A Selective Autophagy Pathway for Phase-Separated Endocytic Protein Deposits
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Wilfling, Florian↗
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A proteomic meta-analysis refinement of plasma extracellular vesicles
Extracellular vesicles play major roles in cell-to-cell communication and are excellent biomarker candidates. However, studying plasma extracellular vesicles is challenging due to contaminants. Here, we performed a proteomics meta-analysis of public data to refine the plasma EV composition by separating EV proteins and contaminants into different clusters. We obtained two clusters with a total of 1717 proteins that were depleted of known contaminants and enriched in EV markers with independently validated 71% true-positive. These clusters had 133 clusters of differentiation (CD) antigens and were enriched with proteins from cell-to-cell communication and signaling. We compared our data with the proteins deposited in PeptideAtlas, making our refined EV protein list a resource for mechanistic and biomarker studies. As a use case example for this resource, we validated the type 1 diabetes biomarker proplatelet basic protein in EVs and showed that it regulates apoptosis of β cells and macrophages, two key players in the disease development. Our approach provides a refinement of the EV composition and a resource for the scientific community.
59 BASIC BIOLOGICAL SCIENCES↗
Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease.
Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.
60 APPLIED LIFE SCIENCES↗
pKPDB: a protein data bank extension database of p Ka and pI theoretical values
Abstract Summary pKa values of ionizable residues and isoelectric points of proteins provide valuable local and global insights about their structure and function. These properties can be estimated with reasonably good accuracy using Poisson–Boltzmann and Monte Carlo calculations at a considerable computational cost (from some minutes to several hours). pKPDB is a database of over 12 M theoretical pKa values calculated over 120k protein structures deposited in the Protein Data Bank. By providing precomputed pKa and pI values, users can retrieve results instantaneously for their protein(s) of interest while also saving countless hours and resources that would be spent on repeated calculations. Furthermore, there is an ever-growing imbalance between experimental pKa and pI values and the number of resolved structures. This database will complement the experimental and computational data already available and can also provide crucial information regarding buried residues that are under-represented in experimental measurements. Availability and implementation Gzipped csv files containing p Ka and isoelectric point values can be downloaded from https://pypka.org/pKPDB. To query a single PDB code please use the PypKa free server at https://pypka.org. The pKPDB source code can be found at https://github.com/mms-fcul/pKPDB. Supplementary information Supplementary data are available at Bioinformatics online.
Reis, Pedro B. P. S. (ORCID:0000000335636239)↗
Protein-activated atomic layer deposition for robust crude-oil-repellent hierarchical nano-armored membranes
Atomic layer deposition (ALD) offers unique capabilities to fabricate atomically engineered porous materials with precise pore tuning and multi-functionalization for diverse applications like advanced membrane separations towards sustainable energy-water systems. However, current ALD technique is inhibited on most non-polar polymeric membranes due to lack of accessible nucleation sites. Here, in this study, we report a facile method to efficiently promote ALD coating on hydrophobic surface of polymeric membranes via novel protein activation/sensitization. As a proof of concept, TiO 2 ALD-coated membranes activated by bovine serum albumin exhibit remarkable superhydrophilicity, ultralow underwater crude oil adhesion, and robust tolerance to rigorous environments including acid, alkali, saline, and ethanol. Most importantly, excellent cyclable crude oil-in-water emulsion separation performance can be achieved. The mechanism for activation/sensitization is rooted in reactivity for a particular set of amino acids. Furthermore, the universality of protein-sensitized ALD is demonstrated using common egg white, promising numerous potential usages in biomedical engineering, environmental remediation, lowcarbon manufacturing, catalysis, and beyond.
36 MATERIALS SCIENCE↗
CryoSheet Jet™: A Novel Deposition and Rapid Cooling Method for Cryogenic Sample Preparation (Final Report)
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59 BASIC BIOLOGICAL SCIENCES↗
Immune activation during Pseudomonas infection causes local cell wall remodeling and alters AGP accumulation
The plant cell boundary generally comprises constituents of the primary and secondary cell wall (CW) that are deposited sequentially during development. Although it is known that the CW acts as a barrier against phytopathogens and undergoes modifications to limit their invasion, the extent, sequence, and requirements of the pathogen-induced modifications of the CW components are still largely unknown, especially at the level of the polysaccharide fraction. To address this significant knowledge gap, we adopted the compatible Pseudomonas syringae–Arabidopsis thaliana system. We found that, despite systemic signaling actuation, Pseudomonas infection leads only to local CW modifications. Furthermore, by utilizing a combination of CW and immune signaling-deficient mutants infected with virulent or non-virulent bacteria, we demonstrated that the pathogen-induced changes in CW polysaccharides depend on the combination of pathogen virulence and the host's ability to mount an immune response. This results in a pathogen-driven accumulation of CW hexoses, such as galactose, and an immune signaling-dependent increase in CW pentoses, mainly arabinose, and xylose. Our analyses of CW changes during disease progression also revealed a distinct spatiotemporal pattern of arabinogalactan protein (AGP) deposition and significant modifications of rhamnogalacturonan sidechains. Furthermore, genetic analyses demonstrated a critical role of AGPs, specifically of the Arabinoxylan Pectin Arabinogalactan Protein1, in limiting pathogen growth. Collectively, our results provide evidence for the actuation of significant remodeling of CW polysaccharides in a compatible host-pathogen interaction, and, by identifying AGPs as critical elements of the CW in plant defense, they pinpoint opportunities to improve plants against diverse pathogens.
59 BASIC BIOLOGICAL SCIENCES↗
Bio-inspired incorporation of phenylalanine enhances ionic selectivity in layer-by-layer deposited polyelectrolyte films
Biological protein ion channels inspire advanced ion selective polyelectrolyte membranes by incorporation of phenylalanine, leading to increased ion selectivity.
36 MATERIALS SCIENCE↗
CERES : a cryo-EM re-refinement system for continuous improvement of deposited models
The field of electron cryomicroscopy (cryo-EM) has advanced quickly in recent years as the result of numerous technological and methodological developments. This has led to an increase in the number of atomic structures determined using this method. Recently, several tools for the analysis of cryo-EM data and models have been developed within the Phenix software package, such as phenix.real_space_refine for the refinement of atomic models against real-space maps. Also, new validation metrics have been developed for low-resolution cryo-EM models. To understand the quality of deposited cryo-EM structures and how they might be improved, models deposited in the Protein Data Bank that have map resolutions of better than 5 Å were automatically re-refined using current versions of Phenix tools. The results are available on a publicly accessible web page (https://cci.lbl.gov/ceres). The implementation of a Cryo-EM Re-refinement System ( CERES ) for the improvement of models deposited in the wwPDB, and the results of the re-refinements, are described. Based on these results, contents are proposed for a `cryo-EM Table 1', which summarizes experimental details and validation metrics in a similar way to `Table 1' in crystallography. The consistent use of robust metrics for the evaluation of cryo-EM models and data should accompany every structure deposition and be reported in scientific publications.
59 BASIC BIOLOGICAL SCIENCES↗
Ion permeation in potassium ion channels
The study of ion channels dates back to the 1950s and the groundbreaking electrophysiology work of Hodgin and Huxley, who used giant squid axons to probe how action potentials in neurons were initiated and propagated. More recently, several experiments using different structural biology techniques and approaches have been conducted to try to understand how potassium ions permeate through the selectivity filter of potassium ion channels. Two mechanisms of permeation have been proposed, and each of the two mechanisms is supported by different experiments. The key structural biology experiments conducted so far to try to understand how ion permeation takes place in potassium ion channels are reviewed and discussed. Protein crystallography has made, and continues to make, key contributions in this field, often through the use of anomalous scattering. Other structural biology techniques used to study the contents of the selectivity filter include solid-state nuclear magnetic resonance and two-dimensional infrared spectroscopy, both of which make clever use of isotopic labeling techniques, while molecular-dynamics simulations of ion flow through the selectivity filter have been enabled by the growing number of potassium ion channel structures deposited in the Protein Data Bank.
59 BASIC BIOLOGICAL SCIENCES↗
What are the current limits on determination of protonation state using neutron macromolecular crystallography?
The rate of deposition of models determined by neutron diffraction, or a hybrid approach that combines X-ray and neutron diffraction, has increased in recent years. The benefit of neutron diffraction is that hydrogen atom (H) positions are detectable, allowing for the determination of protonation state and water molecule orientation. This study analyses all neutron models deposited in the Protein Data Bank to date, focusing on protonation state and properties of H (or deuterium, D) atoms as well as the details of water molecules. In particular, clashes and hydrogen bonds involving H or D atoms are investigated. As water molecules are typically the least reproducible part of a structural model, their positions in neutron models were compared to those in homologous high-resolution X-ray structures. For models determined by joint refinement against X-ray and neutron data, the water structure comparison was also carried out for models re-refined against the X-ray data alone. The homologues have generally fewer conserved water molecules where X-ray only was used and the positions of equivalent waters vary more than in the case of the hybrid X-ray model. As neutron diffraction data are generally less complete than X-ray data, the influence of neutron data completeness on nuclear density maps was also analyzed. Here, we observe and discuss systematic map quality deterioration as result of data incompleteness.
Hydrogen bond↗
Improved joint X-ray and neutron refinement procedure in Phenix
Neutron diffraction is one of the three crystallographic techniques (X-ray, neutron and electron diffraction) used to determine the atomic structures of molecules. Its particular strengths derive from the fact that H (and D) atoms are strong neutron scatterers, meaning that their positions, and thus protonation states, can be derived from crystallographic maps. However, because of technical limitations and experimental obstacles, the quality of neutron diffraction data is typically much poorer (completeness, resolution and signal to noise) than that of X-ray diffraction data for the same sample. Further, refinement is more complex as it usually requires additional parameters to describe the H (and D) atoms. The increase in the number of parameters may be mitigated by using the `riding hydrogen' refinement strategy, in which the positions of H atoms without a rotational degree of freedom are inferred from their neighboring heavy atoms. However, this does not address the issues related to poor data quality. Therefore, neutron structure determination often relies on the presence of an X-ray data set for joint X-ray and neutron (XN) refinement. In this approach, the X-ray data serve to compensate for the deficiencies of the neutron diffraction data by refining one model simultaneously against the X-ray and neutron data sets. To be applicable, it is assumed that both data sets are highly isomorphous, and preferably collected from the same crystals and at the same temperature. However, the approach has a number of limitations that are discussed in this work by comparing four separately re-refined neutron models. To address the limitations, a new method for joint XN refinement is introduced that optimizes two different models against the different data sets. This approach is tested using neutron models and data deposited in the Protein Data Bank. The efficacy of refining models with H atoms as riding or as individual atoms is also investigated.
36 MATERIALS SCIENCE↗
Development of a TSR-based method for understanding structural relationships of cofactors and local environments in photosystem I
All chemical forms of energy and oxygen on Earth are generated via photosynthesis where light energy is converted into redox energy by two photosystems (PS I and PS II). There is an increasing number of PS I 3D structures deposited in the Protein Data Bank (PDB). The Triangular Spatial Relationship (TSR)-based algorithm converts 3D structures into integers (TSR keys). A comprehensive study was conducted, by taking advantage of the PS I 3D structures and the TSR-based algorithm, to answer three questions: (i) Are electron cofactors including P700, A -1 and A 0 , which are chemically identical chlorophylls, structurally different? (ii) There are two electron transfer chains (A and B branches) in PS I. Are the cofactors on both branches structurally different? (iii) Are the amino acids in cofactor binding sites structurally different from those not in cofactor binding sites? The key contributions and important findings include: (i) a novel TSR-based method for representing 3D structures of pigments as well as for quantifying pigment structures was developed; (ii) the results revealed that the redox cofactor, P700, are structurally conserved and different from other redox factors. Similar situations were also observed for both A -1 and A 0 ; (iii) the results demonstrated structural differences between A and B branches for the redox cofactors P700, A -1 , A 0 and A 1 as well as their cofactor binding sites; (iv) the tryptophan residues close to A 0 and A 1 are structurally conserved; (v) The TSR-based method outperforms the Root Mean Square Deviation (RMSD) and the Ultrafast Shape Recognition (USR) methods. The structural analyses of redox cofactors and their binding sites provide a foundation for understanding the unique chemical and physical properties of each redox cofactor in PS I, which are essential for modulating the rate and direction of energy and electron transfers.
59 BASIC BIOLOGICAL SCIENCES↗
Assessing Metal Ion Assignment Accuracy in Protein Data Bank Models via Elemental Spectroscopy
Accurate representation of metal ions in macromolecular structures is critical for chemical interpretation, computational modeling, and machine-learning methods that rely on Protein Data Bank (PDB) entries. However, the elemental identity of metals modeled in crystallographic structures is often inferred indirectly and rarely validated experimentally. Here, we combine Particle Induced X-ray Emission (PIXE) and X-ray Fluorescence Spectroscopy (XRFS) to determine the elemental composition of protein samples used to generate 70 deposited metalloprotein crystal structures. By analyzing the original protein material employed for crystallization, but before the addition of crystallization buffer solutions, we assess whether the modeled metal ions in deposited structures are consistent with experimentally detectable elemental content. We find that in a majority of cases, the metals modeled in the corresponding PDB entries are inconsistent with the metals present in the protein samples before crystallization, or that additional metals are present but not represented in the structural models. Spectroscopic results were integrated with automated crystallographic validation metrics, including real-space Z-difference (RSZD) analysis and systematic rerefinement, to evaluate atomic-number mismatch at metal sites. PIXE and XRFS show strong agreement for dominant elemental signals and provide complementary, scalable approaches for identifying suspect metal assignments. This work does not address physiological or functional metalation but instead highlights a widespread data integrity issue in deposited macromolecular structures, PDB-wide. These results establish an experimentally corroborated link between elemental identity and crystallographic validation metrics, enabling the large-scale detection of chemically inconsistent annotations in structural databases used for computational modeling and machine learning.
Crystallization↗
Indirect electrochemical method for high accuracy quantification of protein adsorption on gold surfaces
We demonstrate the use of copper underpotential deposition (UPD) for measurement of the electrochemically active surface area of a protein-modified gold electrode. The kinetics of adsorption and the subsequent structural rearrangement of β-casein, as a model protein, on the surface of a gold electrode have been followed over time. The protein-free surface area was determined by measuring the charge derived from UPD copper deposition on the non-blocked surface area at different stages of adsorption. The behaviour of β-casein adsorption as a function of surface roughness and the microstructure of the gold electrode, as well as the concentration of the protein solution, are studied using this method.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Rictor-targeting exosomal microRNA-16 ameliorates lung fibrosis by inhibiting the mTORC2-SPARC axis
Highlights: • Exosomal miR-16 increases in sera of experimental pulmonary fibrosis model mice. • miR-16 interferes with the mTORC2 component, Rictor. • miR-16 ameliorates pulmonary fibrosis by targeting the mTORC2-SPARC axis. Idiopathic pulmonary fibrosis (IPF), a progressive disorder of unknown etiology, is characterized by pathological lung fibroblast activation and proliferation resulting in abnormal deposition of extracellular matrix proteins within the lung parenchyma. The pathophysiological roles of exosomal microRNAs in pulmonary fibrosis remain unclear; therefore, we aimed to identify and characterize fibrosis-responsive exosomal microRNAs. We used microRNA array analysis and profiled the expression of exosome-derived miRNA in sera of C57BL/6 mice exhibiting bleomycin-induced pulmonary fibrosis. The effect of microRNAs potentially involved in fibrosis was then evaluated in vivo and in vitro. The expression of exosomal microRNA-16 was increased by up to 8.0-fold on day 14 in bleomycin-treated mice, compared to vehicle-treated mice. MicroRNA-16 mimic administration on day 14 after bleomycin challenge ameliorated pulmonary fibrosis and suppressed lung and serum expression of secreted protein acidic and rich in cysteine (SPARC). Pretreatment of human lung fibroblasts with the microRNA-16 mimic decreased the expression of rapamycin-insensitive companion of mTOR (Rictor) and TGF-β1–induced expression of SPARC. This is the first study reporting the anti-fibrotic properties of microRNA-16 and demonstrating that these effects occur via the mTORC2 pathway. These findings support that microRNA-16 may be a promising therapeutic target for IPF.
60 APPLIED LIFE SCIENCES↗
Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers
Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient’s specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox’s proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.
59 BASIC BIOLOGICAL SCIENCES↗
Structural genomics of bacterial drug targets: Application of a high-throughput pipeline to solve 58 protein structures from pathogenic and related bacteria
Antibiotic resistance remains a leading cause of severe infections worldwide. Small changes in protein sequence can impact antibiotic efficacy. Here, we report deposition of 58 X-ray crystal structures of bacterial proteins that are known targets for antibiotics, which expands knowledge of structural variation to support future antibiotic discovery or modifications.
PDB↗