Identification and Functional Analysis of a de novo IKZF3 Mutation in a Pediatric Patient with Combined Immunodeficiency.

AIOLOS, a vital member of the IKAROS protein family, plays a significant role in lymphocyte development and function through DNA binding and protein-protein interactions. Mutations in the IKZF3 gene, which encodes AIOLOS, lead to a rare combined immunodeficiency often linked with infections and malignancy. In this study, we evaluated a 1-year-4-month-old female patient presenting with recurrent infections, diarrhea, and failure to thrive. Laboratory investigations revealed decreased T lymphocyte and immunoglobulin levels. Through whole-exome and Sanger sequencing, we discovered a de novo mutation in IKZF3 (NM_012481; exon 5 c.571G > C, p.Gly191Arg), corresponding to the third DNA-binding zinc finger region of the encoded protein AIOLOS. Notably, the patient with the AIOLOS G191R mutation showed reduced recent thymic emigrants in naïve CD4+T cells compared to healthy counterparts of the same age, while maintaining normal levels of Th1, Th2, Th17, Treg, and Tfh cells. This mutation also resulted in decreased switched memory B cells and lower CD23 and IgM expression. In vitro studies revealed that AIOLOS G191R does not impact the expression of AIOLOS but compromises its stability, DNA binding and pericentromeric targeting. Furthermore, AIOLOS G191R demonstrated a dominant-negative effect over the wild-type protein. This case represents the first reported instance of a mutation in the third DNA-binding zinc finger region of AIOLOS highlighting its pivotal role in immune cell functionality.

Role of TRAK1 variants in epilepsy: genotype-phenotype analysis in a pediatric case of epilepsy with developmental disorder.

Purpose: The TRAK1 gene is mapped to chromosome 3p22.1 and encodes trafficking protein kinesin binding 1. The aim of this study was to investigate the genotype-phenotype of TRAK1-associated epilepsy. Methods: Trio-based whole-exome sequencing was performed on a cohort of 98 patients with epilepsy of unknown etiologies. Protein modeling and the VarCards database were used to predict the damaging effects of the variants. Detailed neurological phenotypes of all patients with epilepsy having TRAK1 variants were analyzed to assess the genotype-phenotype correlations. Results: A novel TRAK1 compound heterozygous variant comprising variant c.835C > T, p.Arg279Cys and variant c.2560A > C, p.Lys854Gln was identified in one pediatric patient. Protein modeling and VarCards database analyses revealed that the variants were damaging. The patient received a diagnosis of early infantile epileptic spasms with a developmental disorder; he became seizure-free through valproate and adrenocorticotropic hormone treatment. Further results for six variants in 12 patients with epilepsy indicated that biallelic TRAK1 variants (including homozygous or compound heterozygous variants) were associated with epilepsy with developmental disorders. Among these patients, eight (67%) had epileptic spasms and seven (58%) were intractable to anti-seizure medicines. Moreover, eight patients experienced refractory status epilepticus, of which seven (88%) died in early life. To our knowledge, this is the first reported case of epilepsy caused by TRAK1 compound heterozygous variants. Conclusion: Biallelic TRAK1 variants can cause epilepsy and developmental disorders. In these patients, seizures progress to status epilepticus, suggesting a high risk for poor outcomes and the requirement of early treatment.

Risk factor analysis and nomogram prediction model construction for NEC complicated by intestinal perforation.

OBJECTIVE: To investigate the clinical characteristics of neonatal necrotizing enterocolitis (NEC) complicated by intestinal perforation and predict the incidence of intestinal perforation in NEC. METHODS: Neonates diagnosed with NEC at the Affiliated Hospital of Zunyi Medical University from January 2012 to May 2022 were enrolled, and the clinical data were collected and analyzed retrospectively. The patients were divided into two groups based on intestinal perforation occurrence or not. Mann-Whitney U tests, t-tests, chi-square tests, and fisher's exact tests were performed between-group comparisons. Logistic and lasso regressions were applied to screen independent risk factors for concomitant bowel perforation, and R software (RMS package) was used to formulate the nomogram prediction model. In addition, the receiver operating curve (ROC) and the calibration curve were drawn to verify the predictive power, while decision curve analysis (DCA) was constructed to evaluate the clinical applicability of the nomogram model. RESULTS: One hundred eighty neonates with NEC were included, of which 48 had intestinal perforations, and 132 did not; the overall incidence of intestinal perforation was 26.67% (48/180). Bloody stool (OR = 5.60), APTT ≥ 50 s (OR = 3.22), thrombocytopenia (OR = 4.74), and hypoalbuminemia (OR = 5.56) were identified as independent risk variables for NEC intestinal perforation (P < 0.05) through multivariate logistic regression analysis. These factors were then applied to develop a nomogram prediction model (C-index = 0.838) by using the R software. The area under the curve (AUC) for the nomogram in the training and validation cohorts were 0.838 (95% Cl: 0.768, 0.908) and 0.802 (95% CI: 0.659, 0.944), respectively. The calibration curve shown that the nomogram has a good predictive ability for predicting the risk of intestinal perforation occurrence. And the decision curve and clinical impact curve analyses demonstrated good clinical utility of the nomogram model. CONCLUSION: We found that Bloody stool, APTT ≥ 50 s, Thrombocytopenia, and hypoalbuminemia could be used as independent risk factors for predicting intestinal perforation in neonates with NEC. The nomogram model based on these variables had high predictive values to identify NEC patients with intestinal perforation.

Investigation of FRMPD4 variants associated with X-linked epilepsy.

BACKGROUND: The etiology of unexplained epilepsy in most patients remains unclear. Variants of FRMPD4 are suggested to be associated with neurodevelopmental disorders. Therefore, we screened for disease-causing FRMPD4 variants in patients with epilepsy. METHODS: Trios-based whole-exome sequencing was conducted on a cohort of 85 patients with unexplained epilepsy, their parents, and extended family members. Additional cases with FRMPD4 variants were identified from the China Epilepsy Gene Matching Platform V.1.0. The frequency of variants was analyzed, and their subregional effects were predicted using in silico tools. The genotype-phenotype correlation of the newly defined causative genes and protein stability were analyzed using I-Mutant V.3.0 and Grantham scores. RESULTS: Two novel missense variants of FRMPD4 were identified in two families. Using the gene matching platform, we identified three additional novel missense variants. These variants presented at low or no allele frequencies in the gnomAD database. All the variants were located outside the three FRMPD4 main domains (WW, PDZ, and FERM). In silico analyses revealed that the variants were damaging and were predicted to be the least stable. All patients eventually became seizure-free. Eight of the 21 patients with FRMPD4 variants had epilepsy, of which five (63%) had missense variants located outside the domains, two had deletions involving exon 2, and one had a frameshift variant located outside the domains. Patients with epilepsy caused by missense variants were often free of intellectual disabilities (4/5), whereas patients with epilepsy caused by truncated variants had intellectual disabilities and structural brain abnormalities (3/3). CONCLUSIONS: The FRMPD4 gene is potentially associated with epilepsy. The genotype-phenotype correlation of FRMPD4 variants indicated that differences in variant types and locations of FRMPD4 may explain their phenotypic variation.

Rapidly detecting the carcinogen acetaldehyde: preparation and application of a flower-like MoS2 cataluminescence sensor at low working temperature.

In this paper, a cataluminescence (CTL) gas sensor based on flower-like molybdenum disulfide (MoS2) is developed. The experimental results show that it has high sensitivity and selectivity to acetaldehyde. The CTL sensor has the advantage of fast response; the response time is about 3 s and the recovery time is about 40 s. The optimal working temperature of this sensor is 174 °C, which is lower than that of the CTL sensors used for acetaldehyde detection in many other reports. Under the optimized conditions, the CTL signal intensity shows a good linear relationship with acetaldehyde concentration (R2 = 0.9991) within the concentration range of 40-2000 ppm, and the detection limit (LOD) is 3.75 ppm. The selectivity experiment results show that the sensor has an obvious response to acetaldehyde and a very weak response to acetic acid, and has no response to many other VOCs (ether, cyclohexane, butyl ether, carbon tetrachloride, ethanol, toluene, formaldehyde, glycerol, trichloromethane and xylene). After 8 repeated measurements for four weeks, the relative standard deviation (RSD) of the CTL sensor is 1.03%, indicating that it has good reproducibility and stability, which shows that the CTL sensor has a promising prospect for the detection of acetaldehyde.

In situ decrypting plasmonic nanoparticle size-controlled phosphorylation of epidermal growth factor receptor in living cells.

Recently, interaction between epidermal growth factor receptor (EGFR) and EGFR-targeted nanoprobes is a hot topic. Here, we use dark field microscope (DFM) observe different aggregations of EGFR-targeted nanoprobes in diverticulum. Different aggregation states are related to phosphorylation of EGFR. EGFR phosphorylation can be adjusted by gold nanoparticles (GNPs) size.

Entanglement generation by strong coupling between surface lattice resonance and exciton in an Al nanoarray-coated WS2 quantum emitter.

Strong light-matter interaction plays a central role in realizing quantum photonic technologies. The entanglement state, which results from the hybridization of excitons and cavity photons, forms the foundation of quantum information science. In this work, an entanglement state is achieved by manipulating the mode coupling between surface lattice resonance and quantum emitter into the strong coupling regime. At the same time, a Rabi splitting of 40 meV is observed. A full quantum model based on the Heisenberg picture is used to describe this unclassical phenomenon, and it perfectly explains the interaction and dissipation process. In addition, the observed concurrency degree of the entanglement state is 0.5, presenting the quantum nonlocality. This work effectively contributes to the understanding of nonclassical quantum effects arising from strong coupling and will intrigue more interesting potential applications in quantum optics.

A Prognostic Model of Seven Immune Genes to Predict Overall Survival in Childhood Acute Myeloid Leukemia.

Background: Acute myeloid leukemia (AML) is one of the most common hematological malignancies and accounts for about 20% of childhood leukemias. Currently, immunotherapy is one of the recommended treatment schemes for recurrent AML patients to improve their survival rates. Nonetheless, low remission and high mortality rates are observed in recurrent AML and challenge the prognosis of AML patients. To address this problem, we aimed to establish and verify a reliable prognostic risk model using immune-related genes to improve the prognostic evaluation and recommendation for personalized treatment of AML. Methods: Transcriptome data and clinical data were acquired from the TARGET database while immune genes were sourced from InnateDB and ImmPort Shared databases. The mRNA expression profile matrix of immune genes from 62 normal samples and 1408 AML cases was extracted from the transcriptome data and subjected to differential expression (DE) analysis. The entire cohort of DE immune genes was randomly divided into the test group and training group. The prognostic model associated with immune genes was constructed using the training group. The test group and entire cohort were employed for model validation. Lastly, we analyzed the potential clinical application of the model and its association with immune cell infiltration. Results: In total, 751 DE immune genes were differentially regulated, including 552 upregulated and 199 downregulated. Based on these DE genes, we developed and validated a prognostic risk model composed of seven immune genes, GDF1, TPM2, IL1R1, PSMD4, IL5RA, DHCR24, and IL12RB2. This model is able to predict the 5-year survival rate more accurately compared with age, gender, and risk stratification. Further analysis showed that CD8+ T-cell contents and neutrophil infiltration decreased but macrophage infiltration increased as the risk score increased. Conclusions: A seven-immune gene model of AML was developed and validated. We propose this model as an independent prognostic variable able to estimate the 5-year survival rate. In addition, the model can also reflect the immune microenvironment of AML patients.

Progranulin regulates the development and function of NKT2 cells through EZH2 and PLZF.

T helper 2 (Th2) cytokine production by invariant natural killer T (iNKT) cells is involved in the development of asthma, but the regulation of Th2 cytokines in iNKT cells remains unknown. Although it is known that progranulin (PGRN) induces the production of Th2 cytokines in iNKT cells in vivo, the underlying mechanism is not clear. This study aims to investigate the role of PGRN in iNKT cells. The effects of PGRN on the differentiation of iNKT cells was detected by flow cytometry. Then stimulation of iNKT cells and airway resistance were carried out to evaluate the function of PGRN on iNKT cells. Furthermore, the mechanisms of PGRN in regulating iNKT cells was investigated by RT-PCR, WB, confocal and luciferase reporter assays. The absolute number of iNKT cells decreased in PGRN KO mice despite an increase in the percentage of iNKT cells. Furthermore, analyzing the subsets of iNKT cells, we found that NKT2 cells and their IL-4 production were reduced. Mechanistically, the decrease in NKT2 cells in the PGRN KO mice was caused by increased expression of enhancer of zeste homolog 2 (EZH2), that in turn caused increased degradation and altered nuclear localization of PLZF. Interestingly, PGRN signaling decreased expression of EZH2 and treatment of the PGRN KO mice with the EZH2 specific inhibitor GSK343 rescued the defect in NKT2 differentiation, IL-4 generation, and PLZF expression. Altogether, We have revealed a new pathway (PGRN-EZH2-PLZF), which regulates the Th2 responses of iNKT cells and provides a potentially new target for asthma treatment.

Iodide etching for one-step quantitative assay of the number of DNA molecules capped on gold nanoparticles.

Developing a direct method to easily quantify the number of DNA capped on gold nanoparticles (GNPs) is of great significance. Herein, we found that the high concentration of iodine ion (I-) can not only replace the ligands on the surface of GNPs but can also completely etch the particles by virtue of its strong reducibility. According to this finding, a mild, cost-effective, environment-friendly, and non-toxic strategy was constructed to directly and accurately estimate the amount of DNA coupled on GNPs. Due to nanometal surface energy transfer (NSET) that happened between the DNA-FAM donor and the GNPs receptor, the fluorescence was quenched; after incubating with the etching reagent 6 M I-, the recuperative fluorescence was detected directly. This method can easily estimate the number of DNA attached on the GNPs surface by one step. In a nutshell, it is a smart strategy to apply iodide etching for DNA quantification on the surface of GNPs, which breaks through the drawbacks of traditional DNA quantification strategies such as pollution, being expensive and even dangerous. This strategy takes a solid step forward for the refinement and optimization of DNA quantification and can also be more effective in detecting the number of other molecules capped on the GNPs surface, indicating that the iodide etching method is greatly helpful in bio-detection assays and nanoparticle-based therapeutics.

Fast Acquisition of High-Quality Nuclear Magnetic Resonance Pure Shift Spectroscopy via a Deep Neural Network.

Pure shift methods improve the resolution of proton nuclear magnetic resonance spectra at the cost of time. The pure shift yielded by chirp excitation (PSYCHE) method is a promising pure shift method. We propose a method of reconstructing the undersampled PSYCHE spectra based on deep learning to accelerate the spectra acquisition. It only takes 17 s to obtain a high-quality pure shift spectrum. The network can completely remove undersampling artifacts and chunking sidebands and improve the signal-to-noise ratio, obtaining completely clean pure shift spectra. The reconstruction quality is better than the iterative soft thresholding method. In addition, the network can differentiate low-level signals and chunking sidebands with similar intensities in the mixture, remove sidebands, and retain signals, promoting correct mixture analysis.

Effects of preparation parameters on the properties of the crosslinked pectin nanofiber mats.

Pectin nanofiber mats prepared with periodate oxidation-electrospinning-adipic acid dihydrazide crosslinking strategy are promising for biomedical applications. In this study, we systematically examined the effects of electrospinning and crosslinking conditions on the properties of pectin nanofiber mats. The properties of mats were tunable in the range of 200-400 nm fiber size, 11-21% ADH residue content, 13-28 times absorbency, 13°-21° contact angle, 2 weeks or longer degradation time, 1.5-2.2 MPa tensile strength, 40-70% elongation, and 0.25-0.27 g/(cm2·24 h) permeability. Increasing polymer concentration, adipic acid dihydrazide amount, time or temperature could increase fiber size and its tensile strength, and decrease the absorbency, hydrophilicity, degradation rate, and elongation. These results indicate that controlling the process parameters can effectively regulate the properties of pectin nanofiber mats and meet the requirements of various biomedical applications.

A combination of plasma exchange and steroids in the treatment of a-lipoic acid-induced insulin autoimmune syndrome.

Not required for Clinical Vignette.

Gelatin-crosslinked pectin nanofiber mats allowing cell infiltration.

Pectin nanofiber mats are promising tissue engineering scaffolds but suffer from poor cell infiltration. In this study, gelatin, a collagen derived cell adhesive protein, was used to crosslink the electrospun nanofibers of periodate oxidized pectin. Cell culture experiment results demonstrated that cells were able to grow into the gelatin-crosslinked pectin nanofiber mats rather than only spread on mat surface. The nanofiber mats showed moderate mechanical strength, with a maximum tensile strength of up to 2.3 MPa, an ultimate tensile strain of up to 15%, and were capable of degrading gradually over 4 weeks or even longer periods in simulated body fluids. Thus, gelatin-crosslinked pectin nanofiber mats hold a great potential for soft tissue regeneration.

Genome Modeling System: A Knowledge Management Platform for Genomics.

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

WormBase 2012: more genomes, more data, new website.

Since its release in 2000, WormBase (http://www.wormbase.org) has grown from a small resource focusing on a single species and serving a dedicated research community, to one now spanning 15 species essential to the broader biomedical and agricultural research fields. To enhance the rate of curation, we have automated the identification of key data in the scientific literature and use similar methodology for data extraction. To ease access to the data, we are collaborating with journals to link entities in research publications to their report pages at WormBase. To facilitate discovery, we have added new views of the data, integrated large-scale datasets and expanded descriptions of models for human disease. Finally, we have introduced a dramatic overhaul of the WormBase website for public beta testing. Designed to balance complexity and usability, the new site is species-agnostic, highly customizable, and interactive. Casual users and developers alike will be able to leverage the public RESTful application programming interface (API) to generate custom data mining solutions and extensions to the site. We report on the growth of our database and on our work in keeping pace with the growing demand for data, efforts to anticipate the requirements of users and new collaborations with the larger science community.

The BioGRID Interaction Database: 2011 update.

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein interaction data from model organisms and humans (http://www.thebiogrid.org). BioGRID currently holds 347,966 interactions (170,162 genetic, 177,804 protein) curated from both high-throughput data sets and individual focused studies, as derived from over 23,000 publications in the primary literature. Complete coverage of the entire literature is maintained for budding yeast (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe) and thale cress (Arabidopsis thaliana), and efforts to expand curation across multiple metazoan species are underway. The BioGRID houses 48,831 human protein interactions that have been curated from 10,247 publications. Current curation drives are focused on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health. The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. An automated Interaction Management System (IMS) is used to prioritize, coordinate and track curation across international sites and projects. BioGRID provides interaction data to several model organism databases, resources such as Entrez-Gene and other interaction meta-databases. The entire BioGRID 3.0 data collection may be downloaded in multiple file formats, including PSI MI XML. Source code for BioGRID 3.0 is freely available without any restrictions.

CMDS: a population-based method for identifying recurrent DNA copy number aberrations in cancer from high-resolution data.

MOTIVATION: DNA copy number aberration (CNA) is a hallmark of genomic abnormality in tumor cells. Recurrent CNA (RCNA) occurs in multiple cancer samples across the same chromosomal region and has greater implication in tumorigenesis. Current commonly used methods for RCNA identification require CNA calling for individual samples before cross-sample analysis. This two-step strategy may result in a heavy computational burden, as well as a loss of the overall statistical power due to segmentation and discretization of individual sample's data. We propose a population-based approach for RCNA detection with no need of single-sample analysis, which is statistically powerful, computationally efficient and particularly suitable for high-resolution and large-population studies. RESULTS: Our approach, correlation matrix diagonal segmentation (CMDS), identifies RCNAs based on a between-chromosomal-site correlation analysis. Directly using the raw intensity ratio data from all samples and adopting a diagonal transformation strategy, CMDS substantially reduces computational burden and can obtain results very quickly from large datasets. Our simulation indicates that the statistical power of CMDS is higher than that of single-sample CNA calling based two-step approaches. We applied CMDS to two real datasets of lung cancer and brain cancer from Affymetrix and Illumina array platforms, respectively, and successfully identified known regions of CNA associated with EGFR, KRAS and other important oncogenes. CMDS provides a fast, powerful and easily implemented tool for the RCNA analysis of large-scale data from cancer genomes.

BreakDancer: an algorithm for high-resolution mapping of genomic structural variation.

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancer's performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

Recurring mutations found by sequencing an acute myeloid leukemia genome.

BACKGROUND: The full complement of DNA mutations that are responsible for the pathogenesis of acute myeloid leukemia (AML) is not yet known. METHODS: We used massively parallel DNA sequencing to obtain a very high level of coverage (approximately 98%) of a primary, cytogenetically normal, de novo genome for AML with minimal maturation (AML-M1) and a matched normal skin genome. RESULTS: We identified 12 acquired (somatic) mutations within the coding sequences of genes and 52 somatic point mutations in conserved or regulatory portions of the genome. All mutations appeared to be heterozygous and present in nearly all cells in the tumor sample. Four of the 64 mutations occurred in at least 1 additional AML sample in 188 samples that were tested. Mutations in NRAS and NPM1 had been identified previously in patients with AML, but two other mutations had not been identified. One of these mutations, in the IDH1 gene, was present in 15 of 187 additional AML genomes tested and was strongly associated with normal cytogenetic status; it was present in 13 of 80 cytogenetically normal samples (16%). The other was a nongenic mutation in a genomic region with regulatory potential and conservation in higher mammals; we detected it in one additional AML tumor. The AML genome that we sequenced contains approximately 750 point mutations, of which only a small fraction are likely to be relevant to pathogenesis. CONCLUSIONS: By comparing the sequences of tumor and skin genomes of a patient with AML-M1, we have identified recurring mutations that may be relevant for pathogenesis.