Investigation of the Molecular Evolution of Treg Suppression Mechanisms Indicates a Convergent Origin.

Regulatory T cell (Treg) suppression of conventional T cells is a central mechanism that ensures immune system homeostasis. The exact time point of Treg emergence is still disputed. Furthermore, the time of Treg-mediated suppression mechanisms' emergence has not been identified. It is not yet known whether Treg suppression mechanisms diverged from a single pathway or converged from several sources. We investigated the evolutionary history of Treg suppression pathways using various phylogenetic analysis tools. To ensure the conservation of function for investigated proteins, we augmented our study using nonhomology-based methods to predict protein functions among various investigated species and mined the literature for experimental evidence of functional convergence. Our results indicate that a minority of Treg suppressor mechanisms could be homologs of ancient conserved pathways. For example, CD73, an enzymatic pathway known to play an essential role in invertebrates, is highly conserved between invertebrates and vertebrates, with no evidence of positive selection (w = 0.48, p-value < 0.00001). Our findings indicate that Tregs utilize homologs of proteins that diverged in early vertebrates. However, our findings do not exclude the possibility of a more evolutionary pattern following the duplication degeneration−complementation (DDC) model. Ancestral sequence reconstruction showed that Treg suppression mechanism proteins do not belong to one family; rather, their emergence seems to follow a convergent evolutionary pattern.

Investigation of Evolutionary History and Origin of the Tre1 Family Suggests a Role in Regulating Hemocytes Cells Infiltration of the Blood-Brain Barrier.

Understanding the evolutionary relationship between immune cells and the blood-brain barrier (BBB) is important to devise therapeutic strategies. In vertebrates, immune cells follow either a paracellular or a transcellular pathway to infiltrate the BBB. In Drosophila, glial cells form the BBB that regulates the access of hemocytes to the brain. However, it is still not known which diapedesis route hemocytes cells follow. In vertebrates, paracellular migration is dependent on PECAM1, while transcellular migration is dependent on the expression of CAV1. Interestingly Drosophila genome lacks both genes. Tre1 family (Tre1, moody, and Dmel_CG4313) play a diverse role in regulating transepithelial migration in Drosophila. However, its evolutionary history and origin are not yet known. We performed phylogenetic analysis, together with HH search, positive selection, and ancestral reconstruction to investigate the Tre1 family. We found that Tre1 exists in Mollusca, Arthropoda, Ambulacraria, and Scalidophora. moody is shown to be a more ancient protein and it has existed since Cnidaria emergence and has a homolog (e.g., GPCR84) in mammals. The third family member (Dmel_CG4313) seems to only exist in insects. The origin of the family seems to be related to the rhodopsin-like family and in particular family α. We found that opsin is the nearest receptor to have a common ancestor with the Tre1 family that has diverged in sponges. We investigated the positive selection of the Tre1 family using PAML. Tre1 seems to have evolved under negative selection, whereas moody has evolved during positive selection. The sites that we found under positive selection are likely to play a role in the speciation of function in the case of moody. We have identified an SH3 motif, in Tre1 and, moody and Dmel_CG4313. SH3 is known to play a fundamental role in regulating actin movement in a Rho-dependent manner in PECAM1. Our results suggest that the Tre1 family could be playing an important role in paracellular diapedesis in Drosophila.

Interleukins and Interleukin Receptors Evolutionary History and Origin in Relation to CD4+ T Cell Evolution.

Understanding the evolution of interleukins and interleukin receptors is essential to control the function of CD4+ T cells in various pathologies. Numerous aspects of CD4+ T cells' presence are controlled by interleukins including differentiation, proliferation, and plasticity. CD4+ T cells have emerged during the divergence of jawed vertebrates. However, little is known about the evolution of interleukins and their origin. We traced the evolution of interleukins and their receptors from Placozoa to primates. We performed phylogenetic analysis, ancestral reconstruction, HH search, and positive selection analysis. Our results indicated that various interleukins' emergence predated CD4+ T cells divergence. IL14 was the most ancient interleukin with homologs in fungi. Invertebrates also expressed various interleukins such as IL41 and IL16. Several interleukin receptors also appeared before CD4+ T cells divergence. Interestingly IL17RA and IL17RD, which are known to play a fundamental role in Th17 CD4+ T cells first appeared in mollusks. Furthermore, our investigations showed that there is not any single gene family that could be the parent group of interleukins. We postulate that several groups have diverged from older existing cytokines such as IL4 from TGFß, IL10 from IFN, and IL28 from BCAM. Interleukin receptors were less divergent than interleukins. We found that IL1R, IL7R might have diverged from a common invertebrate protein that contained TIR domains, conversely, IL2R, IL4R and IL6R might have emerged from a common invertebrate ancestor that possessed a fibronectin domain. IL8R seems to be a GPCR that belongs to the rhodopsin-like family and it has diverged from the Somatostatin group. Interestingly, several interleukins that are known to perform a critical function for CD4+ T cells such as IL6, IL17, and IL1B have gained new functions and evolved under positive selection. Overall evolution of interleukin receptors was not under significant positive selection. Interestingly, eight interleukin families appeared in lampreys, however, only two of them (IL17B, IL17E) evolved under positive selection. This observation indicates that although lampreys have a unique adaptive immune system that lacks CD4+ T cells, they could be utilizing interleukins in homologous mode to that of the vertebrates' immune system. Overall our study highlights the evolutionary heterogeneity within the interleukins and their receptor superfamilies and thus does not support the theory that interleukins evolved solely in jawed vertebrates to support T cell function. Conversely, some of the members are likely to play conserved functions in the innate immune system.

Paracellular and Transcellular Leukocytes Diapedesis Are Divergent but Interconnected Evolutionary Events.

Infiltration of the endothelial layer of the blood-brain barrier by leukocytes plays a critical role in health and disease. When passing through the endothelial layer during the diapedesis process lymphocytes can either follow a paracellular route or a transcellular one. There is a debate whether these two processes constitute one mechanism, or they form two evolutionary distinct migration pathways. We used artificial intelligence, phylogenetic analysis, HH search, ancestor sequence reconstruction to investigate further this intriguing question. We found that the two systems share several ancient components, such as RhoA protein that plays a critical role in controlling actin movement in both mechanisms. However, some of the key components differ between these two transmigration processes. CAV1 genes emerged during Trichoplax adhaerens, and it was only reported in transcellular process. Paracellular process is dependent on PECAM1. PECAM1 emerged from FASL5 during Zebrafish divergence. Lastly, both systems employ late divergent genes such as ICAM1 and VECAM1. Taken together, our results suggest that these two systems constitute two different mechanical sensing mechanisms of immune cell infiltrations of the brain, yet these two systems are connected. We postulate that the mechanical properties of the cellular polarity is the main driving force determining the migration pathway. Our analysis indicates that both systems coevolved with immune cells, evolving to a higher level of complexity in association with the evolution of the immune system.

What has single-cell RNA sequencing revealed about microglial neuroimmunology?

The use of single-cell RNA sequencing (scRNA-seq) in microglial research is increasing rapidly. The basic workflow of this approach consists of isolating single cells, followed by sequencing. scRNA-seq is capable of examining microglial heterogeneity on a cellular level. However, the results gained from applying this technique suffer from discrepancies due to differences between applied methods characteristics such as the number of cells sequenced and the depth of sequencing. This review aims to shed more light on the recent developments that happened in this field and how they are related to the methods used. To do that, we track the progress and limitations of various scRNA-seq methods currently available. The review then summarizes the current knowledge gained using scRNA-seq in the field of microglia, including novel subpopulations associated with function and development under homeostasis as well during several pathological conditions such as Alzheimer, lipopolysaccharide response, and HIV in relation to the methods employed. Our review points out that despite major developments found using this technique, current scRNA-seq methods suffer from high cost, low yields, and nonstandardization of generated data. Additional development of scRNA-seq methods will raise our awareness of microglia's heterogeneity and plasticity under healthy and pathological conditions.

Implicit-descriptor ligand-based virtual screening by means of collaborative filtering.

Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands' structural and physicochemical properties in a vectorized form. Of particular importance to current methods are the extent to which molecular fingerprints describe a particular ligand and what metric sufficiently captures similarity among ligands. In this work, we propose and evaluate methods that do not require explicit feature vectorization through fingerprinting, but, instead, provide implicit descriptors based only on other known assays. Our methods are based upon well known collaborative filtering algorithms used in recommendation systems. Our implicit descriptor method does not require any fingerprint similarity search, which makes the method free of the bias arising from the empirical nature of the fingerprint models. We show that implicit methods significantly outperform traditional machine learning methods, and the main strengths of implicit methods are their resilience to target-ligand sparsity and high potential for spotting promiscuous ligands.

A Python tool to set up relative free energy calculations in GROMACS.

Free energy calculations based on molecular dynamics (MD) simulations have seen a tremendous growth in the last decade. However, it is still difficult and tedious to set them up in an automated manner, as the majority of the present-day MD simulation packages lack that functionality. Relative free energy calculations are a particular challenge for several reasons, including the problem of finding a common substructure and mapping the transformation to be applied. Here we present a tool, alchemical-setup.py, that automatically generates all the input files needed to perform relative solvation and binding free energy calculations with the MD package GROMACS. When combined with Lead Optimization Mapper (LOMAP; Liu et al. in J Comput Aided Mol Des 27(9):755-770, 2013), recently developed in our group, alchemical-setup.py allows fully automated setup of relative free energy calculations in GROMACS. Taking a graph of the planned calculations and a mapping, both computed by LOMAP, our tool generates the topology and coordinate files needed to perform relative free energy calculations for a given set of molecules, and provides a set of simulation input parameters. The tool was validated by performing relative hydration free energy calculations for a handful of molecules from the SAMPL4 challenge (Mobley et al. in J Comput Aided Mol Des 28(4):135-150, 2014). Good agreement with previously published results and the straightforward way in which free energy calculations can be conducted make alchemical-setup.py a promising tool for automated setup of relative solvation and binding free energy calculations.

Guidelines for the analysis of free energy calculations.

Free energy calculations based on molecular dynamics simulations show considerable promise for applications ranging from drug discovery to prediction of physical properties and structure-function studies. But these calculations are still difficult and tedious to analyze, and best practices for analysis are not well defined or propagated. Essentially, each group analyzing these calculations needs to decide how to conduct the analysis and, usually, develop its own analysis tools. Here, we review and recommend best practices for analysis yielding reliable free energies from molecular simulations. Additionally, we provide a Python tool, alchemical-analysis.py, freely available on GitHub as part of the pymbar package (located at http://github.com/choderalab/pymbar), that implements the analysis practices reviewed here for several reference simulation packages, which can be adapted to handle data from other packages. Both this review and the tool covers analysis of alchemical calculations generally, including free energy estimates via both thermodynamic integration and free energy perturbation-based estimators. Our Python tool also handles output from multiple types of free energy calculations, including expanded ensemble and Hamiltonian replica exchange, as well as standard fixed ensemble calculations. We also survey a range of statistical and graphical ways of assessing the quality of the data and free energy estimates, and provide prototypes of these in our tool. We hope this tool and discussion will serve as a foundation for more standardization of and agreement on best practices for analysis of free energy calculations.

Perspective: Alchemical free energy calculations for drug discovery.

Computational techniques see widespread use in pharmaceutical drug discovery, but typically prove unreliable in predicting trends in protein-ligand binding. Alchemical free energy calculations seek to change that by providing rigorous binding free energies from molecular simulations. Given adequate sampling and an accurate enough force field, these techniques yield accurate free energy estimates. Recent innovations in alchemical techniques have sparked a resurgence of interest in these calculations. Still, many obstacles stand in the way of their routine application in a drug discovery context, including the one we focus on here, sampling. Sampling of binding modes poses a particular challenge as binding modes are often separated by large energy barriers, leading to slow transitions. Binding modes are difficult to predict, and in some cases multiple binding modes may contribute to binding. In view of these hurdles, we present a framework for dealing carefully with uncertainty in binding mode or conformation in the context of free energy calculations. With careful sampling, free energy techniques show considerable promise for aiding drug discovery.

Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations.

Molecular dynamics simulations in explicit solvent were applied to predict the hydration free energies for 23 small organic molecules in blind SAMPL2 test. We found good agreement with experimental results, with an RMS error of 2.82 kcal/mol over the whole set and 1.86 kcal/mol over all the molecules except several hydroxyl-rich compounds where we find evidence for a systematic error in the force field. We tested two different solvent models, TIP3P and TIP4P-Ew, and obtained very similar hydration free energies for these two models; the RMS difference was 0.64 kcal/mol. We found that preferred conformation of the carboxylic acids in water differs from that in vacuum. Surprisingly, this conformational change is not adequately sampled on simulation timescales, so we apply an umbrella sampling technique to include free energies associated with the conformational change. Overall, the results of this test reveal that the force field parameters for some groups of molecules (such as hydroxyl-rich compounds) still need to be improved, but for most compounds, accuracy was consistent with that seen in our previous tests.