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The development of high-throughput genomic technologies associated with recent genetic perturbation techniques such as short hairpin RNA (shRNA), gene trapping, or gene editing (CRISPR/Cas9) has made it possible to obtain large perturbation data sets. These data sets are invaluable sources of information regarding the function of genes, and they offer unique opportunities to reverse engineer gene regulatory networks in specific cell types. Modular response analysis (MRA) is a well-accepted mathematical modeling method that is precisely aimed at such network inference tasks, but its use has been limited to rather small biological systems so far. In this study, we show that MRA can be employed on large systems with almost 1,000 network components. In particular, we show that MRA performance surpasses general-purpose mutual information-based algorithms. Part of these competitive results was obtained by the application of a novel heuristic that pruned MRA-inferred interactions a posteriori . We also exploited a block structure in MRA linear algebra to parallelize large system resolutions.

Although the genes essential for life have been identified in less complex model organisms, their elucidation in human cells has been hindered by technical barriers. We used extensive mutagenesis in haploid human cells to identify approximately 2000 genes required for optimal fitness under culture conditions. To study the principles of genetic interactions in human cells, we created a synthetic lethality network focused on the secretory pathway based exclusively on mutations. This revealed a genetic cross-talk governing Golgi homeostasis, an additional subunit of the human oligosaccharyltransferase complex, and a phosphatidylinositol 4-kinase β adaptor hijacked by viruses. The synthetic lethality map parallels observations made in yeast and projects a route forward to reveal genetic networks in diverse aspects of human cell biology.

The identity of the cytoplasmic DNA receptor that activates the inflammasome has remained elusive. Superti-Furga and colleagues use a proteomics screen to identity AIM2 as the DNA sensor for the inflammasome. Cytoplasmic DNA triggers activation of the innate immune system. Although 'downstream' signaling components have been characterized, the DNA-sensing components remain elusive. Here we present a systematic proteomics screen for proteins that associate with DNA, 'crossed' to a screen for transcripts induced by interferon-β, which identified AIM2 as a candidate cytoplasmic DNA sensor. AIM2 showed specificity for double-stranded DNA. It also recruited the inflammasome adaptor ASC and localized to ASC 'speckles'. A decrease in AIM2 expression produced by RNA-mediated interference impaired DNA-induced maturation of interleukin 1β in THP-1 human monocytic cells, which indicated that endogenous AIM2 is required for DNA recognition. Reconstitution of unresponsive HEK293 cells with AIM2, ASC, caspase-1 and interleukin 1β showed that AIM2 was sufficient for inflammasome activation. Our data suggest that AIM2 is a cytoplasmic DNA sensor for the inflammasome.

Modular response analysis (MRA) is a widely used inference technique developed to uncover directions and strengths of connections in molecular networks under a steady-state condition by means of perturbation experiments. We devised several extensions of this methodology to search genomic data for new associations with a biological network inferred by MRA, to improve the predictive accuracy of MRA-inferred networks, and to estimate confidence intervals of MRA parameters from datasets with low numbers of replicates. The classical MRA computations and their extensions were implemented in a freely available R package called aiMeRA ( https://github.com/bioinfo-ircm/aiMeRA/ ) . We illustrated the application of our package by assessing the crosstalk between estrogen and retinoic acid receptors, two nuclear receptors implicated in several hormone-driven cancers, such as breast cancer. Based on new data generated for this study, our analysis revealed potential cross-inhibition mediated by the shared corepressors NRIP1 and LCoR. We designed aiMeRA for non-specialists and to allow biologists to perform their own analyses.

A systematic study of the intra- and interlaboratory reproducibility of a standardized affinity purification–mass spectrometry protocol demonstrates the high reproducibility of this technique and hints at the feasibility of a large-scale human interactome project through interlaboratory efforts. The characterization of all protein complexes of human cells under defined physiological conditions using affinity purification–mass spectrometry (AP-MS) is a highly desirable step in the quest to understand the phenotypic effects of genomic information. However, such a challenging goal has not yet been achieved, as it requires reproducibility of the experimental workflow and high data consistency across different studies and laboratories. We systematically investigated the reproducibility of a standardized AP-MS workflow by performing a rigorous interlaboratory comparative analysis of the interactomes of 32 human kinases. We show that it is possible to achieve high interlaboratory reproducibility of this standardized workflow despite differences in mass spectrometry configurations and subtle sample preparation–related variations and that combination of independent data sets improves the approach sensitivity, resulting in even more-detailed networks. Our analysis demonstrates the feasibility of obtaining a high-quality map of the human protein interactome with a multilaboratory project.

The antiviral protein IFIT1 is upregulated substantially in the cytoplasm of virus-infected cells. Superti-Furga and colleagues show that IFIT1 is part of a virus-recognition pathway that sequesters specific viral RNA. Antiviral innate immunity relies on the recognition of microbial structures. One such structure is viral RNA that carries a triphosphate group on its 5′ terminus (PPP-RNA). By an affinity proteomics approach with PPP-RNA as the 'bait', we found that the antiviral protein IFIT1 (interferon-induced protein with tetratricopeptide repeats 1) mediated binding of a larger protein complex containing other IFIT family members. IFIT1 bound PPP-RNA with nanomolar affinity and required the arginine at position 187 in a highly charged carboxy-terminal groove of the protein. In the absence of IFIT1, the growth and pathogenicity of viruses containing PPP-RNA was much greater. In contrast, IFIT proteins were dispensable for the clearance of pathogens that did not generate PPP-RNA. On the basis of this specificity and the great abundance of IFIT proteins after infection, we propose that the IFIT complex antagonizes viruses by sequestering specific viral nucleic acids.

Determining relevant targets of promiscuous kinase inhibitors has proven difficult. A combination of two mass spectrometry–based proteomic approaches, RNAi depletion and rescue studies with drug-resistant mutants now reveal that SRC, FYN and EGFR are functionally important targets of dasatinib in lung cancer cells. We describe a strategy for comprehending signaling pathways that are active in lung cancer cells and that are targeted by dasatinib using chemical proteomics to identify direct interacting proteins combined with immunoaffinity purification of tyrosine-phosphorylated peptides corresponding to activated tyrosine kinases. We identified nearly 40 different kinase targets of dasatinib. These include SRC-family kinase (SFK) members (LYN, SRC, FYN, LCK and YES), nonreceptor tyrosine kinases (FRK, BRK and ACK) and receptor tyrosine kinases (Ephrin receptors, DDR1 and EGFR). Using quantitative phosphoproteomics, we identified peptides corresponding to autophosphorylation sites of these tyrosine kinases that are inhibited in a concentration-dependent manner by dasatinib. Using drug-resistant gatekeeper mutants, we show that SFKs (particularly SRC and FYN), as well as EGFR, are relevant targets for dasatinib action. The combined mass spectrometry–based approach described here provides a system-level view of dasatinib action in cancer cells and suggests both functional targets and a rationale for combinatorial therapeutic strategies.

EGFR-mutated lung adenocarcinoma patients treated with gefitinib and osimertinib show a therapeutic benefit limited by the appearance of secondary mutations, such as EGFRT790M and EGFRC797S. It is generally assumed that these secondary mutations render EGFR completely unresponsive to the inhibitors, but contrary to this, we uncovered here that gefitinib and osimertinib increased STAT3 phosphorylation (p-STAT3) in EGFRT790M and EGFRC797S tumoral cells. Interestingly, we also found that concomitant Notch inhibition with gefitinib or osimertinib treatment induced a p-STAT3-dependent strong reduction in the levels of the transcriptional repressor HES1. Importantly, we showed that tyrosine kinase inhibitor-resistant tumors, with EGFRT790M and EGFRC797S mutations, were highly responsive to the combined treatment of Notch inhibitors with gefitinib or osimertinib, respectively. Finally, in patients with EGFR mutations treated with tyrosine kinase inhibitors, HES1 protein levels increased during relapse and correlated with shorter progression-free survival. Therefore, our results offer a proof of concept for an alternative treatment to chemotherapy in lung adenocarcinoma osimertinib-treated patients after disease progression.

Drug-tolerance has emerged as one of the major non-genetic adaptive processes driving resistance to targeted therapy (TT) in non-small cell lung cancer (NSCLC). However, the kinetics and sequence of molecular events governing this adaptive response remain poorly understood. Here, we combine real-time monitoring of the cell-cycle dynamics and single-cell RNA sequencing in a broad panel of oncogenic addiction such as EGFR-, ALK-, BRAF- and KRAS-mutant NSCLC, treated with their corresponding TT. We identify a common path of drug adaptation, which invariably involves alveolar type 1 (AT1) differentiation and Rho-associated protein kinase (ROCK)-mediated cytoskeletal remodeling. We also isolate and characterize a rare population of early escapers, which represent the earliest resistance-initiating cells that emerge in the first hours of treatment from the AT1-like population. A phenotypic drug screen identify farnesyltransferase inhibitors (FTI) such as tipifarnib as the most effective drugs in preventing relapse to TT in vitro and in vivo in several models of oncogenic addiction, which is confirmed by genetic depletion of the farnesyltransferase. These findings pave the way for the development of treatments combining TT and FTI to effectively prevent tumor relapse in oncogene-addicted NSCLC patients. Emergence of drug tolerant cells drives adaptive targeted therapy resistance in non-small cell lung cancer (NSCLC). Here, the authors identify a common molecular event underpinning resistance to multiple targeted therapies in a panel of mutant NSCLC models that can be targeted with farnesyltransferase inhibition.

Besides the standard parameters used for colorectal cancer (CRC) management, new features are needed in clinical practice to improve progression-free and overall survival. In some cancers, the microenvironment mechanical properties can contribute to cancer progression and metastasis formation, or constitute a physical barrier for drug penetration or immune cell infiltration. These mechanical properties remain poorly known for colon tissues. Using a multidisciplinary approach including clinical data, physics and geostatistics, we characterized the stiffness of healthy and malignant colon specimens. For this purpose, we analyzed a prospective cohort of 18 patients with untreated colon adenocarcinoma using atomic force microscopy to generate micrometer-scale mechanical maps. We characterized the stiffness of normal epithelium samples taken far away or close to the tumor area and selected tumor tissue areas. These data showed that normal epithelium was softer than tumors. In tumors, stroma areas were stiffer than malignant epithelial cell areas. Among the clinical parameters, tumor left location, higher stage, and RAS mutations were associated with increased tissue stiffness. Thus, in patients with CRC, measuring tumor tissue rigidity may have a translational value and an impact on patient care.