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Methylation of cytosine to 5-methylcytosine (5mC) is a prevalent DNA modification found in many organisms. Sequential oxidation of 5mC by ten-eleven translocation (TET) dioxygenases results in a cascade of additional epigenetic marks and promotes demethylation of DNA in mammals.sup.1,2. However, the enzymatic activity and function of TET homologues in other eukaryotes remains largely unexplored. Here we show that the green alga Chlamydomonas reinhardtii contains a 5mC-modifying enzyme (CMD1) that is a TET homologue and catalyses the conjugation of a glyceryl moiety to the methyl group of 5mC through a carbon-carbon bond, resulting in two stereoisomeric nucleobase products. The catalytic activity of CMD1 requires Fe(ii) and the integrity of its binding motif His-X-Asp, which is conserved in Fe-dependent dioxygenases.sup.3. However, unlike previously described TET enzymes, which use 2-oxoglutarate as a co-substrate.sup.4, CMD1 uses l-ascorbic acid (vitamin C) as an essential co-substrate. Vitamin C donates the glyceryl moiety to 5mC with concurrent formation of glyoxylic acid and CO.sub.2. The vitamin-C-derived DNA modification is present in the genome of wild-type C. reinhardtii but at a substantially lower level in a CMD1 mutant strain. The fitness of CMD1 mutant cells during exposure to high light levels is reduced. LHCSR3, a gene that is critical for the protection of C. reinhardtii from photo-oxidative damage under high light conditions, is hypermethylated and downregulated in CMD1 mutant cells compared to wild-type cells, causing a reduced capacity for photoprotective non-photochemical quenching. Our study thus identifies a eukaryotic DNA base modification that is catalysed by a divergent TET homologue and unexpectedly derived from vitamin C, and describes its role as a potential epigenetic mark that may counteract DNA methylation in the regulation of photosynthesis. An algal TET dioxygenase homologue, CMD1, uses vitamin C as a glycerol donor to modify 5-methylcytosine and helps to regulate gene transcription in response to high light levels., Author(s): Jian-Huang Xue [sup.1] , Guo-Dong Chen [sup.1] , Fuhua Hao [sup.2] , Hui Chen [sup.1] [sup.3] , Zhaoyuan Fang [sup.1] , Fang-Fang Chen [sup.4] , Bo Pang [sup.5] , [...]

Human lung adenosquamous cell carcinoma (LUAS), containing both adenomatous and squamous pathologies, harbors strong plasticity and is significantly associated with poor prognosis. We established an up-to-date comprehensive genomic and transcriptomic landscape of LUAS in 109 Chinese specimens and demonstrated LUAS development via adeno-to-squamous transdifferentiation. Unsupervised transcriptomic clustering and dynamic network biomarker analysis identified an inflammatory subtype as the critical transition stage during LUAS development. Dynamic dysregulation of the counteracting lineage-specific transcription factors (TFs), containing adenomatous TFs NKX2-1 and FOXA2, and squamous TFs TP63 and SOX2, finely tuned the lineage transition via promoting CXCL3/5-mediated neutrophil infiltration. Genomic clustering identified the most malignant subtype featured with STK11-inactivation, and targeting LSD1 through genetic deletion or pharmacological inhibition almost eradicated STK11-deficient lung tumors. These data collectively uncover the comprehensive molecular landscape, oncogenic driver spectrum and therapeutic vulnerability of Chinese LUAS. [ABSTRACT FROM AUTHOR]

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective. [ABSTRACT FROM AUTHOR]

Somatic mutations of the chromatin remodeling gene ARID2 are observed in ∼7% of human lung adenocarcinomas (LUADs). However, the role of ARID2 in the pathogenesis of LUADs remains largely unknown. Here we find that ARID2 expression is decreased during the malignant progression of both human and mice LUADs. Using two KrasG12D -based genetically engineered murine models, we demonstrate that ARID2 knockout significantly promotes lung cancer malignant progression and shortens overall survival. Consistently, ARID2 knockdown significantly promotes cell proliferation in human and mice lung cancer cells. Through integrative analyses of ChIP-Seq and RNA-Seq data, we find that Hspa1a is up-regulated by Arid2 loss. Knockdown of Hspa1a specifically inhibits malignant progression of Arid2 -deficient but not Arid2 -wt lung cancers in both cell lines as well as animal models. Treatment with an HSPA1A inhibitor could significantly inhibit the malignant progression of lung cancer with ARID2 deficiency. Together, our findings establish ARID2 as an important tumor suppressor in LUADs with novel mechanistic insights, and further identify HSPA1A as a potential therapeutic target in ARID2-deficient LUADs. [ABSTRACT FROM AUTHOR]

Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments. This article proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) and an ensemble of classifiers is used to deliver 2D modality-related decision. The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles, as in many current commercial iris recognition sensors. The map of normal vectors is used to assess the convexity of the observed iris surface. The combination of these two approaches has been applied to detect whether a subject is wearing a textured contact lens to disguise their identity. Extensive experiments with NDCLD’15 dataset, and a newly collected NDIris3D dataset show that the proposed method is highly robust under various open-set testing scenarios, and that it outperforms all available open-source iris PAD methods tested in identical scenarios. The source code and the newly prepared benchmark are made available along with this article. [ABSTRACT FROM AUTHOR]

Accumulating evidence indicates that the alternative splicing program undergoes extensive changes during cancer development and progression. The RNA-binding protein QKI-5 is frequently downregulated and exhibits anti-tumor activity in lung cancer. Howeve-r, little is known about the functional targets and regulatory mechanism of QKI-5. Here, we report that upregulation of exon 14 inclusion of cytoskeletal gene Adducin 3 (ADD3) significantly correlates with a poor prognosis in lung cancer. QKI-5 inhibits cell proliferation and migration in part through suppressing the splicing of ADD3 exon 14. Through genome-wide mapping of QKI-5 binding sites in vivo at nucleotide resolution by iCLIP-seq analysis, we found that QKI-5 regulates alternative splicing of its target mRNAs in a binding position-dependent manner. By binding to multiple sites in an upstream intron region, QKI-5 represses the splicing of ADD3 exon 14. We also identified several QKI mutations in tumors, which cause dysregulation of the splicing of QKI targets ADD3 and NUMB. Taken together, our results reveal that QKI-mediated alternative splicing of ADD3 is a key lung cancer-associated splicing event, which underlies in part the tumor suppressor function of QKI. [ABSTRACT FROM AUTHOR]

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples. [ABSTRACT FROM AUTHOR]

Background: Isocitrate dehydrogenase wild-type (WT) glioblastoma (GBM) accounts for 90% of all GBMs, yet only 27% of isocitrate dehydrogenase WT-GBMs have p53 mutations. However, the tumor surveillance function of WT-p53 in GBM is subverted by mechanisms that are not fully understood.Methods: We investigated the proteolytic inactivation of WT-p53 by asparaginyl endopeptidase (AEP) and its effects on GBM progression in cancer cells, murine models, and patients' specimens using biochemical and functional assays. The sera of healthy donors (n = 48) and GBM patients (n = 20) were examined by enzyme-linked immunosorbent assay. Furthermore, effects of AEP inhibitors on GBM progression were evaluated in murine models (n = 6-8 per group). The statistical significance between groups was determined using two-tailed Student t tests.Results: We demonstrate that AEP binds to and directly cleaves WT-p53, resulting in the inhibition of WT-p53-mediated tumor suppressor function in both tumor cells and stromal cells via extracellular vesicle communication. High expression of uncleavable p53-N311A-mutant rescue AEP-induced tumorigenesis, proliferation, and anti-apoptotic abilities. Knock down or pharmacological inhibition of AEP reduced tumorigenesis and prolonged survival in murine models. However, overexpression of AEP promoted tumorigenesis and shortened the survival time. Moreover, high AEP levels in GBM tissues were associated with a poor prognosis of GBM patients (n = 83; hazard ratio = 3.94, 95% confidence interval = 1.87 to 8.28; P < .001). A correlation was found between high plasma AEP levels and a larger tumor size in GBM patients (r = 0.6, P = .03), which decreased dramatically after surgery.Conclusions: Our results indicate that AEP promotes GBM progression via inactivation of WT-p53 and may serve as a prognostic and therapeutic target for GBM. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1 , HLA-DQB1 , MMP2 , and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related death worldwide. Aberrant splicing has been implicated in lung tumorigenesis. However, the functional links between splicing regulation and lung cancer are not well understood. Here we identify the RNA-binding protein QKI as a key regulator of alternative splicing in lung cancer. We show that QKI is frequently down-regulated in lung cancer, and its down-regulation is significantly associated with a poorer prognosis. QKI-5 inhibits the proliferation and transformation of lung cancer cells both in vitro and in vivo. Our results demonstrate that QKI-5 regulates the alternative splicing of NUMB via binding to two RNA elements in its pre-mRNA, which in turn suppresses cell proliferation and prevents the activation of the Notch signaling pathway. We further show that QKI-5 inhibits splicing by selectively competing with a core splicing factor SF1 for binding to the branchpoint sequence. Taken together, our data reveal QKI as a critical regulator of splicing in lung cancer and suggest a novel tumor suppression mechanism involving QKI-mediated regulation of the Notch signaling pathway. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented that discusses the identification of RET gene fusion by exon array analyses in lung cancer patients in East Asia who are never smokers.

Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the 'over-counting' bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called 'Gene Associaqtion Network-based Pathway Analysis' (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation. [ABSTRACT FROM AUTHOR]

Abstract: Phosphate signaling allows unicellular organisms and higher plants to respond and adapt to phosphate starvation efficiently. Four major adaptive processes include root system development, phosphate mobilization, phosphate transport and metabolism. Phosphite, a non-metabolic analog of phosphate, specifically attenuates several phosphate starvation responses, supporting the hypothesis that plants have a phosphate-sensing machinery comparable to that of unicellular organisms. Biochemical and molecular approaches have characterized acid phosphatases, RNases, high-affinity phosphate transporters, and metabolic enzymes as executive proteins in phosphate signaling. Mutant screening in Arabidopsis thaliana have identified several phosphate signaling regulators, such as PHR1, PHO2 and PHF1. PHO2, microRNA399 family and AtIPS1/At4 family represent a novel circuit in phosphate signaling. Microarray studies in A. thaliana and Oryza sativa suggest regulation at the mRNA level can be an important mechanism of phosphate signaling, and that the leaf and the root may occupy two separate phosphate signaling programs. In addition, phosphate signaling is suggested to interact with hormone and sugar signaling pathways. Finally, an integrated model summarizing the contemporary understanding of phosphate signaling, mainly in A. thaliana and O. sativa, is presented. [Copyright &y& Elsevier]

A correction notice to article "Nonintrusive Appliance Identification With Appliance-Specific Networks" is presented which appear in the previous issue of the periodical.

Keywords: alternative splicing; splicing network; splicing modules; cancer splicing; prognosis 3 This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics Alternative splicing, splicing network, splicing modules, cancer splicing, prognosis. [Extracted from the article]

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn., (© The Author(s) 2024. Published by Oxford University Press.)

Human lung adenosquamous cell carcinoma (LUAS), containing both adenomatous and squamous pathologies, exhibits strong cancer plasticity. We find that ALK rearrangement is detectable in 5.1-7.5% of human LUAS, and transgenic expression of EML4-ALK drives lung adenocarcinoma (LUAD) formation initially and squamous transition at late stage. We identify club cells as the main cell-of-origin for squamous transition. Through recapitulating lineage transition in organoid system, we identify JAK-STAT signaling, activated by EML4-ALK phase separation, significantly promotes squamous transition. Integrative study with scRNA-seq and immunostaining identify a plastic cell subpopulation in ALK-rearranged human LUAD showing squamous biomarker expression. Moreover, those relapsed ALK-rearranged LUAD show notable upregulation of squamous biomarkers. Consistently, mouse squamous tumors or LUAD with squamous signature display certain resistance to ALK inhibitor, which can be overcome by combined JAK1/2 inhibitor treatment. This study uncovers strong plasticity of ALK-rearranged tumors in orchestrating phenotypic transition and drug resistance and proposes a potentially effective therapeutic strategy., (© 2024 Qin et al.)

Tuberous sclerosis complex subunit 1 ( TSC1 ) and 2 ( TSC2 ) are frequently mutated in non-small cell lung cancer (NSCLC), however, their effects on antitumor immunity remained unexplored. A CRISPR screening in murine Kras G12D / Trp53 -/- (KP) model identified Tsc1 and Tsc2 as potent regulators of programmed cell death ligand 1 (Pd-l1) expression in vitro and sensitivity to anti-programmed cell death receptor 1 (PD-1) treatment in vivo. TSC1 or TSC2 knockout (KO) promoted the transcriptional and membrane expression of PD-L1 in cell lines. TSC2 -deficient tumors manifested an inflamed microenvironment in patient samples and The Cancer Genome Atlas dataset. In syngeneic murine models, KP- Tsc2 -KO tumors showed notable response to anti-PD-1 antibody treatment, but Tsc2 -wild-type tumors did not. Patients with TSC1 / TSC2 -mutant NSCLC receiving immune checkpoint blockade (ICB) had increased durable clinical benefit and survival. Collectively, TSC1 / TSC2 loss defines a distinct subtype of NSCLC characterized as inflamed tumor microenvironment and superior sensitivity to ICB.

Cancer has been generally defined as a cluster of systematic malignant pathogenesis involving abnormal cell growth. Genetic mutations derived from environmental factors and inherited genetics trigger the initiation and progression of cancers. Although several well-known factors affect cancer, mutation features and rules that affect cancers are relatively unknown due to limited related studies. In this study, a computational investigation on mutation profiles of cancer samples in 27 types was given. These profiles were first analyzed by the Monte Carlo Feature Selection (MCFS) method. A feature list was thus obtained. Then, the incremental feature selection (IFS) method adopted such list to extract essential mutation features related to 27 cancer types, find out 207 mutation rules and construct efficient classifiers. The top 37 mutation features corresponding to different cancer types were discussed. All the qualitatively analyzed gene mutation features contribute to the distinction of different types of cancers, and most of such mutation rules are supported by recent literature. Therefore, our computational investigation could identify potential biomarkers and prediction rules for cancers in the mutation signature level., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Chen, Zhou, Zeng, Pan, Zhang, Huang, Fang and Cai.)

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less "reliable" gene expression to more "reliable" gene-gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN., (Copyright © 2021 Beijing Institute of Genomics. Published by Elsevier B.V. All rights reserved.)

We demonstrate coupling to and control over the broadening and dispersion of a mid-infrared leaky mode, known as the Berreman mode, in samples with different dielectric environments. We fabricate subwavelength films of AlN, a mid-infrared epsilon-near-zero material that supports the Berreman mode, on materials with a weakly negative permittivity, strongly negative permittivity, and positive permittivity. Additionally, we incorporate ultra-thin AlN layers into a GaN/AlN heterostructure, engineering the dielectric environment above and below the AlN. In each of the samples, coupling to the Berreman mode is observed in angle-dependent reflection measurements at wavelengths near the longitudinal optical phonon energy. The measured dispersion of the Berreman mode agrees well with numerical modes. Differences in the dispersion and broadening for the different materials is quantified, including a 13 cm -1 red-shift in the energy of the Berreman mode for the heterostructure sample.

Despite advancements in treatment options, the overall cure and survival rates for non-small cell lung cancers (NSCLC) remain low. While small-molecule inhibitors of epigenetic regulators have recently emerged as promising cancer therapeutics, their application in patients with NSCLC is limited. To exploit epigenetic regulators as novel therapeutic targets in NSCLC, we performed pooled epigenome-wide CRISPR knockout screens in vitro and in vivo and identified the histone chaperone nucleophosmin 1 ( Npm1 ) as a potential therapeutic target. Genetic ablation of Npm1 significantly attenuated tumor progression in vitro and in vivo . Furthermore, KRAS -mutant cancer cells were more addicted to NPM1 expression. Genetic ablation of Npm1 rewired the balance of metabolism in cancer cells from predominant aerobic glycolysis to oxidative phosphorylation and reduced the population of tumor-propagating cells. Overall, our results support NPM1 as a therapeutic vulnerability in NSCLC. SIGNIFICANCE: Epigenome-wide CRISPR knockout screens identify NPM1 as a novel metabolic vulnerability and demonstrate that targeting NPM1 is a new therapeutic opportunity for patients with NSCLC., (©2020 American Association for Cancer Research.)