Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.

Technical Comment on "Inhibition mechanism of NKCC1 involves the carboxyl terminus and long-range conformational coupling".

Flygaard, Habeck and Nissen question claims on bumetanide and furosemide binding to sodium-potassium-chloride cotransporter NKCC1.

AraC interacts with p75NTR transmembrane domain to induce cell death of mature neurons.

Cytosine arabinoside (AraC) is one of the main therapeutic treatments for several types of cancer, including acute myeloid leukaemia. However, after a high-dose AraC chemotherapy regime, patients develop severe neurotoxicity and cell death in the central nervous system leading to cerebellar ataxia, dysarthria, nystagmus, somnolence and drowsiness. AraC induces apoptosis in dividing cells. However, the mechanism by which it leads to neurite degeneration and cell death in mature neurons remains unclear. We hypothesise that the upregulation of the death receptor p75NTR is responsible for AraC-mediated neurodegeneration and cell death in leukaemia patients undergoing AraC treatment. To determine the role of AraC-p75NTR signalling in the cell death of mature neurons, we used mature cerebellar granule neurons' primary cultures from p75NTR knockout and p75NTRCys259 mice. Evaluation of neurite degeneration, cell death and p75NTR signalling was done by immunohistochemistry and immunoblotting. To assess the interaction between AraC and p75NTR, we performed cellular thermal shift and AraTM assays as well as Homo-FRET anisotropy imaging. We show that AraC induces neurite degeneration and programmed cell death of mature cerebellar granule neurons in a p75NTR-dependent manner. Mechanistically, Proline 252 and Cysteine 256 residues facilitate AraC interaction with the transmembrane domain of p75NTR resulting in uncoupling of p75NTR from the NFκB survival pathway. This, in turn, exacerbates the activation of the cell death/JNK pathway by recruitment of TRAF6 to p75NTR. Our findings identify p75NTR as a novel molecular target to develop treatments for counteract AraC-mediated cell death of mature neurons.

Bayesian methods in integrative structure modeling.

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Fast-forward on P-type ATPases: recent advances on structure and function.

P-type ATPase are present in nearly all organisms. They maintain electrochemical gradients for many solutes, in particular ions, they control membrane lipid asymmetry, and are crucial components of intricate signaling networks. All P-type ATPases share a common topology with a transmembrane and three cytoplasmic domains and their transport cycle follows a general scheme - the Post-Albers-cycle. Recently, P-type ATPase research has been advanced most significantly by the technological advancements in cryo-EM analysis, which has elucidated many new P-type ATPase structures and mechanisms and revealed several new ways of regulation. In this review, we highlight the progress of the field and focus on special features that are present in the five subfamilies. Hence, we outline the new intersubunit transport model of KdpFABC, the ways in which heavy metal pumps have evolved to accommodate various substrates, the strategies Ca2+ pumps utilize to adapt to different environmental needs, the intricate molecular builds of the ion binding sites in Na,K- and H,K-ATPases, the remarkable hexameric assembly of fungal proton pumps, the many ways in which P4-ATPase lipid flippases are regulated, and finally the deorphanization of P5 pumps. Interestingly many of the described features are found in more than one of the five subfamilies, and mixed and matched together to provide optimal function and precise regulation.

eGFP as an All-in-One Tag for Purification of Membrane Proteins.

Within the last decade, cryo-electron microscopy has revolutionized our understanding of membrane proteins, but they still represent challenging targets for biochemical and structural studies. The first obstacle is often to obtain high production levels of correctly folded target protein. In these cases, the use of eGFP tags is an efficient strategy, as it allows rapid screenings of expression systems, constructs, and detergents for solubilization. Additionally, eGFP tags can now be used for affinity purification with recently developed nanobodies. Here we present a series of methods based on enhanced green fluorescent protein (eGFP) fluorescence to efficiently screen for production and stabilization of detergent-solubilized eGFP-tagged membrane proteins produced in S. cerevisiae via in-gel fluorescence SDS-PAGE and fluorescence-detection size-exclusion chromatography (FSEC). Additionally, we present a protocol describing the production of affinity resin based on eGFP-binding nanobodies produced in E. coli. We showcase the purification of human ATP7B, a copper transporting P-type ATPase, as an example of the applicability of the methods.

Cryo-EM structure of the human NKCC1 transporter reveals mechanisms of ion coupling and specificity.

The sodium-potassium-chloride transporter NKCC1 of the SLC12 family performs Na+ -dependent Cl- - and K+ -ion uptake across plasma membranes. NKCC1 is important for regulating cell volume, hearing, blood pressure, and regulation of hyperpolarizing GABAergic and glycinergic signaling in the central nervous system. Here, we present a 2.6 Å resolution cryo-electron microscopy structure of human NKCC1 in the substrate-loaded (Na+ , K+ , and 2 Cl- ) and occluded, inward-facing state that has also been observed for the SLC6-type transporters MhsT and LeuT. Cl- binding at the Cl1 site together with the nearby K+ ion provides a crucial bridge between the LeuT-fold scaffold and bundle domains. Cl- -ion binding at the Cl2 site seems to undertake a structural role similar to conserved glutamate of SLC6 transporters and may allow for Cl- -sensitive regulation of transport. Supported by functional studies in mammalian cells and computational simulations, we describe a putative Na+ release pathway along transmembrane helix 5 coupled to the Cl2 site. The results provide insight into the structure-function relationship of NKCC1 with broader implications for other SLC12 family members.

Structure and Function of Na,K-ATPase-The Sodium-Potassium Pump.

Na,K-ATPase is an ubiquitous enzyme actively transporting Na-ions out of the cell in exchange for K-ions, thereby maintaining their concentration gradients across the cell membrane. Since its discovery more than six decades ago the Na-pump has been studied extensively and its vital physiological role in essentially every cell has been established. This article aims at providing an overview of well-established biochemical properties with a focus on Na,K-ATPase isoforms, its transport mechanism and principle conformations, inhibitors, and insights gained from crystal structures. © 2021 American Physiological Society. Compr Physiol 11:1-21, 2021.

Non-negative blind deconvolution for signal processing in a CRISPR-edited iPSC-cardiomyocyte model of dilated cardiomyopathy.

We developed an integrated platform for analysis of parameterized data from human disease models. We report a non-negative blind deconvolution (NNBD) approach to quantify calcium (Ca2+ ) handling, beating force and contractility in human-induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) at the single-cell level. We employed CRISPR/Cas gene editing to introduce a dilated cardiomyopathy (DCM)-causing mutation in troponin T (TnT), TnT-R141W, into wild-type control iPSCs (MUT). The NNDB-based method enabled data parametrization, fitting and analysis in wild-type controls versus isogenic MUT iPSC-CMs. Of note, Cas9-edited TnT-R141W iPSC-CMs revealed significantly reduced beating force and prolonged contractile event duration. The NNBD-based platform provides an alternative framework for improved quantitation of molecular disease phenotypes and may contribute to the development of novel diagnostic tools.

Bayesian Random Tomography of Particle Systems.

Random tomography is a common problem in imaging science and refers to the task of reconstructing a three-dimensional volume from two-dimensional projection images acquired in unknown random directions. We present a Bayesian approach to random tomography. At the center of our approach is a meshless representation of the unknown volume as a mixture of spherical Gaussians. Each Gaussian can be interpreted as a particle such that the unknown volume is represented by a particle cloud. The particle representation allows us to speed up the computation of projection images and to represent a large variety of structures accurately and efficiently. We develop Markov chain Monte Carlo algorithms to infer the particle positions as well as the unknown orientations. Posterior sampling is challenging due to the high dimensionality and multimodality of the posterior distribution. We tackle these challenges by using Hamiltonian Monte Carlo and a global rotational sampling strategy. We test the approach on various simulated and real datasets.

A graph-based algorithm for detecting rigid domains in protein structures.

BACKGROUND: Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. RESULTS: We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. CONCLUSIONS: The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/ .

Architecture of the flexible tail tube of bacteriophage SPP1.

Bacteriophage SPP1 is a double-stranded DNA virus of the Siphoviridae family that infects the bacterium Bacillus subtilis. This family of phages features a long, flexible, non-contractile tail that has been difficult to characterize structurally. Here, we present the atomic structure of the tail tube of phage SPP1. Our hybrid structure is based on the integration of structural restraints from solid-state nuclear magnetic resonance (NMR) and a density map from cryo-EM. We show that the tail tube protein gp17.1 organizes into hexameric rings that are stacked by flexible linker domains and, thus, form a hollow flexible tube with a negatively charged lumen suitable for the transport of DNA. Additionally, we assess the dynamics of the system by combining relaxation measurements with variances in density maps.

Bayesian inference of chromatin structure ensembles from population-averaged contact data.

Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.

Auto-regulation of Rab5 GEF activity in Rabex5 by allosteric structural changes, catalytic core dynamics and ubiquitin binding.

Intracellular trafficking depends on the function of Rab GTPases, whose activation is regulated by guanine exchange factors (GEFs). The Rab5 GEF, Rabex5, was previously proposed to be auto-inhibited by its C-terminus. Here, we studied full-length Rabex5 and Rabaptin5 proteins as well as domain deletion Rabex5 mutants using hydrogen deuterium exchange mass spectrometry. We generated a structural model of Rabex5, using chemical cross-linking mass spectrometry and integrative modeling techniques. By correlating structural changes with nucleotide exchange activity for each construct, we uncovered new auto-regulatory roles for the ubiquitin binding domains and the Linker connecting those domains to the catalytic core of Rabex5. We further provide evidence that enhanced dynamics in the catalytic core are linked to catalysis. Our results suggest a more complex auto-regulation mechanism than previously thought and imply that ubiquitin binding serves not only to position Rabex5 but to also control its Rab5 GEF activity through allosteric structural alterations.

A probabilistic network model for structural transitions in biomolecules.

Biological macromolecules often undergo large conformational rearrangements during a functional cycle. To simulate these structural transitions with full atomic detail typically demands extensive computational resources. Moreover, it is unclear how to incorporate, in a principled way, additional experimental information that could guide the structural transition. This article develops a probabilistic model for conformational transitions in biomolecules. The model can be viewed as a network of anharmonic springs that break, if the experimental data support the rupture of bonds. Hamiltonian Monte Carlo in internal coordinates is used to infer structural transitions from experimental data, thereby sampling large conformational transitions without distorting the structure. The model is benchmarked on a large set of conformational transitions. Moreover, we demonstrate the use of the probabilistic network model for integrative modeling of macromolecular complexes based on data from crosslinking followed by mass spectrometry.

An evolutionarily conserved glycine-tyrosine motif forms a folding core in outer membrane proteins.

An intimate interaction between a pair of amino acids, a tyrosine and glycine on neighboring ß-strands, has been previously reported to be important for the structural stability of autotransporters. Here, we show that the conservation of this interacting pair extends to nearly all major families of outer membrane ß-barrel proteins, which are thought to have originated through duplication events involving an ancestral ßß hairpin. We analyzed the function of this motif using the prototypical outer membrane protein OmpX. Stopped-flow fluorescence shows that two folding processes occur in the millisecond time regime, the rates of which are reduced in the tyrosine mutant. Folding assays further demonstrate a reduction in the yield of folded protein for the mutant compared to the wild-type, as well as a reduction in thermal stability. Taken together, our data support the idea of an evolutionarily conserved 'folding core' that affects the folding, membrane insertion, and thermal stability of outer membrane protein ß-barrels.

Data-driven coarse graining of large biomolecular structures.

Advances in experimental and computational techniques allow us to study the structure and dynamics of large biomolecular assemblies at increasingly higher resolution. However, with increasing structural detail it can be challenging to unravel the mechanism underlying the function of molecular machines. One reason is that atomistic simulations become computationally prohibitive. Moreover it is difficult to rationalize the functional mechanism of systems composed of tens of thousands to millions of atoms by following each atom's movements. Coarse graining (CG) allows us to understand biological structures from a hierarchical perspective and to gradually zoom into the adequate level of structural detail. This article introduces a Bayesian approach for coarse graining biomolecular structures. We develop a probabilistic model that aims to represent the shape of an experimental structure as a cloud of bead particles. The particles interact via a pairwise potential whose parameters are estimated along with the bead positions and the CG mapping between atoms and beads. Our model can also be applied to density maps obtained by cryo-electron microscopy. We illustrate our approach on various test systems.

Bayesian Modeling of Biomolecular Assemblies with Cryo-EM Maps.

A growing array of experimental techniques allows us to characterize the three-dimensional structure of large biological assemblies at increasingly higher resolution. In addition to X-ray crystallography and nuclear magnetic resonance in solution, new structure determination methods such cryo-electron microscopy (cryo-EM), crosslinking/mass spectrometry and solid-state NMR have emerged. Often it is not sufficient to use a single experimental method, but complementary data need to be collected by using multiple techniques. The integration of all datasets can only be achieved by computational means. This article describes Inferential structure determination, a Bayesian approach to integrative modeling of biomolecular complexes with hybrid structural data. I will introduce probabilistic models for cryo-EM maps and outline Markov chain Monte Carlo algorithms for sampling model structures from the posterior distribution. I will focus on rigid and flexible modeling with cryo-EM data and discuss some of the computational challenges of Bayesian inference in the context of biomolecular modeling.