![]() In addition to genetic variants, psychological correlates have long been found to be linked with suicidal idea, attempts and completion (Duberstein, 1995 Corruble et al., 1999 Goldney et al., 2002 Useda et al., 2004 Brezo et al., 2006). Moreover, the variance of SA is reported to be explained by a very small proportion (around 5%) if considering genetic effect alone, although the effect is jointly generated by hundreds or thousands of common variants (Mullins et al., 2014 Sokolowski et al., 2016). However, it is believed that GWAS is still underpowered to detect SA risk variants with small effect sizes in a classic SNP-by-SNP analysis as the genetic basis of SA is likely to be highly polygenic (Sokolowski et al., 2014). With the advances in genomic technologies, it is possible to investigate the genetic architecture of SA in a whole-genome level using GWAS (Perlis et al., 2010 Schosser et al., 2011 Willour et al., 2012 Mullins et al., 2014 Galfalvy et al., 2015). Additionally, human imaging genetics study suggested that this variant had a significant influence on prefrontal cortex in MDD as well as brain grey and white matter structure and function in bipolar disorder (BPD) (Rietschel et al., 2010 Benedetti et al., 2018). Of note, a common variant rs7713917, located in a putative regulatory region of the gene, was found to be associated with major depression disorder (MDD) in a genome-wide association study (GWAS) with a follow-up replication sample set (Rietschel et al., 2010). Moreover, some common or rare variants in the HOMER1 gene were reported to be associated with susceptibility to the risk of mental disorders and SA in Chinese and Caucasian (Strauss et al., 2012 Rao et al., 2016 Rao et al., 2017). Thus, HOMER1 is involved in the regulation of the function of postsynaptic receptors, such as serotonin receptor, dopamine D1 receptor and N-methyl- d-aspartate (NMDA) glutamate receptor (Dell'aversano et al., 2009 Iasevoli et al., 2009). In postsynaptic density, Homer Scaffolding Protein 1 (HOMER1) is a key molecule at the postsynaptic membrane and constructs a polymeric network at postsynaptic density with another key protein SHANK3 (Hayashi et al., 2009). For genetic variants, most of the previous studies focused on the neurotransmitter system, mainly including the serotonergic, dopaminergic and glutamatergic systems. Moreover, the number of suicide victims in China has been estimated to account for approximately 42% of suicide death all over the world (Phillips et al., 2002 Weiyuan, 2009).Īlthough the pathogenesis of SA is still not explained clearly, it is generally believed that SA is affected by a combination of genetic variants, environmental factors, psychiatric disorders and psychological correlates (Sher, 2011 Beghi et al., 2013). The suicide rate in Chinese population is estimated to be around 12.7 per 100,000 (World_Health_Organization., Retrieved 2nd Oct. It is estimated that three-quarters of suicide death occurs in developing countries particularly in China and India (World_Health_Organization., Retrieved 2nd Oct. 1% of suicide attempters complete suicide within one year and >5% of attempters die by suicide within 10 years (Tintinalli, 2010 Chang et al., 2011), which make completed suicide the 10th leading cause of death all over the world (Hawton and van Heeringen, 2009 Varnik, 2012). Even worse, SA is the most accurate predictor of completed suicide, i.e. We anticipate that the PSAP predictor provides a useful tool for future research aimed at identifying phase separating proteins in health and disease.Every year an estimated 10 to 20 million people had suicide attempts (SA) globally with both acute injuries and long-term disability (Bertolote and Fleischmann, 2002). Through comparison with PPS databases, existing predictors, and experimental evidence, we demonstrate the validity and advantages of the PSAP classifier. Here, we present a phase separation analysis and prediction (PSAP) machine-learning classifier that, based solely on the amino acid content of a training set of known PPS proteins, can determine the phase separation likelihood for each protein in a given proteome. While computational tools for predicting PPS have been developed, obtaining a proteome-wide overview of PPS probabilities has remained challenging. The reported number of proteins with the capacity to mediate protein phase separation (PPS) is continuously growing. Recent advances show that phase separation is essential for cellular homeostasis by regulating basic cellular processes, including transcription and signal transduction. ![]() Membraneless organelles are liquid condensates, which form through liquid-liquid phase separation.
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