Biology subjects

Vidal, G.

Publications and source records attributed to Vidal, G..

5 recordsLinked to original sources

bioRxiv2024

OPA1 and disease-causing mutants perturb mitochondrial nucleoid distribution

Optic atrophy protein 1 (OPA1) mediates inner mitochondrial membrane (IMM) fusion and cristae organization. Mutations in OPA1 cause autosomal dominant optic atrophy (ADOA), a leading cause of blindness. Cells from ADOA patients show impaired mitochondrial fusion, cristae structure, bioenergetic function, and mitochondrial DNA (mtDNA) integrity. The mtDNA encodes electron transport chain subunits and is packaged into nucleoids spread within the mitochondrial population. Nucleoids interact with the IMM, and their distribution is tightly linked to mitochondrial fusion and cristae shaping. Yet, little is known about the physio-pathological relevance of nucleoid distribution. We studied the effect of OPA1 and ADOA-associated mutants on nucleoid distribution using high-resolution confocal microscopy. We applied a novel model incorporating the mitochondrial context, separating nucleoid distribution into the array in the mitochondrial population and intramitochondrial longitudinal distribution. Opa1-null cells showed decreased mtDNA levels and nucleoid abundance. Also, loss of Opa1 lead to an altered distribution of nucleoids in the mitochondrial population, loss of cristae periodicity, and altered nucleoids to cristae proximity partly rescued by OPA1 isoform 1. Overexpression of WT OPA1 or ADOA-causing mutants c.870+5G>A or c.2713C>T in WT cells, showed perturbed nucleoid array in the mitochondria population associated with cristae disorganization. Opa1-null and cells overexpressing ADOA mutants accumulated mitochondria without nucleoids. Interestingly, intramitochondrial nucleoid distribution was only altered in Opa1-null cells. Altogether, our results highlight the relevance of OPA1 in nucleoid distribution in the mitochondrial landscape and at a single-organelle level and shed light on new components of ADOA etiology.

cell biology↗

bioRxiv2022

Experimental Data Connector (XDC): Integrating the Capture of Experimental Data and Metadata Using Standard Formats and Digital Repositories

Accelerating the development of synthetic biology applications requires reproducible experimental findings. Different standards and repositories exist to exchange experimental data and metadata. However, the associated software tools often do not support a uniform data capture, encoding, and exchange of information. A connection between digital repositories is required to prevent siloing and loss of information. To this end, we developed the Experimental Data Connector (XDC). It captures experimental data and related metadata by encoding it in standard formats and storing the converted data in digital repositories. Experimental data is then uploaded to Flapjack and the metadata to SynBioHub in a consistent manner linking these repositories. This produces complete connected experimental datasets that are exchangeable. The information is captured using a single template Excel Workbook, which can be integrated into existing experimental workflow automation processes. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/520467v1_ufig3.gif" ALT="Figure 3"> View larger version (50K): org.highwire.dtl.DTLVardef@1c5f886org.highwire.dtl.DTLVardef@3f2a6dorg.highwire.dtl.DTLVardef@fa6cb2org.highwire.dtl.DTLVardef@f63236_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Phase-based genetic logic circuits

Bio-computation is the implementation of computational operations using biological substrates, such as cells engineered with synthetic genetic circuits. These genetic circuits can be composed of DNA parts with specific functions, such as promoters that initiate transcription, ribosome binding sites that initiate translation, and coding sequences that are translated into proteins. Compositions of such parts can encode genetic circuits in which the proteins produced by genes regulate each other in different ways. The expression level of each gene may be considered as a signal which may be high or low, encoding binary logic, and the combinations of genes can then encode logic circuits. This is equivalent to the level-based logic used in modern electronic computers in which the voltage forms the logical high or low states. A different approach to binary logic is to encode the high and low states in the phase of an oscillating signal. In this approach a signal in phase with a reference represents high, and a signal antiphase with the reference represents the logical low state. We present here designs and models for phase-based genetic NOT, OR, AND, MAJORITY and complementary MAJORITY gates, which together form multiple complete logical sets. We derive analytical expressions for the optimal model parameters for circuit function. Our simulation results suggest that this approach to genetic logic is feasible and could be less sensitive to gate input-output mismatch than level-based genetic logic. To demonstrate the scaleability of phase-based genetic logic, we used our complementary MAJORITY and NOT gates to design and simulate a fully functional 4-bit ripple adder circuit, and showed that it was robust to molecular noise.

synthetic biology↗

bioRxiv2021

LOICA: Logical Operators for Integrated Cell Algorithms

1Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.

synthetic biology↗

bioRxiv2020

Flapjack: a data management and analysis tool for genetic circuit characterization

Characterization is fundamental to the design, build, test, learn (DBTL) cycle for engineering synthetic genetic circuits. Components must be described in such a way as to account for their behavior in a range of contexts. Measurements and associated metadata, including part composition, constitute the test phase of the DBTL cycle. These data may consist of measurements of thousands of circuits, measured in hundreds of conditions, in multiple assays potentially performed in different labs and using different techniques. In order to inform the learn phase this large volume of data must be filtered, collated, and analyzed. Characterization consists of using this data to parameterize models of component function in different contexts, and combining them to predict behaviors of novel circuits. Tools to store, organize, share, and analyze large volumes of measurement and metadata are therefore essential to linking the test phase to the build and learn phases, closing the loop of the DBTL cycle. Here we present such a system, implemented as a web app with a backend data registry and analysis engine. An interactive frontend provides powerful querying, plotting and analysis tools, and we provide a REST API and Python package for full integration with external build and learn software. All measurements are associated to circuit part composition via SBOL. We demonstrate our tool by characterizing a range of genetic components and circuits according to composition and context.

synthetic biology↗