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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2023

Prediction of DNA i-Motifs Via Machine Learning

i-Motifs (iMs), are secondary structures formed in cytosine-rich DNA sequences and are involved in multiple functions in the genome. Although putative iM forming sequences are widely distributed in the human genome, the folding status and strength of putative iMs vary dramatically. Much previous research on iM has focused on assessing the iM folding properties using biophysical experiments. However, there are no dedicated computational tools for predicting the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based CUT&Tag sequencing data to predict the folding status and an Extreme Gradient Boosting regressor to estimate the folding strength according to both literature biophysical data and our in-house biophysical experiments. iM-Seeker predicts DNA iM folding status with a classification accuracy of 81% and estimates the folding strength with coefficient of determination (R2) of 0.642 on the test set. Model interpretation confirms that the nucleotide composition of the C-rich sequence significantly affects iM stability, with a positive correlation with sequences containing cytosine and thymine and a negative correlation with guanine and adenine. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/571121v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@11f4398org.highwire.dtl.DTLVardef@18eb7e9org.highwire.dtl.DTLVardef@cc49c6org.highwire.dtl.DTLVardef@1ff91a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗
bioRxiv2024

Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis

The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations. Author summaryTo make the best use of all the data collected in neuroscientific experiments, we need to develop appropriate analysis tools. In extracellular electrophysiological recordings, that is, measurements of electrical signals outside of cells produced by neural activity, the low-frequency part of the signal referred to as the local field potential (LFP) is often difficult to interpret due to the many neurons and biophysical processes contributing to this signal. Statistical tools have been used to decompose the recorded LFP with the aim of disentangling contributions from different neural populations and pathways. However, these methods are based on assumptions that can be invalid for LFP in the structure of interest. In this study, we extend and validate a method called laminar population analysis (LPA), which is based on physiological rather than statistical assumptions. We tested, developed, and validated LPA using simulated data from a large-scale, biophysically detailed model of mouse primary visual cortex. We found that LPA is able to tease apart several of the most salient contributions from different external inputs as well as the total contribution from recurrent activity within the primary visual cortex. We also demonstrate the application of LPA on experimentally recorded LFP.

neuroscience↗
bioRxiv2024

Advancing Transcription Factor Binding Site Prediction Using DNA Breathing Dynamics and Sequence Transformers via Cross Attention

Understanding the impact of genomic variants on transcription factor binding and gene regulation remains a key area of research, with implications for unraveling the complex mechanisms underlying various functional effects. Our study delves into the role of DNAs biophysical properties, including thermodynamic stability, shape, and flexibility in transcription factor (TF) binding. We developed a multi-modal deep learning model integrating these properties with DNA sequence data. Trained on ChIP-Seq (chromatin immunoprecipitation sequencing) data in vivo involving 690 TF-DNA binding events in human genome, our model significantly improves prediction performance in over 660 binding events, with up to 9.6% increase in AUROC metric compared to the baseline model when using no DNA biophysical properties explicitly. Further, we expanded our analysis to in vitro high-throughput Systematic Evolution of Ligands by Exponential enrichment (SELEX) and Protein Binding Microarray (PBM) datasets, comparing our model with established frameworks. The inclusion of DNA breathing features consistently improved TF binding predictions across different cell lines in these datasets. Notably, for complex ChIP-Seq datasets, integrating DNABERT2 with a cross-attention mechanism provided greater predictive capabilities and insights into the mechanisms of disease-related non-coding variants found in genome-wide association studies. This work highlights the importance of DNA biophysical characteristics in TF binding and the effectiveness of multi-modal deep learning models in gene regulation studies.

bioinformatics↗
bioRxiv2024

Trajectory inference from single-cell genomics data with a process time model

Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment.

bioinformatics↗
bioRxiv2024

Molecular insights into the interaction between a disordered protein and a folded RNA

Intrinsically disordered protein regions (IDRs) are well-established as contributors to intermolecular interactions and the formation of biomolecular condensates. In particular, RNA-binding proteins (RBPs) often harbor IDRs in addition to folded RNA-binding domains that contribute to RBP function. To understand the dynamic interactions of an IDR-RNA complex, we characterized the RNA-binding features of a small (68 residues), positively charged IDR-containing protein, SERF. At high concentrations, SERF and RNA undergo charge-driven associative phase separation to form a protein- and RNA-rich dense phase. A key advantage of this model system is that this threshold for demixing is sufficiently high that we could use solution-state biophysical methods to interrogate the stoichiometric complexes of SERF with RNA in the one-phase regime. Herein, we describe our comprehensive characterization of SERF alone and in complex with a small fragment of the HIV-1 TAR RNA (TAR) with complementary biophysical methods and molecular simulations. We find that this binding event is not accompanied by the acquisition of structure by either molecule; however, we see evidence for a modest global compaction of the SERF ensemble when bound to RNA. This behavior likely reflects attenuated charge repulsion within SERF via binding to the polyanionic RNA and provides a rationale for the higher-order assembly of SERF in the context of RNA. We envision that the SERF-RNA system will lower the barrier to accessing the details that support IDR-RNA interactions and likewise deepen our understanding of the role of IDR-RNA contacts in complex formation and liquid-liquid phase separation. SIGNIFICANCESubcellular organization through the formation of biomolecular condensates has emerged as an important contributor to myriad cellular functions, with implications in homeostasis, stress response, and disease. To understand the general and specific principles that support condensate formation, we must interrogate the interactions and assembly of their constituent biomolecules. To this end, this study introduces a simple model system comprised of a small, disordered protein and small RNA that undergo charge-driven, associative phase separation. In addition to extensive biophysical characterization of these molecules and their complex, we also generate new insights into mode of interaction and assembly between an unstructured protein and a structured RNA.

biochemistry↗
bioRxiv2024

Metabolically-driven flows enable exponential growth in macroscopic multicellular yeast

The ecological and evolutionary success of multicellular lineages is due in no small part to their increased size relative to unicellular ancestors. However, large size also poses biophysical challenges, especially regarding the transport of nutrients to all cells; these constraints are typically overcome through multicellular innovations (e.g., a circulatory system). Here we show that an emergent biophysical mechanism -- spontaneous fluid flows arising from metabolically-generated density gradients -- can alleviate constraints on nutrient transport, enabling exponential growth in nascent multicellular clusters of yeast lacking any multicellular adaptations for nutrient transport or fluid flow. Surprisingly, beyond a threshold size, the metabolic activity of experimentally-evolved snowflake yeast clusters drives large-scale fluid flows that transport nutrients throughout the cluster at speeds comparable to those generated by the cilia of extant multicellular organisms. These flows support exponential growth at macroscopic sizes that theory predicts should be diffusion limited. This work demonstrates how simple physical mechanisms can act as a biophysical scaffold to support the evolution of multicellularity by opening up phenotypic possibilities prior to genetically-encoded innovations. More broadly, our findings highlight how cooption of conserved physical processes is a crucial but underappreciated facet of evolutionary innovation across scales.

evolutionary biology↗
bioRxiv2024

Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

Machine learning (ML) is changing the world of computational protein design, with data- driven methods surpassing biophysical-based methods in experimental success rates. However, they are most often reported as case studies, lack integration and standardization across platforms, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid probabilities inside the Rosetta software framework that allows for the side-by-side comparison of these models. Subsequently, existing protein fitness landscapes were used to benchmark novel self- supervised machine learning methods in realistic protein design settings. We focused on the traditional problems of protein sequence design: sampling and scoring. A major finding of our study is that novel ML approaches are better at purging the sampling space from deleterious mutations. Nevertheless, scoring resulting mutations without model fine-tuning showed no clear improvement over scoring with Rosetta. This study fills an important gap in the field and allows for the first time a comprehensive head-to-head comparison of different ML and biophysical methods. We conclude that ML currently acts as a complement to, rather than a replacement for, biophysical methods in protein design.

bioinformatics↗
bioRxiv2024

Investigating the impact of the interstitial fluid flow and hypoxia interface on cancer biology using a spheroid-on-chip perfusion system

Solid tumours are complex and heterogeneous systems, which exist in a dynamic biophysical microenvironment. Conventional cancer research methods have long relied on two-dimensional (2D) static cultures which neglect the dynamic, three-dimensional (3D) nature of the biophysical tumour microenvironment (TME), especially the role and impact of interstitial fluid flow (IFF). To address this, we undertook a transcriptome-wide analysis of the impact of IFF-like perfusion flow using a spheroid-on-chip microfluidic platform, which allows 3D cancer spheroids to be integrated into extracellular matrices (ECM)-like hydrogels and exposed to continuous perfusion, to mimic IFF in the TME. Importantly, we have performed these studies both in experimental (normoxia) and pathophysiological (hypoxia) oxygen conditions. Our data indicated that gene expression was altered by flow when compared to static conditions, and for the first time showed that these gene expression patterns differed in different oxygen tensions, reflecting a differential role of spheroid perfusion in IFF-like flow in tumour-relevant hypoxic conditions in the biophysical TME. We were also able to identify factors primarily linked with IFF-like conditions which are linked with prognostic value in cancer patients and therefore could correspond to a potential novel biomarker of IFF in cancer. This study therefore highlights the need to consider relevant oxygen conditions when studying the impact of flow in cancer biology, as well as demonstrating the potential of microfluidic models of flow to identify IFF-relevant tumour biomarkers.

cancer biology↗
bioRxiv2024

Dysfunctional mechanotransduction regulates the progression of PIK3CA-driven vascular malformations

Somatic activating mutations in PIK3CA are common drivers of vascular and lymphatic malformations. Despite common biophysical signatures of tissues susceptible to lesion formation, including compliant extracellular matrix and low rates of perfusion, lesions vary in clinical presentation from localized cystic dilatation to diffuse and infiltrative vascular dysplasia. The mechanisms driving the differences in disease severity and variability in clinical presentation and the role of the biophysical microenvironment in potentiating progression are poorly understood. Here, we investigate the role of hemodynamic forces and the biophysical microenvironment in the pathophysiology of vascular malformations, and we identify hemodynamic shear stress and defective endothelial cell mechanotransduction as key regulators of lesion progression. We found that constitutive PI3K activation impaired flow-mediated endothelial cell alignment and barrier function. We show that defective shear stress sensing in PIK3CAE542Kendothelial cells is associated with reduced myosin light chain phosphorylation, junctional instability, and defective recruitment of vinculin to cell-cell junctions. Using 3D microfluidic models of the vasculature, we demonstrate that PIK3CAE542Kmicrovessels apply reduced traction forces and are unaffected by flow interruption. We further found that draining transmural flow resulted in increased sprouting and invasion responses in PIK3CAE542K microvessels. Mechanistically, constitutive PI3K activation decreased cellular and nuclear elasticity resulting in defective cellular tensional homeostasis in endothelial cells which may underlie vascular dilation, tissue hyperplasia, and hypersprouting in PIK3CA-driven venous and lymphatic malformations. Together, these results suggest that defective nuclear mechanics, impaired cellular mechanotransduction, and maladaptive hemodynamic responses contribute to the development and progression of PIK3CA-driven vascular malformations.

bioengineering↗
bioRxiv2024

DendroTweaks: An interactive approach for unraveling dendritic dynamics

Neurons rely on the interplay between two critical components, dendritic morphology and ion channels, to transform synaptic inputs into a sequence of somatic spikes. Detailed biophysical models with active dendrites have been instrumental in exploring this interaction. However, such models can be challenging to understand and validate due to the large number of parameters involved. In this work, we introduce DendroTweaks, a toolbox designed to make detailed biophysical models with active dendrites more intuitive and more interactive. DendroTweaks features a web-based graphical interface, where users can explore single-cell neuronal models and adjust their morphological and biophysical parameters with real-time visual feedback. In particular, DendroTweaks focuses on subcellular properties, such as kinetics and distribution of ion channels, as well as the dynamics and placement of synaptic inputs. The toolbox supports various experimental protocols designed to illuminate how morpho-electric properties map to dendritic events and how these dendritic events shape neuronal output, thereby enhancing model validation. It helps users build high-level, modular model representations and includes a rich set of tools for parsing, generating, and standardizing commonly used neuronal data formats. Finally, it enables model simplification through a built-in morphology reduction algorithm, allowing users to export models for further use in faster, more interpretable networks. By combining extensive visualization capabilities and comprehensive data management functionality, DendroTweaks introduces a novel interactive approach for unraveling dendritic dynamics. This approach will accelerate research on dendritic computations, their underlying mechanisms, and their fundamental role in brain function.

neuroscience↗
bioRxiv2024

Independent derivations of the axis of arrhythmia for predicting drug-induced torsades de pointes

Torsades de pointes is a potentially lethal ventricular arrhythmia that may be caused by many classes of drugs. The likelihood of arrhythmia depends upon the particular combination of cardiac ion-channels that the drug targets. The risk for a given drug can be measured against the axis of arrhythmia, which is a conceptual line in the parameter space of ventricular cardiomyocyte electrophysiology. The axis describes the most potent combination of ion-channel blocks that is theoretically possible. It thus serves as a convenient yardstick for quantifying the pro-arrhythmic risk of novel drugs from their channel-blocking profiles. To date, the axis of arrhythmia has only been derived from biophysical computer simulations. Here, we derive the axis directly from the response profiles of selected ion-channels to known drugs (n=109). The new method provides an independent line of evidence for estimating the axis of arrhythmia. Using step-wise reduction, the two methods were found to converge to identical estimates of the axis in two ion-currents (ICaL and IKr). The resulting axis predicted the pro-arrhythmic risk of the test drugs with 89.9% to 91.7% accuracy, depending on the dose at which the drug was assessed. The axis of arrhythmia offers a practical method for predicting the pro-arrhythmic risk of novel drugs without the need for drug-specific computer simulations. It is the only metric of pro-arrhythmic risk that has been derived from both a biophysical model and a purely statistical model. It thus combines the benefits of biophysical interpretation and computational efficiency in assessing the torsadogenic risk of new drugs prior to clinical trials. Significance StatementMany classes of drugs can cause lethal cardiac arrhythmias by blocking ion-channels that are needed by heart cells for normal rhythm. International safety guidelines thus require all new drugs be tested in animal hearts or cell cultures prior to undertaking clinical trials in humans. Computer models offer an ethical alternative for drug testing that is gaining acceptance in the safety pharmacology community. However, the current corpus of models are usually too complex to apply outside of specialist computing laboratories. We recently proposed a new approach that eliminates the need for drug-specific computer simulations by using a large one-off computer simulation to identify the most pro-arrhythmic drug that is theoretically possible. Thereafter, that hypothetical drug can be used to assess the pro-arrhythmic risk of real drugs using only pen-and-paper calculations. In that study, the hypothetical drug was described by the axis of arrhythmia in the cardiac cell model. In the present study, we propose an alternative method for deriving the axis of arrhythmia directly from drug datasets. The two derivations are independent, yet they arrive at the same result. Together, they provide converging lines of evidence in support of this new approach to drug testing in safety pharmacology.

pharmacology and toxicology↗
bioRxiv2024

Filtered Point Processes Tractably Capture Rhythmic And Broadband Power Spectral Structure in Neural Electrophysiological Recordings

AO_SCPLOWBSTRACTC_SCPLOWNeural electrophysiological recordings arise from interacting rhythmic (oscillatory) and broadband (aperiodic) biological subprocesses. Both rhythmic and broadband processes contribute to the neural power spectrum, which decomposes the variance of a neural recording across frequencies. Although an extensive body of literature has successfully studied rhythms in various diseases and brain states, researchers only recently have systematically studied the characteristics of broadband effects in the power spectrum. Broadband effects can generally be categorized as 1) shifts in power across all frequencies, which correlate with changes in local firing rates and 2) changes in the overall shape of the power spectrum, such as the spectral slope or power law exponent. Shape changes are evident in various conditions and brain states, influenced by factors such as excitation to inhibition balance, age, and various diseases. It is increasingly recognized that broadband and rhythmic effects can interact on a sub-second timescale. For example, broadband power is time-locked to the phase of <1 Hz rhythms in propofol induced unconsciousness. Modeling tools that explicitly deal with both rhythmic and broadband contributors to the power spectrum and that capture their interactions are essential to help improve the interpretability of power spectral effects. Here, we introduce a tractable stochastic forward modeling framework designed to capture both narrowband and broadband spectral effects when prior knowledge or theory about the primary biophysical processes involved is available. Population-level neural recordings are modeled as the sum of filtered point processes (FPPs), each representing the contribution of a different biophysical process such as action potentials or postsynaptic potentials of different types. Our approach builds on prior neuroscience FPP work by allowing multiple interacting processes and time-varying firing rates and by deriving theoretical power spectra and cross-spectra. We demonstrate several properties of the models, including that they divide the power spectrum into frequency ranges dominated by rhythmic and broadband effects, and that they can capture spectral effects across multiple timescales, including sub-second cross-frequency coupling. The framework can be used to interpret empirically observed power spectra and cross-frequency coupling effects in biophysical terms, which bridges the gap between theoretical models and experimental results.

neuroscience↗
bioRxiv2024

Biologically Realistic Computational Primitives of Neocortex Implemented on Neuromorphic Hardware Improve Vision Transformer Performance

Understanding the computational principles of the brain and replicating them on neuromorphic hardware and modern deep learning architectures is crucial for advancing neuro-inspired AI (NeuroAI). Here, we develop an experimentally-constrained biophysical network model of neocortical circuit motifs, focusing on layers 2-3 of the primary visual cortex (V1). We investigate the role of four major cortical interneuron classes in a competitive-cooperative computational primitive and validate these circuit motifs implemented soft winner-take-all (sWTA) computation for gain modulation, signal restoration, and context-dependent multistability. Using a novel parameter mapping technique, we configured IBMs TrueNorth (TN) chip to implement sWTA computations, mirroring biological neural dynamics. Retrospectively, we observed a strong correspondence between the biophysical model and the TN hardware parameters, particularly in the roles of four key inhibitory neuron classes: Parvalbumin (feedforward inhibition), Somatostatin (feedback inhibition), VIP (disinhibition), and LAMP5 (gain normalization). Moreover, the sparse coupling of this sWTA motif was also able to simulate a two-state neural state machine on the TN chip, replicating working memory dynamics essential for cognitive tasks. Additionally, integrating the sWTA computation as a preprocessing layer in the Vision Transformer (ViT) enhanced its performance on the MNIST digit classification task, demonstrating improved generalization to previously unseen data and suggesting a mechanism akin to zero-shot learning. Our approach provides a framework for translating brain-inspired computations to neuromorphic hardware, with potential applications on platforms like Intels Loihi2 and IBMs Northpole. By integrating biophysically accurate models with neuromorphic hardware and advanced machine learning techniques, we offer a comprehensive roadmap for embedding neural computation into NeuroAI systems.

neuroscience↗
bioRxiv2024

Dynamic barriers modulate cohesin positioning and genome folding at fixed occupancy

In mammalian interphase cells, genomes are folded by cohesin loop extrusion limited by directional CTCF barriers. This interplay leads to the enrichment of cohesin at barriers, isolation between neighboring topologically associating domains, and elevated contact frequency between convergent CTCF barriers across the genome. However, recent in vivo measurements present a puzzle: reported residence times for CTCF on chromatin are in the range of a few minutes, while lifetimes for cohesin are much longer. Can the observed features of genome folding result from the action of relatively transient barriers? To address this question, we developed a dynamic barrier model, where CTCF sites switch between bound and unbound states with rates that can be directly compared with biophysical measurements. Using this model, we investigated how barrier dynamics would impact observables for a range of experimental genomic and imaging datasets, including ChIP-seq, Hi-C, and microscopy. We found the interplay of CTCF and cohesin binding timescales influence the strength of each of these features, leaving a signature of barrier dynamics even in the population-averaged snapshots offered by genomic datasets. First, in addition to barrier occupancy, barrier bound times are crucial for instructing features of genome folding. Second, the ratio of boundary to extruder lifetime greatly alters simulated ChIP-seq and simulated Hi-C. Third, large-scale changes in chromosome morphology observed experimentally after increasing extruder lifetime require dynamic barriers. By integrating multiple sources of experimental data, our biophysical model argues that CTCF barrier bound times effectively approach those of cohesin extruder lifetimes. Together, we demonstrate how models that are informed by biophysically measured protein dynamics broaden our understanding of genome folding.

genomics↗
bioRxiv2024

Experimental Design Optimization for Brain Microstructure Imaging via Automatic Differentiation

Accurate characterization of the brain microstructure using diffusion MRI (dMRI) relies on optimal (i.e. information-rich) scanning protocols. Presently, without such protocols, extensive datasets are necessary to image the intricate microarchitecture of the brain by fitting complex biophysical models to the data. This impedes the full realization of dMRIs potential, particularly in time-constrained clinical scans. To tackle this, we introduce a novel framework based on the Cramer-Rao lower bound (CRLB) to optimize dMRI protocols for any multi-compartment biophysical models, to accurately capture features like axonal diameter index and multiple fiber orientations in a voxel. Unlike previous methods, limited in model complexity or parameter scope, our framework handles all model parameters, including water diffusivities, enhancing estimation fidelity. Leveraging automatic differentiation and parallel computing via TensorFlow, this approach systematically explores the entire parameter space for comprehensive scanning protocol optimization. By optimizing dMRI protocols, this work stands to significantly enhance biophysical modeling accuracy, thereby deepening our understanding of the brain microstructure in health and disease.

neuroscience↗
bioRxiv2025

Assembly-Activating Protein Phase Separation Properties Are Required for Adeno-Associated Virus Type 2 Assembly

Adeno-associated virus serotype 2 (AAV2), a non-pathogenic parvovirus reliant on helper viruses, is studied extensively as a potential gene delivery vector. A +1 open reading frame within the cap gene encodes a nonstructural protein of 204-amino-acids termed assembly-activating protein (AAP), which has been attributed a critical role in transporting the viral capsid protein VP3 into the nucleolus for assembly. However, AAP remains poorly characterized because of its relatively late discovery and lack of commercial antibodies. In the absence of other virus proteins, AAP localizes in the nucleolus due to five redundant nuclear and nucleolar localization signals. Additionally, AAP, a predicted intrinsically disordered protein, forms spontaneous dose-dependent nuclear globular condensates, a trait of liquid-liquid phase separated inclusions. Consistent with LLPS biophysical properties, the AAP condensates recovered rapidly from photobleaching and are sensitive to aliphatic diol treatment--moreover, AAP self-oligomerizes. We produced an AAP-specific antibody to analyze the role of this protein during productive AAV2 replication. In this context, we observed that AAP also forms nuclear globular condensates with LLPS biophysical properties in cells co-infected with AAV2 and either herpes simplex virus type 1 (HSV-1) or adenovirus type 5 (AdV-5) as the helper viruses. The screening of AAP deletion mutants revealed that the N-terminal region (amino acids 1-61) is necessary for condensate formation and self-oligomerization. Interestingly, this AAP region contains a predicted alpha-helix spanning amino acids 16 to 45. The substitution in this region of the hydrophobic residues by alanines drastically impaired AAP-LLPS biophysical properties and its ability to trigger AAV2 capsid assembly. Identifying the amino acids involved in assembly and LLPS may improve AAV vector production. Author SummaryAdeno-associated virus serotype 2 (AAV2) is a non-pathogenic virus extensively studied for its potential in gene therapy. It relies on a protein called assembly-activating protein (AAP) to transport its capsid protein, VP3, to the nucleolus for assembly. The 204-amino-acid AAP is not well characterized because it was discovered only relatively recently and commercial antibodies are not availabe, making it challenging to study. Here, we demonstrate that AAP localizes in the nucleolus and forms globular condensates through liquid-liquid phase separation (LLPS), a property characterized by rapid recovery from photobleaching and sensitivity to aliphatic diol treatment. Additionally, we prepared a specific antibody to study AAP during AAV2 co-infection with helper viruses like herpes simplex virus type 1 (HSV-1) or adenovirus type 5 (AdV-5). We found that AAP also forms nuclear condensates with LLPS properties in co-infected cells. We demonstrate that the N-terminal region of AAP (amino acids 1-61) is crucial for condensate formation and self-oligomerization. Within this region, a predicted alpha-helix (amino acids 16-45) is essential, as substituting its hydrophobic residues with alanines significantly impaired the LLPS properties of AAP and its ability to facilitate AAV2 capsid assembly. Identifying these key amino acids may enhance AAV vector production for gene therapy applications.

microbiology↗
bioRxiv2025

Conformational Dynamics and Catalytic Backups in a Hyper-Thermostable Engineered Archaeal Protein Tyrosine Phosphatase

Protein tyrosine phosphatases (PTPs) are a family of enzymes that play important roles in regulating cellular signaling pathways. The activity of these enzymes is regulated by the motion of a catalytic loop that places a critical conserved aspartic acid side chain into the active site for acid-base catalysis upon loop closure. These enzymes also have a conserved phosphate binding loop that is typically highly rigid and forms a well-defined anion binding nest. The intimate links between loop dynamics and chemistry in these enzymes make PTPs an excellent model system for understanding the role of loop dynamics in protein function and evolution. In this context, archaeal PTPs, which have evolved in extremophilic organisms, are highly understudied, despite their unusual biophysical properties. We present here an engineered chimeric PTP (ShufPTP) generated by shuffling the amino acid sequence of five extant hyperthermophilic archaeal PTPs. Despite ShufPTPs high sequence similarity to its natural counterparts, ShufPTP presents a suite of unique properties, including high flexibility of the phosphate binding P-loop, facile oxidation of the active site cysteine, mechanistic promiscuity, and most notably, hyperthermostability, with a denaturation temperature likely >130 {degrees}C (>8 {degrees}C higher than the highest recorded growth temperature of any archaeal strain). Our combined structural, biochemical, biophysical and computational analysis provides insight both into how small steps in evolutionary space can radically modulate the biophysical properties of an enzyme, and showcase the tremendous potential of archaeal enzymes for biotechnology, to generate novel enzymes capable of operating under extreme conditions. Table of Contents Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/645524v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@186bfccorg.highwire.dtl.DTLVardef@19a265borg.highwire.dtl.DTLVardef@14c526corg.highwire.dtl.DTLVardef@1b75920_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗
bioRxiv2025

Characterization of the functional and clinical impacts of CACNA1A missense variants found in neurodevelopmental disorders

CACNA1A encodes the P/Q-type CaV2.1 calcium channels whose function underlies neuronal excitability, presynaptic neurotransmitter release, and Ca2+ signaling in neurons. Pathogenic variants in CACNA1A have been found in individuals with various neurological conditions, including hemiplegic migraine, epilepsy, developmental delay, and ataxia. Clinical presentations can vary significantly between patients, with limited information known about the underlying neurobiology of these different clinical patterns. Adding further complication, prior work on pathogenic missense variants has demonstrated variable impacts on CaV2.1 channel function, sometimes in opposite directions. As such, the relationships between specific coding variants, electrophysiological properties, and clinical phenotypes remain elusive. In this study, we determined the biophysical properties of an allelic series of 42 de novo missense CACNA1A variants discovered in a neurodevelopmental disorder cohort of more than 31,000 individuals, together with the most common eight coding variants found in the general population. We found that all but one de novo variant altered at least one aspect of the channel properties examined, and the majority (70%) of the variants reduced the channel current density. In addition, for variants that encode human CaV2.1 channels (hCaV2.1) with detectable currents, nearly 50% altered how channels respond to membrane potential, while common variations did not significantly change any channel biophysical properties. Coupled with our functional analyses and AlphaMissense prediction, we showed that CaV2.1 missense variants significantly underlie the risk of developmental epileptic encephalopathy. Subsequently, we examined the physiological impact of variant hCaV2.1 using NEURON simulations as an omnibus output of neuronal function and found that abnormal biophysical channel properties have a profound impact on Purkinje cell excitability. Most interestingly, we correlated the clinical phenotype with molecular consequences of missense variants provided by our comprehensive functional analyses and found that distinct CaV2.1 channel molecular function is significantly associated with different clinical outcomes. By analyzing an entire allelic series of CACNA1A de novo changes in a large cohort of individuals with neurodevelopmental disorders, we provide a powerful approach to dissecting the role of missense variants in CACNA1A channelopathy, which in turn may help pave the way for future precision medicine initiatives.

neuroscience↗