A protein network map of head and neck cancer reveals PIK3CA mutant drug sensitivity

I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data

Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.

Protein Interaction Map of APOBEC3 Enzyme Family Reveals Deamination-Independent Role in Cellular Function

Human APOBEC3 enzymes are a family of single-stranded (ss)DNA and RNA cytidine deaminases that act as part of the intrinsic immunity against viruses and retroelements. These enzymes deaminate cytosine to form uracil which can functionally inactivate or cause degradation of viral or retroelement genomes. In addition, APOBEC3s have deamination-independent antiviral activity through protein and nucleic acid interactions. If expression levels are misregulated, some APOBEC3 enzymes can access the human genome leading to deamination and mutagenesis, contributing to cancer initiation and evolution. While APOBEC3 enzymes are known to interact with large ribonucleoprotein complexes, the function and RNA dependence are not entirely understood. To further understand their cellular roles, we determined by affinity purification mass spectrometry (AP-MS) the protein interaction network for the human APOBEC3 enzymes and mapped a diverse set of protein-protein and protein-RNA mediated interactions. Our analysis identified novel RNA-mediated interactions between APOBEC3C, APOBEC3H Haplotype I and II, and APOBEC3G with spliceosome proteins, and APOBEC3G and APOBEC3H Haplotype I with proteins involved in tRNA methylation and ncRNA export from the nucleus. In addition, we identified RNA-independent protein-protein interactions with APOBEC3B, APOBEC3D, and APOBEC3F and the prefoldin family of protein-folding chaperones. Interaction between prefoldin 5 (PFD5) and APOBEC3B disrupted the ability of PFD5 to induce degradation of the oncogene cMyc, implicating the APOBEC3B protein interaction network in cancer. Altogether, the results uncover novel functions and interactions of the APOBEC3 family and suggest they may have fundamental roles in cellular RNA biology, their protein-protein interactions are not redundant, and there are protein-protein interactions with tumor suppressors, suggesting a role in cancer biology. Data are available via ProteomeXchange with the identifier PXD044275.

Analysis and visualization of quantitative proteomics data using FragPipe-Analyst

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

Heterotrimeric G protein signaling without GPCRs: The Gα-binding-and-activating (GBA) motif

Heterotrimeric G proteins (Gαβγ) are molecular switches that relay signals from 7-transmembrane receptors located at the cell surface to the cytoplasm. The function of these receptors is so intimately linked to heterotrimeric G proteins that they are named G protein-coupled receptors (GPCRs), showcasing the interdependent nature of this archetypical receptor-transducer axis of transmembrane signaling in eukaryotes. It is generally assumed that activation of heterotrimeric G protein signaling occurs exclusively by the action of GPCRs, but this idea has been challenged by the discovery of alternative mechanisms by which G proteins can propagate signals in the cell. This review will focus on a general principle of G protein signaling that operates without the direct involvement of GPCRs. The mechanism of G protein signaling reviewed here is mediated by a class of G protein regulators defined by containing an evolutionarily conserved sequence named the Gα-binding-and-activating (GBA) motif. Using the best characterized proteins with a GBA motif as examples, Gα-interacting vesicle-associated protein (GIV)/Girdin and dishevelled-associating protein with a high frequency of leucine residues (DAPLE), this review will cover (i) the mechanisms by which extracellular cues not relayed by GPCRs promote the coupling of GBA motif-containing regulators with G proteins, (ii) the structural and molecular basis for how GBA motifs interact with Gα subunits to facilitate signaling, (iii) the relevance of this mechanism in different cellular and pathological processes, including cancer and birth defects, and (iv) strategies to manipulate GBA-G protein coupling for experimental therapeutics purposes, including the development of rationally engineered proteins and chemical probes.

A foundational atlas of autism protein interactions reveals molecular convergence

Translating high-confidence (hc) autism spectrum disorder (ASD) genes into viable treatment targets remains elusive. We constructed a foundational protein-protein interaction (PPI) network in HEK293T cells involving 100 hcASD risk genes, revealing over 1,800 PPIs (87% novel). Interactors, expressed in the human brain and enriched for ASD but not schizophrenia genetic risk, converged on protein complexes involved in neurogenesis, tubulin biology, transcriptional regulation, and chromatin modification. A PPI map of 54 patient-derived missense variants identified differential physical interactions, and we leveraged AlphaFold-Multimer predictions to prioritize direct PPIs and specific variants for interrogation in Xenopus tropicalis and human forebrain organoids. A mutation in the transcription factor FOXP1 led to reconfiguration of DNA binding sites and altered development of deep cortical layer neurons in forebrain organoids. This work offers new insights into molecular mechanisms underlying ASD and describes a powerful platform to develop and test therapeutic strategies for many genetically-defined conditions.

Protein interaction map of APOBEC3 enzyme family reveals deamination-independent role in cellular function

Human APOBEC3 enzymes are a family of single-stranded (ss)DNA and RNA cytidine deaminases that act as part of the intrinsic immunity against viruses and retroelements. These enzymes deaminate cytosine to form uracil which can functionally inactivate or cause degradation of viral or retroelement genomes. In addition, APOBEC3s have deamination independent antiviral activity through protein and nucleic acid interactions. If expression levels are misregulated, some APOBEC3 enzymes can access the human genome leading to deamination and mutagenesis, contributing to cancer initiation and evolution. While APOBEC3 enzymes are known to interact with large ribonucleoprotein complexes, the function and RNA dependence is not entirely understood. To further understand their cellular roles, we determined by affinity purification mass spectrometry (AP-MS) the protein interaction network for the human APOBEC3 enzymes and map a diverse set of protein-protein and protein-RNA mediated interactions. Our analysis identified novel RNA-mediated interactions between APOBEC3C, APOBEC3H Haplotype I and II, and APOBEC3G with spliceosome proteins, and APOBEC3G and APOBEC3H Haplotype I with proteins involved in tRNA methylation and ncRNA export from the nucleus. In addition, we identified RNA-independent protein-protein interactions with APOBEC3B, APOBEC3D, and APOBEC3F and the prefoldin family of protein folding chaperones. Interaction between prefoldin 5 (PFD5) and APOBEC3B disrupted the ability of PFD5 to induce degradation of the oncogene cMyc, implicating the APOBEC3B protein interaction network in cancer. Altogether, the results uncover novel functions and interactions of the APOBEC3 family and suggest they may have fundamental roles in cellular RNA biology, their protein-protein interactions are not redundant, and there are protein-protein interactions with tumor suppressors, suggesting a role in cancer biology.

Synaptic plasticity via receptor tyrosine kinase/G-protein-coupled receptor crosstalk

Cellular signaling involves a large repertoire of membrane receptors operating in overlapping spatiotemporal regimes and targeting many common intracellular effectors. However, both the molecular mechanisms and the physiological roles of crosstalk between receptors, especially those from different superfamilies, are poorly understood. We find that the receptor tyrosine kinase (RTK) TrkB and the G-protein-coupled receptor (GPCR) metabotropic glutamate receptor 5 (mGluR5) together mediate hippocampal synaptic plasticity in response to brain-derived neurotrophic factor (BDNF). Activated TrkB enhances constitutive mGluR5 activity to initiate a mode switch that drives BDNF-dependent sustained, oscillatory Ca2+ signaling and enhanced MAP kinase activation. This crosstalk is mediated, in part, by synergy between Gβγ, released by TrkB, and Gαq-GTP, released by mGluR5, to enable physiologically relevant RTK/GPCR crosstalk.

High-throughput chemogenetic drug screening reveals PKC-RhoA/PKN as a targetable signaling vulnerability in GNAQ-driven uveal melanoma

Uveal melanoma (UM) is the most prevalent cancer of the eye in adults, driven by activating mutation of GNAQ/GNA11; however, there are limited therapies against UM and metastatic UM (mUM). Here, we perform a high-throughput chemogenetic drug screen in GNAQ-mutant UM contrasted with BRAF-mutant cutaneous melanoma, defining the druggable landscape of these distinct melanoma subtypes. Across all compounds, darovasertib demonstrates the highest preferential activity against UM. Our investigation reveals that darovasertib potently inhibits PKC as well as PKN/PRK, an AGC kinase family that is part of the "dark kinome." We find that downstream of the Gαq-RhoA signaling axis, PKN converges with ROCK to control FAK, a mediator of non-canonical Gαq-driven signaling. Strikingly, darovasertib synergizes with FAK inhibitors to halt UM growth and promote cytotoxic cell death in vitro and in preclinical metastatic mouse models, thus exposing a signaling vulnerability that can be exploited as a multimodal precision therapy against mUM.

The permissive binding theory of cancer

The later stages of cancer, including the invasion and colonization of new tissues, are actively mysterious compared to earlier stages like primary tumor formation. While we lack many details about both, we do have an apparently successful explanatory framework for the earlier stages: one in which genetic mutations hold ultimate causal and explanatory power. By contrast, on both empirical and conceptual grounds, it is not currently clear that mutations alone can explain the later stages of cancer. Can a different type of molecular change do better? Here, I introduce the "permissive binding theory" of cancer, which proposes that novel protein binding interactions are the key causal and explanatory entity in invasion and metastasis. It posits that binding is more abundant at baseline than we observe because it is restricted in normal physiology; that any large perturbation to physiological state revives this baseline abundance, unleashing many new binding interactions; and that a subset of these cause the cellular functions at the heart of oncogenesis, especially invasion and metastasis. Significant physiological perturbations occur in cancer cells in very early stages, and generally become more extreme with progression, providing interactions that continually fuel invasion and metastasis. The theory is compatible with, but not limited to, causal roles for the diverse molecular changes observed in cancer (e.g. gene expression or epigenetic changes), as these generally act causally upstream of proteins, and so may exert their effects by changing the protein binding interactions that occur in the cell. This admits the possibility that molecular changes that appear quite different may actually converge in creating the same few protein complexes, simplifying our picture of invasion and metastasis. If correct, the theory offers a concrete therapeutic strategy: targeting the key novel complexes. The theory is straightforwardly testable by large-scale identification of protein interactions in different cancers.

Gαs is dispensable for β-arrestin coupling but dictates GRK selectivity and is predominant for gene expression regulation by β2-adrenergic receptor

β-arrestins play a key role in G protein-coupled receptor (GPCR) internalization, trafficking, and signaling. Whether β-arrestins act independently of G protein-mediated signaling has not been fully elucidated. Studies using genome-editing approaches revealed that whereas G proteins are essential for mitogen-activated protein kinase activation by GPCRs., β-arrestins play a more prominent role in signal compartmentalization. However, in the absence of G proteins, GPCRs may not activate β-arrestins, thereby limiting the ability to distinguish G protein from β-arrestin-mediated signaling events. We used β2-adrenergic receptor (β2AR) and its β2AR-C tail mutant expressed in human embryonic kidney 293 cells wildtype or CRISPR-Cas9 gene edited for Gαs, β-arrestin1/2, or GPCR kinases 2/3/5/6 in combination with arrestin conformational sensors to elucidate the interplay between Gαs and β-arrestins in controlling gene expression. We found that Gαs is not required for β2AR and β-arrestin conformational changes, β-arrestin recruitment, and receptor internalization, but that Gαs dictates the GPCR kinase isoforms involved in β-arrestin recruitment. By RNA-Seq analysis, we found that protein kinase A and mitogen-activated protein kinase gene signatures were activated by stimulation of β2AR in wildtype and β-arrestin1/2-KO cells but absent in Gαs-KO cells. These results were validated by re-expressing Gαs in the corresponding KO cells and silencing β-arrestins in wildtype cells. These findings were extended to cellular systems expressing endogenous levels of β2AR. Overall, our results support that Gs is essential for β2AR-promoted protein kinase A and mitogen-activated protein kinase gene expression signatures, whereas β-arrestins initiate signaling events modulating Gαs-driven nuclear transcriptional activity.

Synaptic plasticity via receptor tyrosine kinase/G protein-coupled receptor crosstalk

Cellular signaling involves a large repertoire of membrane receptors operating in overlapping spatiotemporal regimes and targeting many common intracellular effectors. However, both the molecular mechanisms and physiological roles of crosstalk between receptors, especially those from different superfamilies, are poorly understood. We find that the receptor tyrosine kinase (RTK), TrkB, and the G protein-coupled receptor (GPCR), metabotropic glutamate receptor 5 (mGluR5), together mediate a novel form of hippocampal synaptic plasticity in response to brain-derived neurotrophic factor (BDNF). Activated TrkB enhances constitutive mGluR5 activity to initiate a mode-switch that drives BDNF-dependent sustained, oscillatory Ca 2+ signaling and enhanced MAP kinase activation. This crosstalk is mediated, in part, by synergy between Gβγ, released by TrkB, and Gα q -GTP, released by mGluR5, to enable a previously unidentified form of physiologically relevant RTK/GPCR crosstalk.

Identifying Significantly Perturbed Subnetworks in Cancer Using Multiple Protein-Protein Interaction Networks

BACKGROUND

The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms.

METHODS

In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously.

RESULTS

To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

Diffusion-enhanced characterization of 3D chromatin structure reveals its linkage to gene regulatory networks and the interactome

The interactome networks at the DNA, RNA, and protein levels are crucial for cellular functions, and the diverse variations of these networks are heavily involved in the establishment of different cell states. We have developed a diffusion-based method, Hi-C to geometry (CTG), to obtain reliable geometric information on the chromatin from Hi-C data. CTG produces a consistent and reproducible framework for the 3D genomic structure and provides a reliable and quantitative understanding of the alterations of genomic structures under different cellular conditions. The genomic structure yielded by CTG serves as an architectural blueprint of the dynamic gene regulatory network, based on which cell-specific correspondence between gene-gene and corresponding protein-protein physical interactions, as well as transcription correlation, is revealed. We also find that gene fusion events are significantly enriched between genes of short CTG distances and are thus close in 3D space. These findings indicate that 3D chromatin structure is at least partially correlated with downstream processes such as transcription, gene regulation, and even regulatory networking through affecting protein-protein interactions.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Advances in tooth agenesis and tooth regeneration

The lack of treatment options for congenital (0.1%) and partial (10%) tooth anomalies highlights the need to develop innovative strategies. Over two decades of dedicated research have led to breakthroughs in the treatment of congenital and acquired tooth loss. We revealed that by inactivating USAG-1, congenital tooth agenesis can be successfully ameliorated during early tooth development and that the inactivation promotes late-stage tooth morphogenesis in double knockout mice. Furthermore, Anti- USAG-1 antibody treatment in mice is effective in tooth regeneration and can be a breakthrough in treating tooth anomalies in humans. With approximately 0.1% of the population suffering from congenital tooth agenesis and 10% of children worldwide suffering from partial tooth loss, early diagnosis will improve outcomes and the quality of life of patients. Understanding the role of pathogenic USAG-1 variants, their interacting gene partners, and their protein functions will help develop critical biomarkers. Advances in next-generation sequencing, mass spectrometry, and imaging technologies will assist in developing companion and predictive biomarkers to help identify patients who will benefit from tooth regeneration.

SEC-TMT facilitates quantitative differential analysis of protein interaction networks

The majority of cellular proteins interact with at least one partner or assemble into molecular-complexes to exert their function. This network of protein-protein interactions (PPIs) and the composition of macromolecular machines differ between cell types and physiological conditions. Therefore, characterizing PPI networks and their dynamic changes is vital for discovering novel biological functions and underlying mechanisms of cellular processes. However, producing an in-depth, global snapshot of PPIs from a given specimen requires measuring tens to thousands of LC-MS/MS runs. Consequently, while recent works made seminal contributions by mapping PPIs at great depth, almost all focused on just 1-2 conditions, generating comprehensive but mostly static PPI networks. In this study we report the development of SEC-TMT, a method that enables identifying and measuring PPIs in a quantitative manner from only 4-8 LC-MS/MS runs per biological sample. This was accomplished by incorporating tandem mass tag (TMT) multiplexing with a size exclusion chromatography mass spectrometry (SEC-MS) work-flow. SEC-TMT reduces measurement time by an order of magnitude while maintaining resolution and coverage of thousands of cellular interactions, equivalent to the gold standard in the field. We show that SEC-TMT provides benefits for conducting differential analyses to measure changes in the PPI network between conditions. This development makes it feasible to study dynamic systems at scale and holds the potential to drive more rapid discoveries of PPI impact on cellular processes.

Oncogenic mutations of PIK3CA lead to increased membrane recruitment driven by reorientation of the ABD, p85 and C-terminus

PIK3CA encoding the phosphoinositide 3-kinase (PI3K) p110α catalytic subunit is frequently mutated in cancer, with mutations occurring widely throughout the primary sequence. The full set of mechanisms underlying how PI3Ks are activated by all oncogenic mutations on membranes are unclear. Using a synergy of biochemical assays and hydrogen deuterium exchange mass spectrometry (HDX-MS), we reveal unique regulatory mechanisms underlying PI3K activation. Engagement of p110α on membranes leads to disengagement of the ABD of p110α from the catalytic core, and the C2 domain from the iSH2 domain of the p85 regulatory subunit. PI3K activation also requires reorientation of the p110α C-terminus, with mutations that alter the inhibited conformation of the C-terminus increasing membrane binding. Mutations at the C-terminus (M1043I/L, H1047R, G1049R, and N1068KLKR) activate p110α through distinct mechanisms, with this having important implications for mutant selective inhibitor development. This work reveals unique mechanisms underlying how PI3K is activated by oncogenic mutations, and explains how double mutants can synergistically increase PI3K activity.

Where protein structure and cell diversity meet

Protein-protein interaction networks - interactomes - are charted with the hope to understand how phenotypes emerge and how they are altered in disease states. Early efforts to map interactomes have focused on the assembly of context agnostic, reference networks. However, recent studies have mapped interactomes across different cell lines and tissues, finding highly variable interactomes due to the rewiring of protein-protein interactions in different contexts. Increasing evidence points to significant links between protein structure and interactome diversity seen across cell types and tissues. We discuss how recent findings support the key role of alternative splicing and phosphorylation, two well-established regulators of protein structural and functional diversity, in defining cell type- and tissue-specific interactomes. Moreover, we show that intrinsically disordered protein regions are most favorably equipped to support interactome rewiring by acting as hubs of protein structure and function regulation.

Multi-omics peripheral and core regions of cancer

Thousands of genes are perturbed by cancer, and these disturbances can be seen in transcriptome, methylation, somatic mutation, and copy number variation omics studies. Understanding their connectivity patterns as an omnigenic neighbourhood in a molecular interaction network (interactome) is a key step towards advancing knowledge of the molecular mechanisms underlying cancers. Here, we introduce a unified connectivity line (CLine) to pinpoint omics-specific omnigenic patterns across 15 curated cancers. Taking advantage of the universality of CLine, we distinguish the peripheral and core genes for each omics aspect. We propose a network-based framework, multi-omics periphery and core (MOPC), to combine peripheral and core genes from different omics into a button-like structure. On the basis of network proximity, we provide evidence that core genes tend to be specifically perturbed in one omics, but the peripheral genes are diversely perturbed in multiple omics. And the core of one omics is regulated by multiple omics peripheries. Finally, we take the MOPC as an omnigenic neighbourhood, describe its characteristics, and explore its relative contribution to network-based mechanisms of cancer. We were able to present how multi-omics perturbations percolate through the human interactome and contribute to an integrated periphery and core.

CORUM: the comprehensive resource of mammalian protein complexes-2022

The CORUM database has been providing comprehensive reference information about experimentally characterized, mammalian protein complexes and their associated biological and biomedical properties since 2007. Given that most catalytic and regulatory functions of the cell are carried out by protein complexes, their composition and characterization is of greatest importance in basic and disease biology. The new CORUM 4.0 release encompasses 5204 protein complexes offering the largest and most comprehensive publicly available dataset of manually curated mammalian protein complexes. The CORUM dataset is built from 5299 different genes, representing 26% of the protein coding genes in humans. Complex information from 3354 scientific articles is mainly obtained from human (70%), mouse (16%) and rat (9%) cells and tissues. Recent curation work includes sets of protein complexes, Functional Complex Groups, that offer comprehensive collections of published data in specific biological processes and molecular functions. In addition, a new graphical analysis tool was implemented that displays co-expression data from the subunits of protein complexes. CORUM is freely accessible at http://mips.helmholtz-muenchen.de/corum/.

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

Development and validation of a novel hypoxia-related signature for prognostic and immunogenic evaluation in head and neck squamous cell carcinoma

Hypoxia plays a critical role in head and neck squamous cell carcinoma (HNSCC) prognosis. However, till now, robust and reliable hypoxia-related prognostic signatures have not been established for an accurate prognostic evaluation in HNSCC patients. This article focused on establishing a risk score model to evaluate the prognosis and guide treatment for HNSCC patients. RNA-seq data and clinical information of 502 HNSCC patients and 44 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database. 433 samples from three Gene Expression Omnibus (GEO) datasets were incorporated as an external validation cohort. In the training cohort, prognostic-related genes were screened and LASSO regression analyses were performed for signature establishment. A scoring system based on SRPX, PGK1, STG1, HS3ST1, CDKN1B, and HK1 showed an excellent prediction capacity for an overall prognosis for HNSCC patients. Patients were divided into high- and low-risk groups, and the survival status of the two groups exhibited a statistically significant difference. Subsequently, gene set enrichment analysis (GSEA) was carried out to explore the underlying mechanisms for the prognosis differences between the high- and low-risk groups. The tumor immune microenvironment was evaluated by CIBERSORT, ESTIMATE, TIDE, and xCell algorithm, etc. Then, we explored the relationships between this prognostic model and the levels of immune checkpoint-related genes. Cox regression analysis and nomogram plot indicated the scoring system was an independent predictor for HNSCC. Moreover, a comparison of predictive capability has been made between the present signature and existing prognostic signatures for HNSCC patients. Finally, we detected the expression levels of proteins encoded by six-HRGs via immunohistochemical analysis in tissue microarray. Collectively, a novel integrated signature considering both HRGs and clinicopathological parameters will serve as a prospective candidate for the prognostic evaluation of HNSCC patients.

IQGAP1 and RNA Splicing in the Context of Head and Neck via Phosphoproteomics

IQGAP1 (IQ motif-containing GTPase-activating protein 1) scaffolds several signaling pathways in mammalian cells that are implicated in carcinogenesis, including the RAS and PI3K pathways that involve multiple protein kinases. IQGAP1 has been shown to promote head and neck squamous cell carcinoma (HNSCC); however, the underlying mechanism(s) remains unclear. Here, we report a mass spectrometry-based analysis identifying differences in phosphorylation of cellular proteins in vivo and in vitro in the presence or absence of IQGAP1. By comparing the esophageal phosphoproteome profiles between Iqgap1+/+ and Iqgap1-/- mice, we identified RNA splicing as one of the most altered cellular processes. Serine/arginine-rich splicing factor 6 (SRSF6) was the protein with the most downregulated levels of phosphorylation in Iqgap1-/- tissue. We confirmed that the absence of IQGAP1 reduced SRSF6 phosphorylation both in vivo and in vitro. We then expanded our analysis to human normal oral keratinocytes. Again, we found factors involved in RNA splicing to be highly altered in the phosphoproteome profile upon genetic disruption of IQGAP1. Both the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Cancer Genome Atlas (TCGA) data sets indicate that phosphorylation of splicing-related proteins is important in HNSCC prognosis. The Biological General Repository for Interaction Datasets (BioGRID) repository also suggested multiple interactions between IQGAP1 and splicing-related proteins. Based on these collective observations, we propose that IQGAP1 regulates the phosphorylation of splicing proteins, which potentially affects their splicing activities and, therefore, contributes to HNSCC. Raw data are available from the MassIVE database with identifier MSV000087770.

Activation of circ_0072088/miR-1261/PIK3CA pathway accelerates lung adenocarcinoma progression

BACKGROUND

Circular RNAs (circRNAs) are involved in the tumorigenesis and progression of lung adenocarcinoma (LUAD). This study aimed to determine the role of circ_0072088 in LUAD.

METHODS

The existence and expression of circ_0072088 in human LUAD tissues and cell lines were determined through Sanger sequencing, quantitative reverse transcription-polymerase chain reaction, and fluorescence in situ hybridization (FISH). Subsequently, the biological role of circ_0072088 was examined using loss-of-function assays in H1299 cells. Moreover, circ_0072088/miR-1261/PIK3CA pathway-mediated biological effects in H1299 were verified using bioinformatic prediction and experiments, including interaction analysis (FISH, luciferase reporter, and RNA-pulldown assays), and tumor biological function test (CCK8 and colony formation, wound healing, and transwell assays). Finally, miR-1261 and PIK3CA expression and LUAD patient survival were further analyzed using FISH, immunohistochemical staining, and the Kaplan-Meier plotter database, respectively.

RESULTS

First, an increase in circ_0072088 was confirmed in human LUAD tissues. Thereafter, it was mainly localized in the cytoplasm and was found to enhance cell proliferation, migration, and invasion of H1299 cells. Mechanistically, circ_0072088 directly downregulated miR-1261 expression, whereas increased PIK3CA gene expression was associated with poor overall survival of LUAD patients. The activation of the circ_0072088/miR-1261/PIK3CA regulatory pathway may play a significant role in the tumorigenesis and progression of LUAD.

CONCLUSIONS

Circ_0072088-dependent regulation of miR-1261/PIK3CA is important for cell proliferation, migration, and invasion during the tumorigenesis and progression of LUAD, warranting the need to consider the circ_0072088/miR-1261/PIK3CA regulatory pathway as a potential therapeutic target in patients with lung adenocarcinoma.

From systems to structure - using genetic data to model protein structures

Understanding the effects of genetic variation is a fundamental problem in biology that requires methods to analyse both physical and functional consequences of sequence changes at systems-wide and mechanistic scales. To achieve a systems view, protein interaction networks map which proteins physically interact, while genetic interaction networks inform on the phenotypic consequences of perturbing these protein interactions. Until recently, understanding the molecular mechanisms that underlie these interactions often required biophysical methods to determine the structures of the proteins involved. The past decade has seen the emergence of new approaches based on coevolution, deep mutational scanning and genome-scale genetic or chemical-genetic interaction mapping that enable modelling of the structures of individual proteins or protein complexes. Here, we review the emerging use of large-scale genetic datasets and deep learning approaches to model protein structures and their interactions, and discuss the integration of structural data from different sources.

Oncogenic mutations ofPIK3CAlead to increased membrane recruitment driven by reorientation of the ABD, p85 and C-terminus.. [DOI: 10.1101/2022.04.05.487205]

PIK3CAencoding the phosphoinositide 3-kinase (PI3K) p110α catalytic subunit is frequently mutated in cancer, with mutations occurring widely throughout the primary sequence. The full set of mechanisms underlying how PI3Ks are activated by all oncogenic mutations on membranes are unclear. Using a synergy of biochemical assays and hydrogen deuterium exchange mass spectrometry (HDX-MS), we reveal unique regulatory mechanisms underlying PI3K activation. Engagement of p110α on membranes leads to disengagement of the ABD of p110α from the catalytic core, and the C2 domain from the iSH2 domain of the p85 regulatory subunit. PI3K activation also requires reorientation of the p110α C-terminus, with mutations that alter the inhibited conformation of the C-terminus increasing membrane binding. Mutations at the C-terminus (M1043I/L, H1047R, G1049R, and N1068KLKR) activate p110α through distinct mechanisms, with this having important implications for mutant selective inhibitor development. This work reveals unique mechanisms underlying how PI3K is activated by oncogenic mutations, and explains how double mutants can synergistically increase PI3K activity.

Application of modular response analysis to medium- to large-size biological systems

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.

Analysis of the epidermal growth factor receptor/phosphoinositide‐dependent protein kinase‐1 axis in tumor of the external auditory canal in response to epidermal growth factor stimulation

Objectives

The epidermal growth factor receptor (EGFR) is related to the invasion and metastasis of external auditory canal (EAC) squamous cell carcinoma (SCC). The phosphoinositide‐dependent protein kinase‐1 (PDPK1) accelerates tumor cell growth through anti‐apoptotic signaling under the influence of downstream EGFR‐mediated signaling pathways. In this study, we investigated the EGFR/PDPK1 axis in the EAC under EGF stimulation.

Methods

We confirmed EGFR and PDPK1 expression in human EACSCC specimens immunohistochemically. We next transfected the EGF expression vector in the mouse EAC and then conducted a PDPK1 inhibitory experiment. Immunohistochemical analysis was performed in the mouse EAC, using anti‐EGF, anti‐EGFR, anti‐PDPK1, and anti‐Ki67 antibodies. Immunohistochemical analysis of cleaved caspase‐3 and terminal deoxy(d)‐UTP nick end labeling (TUNEL) detection assays were also performed for the assessment of apoptosis in the inhibitory experiment.

Results

Immunohistochemical analysis revealed overexpression and colocalization of EGFR and PDPK1 in human EACSCC specimens. The growth of a protuberant tumor was observed in the mouse EAC in which EGF expression vector was transfected, and EGF, EGFR, PDPK1, and Ki67 labeling indexes (LIs) were significantly increased. PDPK1 inhibition then induced normal epithelial appearance in the EAC. Moreover, EGF, EGFR, PDPK1, and Ki67 LIs were decreased, and cleaved caspase‐3 and TUNEL LIs were increased in the EAC.

Conclusion

We demonstrated the possibility that PDPK1 plays an important role in EACSCC.

Level of Evidence: NA.

Contemporary Molecular Analyses of Malignant Tumors for Precision Treatment and the Implication in Oral Squamous Cell Carcinoma

New molecular tests and methods, in addition to morphology-based diagnosis, are widely used as a new standard of care in many tumors. “One-size-fits-all medicine” is now shifting to precision medicine. This review is intended to discuss the key steps toward to development of precision medicine and its implication in oral squamous cell carcinoma. The challenges and opportunities of precision medicine in oral cancer will be sequentially discussed based on the four steps of precision medicine: identification/detection, diagnosis, treatment and monitoring.

Understanding the Pattern of Oropharyngeal Cancers from North-East Romanian Patients

Background: Human papilloma virus (HPV) is acknowledged as a risk factor for oropharyngeal squamous cellular cancers (OPSCC), of which the dominant types are tonsillar (TSCC) and base of tongue cancer (BOTSCC). Objective: To assess the role of HPV in selected OPSCC cases, from Romanian patients by sensitive and complementary molecular assays. Material and Methods: Fifty-four formalin fixed paraffin embedded (FFPE) OPSCC samples were analyzed for HPV DNA by a PCR-based bead-based multiplex-assay. Thirty-four samples were tested for HPV RNA and for overexpression of p16INK4a by immunohistochemistry. Twenty samples were evaluated by Competitive Allele-Specific Taqman PCR (CAST-PCR) for fibroblast growth factor receptor 3 protein (FGFR3) status. Results: A total of 33.3% (18/54) OPSCC samples were positive for HPV DNA. HPV16 was the most frequent type (30%, 16/54); followed by HPV18 (3.7%, 2/54); and 1 sample (1.8%) was positive for both HPV16 and 18. HPV18 E6*I was detected in a HPV18 DNA-positive oropharynx tumor. Four samples positive for HPV16 were also positive for p16INK4a. All the tested samples were negative for FGFR3. Conclusions: The increased HPV16 prevalence is in line with similar studies and is a new confirmation that HPV16 is the most prevalent type in our country; supporting the potential benefit of prophylactic vaccines. Overall, there is no concordance between DNA and any of the two other analytes that are considered being markers of HPV-driven cancers. There is a need to explore novel screening strategies that could be broadly used in the clinical routine to initiate preventive measures.

Identifying cancer drivers

[Figure: see text].

A protein interaction landscape of breast cancer

[Figure: see text].