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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 685 records · Page 38Linked to original sources

bioRxiv2024

Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase

Bioluminescent indicators are power tools for studying dynamic biological processes. In this study, we present the generation of novel bioluminescent indicators by modifying the luciferin molecule with an analyte-binding moiety. Specifically, we have successfully developed the first bioluminescent indicator for potassium ions (K+), which are critical electrolytes in biological systems. Our approach involved the design and synthesis of a K+-binding luciferin named potassiorin. Additionally, we engineered a luciferase enzyme called BRIPO (bioluminescent red indicator for potassium) to work synergistically with potassiorin, resulting in optimized K+-dependent bioluminescence responses. Through extensive validation in cell lines, primary neurons, and live mice, we demonstrated the efficacy of this new tool for detecting K+. Our research demonstrates an innovative concept of incorporating sensory moieties into luciferins to modulate luciferase activity. This approach has great potential for developing a wide range of bioluminescent indicators, advancing bioluminescence imaging (BLI), and enabling the study of various analytes in biological systems. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/581057v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1ffeb68org.highwire.dtl.DTLVardef@b83871org.highwire.dtl.DTLVardef@81b34corg.highwire.dtl.DTLVardef@1224b0b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Specialisation and plasticity in a primitively social insect

Biological systems not only have the remarkable capacity to build and maintain complex spatio-temporal structures in noisy environments, they can also rapidly break up and rebuild such structures. How such systems can simultaneously achieve both robust specialisation and plasticity is poorly understood. Here we use primitive societies of Polistes wasps as a model system where we experimentally perturb the social structure by removing the queen and follow the re-establishment of the social steady state over time. We combine a unique experimental strategy correlating time-resolved measurements across vastly different scales with a theoretical approach. We show that Polistes integrates antagonistic processes on multiple scales to distinguish between extrinsic and intrinsic perturbations and thereby achieve both robust specialisation and rapid plasticity. The long-term stability of the social structure relies on dynamic DNA methylation which controls transcriptional noise. Such dynamics provide a general principle of how both specialization and plasticity can be achieved in biological systems. One Sentence SummaryA primitive social insect simultaneously achieves specialisation and plasticity by integrating antagonistic dynamics on different scales. HighlightsO_LIWe employ a unique experimental approach correlating dynamics of societies, individuals, and epigenetic gene regulation C_LIO_LIA social insect simultaneously achieves specialisation and plasticity by integrating antagonistic processes on different spatial scales C_LIO_LIRegulation of population-level noise by DNA methylation ensures long-term stability of phenotypic specialisation C_LI

biophysics↗

bioRxiv2026

OPTIMIS: Optimizing Personalized Therapies through Integrated Multiscale Intelligent Simulation

Controlling complex biological systems across multiple scales remains a major challenge in computational medicine, because whole-body disease behavior is closely shaped by noisy cellular events at much smaller scales. Standard deterministic models often miss this molecular variability, while fully stochastic simulations are too slow for the repeated, high-throughput interactions needed to train artificial intelligence. To address this problem, we developed a new AI-based framework that combines a discrete stochastic Gillespie algorithm for microscale receptor dynamics with continuous, nonlinear ordinary differential equations for systemic macroscale behavior. To reach the speed needed for deep reinforcement learning (RL), we compress this hybrid system into a differentiable Neural ODE surrogate that acts as a fast digital twin. As a proof of concept, we applied this framework to engineered cellular therapy and used RL agents to learn dynamic, closed-loop treatment policies inside the surrogate environment. By tracking microscopic, unpredictable cellular activity as an early-warning signal, the AI learned to continuously adjust the drug dose--anticipating and stopping dangerous immune reactions before they could spiral out of control. This computational advance improved successful control rates to more than 70% in highly unstable simulated phenotypes and provides a practical, general framework for adaptive intervention in multiscale biological systems.

bioinformatics↗

bioRxiv2020

CloneSeq: A Highly Sensitive Single-cell Analysis Platform for Comprehensive Characterization of Cells from 3D Culture

Single-cell assays have revealed the scope and importance of heterogeneity in many biological systems. However, in many cases, single cell limited sensitivity is a major hurdle for uncovering the full range of cellular variation. To overcome this limitation, we developed a complementary single cell technology, CloneSeq that combines clonal expansion under controlled culture conditions inside three-dimensional (3D) hydrogel spheres and droplet-based RNA sequencing (RNA-seq). We show that unlike single cell transcriptomes, clonal cells maintain cell states and share similar transcriptional profiles. CloneSeq analysis of Non-small-cell lung carcinoma (NSCLC) cells revealed the presence of novel cancer-specific subpopulations, including cancer stem-like cells (CSLCs). Standard single cell RNA-seq assays as well as cell-to-clone tracing by genetic barcoding failed to identify these rare CSLCs. In addition to CSLCs, clonal expansion within 3D soft microenvironments supported cellular stemness of embryonic stem cells (ESCs) that retained their pluripotent state in the absence of pluripotent media and improved epigenetic reprogramming efficiency of mouse embryonic fibroblasts. Our results demonstrate the capacity of CloneSeq, which can be effectively adapted to different biological systems, to discover rare and previously hidden subpopulations of cells, including CSLCs, by leveraging the broader expression space within clones.

molecular biology↗

bioRxiv2025

Multi-task spatial omics analytics for high-precision modeling of tissue architecture with STAX

The rapid advancement of spatial omics technology has revolutionized biomedical research, unveiling unprecedented insights into the molecular and cellular architecture of biological systems. Despite this progress, a critical limitation arises from the widespread use of disparate analytical tools within a single workflow, leading to inconsistencies caused by heterogeneous preprocessing pipelines and extensive method-specific hyperparameter tuning. To this end, we aim to unify diverse analytical tasks within a multi-task learning platform, STAX, for diverse spatial omics types. STAX excels across numerous critical applications, including spatial domain identification, spatial slice integration, cohort-level spatial analysis, spatial spot completion, cell-gene co-embedding, expression profile denoising, and 3D spatial multi-slice simulation. Extensive evaluations demonstrate that STAX consistently delivers superior performance, robustness, and biologically meaningful interpretations, establishing it as an indispensable tool for spatial omics research. In short, STAX effectively addresses multiple analytical challenges in spatial omics, empowering researchers with a unified platform to accelerate biomedical discoveries and deepen understanding of complex biological systems.

bioinformatics↗

bioRxiv2022

Universal dynamics of biological pattern formation in spatio-temporal morphogen variations

In biological systems, chemical signals termed morphogens self-organise into patterns that are vital for many physiological processes. As observed by Turing in 1952, these patterns are in a state of continual development, and are usually transitioning from one pattern into another. How do cells robustly decode these spatio-temporal patterns into signals in the presence of confounding effects caused by unpredictable or heterogeneous environments? Here, we answer this question by developing a general theory of pattern formation in spatio-temporal variations of pre-pattern morphogens, which determine gene-regulatory network parameters. Through mathematical analysis, we identify universal dynamical regimes that apply to wide classes of biological systems. We apply our theory to two paradigmatic pattern-forming systems, and predict that they are robust with respect to non-physiological morphogen variations. More broadly, our theoretical framework provides a general approach to classify the emergent dynamics of pattern-forming systems based on how the bifurcations in their governing equations are traversed.

biophysics↗

bioRxiv2023

Host-encoded CTCF regulates human cytomegalovirus latency via chromatin looping

Human cytomegalovirus (HCMV) is a prevalent pathogen that establishes life-long latent infection in hematopoietic cells. While this infection is usually asymptomatic, immune dysregulation leads to viral reactivation, which can cause significant morbidity and mortality. However, the mechanisms underpinning reactivation remain incompletely understood. The HCMV major immediate early promoter (MIEP)/enhancer is a key factor in this process, as its transactivation from a repressed to active state helps drive viral gene transcription necessary for reactivation from latency. Numerous host transcription factors bind the MIE locus and recruit repressive chromatin modifiers, thus impeding virus reactivation. One such factor is CCCTC-binding protein (CTCF), a highly conserved host zinc finger protein that mediates chromatin conformation and nuclear architecture. However, the mechanisms by which CTCF contributes to HCMV latency were previously unexplored. Here, we confirm CTCF binds two convergent sites within the MIE locus during latency in primary CD14+ monocytes, and following cellular differentiation, CTCF association is lost as the virus reactivates. While mutation of the MIE enhancer CTCF binding site does not impact viral lytic growth in fibroblasts, this mutant virus fails to maintain latency in myeloid cells. Furthermore, we show the two convergent CTCF binding sites allow looping to occur across the MIEP, supporting transcriptional repression during latency. Indeed, looping between the two sites diminishes during virus reactivation, concurrent with activation of MIE transcription. Taken together, our data reveal that three-dimensional chromatin looping aids in the regulation of HCMV latency, and provides insight into promoter/enhancer regulation that may prove broadly applicable across biological systems. Significance StatementHuman cytomegalovirus (HCMV) remains an important healthcare consideration driven by disease in at-risk populations associated with reactivation of this virus from latent infection. We show here the establishment of latency is aided by a host nuclear architectural protein, CTCF. By binding two convergent sites on the virus major immediate early promoter/enhancer region, which largely acts as a switch from latency to reactivation, CTCF anchors a chromatin loop such that the virus promoter is maintained in a transcriptionally repressed state. Upon differentiation of cells, CTCF protein levels decrease, and this loop is alleviated as the virus reactivates. Our findings reveal further insight into the regulation of HCMV latency and promoter/enhancer elements, which is broadly applicable across biological systems.

microbiology↗

bioRxiv2022

A synergistic activation of RARb and RARg nuclear receptors restores cell-types specialization during stem cells differentiation by hijacking RARa-controlled program

How cells respond to different external cues to develop along defined cell lineages to form complex tissues is a major question in systems biology. Here, we investigated the potential of retinoic acid receptor (RARs)-selective synthetic agonists to activate the gene-regulatory programs driving cell specialization during nervous tissue formation from P19 stem cells. Specifically, we found that the synergistic activation of the RAR{beta} and RAR{gamma} by selective ligands (BMS641 or BMS961) induces cell maturation to specialized neuronal subtypes, as well as to astrocytes and oligodendrocyte precursors. Using RAR istoype knockout lines exposed to RAR-specific agonists, interrogated by global transcriptome landscaping and in silico modeling of transcription regulatory signal propagation, revealed major RAR-driven gene programs essential for optimal neuronal cell specialization, and hijacked by the synergistic activation of the RAR{beta} and RAR{gamma} receptors. Overall, this study provides a systems biology view of the gene programs accounting for the previously observed redundancy between RAR receptors, paving the way towards their potential use for directing cell specialization during nervous tissue formation.

molecular biology↗

bioRxiv2024

Biological changes, political ideology, and scientific communication shape human perceptions of pollen seasons

Climate change is changing the timing and intensity of pollen seasons, thereby increasing human exposure to allergenic pollen. Climate-driven changes in pollen seasons present a unique opportunity to craft messaging aimed at communicating climate change impacts on public health through changes in the biological systems. However, it is unclear how pollen seasons are experienced and understood by the public, including how well we detect pollen seasons or what factors we view as responsible for changes in pollen seasons. Here, we use social media data in the United States from 2012 to 2022 to assess public perceptions of pollen seasons at large spatiotemporal scales. We show that pollen seasons detected by social media (Twitter) users align well with natural pollen seasons. Attribution of changing pollen seasons, however, varies based on political ideology: liberal users, compared to conservative users, are more likely to attribute changing pollen seasons to climate change. Mass media and scientific experts play a key role in communicating how climate change drives changing pollen seasons. Our findings reveal how changes in biological systems, political ideology, and scientific communications collectively shape public perceptions of pollen seasons under climate change. Our findings constitute a step towards more effective climate change impact communication and public health intervention design.

scientific communication and education↗

bioRxiv2019

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy - when a single mutation affects multiple traits - is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (QTL) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

evolutionary biology↗

bioRxiv2020

ConnecTF: A platform to build gene networks by integrating transcription factor-target gene interactions

Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise is identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge is constructing GRNs that involve hundreds of TFs and hundreds of thousands of interactions with their genome-wide target genes validated by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent web-based platform for constructing validated GRNs and to refine inferred GRNs via combined analysis of genome-wide studies of TF-target gene binding, TF-target regulation and other TF-centric omic data. We demonstrate the functionality of ConnecTF in three case studies, showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used to conduct precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. In case study 3, we use ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s, to its indirect targets, in an approach called Network Walking. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, and 839,210 TF-target interactions for 139 TFs in maize. The database and tools in ConnecTF should advance the exploration of GRNs in plant systems biology applications for models and crops.

plant biology↗

bioRxiv2021

Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes

In order to advance the mission of in silico cell biology, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes, the number of independent or weakly coupled subsystems increases, and the number of global system states increase exponentially, making the sampling of all distinct global states unfeasible. In this work, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits. Significance StatementMolecular simulations of proteins are often interpreted using Markov state models (MSMs), in which each protein configuration is assigned to a global state. As we explore larger and more complex biological systems, the size of this global state space will face a combinatorial explosion, rendering it impossible to gather sufficient sampling data. In this work, we introduce an approach to decompose a system of interest into separable subsystems. We show that MSMs built for each subsystem can be later coupled to reproduce the behaviors of the global system. To aid in the choice of decomposition we also describe a score to quantify its goodness. This decomposition strategy has the promise to enable robust modeling of complex biomolecular systems.

biophysics↗

bioRxiv2024

Integrative analysis of angiogenic signaling in obesity: capillary features and VEGF binding kinetics

Obesity is a global health crisis, with its prevalence particularly severe in the United States, where over 42% of adults are classified as obese. Obesity is driven by complex molecular and tissue-level mechanisms that remain poorly understood. Among these, angiogenesis--primarily mediated by vascular endothelial growth factor (VEGF-A)--is critical for adipose tissue expansion but presents unique challenges for therapeutic targeting due to its intricate regulation. Systems biology approaches have advanced our understanding of VEGF-A signaling in vascular diseases, but their application to obesity is limited by scattered and sometimes contradictory data. To address this gap, we performed a comprehensive analysis of the existing literature to synthesize key findings, standardize data, and provide a holistic perspective on the adipose vascular microenvironment. The data mining revealed five key findings: (1) obesity increases adipocyte size by 78%; (2) vessel density in adipose tissue decreases by 51% in obese mice, with vessels being 47-58% smaller and 4-9 times denser in comparison with tumor vessels; (3) capillary basement membrane thickness remains similar regardless of obesity; (4) VEGF-A shows the strongest binding affinity for VEGFR1, with four times stronger affinity for VEGFR2 than for NRP1; and (5) binding affinities measured by radioligand binding assay and surface plasmon resonance (SPR) are significantly different. These consolidated findings provide essential parameters for systems biology modeling, new insights into obesity-induced changes in adipose tissue, and a foundation for developing angiogenesis-targeting therapies for obesity.

bioengineering↗

bioRxiv2026

Deep Learning for Cross-Domain Spatial Transcriptomic Modeling of Tissue Repair

Spatial transcriptomics enables investigation of tissue organization while preserving molecular and spatial information within intact tissues. However, existing computational methods primarily focus on clustering and batch integration and provide limited characterization of higher-order spatial organization and transferable tissuestate dynamics across heterogeneous biological systems. This study introduces a cross-domain spatial transcriptomic framework centered on recurrence-based latent tissuestate analysis, pathological fragmentation quantification, and transferable representation learning between wound repair and tumor-associated microenvironments. Human spatial transcriptomic datasets spanning cutaneous wound healing, oral squamous cell carcinoma, and head and neck squamous cell carcinoma were integrated within a graph-based latent embedding framework. Recurrence analysis was applied within latent transcriptomic space to characterize spatial organization and remodeling dynamics. A pathological fragmentation index quantified intra-tissue spatial disorganization from recurrence structure. The learned latent embeddings achieved a mean silhouette score of 0.79, demonstrating coherent separation of tissue states. Recurrence analysis revealed progressive restoration of spatial organization during wound remodeling, whereas tumor-associated tissues exhibited increased fragmentation and heterogeneous recurrence structure. Independent single-cell RNA-seq reference atlases demonstrated reproducible multicellular enrichment patterns within latent spatial niches. The proposed framework demonstrates that recurrence-inspired latent spatial analysis may provide biologically interpretable characterization of tissue organization and pathological remodeling across heterogeneous biological systems.

bioinformatics↗

bioRxiv2022

In situ genetically targeted chemical assembly of polymers on living neuronal membranes

Multicellular biological systems, most notably living neural networks, exhibit highly complex physical organization properties that pose challenges for building cell-specific and biocompatible interfaces. We developed a novel approach to genetically program cells to chemically assemble artificial structures that modify the electrical properties of neurons in situ, opening up the possibility of minimally-invasive cell-specific interfaces with neural circuits in living animals. However, the efficiency and biocompatibility of this approach were challenged by limited membrane targeting of the constructed material. Here, we report a method with significantly improved molecular construct properties, which expresses highly localized enzymes targeted to the plasma membrane of primary neurons with minimal intracellular retention. Polymers synthesized in situ by this approach form dense clusters on the targeted cell membrane, and neurons remain viable after polymerization. This platform can be readily extended to incorporate a broad range of materials onto the surface membranes of specific cells within complex tissues, using chemistry that may further enable the next generation of interfaces with living biological systems.

bioengineering↗

bioRxiv2025

Controlling the synchronization and symmetry breaking of coupled bacterial pili on active biofilm carpets

In the low Reynolds number regime, active biological systems utilize nonreciprocal cyclic activities to achieve motility, as seen in the spinning of bacterial flagella and the beating of cilia. Coupling among these active mechanical components leads to synchronization, and emergence of metachronal waves. Here, we report that biofilms of Pseudomonas nitroreducens form active carpet-like surfaces textured with diverse topological defects, generating Mexican-wave-like collective behavior in which bacteria periodically lift up. On these active surfaces, nonreciprocally coupled extension and retraction activities of bacterial pili drives these collective oscillations. Surprisingly, this collective behavior exhibits left-right asymmetry across the biofilm driving unidirectionally propagating waves. We discover that this directionality is primarily governed by an aging-related frequency gradient across the biofilm. Leveraging these insights, we further demonstrate the ability to control the collective dynamics of these waves, including symmetry breaking, transitions from spiral waves into target and propagating plane waves by manipulating the elastic properties of biofilms. Overall, our findings illuminate the fundamental role of nonreciprocally interacting active components in regulating synchronization, collective dynamics, and symmetry-breaking phenomena in biological systems.

biophysics↗

bioRxiv2026

A Multiscale Signaling--Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease

Red blood cell (RBC) clearance by macrophages maintains blood homeostasis and is dysregulated in the hemolytic disorder sickle cell disease (SCD) and the lysosomal storage disorder Gaucher disease (GD), where biophysical and biochemical alterations promote premature phagocytosis. We develop a multiscale hybrid modeling framework integrating signaling dynamics, biophysical simulations, and machine learning to investigate the mechanisms governing RBC phagocytosis in these diseases. Our approach couples a systems biology model of macrophage-RBC signaling with Dissipative Particle Dynamics (DPD) simulations of molecular diffusion and membrane interactions, and leverages Physics-Informed Neural Networks (PINNs) for robust parameter inference. The DPD framework provides mechanistic insight into antibody diffusion, receptor engagement, and membrane-level interactions during macrophage-RBC contact, generating spatially resolved trajectories of CD47-SIRP signaling and antibody-receptor binding that serve as intermediate observables constraining the signaling model. The model accurately captures differential phagocytic responses between healthy and altered RBCs, revealing diminished inhibitory signaling and changes in SHP1-mediated pathways in both SCD and GD. Identifiability analysis combining Fisher Information Matrix diagnostics and profile likelihood confirms that parameters governing the CD47-SIRP-SHP1 axis are among the most robustly recoverable, and simulations of therapeutic perturbations with anti-SIRP antibodies demonstrate modulation of engulfment outcomes. We further employ Physics-Informed Kolmogorov-Arnold Networks (PIKANs) as an alternative to standard PINNs, demonstrating improved robustness under noise and sampling variability. More broadly, our multiscale platform linking biophysical simulation with systems-level inference is generalizable, offering mechanistic insights and computational tools for therapeutic exploration in diseases involving dysregulated phagocytosis. Significance statementRed blood cells are normally removed from circulation by macrophages through tightly regulated molecular signals. In diseases such as sickle cell disease and Gaucher disease, this clearance process becomes abnormal, contributing to anemia and other complications. However, the mechanisms linking the physical properties of red blood cells to immune signaling remain poorly understood. Here we develop a multiscale computational framework that combines particle-based biophysical simulations, systems biology models, and physics-informed machine learning. This approach provides a quantitative framework to interpret how changes in red blood cell mechanics and surface signaling disrupt the CD47-SIRP inhibitory pathway that normally prevents phagocytosis. The framework provides a predictive platform for studying immune clearance and may help guide therapeutic strategies targeting red blood cell-macrophage interactions.

biophysics↗

bioRxiv2019

Complete genome sequence and annotation of the laboratory reference strain Shigella flexneri serovar 5a M90T and genome-wide transcription start site determination

BackgroundShigella is a Gram-negative facultative intracellular bacterium that causes bacillary dysentery in humans. Shigella invades cells of the colonic mucosa owing to its virulence plasmid-encoded Type 3 Secretion System (T3SS), and multiplies in the target cell cytosol. Although the laboratory reference strain S. flexneri serotype 5a M90T has been extensively used to understand the molecular mechanisms of pathogenesis, its complete genome sequence is not available, thereby greatly limiting studies employing high-throughput sequencing and systems biology approaches.\n\nResultsWe have sequenced, assembled, annotated and manually curated the full genome of S. flexneri 5a M90T. This yielded two complete circular contigs, the chromosome and the virulence plasmid (pWR100). To obtain the genome sequence, we have employed long-read PacBio DNA sequencing followed by polishing with Illumina RNA-seq data. This provides a new pipeline to prepare gapless, highly accurate genome sequences. Furthermore, we have performed genome-wide analysis of transcriptional start sites and determined the length of 5 untranslated regions (5-UTRs) at typical culture conditions for the inoculum of in vitro infection experiments. We identified 6,723 primary TSS (pTSS) and 7,328 secondary TSS (sTSS). The S. flexneri 5a M90T annotated genome sequence and the transcriptional start sites are integrated into RegulonDB (http://regulondb.ccg.unam.mx) and RSAT (http://embnet.ccg.unam.mx/rsat/) to use its analysis tools in S. flexneri 5a M90T genome.\n\nConclusionsWe provide the first complete genome for S. flexneri serotype 5a, specifically the laboratory reference strain M90T. Our work opens the possibility of employing S. flexneri M90T in high-quality systems biology studies such as transcriptomic and differential expression analyses or in genome evolution studies. Moreover, the catalogue of TSS that we report here can be used in molecular pathogenesis studies as a resource to know which genes are transcribed before infection of host cells. The genome sequence, together with the analysis of transcriptional start sites, is also a valuable tool for precise genetic manipulation of S. flexneri 5a M90T. The hybrid pipeline that we report here combining genome sequencing with long-reads technology and polishing with RNAseq data defines a powerful strategy for genome assembly, polishing and annotation in any type of organism.

microbiology↗