Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.

Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.

Cell-specific IL-1R1 regulates the regional heterogeneity of microglial displacement of GABAergic synapses and motor learning ability.

Microglia regulate synaptic function in various ways, including the microglial displacement of the surrounding GABAergic synapses, which provides important neuroprotection from certain diseases. However, the physiological role and underlying mechanisms of microglial synaptic displacement remain unclear. In this study, we observed that microglia exhibited heterogeneity during the displacement of GABAergic synapses surrounding neuronal soma in different cortical regions under physiological conditions. Through three-dimensional reconstruction, in vitro co-culture, two-photon calcium imaging, and local field potentials recording, we found that IL-1ß negatively modulated microglial synaptic displacement to coordinate regional heterogeneity in the motor cortex, which impacted the homeostasis of the neural network and improved motor learning ability. We used the Cre-Loxp system and found that IL-1R1 on glutamatergic neurons, rather than that on microglia or GABAergic neurons, mediated the negative effect of IL-1ß on synaptic displacement. This study demonstrates that IL-1ß is critical for the regional heterogeneity of synaptic displacement by coordinating different actions of neurons and microglia via IL-1R1, which impacts both neural network homeostasis and motor learning ability. It provides a theoretical basis for elucidating the physiological role and mechanism of microglial displacement of GABAergic synapses.

A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0].

The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.

Swollen inguinal lymph nodes with low fever and night sweat: diagnosis and treatment of case of cat-scratch disease lymphadenitis with sinus formation.

A 52-year-old woman complained of inguinal lymph node enlargement, low fever and night sweats for 20 days. After pathological biopsy and metagenomic sequencing, she was diagnosed as having Bartonella henselae infection. Her lymph nodes were accompanied by multiple ulcers in the affected area and sinus formation. Azithromycin was administered according to the Sanford Guide to Antimicrobial Therapy 2020, combined with wound repair and partial resection of inguinal lymph nodes. The patient showed good recovery after the operation. In all, lymphadenitis associated with B. henselae infection is difficult to diagnose. Lymphadenitis with suppuration and sinus formation needs multidisciplinary consultation. When the causal pathogen is unknown, metagenomic sequencing is recommended for a definite diagnosis.

Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow.

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.

Mechanistic Insights into Passive Membrane Permeability of Drug-like Molecules from a Weighted Ensemble of Trajectories.

Passive permeability of a drug-like molecule is a critical property assayed early in a drug discovery campaign that informs a medicinal chemist how well a compound can traverse biological membranes, such as gastrointestinal epithelial or restrictive organ barriers, so it can perform a specific therapeutic function. However, the challenge that remains is the development of a method, experimental or computational, which can both determine the permeation rate and provide mechanistic insights into the transport process to help with the rational design of any given molecule. Typically, one of the following three methods are used to measure the membrane permeability: (1) experimental permeation assays acting on either artificial or natural membranes; (2) quantitative structure-permeability relationship models that rely on experimental values of permeability or related pharmacokinetic properties of a range of molecules to infer those for new molecules; and (3) estimation of permeability from the Smoluchowski equation, where free energy and diffusion profiles along the membrane normal are taken as input from large-scale molecular dynamics simulations. While all these methods provide estimates of permeation coefficients, they provide very little information for guiding rational drug design. In this study, we employ a highly parallelizable weighted ensemble (WE) path sampling strategy, empowered by cloud computing techniques, to generate unbiased permeation pathways and permeability coefficients for a set of drug-like molecules across a neat 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylcholine membrane bilayer. Our WE method predicts permeability coefficients that compare well to experimental values from an MDCK-LE cell line and PAMPA assays for a set of drug-like amines of varying size, shape, and flexibility. Our method also yields a series of continuous permeation pathways weighted and ranked by their associated probabilities. Taken together, the ensemble of reactive permeation pathways, along with the estimate of the permeability coefficient, provides a clearer picture of the microscopic underpinnings of small-molecule membrane permeation.

WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications.

The weighted ensemble (WE) family of methods is one of several statistical mechanics-based path sampling strategies that can provide estimates of key observables (rate constants and pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on the major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE methods. The key features of the new WESTPA 2.0 software enhance the efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation data sets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and "binless" WE strategies.

Normal mode analysis of membrane protein dynamics using the vibrational subsystem analysis.

The vibrational subsystem analysis is a useful approach that allows for evaluating the spectrum of modes of a given system by integrating out the degrees of freedom accessible to the environment. The approach could be utilized for exploring the collective dynamics of a membrane protein (system) coupled to the lipid bilayer (environment). However, the application to membrane proteins is limited due to high computational costs of modeling a sufficiently large membrane environment unbiased by end effects, which drastically increases the size of the investigated system. We derived a recursive formula for calculating the reduced Hessian of a membrane protein embedded in a lipid bilayer by decomposing the membrane into concentric cylindrical domains with the protein located at the center. The approach allows for the design of a time- and memory-efficient algorithm and a mathematical understanding of the convergence of the reduced Hessian with respect to increasing membrane sizes. The application to the archaeal aspartate transporter GltPh illustrates its utility and efficiency in capturing the transporter's elevator-like movement during its transition between outward-facing and inward-facing states.

ClustENMD: efficient sampling of biomolecular conformational space at atomic resolution.

SUMMARY: Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace. AVAILABILITY AND IMPLEMENTATION: ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rutaecarpine enhances the anti-diabetic activity and hepatic distribution of metformin via up-regulation of Oct1 in diabetic rats.

Diabetes mellitus is a chronic metabolic disorder with multiple complications, patients who receive metformin may have a simultaneous intake of herbal medicine containing rutaecarpine due to cardiovascular protection and hypolipidemic effects of rutaecarpine. There might be drug interactions between metformin and rutaecarpine. This study aimed to investigate the effects of rutaecarpine on the pharmacodynamics and pharmacokinetics of metformin in diabetic rats.The diabetic rat model was induced with high-fat diet and low dose streptozotocin. Metformin with or without rutaecarpine was administered by oral gavage for 42 days. Pharmacodynamics and pharmacokinetics parameters were evaluated.The pharmacodynamics results revealed that co-administration of rutaecarpine with metformin resulted in a remarkable reduction of serum glucose and lipid profiles in diabetic rats compared to metformin treated alone. The pharmacokinetics results showed that co-treatments of rutaecarpine with metformin did not affect the systemic exposure and renal distribution of metformin, but increased metformin concentration in liver. Furthermore, rutaecarpine increased Oct1-mediated metformin uptake into hepatocytes by upregulation of Oct1 expression in the liver.The above data indicate that rutaecarpine enhanced the anti-diabetic effect of metformin, which may be associated with the increased hepatic distribution of metformin through up-regulation of Oct1 in response to rutaecarpine.

ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with Python.

SUMMARY: ProDy, an integrated application programming interface developed for modelling and analysing protein dynamics, has significantly evolved in recent years in response to the growing data and needs of the computational biology community. We present major developments that led to ProDy 2.0: (i) improved interfacing with databases and parsing new file formats, (ii) SignDy for signature dynamics of protein families, (iii) CryoDy for collective dynamics of supramolecular systems using cryo-EM density maps and (iv) essential site scanning analysis for identifying sites essential to modulating global dynamics. AVAILABILITY AND IMPLEMENTATION: ProDy is open-source and freely available under MIT License from https://github.com/prody/ProDy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HiDeF: identifying persistent structures in multiscale 'omics data.

In any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here, we use the concept of persistent homology, drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Superantigenic character of an insert unique to SARS-CoV-2 spike supported by skewed TCR repertoire in patients with hyperinflammation.

Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID-19 is a newly recognized condition in children with recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. These children and adult patients with severe hyperinflammation present with a constellation of symptoms that strongly resemble toxic shock syndrome, an escalation of the cytotoxic adaptive immune response triggered upon the binding of pathogenic superantigens to T cell receptors (TCRs) and/or major histocompatibility complex class II (MHCII) molecules. Here, using structure-based computational models, we demonstrate that the SARS-CoV-2 spike (S) glycoprotein exhibits a high-affinity motif for binding TCRs, and may form a ternary complex with MHCII. The binding epitope on S harbors a sequence motif unique to SARS-CoV-2 (not present in other SARS-related coronaviruses), which is highly similar in both sequence and structure to the bacterial superantigen staphylococcal enterotoxin B. This interaction between the virus and human T cells could be strengthened by a rare mutation (D839Y/N/E) from a European strain of SARS-CoV-2. Furthermore, the interfacial region includes selected residues from an intercellular adhesion molecule (ICAM)-like motif shared between the SARS viruses from the 2003 and 2019 pandemics. A neurotoxin-like sequence motif on the receptor-binding domain also exhibits a high tendency to bind TCRs. Analysis of the TCR repertoire in adult COVID-19 patients demonstrates that those with severe hyperinflammatory disease exhibit TCR skewing consistent with superantigen activation. These data suggest that SARS-CoV-2 S may act as a superantigen to trigger the development of MIS-C as well as cytokine storm in adult COVID-19 patients, with important implications for the development of therapeutic approaches.

An insertion unique to SARS-CoV-2 exhibits superantigenic character strengthened by recent mutations.

Multisystem Inflammatory Syndrome in Children (MIS-C) associated with Coronavirus Disease 2019 (COVID-19) is a newly recognized condition in which children with recent SARS-CoV-2 infection present with a constellation of symptoms including hypotension, multiorgan involvement, and elevated inflammatory markers. These symptoms and the associated laboratory values strongly resemble toxic shock syndrome, an escalation of the cytotoxic adaptive immune response triggered upon the binding of pathogenic superantigens to MHCII molecules and T cell receptors (TCRs). Here, we used structure-based computational models to demonstrate that the SARS-CoV-2 spike (S) exhibits a high-affinity motif for binding TCR, interacting closely with both the α- and ß-chains variable domains' complementarity-determining regions. The binding epitope on S harbors a sequence motif unique to SARS-CoV-2 (not present in any other SARS coronavirus), which is highly similar in both sequence and structure to bacterial superantigens. Further examination revealed that this interaction between the virus and human T cells is strengthened in the context of a recently reported rare mutation (D839Y/N/E) from a European strain of SARS-CoV-2. Furthermore, the interfacial region includes selected residues from a motif shared between the SARS viruses from the 2003 and 2019 pandemics, which has intracellular adhesion molecule (ICAM)-like character. These data suggest that the SARS-CoV-2 S may act as a superantigen to drive the development of MIS-C as well as cytokine storm in adult COVID-19 patients, with important implications for the development of therapeutic approaches.

Identifying persistent structures in multiscale 'omics data.

In any 'omics study, the scale of analysis can dramatically affect the outcome. For instance, when clustering single-cell transcriptomes, is the analysis tuned to discover broad or specific cell types? Likewise, protein communities revealed from protein networks can vary widely in sizes depending on the method. Here we use the concept of "persistent homology", drawn from mathematical topology, to identify robust structures in data at all scales simultaneously. Application to mouse single-cell transcriptomes significantly expands the catalog of identified cell types, while analysis of SARS-COV-2 protein interactions suggests hijacking of WNT. The method, HiDeF, is available via Python and Cytoscape.

Involvement of organic anion-transporting polypeptides and organic cation transporter in the hepatic uptake of jatrorrhizine.

Jatrorrhizine possesses a wide spectrum of pharmacological activities. However, the mechanism underlying hepatic uptake of jatrorrhizine remains unclear.Rat liver slices, isolated rat hepatocytes and human embryonic kidney 293 (HEK293) cells stably expressing human organic anion-transporting polypeptide (OATP) and organic cation transporter (OCT) were used to evaluate the hepatic uptake of jatrorrhizine in this study.Uptake of jatrorrhizine in rat liver slices and isolated rat hepatocytes was significantly inhibited by glycyrrhizic acid (Oatp1b2 inhibitor) and prazosin (Oct1 inhibitor), but not by ibuprofen (Oatp1a1 inhibitor) or digoxin (Oatp1a4 inhibitor). Uptake of jatrorrhizine in OATP1B3 and OCT1-HEK293 cells indicated a saturable process with the Km of 8.20 ± 1.28 and 4.94 ± 0.55 µM, respectively. However, the transcellular transport of jatrorrhizine in OATP1B1-HEK293 cells was not observed. Rifampicin (OATP inhibitor) for OATP1B3-HEK293 cells and prazosin for OCT1-HEK293 cells could inhibit the uptake of jatrorrhizine with the IC50 of 5.49 ± 1.05 and 2.77 ± 0.72 µM, respectively.The above data indicate that hepatic uptake of jatrorrhizine is involved in both OATP and OCT, which may have important roles in jatrorrhizine liver disposition and potential drug-drug interactions.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior.

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

Differences in the intrinsic spatial dynamics of the chromatin contribute to cell differentiation.

Advances in chromosome conformation capture techniques as well as computational characterization of genomic loci structural dynamics open new opportunities for exploring the mechanistic aspects of genome-scale differences across different cell types. We examined here the dynamic basis of variabilities between different cell types by investigating their chromatin mobility profiles inferred from Hi-C data using an elastic network model representation of the chromatin. Our comparative analysis of sixteen cell lines reveals close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations emerge in the detailed spatial mobilities of genomic loci. Closer examination reveals that the differences in spatial dynamics mainly originate from the difference in the frequencies of their intrinsically accessible modes of motion. Thus, even though the chromosomes of different types of cells have access to similar modes of collective movements, not all modes are deployed by all cells, such that the effective mobilities and cross-correlations of genomic loci are cell-type-specific. Comparison with RNA-seq expression data reveals a strong overlap between highly expressed genes and those distinguished by high mobilities in the present study, in support of the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation.

Trimerization of dopamine transporter triggered by AIM-100 binding: Molecular mechanism and effect of mutations.

Recent work demonstrated the propensity of dopamine transporters (DATs) to form trimers or higher oligomers, enhanced upon binding a furopyrimidine, AIM-100. AIM-100 binding promotes DAT endocytosis and thereby moderates dopaminergic transmission. Despite the neurobiological significance of these events, the molecular mechanisms that underlie the stabilization of DAT trimer and the key interactions that modulate the trimerization of DAT, and not serotonin transporter SERT, remain unclear. In the present study, we determined three structural models, termed trimer-W238, -C306 and -Y303, for possible trimerization of DATs . To this aim, we used structural data resolved for DAT and its structural homologs that share the LeuT fold, advanced computational modeling and simulations, site-directed mutagenesis experiments and live-cell imaging assays. The models are in accord with the versatility of LeuT fold to stabilize dimeric or higher order constructs. Selected residues show a high propensity to occupy interfacial regions. Among them, D231-W238 in the extracellular loop EL2, including the intersubunit salt-bridge forming pair D231/D232-R237 (not present in SERT) (in trimer-W238), the loop EL3 (trimers-C306 and -Y303), and W497 on the intracellularly exposed IL5 loop (trimer-C306) and its spatial neighbors (e.g. K525) near the C-terminus are computationally predicted and experimentally confirmed to play important roles in enabling the correct folding and/or oligomerization of DATs in the presence of AIM-100. The study suggests the possibility of controlling the effective transport of dopamine by altering the oligomerization state of DAT upon small molecule binding, as a possible intervention strategy to modulate dopaminergic signaling. This article is part of the issue entitled 'Special Issue on Neurotransmitter Transporters'.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity.

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.