Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Insulin resistance and not steatosis is associated with modifications in oxidative stress markers in chronic hepatitis C, non-3 genotype

Background: Modifications of oxidative stress are reported in hepatitis C. the relationship between insulin resistance (IR), steatosis and oxidative stress is not established. Materials and methods: One hundred and eighty-seven HCV-RNA patients were assessed by determination of biochemical, metabolic and viral features, HOMA-IR and morphological alterations. in the 52-non-3 genotypes sub-group and 35 healthy individuals, thiobarbituric acid (TBARS), total glutathione (total-GSH), vitamins C and E, lycopene, beta-carotene, glutathione peroxidase (GPx), catalase and superoxide dismutase were determined. Results: in non-3 genotype patients, steatosis was associated with higher values of BMI, HOMA-IR and triglycerides. in the 52-HCV sub-group, values of TBARS, GPx and total-GSH differ from the control group. Despite these, differences could not be observed according to the presence of steatosis, patients with IR presented significant differences regarding total-GSH (p = 0.019), beta-carotene (p = 0.006), lycopene (p = 0.005) and GPx (p = 0.009). Conclusion: in non-3 genotype HCV carries, IR, and not steatosis, is associated with modifications in serum levels of oxidative stress.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Federal de São Paulo, Div Gastroenterol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Biol Sci, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Pathol, São Paulo, BrazilUniversidade Federal de São Paulo, Div Gastroenterol, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Biol Sci, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Pathol, São Paulo, BrazilFAPESP: 02/05260-6Web of Scienc

Excessive Iodine Intake and Ultrasonographic Thyroid Abnormalities in Schoolchildren

High nutritional levels of iodine may induce a higher prevalence of autoimmune thyroiditis,hypothyroidism, goiter, as well as hyperthyroidism, mostly in the elderly. This study assessed thyroid volume and ultrasonographic abnormalities as well as urinary iodine excretion (UIE) in 964 schoolchildren living in an iodine-sufficient area in southern Brazil. Thyroid volume correlated with age and body surface area in boys and girls. In 76.8% of the children, UIE was above 300 mu g/l, with higher levels among boys compared to girls (484.2 mu g/l vs 435.3 mu g/l, p <0.001). Thyroid abnormalities detected by ultrasonography included hemiagenesis (0.5%), nodules (0.2%), cysts (0.7%), and hypoechogenicity (11.7%). Goiter was present in 1.9% of the children. Hypoechogenicity, a relevant marker of autoimmune thyroiditis, was the most common abnormality found in our study, and this may be linked to excessive iodine intake

RBD and Spike DNA-Based Immunization in Rabbits Elicited IgG Avidity Maturation and High Neutralizing Antibody Responses against SARS-CoV-2

Neutralizing antibodies (nAbs) are a critical part of coronavirus disease 2019 (COVID-19) research as they are used to gain insight into the immune response to severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections. Among the technologies available for generating nAbs, DNA-based immunization methods are an alternative to conventional protocols. In this pilot study, we investigated whether DNA-based immunization by needle injection in rabbits was a viable approach to produce a functional antibody response. We demonstrated that three doses of DNA plasmid carrying the gene encoding the full-length spike protein (S) or the receptor binding domain (RBD) of SARS-CoV-2 induced a time-dependent increase in IgG antibody avidity maturation. Moreover, the IgG antibodies displayed high cross neutralization by live SARS-CoV-2 and pseudoviruses neutralization assays. Thus, we established a simple, low cost and feasible DNA-based immunization protocol in rabbits that elicited high IgG avidity maturation and nAbs production against SARS-CoV-2, highlighting the importance of DNA-based platforms for developing new immunization strategies against SARS-CoV-2 and future emerging epidemics