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bioRxiv2019

Chromatin structure changes in Daphnia populations upon exposure to environmental cues - or - The discovery of Wolterecks \"Matrix\"

Phenotypic plasticity is an important feature of biological systems that is likely to play a major role in the future adaptation of organisms to the ongoing global changes. It may allow an organism to produce alternative phenotypes in responses to environmental cues. Modifications in the phenotype can be reversible but are sometimes enduring and can even span over generations. The notion of phenotypic plasticity was conceptualized in the early 20th century by Richard Woltereck. He introduced the idea that the combined relations of a phenotypic character and all environmental gradients that influence on it can be defined as \"norm of reaction\". Norms of reaction are specific to species and to lineages within species, and they are heritable. He postulated that reaction norms can progressively be shifted over generations depending on the environmental conditions. One of his biological models was the water-flee daphnia. Woltereck proposed that enduring phenotypic modifications and gene mutations could have similar adaptive effects, and he postulated that their molecular bases would be different. Mutations occurred in genes, while enduring modifications were based on something he called the Matrix. He suggested that this matrix (i) was associated with the chromosomes, (ii) that it was heritable, (iii) it changed during development of the organisms, and (iv) that changes of the matrix could be simple chemical substitutions of an unknown, but probably polymeric molecule. We reasoned that the chromatin has all postulated features of this matrix and revisited Wolterecks classical experiments with daphnia. We developed a robust and rapid ATAC-seq technique that allows for analyzing chromatin of individual daphnia and show here (i) that this technique can be used with minimal expertise in molecular biology, and (ii) we used it to identify open chromatin structure in daphnia exposed to different environmental cues. Our result indicates that chromatin structure changes consistently in daphnia upon this exposure confirming Wolterecks classical postulate.

evolutionary biology↗

bioRxiv2019

Kinetic control of stationary flux ratios for a wide range of biochemical processes

One of the most intriguing features of biological systems is their ability to regulate the steady-state fluxes of the underlying biochemical reactions, however the regulatory mechanisms and their physicochemical properties are not fully understood. Fundamentally, flux regulation can be explained with a chemical kinetic formalism describing the transitions between discrete states with the reaction rates defined by an underlying free-energy landscape. Which features of the energy landscape affect the flux distribution? Here we prove that the ratios of the steady-state fluxes of quasi-first-order biochemical processes are invariant to energy perturbations of the discrete states and are only affected by the energy barriers. In other words, the non-equilibrium flux distribution is under kinetic and not thermodynamic control. We illustrate the generality of this result for three biological processes. For the network describing protein folding along competing pathways, the probabilities of proceeding via these pathways are shown to be invariant to the stability of the intermediates or to the presence of additional misfolded states. For the network describing protein synthesis, the error rate and the energy expenditure per peptide bond is proven to be independent of the stability of the intermediate states. For molecular motors such as myosin-V, the ratio of forward to backward steps and the number of ATP molecules hydrolyzed per step is demonstrated to be invariant to energy perturbations of the intermediate states. These findings place important constraints on the ability of mutations and drug perturbations to affect the steady-state flux distribution for a wide class of biological processes.

biophysics↗

bioRxiv2026

Plant cell-based 4D engineered living materials

Engineered living materials (ELMs) seek to bridge the gap between static engineered matter and dynamic biological systems. Photosynthetic ELMs have emerged as a promising route towards autonomous and sustainable material systems; however, they remain largely based on microorganisms or isolated organelles, whereas the potential of living plant cells as multifunctional, adaptive building blocks remains largely unexplored. Here we establish plant cells as a distinct chassis for engineered living materials and demonstrate that their intrinsic metabolic plasticity, cell-wall biochemistry and mechanics can be harnessed to create adaptive, evolvable and programmable materials. Using Arabidopsis thaliana suspension cells and printable pectin-alginate bioinks, we fabricate two- and three-dimensional living materials that support growth, metabolic plasticity and, long-term viability. Non-photosynthetic ELMs undergo light-induced metabolic reprogramming to acquire photosynthetic activity, demonstrating adaptive behaviour, while continuous cell growth dynamically remodels the mechanical properties of the material, enabling evolution of material properties through intrinsic biological processes. We further exploit endogenous cell wall peroxidases to catalyse the biofabrication of conducting polymers, enabling programmable acquisition of electrochemical functionality without genetic modification. By integrating living plant cells with auxetic and honeycomb architectures, we show that biological activity and structural design jointly determine emergent mechanical behaviour, revealing a principle of geometry-dependent biological reinforcement for living mechanical metamaterials. Centimetre-scale plant ELMs retain robust photosynthetic activity, with electron transport rates approaching those of native Arabidopsis tissues despite the absence of leaf architecture. Together, these findings establish plant cells as versatile building blocks for multifunctional living materials and lay the foundation for self-powered autonomous materials with potential applications in soft robotics, bioelectronics, and carbon capture.

bioengineering↗

bioRxiv2017

A novel mathematical method for disclosing oscillations ingene transcription: a comparative study

Circadian rhythmicity, the 24-hour cycle responsive to light and dark, is determined by periodic oscillations in gene transcription. This phenomenon has broad ramifications in physiologic function. Recent work has disclosed more cycles in gene transcription, and to the uncovering of these we apply a novel signal processing methodology known as the pencil method and compare it to conventional parametric, nonparametric, and statistical methods. Methods: In order to assess periodicity of gene expression over time, we analyzed a database derived from livers of mice entrained to a 12-hour light/12-hour dark cycle. We also analyzed artificially generated signals to identify differences between the pencil decomposition and other alternative methods.\n\nResultsThe pencil decomposition revealed hitherto-unsuspected oscillations in gene transcription with 12-hour periodicity. The pencil method was robust in detecting the 24-hour circadian cycle that was known to exist, as well as confirming the existence of shorter-period oscillations. A key consequence of this approach is that orthogonality of the different oscillatory components can be demonstrated. thus indicating a biological independence of these oscillations, that has been subsequently confirmed empirically by knocking out the gene responsible for the 24-hour clock.\n\nConclusionSystem identification techniques can be applied to biological systems and can uncover important characteristics that may elude visual inspection of the data. Significance: The pencil method provides new insights on the essence of gene expression and discloses a wide variety of oscillations in addition to the well-studied circadian pattern. This insight opens the door to the study of novel mechanisms by which oscillatory gene expression signals exert their regulatory effect on cells to influence human diseases.

cell biology↗

bioRxiv2023

Diversity at single nucleotide to pangenome scales among sulfur cycling bacteria in salt marshes

Sulfur-oxidizing and sulfate-reducing bacteria in salt marsh sediments are major controllers of ecosystem-scale carbon cycling. Cross-site comparisons of S-cycling communities are difficult given the rampant uncultured microbial diversity in sediment, yet comparisons are essential for revealing biogeographic, phylogenetic and functionally significant variation. Here, we use deep shotgun metagenomic sequencing data to construct and compare metagenome-assembled genomes (MAGs) of sulfur-cycling bacteria from Massachusetts and Alabama salt marshes that contrast in seasonality and sediment organic matter content. Samples were collected from sediments under Sporobolus alterniflorus and Sporobolus pumilus in separate MA vegetation zones, and under Sporobolus alterniflorus and Juncus roemerianus co-rooted in AL marsh. We grouped metagenomic data by plant species and site and identified 38 MAGs that included pathways for dissimilatory sulfate reduction or sulfide oxidation. Phylogenetic analyses indicated that 30 of the 38 were affiliated with uncultivated lineages. Read-mapping to MAGs showed significant differentiation of AL and MA samples, differentiation of samples taken in S. alterniflorus and S. pumilus vegetation zones in MA, but no differentiation of samples taken under S. alterniflorus and J. roemerianus that were rooted together in AL marsh. Pangenomic analyses of eight ubiquitous MAGs also detected site- and vegetation-specific genomic features, including varied sulfur-cycling operons, carbon fixation pathways, fixed single nucleotide variants, and active diversity-generating retroelements. This genetic diversity, detected at multiple scales even within uncultured groups, suggests evolutionary relationships affected by distance and local environment, and demonstrates differential microbial capacities for sulfur and carbon cycling in salt marsh sediments. ImportanceSalt marshes are known for their significant carbon storage capacity, and sulfur cycling is closely linked with the ecosystem-scale carbon cycling in these ecosystems. Sulfate reducers are the major decomposers in salt marsh systems, and sulfur-oxidizing bacteria remove sulfide, a toxic byproduct of sulfate reduction, supporting the productivity of marsh plants. To date, the complexity of coastal environments, heterogeneity of the rhizosphere, high microbial diversity and uncultured majority hindered our understanding of the genomic diversity of sulfur-cycling microbes in salt marshes. Here we use comparative genomics to overcome these challenges and provide an in-depth characterization of microbial diversity in salt marshes. We characterize sulfur-cycling communities across distinct sites and plant species and uncover extensive genomic diversity at the taxon level and specific genomic features present in MAGs affiliated with sulfur-cycling uncultivated lineages. Our work provides insights into the partnerships in salt marshes and a roadmap for multiscale analyses of diversity in complex biological systems.

ecology↗

bioRxiv2020

msiPL: Non-linear Manifold and Peak Learning of Mass Spectrometry Imaging Data Using Artificial Neural Networks

Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving clinical diagnosis, biomarker discovery, metabolomics research and pharmaceutical applications. The large data size and high dimensional nature of MSI pose computational and memory complexities that hinder accurate identification of biologically-relevant molecular patterns. We propose msiPL, a robust and generic probabilistic generative model based on a fully-connected variational autoencoder for unsupervised analysis and peak learning of MSI data. The method can efficiently learn and visualize the underlying non-linear spectral manifold, reveal biologically-relevant clusters of tumor heterogeneity and identify underlying informative m/z peaks. The method provides a probabilistic parametric mapping to allow a trained model to rapidly analyze a new unseen MSI dataset in a few seconds. The computational model features a memory-efficient implementation using a minibatch processing strategy to enable the analyses of big MSI data (encompassing more than 1 million high-dimensional datapoints) with significantly less memory. We demonstrate the robustness and generic applicability of the application on MSI data of large size from different biological systems and acquired using different mass spectrometers at different centers, namely: 2D Matrix-Assisted Laser Desorption Ionization (MALDI) Fourier Transform Ion Cyclotron Resonance (FT ICR) MSI data of human prostate cancer, 3D MALDI Time-of-Flight (TOF) MSI data of human oral squamous cell carcinoma, 3D Desorption Electrospray Ionization (DESI) Orbitrap MSI data of human colorectal adenocarcinoma, 3D MALDI TOF MSI data of mouse kidney, and 3D MALDI FT ICR MSI data of a patient-derived xenograft (PDX) mouse brain model of glioblastoma. SignificanceMass spectrometry imaging (MSI) provides detailed molecular characterization of a tissue specimen while preserving spatial distributions. However, the complex nature of MSI data slows down the processing time and poses computational and memory challenges that hinder the analysis of multiple specimens required to extract biologically relevant patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Here, we present a generative probabilistic deep-learning model that can analyze and non-linearly visualize MSI data independent of the nature of the specimen and of the MSI platform. We demonstrate robustness of the method with application to different tissue types, and envision it as a new generation of rapid and robust analysis for mass spectrometry data.

bioinformatics↗

bioRxiv2023

A single-cell transposable element atlas of human cell identity

Single cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex biological systems. However, most sequencing studies overlook the contribution of transposable element (TE) expression to the transcriptome. In both scRNA-seq and bulk tissue RNA sequencing (RNA-seq), quantification of TE expression is challenging due to repetitive sequence content and poorly characterized TE gene models. Here, we developed a tool and analysis pipeline for Single cell Transposable Element Locus Level Analysis of scRNA Sequencing (Stellarscope) that reassigns multi-mapped reads to specific genomic loci using an expectation-maximization algorithm. Using Stellarscope, we built an atlas of TE expression in human PBMCs. We found that locus-specific TEs delineate cell types and define new cell subsets not identified by standard mRNA expression profiles. Altogether, this study provides comprehensive insights into the influence of transposable elements in human biology.

genomics↗

bioRxiv2019

Network analysis allows to unravel breast cancer molecular features and to identify novel targets

The behaviour of complex biological systems is determined by the orchestrated activity of many components interacting with each other, and can be investigated by networks. In particular, gene co-expression networks have been widely used in the past years thanks to the increasing availability of huge gene expression databases. Breast cancer is a heterogeneous disease usually classified either according to immunohistochemical features or by expression profiling, which identifies the 5 subtypes luminal A, luminal B, basal-like, HER2-positive and normal-like. Basal-like tumours are the most aggressive subtype, for which so far no targeted therapy is available.\n\nMaking use of the WGCNA clustering method to reconstruct breast cancer transcriptional networks from the METABRIC breast cancer dataset, we developed a platform to address specific questions related to breast cancer biology. In particular, we obtained gene modules significantly correlated with survival and age of onset, useful to understand how molecular features and gene expression patterns are organized in breast cancer. We next generated subtype-specific gene networks and in particular identified two modules that are significantly more connected in basal-like breast cancer with respect to all other subtypes, suggesting relevant biological functions. We demonstrate that network centrality (kWithin) is a suitable measure to identify relevant genes, since we could show that it correlates with clinical features and that it provides a mean to select potential upstream regulators of a module with high reliability. Finally, we showed the feasibility of adding meaning to the networks by combining them with independently obtained data related to activated pathways.\n\nIn conclusion, our platform allows to identify groups of genes highly relevant in breast cancer and possibly amenable to drug targeting, due to their ability to regulate survival-related gene networks. This approach could be successfully extended to other BC subtypes, and to all tumor types for which enough expression data are available.

cancer biology↗

bioRxiv2019

Development of a genetic toolset for the highly engineerable and metabolically versatile Acinetobacter baylyi ADP1

One primary objective of synthetic biology is to improve the sustainability of chemical manufacturing. Biological systems can utilize a variety of carbon sources, including waste streams that pose challenges to traditional chemical processing such as lignin biomass, providing opportunity for remediation and valorization of these materials. Success, however, depends on identifying microorganisms that are both metabolically versatile and engineerable. This has been a historic hindrance. Here, we leverage the facile genetics of the metabolically versatile bacterium Acinetobacter baylyi ADP1 to create easy and rapid molecular cloning workflows, a promoter library, ribosomal binding site (RBS) variants, and an unprecedented number of bacterial chromosomal protein expression sites and variants. Moreover, we demonstrate the utility of these tools by examining ADP1s catabolic repression regulation, creating a strain with improved potential for lignin bioprocessing. Taken together, this work establishes ADP1 as an ideal host for a variety of sustainability and synthetic biology applications.

synthetic biology↗

bioRxiv2026

SPCoral: diagonal integration of spatial multi-omics across diverse modalities and technologies

Spatial multi-omics is indispensable for decoding the comprehensive molecular landscape of biological systems. However, the integration of multi-omics remains largely unresolved due to inherent disparities in molecular features, spatial morphology, and resolution. Here we developed SPCoral for diagonal integration of spatial multiomics across adjacent slices. SPCoral extracts spatial covariation patterns via graph attention networks, followed by the use of optimal transport to identify high-confidence anchors in an unsupervised, feature-independent manner. SPCoral utilizes a crossmodality attention network to enable seamless cross-resolution feature integration alongside robust cross-omics prediction. Comprehensive benchmarking demonstrates SPCorals superior performance across different technologies, modalities and varied resolutions. The integrated multi-omics representation further improves spatial domain identification, effectively augments experimental data, enables cross-modal association analysis, and facilitates cell-cell communication. SPCoral exhibits good scalability with data size, reveals biological insights that are not attainable using a single modality. In summary, SPCoral offers a powerful framework for spatial multi-omics integration across various technologies and biological scenarios.

bioinformatics↗

bioRxiv2020

The key to complexity in interacting systems with multiple strains

Ecological community structure, persistence and stability are shaped by multiple forces, acting on multiple scales. These include patterns of resource use and limitation, spatial heterogeneities, drift and migration. Pathogen strains co-circulating in a host population are a special type of an ecological community. They compete for colonization of susceptible hosts, and sometimes interact via altered susceptibilities to co-colonization. Diversity in such pairwise interaction traits enables the multiple strains to create dynamically their niches for growth and persistence, and engineer their common environment. How such a network of interactions with others mediates collective coexistence remains puzzling analytically and computationally difficult to simulate. Furthermore, the gradients modulating stability-complexity regimes in such multi-player systems remain poorly understood. In a recent study, we presented an analytic framework for N-type coexistence in an SIS epidemiological system with co-colonization interactions. The multi-strain complexity was reduced from O(N2) dimensions of population structure to only N equations for strain frequency evolution on a long timescale. Here, we examine the key drivers of coexistence regimes in such a system. We find the ratio of single to co-colonization critically determines the type of equilibrium for multi-strain dynamics. This key quantity in the model encodes a trade-off between overall transmission intensity R0 and mean interaction coefficient in strain space k. Preserving a given coexistence regime, under fixed trait variation, can only be achieved from a balance between higher competition in favourable environments, and higher cooperation in harsher environments, consistent with the stress gradient hypothesis in ecology. Multi-strain coexistence regimes are more stable when is small, whereas as increases, dynamics tends to increase in complexity. There is an intermediate ratio that maximizes the existence and stability of a unique coexistence equilibrium between strains. This framework provides a foundation for linking invariant principles in collective coexistence across biological systems, and for understanding critical shifts in community dynamics, driven by simple and random pairwise interactions but potentiated by mean-field and environmental gradients.

ecology↗

bioRxiv2026

Harnessing Escherichia coli motility to engineer bacterial Voronoi patterns

Cell motility drives spatial pattern formation across diverse biological systems. Here, we engineer Escherichia coli motility in semi-solid agar to control Voronoi patterns in two and three dimensions, partitioning space into regions closest to their respective inoculation seeds. Consistent with our reaction-diffusion model, we observed that collisions between expansion fronts generate either biomass depletion (''gaps'') or accumulation (''anti-gaps''), governed by the relative diffusion rates of bacteria and nutrients. By engineering strains with distinct expansion rates and tuneable motility, and by integrating these experimental data into a dynamic Voronoi model, we achieved precise control over pattern geometry. This enabled the generation of gaps with varying widths, curved boundaries, asymmetric structures, seedless regions, and complex composite patterns. Together, these findings establish bacterial Voronoi patterns as a programmable platform for engineering multicellular spatial organization, with potential applications in synthetic biology and materials science.

synthetic biology↗

bioRxiv2025

Residual blinking-driven channel alignment for multicolor single-molecule localization microscopy

Given the nanometer-scale resolution of single-molecule localization microscopy (SMLM), sequential multicolor SMLM is inherently challenged by inter-channel misalignment arising from unpredictable sample drift during channel switching. Traditional fiducial-based strategies are limited by added complexity in sample preparation. Here, we report a residual blinking-driven method for channel alignment in multicolor SMLM, termed ReBling alignment. By exploiting residual blinking events under dual-wavelength excitation, our method extracts shared localizations as intrinsic registration anchors and performs accurate cross-correlation-based alignment. We validated ReBling alignment across biological systems featuring distinct spatial patterns, including continuous patterned structures (mitochondria-microtubule co-imaging, dual-labeled microtubules), and discrete point-cluster assemblies (dual-color nuclear pore complex components), demonstrating its broad applicability and reliability. These results establish ReBling alignment as a practical, versatile, fiducial-free solution for accurate channel registration in sequential multicolor SMLM, enabling more precise and reliable characterization of complex cellular architecture and molecular interactions.

cell biology↗

bioRxiv2026

Long-term memory performance optimization via Neural network-based curve fitting in Drosophila

Long-term memory (LTM) formation typically requires extensive training or highly salient experiences, limiting learning efficiency. Operant conditioning is generally thought to produce stronger memory than classical conditioning because of its active learning component. Unexpectedly, however, laser-based social conditioning in Drosophila melanogaster revealed that while classical paradigms yielded lower short-term memory (STM) scores but higher LTM retention, operant paradigms exhibited higher STM scores followed by rapid LTM decay. To resolve this discrepancy, we employed the AI Complex Systems Response (AI-CSR) framework, which reconstructs high-dimensional learning landscapes from sparse sampling and predicts globally optimal training conditions. Following AI-CSR optimization, operant conditioning produced a twofold increase in LTM scores, yielding the strongest 24-hour social memory performance reported in flies to date and revealing the expected superiority of active learning, which had conversely shown poorer performance under standard training protocols. In contrast, AI-CSR did not further enhance classical conditioning performance but reduced training time by 50%. Single-cell RNA sequencing revealed expanded neuronal recruitment marked by the activation and inhibition of various gene combinations. Together, these findings link circuit-level reorganization with molecular programs underlying efficient long-term memory and demonstrate how AI-guided optimization can uncover latent learning capacity in biological systems.

animal behavior and cognition↗

bioRxiv2017

Combined aptamer and transcriptome sequencing of single cells

The transcriptome and proteome encode distinct information that is important for characterizing heterogeneous biological systems. We demonstrate a method to simultaneously characterize the transcriptomes and proteomes of single cells at high throughput using aptamer probes and droplet-based single cell sequencing. With our method, we differentiate distinct cell types based on aptamer surface binding and gene expression patterns. Aptamers provide advantages over antibodies for single cell protein characterization, including rapid, in vitro, and high-purity generation via SELEX, and the ability to amplify and detect them with PCR and sequencing.

cell biology↗

bioRxiv2025

Robust ciliary flows protect early Xenopus embryos from pathogens independent of multiciliated cell patterning

During the early stages of development, the skin of the Xenopus embryo is covered by around two thousand evenly distributed multiciliated cells (MCCs). This striking spatial distribution is believed to maximise the generation of superficial flows at the scale of the embryo. However, the specific role of regular MCC distribution and the physiological function of vigorous ciliary activity remain elusive. We investigate the extent to which superficial flows provide protection against external pathogens before the immune system matures, by combining experimental and computational approaches. First, we cultivated epithelial explants in order to quantify the distribution of MCCs, beating frequencies, and three-dimensional fluid flows. We then use these data to validate a computational fluid dynamics model. Using this model, seeded with structural data from whole embryos, our simulations reveal that the collective ciliary beatings create a robust liquid shield along the embryonic flank, which is highly effective at clearing pathogens from the vicinity of the epithelial surface. Through parametric analyses, we further demonstrate that this protective function is remarkably resilient. Indeed, the effectiveness of pathogen clearance is primarily governed by the overall characteristic velocity of the cilia and is less affected by moderate variations in MCC density and spatial organisation. Our findings suggest that, rather than optimising energy consumption, the biological system prioritises functional robustness to ensure reliable protection.

biophysics↗

bioRxiv2026

Bayesian Inference of Gene Regulatory Networks at Stochastic Steady State

Gene Regulatory Networks (GRNs) form the regulatory back-bone that coordinates gene expression. The architecture of GRNs shapes their function and constraints the biochemical pathways through which information flows. Inferring the structure of regulatory interactions is thus essential for understanding biological systems, and designing targeted therapies. Despite substantial progress in GRN inference, most approaches - from statistical methods to deep learning - do not take into account fundamental biochemical processes that drive regulatory dynamics. To address this shortcoming, here we present a novel Bayesian inference approach based on using the Chemical Langevin Equation (CLE) as a model of gene expression dynamics at stochastic equilibrium. Inter-actions in GRNs are sparse, and we thus use a regularized horseshoe prior enabling selective shrinkage of unsupported interactions while identifying strong regulatory edges. We evaluate our method using synthetic gene expression data, allowing for benchmarking against a known ground truth. Our approach allows us to infer kinetic parameters, identify network structure, and infer regulatory cycles without the need to observe transient dynamics. This Bayesian alternative to current methods thus provides both biological interpretability and structural identifiability in GRN inference.

bioinformatics↗

bioRxiv2022

GroovDB: A database of ligand-inducible transcription factors

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multi-layer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically-responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data has been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides additional information that provides potential insights into alternative ligands and systems biology.

synthetic biology↗