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Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing,ex vivodrug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predictex vivodrug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients.One Sentence Summary:Integrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

Objective: Pancreatic ductal adenocarcinoma (PDAC) is a disease with high mortality. Many so-called “junk” noncoding RNAs need to be discovered in PDAC. The purpose of this study was therefore to investigate the function and regulatory mechanism of the long noncoding RNA MEG3 in PDAC. Methods: The Gene Expression Omnibus database (GEO database) was used to determine the differential expression of long noncoding RNAs in PDAC, and MEG3 was selected for subsequent verification. Tissue and cell samples were used to verify MEG3 expression, followed by functional detection in vitro and in vivo. Microarrays were used to characterize long noncoding RNA and mRNA expression profiles. Competing endogenous RNA analyses were used to detect differential MEG3 and relational miRNA expression in PDAC. Finally, promoter analyses were conducted to explain the downregulation of MEG3 PDAC. Results: We generated a catalogue of PDAC-associated long noncoding RNAs in the GEO database. The ectopic expression of MEG3 inhibited PDAC growth and metastasis in vitro and in vivo, which was statistically significant (P < 0.05). Microarray analysis showed that multiple microRNAs interacted with MEG3. We also showed that MEG3, as a competing endogenous RNA, directly sponged miR-374a-5p to regulate PTEN expression. The transcription factor, Sp1, recruited EZH2 and HDAC3 to the promoter and transcriptionally repressed MEG3 expression. Finally, clinical data showed that MEG3 and miR-374a-5p expressions were correlated with clinicopathological features. Statistically, Sp1, EZH2, HDAC3, and miR-374a-5p were negatively correlated with MEG3 (P < 0.05). Conclusions: Reduced MEG3 levels played a crucial role in the PDAC malignant phenotype, which provided insight into novel and effective molecular targets of MEG3 for pancreatic cancer treatment.

The oncogene Ras and the tumor suppressor gene p53 are frequently co-mutated in human cancer and mutations in Ras and p53 can cooperate to generate a more malignant cell state. To discover novel druggable targets for cancers carrying co-mutations in Ras and p53, we performed arrayed, kinome focused siRNA and oncology drug phenotypic screening utilizing a set of syngeneic Ras mutant squamous cell carcinoma (SCC) cell lines that also carried co-mutations in selected p53 pathway genes. These cell lines were derived from SCCs from carcinogen-treated inbred mice which harbored germline deletions or mutations in Trp53, p19

SummarySynthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, an integrated resource and framework to facilitate the prediction of context-specific SLIs by using cloud-based technologies. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the multiple comparable prediction approaches available. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential SLI partners, using multiple computational strategies. Context-specific synthetic lethality potential can also be compared using the framework. We demonstrate various use cases for our cloud-based computational resource and the utility of this approach for customizable and extensible computational inference of SLIs.

Hepatocellular carcinoma is the third leading cause of deaths from cancer worldwide. Infection with the hepatitis B virus is one of the leading risk factors for developing hepatocellular carcinoma, particularly in East Asia1. Although surgical treatment may be effective in the early stages, the five-year overall rate of survival after developing this cancer is only 50–70%2. Here, using proteomic and phospho-proteomic profiling, we characterize 110 paired tumour and non-tumour tissues of clinical early-stage hepatocellular carcinoma related to hepatitis B virus infection. Our quantitative proteomic data highlight heterogeneity in early-stage hepatocellular carcinoma: we used this to stratify the cohort into the subtypes S-I, S-II and S-III, each of which has a different clinical outcome. S-III, which is characterized by disrupted cholesterol homeostasis, is associated with the lowest overall rate of survival and the greatest risk of a poor prognosis after first-line surgery. The knockdown of sterol O-acyltransferase 1 (SOAT1)—high expression of which is a signature specific to the S-III subtype—alters the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma. Finally, on the basis of a patient-derived tumour xenograft mouse model of hepatocellular carcinoma, we found that treatment with avasimibe, an inhibitor of SOAT1, markedly reduced the size of tumours that had high levels of SOAT1 expression. The proteomic stratification of early-stage hepatocellular carcinoma presented in this study provides insight into the tumour biology of this cancer, and suggests opportunities for personalized therapies that target it. A subtype of early-stage hepatocellular carcinoma characterized by disrupted cholesterol homeostasis and associated with a poor prognosis responds to treatment with the SOAT1 inhibitor avasimibe in a patient-derived xenograft mouse model.

Cells are complex systems in which many functions are performed by different genetically defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we develop a functional module states framework. Using this framework, we (1) define the drug-induced transcriptional state space for breast cancer cell lines using large public gene expression datasets and reveal that the transcriptional states are associated with drug concentration and drug targets, (2) identify potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown-associated cancer dependency, and (3) use functional module states to predict transcriptional state-dependent drug sensitivity and build prediction models for drug response. This approach demonstrates a similar prediction performance as approaches using high-dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

Tumor heterogeneity among different patients result in different response to anti-cancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a Tumor online Prognostic analysis Platform (ToPP) to explore the prognosis associated genetic features in multi-omic levels in different tumor types. The ToPP platform integrated eight multi-omics features and clinical data for 55 tumor types. It provides multiple ways for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features such as genetic variation (mutation, CNV, methylation) and clinical characteristics; 2) Automatic construction of prognostic model with variable selection, transformation and model evaluation for both public data and custom data; 3) Pancancer prognostic analysis in multi-omics data based on tumor related pathways; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network such as miRNA-gene regulation relationship, gene mutation-expression relationship; 6) Prognostic study of custom data and comparison with public datasets. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtype and online prognostic modeling. The web-server is freely available at http://www.biostatistics.online/topp/index.php. Funding: This work was supported by Shanghai Municipal Health Commission and Collaborative Innovation Cluster Project (No. 2019CXJQ02). It was also supported by National Natural Science Foundation of China grants (Nos. 31878209, 31671377), Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01, 20692191500), Beihang University & Capital Medical University Plan (BHME-201904) and the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, ECNU. Declaration of Interest: The authors declare that they have no competing interests.

With the rapid development of next generation sequencing and mass spectrometry based high throughput technologies, large scale multiple omics data for the same cohorts of patients are accumulated. Genome, epigenome, transcriptome, proteome and metabolome are widely measured for the investigation of disease models. A comprehensive biological model can be generated only when different levels of genetic flow information are considered, because meaningful integration of omics data can compensate for missing interaction links, bring more evidence to a point of biological interest. Current representative multiple omics integration methods may be classified under two hypotheses: multiple level regulation and multiple dimension contribution. Following the two hypotheses, sequential integration and simultaneous integration strategies are viewed, within each strategy dozens of computational methods have been developed to integrate multiple omics data for different biological goals.

SummaryCells are complex systems in which many functions are performed by different genetically-defined and encoded functional modules. To systematically understand how these modules respond to drug or genetic perturbations, we developed a Functional Module States framework. Using this framework, we 1) defined the drug induced transcriptional state space for breast cancer cell lines using large public gene expression datasets, and revealed that the transcriptional states are associated with drug concentration and drug targets; 2) identified potential targetable vulnerabilities through integrative analysis of transcriptional states after drug treatment and gene knockdown associated cancer dependency; and 3) used functional module states to predict transcriptional state-dependent drug sensitivity and built prediction models using the functional module states for drug response. This approach demonstrates a similar prediction performance as do approaches using high dimensional gene expression values, with the added advantage of more clearly revealing biologically relevant transcriptional states and key regulators.

Author(s): Su, Yapeng; Chen, Daniel; Lausted, Christopher; Yuan, Dan; Choi, Jongchan; Dai, Cheng; Voillet, Valentin; Scherler, Kelsey; Troisch, Pamela; Duvvuri, Venkata R; Baloni, Priyanka; Qin, Guangrong; Smith, Brett; Kornilov, Sergey; Rostomily, Clifford; Xu, Alex; Li, Jing; Dong, Shen; Rothchild, Alissa; Zhou, Jing; Murray, Kim; Edmark, Rick; Hong, Sunga; Jones, Lesley; Zhou, Yong; Roper, Ryan; Mackay, Sean; O'Mahony, D Shane; Dale, Christopher R; Wallick, Julie A; Algren, Heather A; Michael, Zager A; Magis, Andrew; Wei, Wei; Price, Nathan D; Huang, Sui; Subramanian, Naeha; Wang, Kai; Hadlock, Jennifer; Hood, Leroy; Aderem, Alan; Bluestone, Jeffrey A; Lanier, Lewis L; Greenberg, Phil; Gottardo, Raphael; Davis, Mark M; Goldman, Jason D; Heath, James R | Abstract: Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 + and CD4 + T cells, and cytotoxic CD4 + T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Newborn microglia rapidly replenish the whole brain after selective elimination of most microglia (>99%) in adult mice. Previous studies reported that repopulated microglia were largely derived from microglial progenitor cells expressing nestin in the brain. However, the origin of these repopulated microglia has been hotly debated. In this study, we investigated the origin of repopulated microglia by a series of fate-mapping approaches. We first excluded the blood origin of repopulated microglia via parabiosis. With different transgenic mouse lines, we then demonstrated that all repopulated microglia were derived from the proliferation of the few surviving microglia (

A computational model based on random forest algorithm for identifying synergistic anti-cancer drug combinations is proposed.The model is based on features of biology network and gene expression profiles.Twenty-eight synergistic drug combinations were predicted, three out of which were reported, and the rest could be candidates for further investigations. ObjectiveSynergistic drug combinations are promising therapies for cancer treatment. However, effective prediction of synergistic drug combinations is quite challenging as mechanisms of drug synergism are still unclear. Various features such as drug response, and target networks may contribute to prediction of synergistic drug combinations. In this study, we aimed to construct a computational model to predict synergistic drug combinations. MethodsWe designed drug physicochemical features and network features, including drug chemical structure similarity, target distance in proteinprotein network and targeted pathway similarity. At the same time, we designed fifteen pharmacogenomics features using drug treated gene expression profiles based on the background of cancer-related biology network. Based on these eighteen features, we built a prediction model for Synergistic Drug combination using Random forest algorithm (SyDRa). ResultsOur model achieved a quite good performance with AUC value of 0.89 and Out-of-bag estimate error rate of 0.15 in training dataset. Using the random anti-cancer drug combinations which have transcriptional profile data in the Connectivity Map dataset as the testing dataset, we identified 28 potentially synergistic drug combinations, three out of which had been reported to be effective drug combinations by literatures. ConclusionsWe studied eighteen features for drug combinations and built a computational model using random forest algorithm. The model was evaluated using an independent test dataset. Our model provides an efficient strategy to identify potentially synergistic drug combinations for cancer and may help reduce the search space for high-throughput synergistic drug combinations screening.

Background The molecular origin of cancer drug resistance is not apparent for most cases of acute myeloid leukemia (AML). Clonal evolution appears to be associated with increasing drug resistance. We sought to determine whether the mutation patterns are associated with drug susceptibility in AML. Methods Seventy-two patient blood or marrow samples were enriched for CD34+ blasts by immunomagnetic bead selection. High throughput drug sensitivity screens were performed for 223 drugs after a 72-hour exposure to 8-12 customized drug concentrations (within the range of 5pM to 100µM) of each drug spanning 4-5 logs. Post exposure viability was determined using CellTiter Glo luminescent reagent. XLFit was used to analyze the data and generate dose response curves based on a standard 4-parameter logistic fit. Mutation analysis was performed by MyAML™ utilizing next generation sequencing (NGS) to analyze the 3' and 5' UTRs and exonic regions of 194 AML-associated genes and genomic breakpoints. Co-mutation and mutual exclusivity scores of gene aberrations were computed using three different statistical methods on three large publicly available datasets: 1) TCGA-AML, 2) BeatAML, and 3) data from 1540 patients (Moritz Gerstung, et al., Nat Genet. 2017). The scores for each co-mutation or mutually exclusive gene pair were then logarithmically transformed from the aggregated p-value using a robust rank aggregation method and used to construct a graph from which communities of co-mutated genes could be detected, resulting in higher mutual exclusivity between modules and higher co-occurrence within modules. Results We identified five main groups of co-mutations, including the following: 1) RUNX1 group, 2) CEBPA group, 3) NPM1 group, 4) TP53 group, and 5) RAS group (see Fig 1A). The co-mutation community in the RUNX1 group is featured with transcriptional dysregulation (RUNX1, ASXL1 and EZH2), and dysregulation in splicing (U2AF1, SRSF2 or SF3B1). The TP53 and CEBPA groups exhibit transcriptional dysregulation, and transcription factor alterations. The NPM1 and RAS groups exhibit signaling alteration, particularly the Ras/MAPK signaling pathway. Among each co-mutation community, the variant allele frequencies (VAFs) exhibited differences for specific mutations. For example, in the NPM1 group, FLT3 exhibits the lowest VAF, followed by NPM1 while DNMT3A shows the highest VAF. The five co-mutation groups are associated with different overall survival, including better survival for the CEBPA group and poorer survival for the TP53 group. Within each co-mutation group, specific associations with overall survival were detected, including better survival for NPM1-RAD21 co-mutated vs. wild type (WT), better survival for WT vs. ASXL1-RUNX1 co-mutated, worse survival for DNMT3A-FLT3 co-mutated vs. WT, and others. We also found that the co-mutation clusters were different between the de novo and relapse groups. Most importantly, we found significant correlations with drug sensitivity for the different co-occurring mutation groups. Cells from patients with mutations in the NPM1 co-mutation group show higher sensitivity to most of the drugs, including the tyrosine kinases inhibitors and PI3K-AKT-MTOR inhibitors. Cells from patients with mutations in the RAS cluster exhibited sensitivity to MEK inhibitors (Fig 1B), while cells from patients with mutations in the TP53 cluster show resistance to many drugs, including the MDM2 inhibitor AMG232 (Fig 1C). By mapping the mutated genes and drug targets into signaling pathways, we found that mutations in downstream signaling of the drug target exhibited resistance, while mutations in upstream signaling conferred higher sensitivity. We also constructed drug sensitivity prediction models based on the co-mutation groups. Conclusion We detected co-mutation groups through integrative analysis of large publicly available AML data sets. Patients with mutations in different groups of genes show different overall survival, while cells from patients with mutations in different co-mutation groups show different drug sensitivity. The co-mutation groups may not only reflect the clonal evolution history of AML, but also serve as important features for drug sensitivity prediction. Disclosures Carson: Invivoscribe, Inc: Current Employment. Patay:Invivoscribe, Inc: Current Employment. Becker:Cardiff Oncology: Research Funding; Novartis: Research Funding; SecuraBio: Research Funding; JW Pharmaceutical: Research Funding; Glycomimetics: Research Funding; Abbvie: Research Funding; Bristol Myers Squibb: Research Funding; Pfizer: Research Funding; Invivoscribe: Research Funding; Accordant Health Services/Caremark: Membership on an entity's Board of Directors or advisory committees. OffLabel Disclosure: There are ~200 drugs on the screening panel, and many would be off label for acute myeloid leukemia.

SummaryWe resolved a mechanism connecting tumor epigenetic plasticity with non-genetic adaptive resistance to therapy, with MAPK inhibition ofBRAF-mutant melanomas providing the model. These cancer cells undergo multiple, reversible drug-induced cell-state transitions, ultimately yielding a drug-resistant mesenchymal-like phenotype. A kinetic series of transcriptome and epigenome data, collected over two months of drug treatment and release, revealed changing levels of thousands of genes and extensive chromatin remodeling. However, a 3-step computational algorithm greatly simplified the interpretation of these changes, and revealed that the whole adaptive process was controlled by a gene module activated within just three days of treatment, with RelA driving chromatin remodeling to establish an epigenetic program encoding long-term phenotype changes. These findings were confirmed across several patient-derived cell lines and in melanoma patients under MAPK inhibitor treatment. Co-targeting BRAF and histone-modifying enzymes arrests adaptive transitions towards drug tolerance in epigenetically plastic melanoma cells and may be exploited therapeutically.

Background Lipid metabolism reprogramming is a hallmark for tumor which contributes to tumorigenesis and progression, but the commonality and difference of lipid metabolism among pan-cancer is not fully investigated. Increasing evidences suggest that the alterations in tumor metabolism, including metabolite abundance and accumulation of metabolic products, lead to local immunosuppression in the tumor microenvironment. An integrated analysis of lipid metabolism in cancers from different tissues using multiple omics data may provide novel insight into the understanding of tumorigenesis and progression. Results Through systematic analysis of the multiple omics data from TCGA, we found that the most-widely altered lipid metabolism pathways in pan-cancer are fatty acid metabolism, arachidonic acid metabolism, cholesterol metabolism and PPAR signaling. Gene expression profiles of fatty acid metabolism show commonalities across pan-cancer, while the alteration in cholesterol metabolism and arachidonic acid metabolism differ with tissue origin, suggesting tissue specific lipid metabolism features in different tumor types. An integrated analysis of gene expression, DNA methylation and mutations revealed factors that regulate gene expression, including the differentially methylated sites and mutations of the lipid genes, as well as mutation and differential expression of the up-stream transcription factors for the lipid metabolism pathways. Correlation analysis of the proportion of immune cells in the tumor microenvironment and the expression of lipid metabolism genes revealed immune-related differentially expressed lipid metabolic genes, indicating the potential crosstalk between lipid metabolism and immune response. Genes related to lipid metabolism and immune response that are associated with poor prognosis were discovered including HMGCS2, GPX2 and CD36, which may provide clues for tumor biomarkers or therapeutic targets. Conclusions Our study provides an integrated analysis of lipid metabolism in pan-cancer, highlights the perturbation of key metabolism processes in tumorigenesis and clarificates the regulation mechanism of abnormal lipid metabolism and effects of lipid metabolism on tumor immune microenvironment. This study also provides new clues for biomarkers or therapeutic targets of lipid metabolism in tumors. Electronic supplementary material The online version of this article (10.1186/s12859-019-2734-4) contains supplementary material, which is available to authorized users.

Gene expression levels with mutation frequency more than 2% and differently expressed in pan-cancer (PDF 333 kb)

We present an integrated analysis of the clinical measurements, immune cells and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention., Highlights • Analysis of serial blood from 139 COVID-19 patients reveals immune coordination • A major immunological shift is seen between mild and moderate infection • Moderate and severe cases exhibit inflammation and a sharp drop in blood nutrients • Novel immune cell subsets emerge in moderate cases and increase with severity, Using serial blood draws from hospitalized COVID-19 patients, Su et al. present an extensive single-cell multi-omics dataset covering the first week infection following clinical diagnosis, which includes information on plasma proteins, metabolites, transcriptomic data, immune receptor sequences, secreted proteins, and electronic health record data. Their integrated analysis identifies a major immunological shift between mild and moderate infection, which includes an increase in inflammation, drop in blood nutrients, and the emergence of novel immune cell subpopulations that intensify with disease severity.

Elucidating the origin of microglia is crucial for understanding their functions and homeostasis. Previous study has indicated that Nestin-positive progenitor cells differentiate into microglia and replenish the brain after depleting most brain microglia. Microglia have also shown the capacity to repopulate the retina after eliminating all retinal microglia. However, the origin(s) of repopulated retinal microglia is/are unknown. In this study, we aim to investigate the origins of repopulated microglia in the retina. Interestingly, we find that repopulated retinal microglia are not derived from Nestin-positive progenitor cells. Instead, they have two origins: the center-emerging microglia are derived from residual microglia in the optic nerve and the periphery-emerging microglia are derived from macrophages in the ciliary body/iris. Therefore, we have for the first time identified the extra-retinal origins of microglia in the adult mammalian retina by using a model of microglial repopulation, which may shed light on the target exploration of therapeutic interventions for retinal degenerative disorders.

Newborn microglia rapidly replenish the whole brain after selective elimination of most microglia (99%) in adult mice. Previous studies reported that repopulated microglia were largely derived from microglial progenitor cells expressing nestin in the brain. However, the origin of these repopulated microglia has been hotly debated. In this study, we investigated the origin of repopulated microglia by a series of fate-mapping approaches. We first excluded the blood origin of repopulated microglia via parabiosis. With different transgenic mouse lines, we then demonstrated that all repopulated microglia were derived from the proliferation of the few surviving microglia (1%). Despite a transient pattern of nestin expression in newly forming microglia, none of repopulated microglia were derived from nestin-positive non-microglial cells. In summary, we conclude that repopulated microglia are solely derived from residual microglia rather than de novo progenitors, suggesting the absence of microglial progenitor cells in the adult brain.

Most low GC Gram-positive bacteria possess an essential walKR two-component system (TCS) for signal transduction involved in regulating cell wall homoeostasis. Despite the well-established intracellular regulatory mechanism, the role of this TCS in extracellular signal recognition and factors that modulate the activity of this TCS remain largely unknown. Here we identify the extracellular receptor of the kinase 'WalK' (erWalK) as a key hub for bridging extracellular signal input and intracellular kinase activity modulation in Staphylococcus aureus. Characterization of the crystal structure of erWalK revealed a canonical Per-Arnt-Sim (PAS) domain for signal sensing. Single amino-acid mutation of potential signal-transduction residues resulted in severely impaired function of WalKR. A small molecule derived from structure-based virtual screening against erWalK is capable of selectively activating the walKR TCS. The molecular level characterization of erWalK will not only facilitate exploration of natural signal(s) but also provide a template for rational design of erWalK inhibitors.

The small- and intermediate-conductance Ca 2+ -activated potassium (SK/IK) channels play important roles in the regulation of excitable cells in both the central nervous and cardiovascular systems. Evidence from animal models has implicated SK/IK channels in neurological conditions such as ataxia and alcohol use disorders. Further, genome-wide association studies have suggested that cardiovascular abnormalities such as arrhythmias and hypertension are associated with single nucleotide polymorphisms that occur within the genes encoding the SK/IK channels. The Ca 2+ sensitivity of the SK/IK channels stems from a constitutively bound Ca 2+ -binding protein: calmodulin. Small-molecule positive modulators of SK/IK channels have been developed over the past decade, and recent structural studies have revealed that the binding pocket of these positive modulators is located at the interface between the channel and calmodulin. SK/IK channel positive modulators can potentiate channel activity by enhancing the coupling between Ca 2+ sensing via calmodulin and mechanical opening of the channel. Here, we review binding pocket studies that have provided structural insight into the mechanism of action for SK/IK channel positive modulators. These studies lay the foundation for structure-based drug discovery efforts that can identify novel SK/IK channel positive

Most low GC Gram-positive bacteria possess an essential walKR two-component system (TCS) for signal transduction involved in regulating cell wall homoeostasis. Despite the well-established intracellular regulatory mechanism, the role of this TCS in extracellular signal recognition and factors that modulate the activity of this TCS remain largely unknown. Here we identify the extracellular receptor of the kinase ‘WalK' (erWalK) as a key hub for bridging extracellular signal input and intracellular kinase activity modulation in Staphylococcus aureus. Characterization of the crystal structure of erWalK revealed a canonical Per-Arnt-Sim (PAS) domain for signal sensing. Single amino-acid mutation of potential signal-transduction residues resulted in severely impaired function of WalKR. A small molecule derived from structure-based virtual screening against erWalK is capable of selectively activating the walKR TCS. The molecular level characterization of erWalK will not only facilitate exploration of natural signal(s) but also provide a template for rational design of erWalK inhibitors., The WalKR signal transduction system is involved in extracellular signal recognition, but the details of this function are not well established. Here, the authors report the crystal structure of this two-component system alongside the characterisation of a small-molecule activator.

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

ErbB4, a receptor tyrosine kinase of the ErbB family, plays crucial roles in cell growth and differentiation, especially in the development of the heart and nervous system. Ligand binding to its extracellular region could modulate the activation process. To understand the mechanism of ErbB4 activation induced by ligand binding, we performed one microsecond molecular dynamics (MD) simulations on the ErbB4 extracellular region (ECR) with and without its endogenous ligand neuregulin1β (NRG1β). The conformational transition of the ECR-ErbB4/NRG1β complex from a tethered inactive conformation to an extended active-like form has been observed, while such large and function-related conformational change has not been seen in the simulation on the ECR-ErbB4, suggesting that ligand binding is indeed the active inducing force for the conformational transition and further dimerization. On the basis of MD simulations and principal component analysis, we constructed a rough energy landscape for the conformational transition of ECR-ErbB4/NRG1β complex, suggesting that the conformational change from the inactive state to active-like state involves a stable conformation. The energy barrier for the tether opening was estimated as ~2.7 kcal/mol, which is very close to the experimental value (1-2 kcal/mol) reported for ErbB1. On the basis of the simulation results, an atomic mechanism for the ligand-induced activation of ErbB4 was postulated. The present MD simulations provide a new insight into the conformational changes underlying the activation of ErbB4.

The principal role of acetylcholinesterase is termination of nerve impulse transmission at cholinergic synapses, by rapid hydrolysis of the neurotransmitter acetylcholine to acetate and choline. Its active site is buried at the bottom of a deep and narrow gorge, at the rim of which is found a second anionic site, the peripheral anionic site. The fact that the active site is so deeply buried has raised cogent questions as to how rapid traffic of substrate and products occurs in such a confined environment. Various theoretical and experimental approaches have been used to solve this problem. Here, multiple conventional molecular dynamics simulations have been performed to investigate the clearance of the product, thiocholine, from the active-site gorge of acetylcholinesterase. Our results indicate that thiocholine is released from the peripheral anionic site via random pathways, while three exit routes appear to be favored for its release from the active site, namely, along the axis of the active-site gorge, and through putative back- and side-doors. The back-door pathway is that via which thiocholine exits most frequently. Our results are in good agreement with kinetic and kinetic-crystallography studies. We propose the use of multiple molecular dynamics simulations as a fast yet accurate complementary tool in structural studies of enzymatic trafficking.

Why are oseltamivir and zanamivir effective against the newly emerged influenza A virus (A/H1N1)?

Quercetin-3-O-amino acid-esters, a new type of quercetin derivatives, were successfully prepared for the first time. Different from quercetin, the novel compounds show higher selectivity as inhibitors against Src tyrosine kinase (IC50 values ranging from 3.2 μM to 9.9 μM) than against EGFR tyrosine kinase. Molecular docking reveals that both hydrophobic and hydrogen bonding interactions are important to the selectivity. Therefore, this study provides a new promising scaffold for further development of new anticancer drugs targeting Src tyrosine kinase., Graphical abstract Novel quercetin-3-O-amino acid-esters as a new class of Src tyrosine kinase inhibitors were successfully prepared for the first time.

Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

Chromosomal translocation, which generates fusion proteins in blood tumor or solid tumor, is considered as one of the major causes leading to cancer. Recent studies suggested that the disordered fragments in a fusion protein might contribute to its carcinogenicity. Here, we investigated the sequence feature near the breakpoints in the fusion partner genes, the structure features of breakpoints in fusion proteins, and the posttranslational modification preference in the fusion proteins. Results show that the breakpoints in the fusion partner genes have both sequence preference and structural preference. At the sequence level, nucleotide combination AG is preferred before the breakpoint and GG is preferred at the breakpoint. At the structural level, the breakpoints in the fusion proteins prefer to be located in the disordered regions. Further analysis suggests the phosphorylation sites at serine, threonine, and the methylation sites at arginine are enriched in disordered regions of the fusion proteins. Using EML4-ALK as an example, we further explained how the fusion protein leads to the protein disorder and contributes to its carcinogenicity. The sequence and structural features of the fusion proteins may help the scientific community to predict novel breakpoints in fusion genes and better understand the structure and function of fusion proteins.

Biological and biomedical research relies on comprehensive understanding of protein-coding transcripts. However, the total number of human proteins is still unknown due to the prevalence of alternative splicing and is much larger than the number of human genes. In this paper, we detected 31,566 novel transcripts with coding potential by filtering our ab initio predictions with 50 RNA-seq datasets from diverse tissues/cell lines. PCR followed by MiSeq sequencing showed that at least 84.1% of these predicted novel splice sites could be validated. In contrast to known transcripts, the expression of these novel transcripts were highly tissue-specific. Based on these novel transcripts, at least 36 novel proteins were detected from shotgun proteomics data of 41 breast samples. We also showed L1 retrotransposons have a more significant impact on the origin of new transcripts/genes than previously thought. Furthermore, we found that alternative splicing is extraordinarily widespread for genes involved in specific biological functions like protein binding, nucleoside binding, neuron projection, membrane organization and cell adhesion. In the end, the total number of human transcripts with protein-coding potential was estimated to be at least 204,950.

Chemokine receptors are G protein-coupled receptors that contain seven trans-membrane domains. CXCR4 and CCR5 as major co-receptors for HIV-1 entry into host cells are implicated in cancer and inflammation. They have been attractive targets for the pharmaceutical industry basing on their roles in HIV disease. Homology modeling, molecular docking, molecular dynamics, Molecular Mechanics/Generalized Born Surface Area and many other computational methods are applied to illustrate the structure, function and binding site of GPCR. Moreover, the high resolution crystal structures of CXCR4 and CCR5 have provided extremely valuable structural information and receptor activation mechanisms, enable structure-based drug discovery for the treatment of HIV-1 infection. We also describe the recent progress about the small molecule antagonists of CXCR4 and CCR5 and the interaction between GPCR and their ligands predicted by molecular docking and molecular dynamics methods. Future research questions and further investigations are outlined to highlight some researches that may be relevant to the advancement of therapies targeting the important receptor related with HIV.