Multiomics Reveals Induction of Neuroblastoma SK-N-BE(2)C Cell Death by Mitochondrial Division Inhibitor 1 through Multiple Effects

Mitochondrial division inhibitor 1 (Mdivi-1) is a well-known synthetic compound aimed at inhibiting dynamin-related protein 1 (Drp1) to suppress mitochondrial fission, making it a valuable tool for studying mitochondrial dynamics. However, its specific effects beyond Drp1 inhibition remain to be confirmed. In this study, we employed integrative proteomics and phosphoproteomics to delve into the molecular responses induced by Mdivi-1 in SK-N-BE(2)C cells. A total of 3070 proteins and 1945 phosphorylation sites were identified, with 880 of them represented as phosphoproteins. Among these, 266 proteins and 97 phosphorylation sites were found to be sensitive to the Mdivi-1 treatment. Functional enrichment analysis unveiled their involvement in serine biosynthesis and extrinsic apoptotic signaling pathways. Through targeted metabolomics, we observed that Mdivi-1 enhanced intracellular serine biosynthesis while reducing the production of C24:1-ceramide. Within these regulated phosphoproteins, dynamic dephosphorylation of proteasome subunit alpha type 3 serine 250 (PSMA3-S250) occurred after Mdivi-1 treatment. Further site-directed mutagenesis experiments revealed that the dephosphorylation-deficient mutant PSMA3-S250A exhibited a decreased cell survival. This research confirms that Mdivi-1's inhibition of mitochondrial division leads to various side effects, ultimately influencing cell survival, rather than solely targeting Drp1 inhibition.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.

Ectopic ATP synthase stimulates the secretion of extracellular vesicles in cancer cells

ABSTARCT

Ectopic ATP synthase on the plasma membrane (eATP synthase) has been found in various cancer types and is a potential target for cancer therapy. However, whether it provides a functional role in tumor progression remains unclear. Here, quantitative proteomics reveals that cancer cells under starvation stress express higher eATP synthase and enhance the production of extracellular vesicles (EVs), which are vital regulators within the tumor microenvironment. Further results show that eATP synthase generates extracellular ATP to stimulate EV secretion by enhancing P2X7 receptor-triggered Ca2+ influx. Surprisingly, eATP synthase is also located on the surface of tumor-secreted EVs. The EVs-surface eATP synthase increases the uptake of tumor-secreted EVs in Jurkat T-cells via association with Fyn, a plasma membrane protein found in immune cells. The eATP synthase-coated EVs uptake subsequently represses the proliferation and cytokine secretion of Jurkat T-cells. This study clarifies the role of eATP synthase on EV secretion and its influence on immune cells.

Spatial and temporal dynamics of ATP synthase from mitochondria toward the cell surface

Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

Secretory autophagy promotes RAB37-mediated insulin secretion under glucose stimulation both in vitro and in vivo

High blood glucose is one of the risk factors for metabolic disease and INS (insulin) is the key regulatory hormone for glucose homeostasis. Hypoinsulinemia accompanied with hyperglycemia was diagnosed in mice with pancreatic β-cells exhibiting autophagy deficiency; however, the underlying mechanism remains elusive. The role of secretory autophagy in the regulation of metabolic syndrome is gaining more attention. Our data demonstrated that increased macroautophagic/autophagic activity leads to induction of insulin secretion in β-cells both in vivo and in vitro under high-glucose conditions. Moreover, proteomic analysis of purified autophagosomes from β-cells identified a group of vesicular transport proteins participating in insulin secretion, implying that secretory autophagy regulates insulin exocytosis. RAB37, a small GTPase, regulates vesicle biogenesis, trafficking, and cargo release. We demonstrated that the active form of RAB37 increased MAP1LC3/LC3 lipidation (LC3-II) and is essential for the promotion of insulin secretion by autophagy, but these phenomena were not observed in rab37 knockout (rab37^-/-) cells and mice. Unbalanced insulin and glucose concentration in the blood was improved by manipulating autophagic activity using a novel autophagy inducer niclosamide (an antihelminthic drug) in a high-fat diet (HFD)-obesity mouse model. In summary, we reveal that secretory autophagy promotes RAB37-mediated insulin secretion to maintain the homeostasis of insulin and glucose both in vitro and in vivo.

Context-dependent gene regulatory network reveals regulation dynamics and cell trajectories using unspliced transcripts

Gene regulatory networks govern complex gene expression programs in various biological phenomena, including embryonic development, cell fate decisions and oncogenesis. Single-cell techniques are increasingly being used to study gene expression, providing higher resolution than traditional approaches. However, inferring a comprehensive gene regulatory network across different cell types remains a challenge. Here, we propose to construct context-dependent gene regulatory networks (CDGRNs) from single-cell RNA sequencing data utilizing both spliced and unspliced transcript expression levels. A gene regulatory network is decomposed into subnetworks corresponding to different transcriptomic contexts. Each subnetwork comprises the consensus active regulation pairs of transcription factors and their target genes shared by a group of cells, inferred by a Gaussian mixture model. We find that the union of gene regulation pairs in all contexts is sufficient to reconstruct differentiation trajectories. Functions specific to the cell cycle, cell differentiation or tissue-specific functions are enriched throughout the developmental process in each context. Surprisingly, we also observe that the network entropy of CDGRNs decreases along differentiation trajectories, indicating directionality in differentiation. Overall, CDGRN allows us to establish the connection between gene regulation at the molecular level and cell differentiation at the macroscopic level.

BIC: a database for the transcriptional landscape of bacteria in cancer

Microbial communities are massively resident in the human body, yet dysbiosis has been reported to correlate with many diseases, including various cancers. Most studies focus on the gut microbiome, while the bacteria that participate in tumor microenvironments on site remain unclear. Previous studies have acquired the bacteria expression profiles from RNA-seq, whole genome sequencing, and whole exon sequencing in The Cancer Genome Atlas (TCGA). However, small-RNA sequencing data were rarely used. Using TCGA miRNA sequencing data, we evaluated bacterial abundance in 32 types of cancer. To uncover the bacteria involved in cancer, we applied an analytical process to align unmapped human reads to bacterial references and developed the BIC database for the transcriptional landscape of bacteria in cancer. BIC provides cancer-associated bacterial information, including the relative abundance of bacteria, bacterial diversity, associations with clinical relevance, the co-expression network of bacteria and human genes, and their associated biological functions. These results can complement previously published databases. Users can easily download the result plots and tables, or download the bacterial abundance matrix for further analyses. In summary, BIC can provide information on cancer microenvironments related to microbial communities. BIC is available at: http://bic.jhlab.tw/.

pubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Stratification of lncRNA modulation networks in breast cancer

Background

Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs).

Methods

We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules.

Results

We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer.

Conclusions

We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01236-6.

A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis

Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.

Identification of Entry Inhibitors against Delta and Omicron Variants of SARS-CoV-2

Entry inhibitors against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are urgently needed to control the outbreak of coronavirus disease 2019 (COVID-19). This study developed a robust and straightforward assay that detected the molecular interaction between the receptor-binding domain (RBD) of viral spike protein and the angiotensin-converting enzyme 2 (ACE2) receptor in just 10 min. A drug library of 1068 approved compounds was used to screen for SARS-CoV2 entry inhibition, and 9 active drugs were identified as specific pseudovirus entry inhibitors. A plaque reduction neutralization test using authentic SARS-CoV-2 virus in Vero E6 cells confirmed that 2 of these drugs (Etravirine and Dolutegravir) significantly inhibited the infection of SARS-CoV-2. With molecular docking, we showed that both Etravirine and Dolutegravir are preferentially bound to primary ACE2-interacting residues on the RBD domain, implying that these two drug blocks may prohibit the viral attachment of SARS-CoV-2. We compared the neutralizing activities of these entry inhibitors against different pseudoviruses carrying spike proteins from alpha, beta, gamma, and delta variants. Both Etravirine and Dolutegravir showed similar neutralizing activities against different variants, with EC50 values between 4.5 to 5.8 nM for Etravirine and 10.2 to 22.9 nM for Dolutegravir. These data implied that Etravirine and Dolutegravir may serve as general spike inhibitors against dominant viral variants of SARS-CoV-2.

Phylotranscriptomic patterns of network stochasticity and pathway dynamics during embryogenesis

MOTIVATION

The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff's hypothesis.

RESULTS

We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff's hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Drug Repurposing for the Identification of Compounds with Anti-SARS-CoV-2 Capability via Multiple Targets

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading worldwide, causing hundreds of millions of infections. Despite the development of vaccines, insufficient protection remains a concern. Therefore, the screening of drugs for the treatment of coronavirus disease 2019 (COVID-19) is reasonable and necessary. This study utilized bioinformatics for the selection of compounds approved by the U.S. Food and Drug Administration with therapeutic potential in this setting. In addition, the inhibitory effect of these compounds on the enzyme activity of transmembrane protease serine 2 (TMPRSS2), papain-like protease (PLpro), and 3C-like protease (3CLpro) was evaluated. Furthermore, the capability of compounds to attach to the spike-receptor-binding domain (RBD) was considered an important factor in the present assessment. Finally, the antiviral potency of compounds was validated using a plaque reduction assay. Our funnel strategy revealed that tamoxifen possesses an anti-SARS-CoV-2 property owing to its inhibitory performance in multiple assays. The proposed time-saving and feasible strategy may accelerate drug screening for COVID-19 and other diseases.

Modular signature of long non-coding RNA association networks as a prognostic biomarker in lung cancer

BACKGROUND

Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD).

RESULTS

We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient's survival, including module N1 (HR = 0.62, 95% CI = 0.46-0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50-0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52-0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50-0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37-0.69, p value = 2.00e-05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT).

CONCLUSION

Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer.

Identification of a gut microbiota member that ameliorates DSS-induced colitis in intestinal barrier enhanced Dusp6-deficient mice

Strengthening the gut epithelial barrier is a potential strategy for management of gut microbiota-associated illnesses. Here, we demonstrate that dual-specificity phosphatase 6 (Dusp6) knockout enhances baseline colon barrier integrity and ameliorates dextran sulfate sodium (DSS)-induced colonic injury. DUSP6 mutation in Caco-2 cells enhances the epithelial feature and increases mitochondrial oxygen consumption, accompanied by altered glucose metabolism and decreased glycolysis. We find that Dusp6-knockout mice are more resistant to DSS-induced dysbiosis, and the cohousing and fecal microbiota transplantation experiments show that the gut/fecal microbiota derived from Dusp6-knockout mice also confers protection against colitis. Further culturomics and mono-colonialization experiments show that one gut microbiota member in the genus Duncaniella confers host protection from DSS-induced injury. We identify Dusp6 deficiency as beneficial for shaping the gut microbiota eubiosis necessary to protect against gut barrier-related diseases.

Targeting protein interaction networks in mitochondrial dynamics for cancer therapy

Mitochondria are crucial organelles that provide energy via oxidative phosphorylation in eukaryotic cells and also have critical roles in growth, division, and the cell cycle, as well as the rapid adaptation required to meet the metabolic needs of the cell. Mitochondrial processes are highly dynamic; fusion and fission can vary with cell type, cellular context, and stress levels. Accumulating evidence demonstrates that an imbalance in mitochondrial dynamics leads to death in numerous types of human cancer cells. Therefore, modulating mitochondrial dynamics could be a therapeutic target. In this review, we provide an overview of the protein interaction networks involved in mitochondrial dynamics as effective and feasible drug targets and discuss the related potential therapeutic strategies for cancer.

Identification of cell states using super-enhancer RNA

BACKGROUND

A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states.

RESULTS

Here, we defined super-enhancer RNAs as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process.

CONCLUSIONS

The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

LncTx: A network-based method to repurpose drugs acting on the survival-related lncRNAs in lung cancer

Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein-protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.

Proteomic Analysis Reveals That Metformin Suppresses PSMD2, STIP1, and CAP1 for Preventing Gastric Cancer AGS Cell Proliferation and Migration

Metformin is one of the most widely used anti-diabetic drugs in type-II diabetes treatment. The mechanism of decreasing blood glucose is believed to suppress hepatic gluconeogenesis by increasing muscular glucose uptake and insulin sensitivity. Recent studies suggest that metformin may reduce cancer risk; however, its anticancer mechanism in gastric cancers remains unclear. Here, we aim to evaluate the anticancer effects of metformin on human gastric adenocarcinoma (AGS) cells. Our results showed that metformin inhibited AGS cell proliferation in a dose-dependent manner. Using small-scale quantitative proteomics, we identified 177 differentially expressed proteins upon metformin treatment; among these, nine proteins such as 26S proteasome non-ATPase regulatory subunit 2 (PSMD2), stress-induced phosphoprotein 1 (STIP1), and adenylyl cyclase-associated protein 1 (CAP1) were significantly altered. We found that metformin induced cell cycle arrest at the G0/G1 phase, suppressed cell migration, and affected cytoskeleton distribution. Additionally, patients with highly expressed PSMD2, STIP1, and CAP1 have a poor clinical outcome. Our study provides a novel view of developing therapies for gastric cancer.

DockCoV2: a drug database against SARS-CoV-2

The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Enhancement of the IFN-β-induced host signature informs repurposed drugs for COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causative agent for the outbreak of coronavirus disease 2019 (COVID-19). This global pandemic is now calling for efforts to develop more effective COVID-19 therapies. Here we use a host-directed approach, which focuses on cellular responses to diverse small-molecule treatments, to identify potentially effective drugs for COVID-19. This framework looks at the ability of compounds to elicit a similar transcriptional response to IFN-β, a type I interferon that fails to be induced at notable levels in response to SARS-CoV-2 infection. By correlating the perturbation profiles of ~3,000 small molecules with a high-quality signature of IFN-β-responsive genes in primary normal human bronchial epithelial cells, our analysis revealed four candidate COVID-19 compounds, namely homoharringtonine, narciclasine, anisomycin, and emetine. We experimentally confirmed that the predicted compounds significantly inhibited SARS-CoV-2 replication in Vero E6 cells at nanomolar, relatively non-toxic concentrations, with half-maximal inhibitory concentrations of 165.7 nM, 16.5 nM, and 31.4 nM for homoharringtonine, narciclasine, and anisomycin, respectively. Together, our results corroborate a host-centric strategy to inform protective antiviral therapies for COVID-19.

Multiomics Reveals Ectopic ATP Synthase Blockade Induces Cancer Cell Death via a lncRNA-mediated Phospho-signaling Network

The EGFR tyrosine kinase inhibitor gefitinib is commonly used for lung cancer patients. However, some patients eventually become resistant to gefitinib and develop progressive disease. Here, we indicate that ecto-ATP synthase, which ectopically translocated from mitochondrial inner membrane to plasma membrane, is considered as a potential therapeutic target for drug-resistant cells. Quantitative multi-omics profiling reveals that ecto-ATP synthase inhibitor mediates CK2-dependent phosphorylation of DNA topoisomerase IIα (topo IIα) at serine 1106 and subsequently increases the expression of long noncoding RNA, GAS5. Additionally, we also determine that downstream of GAS5, p53 pathway, is activated by ecto-ATP synthase inhibitor for regulation of programed cell death. Interestingly, GAS5-proteins interactomic profiling elucidates that GAS5 associates with topo IIα and subsequently enhancing the phosphorylation level of topo IIα. Taken together, our findings suggest that ecto-ATP synthase blockade is an effective therapeutic strategy via regulation of CK2/phospho-topo IIα/GAS5 network in gefitinib-resistant lung cancer cells.

Characterizing the cancer-associated microbiome with small RNA sequencing data

The microbiome is recognized as a quasi-organ in the human body. When dysbiosis of the microbiome occurs, this variation may contribute to alterations in the microenvironment, potentially inducing an inflammatory immune response and providing a niche for neoplastic growth. However, there is limited evidence regarding the correlation and interaction between the microbiome and tumorigenesis. By utilizing microRNA sequencing data of patients with colon and rectal cancer from The Cancer Genome Atlas, we designed a novel analytical process to extract non-human small RNA sequences and align them with the microbial genome to obtain a comprehensive view of the cancer-associated microbiome. In the present study, we identified >1000 genera among 630 colorectal samples and clustered these samples into three distinctive colorectal enterotypes. Furthermore, we found 12 genera from these clusters that are associated with cancer stages and revealed their putative functions. Our results indicate that the proposed analytical approach can effectively determine the cancer-associated microbiome. It may be readily applied to explore other types of cancer, in which specimens of the microbiome are difficult to collect.

C1GALT1 is associated with poor survival and promotes soluble Ephrin A1-mediated cell migration through activation of EPHA2 in gastric cancer

C1GALT1 controls the crucial step of GalNAc-type O-glycosylation and is associated with both physiologic and pathologic conditions, including cancers. EPH receptors comprise the largest family of receptor tyrosine kinases (RTKs) and modulate a diverse range of developmental processes and human diseases. However, the role of C1GALT1 in the signaling of EPH receptors remains largely overlooked. Here, we showed that C1GALT1 high expression in gastric adenocarcinomas correlated with adverse clinicopathologic features and is an independent prognostic factor for poor overall survival. Silencing or loss of C1GALT1 inhibited cell viability, migration, invasion, tumor growth and metastasis, as well as increased apoptosis and cytotoxicity of 5-fluorouracil in AGS and MKN45 cells. Phospho-RTK array and western blot analysis showed that C1GALT1 depletion suppressed tyrosine phosphorylation of EPHA2 induced by soluble Ephrin A1-Fc. O-glycans on EPHA2 were modified by C1GALT1 and both S277A and T429A mutants, which are O-glycosites on EPHA2, dramatically enhanced phosphorylation of Y588, suggesting that not only overall O-glycan structures but also site-specific O-glycosylation can regulate EPHA2 activity. Furthermore, depletion of C1GALT1 decreased Ephrin A1-Fc induced migration and reduced Ephrin A1 binding to cell surfaces. The effects of C1GALT1 knockdown or knockout on cell invasiveness in vitro and in vivo were phenocopied by EPHA2 knockdown in gastric cancer cells. These results suggest that C1GALT1 promotes phosphorylation of EPHA2 and enhances soluble Ephrin A1-mediated migration primarily by modifying EPHA2 O-glycosylation. Our study highlights the importance of GalNAc-type O-glycosylation in EPH receptor-regulated diseases and identifies C1GALT1 as a potential therapeutic target for gastric cancer.

Investigating the role of super-enhancer RNAs underlying embryonic stem cell differentiation

BACKGROUND

Super-enhancer RNAs (seRNAs) are a kind of noncoding RNA transcribed from super-enhancer regions. The regulation mechanism and functional role of seRNAs are still unclear. Although super-enhancers play a critical role in the core transcriptional regulatory circuity of embryonic stem cell (ESC) differentiation, whether seRNAs have similar properties should be further investigated.

RESULTS

We analyzed cap analysis gene expression sequencing (CAGE-seq) datasets collected during the differentiation of embryonic stem cells (ESCs) to cardiomyocytes to identify the seRNAs. A non-negative matrix factorization algorithm was applied to decompose the seRNA profiles and reveal two hidden stages during the ESC differentiation. We further identified 95 and 78 seRNAs associated with early- and late-stage ESC differentiation, respectively. We found that the binding sites of master regulators of ESC differentiation, including NANOG, FOXA2, and MYC, were significantly observed in the loci of the stage-specific seRNAs. Based on the investigation of genes coexpressed with seRNA, these stage-specific seRNAs might be involved in cardiac-related functions such as myofibril assembly and heart development and act in trans to regulate the co-expressed genes.

CONCLUSIONS

In this study, we used a computational approach to demonstrate the possible role of seRNAs during ESC differentiation.

Quantitative Proteomics of Th-MYCN Transgenic Mice Reveals Aurora Kinase Inhibitor Altered Metabolic Pathways and Enhanced ACADM To Suppress Neuroblastoma Progression

Neuroblastoma is a neural crest-derived embryonal tumor and accounts for about 15% of all cancer deaths in children. MYCN amplification is associated with aggressive and advanced stage of high-risk neuroblastoma, which remains difficult to treat and exhibits poor survival under current multimodality treatment. Here, we analyzed the transcriptomic profiles of neuroblastoma patients and showed that aurora kinases lead to poor survival and had positive correlation with MYCN amplification and high-risk disease. Further, pan-aurora kinase inhibitor (tozasertib) treatment not only induces cell-cycle arrest and suppresses cell proliferation, migration, and invasion ability in MYCN-amplified (MNA) neuroblastoma cell lines, but also inhibits tumor growth and prolongs animal survival in Th-MYCN transgenic mice. Moreover, we performed quantitative proteomics and identified 150 differentially expressed proteins after tozasertib treatment in the Th-MYCN mouse model. The functional and network-based enrichment revealed that tozasertib alters metabolic processes and identified a mitochondrial flavoenzyme in fatty acid β-oxidation, ACADM, which is correlated with aurora kinases and neuroblastoma patient survival. Our findings indicate that the aurora kinase inhibitor could cause metabolic imbalance, possibly by disturbing carbohydrate and fatty acid metabolic pathways, and ACADM may be a potential target in MNA neuroblastoma.

Activation of Aryl Hydrocarbon Receptor by Kynurenine Impairs Progression and Metastasis of Neuroblastoma

Neuroblastoma is the most common malignant disease of infancy, and amplification of the MYCN oncogene is closely associated with poor prognosis. Recently, expression of MYCN was shown to be inversely correlated with aryl hydrocarbon receptor (AHR) expression in neuroblastoma, and overexpression of AHR downregulated MYCN expression, promoting cell differentiation. Therefore, we further investigated the potential of AHR to serve as a prognostic indicator or a therapeutic target in neuroblastoma. First, the clinical significance of AHR in neuroblastoma was examined. Positive AHR immunostaining strongly correlated with differentiated histology of neuroblastoma and predicted better survival for patients. The mouse xenograft model showed that overexpression of AHR significantly suppressed neuroblastoma tumor growth. In addition, activation of AHR by the endogenous ligand kynurenine inhibited cell proliferation and promoted cell differentiation in vitro and in vivo. kynurenine treatment also upregulated the expression of KISS1, a tumor metastasis suppressor, and attenuated metastasis in the xenograft model. Finally, analysis of KISS1 levels in neuroblastoma patient tumors using the R2: Genomics Analysis and Visualization Platform revealed that KISS1 expression positively correlated with AHR, and high KISS1 expression predicted better survival for patients. In conclusion, our results indicate that AHR is a novel prognostic biomarker for neuroblastoma, and that overexpression or activation of AHR offers a new therapeutic possibility for patients with neuroblastoma. SIGNIFICANCE: These findings show that AHR may function as a tumor suppressor in childhood neuroblastoma, potentially influencing the aetiologic and therapeutic targeting of the disease.

Perturbational Gene-Expression Signatures for Combinatorial Drug Discovery

Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases.

•

Compound signatures targeting non-oncogene addiction for combinatorial drug discovery

•

These signatures are reproducible and linked to cancer hallmarks and drug sensitivity

•

Two synergistic drug combinations are experimentally confirmed in vitro

Therapeutic Targeting of Non-oncogene Dependencies in High-risk Neuroblastoma

PURPOSE

Neuroblastoma is a pediatric malignancy of the sympathetic nervous system with diverse clinical behaviors. Genomic amplification of MYCN oncogene has been shown to drive neuroblastoma pathogenesis and correlate with aggressive disease, but the survival rates for those high-risk tumors carrying no MYCN amplification remain equally dismal. The paucity of mutations and molecular heterogeneity has hindered the development of targeted therapies for most advanced neuroblastomas. We use an alternative method to identify potential drugs that target nononcogene dependencies in high-risk neuroblastoma.

EXPERIMENTAL DESIGN

By using a gene expression-based integrative approach, we identified prognostic signatures and potentially effective single agents and drug combinations for high-risk neuroblastoma.

RESULTS

Among these predictions, we validated in vitro efficacies of some investigational and marketed drugs, of which niclosamide, an anthelmintic drug approved by the FDA, was further investigated in vivo. We also quantified the proteomic changes during niclosamide treatment to pinpoint nucleoside diphosphate kinase 3 (NME3) downregulation as a potential mechanism for its antitumor activity.

CONCLUSIONS

Our results establish a gene expression-based strategy to interrogate cancer biology and inform drug discovery and repositioning for high-risk neuroblastoma.

RNA-Binding Proteomics Reveals MATR3 Interacting with lncRNA SNHG1 To Enhance Neuroblastoma Progression

The interaction of long noncoding RNAs (lncRNAs) with one or more RNA-binding proteins (RBPs) is important to a plethora of cellular and physiological processes. The lncRNA SNHG1 was reported to be aberrantly expressed and associated with poor patient prognosis in several cancers including neuroblastoma. However, the interacting RBPs and biological functions associated with SNHG1 in neuroblastoma remain unknown. In this study, we identified 283, 31, and 164 SNHG1-interacting proteins in SK-N-BE(2)C, SK-N-DZ, and SK-N-AS neuroblastoma cells, respectively, using a RNA-protein pull-down assay coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Twenty-four SNHG1-interacting RBPs were identified in common from these three neuroblastoma cell lines. RBPs MATR3, YBX1, and HNRNPL have the binding sites for SNHG1 predicted by DeepBind motif analysis. Furthermore, the direct binding of MATR3 with SNHG1 was validated by Western blot and confirmed by RNA immunoprecipitation assay (RIP). Coexpression analysis revealed that the expression of SNHG1 is positively correlated with MATR3 ( P = 3.402 × 10^-13). The high expression of MATR3 is associated with poor event-free survival ( P = 0.00711) and overall survival ( P = 0.00064). Biological functions such as ribonucleoprotein complex biogenesis, RNA processing, and RNA splicing are significantly enriched and in common between SNHG1 and MATR3. In conclusion, we identified MATR3 as binding to SNHG1 and the interaction might be involved in splicing events that enhance neuroblastoma progression.

Dynamics of alternative polyadenylation in human preimplantation embryos

Alternative polyadenylation (APA) affects the length of the 3' untranslated region (3'-UTR) and the regulation of microRNAs. Previous studies have shown that cancer cells tend to have shorter 3'-UTRs than normal cells. A plausible explanation for this is that it enables cancer cells to escape the regulation of microRNAs. Here, we extend this concept to an opposing context: changes in 3'-UTR length in the development of the human preimplantation embryo. Unlike cancer cells, during early development 3'-UTRs tended to become longer, and gene expression was negatively correlated with 3'-UTR length. Moreover, our functional enrichment results showed that length changes are part of the development mechanism. We also investigated the analogy of 3'-UTR length variation with respect to lncRNAs and found that, similarly, lncRNA length tended to increase during embryo development.

A Large-Scale Gene Expression Intensity-Based Similarity Metric for Drug Repositioning

Biological systems often respond to a specific environmental or genetic perturbation without pervasive gene expression changes. Such robustness to perturbations, however, is not reflected on the current computational strategies that utilize gene expression similarity metrics for drug discovery and repositioning. Here we propose a new expression-intensity-based similarity metric that consistently achieved better performance than other state-of-the-art similarity metrics with respect to the gold-standard clustering of drugs with known mechanisms of action. The new metric directly emphasizes the genes exhibiting the greatest changes in expression in response to a perturbation. Using the new framework to systematically compare 3,332 chemical and 3,934 genetic perturbations across 10 cell types representing diverse cellular signatures, we identified thousands of recurrent and cell type-specific connections. We also experimentally validated two drugs identified by the analysis as potential topoisomerase inhibitors. The new framework is a valuable resource for hypothesis generation, functional testing, and drug repositioning.

•

Intensity-based similarity metric surpasses other standard metrics in drug clustering

•

This metric was applied to compare thousands of compounds for drug repurposing

•

Two drugs are experimentally confirmed as potential topoisomerase inhibitors

High-risk, Expression-Based Prognostic Long Noncoding RNA Signature in Neuroblastoma

Background

Current clinical risk factors stratify patients with neuroblastoma (NB) for appropriate treatments, yet patients with similar clinical behaviors evoke variable responses. MYCN amplification is one of the established drivers of NB and, when combined with high-risk displays, worsens outcomes. Growing high-throughput transcriptomics studies suggest long noncoding RNA (lncRNA) dysregulation in cancers, including NB. However, expression-based lncRNA signatures are altered by MYCN amplification, which is associated with high-risk, and patient prognosis remains limited.

Methods

We investigated RNA-seq-based expression profiles of lncRNAs in MYCN status and risk status in a discovery cohort (n = 493) and validated them in three independent cohorts. In the discovery cohort, a prognostic association of lncRNAs was determined by univariate Cox regression and integrated into a signature using the risk score method. A novel risk score threshold selection criterion was developed to stratify patients into risk groups. Outcomes by risk group and clinical subgroup were assessed using Kaplan-Meier survival curves and multivariable Cox regression. The performance of lncRNA signatures was evaluated by receiver operating characteristic curve. All statistical tests were two-sided.

Results

In the discovery cohort, 16 lncRNAs that were differentially expressed (fold change ≥ 2 and adjusted P ≤ 0.01) integrated into a prognostic signature. A high risk score group of lncRNA signature had poor event-free survival (EFS; P < 1E-16). Notably, lncRNA signature was independent of other clinical risk factors when predicting EFS (hazard ratio = 3.21, P = 5.95E-07). The findings were confirmed in independent cohorts (P = 2.86E-02, P = 6.18E-03, P = 9.39E-03, respectively). Finally, the lncRNA signature had higher accuracy for EFS prediction (area under the curve = 0.788, 95% confidence interval = 0.746 to 0.831).

Conclusions

Here, we report the first (to our knowledge) RNA-seq 16-lncRNA prognostic signature for NB that may contribute to precise clinical stratification and EFS prediction.

Co-expression analysis identifies long noncoding RNA SNHG1 as a novel predictor for event-free survival in neuroblastoma

Despite of the discovery of protein therapeutic targets and advancement in multimodal therapy, the survival chance of high-risk neuroblastoma (NB) patients is still less than 50%. MYCN amplification is a potent driver of NB, which exerts its oncogenic activity through either activating or inhibiting the transcription of target genes. Recently, long noncoding RNAs (lncRNAs) are reported to be altered in cancers including NB. However, lncRNAs that are altered by MYCN amplification and associated with outcome in high-risk NB patients are limitedly discovered. Herein, we examined the expression profiles of lncRNAs and protein-coding genes between MYCN amplified and MYCN non-amplified NB from microarray (n = 47) and RNA-seq datasets (n = 493). We identified 6 lncRNAs in common that were differentially expressed (adjusted P ≤ 0.05 and fold change ≥ 2) and subsequently validated by RT-qPCR. The co-expression analysis reveals lncRNA, SNHG1 and coding gene, TAF1D highly co-expressed in NB. Kaplan-Meier analysis shows that higher expression of SNHG1 is significantly associated with poor patient survival. Importantly, multivariate analysis confirms high expression of SNHG1 as an independent prognostic marker for event-free survival (EFS) (HR = 1.58, P = 2.36E-02). In conclusion, our study unveils that SNHG1 is up-regulated by MYCN amplification and could be a potential prognostic biomarker for high-risk NB intervention.

A multidisciplinary team care approach improves outcomes in high-risk pediatric neuroblastoma patients

We assessed the impact of a multidisciplinary team care program on treatment outcomes in neuroblastoma patients. Newly diagnosed neuroblastoma patients received treatment under the Taiwan Pediatric Oncology Group (TPOG) N2002 protocol at the National Taiwan University Hospital beginning in 2002. A multidisciplinary team care approach that included nurse-led case management for patients treated under this protocol began in January 2010. Fifty-eight neuroblastoma patients, including 29 treated between 2002 and 2009 (Group 1) and 29 treated between 2010 and 2014 (Group 2), were enrolled in the study. The 5-year overall survival (OS) and event-free survival (EFS) rates for all 58 patients were 59% and 54.7%, respectively. Group 2 patients, who were treated after implementation of the multidisciplinary team care program, had better 3-year EFS (P = 0.046), but not OS (P = 0.16), rates than Group 1 patients. In a multivariate analysis, implementation of the multidisciplinary team approach was the only significant independent prognostic factor for neuroblastoma patients. In further subgroup analyses, the multidisciplinary team approach improved EFS, but not OS, in patients with stage 4 disease, those in the high-risk group, and those with non-MYCN amplified tumors. These data indicate a multidisciplinary team care approach improved survival outcomes in high-risk neuroblastoma patients. However, further investigation will be required to evaluate the long-term effects of this approach over longer follow-up periods.

Unveiling MYCN regulatory networks in neuroblastoma via integrative analysis of heterogeneous genomics data

MYCN, an oncogenic transcription factor of the Myc family, is a major driver of neuroblastoma tumorigenesis. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets for neuroblastoma therapy. Here we perform ChIP-sequencing and small RNA-sequencing of neuroblastoma cells to determine the MYCN-binding sites and MYCN-associated microRNAs, and integrate various types of genomic data to construct MYCN regulatory networks. The overall analysis indicated that MYCN-regulated genes were involved in a wide range of biological processes and could be used as signatures to identify poor-prognosis MYCN-non-amplified patients. Analysis of the MYCN binding sites showed that MYCN principally served as an activator. Using a computational approach, we identified 32 MYCN co-regulators, and some of these findings are supported by previous studies. Moreover, we investigated the interplay between MYCN transcriptional and microRNA post-transcriptional regulations and identified several microRNAs, such as miR-124-3p and miR-93-5p, which may significantly contribute to neuroblastoma pathogenesis. We also found MYCN and its regulated microRNAs acted together to repress the tumor suppressor genes. This work provides a comprehensive view of MYCN regulations for exploring therapeutic targets in neuroblastoma, as well as insights into the mechanism of neuroblastoma tumorigenesis.

MCM2-regulated functional networks in lung cancer by multi-dimensional proteomic approach

DNA replication control is vital for maintaining genome stability and the cell cycle, perhaps most notably during cell division. Malignancies often exhibit defective minichromosome maintenance protein 2 (MCM2), a cancer proliferation biomarker that serves as a licensing factor in the initiation of DNA replication. MCM2 is also known to be one of the ATPase active sites that facilitates conformational changes and drives DNA unwinding at the origin of DNA replication. However, the biological networks of MCM2 in lung cancer cells via protein phosphorylation remain unmapped. The RNA-seq datasets from The Cancer Genome Atlas (TCGA) revealed that MCM2 overexpression is correlated with poor survival rate in lung cancer patients. To uncover MCM2-regulated functional networks in lung cancer, we performed multi-dimensional proteomic approach by integrating analysis of the phosphoproteome and proteome, and identified a total of 2361 phosphorylation sites on 753 phosphoproteins, and 4672 proteins. We found that the deregulation of MCM2 is involved in lung cancer cell proliferation, the cell cycle, and migration. Furthermore, HMGA1^S99 phosphorylation was found to be differentially expressed under MCM2 perturbation in opposite directions, and plays an important role in regulating lung cancer cell proliferation. This study therefore enhances our capacity to therapeutically target cancer-specific phosphoproteins.

DynaPho: a web platform for inferring the dynamics of time-series phosphoproteomics

Summary

Large-scale phosphoproteomics studies have improved our understanding of dynamic cellular signaling, but the downstream analysis of phosphoproteomics data is still a bottleneck. We develop DynaPho, a useful web-based tool providing comprehensive and in-depth analyses of time-course phosphoproteomics data, making analysis intuitive and accessible to non-bioinformatics experts. The tool currently implements five analytic modules, which reveal the transition of biological pathways, kinase activity, dynamics of interaction networks and the predicted kinase-substrate associations. These features can assist users in translating their larger-scale time-course phosphoproteomics data into valuable biological discoveries.

Availability and implementation

DynaPho is freely available at http://dynapho.jhlab.tw/ .

Contact

hsuancheng@ym.edu.tw or yukijuan@ntu.edu.tw .

Supplementary information

Supplementary data are available at Bioinformatics online.

Abstract 359: C1GALT1 activates multiple RTKs and promotes malignant behaviors in pancreatic adenocarcinoma cells

Pancreatic adenocarcinoma (PDAC), stands up to the 4th in all cancer mortalities and remains the most in progress in America. Curative resection remains the most and only potential strategy but only 15~20% of patients are candidates. Even though, many of them have dismal prognosis. Aberrant glycosylation illustrates an attribution of cancer, especially fot promoting malignant behaviors in adenocarcinomas. Altered expression of glycans, such as GalNAcα1-O-S/T (Tn antigen) and Galβ1,3-GalNAc-S/T (T antigen), is hallmark of cancers. Core 1 β1,3-galactosyltransferase (C1GALT1) catalyzes the transfer of Gal to Tn antigen to form T antigen. C1GALT1 is reported to contribute to clinicopathological features and malignant phenotypes in various cancers. However, the expression and function of C1GALT1 in PDAC remain unrevealed. Our data showed that C1GALT1 was overexpressed in PDAC tissues and correlated with poor prognosis. C1GALT1 knockdown inhibited viability, migration, and invasion analyzed by MTT, transwell migration, and Matrigel invasion assays, respectively, in PDAC cells, associated with increased Tn antigen but decreased T antigen expression. Conversely, C1GALT1 overexpression promoted these malignant behaviors. C1GALT1 knockdown inhibited AKT and ERK activities in PDAC cells. Consistently, phospho-receptor tyrosine kinase (p-RTK) array showed decreased phosphorylation of several RTKs including EGFR, IGF1R, and FLT3. Additionally, flow cytometry of C1GALT1 knockdown cells showed enhanced apoptosis, which was associated with decreased catalase, Bcl-x, and death receptor DR5 revealed by apoptosis array. We also found that C1GALT1 knockdown arrested G1 progression in PDAC cells. Furthermore, gene set enrichment analysis of cDNA microarray indicated that C1GALT1 regulated cell cycle, cytoskeleton, and chromatin structure in PDAC cells. These findings indicate that higher C1GALT1 expression predicts worse survival of PDAC and enhances malignant characters in PDAC cells, highlighting a fundamental role of C1GALT1 in PDAC development.

Note: This abstract was not presented at the meeting.

Citation Format: Ting-Chun Kuo, Hsueh-Fen Juan, Yu-Wen Tien, Min-Chuan Huang. C1GALT1 activates multiple RTKs and promotes malignant behaviors in pancreatic adenocarcinoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 359. doi:10.1158/1538-7445.AM2017-359

GALNT6 expression enhances aggressive phenotypes of ovarian cancer cells by regulating EGFR activity

Ovarian cancer is the most lethal of the gynecologic malignancies. N-acetylgalactosaminyltransferase 6 (GALNT6), an enzyme that mediates the initial step of mucin type-O glycosylation, has been reported to regulate mammary carcinogenesis. However, the expression and role of GALNT6 in ovarian cancer are still unclear. Here we showed that high GALNT6 expression correlates with increased recurrence, lymph node metastasis, and chemoresistance in ovarian endometrioid and clear cell carcinomas; and higher GALNT6 levels are significantly associated with poorer patient survivals. GALNT6 knockdown with two independent siRNAs significantly suppressed viability, migration, and invasion of ovarian cancer cells. Using phospho-RTK array and Western blot analyses, we identified EGFR as a critical target of GALNT6. GALNT6 knockdown decreased phosphorylation of EGFR, whereas GALNT6 overexpression increased the phosphorylation. Lectin pull-down assays with Vicia villosa agglutinin (VVA) indicated that GALNT6 was able to modify O-glycans on EGFR. Moreover, the GALNT6-enhanced invasive behavior was significantly reversed by erlotinib, an EGFR inhibitor. Our results suggest that GALNT6 expression is associated with poor prognosis of ovarian cancer and enhances the aggressive behavior of ovarian cancer cells by regulating EGFR activity.

Quantitative proteomics in lung cancer

Lung cancer is the most common cause of cancer-related death worldwide, less than 7% of patients survive 10 years following diagnosis across all stages of lung cancer. Late stage of diagnosis and lack of effective and personalized medicine reflect the need for a better understanding of the mechanisms that underlie lung cancer progression. Quantitative proteomics provides the relative different protein abundance in normal and cancer patients which offers the information for molecular interactions, signaling pathways, and biomarker identification. Here we introduce both theoretical and practical applications in the use of quantitative proteomics approaches, with principles of current technologies and methodologies including gel-based, label free, stable isotope labeling as well as targeted proteomics. Predictive markers of drug resistance, candidate biomarkers for diagnosis, and prognostic markers in lung cancer have also been discovered and analyzed by quantitative proteomic analysis. Moreover, construction of protein networks enables to provide an opportunity to interpret disease pathway and improve our understanding in cancer therapeutic strategies, allowing the discovery of molecular markers and new therapeutic targets for lung cancer.

Identification of lncRNA functions in lung cancer based on associated protein-protein interaction modules

Long non-coding RNAs (lncRNAs) have been found to play important roles in various biological processes; however, many of their functions remain unclear. In this study, we present a novel approach to identify the lncRNA-associated protein-protein interaction (PPI) modules and ascertain their functions in human lung squamous cell carcinoma. We collected lncRNA and mRNA expression profiles of lung squamous cell carcinoma from The Cancer Genome Atlas. To identify the lncRNA-associated PPI modules, lncRNA-mRNA co-expression networks were first constructed based on the mutual ranks of expression correlations. Next, we examined whether the co-expressed mRNAs of a specific lncRNA were closely connected by PPIs. For this, a significantly connected mRNA set was considered to be the lncRNA-associated PPI module. Finally, the prospective functions of a lncRNA was inferred using Gene Ontology enrichment analysis on the associated module. We found that lncRNA-associated PPI modules were subtype-dependent and each subtype had unique molecular mechanisms. In addition, antisense lncRNAs and sense genes tended to be functionally associated. Our results might provide new directions for understanding lncRNA regulations in lung cancer. The analysis pipeline was implemented in a web tool, available at http://lncin.ym.edu.tw/.

Novel Utilization of Terminators in the Design of Biologically Adjustable Synthetic Filters

Terminators, which signal the end of transcription processes, are typically placed behind the last coding sequence of an operon to prevent interference between transcript units in most biologically synthetic systems. Here, we seek to extend the usability of terminators in genetic system design by using terminators as regulatory genetic parts. Terminators with different impacts on their upstream and downstream genes are characterized in detail via dynamic modeling to predict the behavior of the overall genetic system. Some nonlinear effects of terminators observed in our terminator measurements potentially facilitate regulation of gene expression. Through dynamic modeling in silico, we find that such genetic systems may behave like genetic filters. In agreement with the simulations, we successfully implement genetic high-pass and bandpass filters in vivo, demonstrating the potential of using terminators as regulatory parts. The genetic bandpass filter in this work is implemented through the interdependence between genetic parts, in which the termination efficiency of a terminator varies with the strength of the upstream promoter. This design strategy for a bandpass filter requires fewer base pairs than the conventional strategy of concatenating high-pass and low-pass filters. Our results show that this novel utilization of terminators as regulatory parts may provide a new perspective for efficient design of genetic circuits. We believe that further exploration of the complicated dynamics of terminators is important in the development of synthetic biology.

Mucin glycosylating enzyme GALNT2 suppresses malignancy in gastric adenocarcinoma by reducing MET phosphorylation

Glycosylation affects malignancy in cancer. Here, we report that N- acetylgalactosaminyltransferase 2 (GALNT2), an enzyme that mediates the initial step of mucin type-O glycosylation, suppresses malignant phenotypes in gastric adenocarcinoma (GCA) by modifying MET (Hepatocyte growth factor receptor) activity. GALNT2 mRNA and protein were downregulated in GCAs, and this reduction was associated with more advanced disease stage and shorter recurrence-free survival. Suppressing GALNT2 expression in GCA cells increased cell growth, migration, and invasion in vitro, and tumor metastasis in vivo. GALNT2 knockdown enhanced phosphorylation of MET and decreased expression of the Tn antigen on MET. Inhibiting MET activity with PHA665752 decreased the malignant phenotypes caused by GALNT2 knockdown in GCA cells. Our results indicate that GALNT2 suppresses the malignant potential of GCA cells and provide novel insights into the significance of O-glycosylation in MET activity and GCA progression.

Temporal Phosphoproteome Dynamics Induced by an ATP Synthase Inhibitor Citreoviridin

Citreoviridin, one of toxic mycotoxins derived from fungal species, can suppress lung cancer cell growth by inhibiting the activity of ectopic ATP synthase, but has limited effect on normal cells. However, the mechanism of citreoviridin triggering dynamic molecular responses in cancer cells remains unclear. Here, we performed temporal phosphoproteomics to elucidate the dynamic changes after citreoviridin treatment in cells and xenograft model. We identified a total of 829 phosphoproteins and demonstrated that citreoviridin treatment affects protein folding, cell cycle, and cytoskeleton function. Furthermore, response network constructed by mathematical modeling shows the relationship between the phosphorylated heat shock protein 90 β and mitogen-activated protein kinase signaling pathway. This work describes that citreoviridin suppresses cancer cell growth and mitogen-activated protein kinase/extracellular signal-regulated kinase signaling by site-specific dephosphorylation of HSP90AB1 on Serine 255 and provides perspectives in cancer therapeutic strategies.

Integrating Phosphoproteomics and Bioinformatics to Study Brassinosteroid-Regulated Phosphorylation Dynamics in Arabidopsis

BACKGROUND

Protein phosphorylation regulated by plant hormone is involved in the coordination of fundamental plant development. Brassinosteroids (BRs), a group of phytohormones, regulated phosphorylation dynamics remains to be delineated in plants. In this study, we performed a mass spectrometry (MS)-based phosphoproteomics to conduct a global and dynamic phosphoproteome profiling across five time points of BR treatment in the period between 5 min and 12 h. MS coupling with phosphopeptide enrichment techniques has become the powerful tool for profiling protein phosphorylation. However, MS-based methods tend to have data consistency and coverage issues. To address these issues, bioinformatics approaches were used to complement the non-detected proteins and recover the dynamics of phosphorylation events.

RESULTS

A total of 1104 unique phosphorylated peptides from 739 unique phosphoproteins were identified. The time-dependent gene ontology (GO) analysis shows the transition of biological processes from signaling transduction to morphogenesis and stress response. The protein-protein interaction analysis found that most of identified phosphoproteins have strongly connections with known BR signaling components. The analysis by using Motif-X was performed to identify 15 enriched motifs, 11 of which correspond to 6 known kinase families. To uncover the dynamic activities of kinases, the enriched motifs were combined with phosphorylation profiles and revealed that the substrates of casein kinase 2 and mitogen-activated protein kinase were significantly phosphorylated and dephosphorylated at initial time of BR treatment, respectively. The time-dependent kinase-substrate interaction networks were constructed and showed many substrates are the downstream of other signals, such as auxin and ABA signaling. While comparing BR responsive phosphoproteome and gene expression data, we found most of phosphorylation changes were not led by gene expression changes. Our results suggested many downstream proteins of BR signaling are induced by phosphorylation via various kinases, not through transcriptional regulation.

CONCLUSIONS

Through a large-scale dynamic profile of phosphoproteome coupled with bioinformatics, a complicated kinase-centered network related to BR-regulated growth was deciphered. The phosphoproteins and phosphosites identified in our study provide a useful dataset for revealing signaling networks of BR regulation, and also expanded our knowledge of protein phosphorylation modification in plants as well as further deal to solve the plant growth problems.

Knockdown of GALNT1 suppresses malignant phenotype of hepatocellular carcinoma by suppressing EGFR signaling

O-glycosylation is a common protein modification. Aberrant O-glycosylation is associated with many cancers. GALNT1 is a GalNAc-transferase that initiates protein O-glycosylation. We found that GALNT1 is frequently up-regulated in hepatocellular carcinoma (HCC) and is associated with poor patient survival. Overexpression of GALNT1 increased and knockdown decreased HCC cell migration and invasion. Knockdown of GALNT1 inhibited EGF-induced migration and invasion. Knockdown of GALNT1 decreased EGFR activation and increased EGFR degradation, by decreasing EGFR O-glycosylation. This study demonstrates that down-regulation of GALNT1 is sufficient to suppress malignant phenotype of HCC cells by decreasing EGFR signaling. Thus, GALNT1 is a potential target in HCC.