Integrative analysis of the metastatic neuroblastoma transcriptome

Neuroblastoma (NBL), the most common non-Central Nervous System (CNS) solid tumor of childhood, characteristically displays heterogeneous clinical presentation and biological behavior. Previous work has studied the genetic basis of the disease and revealed a low somatic mutation burden. In order to identify novel therapeutic targets and better understand the biology of high-risk NBLs, I investigated whole transcriptome profiles of two cohorts of metastatic NBLs using RNA sequencing. First, I studied changes in splicing pattern in a cohort of 29 patients. V-Myc Avian Myelocytomatosis Viral Oncogene Neuroblastoma Derived Homolog (MYCN) amplified NBLs showed a distinct splicing pattern affecting multiple cancer hallmarks. Six splicing factors have altered expression patterns in MYCN-amplified tumors and cell lines, and binding motifs for these factors were significantly enriched in differentially-spliced genes. ChIP-seq analysis showed direct binding of MYCN to promoter regions of splicing factors PTBP1 and HNRNPA1, demonstrating that MYCN regulates splicing by directly regulating expression of key splicing factors. Furthermore, high expression of PTBP1 and HNRNPA1 was significantly associated with poor overall survival of stage 4 NBL patients (p≤0.05). Knocking down PTBP1, HNRNPA1 and their downstream target PKM2, a pro-tumor-growth isoform, resulted in repression of NBL cell growth. Second, I used whole transcriptome sequencing in a cohort of 150 patients to assess expressed mutations, fusion genes, and gene expression including long non-coding genes to provide clinically-relevant classification and to offer insights into NBL tumor biology. Twenty-four genes including ALK, ATRX and MYCN were recurrently mutated in NBL transcriptomes. In-frame FOXR1 fusions were detected in 4 samples, including 3 cases or 14% of stage 4S NBLs. Unsupervised gene expression analysis revealed four molecular subgroups. MYCN and tumor microenvironment were the primary discriminating signatures in these molecular subgroups. Fifty-eight percent of MYCN-not-amplified samples showed high MYCN signatures, which were potentially contributed by various genomic events such as MYCN activating mutations and FOXR1 fusions. High MYCN signature was significantly associated with poor overall survival in MYCN-not-amplified tumors (p=0.0017). In addition, the tumor microenvironment including stromal and immune cell infiltration significantly contributed to the NBL transcriptional landscape and tumor progression

Mammalian cells in culture actively export specific microRNAs

The discovery of microRNAs (miRNAs) as a new class of regulators of gene expression has triggered an explosion of research, but has left many unanswered questions about how this regulation works and how it is integrated with other regulatory mechanisms. A number of miRNAs have been found to be present in blood plasma and other body fluids of humans and mice in surprisingly high concentrations. This observation was unexpected in two respects: first, the fact that these molecules are present at all outside the cell at significant concentrations; and second, that these molecules appear to be stable outside of the cell. In light of this it has been suggested that the biological function of miRNAs may also extend outside of the cell and mediate cell-cell communication^[1-5]^. Such a system would be expected to export specific miRNAs from cells in response to specific biological stimuli. We report here that after serum deprivation several human cell lines tested do export a spectrum of miRNAs into the culture medium. The export response is substantial and prompt. The exported miRNAs are found both within and outside of microvesicles and exosomes. We have identified some candidate protein components of this system outside the cell, and found one exported protein that plays a role in protecting miRNA from degradation. Our results point to a hitherto unrecognized and uncharacterized miRNA trafficking system in mammalian cells that may involve cell-cell communication

Generalizable Synthetic Image Detection via Language-guided Contrastive Learning

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models. In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. We first augment the training images with carefully-designed textual labels, enabling us to use a joint image-text contrastive learning for the forensic feature extraction. In addition, we formulate the synthetic image detection as an identification problem, which is vastly different from the traditional classification-based approaches. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors by +22.66% accuracy and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED

Prediction of Pedestrians\u27 Red Light Violations Using Deep Learning

Pedestrians are regarded as Vulnerable Road Users (VRUs). Each year, thousands of pedestrians\u27 deaths are caused by traffic crashes, which take up 16% of the total road fatalities and injuries in the U.S. (FHWA, 2018). Crashes can happen if there are interactions between VRUs and motorized transportation. And pedestrians\u27 unexpected crossings, such as red-light violations at the signalized intersections, would expose them to motorized transportation and cause potential collisions. This thesis is intended to predict the pedestrians\u27 red-light violation behaviors at the signalized crosswalks based on an LSTM (Long Short-term Memory) neural network. With video data collected from real traffic scenes, it is found that pedestrians that crossed during the red-light periods are more in danger of being struck by vehicles, from the perspective of Surrogate Safety Measures (SSMs). Pedestrians\u27 features are generated using computer vision techniques. An LSTM model is used to predict pedestrians\u27 red-light violations using these features. The experiment results at one signalized intersection show that the LSTM model achieves an accuracy of 91.6%. Drivers can be more prepared for these unexpected crossing pedestrians if the model is to be implemented in the vehicle-to-infrastructure (V2I) communication system

Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates in yeast

Although many prokaryotes have glycolysis alternatives, it\u27s considered as the only energy-generating glucose catabolic pathway in eukaryotes. Here, we managed to create a hybrid-glycolysis yeast. Subsequently, we identified an inositol pyrophosphatase encoded by OCA5 that could regulate glycolysis and respiration by adjusting 5-diphosphoinositol 1,2,3,4,6-pentakisphosphate (5-InsP7) levels. 5-InsP7 levels could regulate the expression of genes involved in glycolysis and respiration, representing a global mechanism that could sense ATP levels and regulate central carbon metabolism. The hybrid-glycolysis yeast did not produce ethanol during growth under excess glucose and could produce 2.68 g/L free fatty acids, which is the highest reported production in shake flask of Saccharomyces cerevisiae. This study demonstrated the significance of hybrid-glycolysis yeast and determined Oca5 as an inositol pyrophosphatase controlling the balance between glycolysis and respiration, which may shed light on the role of inositol pyrophosphates in regulating eukaryotic metabolism

Comprehensive genomic profiling of cell-free circulating tumor DNA detects response to Ribociclib plus Letrozole in a patient with metastatic breast cancer

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Analysis of cell-free circulating tumor DNA obtained by liquid biopsy is a non-invasive approach that may provide clinically actionable information when conventional tissue biopsy is inaccessible or infeasible. Here, we followed a patient with hormone receptor-positive and human epidermal growth factor receptor (HER) 2-negative breast cancer who developed bone metastases seven years after mastectomy. We analyzed circulating cell-free DNA (cfDNA) extracted from plasma using high-depth massively parallel sequencing targeting 468 cancer-associated genes, and we identified a clonal hotspot missense mutation in the PIK3CA gene (3:178952085, A > G, H1047R) and amplification of the CCND1 gene. Whole-exome sequencing revealed that both alterations were present in the primary tumor. After treatment with ribociclib plus letrozole, the genetic abnormalities were no longer detected in cfDNA. These results underscore the clinical utility of combining liquid biopsy and comprehensive genomic profiling to monitor treatment response in patients with metastasized breast cancer.This work was supported by Fundação para a Ciência e Tecnologia (FCT)/Ministério da Ciência, Tecnologia e Ensino Superior, Portugal, through Fundos do Orçamento de Estado to Instituto de Medicina Molecular João Lobo Antunes (LA/P/0082/2020), and FCT/FEDER/POR Lisboa 2020, Programa Operacional Regional de Lisboa, PORTUGAL 2020 (LISBOA-01-0145-FEDER-016394), and FEDER/POR Lisboa 2020-Programa Operacional Regional de Lisboa, PORTUGAL 2020 (Infogene, 045300). C.S. was a recipient of a FCT fellowship (SFRH/BDE/110544/2015). This work was funded in part by the National Institutes of Health (NIH)/National Cancer Institute (NCI) Cancer Center Support Grant (P30 CA008748; MSK). J.S.R-F. and B.W. are funded in part by the NIH/NCI P50 CA247749 01 grant and a Breast Cancer Research Foundation grant. J.S.R.-F. is also funded in part by a Susan G Komen leadership grant, and B.W. by a Cycle for Survival grant.info:eu-repo/semantics/publishedVersio

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration

Cognition, Aryl Hydrocarbon Receptor Repressor Methylation, and Abstinence Duration-Associated Multimodal Brain Networks in Smoking and Long-Term Smoking Cessation

Cigarette smoking and smoking cessation are associated with changes in cognition and DNA methylation; however, the neurobiological correlates of these effects have not been fully elucidated, especially in long-term cessation. Cognitive performance, percent methylation of the aryl hydrocarbon receptor repressor (AHRR) gene, and abstinence duration were used as references to supervise a multimodal fusion analysis of functional, structural, and diffusion magnetic resonance imaging (MRI) data, in order to identify associated brain networks in smokers and ex-smokers. Correlations among these networks and with smoking-related measures were performed. Cognition-, methylation-, and abstinence duration-associated networks discriminated between smokers and ex-smokers and correlated with differences in fractional amplitude of low frequency fluctuations (fALFF) values, gray matter volume (GMV), and fractional anisotropy (FA) values. Long-term smoking cessation was associated with more accurate cognitive performance, as well as lower fALFF and more GMV in the hippocampus complex. The methylation- and abstinence duration-associated networks positively correlated with smoking-related measures of abstinence duration and percent methylation, respectively, suggesting they are complementary measures. This analysis revealed structural and functional co-alterations linked to smoking abstinence and cognitive performance in brain regions including the insula, frontal gyri, and lingual gyri. Furthermore, AHRR methylation, a promising epigenetic biomarker of smoking recency, may provide an important complement to self-reported abstinence duration