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At least 1,243 records · Page 69Linked to original sources

bioRxiv2018

Nongenic cancer-risk SNPs affect oncogenes, tumor suppressor genes, and immune function

Genome-wide associations studies (GWASes) have identified many germline genetic variants that are associated with an increased risk of developing cancer. However, how these single nucleotide polymorphisms (SNPs) alter biological function in a way that increases cancer risk is still largely unknown. We used a systems biology approach to analyze the regulatory role and functional associations of cancer-risk SNPs in thirteen distinct tissues. Using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis, keeping both cis- and trans-eQTLs, and representing those significant associations as edges in tissue-specific eQTL bipartite networks. We find that each network is organized into highly modular communities that group sets of SNPs together with functionally-related collections of genes. We mapped cancer-risk SNPs to each tissue-specific eQTL network. Although we find in each tissue that cancer-risk SNPs are distributed across the network, they are not uniformly distributed. Rather they are significantly over-represented in a small number of communities. This includes communities enriched for immune response processes as well as communities representing tissue-specific functions. Moreover, cancer-risk SNPs are over-represented in the central \"cores\" of communities, meaning they are more likely to influence the expression of many genes within the same community, thus affecting biological processes. And finally, we find that cancer-risk SNPs preferentially target oncogenes and tumor suppressor genes, suggesting non-genic mutations may still alter the effects of these key cancer-associated genes. This bipartite eQTL network approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

cancer biology↗

bioRxiv2026

Integrative Omics and Network Biology Reveal Transcriptional Changes of Amino Acid Transport in Arabidopsis Susceptibility to Pseudomonas syringae

Plant amino acids function as both pathogen nutrients and essential drivers of systemic immunity. The regulation of amino acid homeostasis through transporters is a essential for mounting a robust and coordinated immune response in plants during pathogen infection. Using systems biology and integrative network science, we investigated bacterial virulence in Arabidopsis. By comparing gene coexpression networks of effector-triggered susceptibility (ETS) and pattern-triggered immunity (PTI), we uncovered a plant amino acid-related processes specifically linked to ETS. Integrating time-series transcriptomics, protein-DNA interactions, and mathematical simulations, we identified ANAC046 as a transcriptional regulator of amino acid processes during ETS. Single-cell RNA-Seq revealed that amino acid transporters are primarily expressed in companion and mesophyll cells, while functional validation confirmed ANAC046s roles in promoting susceptibility. Further integration of transcriptome and interactome data showed that amino acid-related genes interact with key immune hub proteins. Network topology analysis enabled the characterization of seven additional genes involved in plant defense. To support community-wide research, we developed MIData, an open-access platform for pre-analyzed Arabidopsis networks. Together, our findings demonstrate the power of systems-level approaches in uncovering hierarchical regulatory mechanisms underlying plant susceptibility to bacterial pathogens.

plant biology↗

bioRxiv2017

Multiplexed sgRNA Expression Allows Versatile Single Non-repetitive DNA Labeling and Endogenous Gene Regulation

The CRISPR/Cas9 system has made significant contribution to genome editing, gene regulation and chromatin studies in recent years. High-throughput and systematic investigations into the multiplexed biological systems and disease conditions require simultaneous expression and coordinated functioning of multiple sgRNAs. However, current co-transfection based sgRNA co-expression systems remain poorly efficient and virus-based transfection approaches are relatively costly and labor intensive. Here we established a vector-independent method allowing multiple sgRNA expression cassettes to be assembled in series into a single plasmid. This synthetic biology-based strategy excels in its efficiency, controllability and scalability. Taking the flexibility advantage of this all-in-one sgRNA expressing system, we further explored its applications in single non-repetitive genomic locus imaging as well as coordinated gene regulation in live cells. With its strong potency, our method will greatly facilitate the understandings in genome structure, function and dynamics, and will contribute to the systemic investigations into complex physiological and pathological conditions.

synthetic biology↗

bioRxiv2026

Noise analysis of derivative-action biomolecular topologies

Temporal gradient sensing is a fundamental capability observed across diverse natural biological systems, contributing to the coordination of their functions. Harnessing this ability is also of significant interest in synthetic biology, particularly for sensing and control applications. In this work, we focus on a biomolecular topology that exemplifies a broader class of signal-differentiating architectures, while introducing a structural variant of it. We examine their behavior under both nominal and non-ideal conditions, accounting for stochastic noise arising from different sources. Our investigation includes scenarios where these topologies operate independently, as well as when embedded within minimal regulatory architectures based on negative as well as positive feedback. We analyze the stability of the resulting macroscopic dynamics--a prerequisite for practical deployment--and quantify stochastic fluctuations in system output, providing comparisons with the corresponding input/unregulated process. Importantly, our results demonstrate that signal differentiation can be effectively implemented in a biomolecular setting without incurring deleterious noise amplification--a major concern in the utilization of derivative action across disciplines.

synthetic biology↗

bioRxiv2017

Dissection of MAPK signaling specificity through protein engineering in a developmental context

Mitogen-activated protein kinases (MAPK) signaling affects many processes, some of which have different outcomes in the same cell. In Arabidopsis, activation of a MAPK cascade consisting of YODA, MKK4/5 and MPK3/6 inhibits early stages of stomatal developmental, but this ability is lost at the latest stage when guard mother cells (GMCs) transition to guard cells (GCs). Rather than downregulating cascade components, stomatal precursors must have a mechanism to prevent late stage inhibition because the same MKKs and MPKs mediate other physiological responses. Here, we artificially activated the MAPK cascade using MKK7, another MKK that can modulate stomatal development, and found that inhibition of stomatal development is still possible in GMCs. This suggests that MKK4/5, but not MKK7, are specifically prevented from inhibiting stomatal development. To identify regions of MKKs responsible for cell-type specific regulation, we used a domain swap approach with MKK7 and a battery of in vitro and in vivo kinase assays. We found that N-terminal regions of MKK5 and MKK7 establish specific signal-to-output connections like they do in other organisms, but they do so in combination with previously undescribed modules in the C-terminus. One of these modules encodes the GMC-specific regulation of MKK5, that when swapped with MKK7s, allows MKK5 to mediate robust inhibition of late stomatal development. Because MKK structure is conserved across species, the identification of new MKK specificity modules and signaling rules furthers our understanding of how eukaryotes create specificity in complex biological systems.

plant biology↗

bioRxiv2024

Development and Characterization of Recombinant ADP-Ribose Binding Reagents that Allow Simultaneous Detection of Mono and Poly ADP-Ribose

ADP-ribosylation (ADPRylation) is a post-translational modification (PTM) of proteins mediated by the activity of a variety of ADP-ribosyltransferase (ART) enzymes, such as the Poly (ADP-ribose) Polymerase (PARP) family of proteins. This PTM is diverse in both form and biological functions, which makes it a highly interesting modification, but difficult to study due to limitations in reagents available to detect the diversity of ADP-ribosylation. Recently we developed a set of recombinant antibody-like ADP-ribose binding proteins, using naturally occurring ADPR binding domains (ARBDs) that include macrodomains and WWE domains, that have been functionalized by fusion to the constant "Fc" region of rabbit immunoglobulin. Herein, we present an expansion of this biological toolkit, where we have replaced the rabbit Fc sequence with two other species, the Fc for mouse and goat immunogloblulin. Characterization of the new reagents indicates that they can be detected in a species-dependent manner, recognize specific ADP-ribose moieties, and excitingly, can be used in various antibody-based assays by co-staining. The expansion of this tool will allow for more multiplexed assessments of the complexity of ADPRylation biology in many biological systems.

molecular biology↗

bioRxiv2018

Tradeoffs and cultural diversity

Culture is humanitys prime adaptation. Which cultural traits contribute to adaptive value at the level of cultural groups, and how they do so, is, however, unclear and debated. Here, we apply an approach from systems biology, known as Pareto task inference (ParTI), to bring a fresh perspective to these questions. ParTI considers systems that need to perform multiple tasks. No system can be optimal at all tasks at once, leading to a fundamental tradeoff. These tradeoffs result in specific patterns in multivariate trait data: data falls inside polygons in trait space, whose vertices are trait combinations that are adaptive for each of the tasks. ParTI can infer the number of adaptive tasks and their nature without need for a-priori assumptions, based on the vertices of these polygons. Here, we applied ParTI to two datasets of cultural traits, on Austronesian cultures and modern hunter-gatherers, adjusting for phylogeny and spatial diffusion effects. We find evidence that these independent datasets show a tradeoff between the same three tasks. We identify the tasks as resource defense, resource competition, and mobility/exchange. Specific combinations of cultural traits are adaptive for each of these tasks. This approach can be widely applied to any large multivariate dataset to study trade-offs in cultural evolution.

evolutionary biology↗

bioRxiv2026

Synaptation - a missing concept in the science of evolved complexity

The concept of synaptation is introduced to explain a distinct evolutionary origin of selected effects, in which natural selection operates through emergent cross-level interactions within collectives lacking shared inheritance. The traditional framework excludes such origins from the adaptation-exaptation taxonomy. Yet individual organism fitness is often mediated by collective, non-linear interactions within systems that cannot themselves sustain heredity. To address this, multilevel selection (MLS) theory is extended in the form of synaptive MLS, a process through which heterogeneous task groups evolve emergent traits, called synaptations, whose evolutionary origin lies in irreducible, cross-level selective covariance. Synaptive MLS differs from trait-group selection because collective function depends on causal entanglement among heterogenous entities with diverse fates, rather than a common trait that links entities to a shared fate. This framework applies to conspecific divisions of labor, multi-species consortia and holobiont integration, where coordination of distinct roles yields emergent fitness effects. Synaptation recognizes how natural selection can generate novel emergent functions by coordinating adaptive, exaptive, and neutral organism-level traits whose lower-level roles scaffold higher-level selected effects. Synaptation and adaptation define the endpoints of a continuum of selected-effect complexity. Recognition of this continuum extends the Darwinian paradigm to adaptive complexity in systems that fail classical heredity requirements and offers an evolutionary lens for understanding relational phenomena such as eubiosis, dysbiosis, and rebiosis in holobionts. Significance StatementNatural selection is typically understood to require heredity, yet some complex and functionally integrated biological systems in nature lack a mechanism for collective reproduction. This project introduces synaptation, a theoretical framework that recognizes a distinct evolutionary origin of selected effects, in which fitness consequences arise through non-additive, cross-level interactions rather than through collective reproduction. Synaptive multilevel selection explains how functionally integrated traits can originate by natural selection in non-reproducing collectives, bridging molecular, ecological, and social forms of cooperation. By revealing how coordination of lower-level traits can scaffold the emergence of higher-level selected effects, this framework extends the reach of evolutionary theory and offers new insight into the eco-evolutionary dynamics that sustain collective health, stability, and resilience in multi-species systems.

evolutionary biology↗

bioRxiv2017

mixOmics: an R package for ‘omics feature selection and multiple data integration

The advent of high throughput technologies has led to a wealth of publicly available omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a molecular signature) to explain or predict biological conditions, but mainly for a single type of omics. In addition, commonly used methods are univariate and consider each biological feature independently.\n\nWe introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a system biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of omics data available from the package.

bioinformatics↗

bioRxiv2020

Resonating neurons stabilize heterogeneous grid-cell networks

A central theme that governs the functional design of biological networks is their ability to sustain stable function despite widespread parametric variability. Here, we investigated the impact of distinct forms of biological heterogeneities on the stability of a two-dimensional continuous attractor network (CAN) implicated in grid-patterned activity generation. We show that increasing degrees of biological heterogeneities progressively disrupted the emergence of grid- patterned activity and resulted in progressively large perturbations in low-frequency neural activity. We postulated that targeted suppression of low-frequency perturbations could ameliorate heterogeneity-induced disruptions of grid-patterned activity. To test this, we introduced intrinsic resonance, a physiological mechanism to suppress low-frequency activity, either by adding an additional high-pass filter (phenomenological) or by incorporating a slow negative feedback loop (mechanistic) into our model neurons. Strikingly, CAN models with resonating neurons were resilient to the incorporation of heterogeneities and exhibited stable grid-patterned firing. We found CAN networks with mechanistic resonators to be more effective in targeted suppression of low-frequency activity, with the slow kinetics of the negative feedback loop essential in stabilizing these networks. As low-frequency perturbations (1/f noise) are pervasive across biological systems, our analyses suggest a universal role for mechanisms that suppress low- frequency activity in stabilizing heterogeneous biological networks.

neuroscience↗

bioRxiv2021

AlphaFold-aware prediction of protein-DNA binding sites using graph transformer

Protein-DNA interactions play crucial roles in the biological systems, and identifying protein-DNA binding sites is the first step for mechanistic understanding of various biological activities (such as transcription and repair) and designing novel drugs. How to accurately identify DNA-binding residues from only protein sequence remains a challenging task. Currently, most existing sequence-based methods only consider contextual features of the sequential neighbors, which are limited to capture spatial information. Based on the recent breakthrough in protein structure prediction by AlphaFold2, we propose an accurate predictor, GraphSite, for identifying DNA-binding residues based on the structural models predicted by AlphaFold2. Here, we convert the binding site prediction problem into a graph node classification task and employ a transformer-based variant model to take the protein structural information into account. By leveraging predicted protein structures and graph transformer, GraphSite substantially improves over the latest sequence-based and structure-based methods. The algorithm is further confirmed on the independent test set of 181 proteins, where GraphSite surpasses the state-of-the-art structure-based method by 16.4% in AUPR and 11.2% in MCC, respectively. We provide the datasets, the predicted structures, and the source codes along with the pre-trained models of GraphSite at https://github.com/biomed-AI/GraphSite. The GraphSite web server is freely available at https://biomed.nscc-gz.cn/apps/GraphSite.

bioinformatics↗

bioRxiv2022

User-friendly electron microscopy protocols for the visualization of biological macromolecular complexes in three dimensions: Visualization of planta clathrin-coated vesicles at ultrastructural resolution

Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the pseudo 3D morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.

plant biology↗

bioRxiv2023

Thermodynamically-consistent, reduced models of gene regulatory networks

Synthetic biology aims to engineer novel functionalities into biological systems. While the approach has to date been predominantly applied to single cells, a richer set of biological phenomena can be engineered by applying synthetic biology to cell populations. To rationally design cell populations, we require mathematical models that link between intracellular biochemistry and intercellular interactions. In this study, we develop a kinetic model of gene expression that is suitable for incorporation into agent-based models of cell populations. To be scalable to large cell populations, models of gene expression should be both computationally efficient and compliant with the laws of physics. We satisfy the first requirement by applying a model reduction scheme to translation, and the second requirement by formulating models using bond graphs. Our reduced model is significantly faster to simulate than the full model, and faithfully reproduces important behaviours of the full model. We couple separate models of gene expression to build models of the toggle switch and repressilator. With these models, we explore the effects of resource availability and cell-to-cell heterogeneity on circuit behaviour. The modelling approaches developed in this study are a bridge towards engineering collective cell behaviours such as synchronisation and division of labour.

synthetic biology↗

bioRxiv2024

From resonance to chaos: modulating spatiotemporal patterns through a synthetic optogenetic oscillator

Oscillations are a recurrent phenomenon in biological systems across scales, including circadian clocks, metabolic oscillations and embryonic genetic oscillators. Despite their fundamental significance in biology, deciphering core principles of biological oscillators is very challenging due to the multiscale complexity of genetic networks and the difficulty in perturbing organisms in vivo. In this study, we tackle this challenge by re-designing the well-characterised synthetic oscillator, known as "repressilator", in Escherichia coli and controlling it using optogenetics, thus introducing the "optoscillator". When we apply periodic light pulses, the optoscillator behaves as a forced oscillator. Bacterial colonies harboring synthetic oscillators manifest oscillations as spatial ring patterns. Leveraging this feature, we systematically investigate the number, intensity and sharpness of the rings under different regimes of light exposure. By integrating experimental approaches with mathematical modeling, we show that this simple oscillatory circuit can generate complex dynamics that, depending on the external periodic forcing, are transformed into distinct spatial patterns. We report the observation of synchronisation, resonance, undertone and period doubling. Furthermore, we present evidence supporting the existence of a chaotic regime. This work highlights the intricate spatiotemporal patterns accessible by synthetic oscillators and underscores the potential of our approach in understanding the underlying principles governing biological oscillations.

synthetic biology↗

bioRxiv2024

LucaOne: Generalized Biological Foundation Model with Unified Nucleic Acid and Protein Language

AbstractThe language of biology, encoded in DNA, RNA, and proteins, forms the foundation of life but remains challenging to decode due to its complexity. Traditional computational methods often struggle to integrate information across these molecules, limiting a comprehensive understanding of biological systems. Advances in Natural Language Processing (NLP) with pre-trained models offer new possibilities for interpreting biological language. Here, we introduce LucaOne, a pre-trained foundation model trained on nucleic acid and protein sequences from 169,861 species. Through large-scale data integration and semisupervised learning, LucaOne demonstrates an understanding of key biological principles, such as DNA-Protein translation. Using few-shot learning, it effectively comprehends the central dogma of molecular biology and performs competitively on tasks involving DNA, RNA, or protein inputs. Our results highlight the potential of unified foundation models to address complex biological questions, providing an adaptable framework for bioinformatics research and enhancing the interpretation of lifes complexity.

bioinformatics↗

bioRxiv2025

Foraging Ants as Liquid Brains: Movement Heterogeneity Shapes Collective Efficiency

Liquid brains conceptualize living systems operating without central control, where collective outcomes emerge from local but dynamic interactions. Therefore, movement is expected to shape the connectivity among individuals, allowing the system to optimize its efficiency. We empirically measured ant movement behavior across large spatiotemporal scales, closely reflecting the ecology of our model species, Aphaenogaster senilis. We then incorporated this into a liquid brain framework, enabling a quantitative replication of ant foraging efficiency and their spatiotemporal dynamics. Our results highlight that a simple feedback mechanism explains the foraging patterns of this species. Indeed, such feedback is modulated by adjusting the proportion of two coexisting movement behaviors: while the recruits facilitated information transfer and food exploitation by aggregating closely to the nest, the scouts mostly bypassed this feedback, enabling the discovery of alternative food sources. These findings underscore how complex systems frameworks can benefit from empirical insights, enhancing our understanding of the mechanisms underlying collective intelligence in biological systems.

ecology↗

bioRxiv2025

ProtHGT: Heterogeneous Graph Transformers for Automated Protein Function Prediction Using Biological Knowledge Graphs and Language Models

MotivationThe rapid accumulation of protein sequence data, coupled with the slow pace of experimental annotations, creates a critical need for computational methods to predict protein functions. Existing models often rely on limited data types, such as sequence-based features or protein-protein interactions (PPIs), failing to capture the complex molecular relationships in biological systems. To address this, we developed ProtHGT, a heterogeneous graph transformer-based model that integrates diverse biological datasets into a unified framework using knowledge graphs for accurate and interpretable protein function prediction. ResultsProtHGT achieves state-of-the-art performance on benchmark datasets, demonstrating its ability to outperform current graph-based and sequence-based approaches. By leveraging diverse biological entity types and highly representative protein language model embeddings at the input level, the model effectively learns complex biological relationships, enabling accurate predictions across all Gene Ontology (GO) sub-ontologies. Ablation analyses highlight the critical role of heterogeneous data integration in achieving robust predictions. Finally, our use-case study has indicated that its possible to interpret ProtHGTs predictions via exploring the related parts of our input biological knowledge graph, offering plausible explanations to build or test new hypotheses. Availability and ImplementationProtHGT is available as a programmatic tool on Github and as a web service at Hugging Face. ContactTo whom the correspondence should be addressed: Tunca Do[g]an (tuncadogan@gmail.com)

bioinformatics↗

bioRxiv2019

DNA-BOT: A low-cost, automated DNA assembly platform for synthetic biology

Multi-part DNA assembly is the physical starting point for many projects in Synthetic and Molecular Biology. The ability to explore a genetic design space by building extensive libraries of DNA constructs is essential for creating programmed biological systems that perform the desired functions. With multiple DNA assembly methods and standards adopted in the Synthetic Biology community, automation of the DNA assembly process has received serious attention in recent years. Importantly, automating DNA assembly enables larger builds using less researcher time, increasing the accessible design space. However, these benefits currently incur high costs for both equipment and consumables. Here, we address this limitation by introducing low-cost DNA assembly with BASIC on OpenTrons (DNA-BOT). For this purpose, we developed an open-source software package dnabot (https://github.com/BASIC-DNA-ASSEMBLY/dnabot). We demonstrate the performance of DNA-BOT by simultaneously assembling 88 constructs composed of 10 genetic parts, exploring the promoter, ribosome binding site (RBS) and gene order design space for a 3-gene operon. All 88 constructs were assembled with high accuracy, at a cost of $1.50 - $5.50 per construct. This illustrates the efficiency, accuracy and affordability of DNA-BOT making it accessible for most labs and democratising automated DNA assembly.

synthetic biology↗