Random forest and live single-cell metabolomics reveal metabolic profiles of human macrophages upon polarization.

Human macrophages are innate immune cells with diverse, functionally distinct phenotypes, namely, pro-inflammatory M1 and anti-inflammatory M2 macrophages. Both are involved in multiple physiological and pathological processes, including would healing, infection, and cancer. However, the metabolic differences between these phenotypes are largely unexplored at single-cell resolution. To address this knowledge gap, an untargeted live single-cell mass spectrometry-based metabolomic profiling coupled with a machine-learning data analysis approach was developed to investigate the metabolic profile of each phenotype at the single-cell level. Results show that M1 and M2 macrophages have distinct metabolic profiles, with differential levels of fatty acyls, glycerophospholipids, and sterol lipids, which are important components of plasma membrane and involved in multiple biological processes. Furthermore, we could discern several putatively annotated molecules that contribute to inflammatory response of macrophages. The combination of random forest and live single-cell metabolomics provided an in-depth profile of the metabolome of primary human M1 and M2 macrophages at the single-cell level for the first time, which will pave the way for future studies targeting the differentiation of other immune cells.

Decoding chirality in circuit topology of a self entangled chain through braiding.

Circuit topology employs fundamental units of entanglement, known as soft contacts, for constructing knots from the bottom up, utilizing circuit topology relations, namely parallel, series, cross, and concerted relations. In this article, we further develop this approach to facilitate the analysis of chirality, which is a significant quantity in polymer chemistry. To achieve this, we translate the circuit topology approach to knot engineering into a braid-theoretic framework. This enables us to calculate the Jones polynomial for all possible binary combinations of contacts in cross or concerted relations and to show that, for series and parallel relations, the polynomial factorises. Our results demonstrate that the Jones polynomial provides a powerful tool for analysing the chirality of molecular knots constructed using circuit topology. The framework presented here can be used to design and engineer a wide range of entangled chain with desired chiral properties, with potential applications in fields such as materials science and nanotechnology.

Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) lack a stable native conformation, making it challenging to characterize their structure and dynamics. Key topological motifs with fundamental biological relevance are often hidden in the conformational noise, eluding detection. Here, we develop a circuit topology toolbox to extract conformational patterns, critical contacts, and timescales from simulated dynamics of intrinsically disordered proteins. We follow the dynamics of IDPs by providing a smart low-dimensionality representation of their three-dimensional (3D) configuration in the topology space. Such an approach allows us to quantify topological similarity in dynamic systems, therefore providing a pipeline for structural comparison of IDPs.

Circuit topology predicts pathogenicity of missense mutations.

The contact topology of a protein determines important aspects of the folding process. The topological measure of contact order has been shown to be predictive of the rate of folding. Circuit topology is emerging as another fundamental descriptor of biomolecular structure, with predicted effects on the folding rate. We analyze the residue-based circuit topological environments of 21 K mutations labeled as pathogenic or benign. Multiple statistical lines of reasoning support the conclusion that the number of contacts in two specific circuit topological arrangements, namely inverse parallel and cross relations, with contacts involving the mutated residue have discriminatory value in determining the pathogenicity of human variants. We investigate how results vary with residue type and according to whether the gene is essential. We further explore the relationship to a number of structural features and find that circuit topology provides nonredundant information on protein structures and pathogenicity of mutations. Results may have implications for the polymer physics of protein folding and suggest that "local" topological information, including residue-based circuit topology and residue contact order, could be useful in improving state-of-the-art machine learning algorithms for pathogenicity prediction.

Single cell force spectroscopy of erythrocytes at physiological and febrile temperatures reveals mechano-modulatory effects of atorvastatin.

RBCs are mechanically active cells and constantly deform as they circulate through vasculature. Their mechanical properties can be significantly altered by various pathophysiological conditions, and the alterations in RBC mechanics can, in turn, have functional consequences. Although numerous mechanical studies have been conducted on RBCs, surprisingly, strain-rate and temperature dependent mechanics of RBCs have not been systematically examined, and current data is primarily based on measurements at room temperature. Here, we have used state-of-the-art single-cell optical tweezers to probe atorvastatin-induced changes of RBC mechanics and its strain-rate dependency at physiologically and medically relevant temperatures. Our data indicate that RBC mechanics is strain-rate and temperature dependent, and atorvastatin treatment softens RBCs at physiological temperature, but not at febrile temperature. The observed mechanical change is a notable side effect of the drug in some therapeutic applications. However, the mechano-modulatory effects of atorvastatin on erythrocytes at physiological temperature might offer new therapeutic possibilities for diseases related to blood cell mechanics.

Alternative modes of client binding enable functional plasticity of Hsp70.

The Hsp70 system is a central hub of chaperone activity in all domains of life. Hsp70 performs a plethora of tasks, including folding assistance, protection against aggregation, protein trafficking, and enzyme activity regulation, and interacts with non-folded chains, as well as near-native, misfolded, and aggregated proteins. Hsp70 is thought to achieve its many physiological roles by binding peptide segments that extend from these different protein conformers within a groove that can be covered by an ATP-driven helical lid. However, it has been difficult to test directly how Hsp70 interacts with protein substrates in different stages of folding and how it affects their structure. Moreover, recent indications of diverse lid conformations in Hsp70-substrate complexes raise the possibility of additional interaction mechanisms. Addressing these issues is technically challenging, given the conformational dynamics of both chaperone and client, the transient nature of their interaction, and the involvement of co-chaperones and the ATP hydrolysis cycle. Here, using optical tweezers, we show that the bacterial Hsp70 homologue (DnaK) binds and stabilizes not only extended peptide segments, but also partially folded and near-native protein structures. The Hsp70 lid and groove act synergistically when stabilizing folded structures: stabilization is abolished when the lid is truncated and less efficient when the groove is mutated. The diversity of binding modes has important consequences: Hsp70 can both stabilize and destabilize folded structures, in a nucleotide-regulated manner; like Hsp90 and GroEL, Hsp70 can affect the late stages of protein folding; and Hsp70 can suppress aggregation by protecting partially folded structures as well as unfolded protein chains. Overall, these findings in the DnaK system indicate an extension of the Hsp70 canonical model that potentially affects a wide range of physiological roles of the Hsp70 system.

Lassa hemorrhagic shock syndrome-on-a-chip.

Lack of experimental human models hinders research on Lassa hemorrhagic fever and the development of treatment strategies. Here, we report the first chip-based model for Lassa hemorrhagic syndrome. The chip features a microvessel interfacing collagen network as a simple mimic for extracellular matrix, allowing for quantitative and real-time vascular integrity assessment. Luminal infusion of Lassa virus-like particles led to a dramatic increase in vascular permeability in a viral load-dependent manner. Using this platform, we showed that Fibrin-derived peptide FX06 can be used to suppress the vascular integrity loss. This simple chip-based model proved promising in the assessment of disease severity and provides an easy-to-use platform for future investigation of Lassa pathogenesis and drug development in a human-like setting.

Disease evolution in reaction networks: Implications for a diagnostic problem.

We study the time evolution of symptoms (signs) with some defects in the dynamics of a reaction network as a (microscopic) model for the progress of disease phenotypes. To this end, we take a large population of reaction networks and follow the stochastic dynamics of the system to see how the development of defects affects the macroscopic states of the signs probability distribution. We start from some plausible definitions for the healthy and disease states along with a dynamical model for the emergence of diseases by a reverse simulated annealing algorithm. The healthy state is defined as a state of maximum objective function, which here is the sum of mutual information between a subset of signal variables and the subset of assigned response variables. A disease phenotype is defined with two parameters controlling the rate of mutations in reactions and the rate of accepting mutations that reduce the objective function. The model can provide the time dependence of the sign probabilities given a disease phenotype. This allows us to obtain the accuracy of diagnosis as a function of time by using a probabilistic model of signs and diseases. The trade-off between the diagnosis accuracy (increasing in time) and the objective function (decreasing in time) can be used to suggest an optimal time for medical intervention. Our model would be useful in particular for a dynamical (history-based) diagnostic problem, to estimate the likelihood of a disease hypothesis given the temporal evolution of the signs.

Elasticity of connected semiflexible quadrilaterals.

Using the positional-orientational propagator of a semiflexible filament in the weakly bending regime, we analytically calculate the probability densities associated with the fluctuating tip and the corners of a grafted system of connected quadrilaterals. We calculate closed analytic expressions for the probability densities within the framework of the worm-like chain model, which are valid in the weakly bending regime. The probability densities give the physical quantities related to the elasticity of the system such as the force-extension relation in the fixed extension ensemble, the Poisson's ratio and the average of the force exerted to a confining stiff planar wall by the fluctuating tip of the system. Our analysis reveals that the force-extension relations depend on the contour length of the system (material content), the bending stiffness (chemical nature), the geometrical angle and the number of the quadrilaterals, while the Poisson's ratio depends only on the geometrical angle and the number of the quadrilaterals, and is thus a purely geometric property of the system.

Topological principles of protein folding.

What is the topology of a protein and what governs protein folding to a specific topology? This is a fundamental question in biology. The protein folding reaction is a critically important cellular process, which is failing in many prevalent diseases. Understanding protein folding is also key to the design of new proteins for applications. However, our ability to predict the folding of a protein chain is quite limited and much is still unknown about the topological principles of folding. Current predictors of folding kinetics, including the contact order and size, present a limited predictive power, suggesting that these models are fundamentally incomplete. Here, we use a newly developed mathematical framework to define and extract the topology of a native protein conformation beyond knot theory, and investigate the relationship between native topology and folding kinetics in experimentally characterized proteins. We show that not only the folding rate, but also the mechanistic insight into folding mechanisms can be inferred from topological parameters. We identify basic topological features that speed up or slow down the folding process. The approach enabled the decomposition of protein 3D conformation into topologically independent elementary folding units, called circuits. The number of circuits correlates significantly with the folding rate, offering not only an efficient kinetic predictor, but also a tool for a deeper understanding of theoretical folding models. This study contributes to recent work that reveals the critical relevance of topology to protein folding with a new, contact-based, mathematically rigorous perspective. We show that topology can predict folding kinetics when geometry-based predictors like contact order and size fail.

Deciphering Metabolic Heterogeneity by Single-Cell Analysis.

Single-cell analysis provides insights into cellular heterogeneity and dynamics of individual cells. This Feature highlights recent developments in key analytical techniques suited for single-cell metabolic analysis with a special focus on mass spectrometry-based analytical platforms and RNA-seq as well as imaging techniques that reveal stochasticity in metabolism.

Cornea-Derived Mesenchymal Stromal Cells Therapeutically Modulate Macrophage Immunophenotype and Angiogenic Function.

Macrophages are crucial drivers of inflammatory corneal neovascularization and thus are potential targets for immunomodulatory therapies. We hypothesized that therapeutic use of cornea-derived mesenchymal stromal cells (cMSCs) may alter the function of macrophages. We found that cMSCs can modulate the phenotype and angiogenic function of macrophages. In vitro, cMSCs induce apoptosis of macrophages while preferentially promoting a distinct CD14hi CD16hi CD163hi CD206hi immunophenotype that has significantly reduced angiogenic effects based on in vitro angiogenesis assays. In vivo, application of cMSCs to murine corneas after injury leads to reduced macrophage infiltration and higher expression of CD206 in macrophages. Macrophages cocultured ("educated") by cMSCs express significantly higher levels of anti-angiogenic and anti-inflammatory factors compared with control macrophages. In vivo, injured corneas treated with cMSC-educated macrophages demonstrate significantly less neovascularization compared with corneas treated with control macrophages. Knocking down the expression of pigment epithelial derived factor (PEDF) in cMSCs significantly abrogates its modulating effects on macrophages, as shown by the reduced rate of apoptosis, decreased expression of sFLT-1/PEDF, and increased expression of vascular endothelial growth factor-A in the cocultured macrophages. Similarly, cMSCs isolated from PEDF knockout mice are less effective compared with wild-type cMSCs at inhibiting macrophage infiltration when applied to wild-type corneas after injury. Overall, these results demonstrate that cMSCs therapeutically suppress the angiogenic capacity of macrophages and highlight the role of cMSC secreted PEDF in the modulation of macrophage phenotype and function. Stem Cells 2018;36:775-784.

Mapping a single-molecule folding process onto a topological space.

Physics of protein folding has been dominated by conceptual frameworks including the nucleation-propagation mechanism and the diffusion-collision model, and none address the topological properties of a chain during a folding process. Single-molecule interrogation of folded biomolecules has enabled real-time monitoring of folding processes at an unprecedented resolution. Despite these advances, the topology landscape has not been fully mapped for any chain. Using a novel circuit topology approach, we map the topology landscape of a model polymeric chain. Inspired by single-molecule mechanical interrogation studies, we restrained the ends of a chain and followed fold nucleation dynamics. We find that, before the nucleation, transient local entropic loops dominate. Although the nucleation length of globules is dependent on the cohesive interaction, the ultimate topological states of the collapsed polymer are largely independent of the interaction but depend on the speed of the folding process. After the nucleation, transient topological rearrangements are observed that converge to a steady-state, where the fold grows in a self-similar manner.

Reshaping of the conformational search of a protein by the chaperone trigger factor.

Protein folding is often described as a search process, in which polypeptides explore different conformations to find their native structure. Molecular chaperones are known to improve folding yields by suppressing aggregation between polypeptides before this conformational search starts, as well as by rescuing misfolds after it ends. Although chaperones have long been speculated to also affect the conformational search itself--by reshaping the underlying folding landscape along the folding trajectory--direct experimental evidence has been scarce so far. In Escherichia coli, the general chaperone trigger factor (TF) could play such a role. TF has been shown to interact with nascent chains at the ribosome, with polypeptides released from the ribosome into the cytosol, and with fully folded proteins before their assembly into larger complexes. To investigate the effect of TF from E. coli on the conformational search of polypeptides to their native state, we investigated individual maltose binding protein (MBP) molecules using optical tweezers. Here we show that TF binds folded structures smaller than one domain, which are then stable for seconds and ultimately convert to the native state. Moreover, TF stimulates native folding in constructs of repeated MBP domains. The results indicate that TF promotes correct folding by protecting partially folded states from distant interactions that produce stable misfolded states. As TF interacts with most newly synthesized proteins in E. coli, we expect these findings to be of general importance in understanding protein folding pathways.

On the role of topology in regulating transcriptional cascades.

We study the impact of topology on the response of a transcriptional cascade with certain circuit topologies to a constant and time-varying input signal. We systematically analyze the response of the output to activating and repressing cascades. We identify two types of responses for a linear cascade, namely the "Decaying mode", where the input signal becomes exceedingly weaker as it propagates, and the "Bistable mode", where the input signal can either be amplified or die out in the pathway. We examine how the transition occurs from one mode to the other as we add coherent and/or incoherent feed-forward loops in an otherwise linear cascade. We find that pathways with at least one incoherent feedforward loop can perform adaptive responses with the quality of response varying among different topologies. Furthermore, we study the origin of a (non)monotonic input-output profile for various circuit topologies over a wide range of parameter space. For a time-varying input signal, we identify some circuit topologies that are more prone to noise propagation than others that are more reliable in blocking out high-amplitude fluctuations. We discuss the effect of cell to cell variation in protein expression on the output of a linear cascade and compare the robustness of activating and repressing cascades to noise propagations. In the end, we apply our model to study an example of a transcription cascade that guides the development of Bacillus subtilis spores and discuss an example from a metabolic pathway where a transition from the decaying to bistable mode can occur by changing the topology of interactions in the pathway.

Topology of internally constrained polymer chains.

Linear chains with intra-chain contacts can adopt different topologies and allow transitions between them, but it remains unclear how this process can be controlled. This question is important to systems ranging from proteins to chromosomes, which can adopt different conformations that are key to their function and toxicity. Here, we investigate how the topological dynamics of a simple linear chain is affected by interactions with a binding partner, using Monte Carlo and Molecular Dynamics simulations. We show that two point contacts with a binding partner are sufficient to accelerate or decelerate the formation of particular topologies within linear chains. Computed ''folding-time landscapes" that detail the folding time within the topology space show that such contacts deform these landscapes and hence alter the occupation probability of topological states. The results provide a mechanism by which chain topologies can be controlled externally, which opens up the possibility of regulating topological dynamics and the formation of more complex topologies. The findings may have important implications for understanding the mechanism of chaperone action as well as genome architecture and evolution.

Neuropeptide substance P and the immune response.

Substance P is a peptide mainly secreted by neurons and is involved in many biological processes, including nociception and inflammation. Animal models have provided insights into the biology of this peptide and offered compelling evidence for the importance of substance P in cell-to-cell communication by either paracrine or endocrine signaling. Substance P mediates interactions between neurons and immune cells, with nerve-derived substance P modulating immune cell proliferation rates and cytokine production. Intriguingly, some immune cells have also been found to secrete substance P, which hints at an integral role of substance P in the immune response. These communications play important functional roles in immunity including mobilization, proliferation and modulation of the activity of immune cells. This review summarizes current knowledge of substance P and its receptors, as well as its physiological and pathological roles. We focus on recent developments in the immunobiology of substance P and discuss the clinical implications of its ability to modulate the immune response.

The Trigger Factor Chaperone Encapsulates and Stabilizes Partial Folds of Substrate Proteins.

How chaperones interact with protein chains to assist in their folding is a central open question in biology. Obtaining atomistic insight is challenging in particular, given the transient nature of the chaperone-substrate complexes and the large system sizes. Recent single-molecule experiments have shown that the chaperone Trigger Factor (TF) not only binds unfolded protein chains, but can also guide protein chains to their native state by interacting with partially folded structures. Here, we used all-atom MD simulations to provide atomistic insights into how Trigger Factor achieves this chaperone function. Our results indicate a crucial role for the tips of the finger-like appendages of TF in the early interactions with both unfolded chains and partially folded structures. Unfolded chains are kinetically trapped when bound to TF, which suppresses the formation of transient, non-native end-to-end contacts. Mechanical flexibility allows TF to hold partially folded structures with two tips (in a pinching configuration), and to stabilize them by wrapping around its appendages. This encapsulation mechanism is distinct from that of chaperones such as GroEL, and allows folded structures of diverse size and composition to be protected from aggregation and misfolding interactions. The results suggest that an ATP cycle is not required to enable both encapsulation and liberation.

Distance measures and evolution of polymer chains in their topological space.

Conformational transitions are ubiquitous in biomolecular systems, have significant functional roles and are subject to evolutionary pressures. Here we provide a first theoretical framework for topological transition, i.e. conformational transitions that are associated with changes in molecular topology. For folded linear biomolecules, arrangement of intramolecular contacts is identified as a key topological property, termed as circuit topology. Distance measures are proposed as reaction coordinates to represent progress along a pathway from initial topology to final topology. Certain topological classes are shown to be more accessible from a random topology. We study dynamic stability and pathway degeneracy associated with a topological reaction and found that off-pathways might seriously hamper evolution to desired topologies. Finally we present an algorithm for estimating the number of intermediate topologies visited during a topological reaction. The results of this study are relevant to, among others, structural studies of RNA and proteins, analysis of topologically associated domains in chromosomes, and molecular evolution.

Label-free characterization of biomembranes: from structure to dynamics.

We review recent progress in the study of the structure and dynamics of phospholipid membranes and associated proteins, using novel label-free analytical tools. We describe these techniques and illustrate them with examples highlighting current capabilities and limitations. Recent advances in applying such techniques to biological and model membranes for biophysical studies and biosensing applications are presented, and future prospects are discussed.