SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Predicting a Stable Dimeric Form of the PD1-PDL1 Complex: Implications for Understanding the PD1 Activation Mechanism.

The activation of T cells is typically accompanied by inhibitory mechanisms within which the programmed cell death (PD1) receptor stands out. Upon binding the ligands PDL1 and PDL2, PD1 drives T cells to an unresponsive state called exhaustion, characterized by a markedly decreased capacity to exert effector functions. For this reason, PD1 has become one of the most important targets in cancer immunotherapy. Despite the numerous studies about PD1 signaling modulation, how the PD1 signaling is activated upon the ligands' binding remains an open question. Several experimental facts suggest that the activation of the PD1-PLD1 pathway depends on the interaction with an unknown partner at the cellular membrane. In this work, we investigate the possibility that the target of PD1-PDL1 is the same PD1-PDL1 complex. We combined molecular docking with molecular dynamics and umbrella sampling simulations to explore different binding modes and assess the complexes' stability. We predicted a stable dimeric form of the extracellular domains of the PD1-PDL1 complex. This dimeric complex has an affinity comparable to the PD1-PDL1 interaction and resembles the form of a linear lattice. We proposed a new model for PD1 activation where the PD1-PDL1 dimeric form could facilitate the interaction of the intracellular domains of PD1 and the further binding and activation of the SHP2 phosphatase. This model might explain the inhibitory effect of anti-PD1/PDL1 antibodies through the prevention of the formation of the PD1-PDL1 dimers and, subsequently, the abrogation of the SHP2 phosphatase activation.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry.

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

Unraveling a Conserved Conformation of the FG Loop upon the Binding of Natural Ligands to the Human and Murine PD1.

The activation of T cells is normally accompanied by inhibitory mechanisms within which the PD1 receptor stands out. PD1 drives T cells to an unresponsive state called exhaustion, characterized by a markedly decreased capacity to exert effector functions upon binding the ligands PDL1 and PDL2. For this reason, PD1 has become one of the most important targets in cancer immunotherapy. Despite the numerous studies about PD1 signaling modulation, how the PD1 signaling pathway is activated upon the ligands' binding remains an open question. In this work, we used molecular dynamics simulations to assess the differences of the PD1 motion in the free state and in complex with the ligands. We found that, in both human and murine systems, the binding of PDL1 and PDL2 stabilizes the conformation of the FG loop similarly. This result, combined with the conservation of the FG loop residues across species, suggests that the conformation of the FG loop is somehow related to the signaling process. We also found a high similarity between the PD1-PDL1 structures with the variable region of an antibody structure, where the FG loop occupies a similar position to the CDR3 light chain.

Differential Effects of IL2Rα and IL15Rα over the Stability of the Common Beta-Gamma Signaling Subunits of the IL2 and IL15 Receptors.

Interleukin (IL) 2 and IL15 are two members of the common gamma chain cytokine family, involved in the regulation of the T cell differentiation process. Both molecules use a specific alpha subunit, IL2Rα and IL15Rα, and share the same beta and gamma chains signaling receptors. The presence of the specific alpha subunit modulates the T cell ability to compete for both soluble cytokines while the beta and gamma subunits are responsible for the signal transduction. Recent experimental results point out that the specific alpha subunits modulate the capacity of IL2 and IL15 to induce the differentiation of stimulated T cells. In other membrane receptors, the outcome of the signal transduction has been associated with the strength of the interaction of the signaling subunits. Here, we investigate how IL2Rα and IL15Rα modulate the stability of their signaling complexes by combining molecular dynamics simulations and free energy calculations. Our simulations predict that IL2Rα binding destabilizes the ß-γc interaction mediated by IL2, while IL15Rα has the opposite effect. These results explain the ability of IL2Rα and IL15Rα to modulate the signaling outcome and suggest new strategies for the development of better CD8+ T cell differentiation protocols for adoptive cell transfer (ACT).

Exploring the conformational dynamics of PD1 in complex with different ligands: What we can learn for designing novel PD1 signaling blockers?

Activation of T cells triggers the expression of regulatory molecules like the programmed cell death 1 (PD1) protein. The association of PD1 with the natural ligands PDL1 and PDL2 induces an inhibitory signal that prevents T cells from proliferating and exerting effector functions. However, little is known about how the binding of the ligands induce the PD1 inhibitory signal over T cells effector functions. Here, we explore the dynamics of PD1 free, and in complex with different PDL1 variants as well as the therapeutic antibodies nivolumab and pembrolizumab in order to assess the conformational changes in PD1 related to the signaling process. Our simulations suggest a pre-conformational selection mechanism for the binding of the different PDL1 variants, while an induced-fit model fits better for the molecular recognition process of the therapeutic antibodies. A deep analysis of the changes on PD1 movement upon the binding to different ligands revealed that as larger is the difference in the conformation adopted by loop C'D with respect to the complex with PDL1 is higher the ligand ability to reduce the PD1 inhibitory signaling. This behavior suggests that targeting specific conformations of this loop can be useful for designing therapies able to recover T cells effector functions.

Expression of Membrane Progesterone Receptors in Eutopic and Ectopic Endometrium of Women with Endometriosis.

Endometriosis is one of the most frequent gynecological diseases in reproductive age women, but its etiology is not completely understood. Endometriosis is characterized by progesterone resistance, which has been explained in part by a decrease in the expression of the intracellular progesterone receptor in the ectopic endometrium. Progesterone action is also mediated by nongenomic mechanisms via membrane progesterone receptors (mPRs) that belong to the class II members of the progesterone and adipoQ receptor (PAQR) family. The aim of the present study was to evaluate the expression at mRNA and protein levels of mPR members in the eutopic and ectopic endometrium of women with endometriosis. Total RNA and total protein were isolated from control endometrium (17 samples), eutopic endometrium (17 samples), and ectopic endometrium (9 samples). The expression of PAQR7 (mPRα), PAQR8 (mPRß), and PAQR6 (mPRδ) at mRNA and protein levels was evaluated by RT-qPCR and Western blot, whereas PAQR5 (mPRγ) gene expression was evaluated by RT-qPCR. Statistical analysis between comparable groups was performed using one-way ANOVA followed by Tukey's multiple comparisons test with a confidence interval of 95 %. The analysis of gene expression showed that PAQR7 and PAQR5 expression was lower in both eutopic and ectopic endometrium as compared to the endometrium of women without endometriosis, whereas the expression of PAQR8 and PAQR6 was only reduced in eutopic endometrium. Furthermore, mPRα and mPRß protein content was decreased in the ectopic endometrium of women with endometriosis. Our results demonstrate a decrease in the expression and protein content of mPRs in eutopic and ectopic endometrium of patients with endometriosis, which could contribute to the progesterone resistance observed in patients with this disease.

Affinity-matured variants derived from nimotuzumab keep the original fine specificity and exhibit superior biological activity.

Nimotuzumab is a humanized monoclonal antibody against the Epidermal Growth Factor Receptor with a long history of therapeutic use, recognizing an epitope different from the ones targeted by other antibodies against the same antigen. It is also distinguished by much less toxicity resulting in a better safety profile, which has been attributed to its lower affinity compared to these other antibodies. Nevertheless, the ideal affinity window for optimizing the balance between anti-tumor activity and toxic effects has not been determined. In the current work, the paratope of the phage-displayed nimotuzumab Fab fragment was evolved in vitro to obtain affinity-matured variants. Soft-randomization of heavy chain variable region CDRs and phage selection resulted in mutated variants with improved binding ability. Two recombinant antibodies were constructed using these variable regions, which kept the original fine epitope specificity and showed moderate affinity increases against the target (3-4-fold). Such differences were translated into a greatly enhanced inhibitory capacity upon ligand-induced receptor phosphorylation on tumor cells. The new antibodies, named K4 and K5, are valuable tools to explore the role of affinity in nimotuzumab biological properties, and could be used for applications requiring a fine-tuning of the balance between binding to tumor cells and healthy tissues.

Quantitative assessment of extracellular IL-1 regulation.

IL-1 system is involved in the induction and maintenance of chronic inflammation associated with several autoimmune diseases and cancer, mainly due to its capacity to promote the secretion of inflammatory mediators. For this reason, several intracellular and extracellular mechanisms for this system have been fixed during the evolution. In spite of the large description of molecular interactions between IL-1 ligands and receptors, little is known about the relevance and limits of the extracellular regulatory mechanims in different scenarios. To tackle this problem, we developed and calibrated a mathematical model including all the known interactions between IL-1 ligands and IL-1Rs and calibrate it with experimental data of IL-1 binding to different cells. The model predicts that, independently on the IL-1Rs expression, IL-1α has more ability than IL-1ß to induce IL-1 signaling, which suggests that both ligands can be equally relevant for the IL-1 related inflammation. On the other hand, at the cell level, IL-1 signaling is mainly controlled by IL-1R1 and IL-1R3 and not by IL-1R2. Moreover, the soluble form of IL-1R1 and IL-1RA have the highest capacity to prevent IL-1α while IL-1R2 and IL-1R1 and IL-1RA have a similar capacity to prevent IL-1ß signaling. The soluble IL-1R3 has the lowest capacity to prevent IL-1 signaling and preferentially inhibits cells with low number of IL-1R3. In general, model predictions suggest several ways in which IL-1 controlling system may fail, developing IL-1 related inflammation.

Quantitative Contribution of IL2Rγ to the Dynamic Formation of IL2-IL2R Complexes.

Interleukin-2 (IL2) is a growth factor for several immune cells and its function depends on its binding to IL2Rs in the cell membrane. The most accepted model for the assembling of IL2-IL2R complexes in the cell membrane is the Affinity Conversion Model (ACM). This model postulates that IL2R receptor association is sequential and dependent on ligand binding. Most likely free IL2 binds first to IL2Rα, and then this complex binds to IL2Rß, and finally to IL2Rγ (γc). However, in previous mathematical models representing this process, the binding of γc has not been taken into account. In this work, the quantitative contribution of the number of IL2Rγ chain to the IL2-IL2R apparent binding affinity and signaling is studied. A mathematical model of the affinity conversion process including the γ chain in the dynamic, has been formulated. The model was calibrated by fitting it to experimental data, specifically, Scatchard plots obtained using human cell lines. This paper demonstrates how the model correctly explains available experimental observations. It was estimated, for the first time, the value of the kinetic coefficients of IL2-IL2R complexes interaction in the cell membrane. Moreover, the number of IL2R components in different cell lines was also estimated. It was obtained a variable distribution in the number of IL2R components depending on the cell type and the activation state. Of most significance, the study predicts that not only the number of IL2Rα and IL2Rß, but also the number of γc determine the capacity of the cell to capture and retain IL2 in signalling complexes. Moreover, it is also showed that different cells might use different pathways to bind IL2 as consequence of its IL2R components distribution in the membrane.