Summary 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.

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.

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.

The Java bytecode language is emerging as a soft.-ware distribution standard. W i t h major vendors conimatted t o porting the Java run-time environment to their platforms, Java bytecode programs are expected to run without modification on multiple platforms. TFiese first generation run-time environmen,ts rely o n an, interpreter t o bridge the gap between the bytecode instructions and the native hardware. However, Java interpreters cause performance problems with, niicroarch,itectural features such as the caches and the Brunch Turget Buffer. S o m e of these problems can, be solved b y translating Java bytecode t o native code. I n this paper we compare the performance of code run through the SUN Java interpreter t o code compiled through Caffeine, a bytecode t o native code translator, as well as t o compaled C/C++ versions of the code, using large applications and common bench"&.W e discuss th,e reasons f o r several performance problems incurred b y both, approaches t o running Java code, and ezamine possible solutions.

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.