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.

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.

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.

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.

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.

DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome.

Acute myeloid leukaemia is a highly malignant haematopoietic tumour that affects about 13,000 adults in the United States each year. The treatment of this disease has changed little in the past two decades, because most of the genetic events that initiate the disease remain undiscovered. Whole-genome sequencing is now possible at a reasonable cost and timeframe to use this approach for the unbiased discovery of tumour-specific somatic mutations that alter the protein-coding genes. Here we present the results obtained from sequencing a typical acute myeloid leukaemia genome, and its matched normal counterpart obtained from the same patient's skin. We discovered ten genes with acquired mutations; two were previously described mutations that are thought to contribute to tumour progression, and eight were new mutations present in virtually all tumour cells at presentation and relapse, the function of which is not yet known. Our study establishes whole-genome sequencing as an unbiased method for discovering cancer-initiating mutations in previously unidentified genes that may respond to targeted therapies.

Somatic mutations affect key pathways in lung adenocarcinoma.

Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well-classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus are probably involved in carcinogenesis. The frequently mutated genes include tyrosine kinases, among them the EGFR homologue ERBB4; multiple ephrin receptor genes, notably EPHA3; vascular endothelial growth factor receptor KDR; and NTRK genes. These data provide evidence of somatic mutations in primary lung adenocarcinoma for several tumour suppressor genes involved in other cancers--including NF1, APC, RB1 and ATM--and for sequence changes in PTPRD as well as the frequently deleted gene LRP1B. The observed mutational profiles correlate with clinical features, smoking status and DNA repair defects. These results are reinforced by data integration including single nucleotide polymorphism array and gene expression array. Our findings shed further light on several important signalling pathways involved in lung adenocarcinoma, and suggest new molecular targets for treatment.