Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Signal-processing and adaptive prototissue formation in metabolic DNA protocells.

ABSTRACT: The fundamental life-defining processes in living cells, such as replication, division, adaptation, and tissue formation, occur via intertwined metabolic reaction networks that process signals for downstream effects with high precision in a confined, crowded environment. Hence, it is crucial to understand and reenact some of these functions in wholly synthetic cell-like entities (protocells) to envision designing soft materials with life-like traits. Herein, we report on all-DNA protocells composed of a liquid DNA interior and a hydrogel-like shell, harboring a catalytically active DNAzyme, that converts DNA signals into functional metabolites that lead to downstream adaptation processes via site-selective strand displacement reactions. The downstream processes include intra-protocellular phenotype-like changes, prototissue formation via multivalent interactions, and chemical messenger communication between active sender and dormant receiver cell populations for sorted heteroprototissue formation. The approach integrates several tools of DNA-nanoscience in a synchronized way to mimic life-like behavior in artificial systems for future interactive materials.

SUBMITTER: Samanta A

PROVIDER: S-EPMC9270428 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Functional and morphological adaptation in DNA protocells via signal processing prompted by artificial metalloenzymes.

Project description:For life to emerge, the confinement of catalytic reactions within protocellular environments has been proposed to be a decisive aspect to regulate chemical activity in space1. Today, cells and organisms adapt to signals2-6 by processing them through reaction networks that ultimately provide downstream functional responses and structural morphogenesis7,8. Re-enacting such signal processing in de novo-designed protocells is a profound challenge, but of high importance for understanding the design of adaptive systems with life-like traits. We report on engineered all-DNA protocells9 harbouring an artificial metalloenzyme10 whose olefin metathesis activity leads to downstream morphogenetic protocellular responses with varying levels of complexity. The artificial metalloenzyme catalyses the uncaging of a pro-fluorescent signal molecule that generates a self-reporting fluorescent metabolite designed to weaken DNA duplex interactions. This leads to pronounced growth, intraparticular functional adaptation in the presence of a fluorescent DNA mechanosensor11 or interparticle protocell fusion. Such processes mimic chemically transduced processes found in cell adaptation and cell-to-cell adhesion. Our concept showcases new opportunities to study life-like behaviour via abiotic bioorthogonal chemical and mechanical transformations in synthetic protocells. Furthermore, it reveals a strategy for inducing complex behaviour in adaptive and communicating soft-matter microsystems, and it illustrates how dynamic properties can be upregulated and sustained in micro-compartmentalized media.

| S-EPMC7610402 | biostudies-literature

The evolutionary dynamics of metabolic protocells.

Project description:Protocell multilevel selection models have been proposed to study the evolutionary dynamics of vesicles encapsulating a set of replicating, competing and mutating sequences. The frequency of the different sequence types determines protocell survival through a fitness function. One of the defining features of these models is the genetic load generated when the protocell divides and its sequences are assorted between the offspring vesicles. However, these stochastic assortment effects disappear when the redundancy of each sequence type is sufficiently high. The fitness dependence of the vesicle with its sequence content is usually defined without considering a realistic account on how the lower level dynamics would specify the protocell fitness. Here, we present a protocell model with a fitness function determined by the output flux of a simple metabolic network with the aim of understanding how the evolution of both kinetic and topological features of metabolism would have been constrained by the particularities of the protocell evolutionary dynamics. In our model, the sequences inside the vesicle are both the carriers of information and Michaelis-Menten catalysts exhibiting saturation. We found that the saturation of the catalysts controlling the metabolic fluxes, achievable by modifying the kinetic or stoichiometric parameters, provides a mechanism to ameliorate the assortment load by increasing the redundancy of the catalytic sequences required to achieve the maximum flux. Regarding the network architecture, we conclude that combinations of parallel network motifs and bimolecular catalysts are a robust way to increase the complexity of the metabolism enclosed by the protocell.

| S-EPMC6070278 | biostudies-literature

Catalytic processing in ruthenium-based polyoxometalate coacervate protocells.

Project description:The development of programmable microscale materials with cell-like functions, dynamics and collective behaviour is an important milestone in systems chemistry, soft matter bioengineering and synthetic protobiology. Here, polymer/nucleotide coacervate micro-droplets are reconfigured into membrane-bounded polyoxometalate coacervate vesicles (PCVs) in the presence of a bio-inspired Ru-based polyoxometalate catalyst to produce synzyme protocells (Ru4PCVs) with catalase-like activity. We exploit the synthetic protocells for the implementation of multi-compartmentalized cell-like models capable of collective synzyme-mediated buoyancy, parallel catalytic processing in individual horseradish peroxidase-containing Ru4PCVs, and chemical signalling in distributed or encapsulated multi-catalytic protocell communities. Our results highlight a new type of catalytic micro-compartment with multi-functional activity and provide a step towards the development of protocell reaction networks.

| S-EPMC6941959 | biostudies-literature

Genomic signal processing for DNA sequence clustering.

Project description:Genomic signal processing (GSP) methods which convert DNA data to numerical values have recently been proposed, which would offer the opportunity of employing existing digital signal processing methods for genomic data. One of the most used methods for exploring data is cluster analysis which refers to the unsupervised classification of patterns in data. In this paper, we propose a novel approach for performing cluster analysis of DNA sequences that is based on the use of GSP methods and the K-means algorithm. We also propose a visualization method that facilitates the easy inspection and analysis of the results and possible hidden behaviors. Our results support the feasibility of employing the proposed method to find and easily visualize interesting features of sets of DNA data.

| S-EPMC5786891 | biostudies-literature

Supramolecular Nanoscaffolds within Cytomimetic Protocells as Signal Localization Hubs.

Project description:The programmed construction of functional synthetic cells requires spatial control over arrays of biomolecules within the cytomimetic environment. The mimicry of the natural hierarchical assembly of biomolecules remains challenging due to the lack of an appropriate molecular toolbox. Herein, we report the implementation of DNA-decorated supramolecular assemblies as dynamic and responsive nanoscaffolds for the localization of arrays of DNA signal cargo within hierarchically assembled complex coacervate protocells. Protocells stabilized with a semipermeable membrane allow trafficking of single-stranded DNA between neighboring protocells. DNA duplex operations demonstrate the responsiveness of the nanoscaffolds to different input DNA strands via the reversible release of DNA cargo. Moreover, a second population of coacervate protocells with nanoscaffolds featuring a higher affinity for the DNA cargo enabled chemically programmed communication between both protocell populations. This combination of supramolecular structure and function paves the way for the next generation of protocells imbued with programmable, lifelike behaviors.

| S-EPMC7243252 | biostudies-literature

Non-Adaptive Methods for Fetal ECG Signal Processing: A Review and Appraisal.

Project description:Fetal electrocardiography is among the most promising methods of modern electronic fetal monitoring. However, before they can be fully deployed in the clinical practice as a gold standard, the challenges associated with the signal quality must be solved. During the last two decades, a great amount of articles dealing with improving the quality of the fetal electrocardiogram signal acquired from the abdominal recordings have been introduced. This article aims to present an extensive literature survey of different non-adaptive signal processing methods applied for fetal electrocardiogram extraction and enhancement. It is limiting that a different non-adaptive method works well for each type of signal, but independent component analysis, principal component analysis and wavelet transforms are the most commonly published methods of signal processing and have good accuracy and speed of algorithms.

| S-EPMC6263968 | biostudies-literature

DNA-based communication in populations of synthetic protocells.

Project description:Developing molecular communication platforms based on orthogonal communication channels is a crucial step towards engineering artificial multicellular systems. Here, we present a general and scalable platform entitled 'biomolecular implementation of protocellular communication' (BIO-PC) to engineer distributed multichannel molecular communication between populations of non-lipid semipermeable microcapsules. Our method leverages the modularity and scalability of enzyme-free DNA strand-displacement circuits to develop protocellular consortia that can sense, process and respond to DNA-based messages. We engineer a rich variety of biochemical communication devices capable of cascaded amplification, bidirectional communication and distributed computational operations. Encapsulating DNA strand-displacement circuits further allows their use in concentrated serum where non-compartmentalized DNA circuits cannot operate. BIO-PC enables reliable execution of distributed DNA-based molecular programs in biologically relevant environments and opens new directions in DNA computing and minimal cell technology.

| S-EPMC6451639 | biostudies-literature

Interdependence of signal processing and analysis of urine 1H NMR spectra for metabolic profiling.

Project description:Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.

| S-EPMC2789356 | biostudies-literature

Creating complex protocells and prototissues using simple DNA building blocks.

Project description:Building synthetic protocells and prototissues hinges on the formation of biomimetic skeletal frameworks. Recreating the complexity of cytoskeletal and exoskeletal fibers, with their widely varying dimensions, cellular locations and functions, represents a major material hurdle and intellectual challenge which is compounded by the additional demand of using simple building blocks to ease fabrication and control. Here we harness simplicity to create complexity by assembling structural frameworks from subunits that can support membrane-based protocells and prototissues. We show that five oligonucleotides can anneal into nanotubes or fibers whose tunable thicknesses and lengths spans four orders of magnitude. We demonstrate that the assemblies' location inside protocells is controllable to enhance their mechanical, functional and osmolar stability. Furthermore, the macrostructures can coat the outside of protocells to mimic exoskeletons and support the formation of millimeter-scale prototissues. Our strategy could be exploited in the bottom-up design of synthetic cells and tissues, to the generation of smart material devices in medicine.

| S-EPMC10006096 | biostudies-literature

Genomic signal processing methods for computation of alignment-free distances from DNA sequences.

Project description:Genomic signal processing (GSP) refers to the use of digital signal processing (DSP) tools for analyzing genomic data such as DNA sequences. A possible application of GSP that has not been fully explored is the computation of the distance between a pair of sequences. In this work we present GAFD, a novel GSP alignment-free distance computation method. We introduce a DNA sequence-to-signal mapping function based on the employment of doublet values, which increases the number of possible amplitude values for the generated signal. Additionally, we explore the use of three DSP distance metrics as descriptors for categorizing DNA signal fragments. Our results indicate the feasibility of employing GAFD for computing sequence distances and the use of descriptors for characterizing DNA fragments.

| S-EPMC4230918 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data