Identification of a novel compound heterozygous mutation and a homozygous mutation of SLURP1 in Chinese families with Mal de Meleda.

BACKGROUND: Mal de Meleda is an autosomal recessive palmoplantar keratoderma, with SLURP1 identified as the pathogenic gene responsible. Although over 20 mutations in SLURP1 have been reported, only the mutation c.256G > A (p.G87R) has been detected in Chinese patients. Here, we report a novel heterozygous SLURP1 mutation in a Chinese family. METHODS: We assessed the clinical manifestations of two Chinese patients with Mal de Meleda and collected specimens from the patients and other family members for whole-exome and Sanger sequencing. We used algorithms (MutationTaster, SIFT, PolyPhen-2, PROVEAN, PANTHER, FATHMM, mCSM, SDM and DUET) to predict the pathogenetic potential of the mutation detected. We also employed AlphaFold2 and PyMOL for protein structure analysis. RESULTS: Both patients displayed the typical manifestation of palmoplantar keratoderma. In Proband 1, we detected a novel compound heterozygous mutation (c.243C > A and c.256G > A) in exon 3 of SLURP1. Proband 2 was an adult female born to a consanguineous family and carried a homozygous mutation (c.211C > T). Algorithms indicated both mutations to be probably disease causing. We used AlphaFold2 to predict the protein structure of these mutations and found that they cause instability, as shown by PyMOL. CONCLUSIONS: Our study identified a novel compound heterozygous mutation (c.243C > A and c.256G > A) in a Chinese patient with Mal de Meleda that has the potential to cause instability in protein structure. Moreover, this study expands on the existing knowledge of SLURP1 mutations and contributes to knowledge of Mal de Meleda.

Prospective Prediction of First Onset of Nonsuicidal Self-Injury in Adolescent Girls.

OBJECTIVE: Adolescent nonsuicidal self-injury (NSSI) is a significant risk factor for suicidal behavior and an important clinical marker of psychopathology. NSSI is especially common in adolescent girls. A number of psychosocial correlates of adolescent NSSI have been identified, including problems characterized by disinhibition and negative affectivity. However, it is unknown whether these characteristics prospectively predict first-onset NSSI, limiting our understanding of its etiology and prevention. The current study addresses this gap in the literature. METHOD: Participants in the Adolescent Development of Emotion and Personality Traits (ADEPT) project at Stony Brook University who had not experienced NSSI at baseline (462 girls, mean age = 14.39 years, SD = 0.62 years) completed baseline measures of hypothesized risk factors related to problems with disinhibition and negative affectivity, including adolescent psychopathology, personality and clinical traits, and parental psychopathology. First onset of NSSI was monitored at 9-month intervals by in-person and telephone interviews over the next 36 months. RESULTS: There were 42 first onsets of NSSI (9.1%) in the 3 years since baseline. First-onset NSSI was independently predicted by adolescents' low conscientiousness, high avoidance, and parental substance abuse at baseline. The composite risk index predicting first-onset NSSI demonstrated good accuracy for identifying girls who will start self-injuring (area under the curve = 0.78, sensitivity = 0.85, specificity = 0.57). CONCLUSION: These results highlight the role of disinhibition and avoidance in the development of NSSI. The risk index predicting NSSI onset may help to guide the design and application of novel interventions to prevent this condition in adolescent girls.

A systematic evaluation of nucleotide properties for CRISPR sgRNA design.

BACKGROUND: CRISPR is a versatile gene editing tool which has revolutionized genetic research in the past few years. Optimizing sgRNA design to improve the efficiency of target/DNA cleavage is critical to ensure the success of CRISPR screens. RESULTS: By borrowing knowledge from oligonucleotide design and nucleosome occupancy models, we systematically evaluated candidate features computed from a number of nucleic acid, thermodynamic and secondary structure models on real CRISPR datasets. Our results showed that taking into account position-dependent dinucleotide features improved the design of effective sgRNAs with area under the receiver operating characteristic curve (AUC) >0.8, and the inclusion of additional features offered marginal improvement (∼2% increase in AUC). CONCLUSION: Using a machine-learning approach, we proposed an accurate prediction model for sgRNA design efficiency. An R package predictSGRNA implementing the predictive model is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#predictsgrna .

methylDMV: SIMULTANEOUS DETECTION OF DIFFERENTIAL DNA METHYLATION AND VARIABILITY WITH CONFOUNDER ADJUSTMENT.

DNA methylation has emerged as promising epigenetic markers for disease diagnosis. Both the differential mean (DM) and differential variability (DV) in methylation have been shown to contribute to transcriptional aberration and disease pathogenesis. The presence of confounding factors in large scale EWAS may affect the methylation values and hamper accurate marker discovery. In this paper, we propose a exible framework called methylDMV which allows for confounding factors adjustment and enables simultaneous characterization and identification of CpGs exhibiting DM only, DV only and both DM and DV. The proposed framework also allows for prioritization and selection of candidate features to be included in the prediction algorithm. We illustrate the utility of methylDMV in several TCGA datasets. An R package methylDMV implementing our proposed method is available at http://www.ams.sunysb.edu/~pfkuan/softwares.html#methylDMV.