The conformational and mutational landscape of the ubiquitin-like marker for autophagosome formation in cancer

Downregulation of MYBL1 in endothelial cells contributes to atherosclerosis by repressing PLEKHM1-inducing autophagy

MYBL1 is a strong transcriptional activator involved in the cell signaling. However, there is no systematic study on the role of MYBL1 in atherosclerosis. The aim of this study is to elucidate the role and mechanism of MYBL1 in atherosclerosis. GSE28829, GSE43292 and GSE41571 were downloaded from NCBI for differentially expressed analysis. The expression levels of MYBL1 in atherosclerotic plaque tissue and normal vessels were detected by qRT-PCR, Western blot and Immunohistochemistry. Transwell and CCK-8 were used to detect the migration and proliferation of HUVECs after silencing MYBL1. RNA-seq, Western blot, qRT-PCR, Luciferase reporter system, Immunofluorescence, Flow cytometry, ChIP and CO-IP were used to study the role and mechanism of MYBL1 in atherosclerosis. The microarray data of GSE28829, GSE43292, and GSE41571 were analyzed and intersected, and then MYBL1 were verified. MYBL1 was down-regulated in atherosclerotic plaque tissue. After silencing of MYBL1, HUVECs were damaged, and their migration and proliferation abilities were weakened. Overexpression of MYBL1 significantly enhanced the migration and proliferation of HUVECs. MYBL1 knockdown induced abnormal autophagy in HUVEC cells, suggesting that MYBL1 was involved in the regulation of HUVECs through autophagy. Mechanistic studies showed that MYBL1 knockdown inhibited autophagosome and lysosomal fusion in HUVECs by inhibiting PLEKHM1, thereby exacerbating atherosclerosis. Furthermore, MYBL1 was found to repress lipid accumulation in HUVECs after oxLDL treatment. MYBL1 knockdown in HUVECs was involved in atherosclerosis by inhibiting PLEKHM1-induced autophagy, which provided a novel target of therapy for atherosclerosis.

Identifying CTH and MAP1LC3B as ferroptosis biomarkers for prognostic indication in gastric cancer decoding

Gastric cancer (GC), known for its high incidence and poor prognosis, urgently necessitates the identification of reliable prognostic biomarkers to enhance patient outcomes. We scrutinized data from 375 GC patients alongside 32 non-cancer controls, sourced from the TCGA database. A univariate Cox Proportional Hazards Model (COX) regression was employed to evaluate expressions of ferroptosis-related genes. This was followed by the application of Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate COX regression for the development of prognostic models. The composition of immune cell subtypes was quantified utilizing CIBERSORT, with their distribution in GC versus control samples being comparatively analyzed. Furthermore, the correlation between the expressions of Cystathionine Gamma-Lyase (CTH) and Microtubule Associated Protein 1 Light Chain 3 Beta (MAP1LC3B) and the abundance of immune cell subtypes was explored. Our bioinformatics findings underwent validation through immunohistochemical analysis. Our prognostic models integrated CTH and MAP1LC3B. Survival analysis indicated that patients categorized as high-risk, as defined by the model, exhibited significantly lower survival rates compared to their low-risk counterparts. Notably, CTH expression inversely correlated with monocyte levels, while MAP1LC3B expression showed an inverse relationship with the abundance of M2 macrophages. Immunohistochemical validation corroborated lower expressions of CTH and MAP1LC3B in GC tissues relative to control samples, in concordance with our bioinformatics predictions. Our study suggests that the dysregulation of CTH, MAP1LC3B, and the accompanying monocyte-macrophage dynamics could be pivotal in the prognosis of GC. These elements present potential targets for prognostic assessment and therapeutic intervention.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Fascin - F-actin interaction studied by molecular dynamics simulation and protein network analysis

Actin bundles are an important component of cellular cytoskeleton and participate in the movement of cells. The formation of actin bundles requires the participation of many actin binding proteins (ABPs). Fascin is a member of ABPs, which plays a key role in bundling filamentous actin (F-actin) to bundles. However, the detailed interactions between fascin and F-actin are unclear. In this study, we construct an atomic-level structure of fascin - F-actin complex based on a rather poor cryo-EM data with resolution of 20 nm. We first optimized the geometries of the complex by molecular dynamics (MD) simulation and analyzed the binding site and pose of fascin which bundles two F-actin chains. Next, binding free energy of fascin was calculated by MM/GBSA method. Finally, protein structure network analysis (PSNs) was performed to analyze the key residues for fascin binding. Our results show that residues of K22, E27, E29, K41, K43, R110, R149, K358, R408 and K471 on fascin are important for its bundling, which are in good agreement with the experimental data. On the other hand, the consistent results indicate that the atomic-level model of fascin - F-actin complex is reliable. In short, this model can be used to understand the detailed interactions between fascin and F-actin, and to develop novel potential drugs targeting fascin.Communicated by Ramaswamy H. Sarma.

Post-translational modifications of ATG8 proteins - an emerging mechanism of autophagy control

Autophagy is a recycling mechanism involved in cellular homeostasis with key implications for health and disease. The conjugation of the ATG8 family proteins, which includes LC3B (also known as MAP1LC3B), to autophagosome membranes, constitutes a hallmark of the canonical autophagy process. After ATG8 proteins are conjugated to the autophagosome membranes via lipidation, they orchestrate a plethora of protein-protein interactions that support key steps of the autophagy process. These include binding to cargo receptors to allow cargo recruitment, association with proteins implicated in autophagosome transport and autophagosome-lysosome fusion. How these diverse and critical protein-protein interactions are regulated is still not well understood. Recent reports have highlighted crucial roles for post-translational modifications of ATG8 proteins in the regulation of ATG8 functions and the autophagy process. This Review summarizes the main post-translational regulatory events discovered to date to influence the autophagy process, mostly described in mammalian cells, including ubiquitylation, acetylation, lipidation and phosphorylation, as well as their known contributions to the autophagy process, physiology and disease.

PyInteraph2 and PyInKnife2 to Analyze Networks in Protein Structural Ensembles

Due to the complex nature of noncovalent interactions and their long-range effects, analyzing protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) provide a convenient formalism to study protein structures in relation to essential properties such as key residues for structural stability, allosteric communication, and the effects of modifications of the protein. PSNs can be defined according to very different principles, and the available tools have limitations in input formats, supported models, and version control. Other outstanding problems are related to the definition of network cutoffs and the assessment of the stability of the network properties. The protein science community could benefit from a common framework to carry out these analyses and make them easier to reproduce, reuse, and evaluate. We here provide two open-source software packages, PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a reproducible and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and incorporates different network models with the possibility of integrating them into a macronetwork and performing various downstream analyses, including hubs, connected components, and several other centrality measures, and visualizes the networks or further analyzes them thanks to compatibility with Cytoscape.PyInKnife2 that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. We foresee that the modular structure of the code and the supported version control system will promote the transition to a community-driven effort, boost reproducibility, and establish common protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities and maintenance, assistance, and training of new contributors.

Autophagy/Mitophagy Regulated by Ubiquitination: A Promising Pathway in Cancer Therapeutics

Autophagy is essential for organismal development, maintenance of energy homeostasis, and quality control of organelles and proteins. As a selective form of autophagy, mitophagy is necessary for effectively eliminating dysfunctional mitochondria. Both autophagy and mitophagy are linked with tumor progression and inhibition. The regulation of mitophagy and autophagy depend upon tumor type and stage. In tumors, mitophagy has dual roles: it removes damaged mitochondria to maintain healthy mitochondria and energy production, which are necessary for tumor growth. In contrast, mitophagy has been shown to inhibit tumor growth by mitigating excessive ROS production, thus preventing mutation and chromosomal instability. Ubiquitination and deubiquitination are important modifications that regulate autophagy. Multiple E3 ubiquitin ligases and DUBs modulate the activity of the autophagy and mitophagy machinery, thereby influencing cancer progression. In this review, we summarize the mechanistic association between cancer development and autophagy/mitophagy activities regulated by the ubiquitin modification of autophagic proteins. In addition, we discuss the function of multiple proteins involved in autophagy/mitophagy in tumors that may represent potential therapeutic targets.

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.

Early Stages of Misfolding of PAP248-286 at two different pH values: An Insight from Molecular Dynamics Simulations

PAP248-286 peptides, which are highly abundant in human semen, aggregate and form amyloid fibrils that enhance HIV infection. Previous experimental studies have shown that the infection-promoting activity of PAP248-286 begins to increase well before amyloid formation takes place and that pH plays a key role in the enhancement of PAP248-286-related infection. Hence, understanding the early stages of misfolding of the PAP2482-86 peptide is crucial. To this end, we have performed 60 independent MD simulations for a total of 24 µs at two different pH values (4.2 and 7.2). Our data shows that early stages of misfolding of the PAP248-286 peptide is a multistage process and that the first step of the process is a transition from an “I-shaped” structure to a “U-shaped” structure. We further observed that the structure of PAP248-286 at the two different pH values shows significantly different features. At pH 4.2, the peptide has less intra-molecular H-bonds and a reduced α-helical content than at pH 7.2. Moreover, differences in intra-peptide residues contacts are also observed at the two pH values. Finally, free energy landscape analysis shows that there are more local minima in the energy surface of the peptide at pH 7.2 than at pH 4.2. Overall, the present study elucidates the early stages of misfolding of the PAP248-286 peptide at the atomic level, thus possibly opening new avenues in structure-based drug discovery against HIV infection.

Directed Evolution of (R)-2-Hydroxyglutarate Dehydrogenase Improves 2-Oxoadipate Reduction by 2 Orders of Magnitude

Pathway engineering is commonly employed to improve the production of various metabolites but may incur in bottlenecks due to the low catalytic activity of a particular reaction step. The reduction of 2-oxoadipate to (R)-2-hydroxyadipate is a key reaction in metabolic pathways that exploit 2-oxoadipate conversion via α-reduction to produce adipic acid, an industrially important platform chemical. Here, we engineered (R)-2-hydroxyglutarate dehydrogenase from Acidaminococcus fermentans (Hgdh) with the aim of improving 2-oxoadipate reduction. Using a combination of computational analysis, saturation mutagenesis, and random mutagenesis, three mutant variants with a 100-fold higher catalytic efficiency were obtained. As revealed by rational analysis of the mutations found in the variants, this improvement could be ascribed to a general synergistic effect where mutation A206V played a key role since it boosted the enzyme’s activity by 4.8-fold. The Hgdh variants with increased activity toward 2-oxoadipate generated within this study pave the way for the bio-based production of adipic acid.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Detecting Rewiring Events in Protein-Protein Interaction Networks Based on Transcriptomic Data

Proteins rarely carry out their cellular functions in isolation. Instead, eukaryotic proteins engage in about six interactions with other proteins on average. The aggregated protein interactome of an organism forms a “hairy ball”-type protein-protein interaction (PPI) network. Yet, in a typical human cell, only about half of all proteins are expressed at a particular time. Hence, it has become common practice to prune the full PPI network to the subset of expressed proteins. If RNAseq data is available, one can further resolve the specific protein isoforms present in a cell or tissue. Here, we review various approaches, software tools and webservices that enable users to construct context-specific or tissue-specific PPI networks and how these are rewired between two cellular conditions. We illustrate their different functionalities on the example of the interactions involving the human TNR6 protein. In an outlook, we describe how PPI networks may be integrated with epigenetic data or with data on the activity of splicing factors.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.