3, Mfn1, Ifit3, and Ifit1, both in control WT cells and Pink1−/− cells. Thus, again the mitochondrial antiviral signaling factor Mavs showed a selective dependence on mitochondrial dysfunction, whereas the levels of all other antiviral key factors decreased in parallel to the FCCP-triggered autophagic elimination of mitochondria. Fibroblasts tolerate a reduction of mitochondrial mass by prominent glycolysis, whereas neuronal cells depend on mitochondria and will compensate mitophagy with mitochondrial biogenesis [ogenesis . TherefoRSAD2, IFIT3, and IFIT1 in the starvation experiment , but the PINK1-dependent reductions of RSAD2, DDX58, and IFIT3 were not significant. IFIT1 could not be analyzed, since the available antibodies in our hands recognized only the human, but not the murine variant. Overall, these data clearly establish PINK1 as enhancer of anti-microbial responses and particularly of RNA sensors.The substantial upregulations of RNA sensors and their modulation by PINK1 encouraged us to attempt validating these effects also at the protein level, so quantitative immunoblots were performed Fig. . In neurIFIT3 transcript was reduced to 31% (p = 0.0059), while RSAD2 transcript was increased to 307% (p = 0.044)  as an inhibitor of many RNA and DNA viruses. In quantitative immunoblots, IFIT3 protein was reduced to 0.27-fold   Fig. . These d03) Fig. . The RSAThe progression of pathology in PD tissues and its animal and cell models is being documented intensively at the clinical, histology, imaging, neurophysiology, and molecular levels, especially in cases with monogenic pathogenesis –143. TheOur novel progression analysis of global transcriptome profiles in PINK1-deficient brain tissue across lifespan now indicates that subsequent stages of pathology are characterized by the involvement of increasing numbers of subtly dysregulated pathways rather than stronger expression anomalies of individual candidates. This in vivo approach identified several pathways to be prominently PINK1-dependent, in good agreement with previous in vitro findings in most cases. Importantly, for the first time, we establish a temporal order and provide a quantitative value for the significance of each pathway.Srsf10 as a spliceosome component was reproduced by qPCR. Given that spliceosomal alteration is a constant feature in Pink1−/− brain and that Ube3a and Mapk8 are regulated by alternative splicing, we investigated the dysregulation of their splice isoforms and demonstrated a selective effect on the shorter splice isoform of UBE3A, a ubiquitination enzyme responsible for alpha-synuclein degradation [In the scenario documented, a first disease stage is defined by a mild adaptation of the nuclear splicing machinery, which persists without increase throughout all ages. This is completely novel evidence; the notion of a splicing adaptation to PINK1-deficiency is currently supported only by an OMICS study into posttranslational modifications of the brain in a genetic mouse model of PD, where a strongly altered arginine-methylation of the splicing factor PSF was observed, caused either by PINK1 deficiency or by alpha-synuclein gain of function . The traradation .Creb3 and Nfkbia as ER stress and inflammation factors was similarly reproduced by qPCR as the dysregulation of Ube3a as a component of ubiquitination pathways. Thus, both the ubiquitin kinase effects and the early alteration of widespread protein processing support a novel concept, where PINK1 has a general role for subcellular degradation rather than a function restricted to the selective elimination of dysfunctional mitochondria. Vesicular and lysosomal pathway dysregulations are more prominent at early ages upon GSEA bioinformatics than mitochondrial pathways.A second disease stage shows manifest anomalies in the ubiquitin-dependent degradation of proteins and in the protein processing at the endoplasmic reticulum (ER). These data are in excellent agreement with the established role of PINK1 as a ubiquitin kinase . Scarce Pink1−/− mouse, cerebellar tissue shows no change of the effect size in expression dysregulation in parallel with the progression of pathology, e.g., for the components of the splicing machinery. Instead, the number of dysregulated factors and pathways, the significance of the intracellular membrane-bounded organelle enrichment, and the appearance of altered neurotransmission, mitophagy, anti-microbial, and neuroinflammatory profiles were clearly age-associated. Our experimental validation of increased Mapk8 and Nfkbia transcript levels is in agreement with a previous report about Pink1−/− astrocyte JNK1 signaling [A third stage, at old age in the ignaling  and withignaling –156.Indeed, neuroinflammation is well documented in late stages of PD –161. IniThus, we suggest that innate immunity is triggered within neurons via PINK1-associated mitochondrial dysfunction. Indeed, evidence has accumulated over the past 5 years that dysfunctional mitochondria are releasing damage-associated molecular patterns (DAMPs), e.g., the hypomethylated DNA/RNA and formylated peptides which are characteristic for bacteria and the mitochondrial endosymbiont , 177. ThA similar mechanism could operate in PINK1 and PARKIN deficient cells, which are being used as models of PD and are known to have an impairment of selective mitophagy, as was suggested by a recent report of high visibility that PARKIN also mediates resistance to microbial invasion . The PINPink1−/− primary neuron-rich cultures leads to the significant expression dysregulation of the abnormal DNA/RNA sensor Ifit3 and the iron-sulfur-cluster detector Rsad2, confirmed in Pink1-KO+A53T-SNCA double mutant brain, in human neuroblastoma cells, in Pink1−/− MEFs, and in PARK6 patient skin fibroblasts. The starvation dataset links PINK1 deficiency to a transcriptional downregulation of mitochondria-associated innate immunity factors such as MAVS, MFN1, and IFIT3, and the FCCP dataset shows altered expression of MAVS and MFN in MEFs and a PINK1-dependent blunting of MAVS induction in SH-SY5Y cells. MFN1 and MAVS are localized to the mitochondrial outer membrane, respond to an alteration in the mitochondrial proton gradient, and mediate the elimination of dysfunctional mitochondria via autophagy [TBK1, IRF3, and RSAD2. As shown in the poly(I:C) dataset at transcript and protein level, the regulation of IFIT3 and RSAD2 is altered by PINK1 deficiency. RSAD2 upregulation and mitochondrial relocalization triggers the lipogenesis for viral envelope formation [The innate immunity problems of PINK1-deficient cells become detectable even at the postnatal age upon the presence of a stressor. Stress exposure of utophagy –190. In utophagy . The staormation , 192. BePink1−/− brain and Pink1−/− cells are perfectly compatible with previous in vitro observations [Pink1−/− mouse brain, human neuroblastoma cells, and patient fibroblasts serve as additional corroboration in vivo, providing a spatio-temporal framework and identifying crucial molecular mediators.The changes in very young rvations , 193–198rvations . The prePink1-ablation, we focused the studies of histology and ceramides on the brainstem and olfactory bulb, where Parkinson’s disease starts. Furthermore, demonstration of the expected glial pathology uncovered a preferential affection of myelinated tracts. Thus, at present, it is cumbersome to extrapolate the expression data and other findings into a coherent picture of phenomenology and pathology progression in time and space, integrating observations from mouse and man, so further analyses in-depth are needed. (II) The impaired response of Pink1−/−-deficient cells against polyI:C suggests a vulnerability towards viral infections. Although we present suggestive data at the transcript, protein, and cellular level, with several artificial stressors, this issue can only unequivocally be proven once Pink1−/− organisms are exposed to virus in future experiments, with quantification of viability and propagation rates.Still, as an important caveat, we have to mention several limitations of our findings: (I) Various technical approaches were focused on different brain regions. The previously published global transcriptome profiles had focused on midbrain/brainstem, which were selected because they are responsible for the characteristic mid-stage motor deficit of Parkinson’s disease. The re-assessment of these data showed these regions to be less informative within the 2-year lifespan of mice than the cerebellum. Consequently, for a deeper understanding of the subtle early consequences of Our present findings support the concept that neuroinflammation in PD may occur long before neuronal loss and before the formation of alpha-synuclein aggregates. PINK1-mediated dysregulations of antimicrobial defense and dysfunctional mitochondria are activating the innate immunity.Pink1-deficient mouse brain after earlier ubiquitination and ER-associated degradation alterations, so these data might define markers of disease progression. Although PINK1-mutations are a rare cause of PD, PINK1 is upstream of several other Parkinson’s disease genes and its inactivity results in specific mitochondrial dysfunctions as a trigger for the selective neurodegenerative pattern that is frequent [The neuroinflammatory profile appeared in frequent –203, so Additional file 1: Table S1.Pink1−/− brain regions  across the mouse lifespan , showing all expression dysregulations which were consistent in all regions at all ages with significance . ID identifies the Affymetrix microarray chip probe. AveExpr quantifies the average expression strength among the tissues and ages for each transcript. F, P value, and AdjPvalue refer to statistical values. For each column, 3 mutant versus 3 WT tissues were analyzed to represent a specific brain region and age. (XLS 27 kb)Global transcriptome profile of Additional file 2: Fig. S1.Pink1-deficient mouse cerebellar tissue at ages 6 weeks (A), 6 months (B), and 18 months (C), illustrating the network with respect to interactions between the encoded proteins, employing the STRING web-server multiple proteins algorithm. (ZIP 11629 kb)Global transcriptome profile of Additional file 3: Table S2.(A). Significant functional enrichments in the protein-protein-interaction network according to the STRING database is shown regarding GO Cellular component at 6 weeks with all factors of AdjPvalue <0.05 (B), 6 months with all factors of AdjPvalue <0.05 (C), and 18 months with all factors of AdjPvalue <0.01, with the term “intracellular membrane-bounded organelle” being prominent at all 3 ages (D). Prominent enrichments were highlighted in yellow color. In GO Molecular Process at 18 months with all factors of AdjPvalue <0.01 the relevance of stimulus- and stress-dependent pathways is apparent, e.g., endoplasmic reticulum stress, and a significant enrichment of mitophagy factors appears here in lines 495 and 518 (E). Among KEGG pathways at 18 months with all factors of AdjPvalue <0.01, the highest ranked pathways include “MAPK signaling”, “Ubiquitin-mediated proteolysis”, “Protein processing in endoplasmic reticulum” and “Bacterial invasion of epithelial cells” (F). Orange color was used to highlight some MAPK-dependent synaptic signaling pathways with significant enrichment, purple color was used for ubiquitination and protein processing pathways, green color was used for neuroinflammation pathways. (ZIP 366 kb)STRING bioinformatics assessment of all significant changes, including subtle effects. Ranked list of the significant transcript expression changes in PINK1-deficient mouse cerebellar tissue at ages 6 weeks, 6 months, and 18 months, which were used as input for the STRING analysis Additional file 4: Table S3.STRING bioinformatics assessment with high stringency of only >1.5-fold downregulations with significance. Ranked KEGG pathway dysregulations are shown for cerebellum at each age. The prominent MAPK signaling is highlighted in purple color, neuroinflammation pathways in red, ubiquitination pathways in blue, endoplasmic reticulum stress response pathways in orange, the mTOR signaling pathway (mimicked by starvation stress during in vitro experiments) in dark gray, the vesicular pathways in light brown. Subsequently, the interaction diagrams are shown for each age, with components of the prominent MAPK signaling pathway highlighted in red color. Finally, a heatmap shows all components of the MAPK signaling pathway with their expression changes at each age in cerebellum, with clearly progressive downregulation for several factors at the far end. (PDF 643 kb)Additional file 5: Table S4.p value and the False Discovery Rate q values indicate significance according to GSEA recommendations. Neuroinflammation pathways are highlighted in red color, MAPK signaling in purple, Parkinson’s disease in yellow, ubiquitination pathways in blue, endoplasmic reticulum stress response pathways in orange, the mTOR signaling pathway (mimicked by starvation stress during in vitro experiments) in dark gray, the vesicular pathways in light brown. (XLSX 123 kb)GSEA bioinformatics assessment—ranked pathways by age. Ranked pathway dysregulations are shown separately on different datasheets for the predefined Reactome and KEGG gene sets regarding down- and upregulations, and progressive ages. Green background colors for the nominal Additional file 6: Table S5.Pink1−/− cerebellum at the age of 18 months the top dozen dysregulated KEGG pathways and the top dozen dysregulated Reactome pathways are shown with individual components. The nominal p values, false discovery rate q values and family wise error p values are shown on top, the Rank Metric Score indicates the log2 expression values for each factor, and YES reflects significance. (PDF 1540 kb)GSEA bioinformatics assessment—top dozen pathways with components. In Additional file 7: Table S6.GSEA bioinformatics assessment—heat maps. Heat maps to illustrate the progression of expression dysregulation for all individual components of selected pathways of relevance, namely (A) the Reactome pathway “Negative regulators of RIG I and MDA5 signaling”, (B) the Reactome pathway “Antiviral mechanism by IFN stimulated genes”, (C) the Reactome pathway “Antigen processing ubiquitination proteasome degradation”, and (D) the Reactome pathway “Activation of chaperone genes by Xbp1s”. (PDF 135 kb)Additional file 8: Fig. S2.Pink1-deficient mouse cerebellar tissue at the age of 18 months in qPCR analyses represented in bar graphs. (A) Significant downregulation of Srsf10 mRNA and upregulations of Creb3 and Nfkbia mRNAs confirm the alteration within spliceosomal, ER stress and neuroinflammation pathways. The significant dysregulation of a Ube3a splice isoform is particularly interesting as a potential target of the spliceosome alterations and in view of its role in the degradation of alpha-synuclein. The scheme of Ube3a exon intron structure with the location of different Taqman assays was adapted from the Thermo Fisher Scientific internet site. (B) Significant upregulations of Mapk8 mRNA at 3 different exon junctions, together with a scheme of the Mapk8 exon intron structure and the location of 3 different Taqman assays (modified from the Thermo Fisher Scientific internet site). Non-significant changes of the MAPK phosphorylation cascade components Mapk9 and Mapk14 mRNAs demonstrate the selectivity of transcriptional regulation. Significant upregulations in the downstream nuclear transcription regulators Creb3 and Nfkbia in the stress and inflammation response may reflect biological responses to the Mapk8 upregulation. The bar graphs show mean and standard error of the mean (10 Pink1−/− versus 10 WT), illustrating the significance with asterisks (* p < 0.05 and ** p < 0.01). (TIFF 13007 kb)Transcript changes of Additional file 9: Table S7.Pink1−/− and 3 age- and sex- matched WT controls from the same ancestors. FC = fold change. (PDF 60 kb)PINK1-deficient transcriptome profile of primary neuron cultures. Ranked list of significant expression changes in global transcriptome profile of primary neuron cultures at 12 days after dissection of brains at postnatal stage, taken from 3 Additional file 10: Fig. S3.Pink1-deficient mice with chronic stress from A53T-SNCA overexpression. The levels of the stress-triggered pro-inflammatory factor Mapk9, the lipid-droplet associated antiviral effector Rsad2 (viperin), the formylpeptide receptor activating Hebp1, and the immunostimulant Tnf  transcripts were assessed in brain from adult double mutant Pink1−/−+A53T-SNCA mice. These data corroborate the brain tissue dysregulation of two factors that were previously found dysregulated in the global transcriptome profile of Pink1−/− primary neuron cultures, taken from postnatal mice and maintained in vitro over 12 days. Data are displayed as bar graphs, illustrating mean values and standard error of the mean (SEM). Tbp transcript levels were used as loading controls to normalize the data. Significant differences were highlighted with asterisks . (TIFF 17632 kb)Transcription dysregulation of innate immunity factors in the midbrain of 18-month-old Additional file 11: Fig. S4.p < 0.05, ** p < 0.01, *** p < 0.001). (TIFF 538 kb)Transcriptional response of innate immunity factors to 24 h treatment with uncoupling drug FCCP and subsequent mitophagy, in dependence on PINK1. Three independent experiments in SH-SY5Y human neuroblastoma (above) and murine embryonal fibroblast cells (below) documented the expression of key inflammatory factors in untreated versus drug-treated cells, comparing control with PINK1-deficiency. The bar graphs show mean and standard error of the mean, illustrating the significance with asterisks (*