A model-informed method to retrieve intrinsic from apparent cooperativity and project cellular target occupancy for ternary complex-forming compounds.

There is an increasing interest to develop therapeutics that modulate challenging or undruggable target proteins via a mechanism that involves ternary complexes. In general, such compounds can be characterized by their direct affinities to a chaperone and a target protein and by their degree of cooperativity in the formation of the ternary complex. As a trend, smaller compounds have a greater dependency on intrinsic cooperativity to their thermodynamic stability relative to direct target (or chaperone) binding. This highlights the need to consider intrinsic cooperativity of ternary complex-forming compounds early in lead optimization, especially as they provide more control over target selectivity (especially for isoforms) and more insight into the relationship between target occupancy and target response via estimation of ternary complex concentrations. This motivates the need to quantify the natural constant of intrinsic cooperativity (α) which is generally defined as the gain (or loss) in affinity of a compound to its target in pre-bound vs. unbound state. Intrinsic cooperativities can be retrieved via a mathematical binding model from EC50 shifts of binary binding curves of the ternary complex-forming compound with either a target or chaperone relative to the same experiment but in the presence of the counter protein. In this manuscript, we present a mathematical modeling methodology that estimates the intrinsic cooperativity value from experimentally observed apparent cooperativities. This method requires only the two binary binding affinities and the protein concentrations of target and chaperone and is therefore suitable for use in early discovery therapeutic programs. This approach is then extended from biochemical assays to cellular assays (i.e., from a closed system to an open system) by accounting for differences in total ligand vs. free ligand concentrations in the calculations of ternary complex concentrations. Finally, this model is used to translate biochemical potency of ternary complex-forming compounds into expected cellular target occupancy, which could ultimately serve as a way for validation or de-validation of hypothesized biological mechanisms of action.

Efficacy and safety of MAS825 (anti-IL-1ß/IL-18) in COVID-19 patients with pneumonia and impaired respiratory function.

MAS825, a bispecific IL-1ß/IL-18 monoclonal antibody, could improve clinical outcomes in COVID-19 pneumonia by reducing inflammasome-mediated inflammation. Hospitalized non-ventilated patients with COVID-19 pneumonia (n = 138) were randomized (1:1) to receive MAS825 (10 mg/kg single i.v.) or placebo in addition to standard of care (SoC). The primary endpoint was the composite Acute Physiology and Chronic Health Evaluation II (APACHE II) score on Day 15 or on the day of discharge (whichever was earlier) with worst-case imputation for death. Other study endpoints included safety, C-reactive protein (CRP), SARS-CoV-2 presence, and inflammatory markers. On Day 15, the APACHE II score was 14.5 ± 1.87 and 13.5 ± 1.8 in the MAS825 and placebo groups, respectively (P = 0.33). MAS825 + SoC led to 33% relative reduction in intensive care unit (ICU) admissions, ~1 day reduction in ICU stay, reduction in mean duration of oxygen support (13.5 versus 14.3 days), and earlier clearance of virus on Day 15 versus placebo + SoC group. On Day 15, compared with placebo group, patients treated with MAS825 + SoC showed a 51% decrease in CRP levels, 42% lower IL-6 levels, 19% decrease in neutrophil levels, and 16% lower interferon-γ levels, indicative of IL-1ß and IL-18 pathway engagement. MAS825 + SoC did not improve APACHE II score in hospitalized patients with severe COVID-19 pneumonia; however, it inhibited relevant clinical and inflammatory pathway biomarkers and resulted in faster virus clearance versus placebo + SoC. MAS825 used in conjunction with SoC was well tolerated. None of the adverse events (AEs) or serious AEs were treatment-related.

Time matters - in vitro cellular disposition kinetics help rationalizing cellular potency disconnects.

Loss in potency is commonly observed in early drug discovery when moving from biochemical to more complex cellular systems. Among other factors, low permeability is often considered to cause such potency disconnects.We developed a novel cellular disposition assay in MDCK cells to determine passive uptake clearance (PSinf), cell-to-medium ratios at steady-state (Kp) and the time to reach 90% steady-state (TTSS90) from a single experiment in a high-throughput format.The assay was validated using 40 marketed drugs, showing a wide distribution of PSinf and Kp values. The parameters generally correlated with transcellular permeability and lipophilicity, while PSinf data revealed better resolution in the high and low permeability ranges compared to traditional permeability data. A linear relationship between the Kp/PSinf ratio and TTSS90 was mathematically derived and experimentally validated, demonstrating the dependency of TTSS90 on the rate and extent of cellular accumulation.Cellular disposition parameters could explain potency (IC50) disconnects noted for seven Bruton's tyrosine kinase degrader compounds in a cellular potency assay. In contrast to transcellular permeability, PSinf data enabled identification of the compounds with IC50 disconnects based on their time to reach equilibrium. Overall, the novel assay offers the possibility to address potency disconnects in early drug discovery.

High-Dimensional Analysis of Immune Cell Composition Predicts Periprosthetic Joint Infections and Dissects Its Pathophysiology.

Accurate diagnosis of periprosthetic joint infections (PJI) is one of the most widely researched areas in modern orthopedic endoprosthesis. However, our understanding of the immunological basis of this severe complication is still limited. In this study, we developed a flow cytometric approach to precisely characterize the immune cell composition in periprosthetic joints. Using high-dimensional multi-parametric data, we defined, for the first time, the local immune cell populations of artificial joints. We identified significant differences in the cellular distribution between infected and non-infected samples, and revealed that myeloid-derived suppressor cells (MDSCs) act as potential regulators of infiltrating immune cells in PJI. Further, we developed an algorithm to predict septic and aseptic samples with high sensitivity and specificity, that may serve as an indispensable addition to the current criteria of the Musculoskeletal Infection Society. This study describes a novel approach to flow cytometrically analyze the immune cell infiltrate of joint fluid that not only improves our understanding of the pathophysiology of PJI, but also enables the development of a novel screening tool to predict infection status. Our data further suggest that pharmacological targeting of MDSCs represents a novel strategy for addressing PJI.

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line.

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

Protein Structure from Experimental Evolution.

Natural evolution encodes rich information about the structure and function of biomolecules in the genetic record. Previously, statistical analysis of co-variation patterns in natural protein families has enabled the accurate computation of 3D structures. Here, we explored generating similar information by experimental evolution, starting from a single gene and performing multiple cycles of in vitro mutagenesis and functional selection in Escherichia coli. We evolved two antibiotic resistance proteins, ß-lactamase PSE1 and acetyltransferase AAC6, and obtained hundreds of thousands of diverse functional sequences. Using evolutionary coupling analysis, we inferred residue interaction constraints that were in agreement with contacts in known 3D structures, confirming genetic encoding of structural constraints in the selected sequences. Computational protein folding with interaction constraints then yielded 3D structures with the same fold as natural relatives. This work lays the foundation for a new experimental method (3Dseq) for protein structure determination, combining evolution experiments with inference of residue interactions from sequence information. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Computer-guided design of optimal microbial consortia for immune system modulation.

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics.

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.

Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile.

The gastrointestinal tracts of mammals are colonized by hundreds of microbial species that contribute to health, including colonization resistance against intestinal pathogens. Many antibiotics destroy intestinal microbial communities and increase susceptibility to intestinal pathogens. Among these, Clostridium difficile, a major cause of antibiotic-induced diarrhoea, greatly increases morbidity and mortality in hospitalized patients. Which intestinal bacteria provide resistance to C. difficile infection and their in vivo inhibitory mechanisms remain unclear. Here we correlate loss of specific bacterial taxa with development of infection, by treating mice with different antibiotics that result in distinct microbiota changes and lead to varied susceptibility to C. difficile. Mathematical modelling augmented by analyses of the microbiota of hospitalized patients identifies resistance-associated bacteria common to mice and humans. Using these platforms, we determine that Clostridium scindens, a bile acid 7α-dehydroxylating intestinal bacterium, is associated with resistance to C. difficile infection and, upon administration, enhances resistance to infection in a secondary bile acid dependent fashion. Using a workflow involving mouse models, clinical studies, metagenomic analyses, and mathematical modelling, we identify a probiotic candidate that corrects a clinically relevant microbiome deficiency. These findings have implications for the rational design of targeted antimicrobials as well as microbiome-based diagnostics and therapeutics for individuals at risk of C. difficile infection.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

On the need for widespread horizontal gene transfers under genome size constraint.

BACKGROUND: While eukaryotes primarily evolve by duplication-divergence expansion (and reduction) of their own gene repertoire with only rare horizontal gene transfers, prokaryotes appear to evolve under both gene duplications and widespread horizontal gene transfers over long evolutionary time scales. But, the evolutionary origin of this striking difference in the importance of horizontal gene transfers remains by and large a mystery. HYPOTHESIS: We propose that the abundance of horizontal gene transfers in free-living prokaryotes is a simple but necessary consequence of two opposite effects: i) their apparent genome size constraint compared to typical eukaryote genomes and ii) their underlying genome expansion dynamics through gene duplication-divergence evolution, as demonstrated by the presence of many tandem and block repeated genes. In principle, this combination of genome size constraint and underlying duplication expansion should lead to a coalescent-like process with extensive turnover of functional genes. This would, however, imply the unlikely, systematic reinvention of functions from discarded genes within independent phylogenetic lineages. Instead, we propose that the long-term evolutionary adaptation of free-living prokaryotes must have resulted in the emergence of efficient non-phylogenetic pathways to circumvent gene loss. IMPLICATIONS: This need for widespread horizontal gene transfers due to genome size constraint implies, in particular, that prokaryotes must remain under strong selection pressure in order to maintain the long-term evolutionary adaptation of their "mutualized" gene pool, beyond the inevitable turnover of individual prokaryote species. By contrast, the absence of genome size constraint for typical eukaryotes has presumably relaxed their need for widespread horizontal gene transfers and strong selection pressure. Yet, the resulting loss of genetic functions, due to weak selection pressure and inefficient gene recovery mechanisms, must have ultimately favored the emergence of more complex life styles and ecological integration of many eukaryotes. REVIEWERS: This article was reviewed by Pierre Pontarotti, Eugene V Koonin and Sergei Maslov.