Contribution of changes in the orexin system and energy sensors in the brain in depressive disorder - a study in an animal model.

BACKGROUND: Maternal elevated glucocorticoid levels during pregnancy can affect the developing fetus, permanently altering the structure and function of its brain throughout life. Excessive action of these hormones is known to contribute to psychiatric disorders, including depression. MATERIALS: The study was performed in a rat model of depression based on prenatal administration of dexamethasone (DEX) in late pregnancy (0.1 mg/kg, days 14-21). We evaluated the effects of prenatal DEX treatment on the cognition and bioenergetic signaling pathways in the brain of adult male rats, in the frontal cortex and hippocampus, and in response to stress in adulthood, using behavioral and biochemical test batteries. RESULTS: We revealed cognitive deficits in rats prenatally treated with DEX. At the molecular level, a decrease in the orexin A and orexin B levels and downregulation of the AMPK-SIRT1-PGC1α transduction pathway in the frontal cortex of these animals were observed. In the hippocampus, a decreased expression of orexin B was found and changes in the MR/GR ratio were demonstrated. Furthermore, an increase in HDAC5 level triggered by the prenatal DEX treatment in both brain structures and a decrease in MeCP2 level in the hippocampus were reported. CONCLUSIONS: Our study demonstrated that prenatal DEX treatment is associated with cognitive dysfunction and alterations in various proteins leading to metabolic changes in the frontal cortex, while in the hippocampus adaptation mechanisms were activated. The presented results imply that different pathophysiological metabolic processes may be involved in depression development, which may be useful in the search for novel therapies.

The Effects of Prenatal Dexamethasone Exposure on Brain Metabolic Homeostasis in Adulthood: Implications for Depression.

Since depression produces a long-term negative impact on quality of life, understanding the pathophysiological changes implicated in this disorder is urgent. There is growing evidence that demonstrates a key role for dysfunctional energy metabolism in driving the onset of depression; thus, bioenergetic alterations should be extensively studied. Brain metabolism is known to be a glucocorticoid-sensitive process, but the long-lasting consequences in adulthood following high levels of glucocorticoids at the early stages of life are unclear. We examined a possible association between brain energetic changes induced by synthetic glucocorticoid-dexamethasone treatment in the prenatal period and depressive-like behavior. The results show a reduction in the oxidative phosphorylation process, Krebs cycle impairment, and a weakening of the connection between the Krebs cycle and glycolysis in the frontal cortex of animals receiving dexamethasone, which leads to ATP reduction. These changes appear to be mainly due to decreased expression of pyruvate dehydrogenase, impairment of lactate transport to neurons, and pyruvate to the mitochondria. Acute stress in adulthood only slightly modified the observed alterations in the frontal cortex, while in the case of the hippocampus, prenatal exposure to dexamethasone made this structure more sensitive to future adverse factors.

Changes in regulators of lipid metabolism in the brain: a study of animal models of depression and hypothyroidism.

Metabolic disturbances in the brain are assumed to be early changes involved in the pathogenesis of depression, and these alterations may be intensified by a deficiency of thyroid hormones. In contrast to glucose metabolism, the link between altered brain lipids and the pathogenesis of depression is poorly understood, therefore in the present study, we determine transcription factors and enzymes regulating cholesterol and fatty acid biosynthesis in the brain structures in an animal model of depression, hypothyroidism and the coexistence of these diseases.In used model of depression, a decrease in the active form of the transcription factor SREBP-2 in the hippocampus was demonstrated, thus suggesting a reduction in cholesterol biosynthesis. In turn, in the hypothyroidism model, the reduction of cholesterol biosynthesis in the frontal cortex was demonstrated by both the reduction of mature SREBP-2 and the concentration of enzymes involved in cholesterol biosynthesis. The lower expression of LDL receptors in the frontal cortex indicates the restriction of cholesterol uptake into the cells in the model of coexistence of depression and hypothyroidism. Moreover, the identified changes in the levels of SNAP-25, GLP-1R and GLP-2R pointed to disturbances in synaptic plasticity and neuroprotection mechanisms in the examined brain structures.In conclusion, a reduction in cholesterol synthesis in the hippocampus in the model of depression may be the reason for the reduction of synaptic plasticity, whereas a lower level of LDL-R occurring in the frontal cortex in rats from the model of depression and hypothyroidism coexistence could be the reason of anxiogenic and depression-like behaviors.

Overlapping association signals in the genetics of hair-related phenotypes in humans and their relevance to predictive DNA analysis.

Genetic prediction of different hair phenotypes can help reconstruct the physical appearance of an individual whose biological sample is analyzed in criminal and identification cases. Up to date, forensic prediction models for hair colour, hair shape, hair loss and hair greying have been developed, but studies investigating predictability of hair thickness and density traits are missing. First data suggesting overlapping associations in various hair features have emerged in recent years, suggesting partially common genetic basis and molecular mechanisms, and this knowledge can be used for predictive purposes. Here we aim to broaden our understanding of the genetics underlying head, facial and body hair thickness and density traits and examine the association for a set of literature SNPs. We characterize the overlap in SNP association for various hair phenotypes, the extent of genetic interactions and the potential for genetic prediction. The study involved 999 samples from Poland, genotyped for 240 SNPs with targeted next-generation sequencing. Logistic regression methods were applied for association and prediction analyses while entropy-based approach was used for interaction testing. As a result, we refined known associations for monobrow and hairiness (PAX3, 5q13.2, TBX) and identified two novel association signals in IGFBP5 and VDR. Both genes were among top significant loci, showed broad association with different hair-related traits and were implicated in multiple interaction effects. Overall, for 14.7% of SNPs previously associated with head hair loss and/or hair shape, a positive signal of association was revealed with at least one hair feature studied in the current research. Overlap in association with at least two hair-related traits was demonstrated for 24 distinct loci. We showed that the associated SNPs explain ∼5-30% of the variation observed in particular hair traits and allow moderate accuracy of prediction. The highest accuracy was achieved for hairiness level prediction in females (AUC = 0.69 for the "none", 0.69 for the "low" and 0.76 for the "excessive" hairiness category) and monobrow (AUC = 0.69 for the "none", 0.62 for the "slight" and 0.70 for the "significant" monobrow category) with 33% of the variation in hairiness level in females explained by 7 SNPs and age, and 20% of the variation in monobrow captured by 7 SNPs and sex. Our study presents clear evidence of pleiotropy and epistasis in the genetics of hair traits. The acquired knowledge may have practical application in forensics, as well as in the cosmetic industry and anthropological research.

Searching for improvements in predicting human eye colour from DNA.

Increasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

Exploring the possibility of predicting human head hair greying from DNA using whole-exome and targeted NGS data.

BACKGROUND: Greying of the hair is an obvious sign of human aging. In addition to age, sex- and ancestry-specific patterns of hair greying are also observed and the progression of greying may be affected by environmental factors. However, little is known about the genetic control of this process. This study aimed to assess the potential of genetic data to predict hair greying in a population of nearly 1000 individuals from Poland. RESULTS: The study involved whole-exome sequencing followed by targeted analysis of 378 exome-wide and literature-based selected SNPs. For the selection of predictors, the minimum redundancy maximum relevance (mRMRe) method was used, and then two prediction models were developed. The models included age, sex and 13 unique SNPs. Two SNPs of the highest mRMRe score included whole-exome identified KIF1A rs59733750 and previously linked with hair loss FGF5 rs7680591. The model for greying vs. no greying prediction achieved accuracy of cross-validated AUC = 0.873. In the 3-grade classification cross-validated AUC equalled 0.864 for no greying, 0.791 for mild greying and 0.875 for severe greying. Although these values present fairly accurate prediction, most of the prediction information was brought by age alone. Genetic variants explained < 10% of hair greying variation and the impact of particular SNPs on prediction accuracy was found to be small. CONCLUSIONS: The rate of changes in human progressive traits shows inter-individual variation, therefore they are perceived as biomarkers of the biological age of the organism. The knowledge on the mechanisms underlying phenotypic aging can be of special interest to the medicine, cosmetics industry and forensics. Our study improves the knowledge on the genetics underlying hair greying processes, presents prototype models for prediction and proves hair greying being genetically a very complex trait. Finally, we propose a four-step approach based on genetic and epigenetic data analysis allowing for i) sex determination; ii) genetic ancestry inference; iii) greying-associated SNPs assignment and iv) epigenetic age estimation, all needed for a final prediction of greying.

The challenge of predicting human pigmentation traits in degraded bone samples with the MPS-based HIrisPlex-S system.

Identification of human remains is an important part of human DNA analysis studies. STR and mitochondrial DNA markers are well suited for the analysis of degraded biological samples including bone material. However, these DNA markers may be useless when reference material is not available. In these cases, predictive DNA analysis can support the process of human identification by providing investigative leads. Forensic DNA phenotyping has progressed significantly by offering new methods based on massively parallel sequencing technology, but the frequent degradation processes observed in skeletal remains can make analysis of such samples challenging. In this study, we demonstrate the usefulness of a recently established Ion AmpliSeqTM HIrisPlex-S panel using Ion Torrent technology for analyzing bone samples that show different levels of DNA degradation. In total, 63 bone samples at post-mortem intervals up to almost 80 years were genotyped and eye, hair and skin colour predictions were performed using the HIrisPlex-S models. Following the recommended coverage thresholds, it was possible to establish full DNA profiles comprising of 41 DNA variants for 35 samples (55.6%). For 5 samples (7.9%) no DNA profiles were generated. The remaining 23 samples (36.5%) produced partial profiles and showed a clear underperformance of 3 HIrisPlex-S SNPs - rs1545397 (OCA2), rs1470608 (OCA2) and rs10756819 (BNC2), all used for skin colour prediction only. None of the 23 samples gave complete genotypes needed for skin colour prediction was obtained, and in 7 of them (25.9%) the 3 underperformed SNPs were the cause. At the same time, the prediction of eye and hair colour using complete IrisPlex and HIrisPlex profiles could be made for these 23 samples in 20 (87.0%) and 12 cases (52.2%), respectively. Complete HIrisPlex-S profiles were generated from as little as 49 pg of template DNA. Five samples for which the HIrisPlex-S analysis failed, consistently failed in standard STR analysis. Importantly, the 3 underperforming SNPs produced significantly lower number of reads in good quality samples. Nonetheless, the AUC loss resulting from missing data for these 3 SNPs is not considered large (≤0.004) and the prediction of pigmentation from partial profiles is also available in the current HPS tool. The study shows that DNA degradation and the resulting loss of data are the most serious challenge to DNA phenotyping of skeletal remains. Although the newly developed HIrisPlex-S panel has been successfully validated in the current research, primer redesign for the 3 underperforming SNPs in the MPS design should be considered in the future.

HIrisPlex-S system for eye, hair, and skin color prediction from DNA: Massively parallel sequencing solutions for two common forensically used platforms.

Forensic DNA Phenotyping (FDP) provides the ability to predict externally visible characteristics from minute amounts of crime scene DNA, which can help find unknown perpetrators who are typically unidentifiable via conventional forensic DNA profiling. Fundamental human genetics research has led to a better understanding of the specific DNA variants responsible for physical appearance characteristics, particularly eye, hair, and skin color. Recently, we introduced the HIrisPlex-S system for the simultaneous prediction of eye, hair, and skin color based on 41 DNA variants generated from two forensically validated SNaPshot multiplex assays using capillary electrophoresis (CE). Here we introduce massively parallel sequencing (MPS) solutions for the HIrisPlex-S (HPS) system on two MPS platforms commonly used in forensics, Ion Torrent and MiSeq, that cover all 41 DNA variants in a single assay, respectively. Additionally, we present the forensic developmental validation of the two HPS-MPS assays. The Ion Torrent MPS assay, based on Ion AmpliSeq technology, illustrated the successful generation of full HIrisPlex-S genotypic profiles from 100 pg of input control DNA, while the MiSeq MPS assay based on an in-house design yielded complete profiles from 250 pg of input DNA. Assessing simulated forensic casework samples such as saliva, hair (bulb), blood, semen, and low quantity touch DNA, as well as artificially damaged DNA samples, concordance testing, and samples from numerous species, all illustrated the ability of both versions of the HIrisPlex-S MPS assay to produce results that motivate forensic applications. By also providing an integrated bioinformatics analysis pipeline, MPS data can now be analyzed and a file generated for upload to the publically accessible HIrisPlex online webtool (https://hirisplex.erasmusmc.nl). In addition, we updated the website to accept VCF input data for those with genome sequence data. We thus provide a user-friendly and semi-automated MPS workflow from DNA sample to individual eye, hair, and skin color prediction probabilities. Furthermore, we present a 2-person mixture separation tool that not only assesses genotype reliability with regards genotyping confidence but also provides the most fitting mixture scenario for both minor and major contributors, including profile separation. We envision this MPS implementation of the HIrisPlex-S system for eye, hair, and skin color prediction from DNA as a starting point for further expanding MPS-based forensic DNA phenotyping. This may include the future addition of SNPs predictive for more externally visible characteristics, as well as SNPs for bio-geographic ancestry inference, provided the statistical framework for DNA prediction of these traits is in place.

DNA-based predictive models for the presence of freckles.

Freckles or ephelides are hyperpigmented spots observed on skin surface mainly in European and Asian populations. Easy recognition and external visibility make prediction of ephelides, the potentially useful target in the field of forensic DNA phenotyping. Prediction of freckles would be a step forward in sketching the physical appearance of unknown perpetrators or decomposed cadavers for the forensic DNA intelligence purposes. Freckles are especially common in people with pale skin and red hair and therefore it is expected that predisposition to freckles may partially share the genetic background with other pigmentation traits. The first proposed freckle prediction model was developed based on investigation that involved variation of MC1R and 8 SNPs from 7 genes in a Spanish cohort [19]. In this study we examined 113 DNA variants from 46 genes previously associated with human pigmentation traits and assessed their impact on freckles presence in a group of 960 individuals from Poland. Nineteen DNA variants revealed associations with the freckle phenotype and the study also revealed that females have ∼1.8 higher odds of freckles presence comparing to males (p-value = 9.5 × 10-5). Two alternative prediction models were developed using regression methods. A simplified binomial 12-variable model predicts the presence of ephelides with cross-validated AUC = 0.752. A multinomial 14-variable model predicts one of three categories - non-freckled, medium freckled and heavily freckled. The two extreme categories, non-freckled and heavily freckled were predicted with moderately high accuracy of cross-validated AUC = 0.754 and 0.792, respectively. Prediction accuracy of the intermediate category was lower, AUC = 0.657. The study presents novel DNA models for prediction of freckles that can be used in forensic investigations and emphasizes significance of pigmentation genes and sex in predictive DNA analysis of freckles.

Towards broadening Forensic DNA Phenotyping beyond pigmentation: Improving the prediction of head hair shape from DNA.

Human head hair shape, commonly classified as straight, wavy, curly or frizzy, is an attractive target for Forensic DNA Phenotyping and other applications of human appearance prediction from DNA such as in paleogenetics. The genetic knowledge underlying head hair shape variation was recently improved by the outcome of a series of genome-wide association and replication studies in a total of 26,964 subjects, highlighting 12 loci of which 8 were novel and introducing a prediction model for Europeans based on 14 SNPs. In the present study, we evaluated the capacity of DNA-based head hair shape prediction by investigating an extended set of candidate SNP predictors and by using an independent set of samples for model validation. Prediction model building was carried out in 9674 subjects (6068 from Europe, 2899 from Asia and 707 of admixed European and Asian ancestries), used previously, by considering a novel list of 90 candidate SNPs. For model validation, genotype and phenotype data were newly collected in 2415 independent subjects (2138 Europeans and 277 non-Europeans) by applying two targeted massively parallel sequencing platforms, Ion Torrent PGM and MiSeq, or the MassARRAY platform. A binomial model was developed to predict straight vs. non-straight hair based on 32 SNPs from 26 genetic loci we identified as significantly contributing to the model. This model achieved prediction accuracies, expressed as AUC, of 0.664 in Europeans and 0.789 in non-Europeans; the statistically significant difference was explained mostly by the effect of one EDAR SNP in non-Europeans. Considering sex and age, in addition to the SNPs, slightly and insignificantly increased the prediction accuracies (AUC of 0.680 and 0.800, respectively). Based on the sample size and candidate DNA markers investigated, this study provides the most robust, validated, and accurate statistical prediction models and SNP predictor marker sets currently available for predicting head hair shape from DNA, providing the next step towards broadening Forensic DNA Phenotyping beyond pigmentation traits.

Investigating the impact of age-depended hair colour darkening during childhood on DNA-based hair colour prediction with the HIrisPlex system.

Predictive DNA analysis of externally visible characteristics exerts an increasing influence on contemporary forensic and anthropological investigations, with pigmentation traits currently being the most advanced for predictive modelling. Since pigmentation prediction error in some cases may be due to the result of age-related hair colour darkening, and sex influence in eye colour, this study aims to investigate these less explored phenomena on a group of juvenile individuals. Pigmentation phenotypes of children between the age of 6-13 years old were evaluated, in addition to data about their hair colour during early childhood from a select number of these individuals. The HIrisPlex models for DNA-based eye and hair colour prediction were used with input from SNP genotyping using massive parallel sequencing. Analysis of the total group of 476 children showed high accuracy in blue (AUC = 0.89) and brown (AUC = 0.91) eye colour prediction, while hair colour was predicted with AUC = 0.64 for blond, AUC = 0.64 for brown and AUC = 0.97 for red. 70.8% (n = 143) of the total number of children phenotypically blond for hair colour during early childhood progressed to brown during advanced childhood. In 70.6% (n = 101) of those cases, an incorrect blond hair prediction was made during the time of analysis. A noticeable decline in AUC values for blond (from 0.76 to 0.65) and brown (from 0.72 to 0.64) were observed when comparing hair colour prediction outcomes for the phenotypes recorded for the two different time points (at the age of 2-3 and 6-13). The number of incorrect blond hair colour predictions was significantly higher in children with brown hair at age 6-13 who were blond at early childhood (n = 47, 32.9%), relative to children who had brown hair at both time points (n = 6, 9.4%). However, in 28.0% (n = 40) of children who did experience hair colour darkening, HIrisPlex provided the correct prediction for the darkened hair colour phenotype, despite them being blond in early childhood. Our study implies that HIrisPlex can correctly predict adult hair colour in some individuals who experience age-related hair colour darkening during adolescence. However, in most instances prediction seems to default to the pre-adolescent hair colour for individuals with this phenomenon. In the future, the full adolescent age range in which hair colour darkening can occur should be considered in the study samples used for training hair colour prediction models to obtain a more complete picture of the phenomenon and its impact on DNA-based hair colour prediction in adults.

The HIrisPlex-S system for eye, hair and skin colour prediction from DNA: Introduction and forensic developmental validation.

Forensic DNA Phenotyping (FDP), i.e. the prediction of human externally visible traits from DNA, has become a fast growing subfield within forensic genetics due to the intelligence information it can provide from DNA traces. FDP outcomes can help focus police investigations in search of unknown perpetrators, who are generally unidentifiable with standard DNA profiling. Therefore, we previously developed and forensically validated the IrisPlex DNA test system for eye colour prediction and the HIrisPlex system for combined eye and hair colour prediction from DNA traces. Here we introduce and forensically validate the HIrisPlex-S DNA test system (S for skin) for the simultaneous prediction of eye, hair, and skin colour from trace DNA. This FDP system consists of two SNaPshot-based multiplex assays targeting a total of 41 SNPs via a novel multiplex assay for 17 skin colour predictive SNPs and the previous HIrisPlex assay for 24 eye and hair colour predictive SNPs, 19 of which also contribute to skin colour prediction. The HIrisPlex-S system further comprises three statistical prediction models, the previously developed IrisPlex model for eye colour prediction based on 6 SNPs, the previous HIrisPlex model for hair colour prediction based on 22 SNPs, and the recently introduced HIrisPlex-S model for skin colour prediction based on 36 SNPs. In the forensic developmental validation testing, the novel 17-plex assay performed in full agreement with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, as previously shown for the 24-plex assay. Sensitivity testing of the 17-plex assay revealed complete SNP profiles from as little as 63 pg of input DNA, equalling the previously demonstrated sensitivity threshold of the 24-plex HIrisPlex assay. Testing of simulated forensic casework samples such as blood, semen, saliva stains, of inhibited DNA samples, of low quantity touch (trace) DNA samples, and of artificially degraded DNA samples as well as concordance testing, demonstrated the robustness, efficiency, and forensic suitability of the new 17-plex assay, as previously shown for the 24-plex assay. Finally, we provide an update to the publically available HIrisPlex website https://hirisplex.erasmusmc.nl/, now allowing the estimation of individual probabilities for 3 eye, 4 hair, and 5 skin colour categories from HIrisPlex-S input genotypes. The HIrisPlex-S DNA test represents the first forensically validated tool for skin colour prediction, and reflects the first forensically validated tool for simultaneous eye, hair and skin colour prediction from DNA.