Behavioural evidence for a visual and proprioceptive control of head roll in hoverflies (Episyrphus balteatus).

The ability of hoverflies to control their head orientation with respect to their body contributes importantly to their agility and their autonomous navigation abilities. Many tasks performed by this insect during flight, especially while hovering, involve a head stabilization reflex. This reflex, which is mediated by multisensory channels, prevents the visual processing from being disturbed by motion blur and maintains a consistent perception of the visual environment. The so-called dorsal light response (DLR) is another head control reflex, which makes insects sensitive to the brightest part of the visual field. In this study, we experimentally validate and quantify the control loop driving the head roll with respect to the horizon in hoverflies. The new approach developed here consisted of using an upside-down horizon in a body roll paradigm. In this unusual configuration, tethered flying hoverflies surprisingly no longer use purely vision-based control for head stabilization. These results shed new light on the role of neck proprioceptor organs in head and body stabilization with respect to the horizon. Based on the responses obtained with male and female hoverflies, an improved model was then developed in which the output signals delivered by the neck proprioceptor organs are combined with the visual error in the estimated position of the body roll. An internal estimation of the body roll angle with respect to the horizon might explain the extremely accurate flight performances achieved by some hovering insects.

Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics.

The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.

Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations.

Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability: Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.

Vanucizumab mode of action: Serial biomarkers in plasma, tumor, and skin-wound-healing biopsies.

Vanucizumab is a novel bispecific antibody inhibiting vascular endothelial growth factor (VEGF-A) and angiopoietin-2 (Ang-2) that demonstrated safety and anti-tumor activity in part I of a phase I study of 42 patients with advanced solid tumors. Part II evaluated the pharmacodynamic effects of vanucizumab 30 or 15 mg/kg every 2 weeks in 32 patients. Serial plasma samples, paired tumor, and skin-wound-healing biopsies were taken over 29 days to evaluate angiogenic markers. Vanucizumab was associated with marked post-infusion reductions in circulating unbound VEGF-A and Ang-2. By day 29, tumor samples revealed mean reductions in density of microvessels (-32.2%), proliferating vessels (-47.9%) and Ang-2 positive vessels (-62.5%). Skin biopsies showed a mean reduction in density of microvessels (-49.0%) and proliferating vessels (-25.7%). Gene expression profiling of tumor samples implied recruitment and potential activation of lymphocytes. Biopsies were safely conducted. Vanucizumab demonstrated a consistent biological effect on vascular-related biomarkers, confirming proof of concept. Skin-wound-healing biopsies were a valuable surrogate for studying angiogenesis-related mechanisms.

Cellular Resistance Mechanisms to Targeted Protein Degradation Converge Toward Impairment of the Engaged Ubiquitin Transfer Pathway.

Proteolysis targeting chimeras are bifunctional small molecules capable of recruiting a target protein of interest to an E3 ubiquitin ligase that facilitates target ubiquitination followed by proteasome-mediated degradation. The first molecules acting on this novel therapeutic paradigm have just entered clinical testing. Here, by using Bromodomain Containing 4 (BRD4) degraders engaging cereblon and Von Hippel-Lindau E3 ligases, we investigated key determinants of resistance to this new mode of action. A loss-of-function screen for genes required for BRD4 degradation revealed strong dependence on the E2 and E3 ubiquitin ligases as well as for members of the COP9 signalosome complex for both cereblon- and Von Hippel-Lindau-engaging BRD4 degraders. Cancer cell lines raised to resist BRD4 degraders manifested a degrader-specific mechanism of resistance, resulting from the loss of components of the ubiquitin proteasome system. In addition, degrader profiling in a cancer cell line panel revealed a differential pattern of activity of Von Hippel-Lindau- and cereblon-based degraders, highlighting the need for the identification of degradation-predictive biomarkers enabling effective patient stratification.

OptPipe - a pipeline for optimizing metabolic engineering targets.

BACKGROUND: We propose OptPipe - a Pipeline for Optimizing Metabolic Engineering Targets, based on a consensus approach. The method generates consensus hypotheses for metabolic engineering applications by combining several optimization solutions obtained from distinct algorithms. The solutions are ranked according to several objectives, such as biomass and target production, by using the rank product tests corrected for multiple comparisons. RESULTS: OptPipe was applied in a genome-scale model of Corynebacterium glutamicum for maximizing malonyl-CoA, which is a valuable precursor for many phenolic compounds. In vivo experimental validation confirmed increased malonyl-CoA level in case of ΔsdhCAB deletion, as predicted in silico. CONCLUSIONS: A method was developed to combine the optimization solutions provided by common knockout prediction procedures and rank the suggested mutants according to the expected growth rate, production and a new adaptability measure. The implementation of the pipeline along with the complete documentation is freely available at https://github.com/AndrasHartmann/OptPipe .

A Combinatorial Algorithm for Microbial Consortia Synthetic Design.

Synthetic biology has boomed since the early 2000s when it started being shown that it was possible to efficiently synthetize compounds of interest in a much more rapid and effective way by using other organisms than those naturally producing them. However, to thus engineer a single organism, often a microbe, to optimise one or a collection of metabolic tasks may lead to difficulties when attempting to obtain a production system that is efficient, or to avoid toxic effects for the recruited microorganism. The idea of using instead a microbial consortium has thus started being developed in the last decade. This was motivated by the fact that such consortia may perform more complicated functions than could single populations and be more robust to environmental fluctuations. Success is however not always guaranteed. In particular, establishing which consortium is best for the production of a given compound or set thereof remains a great challenge. This is the problem we address in this paper. We thus introduce an initial model and a method that enable to propose a consortium to synthetically produce compounds that are either exogenous to it, or are endogenous but where interaction among the species in the consortium could improve the production line.