A large population-based investigation into the genetics of susceptibility to gastrointestinal infections and the link between gastrointestinal infections and mental illness.

Gastrointestinal infections can be life threatening, but not much is known about the host's genetic contribution to susceptibility to gastrointestinal infections or the latter's association with psychiatric disorders. We utilized iPSYCH, a genotyped population-based sample of individuals born between 1981 and 2005 comprising 65,534 unrelated Danish individuals (45,889 diagnosed with mental disorders and 19,645 controls from a random population sample) in which all individuals were linked utilizing nationwide population-based registers to estimate the genetic contribution to susceptibility to gastrointestinal infections, identify genetic variants associated with gastrointestinal infections, and examine the link between gastrointestinal infections and psychiatric and neurodevelopmental disorders. The SNP heritability of susceptibility to gastrointestinal infections ranged from 3.7% to 6.4% on the liability scale. Significant correlations were found between gastrointestinal infections and the combined group of mental disorders (OR = 2.09; 95% CI: 1.82-2.4, P = 1.87 × 10-25). Correlations with autism spectrum disorder, attention deficit hyperactivity disorder, and depression were also significant. We identified a genome-wide significant locus associated with susceptibility to gastrointestinal infections (OR = 1.13; 95% CI: 1.08-1.18, P = 2.9 × 10-8), where the top SNP was an eQTL for the ABO gene. The risk allele was associated with reduced ABO expression, providing, for the first time, genetic evidence to support previous studies linking the O blood group to gastrointestinal infections. This study also highlights the importance of integrative work in genetics, psychiatry, infection, and epidemiology on the road to translational medicine

Protein structure search and local structure characterization

<p>Abstract</p> <p>Background</p> <p>Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.</p> <p>Results</p> <p>We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at <url>http://140.113.166.178/safast/</url>.</p> <p>Conclusion</p> <p>The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.</p

Lifetime self-reported arthritis is associated with elevated levels of mental health burden: A multi-national cross sectional study across 46 low- and middle-income countries

Population-based studies investigating the relationship of arthritis with mental health outcomes are lacking, particularly among low- and middle-income countries (LMICs). We investigated the relationship between arthritis and mental health (depression spectrum, psychosis spectrum, anxiety, sleep disturbances and stress) across community-dwelling adults aged ≥18 years across 46 countries from the World Health Survey. Symptoms of psychosis and depression were established using questions from the Mental Health Composite International Diagnostic Interview. Severity of anxiety, sleep problems, and stress sensitivity over the preceding 30 days were self-reported. Self-report lifetime history of arthritis was collected, including presence or absence of symptoms suggestive of arthritis: pain, stiffness or swelling of joints over the preceding 12-months. Multivariable logistic regression analyses were undertaken. Overall, 245,706 individuals were included. Having arthritis increased the odds of subclinical psychosis (OR = 1.85; 95%CI = 1.72–1.99) and psychosis (OR = 2.48; 95%CI = 2.05–3.01). People with arthritis were at increased odds of subsyndromal depression (OR = 1.92; 95%CI = 1.64–2.26), a brief depressive episode (OR = 2.14; 95%CI = 1.88–2.43) or depressive episode (OR = 2.43; 95%CI = 2.21–2.67). Arthritis was also associated with increased odds for anxiety (OR = 1.75; 95%CI = 1.63–1.88), sleep problems (OR = 2.23; 95%CI = 2.05–2.43) and perceived stress (OR = 1.43; 95%CI = 1.33–1.53). Results were similar for middle-income and low-income countries. Integrated interventions addressing arthritis and mental health comorbidities are warranted to tackle this considerable burden

Modeling psychiatric disorders: from genomic findings to cellular phenotypes

Major programs in psychiatric genetics have identified 4150 risk loci for psychiatric disorders. These loci converge on a small number of functional pathways, which span conventional diagnostic criteria, suggesting a partly common biology underlying schizophrenia, autism and other psychiatric disorders. Nevertheless, the cellular phenotypes that capture the fundamental features of psychiatric disorders have not yet been determined. Recent advances in genetics and stem cell biology offer new prospects for cell-based modeling of psychiatric disorders. The advent of cell reprogramming and induced pluripotent stem cells (iPSC) provides an opportunity to translate genetic findings into patient-specific in vitro models. iPSC technology is less than a decade old but holds great promise for bridging the gaps between patients, genetics and biology. Despite many obvious advantages, iPSC studies still present multiple challenges. In this expert review, we critically review the challenges for modeling of psychiatric disorders, potential solutions and how iPSC technology can be used to develop an analytical framework for the evaluation and therapeutic manipulation of fundamental disease processes