Cryo-electron Microscopic Analysis of Single-Pass Transmembrane Receptors

Identification of Novel Gene Signature Predicting Lymph Node Metastasis in Papillary Thyroid Cancer via Bioinformatics Analysis and in vitro Validation

Background

Although with a good prognosis of papillary thyroid cancer (PTC), patients with PTC and also experiencing lymph node metastasis (LNM) had higher recurrence and mortality rates. Therefore it was essential to explore novel biomarkers or methods to predict and evaluate the situation in the stages of PTC.

Methods

In this study, mRNA sequence datasets from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were utilized to obtain differentially expressed genes (DEGs) between PTC tumors and normal specimens and DEGs related to lymph node metastasis were identified using weighted gene co-expression network analysis (WGCNA) according to the clinical information. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to query the biological functions and pathways. Furthermore, a protein-protein interaction (PPI) network was constructed using a STRING database and a prognosis model was established using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis based on the LNM-related DEGs. Finally, six hub genes were identified and verified in vitro experiments.

Results

A novel six-gene signature model including COL8A2, MET, FN1, MPZL2, PDLIM4 and CLDN10 was established based on a total of 52 DEGs from the intersection of LNM-related modules identified by WGNCA from TCGA, THCA and GSE60542 to predict the situation of lymph node metastasis in PTC. Those six hub genes were all more highly expressed in PTC tumors and played potential biological functions on the development of PTC in in vitro experiments, which had potential values as diagnostic and therapeutic targets.

Artificial intelligence in drug development: reshaping the therapeutic landscape

Artificial intelligence (AI) is transforming medication research and development, giving clinicians new treatment options. Over the past 30 years, machine learning, deep learning, and neural networks have revolutionized drug design, target identification, and clinical trial predictions. AI has boosted pharmaceutical R&D (research and development) by identifying new therapeutic targets, improving chemical designs, and predicting complicated protein structures. Furthermore, generative AI is accelerating the development and re-engineering of medicinal molecules to cater to both common and rare diseases. Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered "black boxes," making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. Despite these obstacles, AI-driven drug discovery has substantially reduced development times and costs, expediting the process and financial risks of bringing new medicines to market. In the future, AI is expected to continue to impact pharmaceutical innovation positively, making life-saving drug discoveries faster, more efficient, and more widespread.

The MET Oncogene Network of Interacting Cell Surface Proteins

The MET oncogene, encoding the hepatocyte growth factor (HGF) receptor, plays a key role in tumorigenesis, invasion, and resistance to therapy, yet its full biological functions and activation mechanisms remain incompletely understood. A feature of MET is its extensive interaction network, encompassing the following: (i) receptor tyrosine kinases (RTKs); (ii) co-receptors (e.g., CDCP1, Neuropilin1); (iii) adhesion molecules (e.g., integrins, tetraspanins); (iv) proteases (e.g., ADAM10); and (v) other receptors (e.g., CD44, plexins, GPCRs, and NMDAR). These interactions dynamically modulate MET's activation, signaling, intracellular trafficking, and degradation, enhancing its functional versatility and oncogenic potential. This review offers current knowledge on MET's partnerships, focusing on their functional impact on signaling output, therapeutic resistance, and cellular behavior. Finally, we evaluate emerging combination therapies targeting MET and its interactors, highlighting their potential to overcome resistance and improve clinical outcomes. By exploring the complex interplay within the MET network of interacting cell surface proteins, this review provides insights into advancing anti-cancer strategies and understanding the broader implications of RTK crosstalk in oncology.

Engineering signalling pathways in mammalian cells

In mammalian cells, signalling pathways orchestrate cellular growth, differentiation and survival, as well as many other processes that are essential for the proper functioning of cells. Here we describe cutting-edge genetic-engineering technologies for the rewiring of signalling networks in mammalian cells. Specifically, we describe the recombination of native pathway components, cross-kingdom pathway transplantation, and the development of de novo signalling within cells and organelles. We also discuss how, by designing signalling pathways, mammalian cells can acquire new properties, such as the capacity for photosynthesis, the ability to detect cancer and senescent cell markers or to synthesize hormones or metabolites in response to chemical or physical stimuli. We also review the applications of mammalian cells in biocomputing. Technologies for engineering signalling pathways in mammalian cells are advancing basic cellular biology, biomedical research and drug discovery.

Structure and organization of full-length Epidermal Growth Factor Receptor in extracellular vesicles by cryo-electron tomography

We report here transport of the Epidermal Growth Factor Receptor (EGFR), Insulin Receptor, 7-pass transmembrane receptor Smoothened, and 13-pass Sodium-iodide symporter to extracellular vesicles (EVs) for structural and functional studies. Mass spectrometry confirmed the transported proteins as the most abundant in EV membranes, and the presence of many receptor-interacting proteins demonstrates the utility of EVs for characterizing membrane protein interactomes. Cryo-electron tomography of EGFR-containing EVs reveals that EGFR forms clusters in the presence of EGF with a ∼3 nm gap between the inner membrane and cytoplasmic density. EGFR extracellular regions do not form regular arrays, suggesting that clustering is mediated by the intracellular region. Subtomogram averaging of the EGFR extracellular region (ECR) yielded a 15 Å map into which the crystal structure of the ligand-bound EGFR ECR dimer fits well. These findings refine our understanding of EGFR activation, clustering, and signaling, and they establish EVs as a versatile platform for structural and functional characterization of human membrane proteins in a native-like environment.

Significance Statement

Atomic or near-atomic resolution structural studies of proteins embedded in cell membranes have proven challenging. We show that transporting integral membrane proteins to cell-derived extracellular vesicles enables structural and functional studies of human membrane proteins in a native membrane environment. We have used this approach to visualize an active form of full-length Epidermal Growth Factor Receptor (EGFR) and show that it forms clusters in the membrane and projects its cytoplasmic signaling domains ∼3 nm away from the membrane surface. EGFR is essential for normal development, but abnormal EGFR activity is associated with several human cancers and is the target of many anticancer therapies. Our studies refine current models of how ligand binding to EGFR transmits signals across cell membranes.

Exploring structure-function relationships in engineered receptor performance using computational structure prediction

Engineered receptors play increasingly important roles in transformative cell-based therapies. However, the structural mechanisms that drive differences in performance across receptor designs are often poorly understood. Recent advances in protein structural prediction tools have enabled the modeling of virtually any user-defined protein, but how these tools might build understanding of engineered receptors has yet to be fully explored. In this study, we employed structural modeling tools to perform post hoc analyses to investigate whether predicted structural features might explain observed functional variation. We selected a recently reported library of receptors derived from natural cytokine receptors as a case study, generated structural models, and from these predictions quantified a set of structural features that plausibly impact receptor performance. Encouragingly, for a subset of receptors, structural features explained considerable variation in performance, and trends were largely conserved across structurally diverse receptor sets. This work indicates potential for structure prediction-guided synthetic receptor engineering.

Exploring Novel Antidepressants Targeting G Protein-Coupled Receptors and Key Membrane Receptors Based on Molecular Structures

Major Depressive Disorder (MDD) is a complex mental disorder that involves alterations in signal transmission across multiple scales and structural abnormalities. The development of effective antidepressants (ADs) has been hindered by the dominance of monoamine hypothesis, resulting in slow progress. Traditional ADs have undesirable traits like delayed onset of action, limited efficacy, and severe side effects. Recently, two categories of fast-acting antidepressant compounds have surfaced, dissociative anesthetics S-ketamine and its metabolites, as well as psychedelics such as lysergic acid diethylamide (LSD). This has led to structural research and drug development of the receptors that they target. This review provides breakthroughs and achievements in the structure of depression-related receptors and novel ADs based on these. Cryo-electron microscopy (cryo-EM) has enabled researchers to identify the structures of membrane receptors, including the N-methyl-D-aspartate receptor (NMDAR) and the 5-hydroxytryptamine 2A (5-HT2A) receptor. These high-resolution structures can be used for the development of novel ADs using virtual drug screening (VDS). Moreover, the unique antidepressant effects of 5-HT1A receptors in various brain regions, and the pivotal roles of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) and tyrosine kinase receptor 2 (TrkB) in regulating synaptic plasticity, emphasize their potential as therapeutic targets. Using structural information, a series of highly selective ADs were designed based on the different role of receptors in MDD. These molecules have the favorable characteristics of rapid onset and low adverse drug reactions. This review offers researchers guidance and a methodological framework for the structure-based design of ADs.

Aberrant MET Receptor Tyrosine Kinase Signaling in Glioblastoma: Targeted Therapy and Future Directions

Brain tumors represent a heterogeneous group of neoplasms characterized by a high degree of aggressiveness and a poor prognosis. Despite recent therapeutic advances, the treatment of brain tumors, including glioblastoma (GBM), an aggressive primary brain tumor associated with poor prognosis and resistance to therapy, remains a significant challenge. Receptor tyrosine kinases (RTKs) are critical during development and in adulthood. Dysregulation of RTKs through activating mutations and gene amplification contributes to many human cancers and provides attractive therapeutic targets for treatment. Under physiological conditions, the Met RTK, the hepatocyte growth factor/scatter factor (HGF/SF) receptor, promotes fundamental signaling cascades that modulate epithelial-to-mesenchymal transition (EMT) involved in tissue repair and embryogenesis. In cancer, increased Met activity promotes tumor growth and metastasis by providing signals for proliferation, survival, and migration/invasion. Recent clinical genomic studies have unveiled multiple mechanisms by which MET is genetically altered in GBM, including focal amplification, chromosomal rearrangements generating gene fusions, and a splicing variant mutation (exon 14 skipping, METex14del). Notably, MET overexpression contributes to chemotherapy resistance in GBM by promoting the survival of cancer stem-like cells. This is linked to distinctive Met-induced pathways, such as the upregulation of DNA repair mechanisms, which can protect tumor cells from the cytotoxic effects of chemotherapy. The development of MET-targeted therapies represents a major step forward in the treatment of brain tumours. Preclinical studies have shown that MET-targeted therapies (monoclonal antibodies or small molecule inhibitors) can suppress growth and invasion, enhancing the efficacy of conventional therapies. Early-phase clinical trials have demonstrated promising results with MET-targeted therapies in improving overall survival for patients with recurrent GBM. However, challenges remain, including the need for patient stratification, the optimization of treatment regimens, and the identification of mechanisms of resistance. This review aims to highlight the current understanding of mechanisms underlying MET dysregulation in GBM. In addition, it will focus on the ongoing preclinical and clinical assessment of therapies targeting MET dysregulation in GBM.

Exploring the World of Membrane Proteins: Techniques and Methods for Understanding Structure, Function, and Dynamics

In eukaryotic cells, membrane proteins play a crucial role. They fall into three categories: intrinsic proteins, extrinsic proteins, and proteins that are essential to the human genome (30% of which is devoted to encoding them). Hydrophobic interactions inside the membrane serve to stabilize integral proteins, which span the lipid bilayer. This review investigates a number of computational and experimental methods used to study membrane proteins. It encompasses a variety of technologies, including electrophoresis, X-ray crystallography, cryogenic electron microscopy (cryo-EM), nuclear magnetic resonance spectroscopy (NMR), biophysical methods, computational methods, and artificial intelligence. The link between structure and function of membrane proteins has been better understood thanks to these approaches, which also hold great promise for future study in the field. The significance of fusing artificial intelligence with experimental data to improve our comprehension of membrane protein biology is also covered in this paper. This effort aims to shed light on the complexity of membrane protein biology by investigating a variety of experimental and computational methods. Overall, the goal of this review is to emphasize how crucial it is to understand the functions of membrane proteins in eukaryotic cells. It gives a general review of the numerous methods used to look into these crucial elements and highlights the demand for multidisciplinary approaches to advance our understanding.

The Activation Mechanism of the Insulin Receptor: A Structural Perspective

The insulin receptor (IR) is a type II receptor tyrosine kinase that plays essential roles in metabolism, growth, and proliferation. Dysregulation of IR signaling is linked to many human diseases, such as diabetes and cancers. The resolution revolution in cryo-electron microscopy has led to the determination of several structures of IR with different numbers of bound insulin molecules in recent years, which have tremendously improved our understanding of how IR is activated by insulin. Here, we review the insulin-induced activation mechanism of IR, including (a) the detailed binding modes and functions of insulin at site 1 and site 2 and (b) the insulin-induced structural transitions that are required for IR activation. We highlight several other key aspects of the activation and regulation of IR signaling and discuss the remaining gaps in our understanding of the IR activation mechanism and potential avenues of future research.

The three-dimensional structure of insulin and its receptor

Insulin is a peptide hormone essential for maintaining normal blood glucose levels. Individuals unable to secrete sufficient insulin or not able to respond properly to insulin develop diabetes. Since the discovery of insulin its structure and function has been intensively studied with the aim to develop effective diabetes treatments. The three-dimensional crystal structure of this 51 amino acid peptide paved the way for discoveries, outlined in this review, of determinants important for receptor binding and hormone stability that have been instrumental in development of insulin analogs used in the clinic today. Important for the future development of effective diabetes treatments will be a detailed understanding of the insulin receptor structure and function. Determination of the three-dimensional structure of the insulin receptor, a receptor tyrosine kinase, proved challenging but with the recent advent of high-resolution cryo-electron microscopy significant progress has been made. There are now >40 structures of the insulin:insulin receptor complex deposited in the Protein Data Bank. From these structures we have a detailed picture of how insulin binds and activates the receptor. Still lacking are details of the initial binding events and the exact sequence of structural changes within the receptor and insulin. In this review, the focus will be on the most recent structural studies of insulin:insulin receptor complexes and how they have contributed to the current understanding of insulin receptor activation and signaling outcome. Molecular mechanisms underlying insulin receptor signaling bias emerging from the latest structures are described.

Molecular basis for the role of disulfide-linked αCTs in the activation of insulin-like growth factor 1 receptor and insulin receptor

The insulin receptor (IR) and insulin-like growth factor 1 receptor (IGF1R) control metabolic homeostasis and cell growth and proliferation. The IR and IGF1R form similar disulfide bonds linked homodimers in the apo-state; however, their ligand binding properties and the structures in the active state differ substantially. It has been proposed that the disulfide-linked C-terminal segment of α-chain (αCTs) of the IR and IGF1R control the cooperativity of ligand binding and regulate the receptor activation. Nevertheless, the molecular basis for the roles of disulfide-linked αCTs in IR and IGF1R activation are still unclear. Here, we report the cryo-EM structures of full-length mouse IGF1R/IGF1 and IR/insulin complexes with modified αCTs that have increased flexibility. Unlike the Γ-shaped asymmetric IGF1R dimer with a single IGF1 bound, the IGF1R with the enhanced flexibility of αCTs can form a T-shaped symmetric dimer with two IGF1s bound. Meanwhile, the IR with non-covalently linked αCTs predominantly adopts an asymmetric conformation with four insulins bound, which is distinct from the T-shaped symmetric IR. Using cell-based experiments, we further showed that both IGF1R and IR with the modified αCTs cannot activate the downstream signaling potently. Collectively, our studies demonstrate that the certain structural rigidity of disulfide-linked αCTs is critical for optimal IR and IGF1R signaling activation.