SEARCH · Search bioRxiv

Results for “Systems Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,693 records · Page 94Linked to original sources

bioRxiv2022

Effective holistic characterization of small molecule effects using heterogeneous biological networks

The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network that was used to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of CANDO to accurately relate drugs in a multitarget and multiscale context, and paves the way for predicting novel putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.

bioinformatics↗

bioRxiv2025

Proteostasis and Unfolded Protein Response Dynamics in Human Neuron and Mouse Glia Co-culture Reveal Cell-Specific Aging Responses

Proteostasis, or protein homeostasis, is a tightly regulated network of cellular pathways essential for maintaining proper protein folding, trafficking, and degradation. Neurons are particularly vulnerable to proteostasis collapse due to their post-mitotic and long-lived nature and thus represent a unique cell type to understand the dynamics of proteostasis throughout development, maturation, and aging. Here, we utilized a dual-species co-culture model of human excitatory neurons and mouse glia to investigate cell type- specific, age-related changes in the proteostasis network using data-independent acquisition (DIA) LC-MS/MS proteomics. We quantified branch-specific unfolded protein response (UPR) activation by monitoring curated effector proteins downstream of the ATF6, IRE1/XBP1s, and PERK pathways, enabling a comprehensive, unbiased evaluation of UPR dynamics during neuronal aging. Species-specific analysis revealed that aging neurons largely preserved proteostasis, although they showed some signs of collapse, primarily in ER-to-Golgi transport mechanisms. However, these changes were accompanied by upregulation of proteostasis-related machinery and activation of the ATF6 branch, as well as maintenance of the XBP1s and PERK branches of the UPR with age. In contrast, glia exhibited broad downregulation of proteostasis factors and UPR components, independent of neuronal presence. Furthermore, we quantified stimulus-specific modulation of select UPR branches in aged neurons exposed to pharmacologic ER stressors. These findings highlight distinct, cell-type-specific stress adaptations during aging and provide a valuable proteomic resource for dissecting proteostasis and UPR regulation in the aging brain. SignificanceUnderstanding how the unfolded protein response (UPR) and proteostasis network change with age is often studied in model organisms, where pathways are assessed across mixed cell types. Such systems can obscure cell-type-specific regulation. Here, we evaluate age-associated remodeling of the UPR and proteostasis network in a dual-species co-culture of human neurons and mouse glia using DIA proteomics. This approach enables species-specific proteomic profiling without physical separation, supported by a customizable data analysis pipeline. We show that neurons and glia exhibit divergent age-related responses, with neurons maintaining adaptive proteostasis and glia showing broader declines. The analytical framework presented here supports future studies to uncover additional cell-type-specific aging phenotypes or to probe the effects of pharmacologic or physical manipulation of biological systems.

biochemistry↗

bioRxiv2019

Multilevel feedback architecture for adaptive regulation of learning in the insect brain

Modulatory (e.g. dopaminergic) neurons provide \"teaching signals\" that drive associative learning across the animal kingdom, but the circuits that regulate their activity and compute teaching signals are still poorly understood. We provide the first synaptic-resolution connectome of the circuitry upstream of all modulatory neurons in a brain center for associative learning, the mushroom body (MB) of the Drosophila larva. We discovered afferent pathways from sensory neurons, as well as an unexpected large population of 61 feedback neuron pairs that provide one- and two-step feedback from MB output neurons. The majority of these feedback pathways link distinct memory systems (e.g. aversive and appetitive). We functionally confirmed some of the structural pathways and found that some modulatory neurons compare inhibitory input from their own compartment and excitatory input from compartments of opposite valence, enabling them to compute integrated common-currency predicted values across aversive and appetitive memory systems. This architecture suggests that the MB functions as an interconnected ensemble during learning and that distinct types of previously formed memories can regulate future learning about a stimulus. We developed a model of the circuit constrained by the connectome and by the functional data which revealed that the newly discovered architectural motifs, namely the multilevel feedback architecture and the extensive cross-compartment connections, increase the computational performance and flexibility on learning tasks. Together our study provides the most detailed view to date of a recurrent brain circuit that computes teaching signals and provides insights into the architectural motifs that support reinforcement learning in a biological system.

neuroscience↗

bioRxiv2024

Could Quantum-Mediated Bacterial Signaling Explain Adaptive Mutation?

The phenomenon of adaptive mutation in bacteria presents several challenges to classical models of evolution, particularly regarding the observed coordination of mutation patterns across populations. Here, we examine statistical evidence from evolved Escherichia coli populations showing mutation enrichment up to 256-fold above background rates in key metabolic genes, with remarkable temporal stability that appears to transcend known bacterial communication mechanisms. We propose a theoretical framework suggesting that bacteria may utilize quantum coherent oscillations, potentially mediated through synchronized membrane potential fluctuations, to achieve this degree of coordination in adaptive mutation. Building on Sprouffske et al.s findings that high mutation rates can limit adaptive evolution in E. coli, we examined mutation patterns in four key metabolic genes (pykF, topA, cspC, and rpoC) previously identified as targets of selection. While Sprouffske found that extremely high mutation rates impaired adaptation, our analysis reveals that these genes show non-random enrichment patterns (p < 1.76x10-34 for pykF) that maintain remarkable temporal stability across multiple timepoints. Recent advances in quantum biology have demonstrated sustained quantum coherence in biological systems, we present a model for how quantum-mediated bacterial signaling could potentially contribute to adaptive mutation. The framework makes several experimentally testable predictions about mutation patterns, population dynamics, and coherence times in bacterial populations under stress conditions. While substantial experimental validation remains necessary, this work provides specific approaches for investigating potential quantum contributions to bacterial adaptation, with implications for understanding evolutionary mechanisms and bacterial stress responses.

evolutionary biology↗

bioRxiv2021

Sensitive identification of known and unknown protease activities by unsupervised linear motif deconvolution

The cleavage-site specificities for many proteases are not well-understood, restricting the utility of supervised classification methods. We present an algorithm and web interface to overcome this limitation through the unsupervised detection of overrepresented patterns in protein sequence data, providing insight into the mixture of protease activities contributing to a complex system. Here, we apply the RObust LInear Motif Deconvolution (RoLiM) algorithm to confidently detect substrate cleavage patterns for SARS-CoV-2 Mpro protease in N terminome data of an infected human cell line. Using mass spectrometry-based peptide data from a case-control comparison of 341 primary urothelial bladder cancer cases and 110 controls, we identified distinct sequence motifs indicative of increased MMP activity in urine from cancer patients. Evaluation of N terminal peptides from patient plasma post-chemotherapy detected novel Granzyme B/Corin activity. RoLiM will enhance unbiased investigation of peptide sequences to establish the composition of known and uncharacterized protease activities in biological systems.

bioinformatics↗

bioRxiv2023

An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

bioinformatics↗

bioRxiv2019

Some common pathways perturbed in septic shock and cancer

BackgroundSepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests shared biology between the two disease categories. Earlier analysis had revealed cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. This has motivated a more comprehensive comparison of the transcriptomes of SS and cancer. MethodsGene Set Enrichment Analysis was performed to detect the pathways enriched in SS and cancer. Thereafter, hierarchical clustering was applied to identify relative segregation of 17 cancer types in to two groups vis-a-vis SS. Biological significance of the selected pathways was explored by network analysis. Clinical significance of the pathways was tested by survival analysis. A robust classifier of cancer groups was developed based on machine learning. ResultsA total of 66 pathways were observed to be enriched in both SS and cancer. However, clustering segregated cancer types into two categories based on the direction of transcriptomic change. In general, there was up-regulation in SS and one group of cancer (termed Sepsis-Like Cancers, or SLC), but not in other cancers. SLC group mainly consisted of malignancies of the gastrointestinal tract (head and neck, oesophagus, stomach, liver and biliary system) often associated with infection. Machine learning classifier successfully segregated the two cancer groups with high accuracy (> 98%). Additionally, pathway up-regulation was observed to be associated with survival in the SLC group of cancers. ConclusionTranscriptome-based systems biology approach segregates cancer into two groups (SLC and CA) based on similarity with SS. Host response to infection plays a key role in pathogenesis of SS and SLC. However, we hypothesize that some component of the host response is protective in both SS and SLC.

genomics↗

bioRxiv2021

Genome-wide association mapping and systems-level analysis reveal genetic architecture and physiological mechanisms linked with tolerance to flooding during germination in rice

Rice is the staple food of more than half of the worlds population; yet, it faces numerous challenges to meet the rising food demands and worsening climates. An urgent global imperative is to address imminent food shortages through intensive and sustainable agri-food systems and steady genetic gains. Intensification of rice production through direct-seeded rice (DSR) has been progressively practiced but is hindered by poor germination of existing high-yielding varieties in flooded soils. Identifying donors of anaerobic germination (AG) tolerance in rice will expedite the development of varieties suitable for DSR and will lead to enhanced and sustained agricultural productivity. This study aims to dissect the genetic architecture and physiological mechanisms of AG tolerance using systems biology and omics approaches. A Rice Diversity Panel (343 accessions) consisting of 5 subpopulations was screened for AG tolerance under greenhouse conditions, mapped through genome-wide association study (GWAS), and profiled for metabolites. Analyses revealed that most of the AG-tolerant varieties are japonicas with few indicas) and aus. Tolerant japonicas employed better root growth or rapid shoot extension, while tolerant indicas exhibited only the latter. A total of 51 significant GWAS peaks were detected across the genome, some of which were co-localized with known quantitative trait loci while others were novel, more so tolerance was found to involve different genetic controls across subpopulations. AG stress causes distinct biochemical signatures for tolerant genotypes and the profiles contrast among subpopulations implicating divergent metabolic adjustments, including shifts in sugars, intermediates, amino acids, antioxidants, and hormones. This study provides a systems-level approach for underpinning physiological mechanisms of AG tolerance; elucidating phenotypic heterogeneity, genetic architecture, transcriptomic networks, and metabolic landscapes from a genome-wide perspective. ONE SENTENCE SUMMARYThe integration of GWA mapping, gene network analysis and, non-targeted metabolite profiling elucidates genetic architecture and physiological mechanisms of tolerance to germination and early seedling growth under anaerobic conditions in rice.

plant biology↗

bioRxiv2022

Emergence of linkage between cooperative RNA replicators encoding replication and metabolic enzymes thorough experimental evolution

The integration of individually replicating genes into a primitive chromosome is a key evolutionary transition in the development of life, allowing the simultaneous inheritance of genes. However, how this transition occurred is unclear because of the extended size of primitive chromosomes, which replicate slower than unlinked genes. Theoretical studies have suggested that a primitive chromosome can evolve in the presence of cell-like compartments, as the physical linkage prevents the stochastic loss of essential genes upon division, but experimental support for this is lacking. Here, we demonstrate the evolution of a chromosome-like RNA from two cooperative RNA replicators encoding replication and metabolic enzymes. Through their long-term replication in cell-like compartments, linked RNAs emerged with the two cooperative RNAs connected end-to-end. The linked RNAs had different mutation patterns than the two unlinked RNAs, suggesting that they were maintained as partially distinct lineages in the population. Our results provide experimental evidence supporting the plausibility of the evolution of a primitive chromosome from unlinked gene fragments, an important step in the emergence of complex biological systems. Author SummaryThe integration of genes into a chromosome is a fundamental genetic organization in all extant life. The assembly of unlinked genes during prebiotic evolution was likely a major evolutionary transition toward the development of a complex cell. Decades of theoretical studies have suggested a plausible evolutionary pathway to a primitive chromosome from replicating RNA molecules that harbor cooperative genes within a protocell structure. However, demonstrating the evolution of a primitive chromosome in an experimental setup is challenging. We previously developed a cooperative RNA replication system in which two types of RNAs co-replicate using their self-encoded replication and metabolic enzymes. Using this system, in the present study, we demonstrate the evolution of a linkage between the two cooperative RNA replicators in compartments. An evolved "linked" RNA harbored the entire region of both genes, accumulated distinct mutations, and retained the ability to replicate using the two proteins translated from itself. These experimental findings support a prebiotic evolutionary scenario, in which unlinked genes assembled into a single genomic structure.

evolutionary biology↗

bioRxiv2025

AllMetal3D: joint prediction of localization, identity and coordination geometry of common metal ions in proteins

Nature uses a variety of metal ions as cofactors for specific tasks. The three main classes of metals in biological systems are alkali ions (e.g. sodium and potassium), alkaline earth ions (e.g. magnesium and calcium) as well as transition metal ions (e.g. zinc or iron and many others). To date no selective predictor to localize each of these metals in a given protein structure exists. Current methods require either preselecting a specific ion (e.g MIB2 (Lu et al., 2022), ESMBind (Dai et al., 2024) or AlphaFold3 (Abramson et al., 2024) or a location (MIC Shub et al. (2024)). In this work, we describe an extension of the recently introduced Metal3D framework (Durr et al., 2023) that was originally trained on zinc sites to predict the location of all biologically relevant classes of metal ions as well as to classify their coordination geometry. Our model is the first of its kind and even outperforms Metal3D for the prediction of the location of Zn2+ and generalizes well to the other metals in terms of location prediction as well as identity classification. Comparing the model to several other available tools such as MetalSiteHunter, AlphaFold3, MIC, MIB2 and MetalHawk highlights important shortcomings in these tools with respect to data bias, prediction of negative sites and selectivity. However, concerning coordination geometry prediction, similar to other work, we find that our method cannot accurately make correct classifications beyond the most common classes in natural proteins i.e. tetrahedral and octahedral arrangements. AllMetal3D is available as ChimeraX extension, standalone web app as well as python package: https://github.com/lcbc-epfl/allmetal3d

bioinformatics↗

bioRxiv2025

AOP-helpFinder 3.0: from text mining to network visualization of key event relationships, and knowledge integration from multi-sources.

MotivationThe Adverse Outcome Pathways (AOP) framework advances alternative toxicology by prioritizing the mechanisms underlying toxic effects. It organizes existing knowledge in a structured way, tracing the progression from the initial perturbation of a molecular event--caused by various stressors--through key events (KEs) across different biological levels, ultimately leading to adverse outcomes that affect human health and ecosystems. However, the increasing volume of toxicological data presents a significant challenge for integrating all available knowledge effectively. ResultsArtificial intelligence provides powerful methods to analyze and integrate large, heterogeneous data sources. Within this framework, the AOP-helpFinder text mining tool, accessible as a web server, was designed to identify stressor-event and event-event relationships by automatically screening scientific literature in the PubMed database, facilitating the development of AOPs. The proposed new version introduces enhanced functionality by incorporating additional data sources, automatically annotating events from the literature with toxicological database information in a systems biology context. Users can now visualize results as interactive networks directly on the web server. With these advancements, AOP-helpFinder 3.0 offers a robust solution for integrative and predictive toxicology, as demonstrated in a case study exploring toxicological mechanisms associated with radon exposure. AvailabilityAOP-helpFinder is available at https://aop-helpfinder-v3.u-paris-sciences.fr/ Contactkarine.audouze@u-paris.fr Supplementary informationSupplementary data are available on Zenodo (https://zenodo.org/records/15193936) and codes on GitHub (https://github.com/systox1124/AOP-helpFinder).

bioinformatics↗

bioRxiv2018

DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis.

A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large omics or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high \"startup\" costs and poor adherence to procedures when they deviate substantially from an analysts usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individuals existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically Rs package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

bioinformatics↗

bioRxiv2018

Gsmodutils: A python based framework for test-driven genome scale metabolic model development

MotivationGenome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for the continued management of curated large scale models. For example, when genome annotations are updated or new understanding regarding behaviour of is discovered, models often need to be altered to reflect this. This is quickly becoming an issue for industrial systems and synthetic biotechnology applications, which require good quality reusable models integral to the design, build and test cycle.\n\nResultsAs part of an ongoing effort to improve genome scale metabolic analysis, we have developed a test-driven development methodology for the continuous integration of validation data from different sources. Contributing to the open source technology based around COBRApy, we have developed the gsmodutils modelling framework placing an emphasis on test-driven design of models through defined test cases. Crucially, different conditions are configurable allowing users to examine how different designs or curation impact a wide range of system behaviours, minimising error between model versions.\n\nAvailabilityThe software framework described within this paper is open source and freely available from http://github.com/SBRCNottingham/gsmodutils

bioinformatics↗

bioRxiv2020

SimiC: A Single Cell Gene Regulatory Network Inference method with Similarity Constraints

Single-cell RNA-Sequencing has made it possible to infer high-resolution gene regulatory networks (GRNs), providing deep biological insights by revealing regulatory interactions at single-cell resolution. However, current single-cell GRN analysis methods produce only a single GRN per input dataset, potentially missing relationships between cells from different phenotypes. To address this issue, we present SimiC, a single-cell GRN inference method that produces a GRN per phenotype while imposing a similarity constraint that forces a smooth transition between GRNs, allowing for a direct comparison between different states, treatments, or conditions. We show that jointly inferring GRNs can uncover variation in regulatory relationships across phenotypes that would have otherwise been missed. Moreover, SimiC can recapitulate complex regulatory dynamics across a range of systems, both model and non-model alike. Taken together, we establish a new approach to quantitating regulatory architectures between the GRNs of distinct cellular phenotypes, with far-reaching implications for systems biology.

bioinformatics↗

bioRxiv2019

BioSAILs: versatile workflow management for high-throughput data analysis

MotivationHigh-throughput analysis in the current era of systems biology encompasses a range of analysis workflows for different applications, such as transcriptomics, epigenetics, variant discovery, de novo genome assembly, etc. Many research institutes house a genomics core facility that is responsible for generating high-throughput sequencing data and often includes a core bioinformatics team that carries out data analysis. Core teams must both keep track of data and maintain numerous software packages and databases - which must be scalable according to ever-changing research needs - and they must allocate computational resources to them. Typical data analysis pipelines involve multiple software packages and require the ability to install, execute, and maintain software stacks on a variety of hardware/operating system configurations, including high-performance computing (HPC) facilities, stand-alone servers, and Cloud services. At the same time, individual researchers need to analyze their data and share their analysis steps with collaborators. Having to rely on ad hoc scripts and rigid internally developed pipelines is inefficient, difficult to track and maintain, and ultimately, limits the ability to adapt and evolve as research methods progress.\n\nResultsHere, we present BioSAILs (Bioinformatics Standardized Analysis Information Layers), a scientific workflow management system (WMS) developed by the Core Bioinformatics team at NYU Abu Dhabi. BioSAILs comprises two central components, BioX command and HPCRunner command, supported by BioStacks software stacks. BioX structures executable workflows that may be either run directly on a desktop or lab server, or submitted by HPCRunner to HPC infrastructure or the Cloud. BioSAILs is supported by a range of pre-configured, customizable BioStacks software stacks for various applications (e.g. RNA-seq, de novo genome/transcriptome assembly, variant discovery, etc.) Software stacks are built using Conda and BioConda (6), have been pre-packaged into modules using EasyBuild, and can be deployed with or without BioSAILs. Within the BioSAILs WMS, users can also invoke commands that take advantage of containerized software in Docker (10) and Singularity (8). Finally, the BioSAILs web resource provides documentation, blog posts related to various BioSAILs analysis tasks, forums, knowledge base & FAQs, as well as an interactive web-based workflow editor/creator. BioSAILs is production-level software that has been in use as the main WMS at NYU Abu Dhabi for the past 2 years and is continuously developed and maintained by the core bioinformatics team.\n\nAvailabilityBioSAILs is open source software, available under the GNU license. Online documentation, support and guides can be found at biosails.abudhabi.nyu.edu. The public Github project is available at Github - BioSAILs. HPC-Runner and BioX-Workflow can be installed through Conda using the BioConda channel.

bioinformatics↗

bioRxiv2026

A five-dimensional functional state space for fingerprinting disease transcriptomes

High-throughput transcriptomics has transformed disease biology, but its outputs often remain fragmented into gene and pathway lists that are difficult to compare across conditions or use for human-AI interpretation. We developed a five-dimensional (5-D) functional state space that represents disease transcriptomes as coordinated activity patterns across major biological systems. The framework maps transcriptomic signals onto five functional systems, 14 subcategories, and a distinct infrastructure layer, and was implemented as a reproducible pipeline for functional scoring, cross-condition profiling, benchmarking, and large language model (LLM)-assisted interpretation. Applied to wound healing, sepsis, colorectal cancer-related datasets, an extended GEO atlas of 38 complete case-control disease fingerprints spanning diverse disease contexts, and a TCGA-COAD/READ stage benchmark, the approach recovered interpretable disease-state patterns and retained progression-related information under strong compression. It also improved the quantitative grounding of LLM-generated summaries. This framework provides a compact and auditable representation for comparing disease transcriptomes and supporting human-AI biological interpretation. TeaserA five-dimensional functional framework turns complex disease transcriptomes into compact, interpretable fingerprints for human-AI analysis.

bioinformatics↗

bioRxiv2022

Innate Conformational Dynamics Drive Binding Specificity in Anti-Apoptotic Proteins Mcl-1 and Bcl-2

The structurally conserved B-cell Lymphoma 2 (Bcl-2) family of proteins function to promote or inhibit apoptosis through an exceedingly complex web of specific, intrafamilial protein-protein interactions. The critical role of these proteins in lymphomas and other cancers has motivated a widespread interest in understanding the molecular mechanisms that drive specificity in Bcl-2 family interactions. However, the substantial structural similarity amongst Bcl-2 homologues has made it difficult to rationalize the highly specific (and often divergent) binding behavior exhibited by these proteins using conventional structural arguments. In this work, we use millisecond hydrogen deuterium exchange mass spectrometry to explore shifts in conformational dynamics associated with binding partner engagement in Bcl-2 family proteins Bcl-2 and Mcl-1. Using this approach, we reveal that, specifically for Mcl-1, binding specificity arises largely from protein-specific dynamic modes that are accessed in the unbound state. This work has implications for exploring the evolution of internally regulated biological systems composed of structurally similar proteins, and for the development of drugs targeting Bcl-2 family proteins for promotion of apoptosis in cancer. General Interest StatementThis work reveals how a group of proteins, which are highly similar in structure, can form a complex web of highly specific protein-protein interactions that drive programmed cell death (apoptosis) in cancer.

molecular biology↗

bioRxiv2022

The Evolution of the Age of Onset of Resistance to Infectious Disease

AO_SCPLOWBSTRACTC_SCPLOWMany organisms experience an increase in disease resistance as they age but the time of life at which this change occurs varies. Increases in resistance are partially due to prior exposure and physiological constraints but these cannot fully explain the observed patterns of age-related resistance. An alternative explanation is that developing resistance at an earlier age incurs costs to other life-history traits. Here, we explore how trade-offs with host reproduction or mortality affect the evolution of the onset of resistance, depending on when during the hosts life-cycle the costs are paid (only when resistance is developing, only when resistant or throughout the lifetime). We find that the timing of the costs is crucial to determining evolutionary outcomes, often making the difference between resistance developing at an early or late age. Accurate modelling of biological systems therefore relies on knowing not only the shape of trade-offs but also when they take effect. We also find that the evolution of the rate of onset of resistance can result in evolutionary branching. This provides an alternative, possible evolutionary history of populations which are dimorphic in disease resistance, where the rate of onset of resistance has diversified rather than the level of resistance.

evolutionary biology↗