Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanistic Insights into the Activation of Lecithin–Cholesterol Acyltransferase in Therapeutic Nanodiscs Composed of Apolipoprotein A-I Mimetic Peptides and Phospholipids

The mechanistic details behind the activation of lecithin-cholesterol acyltransferase (LCAT) by apolipoprotein A-I (apoA-I) and its mimetic peptides are still enigmatic. Resolving the fundamental principles behind the LCAT activation will facilitate the design of advanced HDL-mimetic therapeutic nanodiscs for LCAT deficiencies and coronary heart disease, and for several targeted drug delivery applications. Here, we have combined coarse-grained molecular dynamics simulations with complementary experiments to gain mechanistic insight into how apoA-I mimetic peptide 22A and its variants attune LCAT activity in peptide-lipid nanodiscs. Results highlight that peptide 22A forms transient antiparallel dimers in the rim of nanodiscs. The dimerization tendency considerably decreases with the removal of C-terminal lysine K22, which has also been shown to reduce the cholesterol esterification activity of LCAT. In addition, our simulations revealed that LCAT prefers to localize to the rim of nanodiscs in a manner that shields the membrane-binding domain (MBD), αA-αA’, and the lid amino acids from the water phase, following the previous experimental evidence. Meanwhile, the location and conformation of LCAT in the rim of nanodisc are spatially more restricted when the active site covering lid of LCAT is in the open form. The average location and spatial dimensions of LCAT in its open form were highly compatible with the electron microscopy images. All peptide 22A variants studied here had a specific interaction site in the open LCAT structure flanked by the lid and MBD domain. The bound peptides showed different tendencies to form antiparallel dimers and, interestingly, the temporal binding site occupancies of the peptide variants affected their in vitro ability to promote LCAT-mediated cholesterol esterification.
Spatial structure alters the site frequency spectrum produced by hitchhiking

The reduction of genetic diversity due to genetic hitchhiking is widely used to find past selective sweeps from sequencing data, but very little is known about how spatial structure affects hitchhiking. We use mathematical modeling and simulations to find the unfolded site frequency spectrum (SFS) left by hitchhiking in the genomic region of a sweep in a population occupying a one-dimensional range. For such populations, sweeps spread as Fisher waves, rather than logistically. We find that this leaves a characteristic three-part SFS at loci very close to the swept locus. Very low frequencies are dominated by recent mutations that occurred after the sweep and are unaffected by hitchhiking. At moderately low frequencies, there is a transition zone primarily composed of alleles that briefly “surfed” on the wave of the sweep before falling out of the wavefront, leaving a spectrum close to that expected in well-mixed populations. However, for moderate-to-high frequencies, there is a distinctive scaling regime of the SFS produced by alleles that drifted to fixation in the wavefront and then were carried throughout the population. For loci slightly farther away from the swept locus on the genome, recombination is much more effective at restoring diversity in one-dimensional populations than it is in well-mixed ones. We find that these signatures of space can be strong even in apparently well-mixed populations with negligible spatial genetic differentiation, suggesting that spatial structure may frequently distort the signatures of hitchhiking in natural populations.
A model of coevolution and local adaptation between hosts and parasites in continuous space

Previous host-parasite coevolutionary theory has focused on understanding the determinants of local adaptation using spatially discrete models. However, these studies fall short of describing patterns of host-parasite local adaptation across spatial scales. In contrast, empirical work demonstrates patterns of adaptation depend on the scale at which they are measured. Here, we propose a model of host-parasite coevolution in continuous space that naturally leads to a scale-dependent definition of local adaptation and a formal definition for the spatial scale of coevolution. In agreement with empirical findings, our model implies patterns of adaptation vary across spatial scales. When measured on spatial scales shorter than the scale of coevolution, we find the farther dispersing species is locally adapted. However, when measured at longer spatial scales, the opposite pattern is observed. We discuss our results in relation to those found using spatially discrete models and to conclusions drawn from empirical studies, and provide an example of our how our results can be used to inform the design of empirical studies.
Monitoring regional astrocyte diversity by cell-type specific proteomic labeling in vivo

Astrocytes exhibit regional heterogeneity in morphology, function and molecular composition to support and modulate neuronal function and signaling in a region-specific manner. To characterize regional heterogeneity of astrocytic proteomes of different brain regions we established an Aldh1l1- MetRSL274G mouse line that allows cell-type specific labeling of newly synthesized proteins in vivo and analyzed astrocytic proteins from four different brain regions by mass spectrometry. Identified proteins are specific for astrocytes and show a high overlap with proteins compiled in ‘AstroProt’, a newly established database for astrocytic proteins. Gene enrichment analysis reveals high overlap among brain regions, and only subtle changes in abundances of key astrocytic proteins for hippocampus, cortex and striatum. However, the cerebellar proteome stands out with proteins being associated with calcium signaling or bipolar disorder. Subregional differences of translation dynamics in single hippocampal astrocytes indicates distinct subregional heterogeneity and highlights the applicability of our toolbox to study dynamic astrocytic proteomes in vivo.
Cryo-EM structure and B-factor refinement with ensemble representation

Cryo-EM is the go-to method for visualizing large, flexible biomolecules. Here, authors introduce a new Gaussian mixture modelling method for cryo-EM modelling tasks, including refinement, composite map generation and ensemble representation. Cryo-EM experiments produce images of macromolecular assemblies that are combined to produce three-dimensional density maps. Typically, atomic models of the constituent molecules are fitted into these maps, followed by a density-guided refinement. We introduce TEMPy-ReFF, a method for atomic structure refinement in cryo-EM density maps. Our method represents atomic positions as components of a Gaussian mixture model, utilising their variances as B-factors, which are used to derive an ensemble description. Extensively tested on a substantial dataset of 229 cryo-EM maps from EMDB ranging in resolution from 2.1-4.9 Å with corresponding PDB and CERES atomic models, our results demonstrate that TEMPy-ReFF ensembles provide a superior representation of cryo-EM maps. On a single-model basis, it performs similarly to the CERES re-refinement protocol, although there are cases where it provides a better fit to the map. Furthermore, our method enables the creation of composite maps free of boundary artefacts. TEMPy-ReFF is useful for better interpretation of flexible structures, such as those involving RNA, DNA or ligands.
Endocytosis at extremes: Formation and internalization of giant clathrin-coated pits under elevated membrane tension

Internalization of clathrin-coated vesicles from the plasma membrane constitutes the major endocytic route for receptors and their ligands. Dynamic and structural properties of endocytic clathrin coats are regulated by the mechanical properties of the plasma membrane. Here, we used conventional fluorescence imaging and multiple modes of structured illumination microscopy (SIM) to image formation of endocytic clathrin coats within live cells and tissues of developing fruit fly embryos. High resolution in both spatial and temporal domains allowed us to detect and characterize distinct classes of clathrin-coated structures. For the first time, we show that membrane tension induces formation of giant coated pits (GCPs) that can be up to two orders of magnitude larger than the canonical clathrin-coated pits. GCPs take longer to form but their mechanism of curvature generation is the same as the canonical pits. We also demonstrate that GCPs can split into smaller fragments during internalization. Considering the supporting roles played by actin filament dynamics in clathrin-mediated endocytosis under mechanically stringent conditions, we suggest that local changes in the coat curvature driven by actin machinery can drive splitting and internalization of GCPs.
Rapid Evolution of the Fine-scale Recombination Landscape in Wild House Mouse (Mus musculus) Populations

Meiotic recombination is an important evolutionary force and essential meiotic process. In many species, recombination events concentrate into “hotspots” defined by the site-specific binding of PRMD9. Rapid evolution of PRDM9’s zinc finger DNA-binding array leads to remarkably abrupt shifts in the genomic distribution of hotspots between species, but the question of how Prdm9 allelic variation shapes the landscape of recombination between populations remains less well understood. Wild house mice (Mus musculus) harbor exceptional Prdm9 diversity, with >100 alleles identified to date, and pose a particularly powerful system for addressing this open question. We employed a coalescent-based approach to construct fine-scale, sex-averaged recombination maps from contemporary patterns of linkage disequilibrium in nine geographically isolated wild house mouse populations, including multiple populations from each of three subspecies. Comparing maps between wild mouse populations and subspecies reveals several themes. First, we report weak fine- and broad-scale recombination map conservation across subspecies and populations, with genetic divergence offering no clear prediction for recombination map divergence. Second, most hotspots are unique to one population, an outcome consistent with minimal sharing of Prdm9 alleles between surveyed populations. Finally, by contrasting aggregate hotspot activity on the X versus autosomes, we uncover evidence for population-specific differences in the degree and direction of sex-dimorphism for recombination. Overall, our findings illuminate the variability of both the broad- and fine-scale recombination landscape in Mus musculus and underscore the functional impact of Prdm9 allelic variation in wild mouse populations.
Proteotype Co-evolution and Diversity in Mammals

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomic variability as reference. The co-variation analysis indicates that transcript and protein expression robustness across mammals remarkably follows functional role, with extracellular matrix-associated expressions being most variable, demonstrating strong transcriptome-proteome co-evolution. Interestingly, the variability control of gene expression is universal at both inter-individual and inter-species scales, but of different extent. RNA metabolism processes particularly show the higher inter-species versus inter-individual variations. Our results further uncover that while ubiquitin-proteasome system is extremely conserved in mammals, the lysosome-mediated protein degradation exhibits a remarkable variation between mammalian lineages. Additionally, the phosphosite profiles reveals phosphorylation co-evolution network independent of protein abundance.
A conserved membrane protein negatively regulates Mce1 complexes in mycobacteria

Tuberculosis continues to pose a serious threat to global health. Mycobacterium tuberculosis, the causative agent of tuberculosis, is an intracellular pathogen that relies on various mechanisms to survive and persist within the host. Among their many virulence factors, mycobacteria encode Mce systems. Some of these systems are implicated in lipid uptake, but the molecular basis for Mce function(s) is poorly understood. To gain insights into the composition and architecture of Mce systems, we characterized the putative Mce1 complex involved in fatty acid transport. We show that the Mce1 system in Mycobacterium smegmatis comprises a canonical ATP-binding cassette transporter, associated with functionally redundant heterohexameric assemblies of substrate-binding proteins. Furthermore, we establish that the conserved membrane protein Mce1N negatively regulates Mce1 function via a unique mechanism involving blocking transporter assembly. Our work offers molecular understanding of Mce complexes, sheds light on mycobacterial lipid metabolism and its regulation, and informs future anti-mycobacterial strategies.
A Cellular Assay for Spike/ACE2 Fusion: Quantification of Fusion-Inhibitory Antibodies after COVID-19 and Vaccination

Not all antibodies against SARS-CoV-2 inhibit viral entry and hence infection. Neutralizing antibodies are more likely to reflect real immunity, however certain of these tests investigate protein/protein interaction rather than the fusion event. Viral and pseudoviral entry assays detect functionally active antibodies, however they are cumbersome and burdened by biosafety and standardization issues. We have developed a Spike/ACE2-dependant cell-to-cell fusion assay, based on a split luciferase. Hela cells stably transduced with Spike and a large fragment of luciferase were co-cultured with Hela cells transduced with ACE2 and the complementary small fragment of luciferase. Within 24h, cell fusion occured allowing the measurement of luminescence. Light emission was abolished in the absence of Spike and reduced in the presence of an inhibitor of Spike-processing proteases. Serum samples from COVID-19-negative, non-vaccinated individuals, or sera from patients at the moment of first symptoms did not lead to a significant reduction of fusion. In contrast, sera from COVID-19-positive patients as well as sera from vaccinated individuals reduced the fusion. In conclusion, we report a new method measuring fusion-inhibitory antibodies in serum, combining the advantage of a functional full Spike/ACE2 interaction with a high degree of standardization, easily allowing automation in a standard bio-safety environment.
Hallucinating protein assemblies

Deep learning generative approaches provide an opportunity to broadly explore protein structure space beyond the sequences and structures of natural proteins. Here we use deep network hallucination to generate a wide range of symmetric protein homo-oligomers given only a specification of the number of protomers and the protomer length. Crystal structures of 7 designs are very close to the computational models (median RMSD: 0.6 Å), as are 3 cryoEM structures of giant rings with up to 1550 residues, C33 symmetry, and 10 nanometer in diameter; all differ considerably from previously solved structures. Our results highlight the rich diversity of new protein structures that can be created using deep learning, and pave the way for the design of increasingly complex nanomachines and biomaterials. One-sentence summary Deep network-based protein design enables the generation of cyclic homo-oligomers across the nanoscopic scale.
Redefining and subtyping of major depression based on brain functional connectivity signatures with high generalization: an ensemble hybrid framework

Current clinical diagnosis of major depression is solely based on subjective symptoms and signs rather than the underlying biological mechanisms, which has hindered the improvement in treatment effectiveness and outcomes. Redefining and subtyping major depressive disorder (MDD) based on neural circuits has been an emerging consensus. Here, we proposed a novel ensemble hybrid deep-learning framework based on graph neural network for redefining and subtyping of MDD with the brain’s “fingerprinting”, i.e. nuanced functional connectivity profiles. A graph neural network (GNN) based supervised model was first employed to discriminate MDD patients from healthy controls. The graph embedding features obtained from the supervised classifier was then employed for subtyping of MDD with an unsupervised clustering by fast search and find of density peaks (CFDP) approach. The generalization accuracy and reproducibility of the proposed framework were further validated with one of the largest resting-state fMRI data set for MDD which included 2428 participants recruited from 25 independent sites. Our classifier showed a high generalization of 75% for leave-one-site-out cross validation. In addition, we obtained three biologically homogenous MDD subtypes characterized by impairments in subcortical-limbic, DMN-FPN, VN-SMN circuits, respectively. Notably, we demonstrated that subtyping of MDD according to graph embedding features showed high robustness and reproducibility across different sites. The proposed framework is not only essential for redefining and subtyping of MDD, but also paves the way towards precision medicine of mental disorders.
MicroFPGA: An affordable FPGA platform for microscope control

Modern microscopy relies increasingly on microscope automation to improve throughput, ensure reproducibility or observe rare events. Automation requires in particular computer control of the important elements of the microscope. Furthermore, optical elements that are usually fixed or manually movable can be placed on electronically-controllable elements. In most cases, a central electronics board is necessary to generate the control signals they require and to communicate with the computer. Fur such tasks, Arduino microcontrollers are widely used due to their low cost and programming entry barrier. However, they are limiting in their performance for applications that require high-speed or multiple parallel processes. Field programmable gate arrays (FPGA) are the perfect technology for high-speed microscope control, as they are capable of processing signals in parallel and with high temporal precision. While plummeting price made the technology available to consumers, a major hurdle remains the complex languages used to configure them. In this work, we used an affordable FPGA, delivered with an open-source and friendly-to-use programming language, to create a versatile microscope control platform called MicroFPGA. It is capable of synchronously triggering cameras and multiple lasers following complex patterns, as well as generating various signals used to control microscope elements such as filter wheels, servomotor stages, flip-mirrors, laser power or acoustooptic modulators. MicroFPGA is open-source and we provide online Micro-Manager, Java, Python and LabVIEW libraries, together with blueprints and tutorials.
Rotavirus Spike Protein VP4 Mediates Viroplasm Assembly by Association to Actin Filaments

The spike protein VP4 participates in diverse steps of the rotavirus (RV) life cycle, including virus-cell attachment, internalization, modulation of endocytosis, virion morphogenesis, and virus egress. Using reverse genetics, we constructed for the first time a recombinant RV, rRV/VP4-BAP, harboring a heterologous peptide in the lectin domain (loop K145-G150) of VP4. ABSTRACT Rotavirus (RV) viroplasms are cytosolic inclusions where both virus genome replication and primary steps of virus progeny assembly take place. A stabilized microtubule cytoskeleton and lipid droplets are required for the viroplasm formation, which involves several virus proteins. The viral spike protein VP4 has not previously been shown to have a direct role in viroplasm formation. However, it is involved with virus-cell attachment, endocytic internalization, and virion morphogenesis. Moreover, VP4 interacts with actin cytoskeleton components, mainly in processes involving virus entrance and egress, and thereby may have an indirect role in viroplasm formation. In this study, we used reverse genetics to construct a recombinant RV, rRV/VP4-BAP, that contains a biotin acceptor peptide (BAP) in the K145-G150 loop of the VP4 lectin domain, permitting live monitoring. The recombinant virus was replication competent but showed a reduced fitness. We demonstrate that rRV/VP4-BAP infection, as opposed to rRV/wt infection, did not lead to a reorganized actin cytoskeleton as viroplasms formed were insensitive to drugs that depolymerize actin and inhibit myosin. Moreover, wild-type (wt) VP4, but not VP4-BAP, appeared to associate with actin filaments. Similarly, VP4 in coexpression with NSP5 and NSP2 induced a significant increase in the number of viroplasm-like structures. Interestingly, a small peptide mimicking loop K145-G150 rescued the phenotype of rRV/VP4-BAP by increasing its ability to form viroplasms and hence improve virus progeny formation. Collectively, these results provide a direct link between VP4 and the actin cytoskeleton to catalyze viroplasm assembly. IMPORTANCE The spike protein VP4 participates in diverse steps of the rotavirus (RV) life cycle, including virus-cell attachment, internalization, modulation of endocytosis, virion morphogenesis, and virus egress. Using reverse genetics, we constructed for the first time a recombinant RV, rRV/VP4-BAP, harboring a heterologous peptide in the lectin domain (loop K145-G150) of VP4. The rRV/VP4-BAP was replication competent but with reduced fitness due to a defect in the ability to reorganize the actin cytoskeleton, which affected the efficiency of viroplasm assembly. This defect was rescued by adding a permeable small-peptide mimicking the wild-type VP4 loop K145-G150. In addition to revealing a new role of VP4, our findings suggest that rRV harboring an engineered VP4 could be used as a new dual vaccination platform providing immunity against RV and additional heterologous antigens.
The effects of data quantity on performance of temporal response function analyses of natural speech processing

In recent years, temporal response function (TRF) analyses of non-invasive recordings of neural activity evoked by continuous naturalistic stimuli have become increasingly popular for characterizing response properties within the auditory hierarchy. However, despite this rise in TRF usage, relatively few educational resources for these tools exist. Here we use a dual-talker continuous speech paradigm to demonstrate how a key parameter of experimental design, the quantity of acquired data, influences TRF analyses fit to either individual data (subject-specific analyses), or group data (generic analyses). We show that although model performance monotonically increases with data quantity, the amount of data required to achieve significant prediction accuracies can vary substantially based on whether the fitted model contains densely (e.g., acoustic envelope) or sparsely (e.g., lexical surprisal) spaced features, especially when the goal of the analyses is to capture the aspect of neural responses uniquely explained by specific features. Moreover, we demonstrate that generic models can exhibit high performance on small amounts of test data (2-8 min), if they are trained on a sufficiently large data set. As such, they may be particularly useful for clinical and multi-task study designs with limited recording time. Finally, we show that the regularization procedure used in fitting TRF models can interact with the quantity of data used to fit the models, with larger training quantities resulting in systematically larger TRF amplitudes. Together, demonstrations in this work should aid the learning process of new users of TRF analyses, and in combination with other tools, such as piloting and power analyses, may serve as a detailed reference for choosing acquisition duration in future studies.