The Impact of Recent Life Events, Internalizing Symptoms, and Emotion Regulation on the Severity of Non-Suicidal Self-Injury in Adolescents: A Mediation Analysis.

Background: Previous studies have demonstrated a strong association between recent stressful life events and non-suicidal self-injury (NSSI) among adolescents. Internalizing symptoms and difficulty in emotion regulation (DER) may mediate this relationship. This study aimed to investigate the relationship between recent stressful life events and NSSI severity in adolescents and the potential moderating role of internalizing symptoms and DER. Methods: A total of 224 adolescent inpatients (78.6% female) participated in the study, with an age range of 12-18 years old. Data on recent stressful life events, internalizing symptoms, DER, and NSSI behaviors were collected using a clinician-rated questionnaire. A structural equation model was used to test the hypothesized model. Results: The rate of NSSI reporting among adolescents in the past 12 months was 65.18%. Recent stressful life events were found to be directly associated with NSSI severity (ß = 0.128, P = 0.023). A chain-mediating effect between recent stressful life events and NSSI was also confirmed (ß = 0.034, P = 0.023), with DER and internalizing symptoms playing a chain-mediating role and DER having a significantly indirect association with NSSI through internalizing symptoms. Conclusion: Recent stressful life events appear to play a role in the etiology of NSSI, particularly punishment and interpersonal relationship events that warrant special attention. DER and internalizing symptoms play a chain-mediating role in the relationship between life events and NSSI. Reducing recent stressful life events, screening for internalizing symptoms, and improving emotion regulation may decrease NSSI behavior among adolescents.

An exploration of the association between family functioning and nonsuicidal self-injury among Chinese adolescents with mood disorders

Background and objectives This study explored the correlation between nonsuicidal self-injury (NSSI) and family functioning among adolescents aged 12 to 17 years with mood disorders. Methods A total of 142 participants were clinically assessed for NSSI, with 85 in the NSSI group and 57 in the non-NSSI group. The correlation between NSSI and family functioning was compared and a regression prediction model was constructed to determine the risk probability of NSSI. Results A significant association was found between family functioning and NSSI (P = 0.017). The correlation between adolescents with NSSI and gender, communication, affective responsiveness, and behaviour control was statistically significant. A nomogram graph and ROC curve were constructed, with an AUC of 0.772. Conclusion The findings support the notion that family functioning is associated with a higher risk for NSSI among adolescents with mood disorders. Furthermore, gender, communication, affective responsiveness, and behaviour control may be contributing factors. (AU)

Chromatin regulation and gene centrality are essential for controlling fitness pleiotropy in yeast.

BACKGROUND: There are a wide range of phenotypes that are due to loss-of-function or null mutations. Previously, the functions of gene products that distinguish essential from nonessential genes were characterized. However, the functions of products of non-essential genes that contribute to fitness remain minimally understood. PRINCIPAL FINDINGS: Using data from Saccharomyces cerevisiae, we investigated several gene characteristics, which we are able to measure, that are significantly associated with a gene's fitness pleiotropy. Fitness pleiotropy is a measurement of the gene's importance to fitness. These characteristics include: 1) whether the gene's product functions in chromatin regulation, 2) whether the regulation of the gene is influenced by chromatin state, measured by chromatin regulation effect (CRE), 3) whether the gene's product functions as a transcription factor (TF) and the number of genes a TF regulates, 4) whether the gene contains TATA-box, and 5) whether the gene's product is central in a protein interaction network. Partial correlation analysis was used to study how these characteristics interact to influence fitness pleiotropy. We show that all five characteristics that were measured are statistically significantly associated with fitness pleiotropy. However, fitness pleiotropy is not associated with the presence of TATA-box when CRE is controlled. In particular, two characteristics: 1) whether the regulation of a gene is more likely to be influenced by chromatin state, and 2) whether the gene product is central in a protein interaction network measured by the number of protein interactions were found to play the most important roles affecting a gene's fitness pleiotropy. CONCLUSIONS: These findings highlight the significance of both epigenetic gene regulation and protein interaction networks in influencing the fitness pleiotropy.

The effects of protein interactions, gene essentiality and regulatory regions on expression variation.

BACKGROUND: Identifying factors affecting gene expression variation is a challenging problem in genetics. Previous studies have shown that the presence of TATA box, the number of cis-regulatory elements, gene essentiality, and protein interactions significantly affect gene expression variation. Nonetheless, the need to obtain a more complete understanding of such factors and how their interactions influence gene expression variation remains a challenge. The growth rates of yeast cells under several DNA-damaging conditions have been studied and a gene's toxicity degree is defined as the number of such conditions that the growth rate of the yeast deletion strain is significantly affected. Since toxicity degree reflects a gene's importance to cell survival under DNA-damaging conditions, we expect that it is negatively associated with gene expression variation. Mutations in both cis-regulatory elements and transcription factors (TF) regulating a gene affect the gene's expression and thus we study the relationship between gene expression variation and the number of TFs regulating a gene. Most importantly we study how these factors interact with each other influencing gene expression variation. RESULTS: Using yeast as a model system, we evaluated the effects of four separate factors and their interactions on gene expression variation: protein interaction degree, toxicity degree, number of TFs, and the presence of TATA box. Results showed that 1) gene expression variation is negatively correlated with the protein interaction degree in the protein interaction network, 2) essential genes tend to have less expression variation than non-essential genes and gene expression variation decreases with toxicity degree, and 3) the number of TFs regulating a gene is the most important factor influencing gene expression variation (R2 = 8-14%). In addition, the number of TFs regulating a gene was found to be an important factor influencing gene expression variation for both TATA-containing and non-TATA-containing genes, but with different association strength. Moreover, gene expression variation was significantly negatively correlated with toxicity degree only for TATA-containing genes. CONCLUSION: The finding that distinct mechanisms may influence gene expression variation in TATA-containing and non-TATA-containing genes, provides new insights into the mechanisms that underlie the evolution of gene expression.

Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations.

The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.