Biology subjects

Salomon, J.

Publications and source records attributed to Salomon, J..

10 recordsLinked to original sources

bioRxiv2026

EpCAM sustains crypt mechanics and prevents aberrant remodeling

Crypt morphogenesis is a fundamental process that drives intestinal growth and maintains its homeostasis. Although intestinal tissue mechanics is recognized as pivotal for early crypt initiation and budding, the molecular mechanisms that couple epithelial forces to mature crypt behavior remain largely unexplored. In particular, while Paneth cells have been proposed to be implicated in the mechanical control of crypt remodeling and fission, the underlying pathways have remained elusive. Here, we identified EpCAM (Epithelial Cell Adhesion Molecule) as a key regulator of epithelial contractility and crypt morphogenesis. EpCAM was preferentially enriched at lateral interfaces of Paneth cells in vivo and in organoid models, where it maintained cortical tension and contractile organization. Loss of EpCAM disrupted crypt cell cortical myosin II-A localization, leading to aberrant Paneth cell apical contractility, enlarged apical surfaces, distorted pyramidal morphology, and altered aspect ratios. Consequently, Paneth cells lose their tight clustering in crypt base, their spatial distribution along the crypt axis becomes irregular, and the collective architecture of the crypt epithelium was compromised. At the tissue level, EpCAM deficiency profoundly perturbed crypt fission, increasing fission frequency while favoring asymmetric outcome both in EPCAM-mutated Congenital Tufting Enteropathy (CTE) patients. Live imaging of EpCAM-knockout mouse organoids confirmed a failure in the spatial control of crypt bifurcation. Collectively, our findings revealed that EpCAM is not required to initiate fission, but is essential to coordinate the mechanical forces and ensure correct division of the crypt. We showed that EpCAM-dependent biomechanical integrity of Paneth cells generates a stabilizing force field at the crypt base, orchestrating coordinated tissue remodeling. Disruption of this program led to unbalanced force transmission, distorted crypt geometry, and aberrant fission outcomes, revealing a critical link between epithelial mechanics and intestinal morphogenesis.

developmental biology↗

bioRxiv2026

Joint modeling of social genetic effects in mono- and pluri-specific groups: case study in intercrops

The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these interspecific social effects. Such species mixtures provide well-documented agricultural benefits, yet few breeding frameworks have integrated the genetics of social interactions. Here, we address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as a concrete and applied model case. We propose a quantitative genetic model that jointly analyzes intra and interspecific interactions within a unifying framework. Breeding values are decomposed into a direct component, shared in mono and mixed-crops, an interspecific social component corresponding to the effect of one species on another, and an intraspecific component that captures the social effects within a mono-genotypic stand of cloned plants. Statistically, this consists in simultaneously fitting several linear mixed models, one per stand type, all having direct breeding values in common. As no open-source software can fit such a complex mixed model, we provide such an implementation in C++ available in the R package plantmix. Simulations across various genetic (co)variance structures and sparse experimental designs showed accurate estimation of all genetic (co)variances and breeding values. With an incomplete, yet balanced design combining sole crops and intercrops, genetic gains in both systems were achievable simultaneously, enabling breeding strategies that progressively integrate intercropping into existing, sole-crop-only schemes. More broadly, this framework allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations, cultivated or not. Article summaryThe genetics of interspecific groups remains largely unexplored, despite the central role of social genetic effects in shaping phenotypic expression within communities. We address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as an example. We propose a quantitative genetic model that jointly analyzes intra- and interspecific interactions within a unifying framework. Simulations showed accurate estimation of genetic (co)variances and breeding values. This framework supports breeding strategies where breeders want to gradually integrate pluri-specific trials in their mono-specific platforms, and allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations.

genetics↗

bioRxiv2026

Triatomine host preference in an experimental cafeteria: the impact of Trypanosoma cruzi infections

BackgroundHematophagous triatomine vectors of the Chagas disease parasite, Trypanosoma cruzi, have a wide bloodmeal host breadth yet mammals are the primary competent reservoirs for T. cruzi. Accordingly, the transmission of T. cruzi is dependent on triatomines encountering mammalian hosts. We hypothesized T. cruzi-infected triatomines exhibit a preference for mammalian versus non-mammalian hosts, as compared to uninfected triatomines. Methodology/Principal FindingsWe used a dual-choice olfactometer to present individual T. cruzi-infected and uninfected Triatoma gerstaeckeri- an important North American triatomine species- to pairwise combinations of no host, dog, human, or chicken. We found that uninfected T. gerstaeckeri preferred dogs over chickens and humans, but T. cruzi-infected T. gerstaeckeri had no host preference. Additionally, T. cruzi-infected T. gerstaeckeri took less time to begin advancing towards hosts (average of 522 s {+/-} 577 s vs 801 s {+/-} 704 s) and spent more time on both sides of the olfactometer compared to uninfected insects As a comparative group,we tested Rhodnius prolixus- a potent South American vector- and found no statistical preference among hosts, yet they advanced towards hosts more often (63.2% vs 47.1%) and faster compared to T. gerstaeckeri. Conclusions/SignificanceThe increased locomotion of R. prolixus and T. cruzi-infected T. gerstaeckeri toward hosts may promote increased transmission of T. cruzi. Dogs are a preferred host for T. gerstaeckeri and may be considered in future host-targeted interventions for triatomine control. Author SummaryVectors are often generalists or opportunists with a wide range of available hosts, yet the parasites they transmit may have a narrower host range. It is in the parasites best interest for the vectors they infect to interact with competent hosts, thereby introducing the setting for parasite manipulation of vectors. We aimed to understand the foraging behaviors of vectors and how those behaviors may be different when the vector is infected with a parasite. We studied the triatomine ( kissing bug) insect vectors of the Chagas disease parasite, Trypanosoma cruzi, using one of North Americas most important vector species in comparison to a potent South American vector species. We tested if parasite-infected triatomines exhibited a preference toward vertebrate hosts that are also competent reservoirs for the T. cruzi (i.e., mammals). We used an experimental cafeteria, in which individual vectors were introduced to a dual-choice olfactometer and allowed to walk toward hosts including restrained humans, dogs, or chickens. We found differences in apparent host preference between species of vectors and between infected vs. uninfected vectors. Uninfected vectors displayed a host preference toward dogs, yet this preference was not significant among infected vectors. Uninfected vectors had higher frequency of locomotion in the arena, suggesting the opportunity for more vector-host contact.

ecology↗

bioRxiv2024

Cross-species vs species-specific models for protein melting temperature prediction

AO_SCPLOWBSTRACTC_SCPLOWProtein melting temperatures are important proxies for stability, and frequently probed in protein engineering campaigns, for instance for enzyme discovery and protein optimization. With the emergence of large datasets of melting temperatures for diverse natural proteins, it has become possible to train models to predict this quantity, and the literature has reported impressive performance values in terms of Spearman rho. The high correlation scores suggest that it should be possible to accurately predict melting temperature changes in engineered variants, and to reliably identify naturally thermostable proteins. However, in practice, results in these settings are often disappointing. In this paper, we explore this apparent discrepancy. We show that Spearman rho over cross-species data gives an overly optimistic impression of prediction performance, and that this metric reflects the ability to distinguish global differences in amino acid composition between species, rather than the specific effects of genetic variation. We proceed by investigating whether cross-species training on melting temperature is beneficial at all, compared to training specific models for each species. We address this question using four different transfer-learning approaches and a fine-tuning procedure. Surprisingly, we consistently find no benefit of cross-species training. We conclude that 1) current models for supervised prediction of melting temperature perform substantially worse than the literature suggests, and 2) that reliable transfer across species is still a challenging problem. An implementation of this work is available at https://github.com/deltadedirac/thermocontrast_tm

bioinformatics↗

bioRxiv2024

Kermut: Composite kernel regression for protein variant effects

Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modeling mutation similarity, which obtains state-of-the-art performance for supervised protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging.

synthetic biology↗

bioRxiv2023

FLOP: Tasks for Fitness Landscapes Of Protein wildtypes

Protein engineering has the potential to create optimized protein variants with improved properties and function. An initial step in the protein optimization process typically consists of a search among natural (wildtype) sequences to find the naturally occurring proteins with the most desirable properties. Promising candidates from this initial discovery phase then form the basis of the second step: a more local optimization procedure, exploring the space of variants separated from this candidate by a number of mutations. While considerable progress has been made on evaluating machine learning methods on single protein datasets, benchmarks of data-driven approaches for global fitness landscape exploration are still lacking. In this paper, we have carefully curated a representative benchmark dataset, which reflects industrially relevant scenarios for the initial wildtype discovery phase of protein engineering. We focus on exploration within a protein family, and investigate the downstream predictive power of various protein representation paradigms, i.e., protein language model-based representations, structure-based representations, and evolution-based representations. Our benchmark highlights the importance of coherent split strategies, and how we can be misled into overly optimistic estimates of the state of the field. The codebase and data can be accessed via https://github.com/petergroth/FLOP.

bioinformatics↗

bioRxiv2023

Assessing the performance of protein regression models

To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain.

bioinformatics↗

bioRxiv2022

Continuous B- to A- Transition in Protein-DNA Binding - How Well Is It Described by Current AMBER Force Fields?

When DNA interacts with a protein, its structure often undergoes significant conformational adaptation. Perhaps the most common is the transition from canonical B-DNA towards the A-DNA form, which is not a two-state, but rather a continuous transition. The A- and B-forms differ mainly in sugar pucker P (north/south) and glycosidic torsion {chi} (high-anti/anti). The combination of A-like P and B-like {chi} (and vice versa) represents the nature of the intermediate states lying between the pure A- and B- forms. In this work, we study how the A/B equilibrium and in particular the A/B intermediate states, which are known to be over-represented at protein-DNA interfaces, are modeled by current AMBER force fields. Eight protein-DNA complexes and their naked (unbound) DNAs were simulated with OL15 and bsc1 force fields as well as an experimental combination OL15{chi}OL3. We found that while the geometries of the A-like intermediate states in the molecular dynamics (MD) simulations agree well with the native X-ray geometries found in the protein-DNA complexes, their populations (stabilities) are significantly underestimated. Different force fields predict different propensities for A-like states growing in the order OL15 < bsc1 < OL15{chi}OL3, but the overall populations of the A-like form are too low in all of them. Interestingly, the force fields seem to predict the correct sequence-dependent A-form propensity, as they predict larger populations of the A-like form in naked (unbound) DNA in those steps that acquire A-like conformations in protein-DNA complexes. The instability of A-like geometries in current force fields may significantly alter the geometry of the simulated protein-DNA complex, destabilize the binding motif, and reduce the binding energy, suggesting that refinement is needed to improve description of protein-DNA interactions in AMBER force fields.

biophysics↗

bioRxiv2021

NetSolP: predicting protein solubility in E. coli using language models

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased. In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences. The predictor is available at https://services.healthtech.dtu.dk/service.php?NetSolP-1.0

bioinformatics↗

bioRxiv2021

Deep protein representations enable recombinant protein expression prediction

A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjusting the recombinant expression vector, adjusting cultivation conditions, or performing codon optimizations. However, such strategies are time-consuming, and an alternative strategy would be to select genes for better compatibility with the recombinant host. Several methods for predicting soluble expression are available; however, they are all optimized for the expression host Escherichia coli and do not consider the possibility of an expressed protein not being soluble. We show that these tools are not suited for predicting expression potential in the industrially important host Bacillus subtilis. Instead, we build a B. subtilis-specific machine learning model for expressibility prediction. Given millions of unlabelled proteins and a small labeled dataset, we can successfully train such a predictive model. The unlabeled proteins provide a performance boost relative to using amino acid frequencies of the labeled proteins as input. On average, we obtain a modest performance of 0.64 area-under-the-curve (AUC) and 0.2 Matthews correlation coefficient (MCC). However, we find that this is sufficient for the prioritization of expression candidates for high-throughput studies. Moreover, the predicted class probabilities are correlated with expression levels. A number of features related to protein expression, including base frequencies and solubility, are captured by the model.

bioinformatics↗