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Osteosarcoma is the most common malignancy of the bone, yet the survival for patients with osteosarcoma is virtually unchanged over the past 30 years. This is principally because development of new therapies is hampered by a lack of recurrent mutations that can be targeted in osteosarcoma. Here, we report that epigenetic changes via mRNA methylation holds great promise to better understand the mechanisms of osteosarcoma growth and to develop targeted therapeutics. In patients with osteosarcoma, the RNA demethylase ALKBH5 was amplified and higher expression correlated with copy-number changes. ALKBH5 was critical for promoting osteosarcoma growth and metastasis, yet it was dispensable for normal cell survival. Methyl RNA immunoprecipitation sequencing analysis and functional studies showed that ALKBH5 mediates its protumorigenic function by regulating m6A levels of histone deubiquitinase USP22 and the ubiquitin ligase RNF40. ALKBH5-mediated m6A deficiency in osteosarcoma led to increased expression of USP22 and RNF40 that resulted in inhibition of histone H2A monoubiquitination and induction of key protumorigenic genes, consequently driving unchecked cell-cycle progression, incessant replication, and DNA repair. RNF40, which is historically known to ubiquitinate H2B, inhibited H2A ubiquitination in cancer by interacting with and affecting the stability of DDB1-CUL4–based ubiquitin E3 ligase complex. Taken together, this study directly links increased activity of ALKBH5 with dysregulation of USP22/RNF40 and histone ubiquitination in cancers. More broadly, these results suggest that m6A RNA methylation works in concert with other epigenetic mechanisms to control cancer growth. Significance: RNA demethylase ALKBH5 upregulates USP22 and RNF40 to inhibit histone H2A ubiquitination and induces expression of key replication and DNA repair–associated genes, driving osteosarcoma progression.

Purpose: The favorable prognosis of stage I and II nasopharyngeal carcinoma (NPC) has motivated a search for biomarkers for the early detection and risk assessment of Epstein-Barr virus (EBV)–associated NPC. Although EBV seropositivity is ubiquitous among adults, a spike in antibodies against select EBV proteins is a harbinger of NPC. A serologic survey would likely reveal which EBV antibodies could discriminate those at risk of developing NPC. Experimental Design: Lysates from a new EBV mammalian expression library were used in a denaturing multiplex immunoblot assay to survey antibodies against EBV in sera collected from healthy individuals who later developed NPC (incident cases) in a prospective cohort from Singapore and validated in an independent cohort from Shanghai, P.R. China. Results: We show that IgA against EBV nuclear antigen 1 (EBNA1) discriminated incident NPC cases from matched controls with 100% sensitivity and 100% specificity up to 4 years before diagnosis in both Singapore and Shanghai cohorts. Incident NPC cases had a greater IgG repertoire against lytic-classified EBV proteins, and the assortment of IgA against EBV proteins detected by the immunoblot assay increased closer to diagnosis. Conclusions: Although NPC tumors consistently harbor latent EBV, the observed heightened systemic and mucosal immunity against lytic-classified antigens years prior to clinical diagnosis is consistent with enhanced lytic transcription. We conclude that an expanding EBV mucosal reservoir (which can be latent and/or lytic) is a risk factor for NPC. This presents an opportunity to identify those at risk of developing NPC using IgA against EBNA1 as a biomarker.

PURPOSE To study the efficacy and safety of the dienogest and the gonadotropin-releasing hormone agonist (GnRH-a) in symptomatic females with uterine adenomyosis. METHODS A total of 127 patients with adenomyosis with a chief complaint of dysmenorrhea were recruited. The first group received 2 mg of dienogest (DNG) daily, whereas the second group received goserelin acetate (GS) (3.6 mg/4 weeks) for 12 weeks. Outpatient follow-up was undertaken after 12 weeks. RESULTS Among 127 women, 56/63 (88.9%) patients completed the treatment in the DNG group, whereas 62/64 (96.9%) patients completed the treatment in the GS group. A significant decrease in dysmenorrhea symptoms as measured by the visual analog scale (VAS) and Carcinoma antigen125 (CA125) after 12 weeks of treatment was observed in both groups (p

The survival rate of cancer has increased significantly during the past two decades for breast, prostate, testicular, and colon cancer, while the brain and pancreatic cancers have a much lower median survival rate that has not improved much over the last forty years. This has imposed the challenge of finding gene markers for early cancer detection and treatment strategies. Different methods including regression-based Cox-PH, artificial neural networks, and recently deep learning algorithms have been proposed to predict the survival rate for cancers. We established in this work a novel graph convolution neural network (GCNN) approach called Surv_GCNN to predict the survival rate for 13 different cancer types using the TCGA dataset. For each cancer type, 6 Surv_GCNN models with graphs generated by correlation analysis, GeneMania database, and correlation + GeneMania were trained with and without clinical data to predict the risk score (RS). The performance of the 6 Surv_GCNN models was compared with two other existing models, Cox-PH and Cox-nnet. The results showed that Cox-PH has the worst performance among 8 tested models across the 13 cancer types while Surv_GCNN models with clinical data reported the best overall performance, outperforming other competing models in 7 out of 13 cancer types including BLCA, BRCA, COAD, LUSC, SARC, STAD, and UCEC. A novel network-based interpretation of Surv_GCNN was also proposed to identify potential gene markers for breast cancer. The signatures learned by the nodes in the hidden layer of Surv_GCNN were identified and were linked to potential gene markers by network modularization. The identified gene markers for breast cancer have been compared to a total of 213 gene markers from three widely cited lists for breast cancer survival analysis. About 57% of gene markers obtained by Surv_GCNN with correlation + GeneMania graph either overlap or directly interact with the 213 genes, confirming the effectiveness of the identified markers by Surv_GCNN.

We develop a novel computational method, NucHMM, to identify functional nucleosome states associated with cell type-specific combinatorial histone marks and nucleosome organization features such as phasing, spacing and positioning. We test it on publicly available MNase-seq and ChIP-seq data in MCF7, H1, and IMR90 cells and identify 11 distinct functional nucleosome states. We demonstrate these nucleosome states are distinctly associated with the splicing potentiality of skipping exons. This advances our understanding of the chromatin function at the nucleosome level and offers insights into the interplay between nucleosome organization and splicing processes. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-021-02465-1.

SARS-CoV-2 vaccines have contributed to the control of COVID-19 in some parts of the world. However, the constant emergence of variants of concern (VOCs) challenges the effectiveness of SARS-CoV-2 vaccines over time. In particular, Omicron contains a high number of mutations in the spike (S) protein gene, on which most vaccines were developed. In this study, we quantitated neutralizing antibodies in vaccine recipients at various times postvaccination using S protein-based pseudoviruses derived from wild type (WT) SARS-CoV-2 and five VOCs including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). We found that two-dose mRNA-1273 and BNT162b2 vaccines elicited robust neutralizing antibodies against WT, Alpha, Beta, Gamma, and Delta, but wanned after 6 months with a faster decline observed for BNT162b2. Both mRNA-1273 and BNT162b2 elicited weak neutralizing antibodies against Omicron. One dose of Ad26.COV2.S vaccine induced weaker neutralizing antibodies against WT and most VOCs than mRNA-1273 and BNT162b2 did but moderate neutralizing antibodies against Delta and Omicron, which lasted for 6 months. These results support current recommendations of the Centers for Disease Control and Prevention for a booster 5 months after full immunization with an mRNA-based vaccine and the use of an mRNA-based vaccine 2 months after Ad26.COV2.S vaccination.

Changes in the function of peritoneal macrophages contribute to the homeostasis of the peritoneal immune microenvironment in endometriosis. The mechanism by which ectopic tissues escape phagocytic clearance by macrophages to achieve ectopic colonization and proliferation is unknown. The expression of CD163 in peritoneal macrophages in patients with endometriosis is increased, with the overexpression of

The negative impact of COVID-19 pandemic has seen SME's struggling around the world. With many quickly adopting digital technologies, such as AI, in their manufacturing or services operations to achieve sustainable development. This study aims to develop a framework that informs AI-enabled sustainable development for SMEs by integrating the relevant research in the field. In this framework, we identify the opportunities that the deployment of AI technology can do to alleviate the plights of SMEs in the post-pandemic era, including the impacts on work, organizations, and performance. We further explore the challenges that SMEs face in AI transformation and recommend strategies to take on those challenges. Finally we propose an agenda for future research based on technological challenges and environmental threats.

Background Deep learning (DL) models are highly vulnerable to adversarial attacks for medical image classification. An adversary could modify the input data in imperceptible ways such that a model could be tricked to predict, say, an image that actually exhibits malignant tumor to a prediction that it is benign. However, adversarial robustness of DL models for medical images is not adequately studied. DL in medicine is inundated with models of various complexity—particularly, very large models. In this work, we investigate the role of model complexity in adversarial settings. Results Consider a set of DL models that exhibit similar performances for a given task. These models are trained in the usual manner but are not trained to defend against adversarial attacks. We demonstrate that, among those models, simpler models of reduced complexity show a greater level of robustness against adversarial attacks than larger models that often tend to be used in medical applications. On the other hand, we also show that once those models undergo adversarial training, the adversarial trained medical image DL models exhibit a greater degree of robustness than the standard trained models for all model complexities. Conclusion The above result has a significant practical relevance. When medical practitioners lack the expertise or resources to defend against adversarial attacks, we recommend that they select the smallest of the models that exhibit adequate performance. Such a model would be naturally more robust to adversarial attacks than the larger models.

Despite aggression being detrimental to children’s physical health, mental health and social development, the dispositional and neurological antecedents of aggression in the child are poorly understood. Here we examined the relationship between trait aggression as measured by Buss and Warren’s Aggression Questionnaire and personality traits measured with Big Five Questionnaire for Children in 77 primary-school children and recorded resting-state brain activity (fractional amplitude of low-frequency fluctuations [fALFF]) and resting-state functional connectivity (rsFC) using functional magnetic resonance imaging. The present results showed that trait aggression was negatively correlated with agreeableness and positively correlated with neuroticism. The brain analyses showed that children with a higher propensity for aggression had a lower fALFF mainly in the left superior temporal gyrus, right parahippocampal gyrus and left supramarginal gyrus. Physical and total aggressions were negatively associated with rsFC between the right parahippocampal gyrus and the right putamen. Further analysis revealed that this rsFC could moderate the influence of neuroticism on total aggression. Moreover, the results suggest the presence of a sex difference in the neurodevelopmental mechanisms underlying aggression in middle childhood. Overall, our findings indicate that aggressive children have lower agreeableness and higher neuroticism, and the underlying neural systems are mainly implicated in social judgment and empathy.

Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing. This poses challenges to train powerful deep learning model with the limited EEG data. Being able to generate EEG data computationally could address this limitation. We propose a novel Wasserstein Generative Adversarial Network with gradient penalty (WGAN-GP) to synthesize EEG data. This network addresses several modeling challenges of simulating time-series EEG data including frequency artifacts and training instability. We further extended this network to a class-conditioned variant that also includes a classification branch to perform event-related classification. We trained the proposed networks to generate one and 64-channel data resembling EEG signals routinely seen in a rapid serial visual presentation (RSVP) experiment and demonstrated the validity of the generated samples. We also tested intra-subject cross-session classification performance for classifying the RSVP target events and showed that class-conditioned WGAN-GP can achieve improved event-classification performance over EEGNet.

Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that can potentially bias the identification of cancer markers. Results In this paper, we introduced several Convolutional Neural Network (CNN) models that take unstructured gene expression inputs to classify tumor and non-tumor samples into their designated cancer types or as normal. Based on different designs of gene embeddings and convolution schemes, we implemented three CNN models: 1D-CNN, 2D-Vanilla-CNN, and 2D-Hybrid-CNN. The models were trained and tested on combined 10,340 samples of 33 cancer types and 731 matched normal tissues of The Cancer Genome Atlas (TCGA). Our models achieved excellent prediction accuracies (93.9-95.0%) among 34 classes (33 cancers and normal). Furthermore, we interpreted one of the models, known as 1D-CNN model, with a guided saliency technique and identified a total of 2,090 cancer markers (108 per class). The concordance of differential expression of these markers between the cancer type they represent and others is confirmed. In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1. Finally, we extended the 1D-CNN model for prediction of breast cancer subtypes and achieved an average accuracy of 88.42% among 5 subtypes. The codes can be found at https://github.com/chenlabgccri/CancerTypePrediction., Comment: 34 pages, 5 figures, This paper was presented at ICIBM June, 2019 at Ohio Columbus, and will be published in BMC Genomics journal. Keywords: Deep Learning; Convolutional Neural Networks, The Cancer Genome Atlas; Cancer type prediction; Cancer gene markers; Breast cancer subtype prediction

Infection by Kaposi’s sarcoma-associated herpesvirus (KSHV) is causally associated with numerous cancers. The mechanism of KSHV-induced oncogenesis remains unclear. By performing a CRISPR-Cas9 screening in a model of KSHV-induced cellular transformation of primary cells, we identified epigenetic regulators that were essential for KSHV-induced cellular transformation. Examination of TCGA data sets of the top 9 genes, including glutamate-rich WD repeat containing 1 (GRWD1), a WD40 family protein upregulated by KSHV, that had positive effects on cell proliferation and survival of KSHV-transformed cells (KMM) but not the matched primary cells (MM), uncovered the predictive values of their expressions for patient survival in numerous types of cancer. We revealed global epigenetic remodeling including H3K4me3 epigenetic active mark in KMM cells compared to MM cells. Knockdown of GRWD1 inhibited cell proliferation, cellular transformation, and tumor formation and caused downregulation of global H3K4me3 mark in KMM cells. GRWD1 interacted with WD repeat domain 5 (WDR5), the core protein of H3K4 methyltransferase complex, and several H3K4me3 methyltransferases, including myeloid leukemia 2 (MLL2). Knockdown of WDR5 and MLL2 phenocopied GRWD1 knockdown, caused global reduction of H3K4me3 mark, and altered the expression of similar sets of genes. Transcriptome sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses further identified common and distinct cellular genes and pathways that were regulated by GRWD1, WDR5, and MLL2. These results indicate that KSHV hijacks the GRWD1-WDR5-MLL2 epigenetic complex to regulate H3K4me3 methylation of specific genes, which is essential for KSHV-induced cellular transformation. Our work has identified an epigenetic complex as a novel therapeutic target for KSHV-induced cancers. IMPORTANCE By performing a genome-wide CRISPR-Cas9 screening, we have identified cellular epigenetic regulators that are essential for KSHV-induced cellular transformation. Among them, GRWD1 regulates epigenetic active mark H3K4me3 by interacting with WDR5 and MLL2 and recruiting them to chromatin loci of specific genes in KSHV-transformed cells. Hence, KSHV hijacks the GRWD1-WDR5-MLL2 complex to remodel cellular epigenome and induce cellular transformation. Since the dysregulation of GRWD1 is associated with poor prognosis in several types of cancer, GRWD1 might also be a critical driver in other viral or nonviral cancers.

Peritoneal macrophages play a significant role in the progression of endometriosis (EM), but their functional differentiation is still unclear, and their phagocytic ability is weak. CD47-signal-regulated protein α (SIRPα) and PD-L1-PD-1 are considered immune checkpoints associated with macrophage phagocytosis. A specific blockade of these two pathways had been shown to increase the phagocytic clearance of cancer cells by macrophages in most cancers. We hypothesized that targeting CD47/PD-L1 in EM could improve the phagocytosis of macrophages, thereby delaying the progression of EM. From localization to quantification, from mRNA to protein, we comprehensively evaluated the expression of CD47 and PD-L1 in EM. We demonstrated that the CD47 expression in ectopic endometrium from patients with EM was significantly increased, but PD-L1 was not. We performed direct co-culture experiments of endometrial stromal cells with macrophages in vitro and in vivo to assess whether ectopic endometrial stromal cells escape macrophage phagocytosis through the CD47-SIRPα signaling pathway. The results showed that targeting CD47 increased the phagocytic capacity of macrophages. Interestingly, we also found that the reduction of CD47 expression promoted apoptosis of endometrial stromal cells. In conclusion, these data suggested that targeting CD47 can effectively target ectopic endometrial stromal cells through a dual mechanism of increased phagocytosis of macrophages and induced apoptosis of ectopic endometrial stromal cells. Thus, immunotherapy based on the CD47-SIRPα signaling pathway has some potential in treating EM, but further mechanistic studies are needed to explore more effective and specific antibodies.

The processes underlying adenomyosis are similar to those of tumor metastasis, and it is defined as progressive invasion by the endometrium and the subsequent creation of ectopic lesions. GRIM-19 regulates cell death via the mitochondrial respiratory chain. Stress following oxygen deprivation can induce tumor cell autophagy, leading to cell invasion and migration. Here, we revealed that GRIM-19 negatively regulates autophagy, and, at least in adenomyosis, decreased expression of GRIM-19 is accompanied by an increased level of autophagy and 5′-adenosine monophosphate-activated protein kinase-Unc-51 like autophagy activating kinase 1 (AMPK-ULK1) activation. Upregulation of GRIM-19 expression in human primary endometrial cells and ISHIKAWA cells inhibits autophagy via the AMPK-ULK1 pathway and helps control cell invasion and migration. In addition, we also identified increased expression of AMPK and ULK1, and higher levels of autophagy in the uterine tissues of GRIM-19+/− mice. Importantly, the function of the GRIM-19-AMPK-ULK1 axis in regulating autophagy in adenomyosis is similar to that of tumor tissues, which may help elucidate the regulation of adenomyosis tumor-like behavior, and is expected to help identify novel targets for the diagnosis and treatment of adenomyosis.

Background Billions of doses of medicines are donated for mass drug administrations in support of the World Health Organization’s “Roadmap to Implementation,” which aims to control, eliminate, and eradicate Neglected Tropical Diseases (NTDs). The supply chain to deliver these medicines is complex, with fragmented data systems and limited visibility on performance. This study empirically evaluates the impact of an online supply chain performance measurement system, “NTDeliver,” providing understanding of the value of information sharing towards the success of global health programs. Methods Retrospective secondary data were extracted from NTDeliver, which included 1,484 shipments for four critical medicines ordered by over 100 countries between February 28, 2006 and December 31, 2018. We applied statistical regression models to analyze the impact on key performance metrics, comparing data before and after the system was implemented. Findings The results suggest information sharing has a positive association with improvement for two key performance indicators: purchase order timeliness (β = 0.941, p = 0.003) and—most importantly—delivery timeliness (β = 0.828, p = 0.027). There is a positive association with improvement for three variables when the data are publicly shared: shipment timeliness (β = 2.57, p = 0.001), arrival timeliness (β = 2.88, p = 0.003), and delivery timeliness (β = 2.82, p = 0.011). Conclusions Our findings suggest that information sharing between the NTD program partners via the NTDeliver system has a positive association with supply chain performance improvements, especially when data are shared publicly. Given the large volume of medicine and the significant number of people requiring these medicines, information sharing has the potential to provide improvements to global health programs affecting the health of tens to hundreds of millions of people., Author summary The supply chain to deliver donated preventive chemotherapy medicines is complex due to the many stakeholders and partnerships participating, as well as challenging because the logistics are further complicated by delivery to remote destinations in developing countries. As mass drug administration (MDA) campaigns involve treating hundreds of thousands to millions of patients in endemic regions within entire countries over the course of days or weeks, close coordination and timing of medicine delivery is critical. Inefficiencies caused by fragmented data systems and limited transparency on supply chain performance further challenges the ability to identify shipment issues and explore the root cause of the issues. Prior to 2016, delivery was performing below standards, lagging as much as 40% below the WHO target of 80% on-time delivery. These delays result in wasted medicine donations, increased program costs, delayed MDAs, or sometimes even completely missed MDAs. In September 2016, an online supply chain performance measurement system, “NTDeliver,” was launched by the NTD Supply Chain Forum (a public-private partnership focused on managing and improving the preventive chemotherapy (PC) donation supply chain) to enhance supply chain performance and information transparency. Our findings suggest that information sharing through NTDeliver is positively associated with performance improvements at key stages in NTD supply chain, especially when information is made publicly accessible, focused on access for country program managers. The study findings support investment in supply chain systems and commitment to data transparency, in the context of a growing focus on supply chain investment in NTD programs.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

A novel deep learning method links in vitro CRISPR screens to the context of cancer genomics and translates to individual tumors., Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines and further to tumors. Here, we present DeepDEP, a deep learning model that predicts cancer dependencies using integrative genomic profiles. It uses a unique unsupervised pretraining that captures unlabeled tumor genomic representations to improve the learning of cancer dependencies. We demonstrated DeepDEP’s improvement over conventional machine learning methods and validated the performance with three independent datasets. By systematic model interpretations, we extended the current dependency maps with functional characterizations of dependencies and a proof-of-concept in silico assay of synthetic essentiality. We applied DeepDEP to pan-cancer tumor genomics and built the first pan-cancer synthetic dependency map of 8000 tumors with clinical relevance. In summary, DeepDEP is a novel tool for investigating cancer dependency with rapidly growing genomic resources.

BackgroundEndometriosis is a common nonmalignant gynecological disorder that affects 10–15% women of reproductive age and causes several symptoms that result in decreased quality of life and a huge social burden. In recent decades, extracellular vesicles (EVs) have gained attention as a potential therapeutic tool; however, the therapeutic effects of EVs against endometriosis have not been reported. Accordingly, in this study, we investigated the feasibility of nanovesicles (NVs) derived from M1 macrophages (M1NVs) in treating endometriosis.MethodsM1NVs were prepared by serial extrusion. Co-culture assays were performed to investigate changes in tube formation and migration/invasion of eutopic endometrial stroma cells (ESCs) obtained from patients with endometriosis (EM-ESCs). A mouse model of endometriosis was established, and mice were treated with phosphate-buffered saline, M0NVs, or M1NVs to evaluate the efficacy and safety of M1NV for treating endometriosis.ResultsM1NVs directly or indirectly inhibited the migration and invasion of EM-ESCs and reduced tube formation. In the mouse model, M1NVs suppressed the development of endometriosis through reprogramming of M2 macrophages, without causing damage to the organs.ConclusionsM1NVs inhibit the development of endometriosis directly, or through repolarizing macrophages from M2 to M1 phenotype. Hence, administration of M1NVs may represent a novel method for the treatment of endometriosis.

Motivation As the most abundant mammalian mRNA methylation, N6-methyladenosine (m6A) exists in >25% of human mRNAs and is involved in regulating many different aspects of mRNA metabolism, stem cell differentiation and diseases like cancer. However, our current knowledge about dynamic changes of m6A levels and how the change of m6A levels for a specific gene can play a role in certain biological processes like stem cell differentiation and diseases like cancer is largely elusive. Results To address this, we propose in this paper FunDMDeep-m6A a novel pipeline for identifying context-specific (e.g. disease versus normal, differentiated cells versus stem cells or gene knockdown cells versus wild-type cells) m6A-mediated functional genes. FunDMDeep-m6A includes, at the first step, DMDeep-m6A a novel method based on a deep learning model and a statistical test for identifying differential m6A methylation (DmM) sites from MeRIP-Seq data at a single-base resolution. FunDMDeep-m6A then identifies and prioritizes functional DmM genes (FDmMGenes) by combing the DmM genes (DmMGenes) with differential expression analysis using a network-based method. This proposed network method includes a novel m6A-signaling bridge (MSB) score to quantify the functional significance of DmMGenes by assessing functional interaction of DmMGenes with their signaling pathways using a heat diffusion process in protein-protein interaction (PPI) networks. The test results on 4 context-specific MeRIP-Seq datasets showed that FunDMDeep-m6A can identify more context-specific and functionally significant FDmMGenes than m6A-Driver. The functional enrichment analysis of these genes revealed that m6A targets key genes of many important context-related biological processes including embryonic development, stem cell differentiation, transcription, translation, cell death, cell proliferation and cancer-related pathways. These results demonstrate the power of FunDMDeep-m6A for elucidating m6A regulatory functions and its roles in biological processes and diseases. Availability and implementation The R-package for DMDeep-m6A is freely available from https://github.com/NWPU-903PR/DMDeepm6A1.0. Supplementary information Supplementary data are available at Bioinformatics online.

Aberrant miR-629-5p expression in several cancer types has been reported. Nonetheless, its potential effect and mechanism of action on tumor growth and metastasis in hepatocellular carcinoma (HCC) have rarely been analyzed. In this study, we found that miR-629-5p was upregulated in HCC tissue samples as compared to matched adjacent-tissue samples. Overexpression of miR-629-5p promoted the proliferation, migration, and invasiveness of human HCC cells in vitro, whereas miR-629-5p knockdown reduced these parameters. Consistently, miR-629-5p overexpression accelerated tumor growth and metastasis in a nude mouse model. Mechanistically, miR-629-5p directly targeted the 3' untranslated region (3'UTR) of the secreted frizzled-related protein 2 (SFRP2) mRNA and suppressed its expression, resulting in the activation of β-catenin. Inhibition of β-catenin abrogated miR-629-5p-induced growth and invasiveness. Collectively, these results suggest that miR-629-5p activates β-catenin signaling by downregulating SFRP2 and thus promotes the growth and metastasis of HCC. These data open up new prospects for HCC treatment.

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes., Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):142

Background The state-of-the-art deep learning based cancer type prediction can only predict cancer types whose samples are available during the training where the sample size is commonly large. In this paper, we consider how to utilize the existing training samples to predict cancer types unseen during the training. We hypothesize the existence of a set of type-agnostic expression representations that define the similarity/dissimilarity between samples of the same/different types and propose a novel one-shot learning model called CancerSiamese to learn this common representation. CancerSiamese accepts a pair of query and support samples (gene expression profiles) and learns the representation of similar or dissimilar cancer types through two parallel convolutional neural networks joined by a similarity function. Results We trained CancerSiamese for cancer type prediction for primary and metastatic tumors using samples from the Cancer Genome Atlas (TCGA) and MET500. Network transfer learning was utilized to facilitate the training of the CancerSiamese models. CancerSiamese was tested for different N-way predictions and yielded an average accuracy improvement of 8% and 4% over the benchmark 1-Nearest Neighbor (1-NN) classifier for primary and metastatic tumors, respectively. Moreover, we applied the guided gradient saliency map and feature selection to CancerSiamese to examine 100 and 200 top marker-gene candidates for the prediction of primary and metastatic cancers, respectively. Functional analysis of these marker genes revealed several cancer related functions between primary and metastatic tumors. Conclusion This work demonstrated, for the first time, the feasibility of predicting unseen cancer types whose samples are limited. Thus, it could inspire new and ingenious applications of one-shot and few-shot learning solutions for improving cancer diagnosis, prognostic, and our understanding of cancer.

Research question Which metabolites are altered in the peritoneal cavity of women with endometriosis? Could the mouse endometriosis model simulate these alterations? Design Thirteen women with endometriosis and seven women with other benign gynaecological diseases, who underwent laparoscopic surgery, were included in this study. None had received hormonal therapy for 3 months before surgery. For the animal experiments, six and five mice were included in the endometriosis and control groups, respectively. Peritoneal fluid from the patients and peritoneal lavage fluid from the mice was collected and analysed. Non-targeted metabolomics via liquid chromatography with tandem mass spectrometry was used to identify the altered metabolites in the peritoneal fluid of endometriosis patients and mouse models. MetaboAnalyst 4.0 was used to visualize the data. Results Several metabolites in the peritoneal cavity were significantly altered in both humans and mice with endometriosis. Concentrations of lysophosphatidylcholine (LysopC) (P=0.017 in patients and P=0.041 in the mouse model) and derivatives of phosphoethanolamine (1-arachidonoyl-sn-glycero-3-phosphoethanolamine in patients, P=0.027; 1-oleoyl-sn-glycero-3-phosphoethanolamine in patients, P=0.0086; and phosphorylethanolamine in the mouse model, P=0.0027) were significantly up-regulated in both, whereas concentrations of acylcarnitines ( l -palmitoylcarnitine, P=0.047; and stearoylcarnitine, P=0.029) and kynurenine (P=0.045) were significantly increased only in humans. The human and mouse samples shared three altered enriched metabolite sets. Conclusions Women with endometriosis show an altered metabolic state in the abdominal cavity. The endometriosis mouse model shared half of the significantly altered metabolite sets found in the abdominal cavity of humans.

Accumulation of dendritic cells (DCs) is a special characteristic of the decidual microenvironment. Decidua-infiltrated DCs show unique phenotypes and functions that promote the establishment of fetal-maternal tolerance. However, the regulatory mechanisms yet to be fully investigated. Decidual stromal cells (DSCs) are the major cellular component of decidua tissue. The interactions between DSCs and decidua-infiltrated immunocytes dictate immune tolerance in early pregnancy. Therefore, in the present study, we explore the effect of early pregnancy DSCs on monocyte-derived DCs and the relevant mechanisms. DSC-conditioned DCs showed altered phenotypes, secretion profiles and Th2 priming potential. G-CSF concentration was significantly up-regulated in the co-culture supernatant between DSCs and DCs. Supplementation of G-CSF neutralizing antibody partly reversed the reprogramming of DCs mediated by DSCs. Furthermore, G-CSF production was promoted by IL-1β, which was mainly produced by DCs and significantly up-regulated after their cultivation with DSCs. Interestingly, the effects of DSC on IL-1β production of DCs occurred in their immature stage but not their mature stage. Lastly, no significant difference of G-CSF was found in DSCs from healthy early pregnancy women and spontaneous abortions (SA) patients. However, DSCs from SA patients secreted less G-CSF in response to exogenous rhIL-1β or DC cultivation. In conclusion, our study bolster the understanding of the decidual immunomodulatory microenvironment during early pregnancy, and brings new insight into the potential clinical value of G-CSF in pregnancy disorders.

Brain–computer Interface (BCI) is actively involved in optimizing the communication medium between the human brain and external devices.Objective. Rapid serial visual presentation (RSVP) is a robust and highly efficient BCI technique in recognizing target objects but suffers from limited target selections. Hybrid BCI systems that combine steady-state visual evoked potential (SSVEP) and RSVP can mitigate this limitation and allow users to operate on multiple targets. Approach. This study proposes a novel hybrid SSVEP-RSVP BCI to improve the performance of classifying the target/non-target objects in a multi-target scenario. In this paradigm, SSVEP stimulation helps in identifying the user’s focus location and RSVP stimuli that elicit event-related potentials differentiate target and non-target objects. Main results. The proposed model achieved an offline accuracy of 81.59% by using 12 electroencephalography (EEG) channels and an online (real-time) accuracy of 78.10% when only four EEG channels are considered. Further, the biomarkers of physiological states are analyzed to assess the cognitive states (mental fatigue and user attention) of the participants based on resting theta and alpha band powers. The results indicate an inverse relationship between the BCI performance and the resting EEG power, validating that the subjects’ performance is affected by physiological states for long-term use of the BCI. Significance. Our findings demonstrate that the combination of SSVEP and RSVP stimuli improves the BCI performance and further enhances the possibility of performing multiple user command tasks, which are inevitable in real-world applications. Additionally, the cognitive state biomarkers discussed imply the need for an efficient and attractive experimental paradigm that reduces the physiological state disparities and provide enhanced BCI performance.

Enhancers are non-coding DNA sequences bound by proteins called transcription factors. They function as distant regulators of gene transcription and participate in the development and maintenance of cell types and tissues. Since experimental validation of enhancers is expensive and time-consuming, many computational methods have been developed to predict enhancers and their strength. However, most of these methods still lack good performance in the prediction of enhancer strength. Here, we present a method to predict Enhancers Strength (i.e., strong and weak) by using Augmented data and Residual Convolutional Neural Network (ES-ARCNN). To train ES-ARCNN, we used two data augmentation tricks (i.e., reverse complement and shift) to previously identified enhancers for enlarging a previously identified dataset of enhancers. We further employed a residual convolutional neural network and trained it using the augmented dataset. Compared with other state-of-the-art methods in the 10-fold cross-validation (CV) test, ES-ARCNN has the best performance with the accuracy of 66.17%, and the tricks of data augmentation can effectively improve the prediction performance. We further tested ES-ARCNN on an independent dataset and obtained 65.5% accuracy, which has more than 4% improvement over the other three existing methods. The results in 10CV and independent tests show that ES-ARCNN can effectively predict the enhancer strength. The transcription factor binding sites (TFBSs) enrichment analysis shows that from the mechanistic perspective, enhancer strength is associated with a higher density of important TFBSs in a tissue. A user-friendly web-application is also provided at http://compgenomics.utsa.edu/ES-ARCNN/ .

Post-testicular maturation of spermatozoa is crucial for attaining the morphological and functional capabilities needed for successful fertilization. Epididymal epithelia offer a favorable environment for spermatozoa that are stored long term in the turtle epididymis; however, sperm-epithelial interactions during storage, which are enormously important for sperm functional and morphological maturation, are still largely unknown in turtles. The present study examined the epididymis during the sperm-storage period (November-April) in the Chinese soft-shelled turtle (Pelodiscus sinensis). Light and transmission electron microscopy were used to determine the cellular features of each epididymal segment (caput, corpus, and cauda) and their epithelial interactions with the spermatozoa. Spermatozoa were mainly located in the lumena of caput, corpus, and cauda epididymides. Numerous spermatozoa were bound to apical surfaces of the epithelia, and several were even embedded in the epithelial cytoplasm of the caput and corpus epididymides. No embedded spermatozoa were found in the cauda epididymis. In all epididymal segments, principal and clear cells showed the synthetic activity, evidenced by a well-developed endoplasmic reticulum network and high and low electron-dense secretory materials, respectively. Principal and clear cells in the caput and corpus segments showed embedded spermatozoa in electron-dense secretions and in the lipid droplets within the cytoplasm. No lysosomes were observed around the embedded spermatozoa. The lumena of the caput and corpus segments showed few apocrine and low electron density secretions. In the lumen of the cauda epididymidis, different secretions, such as holocrine with low and high electron density and their fragmentation, apocrine, and dictyosome, were found and are summarized. Altogether, sperm physical interactions with secretions either in the cytoplasm of epithelium or in the lumen may support the viability, morphological maintenance, and transfer of various proteins involved in long-term sperm storage in the turtle. This interaction could help us to understand the mechanisms of long-term sperm storage and provide more insights into the reproductive strategies of turtle sperm preservation.

N6-Methyladenosine (m6A) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential m6A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction. Comprehensive evaluation of MeTDiff's performance using both simulated and real datasets showed that MeTDiff is much more robust and achieved much higher sensitivity and specificity over exomePeak. The R package “MeTDiff” and additional details are available at: https://github.com/compgenomics/MeTDiff .

3D image-based finite element (FE) and bone volume fraction (BV/TV)/fabric tensor modeling techniques are currently used to determine the apparent stiffness tensor of trabecular bone for assessing its anisotropic elastic behavior. Inspired by the recent success of deep learning (DL) techniques, we hypothesized that DL modeling techniques could be used to predict the apparent stiffness tensor of trabecular bone directly using dual-energy X-ray absorptiometry (DXA) images. To test the hypothesis, a convolutional neural network (CNN) model was trained and validated to predict the apparent stiffness tensor of trabecular bone cubes using their DXA images. Trabecular bone cubes obtained from human cadaver proximal femurs were used to obtain simulated DXA images as input, and the apparent stiffness tensor of the trabecular cubes determined by using micro-CT based FE simulations was used as output (ground truth) to train the DL model. The prediction accuracy of the DL model was evaluated by comparing it with the micro-CT based FE models, histomorphometric parameter based multiple linear regression models, and BV/TV/fabric tensor based multiple linear regression models. The results showed that DXA image-based DL model achieved high fidelity in predicting the apparent stiffness tensor of trabecular bone cubes (R2 = 0.905–0.973), comparable to or better than the histomorphometric parameter based multiple linear regression and BV/TV/fabric tensor based multiple linear regression models, thus supporting the hypothesis of this study. The outcome of this study could be used to help develop DXA image-based DL techniques for clinical assessment of bone fracture risk.

N6-methyladenosine (m6A) is the most abundant form of mRNA modification and controls many aspects of RNA metabolism including gene expression. However, the mechanisms by which m6A regulates cell- and condition-specific gene expression are still poorly understood, partly due to a lack of tools capable of identifying m6A sites that regulate gene expression under different conditions. Here we develop m6A-express, the first algorithm for predicting condition-specific m6A regulation of gene expression (m6A-reg-exp) from limited methylated RNA immunoprecipitation sequencing (MeRIP-seq) data. Comprehensive evaluations of m6A-express using simulated and real data demonstrated its high prediction specificity and sensitivity. When only a few MeRIP-seq samples may be available for the cellular or treatment conditions, m6A-express is particularly more robust than the log-linear model. Using m6A-express, we reported that m6A writers, METTL3 and METTL14, competitively regulate the transcriptional processes by mediating m6A-reg-exp of different genes in Hela cells. In contrast, METTL3 induces different m6A-reg-exp of a distinct group of genes in HepG2 cells to regulate protein functions and stress-related processes. We further uncovered unique m6A-reg-exp patterns in human brain and intestine tissues, which are enriched in organ-specific processes. This study demonstrates the effectiveness of m6A-express in predicting condition-specific m6A-reg-exp and highlights the complex, condition-specific nature of m6A-regulation of gene expression.

To stop the spread of COVID-19 (2019 novel coronavirus), China placed lockdown on social activities across China since mid-January 2020. The government actions significantly affected emissions of atmospheric pollutants and unintentionally created a nationwide emission reduction scenario. In order to assess the impacts of COVID-19 on fine particular matter (PM2.5) levels, we developed a “conditional variational autoencoder” (CVAE) algorithm based on the deep learning to discern unsupervised PM2.5 anomalies in Chines cities during the COVID-19 epidemic. We show that the timeline of changes in number of cities with unsupervised PM2.5 anomalies is consistent with the timeline of WHO's response to COVID-19. Using unsupervised PM2.5 anomaly as a time node, we examine changes in PM2.5 before and after the time node to assess the response of PM2.5 to the COVID-19 lockdown. The rate of decrease of PM2.5 around the time node in northern China is 3.5 times faster than southern China, and decreasing PM2.5 levels in southern China is 3.5 times of that in northern China. Results were also compared with anomalous PM2.5 occurring in Chinese's Spring Festival from 2017 to 2019, PM2.5 anomalies during around Chinese New Year in 2020 differ significantly from 2017 to 2019. We demonstrate that this method could be used to detect the response of air quality to sudden changes in social activities., Graphical abstract Unlabelled Image

Infection by Kaposi’s sarcoma-associated herpesvirus (KSHV) is necessary for the development of Kaposi’s sarcoma (KS), which most often develops in HIV-infected individuals. KS frequently has oral manifestations and KSHV DNA can be detected in oral cells. Numerous types of cancer are associated with the alteration of microbiome including bacteria and virus. We hypothesize that oral bacterial microbiota affects or is affected by oral KS and the presence of oral cell-associated KSHV DNA. In this study, oral and blood specimens were collected from a cohort of HIV/KSHV-coinfected individuals all previously diagnosed with KS, and were classified as having oral KS with any oral cell-associated KSHV DNA status (O-KS, n = 9), no oral KS but with oral cell-associated KSHV DNA (O-KSHV, n = 10), or with neither oral KS nor oral cell-associated KSHV DNA (No KSHV, n = 10). We sequenced the hypervariable V1-V2 region of the 16S rRNA gene present in oral cell-associated DNA by next generation sequencing. The diversity, richness, relative abundance of operational taxonomic units (OTUs) and taxonomic composition of oral microbiota were analyzed and compared across the 3 studied groups. We found impoverishment of oral microbial diversity and enrichment of specific microbiota in O-KS individuals compared to O-KSHV or No KSHV individuals. These results suggest that HIV/KSHV coinfection and oral microbiota might impact one another and influence the development of oral KS., Author summary Kaposi’s sarcoma (KS) is the most common cancer occurring in HIV-infected individuals worldwide, and often involves the mouth. While infection by Kaposi’s sarcoma-associated herpesvirus (KSHV) is necessary for the development of KS, other cofactors remain unclear. In this study, we evaluated the impact of oral bacterial microbiota on the development of oral KS and the presence of oral cell-associated KSHV DNA by studying a cohort of HIV/KSHV-coinfected individuals all previously diagnosed with KS, classified as having oral KS with any oral cell-associated KSHV DNA status (O-KS), no oral KS but with oral cell-associated KSHV DNA (O-KSHV), or with neither oral KS nor oral cell-associated KSHV DNA (No KSHV). We observed impoverishment of oral microbial diversity and enrichment of specific types of microbes in O-KS individuals compared to O-KSHV or No KSHV individuals. Hence, HIV/KSHV coinfection and oral microbiota might impact one another and influence the development of oral KS.

The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor’s genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.

Using a model of oncogenic virus KSHV-driven cellular transformation of primary cells, we have performed a genome-wide CRISPR-Cas9 screening to identify vulnerable genes of cancer cells. This screening is unique in that this virus-induced oncogenesis model does not depend on any cellular genetic alterations and has matched primary and KSHV-transformed cells, which are not available for similar screenings in other types of cancer. We have identified genes that are both growth promoting and growth suppressive in primary and transformed cells, some of which could represent novel proto-oncogenes and tumor suppressors. In particular, we have demonstrated that the exportin XPO1 is a critical factor for the survival of transformed cells. Using a XPO1 inhibitor (KPT-8602) and siRNA-mediated knockdown, we have confirmed the essential role of XPO1 in cell proliferation and in growth transformation of KSHV-transformed cells, as well as of gastric and liver cancer cells. XPO1 inhibition induces cell cycle arrest by activating p53, but the mechanisms of p53 activation differed among different types of cancer cells. p53 activation is dependent on the formation of PML nuclear bodies in gastric and liver cancer cells. Mechanistically, XPO1 inhibition induces relocalization of autophagy adaptor protein p62 (SQSTM1), recruiting p53 for activation in PML nuclear bodies. These results illustrate that XPO1 is a vulnerable target of cancer cells and reveal a novel mechanism for blocking cancer cell proliferation by XPO1 inhibition as well as a novel PML- and p62-mediated mechanism of p53 activation in some types of cancer cells., The abnormal proliferation of cancer cells is driven by deregulated oncogenes or tumor suppressors, among which the cancer-vulnerable genes are attractive therapeutic targets. Targeting mislocalization of oncogenes and tumor suppressors resulting from aberrant nuclear export is effective for inhibiting growth transformation of cancer cells. We performed a clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) screening in a unique model of matched primary and oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV)-transformed cells and identified genes that were growth promoting and growth suppressive for both types of cells, among which exportin XPO1 was demonstrated to be critical for the survival of transformed cells. Using XPO1 inhibitor KPT-8602 and by small interfering RNA (siRNA) knockdown, we confirmed the essential role of XPO1 in cell proliferation and growth transformation of KSHV-transformed cells and in cell lines of other cancers, including gastric cancer and liver cancer. XPO1 inhibition induced cell cycle arrest through p53 activation, but the mechanisms of p53 activation differed among the different types of cancer cells. p53 activation depended on the formation of promyelocytic leukemia (PML) nuclear bodies in gastric cancer and liver cancer cells. Mechanistically, XPO1 inhibition induced relocalization of autophagy adaptor protein p62 (SQSTM1), recruiting p53 for activation in PML nuclear bodies. Taken the data together, we have identified novel growth-promoting and growth-suppressive genes of primary and cancer cells and have demonstrated that XPO1 is a vulnerable target of cancer cells. XPO1 inhibition induces cell arrest through a novel PML- and p62-dependent mechanism of p53 activation in some types of cancer cells.

Cardiovascular disease (CVD) is a significant cause of death worldwide. Because of its major individual differences in genetic background, pathogenesis, and disease progression pattern, the mortality risk rate remains high following conventional Western medicine diagnosis under current guidelines. Traditional Chinese medicine (TCM) has important multi-target, multi-pathway, and multi-layer benefits that can effectively address western medicine deficiencies. It was therefore commonly used in CVD diagnosis. Oxidative stress is also one of the main factors of CVD. Likewise, this main reaction regulator is the nuclear factor erythroid-2-related (Nrf2) factor. When activated, it can be transferred to the nucleus and initiated in the downstream pathway, thus playing an anti-oxidant stress role. As one of the most crucial endogenous protection systems in the body, Nrf2-related / heme oxygenase 1 (Nrf2/HO-1) signaling pathway is Nrf2's most classic approach to playing roles. Recently, various advances have been made to research and explain TCM by manipulating this pathway to treat CVD using modern molecular biology and other approaches. This analysis summarized the relationship between Nrf2/HO-1 signaling route, CVD and TCM. Further, Autodock calculation was also conducted to determine the binding amino acid on this TCM to Nrf2 and HO-1.

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases., Author summary The goal of this work is to identify functional significant m6A-regulated genes and m6A-associated diseases from analyzing an extensive collection of MeRIP-seq data. To achieve this, we first developed Deep-m6A, a CNN model for single-base m6A prediction. To our knowledge, this is the first condition-specific single-base m6A site prediction model that combines mRNA sequence feature and MeRIP-Seq data. The 10-fold cross-validation and test on an independent dataset show that Deep-m6A outperformed two sequence-based models. We applied Deep-m6A followed by network-based analysis using HotNet2 and RWRH to 75 human MeRIP-Seq samples from various cells and tissue under different conditions to globally detect m6A-regulated genes and further predict m6A mediated functions and associated diseases. This is also to our knowledge the first attempt to predict m6A functions and associated diseases using only computational methods in a global manner on a large number of human MeRIP-Seq samples. The predicted functions and diseases show considerable consistent with those reported in the literature, which demonstrated the power of our proposed pipeline to predict potential m6A mediated functions and associated diseases.

Motivation Transcription factor (TF) binds to the promoter region of a gene to control gene expression. Identifying precise TF binding sites (TFBSs) is essential for understanding the detailed mechanisms of TF-mediated gene regulation. However, there is a shortage of computational approach that can deliver single base pair resolution prediction of TFBS. Results In this paper, we propose DeepSNR, a Deep Learning algorithm for predicting TF binding location at Single Nucleotide Resolution de novo from DNA sequence. DeepSNR adopts a novel deconvolutional network (deconvNet) model and is inspired by the similarity to image segmentation by deconvNet. The proposed deconvNet architecture is constructed on top of ‘DeepBind’ and we trained the entire model using TF-specific data from ChIP-exonuclease (ChIP-exo) experiments. DeepSNR has been shown to outperform motif search–based methods for several evaluation metrics. We have also demonstrated the usefulness of DeepSNR in the regulatory analysis of TFBS as well as in improving the TFBS prediction specificity using ChIP-seq data. Availability and implementation DeepSNR is available open source in the GitHub repository (https://github.com/sirajulsalekin/DeepSNR) Supplementary information Supplementary data are available at Bioinformatics online.

Collaboration among writers-readers-erasers of m6A regulates the stability of tumor-specific genes., The importance of RNA methylation in biological processes is an emerging focus of investigation. We report that altering m6A levels by silencing either N6-adenosine methyltransferase METTL14 (methyltransferase-like 14) or demethylase ALKBH5 (ALKB homolog 5) inhibits cancer growth and invasion. METTL14/ALKBH5 mediate their protumorigenic function by regulating m6A levels of key epithelial-mesenchymal transition and angiogenesis-associated transcripts, including transforming growth factor–β signaling pathway genes. Using MeRIP-seq (methylated RNA immunoprecipitation sequencing) analysis and functional studies, we find that these target genes are particularly sensitive to changes in m6A modifications, as altered m6A status leads to aberrant expression of these genes, resulting in inappropriate cell cycle progression and evasion of apoptosis. Our results reveal that METTL14 and ALKBH5 determine the m6A status of target genes by controlling each other’s expression and by inhibiting m6A reader YTHDF3 (YTH N6-methyladenosine RNA binding protein 3), which blocks RNA demethylase activity. Furthermore, we show that ALKBH5/METTL14 constitute a positive feedback loop with RNA stability factor HuR to regulate the stability of target transcripts. We discover that hypoxia alters the level/activity of writers, erasers, and readers, leading to decreased m6A and consequently increased expression of target transcripts in cancer cells. This study unveils a previously undefined role for m6A in cancer and shows that the collaboration among writers-erasers-readers sets up the m6A threshold to ensure the stability of progrowth/proliferation-specific genes, and protumorigenic stimulus, such as hypoxia, perturbs that m6A threshold, leading to uncontrolled expression/activity of those genes, resulting in tumor growth, angiogenesis, and progression.

Understanding how indoor environment affects office worker's performance and developing methods to predict human performance in changing indoor environment have become highly important research topic bearing significant economic and sociological impact. While past research groups have attempted to find predictors for performance they do not provide satisfactory predictions. We conduct in this paper a study to predict human performance by developing a regression model using neurophysiological signals collected from electroencephalogram (EEG), during simulated office-work tasks under different indoor room temperatures (22°C and 30°C). We found that using brain power spectral densities (PSD) from EEG as predictors provides the higher R2 than predictors using skin temperature or heart rate by approximately over 3 folds. Finally, we showed that the predictor using EEG is more robust than regression models using skin temperature and heart rate. Our work shows the potential of using brain signals to accurately predict human office work performance.

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. A knowledge base with the systematic collection and curation of context specific transcriptome-wide methylations is critical for elucidating their biological functions as well as for developing bioinformatics tools. Since its inception in 2014, the Met-DB (Liu, H., Flores, M.A., Meng, J., Zhang, L., Zhao, X., Rao, M.K., Chen, Y. and Huang, Y. (2015) MeT-DB: a database of transcriptome methylation in mammalian cells. Nucleic Acids Res., 43, D197–D203), has become an important resource for methyltranscriptome, especially in the N6-methyl-adenosine (m6A) research community. Here, we report Met-DB v2.0, the significantly improved second version of Met-DB, which is entirely redesigned to focus more on elucidating context-specific m6A functions. Met-DB v2.0 has a major increase in context-specific m6A peaks and single-base sites predicted from 185 samples for 7 species from 26 independent studies. Moreover, it is also integrated with a new database for targets of m6A readers, erasers and writers and expanded with more collections of functional data. The redesigned Met-DB v2.0 web interface and genome browser provide more friendly, powerful, and informative ways to query and visualize the data. More importantly, MeT-DB v2.0 offers for the first time a series of tools specifically designed for understanding m6A functions. Met-DB V2.0 will be a valuable resource for m6A methyltranscriptome research. The Met-DB V2.0 database is available at http://compgenomics.utsa.edu/MeTDB/ and http://www.xjtlu.edu.cn/metdb2.

Background As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. Results We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. Conclusion QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m1A-Seq, Par-CLIP, RIP-Seq, etc. Electronic supplementary material The online version of this article (10.1186/s12859-017-1808-4) contains supplementary material, which is available to authorized users.

We consider the detection of the control or idle state in an asynchronous Steady-state visually evoked potential (SSVEP)-based brain computer interface system. We propose a likelihood ratio test using Canonical Correlation Analysis (CCA) scores calculated from the EEG measurements. The test exploits the state-specific distributions of CCA scores. The algorithm was tested on offline measurements from 42 participants and the results should a significant improvement in detection error rate over the support vector machine classifier. The proposed test is also shown to be robust against training sample size.

N 6-methyladenosine (m6A) and N6, 2′-O-Dimethyladenosine (m6Am) modifications (m6A/m) of messenger RNA mediate diverse cellular functions. Oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV) has latent and lytic replication phases that are essential for the development of KSHV-associated cancers. To date, the role of m6A/m in KSHV replication and tumorigenesis is unclear. Here, we provide mechanistic insights by examining the viral and cellular m6A/m epitranscriptomes during KSHV latent and lytic infection. KSHV transcripts contain abundant m6A/m modifications during latent and lytic replication, and these modifications are highly conserved among different cell types and infection systems. Knockdown of YTHDF2 enhanced lytic replication by impeding KSHV RNA degradation. YTHDF2 binds to viral transcripts and differentially mediates their stability. KSHV latent infection induces 5′UTR hypomethylation and 3′UTR hypermethylation of the cellular epitranscriptome, regulating oncogenic and epithelial-mesenchymal transition pathways. KSHV lytic replication induces dynamic reprograming of epitranscriptome, regulating pathways that control lytic replication. These results reveal a critical role of m6A/m modifications in KSHV lifecycle and provide rich resources for future investigations.

Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the "grouping effect" in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.

Methyltranscriptome is an exciting new area that studies the mechanisms and functions of methylation in transcripts. The MethylTranscriptome DataBase (MeT-DB, http://compgenomics.utsa.edu/methylation/) is the first comprehensive resource for N6-methyladenosine (m(6)A) in mammalian transcriptome. It includes a database that records publicaly available data sets from methylated RNA immunoprecipitation sequencing (MeRIP-Seq), a recently developed technology for interrogating m(6)A methyltranscriptome. MeT-DB includes ∼ 300 k m(6)A methylation sites in 74 MeRIP-Seq samples from 22 different experimental conditions predicted by exomePeak and MACS2 algorithms. To explore this rich information, MeT-DB also provides a genome browser to query and visualize context-specific m(6)A methylation under different conditions. MeT-DB also includes the binding site data of microRNA, splicing factor and RNA binding proteins in the browser window for comparison with m(6)A sites and for exploring the potential functions of m(6)A. Analysis of differential m(6)A methylation and the related differential gene expression under two conditions is also available in the browser. A global perspective of the genome-wide distribution of m(6)A methylation in all the data is provided in circular ideograms, which also act as a navigation portal. The query results and the entire data set can be exported to assist publication and additional analysis.

Gene expression silencing at mRNA level by microRNAs is a well-established form of post-transcriptional regulation.1,2 Such silencing is achieved through microRNA binding to microRNA response elements (MREs) residing mainly in the 3′ untranslated regions of the target mRNA. Over 1000 human microRNAs have been identified,3 and the prevalence of microRNA regulation in a broad range of biological processes and disease often attributes to the fact that a single microRNA can repress hundreds of different mRNAs.4 Interestingly, a single target mRNA often possesses MREs of distinct microRNAs in its 3′ untranslated regions.5 Questions have been raised regarding the need for this redundancy in regulation, and these multiple MREs were once thought to serve as regulatory buffers of different microRNAs. In a recent seminal work, a novel theory, termed the competing endogenous RNA (ceRNA), was proposed to provide a plausible explanation for this interesting phenomenon from a new perspective of gene regulation.6 According to the ceRNA theory, MREs function as letters of this new regulatory system, and ceRNAs, or sets of RNAs including mRNA, pseudogenes, and long noncoding RNAs, can communicate or regulate each other, through competition for common MREs. As such, ceRNA regulatory networks provide a unifying system for regulations among transcriptome-wide RNAs, greatly expanding the functions of RNAs.6 Alteration of this competition between ceRNAs could modify normal state gene expression and in return alter the status of biological pathways to promote an oncogenic program, for example. To that end, a PTEN ceRNA network was uncovered and shown to potentially regulate oncogenesis.6 The fact that this new level of RNA regulations could be prevalent in cells has prompted research to identify ceRNAs of genes related to disease. However, the complexity of ceRNA …

Successful viral infection entails a choreographic regulation of viral gene expression program. Kaposi’s sarcoma–associated herpesvirus (KSHV) encodes numerous miRNAs that regulate viral life cycle. However, few viral targets have been identified due to the lack of information on KSHV 3′ untranslated regions (3′UTRs). Recent genome-wide mapping of KSHV transcripts and 3′UTRs has revealed abundant bicistronic and polycistronic transcripts. The extended 3′UTRs of the 5′ proximal genes of bicistronic and polycistronic transcripts offer additional regulatory targets. Indeed, a genome-wide screening of KSHV 3′UTRs has identified several bicistronic and polycistronic transcripts as the novel targets of viral miRNAs. Together, these works have expanded our knowledge of the unique features of KSHV gene regulation program and provided valuable resources for the research community.

Kaposi's sarcoma-associated herpesvirus (KSHV) encodes over 90 genes and 25 microRNAs (miRNAs). The KSHV life cycle is tightly regulated to ensure persistent infection in the host. In particular, miRNAs, which primarily exert their effects by binding to the 3′ untranslated regions (3′UTRs) of target transcripts, have recently emerged as key regulators of KSHV life cycle. Although studies with RNA cross-linking immunoprecipitation approach have identified numerous targets of KSHV miRNAs, few of these targets are of viral origin because most KSHV 3′UTRs have not been characterized. Thus, the extents of viral genes targeted by KSHV miRNAs remain elusive. Here, we report the mapping of the 3′UTRs of 74 KSHV genes and the effects of KSHV miRNAs on the control of these 3′UTR-mediated gene expressions. This analysis reveals new bicistronic and polycistronic transcripts of KSHV genes. Due to the 5′-distal open reading frames (ORFs), KSHV bicistronic or polycistronic transcripts have significantly longer 3′UTRs than do KSHV monocistronic transcripts. Furthermore, screening of the 3′UTR reporters has identified 28 potential new targets of KSHV miRNAs, of which 11 (39%) are bicistronic or polycistronic transcripts. Reporter mutagenesis demonstrates that miR-K3 specifically targets ORF31-33 transcripts at the lytic locus via two binding sites in the ORF33 coding region, whereas miR-K10a-3p and miR-K10b-3p and their variants target ORF71-73 transcripts at the latent locus through distinct binding sites in both 5′-distal ORFs and intergenic regions. Our results indicate that KSHV miRNAs frequently target the 5′-distal coding regions of bicistronic or polycistronic transcripts and highlight the unique features of KSHV miRNAs in regulating gene expression and life cycle.

As we have officially entered the big data era, we are embracing numerous opportunities but also meeting strong demand on innovative approaches to managing and analyzing the data in the digital world. The speed of data generation is astonishing—from now until 2020, the volume of the digital data is expected to approximately double every two years, and 90% of the data available to us today were generated in just the last two years. Specifically in biological and biomedical research fields, we have witnessed the rapid advances in biotechnologies, especially the next-generation sequencing and single cell technologies, enabling the investigators to create massive amounts of data for genomics and translational research. While analysis of the data from single domain like mutations or gene expression is often the first choice in a project, integrative genomic approaches have more advantages such as robustness in detecting the biological signals or biomarkers and low false discovery rates due to the evidence from multiple domains. Therefore, we have steadily seen more integrative genomic studies during the past several years. Although these approaches are promising and powerful, there are many challenges that are required to be addressed by bioinformaticians, computational biologists, and other scientists. These challenges include, but are not limited to, data quality and process from different technology platforms, sample size and consistency, data missingness, incomplete and inaccurate knowledgebase (e.g., reference networks and pathways), false discovery, lack of novel algorithms for data integration, computational efficiency, data interpretation, and visualization.