The Role of Genetics in the Etiology of Schizophrenia

How has schizophrenia, a condition that significantly reduces an individual's evolutionary fitness, remained common across generations and cultures? Numerous theories about the evolution of schizophrenia have been proposed, most of which are not consistent with modern epidemiological and genetic evidence. Here, we briefly review this evidence and explore the cliff edge model of schizophrenia. It suggests that schizophrenia is the extreme manifestation of a polygenic trait or a combination of traits that, within a normal range of variation, confer cognitive, linguistic, and/or social advantages. Only beyond a certain threshold, these traits precipitate the onset of schizophrenia and reduce fitness. We provide the first mathematical model of this qualitative concept and show that it requires only very weak positive selection of the underlying trait(s) to explain today’s schizophrenia prevalence. This prediction, along with expectations about the effect size of schizophrenia risk alleles, are surprisingly well matched by empirical evidence. The cliff edge model predicts a dynamic change of selection of risk alleles, which explains the contradictory findings of evolutionary genetic studies.

Animals constantly integrate sensory information with prior experience to select behavioral responses appropriate to the current situation. Genetic factors supporting this behavioral flexibility are often disrupted in neuropsychiatric conditions, such as the autism-linked ap2s1 gene which supports acoustically evoked habituation learning. ap2s1 encodes an AP2 endocytosis adaptor complex subunit, although its behavioral mechanisms and importance have been unclear. Here, we show that multiple AP2 subunits regulate acoustically evoked behavior selection and habituation learning in zebrafish. Furthermore, ap2s1 biases escape behavior choice in sensory modality-specific manners, and broadly regulates action selection across sensory contexts. We demonstrate that the AP2 complex functions acutely in the nervous system to modulate acoustically evoked habituation, suggesting several spatially and/or temporally distinct mechanisms through which AP2 regulates escape behavior selection and performance. Altogether, we show the AP2 complex coordinates action selection across diverse contexts, providing a vertebrate model for ap2s1’s role in human conditions including autism spectrum disorder.

Neurological disorders are diseases of the central nervous system with progressive loss of nervous tissue. One of the most difficult problems associated with neurological disorders is that there is no clear treatment for these diseases. In this review, the physiopathology of some neurodegenerative diseases, etiological causes, drugs used and their side effects, and finally the role of probiotics in controlling the symptoms of these neurodegenerative diseases are presented. Recently, researchers have focused more on the microbiome and the gut-brain axis, which may play a critical role in maintaining brain health. Probiotics are among the most important bacteria that have positive effects on the balance of homeostasis via influencing the microbiome. Other important functions of probiotics in alleviating symptoms of neurological disorders include anti-inflammatory properties, short-chain fatty acid production, and the production of various neurotransmitters. The effects of probiotics on the control of abnormalities seen in neurological disorders led to probiotics being referred to as "psychobiotic. Given the important role of the gut-brain axis and the imbalance of the gut microbiome in the etiology and symptoms of neurological disorders, probiotics could be considered safe agents that positively affect the balance of the microbiome as complementary treatment options for neurological disorders.

Schizophrenia is one of the major neuropsychiatric disorders affecting 1% of the population worldwide. Neuroinflammation, neurodevelopment, and oxidative stress are some of the crucial factors that can contribute to the pathogenesis of Schizophrenia. Klotho gene is an antiaging gene whose dysregulated expression can lead to Schizophrenia and aging-like symptoms in patients. Klotho gene expression is regulated by miRNA- 339, which might lead to expression changes of the klotho gene in schizophrenia patients. This study aimed to determine the Role of miRNA- 339–5p in the Regulation of Klotho Gene Expression and its Circulatory Levels in Schizophrenia. In this study total of 60 cases, schizophrenia patients and 30 healthy controls were recruited, and written informed consent was obtained from all the study subjects. The klotho gene and miRNA – 339–5p expressions were done using a reverse transcription polymerase chain reaction. And relative fold change expression was calculated by Livaak’s method, that is 2^-double delta ct. It was found that the klotho gene is around 2.08 times upregulated as compared to healthy control, and miRNA- 339–5p was downregulated and showed an inverse relationship. The present study is the first to evaluate the klotho gene expression and correlate it with miRNA- 339–5p. Further confirmation of the results study should be planned with a large sample size and with drug naïve patients.

Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.

The integration of evolutionary theory into our understanding of mental health provides important insights for understanding why humans are susceptible to mental illnesses. Ultimately, the foundational concepts that underlie evolutionary psychiatry are the same that make up the broader field of evolutionary medicine. In this article, we discuss how these principles can be applied to understand why humans are vulnerable to mental disorders such as anxiety, depression, autism, and schizophrenia.