Biology subjects

Stracquadanio, G.

Publications and source records attributed to Stracquadanio, G..

16 recordsLinked to original sources

bioRxiv2024

APEX: Automated Protein EXpression in Escherichia coli

Heterologous protein expression is an indispensable strategy for generating recombinant proteins. Escherichia coli (E. coli) is the most widely used microbial host for recombinant protein production due to its rapid growth, well-characterised genetics, and ability to produce recombinant proteins in high yields using modern recombinant DNA technology. However, while there is a plethora of robust protein expression protocols for E. coli, these methods are often unsuitable for high-throughput screening due to their significant resource and time consumption; these protocols are also susceptible to operator error and inconsistency. To address these challenges, we have developed Automated Protein EXpression (APEX), a robust and automated protocol for recombinant protein production in E. coli. APEX leverages the accessible, open-source Opentrons OT-2 platform to automate microbial handling and protein expression with high precision and reproducibility. APEX can be configured to perform heat shock transformation, colony selection, colony sampling, microculturing and protein expression induction using a low-cost, minimal OT-2 hardware setup. We further demonstrate the efficacy of our automated transformation workflows using a variety of plasmids (2.7-17.7 kb), and exemplify the automated heterologous expression of a diverse array of proteins (27-222 kDa). Designed with customisation, modularity and user-friendliness in mind, APEX can be easily adapted to the operators needs without requiring any coding expertise. APEX is available at https://github.com/stracquadaniolab/apex-nf under the AGPL3 license.

synthetic biology↗

bioRxiv2024

Synthetic genome modules designed for programmable silencing of functions and chromosomes

Unlike in bacteria, eukaryotes rarely cluster sets of genes in their genomes according to function, instead having most genes spread randomly across different chromosomes and loci. However, with the advent of genome engineering, synthetic co-location of genes that together encode a cell function has now become possible. Here, using Saccharomyces cerevisiae we demonstrate the feasibility of reorganising a set of yeast genes encoding a cell function, tryptophan biosynthesis, into a synthetic genome module by deleting these genes and their regulatory elements from their native genomic loci while in parallel reconstructing them into gene cluster format by synthetic DNA assembly. As part of synthetic module design, loxPsym sequences recognised by Cre recombinase are placed between all module genes, and we leverage these for a novel master regulation system we call dCreSIR. Using dCreSIR we externally control silencing of synthetic modules by targeted binding of chromatin recruiters to loxPsym sites and this leads to inhibition of local transcription. We further show that dCreSIR can go beyond modules and be used to specifically downregulate expression across an entire synthetic yeast chromosome containing loxPsym sites. Together, our work offers insights into yeast genome organisation and establishes new principles and tools for the future design and construction of modular synthetic yeast genomes.

synthetic biology↗

bioRxiv2023

Biofoundry-scale DNA assembly validation using cost-effective high-throughput long read sequencing

Biofoundries are automated high-throughput facilities specialising in the design, construction and testing of engineered/synthetic DNA constructs (plasmids), often from genetic parts. A critical step of this process is assessing the fidelity of the assembled DNA construct to the desired design. Current methods utilised for this purpose are restriction digest or PCR followed by fragment analysis, and sequencing. The Edinburgh Genome Foundry (EGF) has recently established a single-molecule sequencing quality control step using the Oxford Nanopore sequencing technology, along with a companion Nextflow pipeline and a Python package to perform in-depth analysis and generate a detailed report. Our software enables biofoundry scientists and end-users to rapidly analyse sequencing data, without specialised bioinformatics knowledge. In conclusion, we have created a laboratory and software protocol that validates assembled, cloned or edited plasmids, using Nanopore long reads, which can serve as a useful resource for the genetics, synthetic biology and sequencing communities. Author informationAll authors contributed to the design of the sequencing quality control step and pipeline, and the preparation of the manuscript. P.V. wrote the manuscript, designed and implemented the bioinformatics pipeline and interpreted results. S.D. wrote the manuscript, implemented the laboratory protocol and interpreted results. G.S. designed the bioinformatics pipeline. R.F. wrote the manuscript and contributed to the design of the laboratory protocol and pipeline. Address: Edinburgh Genome Foundry (University of Edinburgh), Michael Swann Building, Max Born Crescent, Edinburgh, EH9 3BF, United Kingdom Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/558498v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@a819e5org.highwire.dtl.DTLVardef@3c34ceorg.highwire.dtl.DTLVardef@aef5d8org.highwire.dtl.DTLVardef@8e5d2f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Transcriptome-wide meta-analysis of codon usage in Escherichia coli

The preference for synonymous codons, termed codon usage bias (CUB), is a fundamental feature of coding sequences, with distinct preferences being observed across species, genomes and genes. Accurately quantifying codon usage frequencies is useful for a range of applications, from guiding mRNA vaccine design, to elucidating protein folding and uncovering co-evolutionary relationships. However, current methods are either based on a single genome assembly, lack functional stratification, or are extremely outdated. To address this, we adopted a data-driven approach and developed Codon Usage Bias estimation from RNA-sequencing data (CUBSEQ), a fully automatic meta-analysis pipeline to estimate CUB at the trascriptome-level and for gene panels. Here, we used CUBSEQ to perform, to our knowledge, the largest and most comprehensive CUB analysis of the transcriptome and highly expressed genes in Escherichia coli, using RNA sequencing data from 6,763 samples across 72 strains. By capturing sequence variants of these genes through variant calls, we constructed a per-sample representation of the E. coli transcriptome revealing a rich mutational landscape. We then identified a set of 81 highly expressed genes with consistent expression patterns across strains, sample library size and experimental conditions, and found significant differences in CUB compared to transcriptome-wide genes and alternative codon usage tables. Finally, we found codons with a high relative frequency were often associated with a larger repertoire of isoaccepting tRNAs and not necessarily high tRNA abundance.

genomics↗

bioRxiv2022

Polymer physics of structural evolution in synthetic yeast chromosomes

Yeast SCRaMbLE is an experimental method using designed synthetic yeast chromosomes to generate combinatorial diversity through genome rearrangements. These events occur at designed loxPsym recombination sites through the activity of Cre recombinase. While the synthetic SCRaMbLE system was designed to explore minimal genomes and permit rapid genome evolution, the pattern of recombinations also reflects inherent properties of DNA looping required to coalesce pairs of loxPsym sites. Genomes of yeast strains generated by SCRaMbLE are analyzed here using a new statistical mechanics model, called the SCRaMbLE Polymer Interaction (SPI) model. SPI uses polymer physics to model recombinations, and implements efficient rejection sampling and histogram reweighting algorithms to conduct SCRaMbLE experiments in silico. Using SPI, we found that recombination events observed experimentally are consistent with a random walk scaling exponent ranging between 0.45 and 0.6, which spans values of 0.5 for a Gaussian polymer and 0.588 for a self-avoiding walk. SPI provides a highly accurate tool to study SCRaMbLE recombinations and massively parallel genome recombination experiments.

synthetic biology↗

bioRxiv2022

Laboratory evolution and polyploid SCRaMbLE reveal genomic plasticity to synthetic chromosome defects and rearrangements

We have designed, constructed, and debugged a synthetic 753,096 bp version of Saccharomyces cerevisiae chromosome XIV as part of the international Sc2.0 project. We showed that certain synthetic loxPsym recombination sites can interfere with mitochondrial protein localization, that the deletion of one intron (NOG2) reduced fitness, and that a reassigned stop codon can lead to a growth defect. In parallel to these rational debugging modifications, we used Adaptive Laboratory Evolution to generate a general growth defect suppressor rearrangement in the form of increased TAR1 copy number. We also extended the utility of the Synthetic Chromosome Recombination and Modification by LoxP-mediated Evolution (SCRaMbLE) system by engineering synthetic-wild-type tetraploid hybrid strains that buffer against essential gene loss. The presence of wild-type chromosomes in the hybrid tetraploids increased post-SCRaMbLE viability and heterologous DNA integration, highlighting the plasticity of the S. cerevisiae genome in the presence of rational and non-rational modifications.

synthetic biology↗

bioRxiv2022

Synthetic yeast chromosome XI design enables extrachromosomal circular DNA formation on demand

We describe construction of the 660 kilobase synthetic yeast chromosome XI (synXI) and reveal how synthetic redesign of non-coding DNA elements impact the cell. To aid construction from synthesized 5 to 10 kilobase DNA fragments, we implemented CRISPR-based methods for synthetic crossovers in vivo and used these methods in an extensive process of bug discovery, redesign and chromosome repair, including for the precise removal of 200 kilobases of unexpected repeated sequence. In synXI, the underlying causes of several fitness defects were identified as modifications to non-coding DNA, including defects related to centromere function and mitochondrial activity that were subsequently corrected. As part of synthetic yeast chromosome design, loxPsym sequences for Cre-mediated recombination are inserted between most genes. Using the GAP1 locus from chromosome XI, we show here that targeted insertion of these sites can be used to create extrachromosomal circular DNA on demand, allowing direct study of the effects and propagation of these important molecules. Construction and characterization of synXI has uncovered effects of non-coding and extrachromosomal circular DNA, contributing to better understanding of these elements and informing future synthetic genome design.

synthetic biology↗

bioRxiv2022

Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

The Sc2.0 project is building a eukaryotic synthetic genome from scratch, incorporating thousands of designer features. A major milestone has been achieved with the assembly of all individual Sc2.0 chromosomes. Here, we describe the consolidation of multiple synthetic chromosomes using endoreduplication intercross to generate a strain with 6.5 synthetic chromosomes. Genome-wide chromosome conformation capture and long-read direct RNA sequencing were performed on this strain to evaluate the effects of designer modifications, such as loxPsym site insertion, tRNA relocation, and intron deletion, on 3D chromosome organization and transcript isoform profiles. To precisely map "bugs", we developed a method, CRISPR Directed Biallelic URA3-assisted Genome Scan, or "CRISPR D-BUGS", exploiting directed mitotic recombination in heterozygous diploids. Using this method, we first fine-mapped a synII defect resulting from two loxPsym sites in the 3 UTR of SHM1. This approach was also used to map a combinatorial bug associated with synIII and synX, revealing a highly unexpected genetic interaction that links transcriptional regulation, inositol metabolism and tRNASerCGA abundance. "Starvation" for tRNASerCGA leads to insufficient levels of the key positive inositol biosynthesis regulator, Swi3, which contains tandem UCG codons. Finally, to expedite consolidation, we employed a new method, chromosome swapping, to incorporate the largest chromosome (synIV), thereby consolidating more than half of the Sc2.0 genome in a single strain.

genomics↗

bioRxiv2022

Designing human Sphingosine-1-phosphate lyases using a temporal Dirichlet variational autoencoder

Enzymatic deficiencies cause the accumulation of toxic levels of substrates in a cell and are associated with life-threatening pathologies. Restoring physiological enzymes levels by injecting a recombinant version of the defective enzyme could provide a viable therapeutic option. However, these enzyme replacement therapies have had limited success, as the recombinant enzymes are less catalytically active, cause immune response and are difficult to manufacture. Moreover, the vast sequence design space makes finding enzymes with desired therapeutic properties extremely challenging. Here, we present a new enzyme engineering framework, which builds on recent advances in deep learning, variational calculus and natural language processing, to design variants of human enzymes with biochemical features comparable to the wild type protein as a way to rapidly build targeted libraries for downstream screening. We applied our method to design variants of human Sphyngosine-1-phosphate lyase (HsS1PL) as potential therapeutic treatments for nephrotic syndrome type 14 (NPHS14), and characterized their biochemical properties through extensive sequence and molecular dynamics analyses.

bioinformatics↗

bioRxiv2021

Discovering cancer driver genes and pathways using stochastic block model graph neural networks

The identification of genes and pathways responsible for the transformation of normal cells into malignant ones represents a pivotal step to understand the aetiology of cancer, to characterise progression and relapse, and to ultimately design targeted therapies. The advent of high-throughput omic technologies has enabled the discovery of a significant number of cancer driver genes, but recent genomic studies have shown these to be only necessary but not sufficient to trigger tumorigenesis. Since most biological processes are the results of the interaction of multiple genes, it is then conceivable that tumorigenesis is likely the result of the action of networks of cancer driver and non-driver genes. Here we take advantage of recent advances in graph neural networks, combined with well established statistical models of network structure, to build a new model, called Stochastic Block Model Graph Neural Network (SBM-GNN), which predicts cancer driver genes and cancer mediating pathways directly from high-throughput omic experiments. Experimental analysis of synthetic datasets showed that our model can correctly predict genes associated with cancer and recover relevant pathways, while outperforming other state-of-the-art methods. Finally, we used SBM-GNN to perform a pan-cancer analysis, where we found genes and pathways directly involved in the hallmarks of cancer controlling genome stability, apoptosis, immune response, and metabolism.

bioinformatics↗

bioRxiv2019

Germline and somatic genetic variants in the p53 pathway interact to affect cancer risk, progression and drug response

Insights into oncogenesis derived from cancer susceptibility loci could facilitate better cancer management and treatment through precision oncology. However, therapeutic applications have thus far been limited by our current lack of understanding regarding both their interactions with somatic cancer driver mutations and their influence on tumorigenesis. Here, by integrating germline datasets relating to cancer susceptibility with tumour data capturing somatically-acquired genetic variation, we provide evidence that single nucleotide polymorphism (SNPs) and somatic mutations in the p53 tumor suppressor pathway can interact to influence cancer development, progression and treatment response. We go on to provide human genetic evidence of a tumor-promoting role for the pro-survival activities of p53, which supports the development of more effective therapy combinations through their inhibition in cancers retaining wild-type p53. SignificanceWe describe significant interactions between heritable and somatic genetic variants in the p53 pathway that affect cancer susceptibility, progression and treatment response. Our results offer evidence of how cancer susceptibility SNPs can interact with cancer driver genes to affect cancer progression and identify novel therapeutic targets.

cancer biology↗

bioRxiv2019

Heritable genetic variants in key cancer genes link cancer risk with anthropometric traits

Inherited genetic variants in tumour suppressors and oncogenes can increase the cancer risk, but little is known about their influence on anthropometric traits. Through the integration of inherited and somatic cancer genetic data, we define functional single nucleotide polymorphisms (SNPs) associated with cancer risk and explore potential pleiotropic associations with anthropometic traits in a cohort of 500,000 individuals. We identify three regulatory SNPs for three important cancer genes that associate with both anthropometric traits and cancer risk. We describe a novel association of a SNP in TP53 (rs78378222) with height, lean body mass measures and basal metabolic rate, as well as validating its known associations with brain and non-melanomatous skin cancer susceptibility. Our results clearly demonstrate that heritable variants in key cancer genes can associate with both differential cancer risk and anthropometric traits in the general population, thereby lending support for a role of genetics in linking these human phenotypes.

genetics↗

bioRxiv2019

Design and assembly of DNA molecules using multi-objective optimisation

Rapid engineering of biological systems is currently hindered by limited integration of manufacturing constraints into the design process, ultimately limiting the yield of many synthetic biology workflows.\n\nHere we tackle DNA engineering as a multi-objective optimization problem aiming at finding the best tradeoff between design requirements and manufacturing constraints. We developed a new open-source algorithm for DNA engineering, called Multi-Objective Optimisation algorithm for DNA Design and Assembly (MOODA), available as a Python package and web application at http://mooda.stracquadaniolab.org.\n\nExperimental results show that our method provides near optimal constructs and scales linearly with design complexity, effectively paving the way to rational engineering of DNA molecules from genes to genomes.

synthetic biology↗

bioRxiv2019

A unified framework for geneset network analysis

Gene and protein interaction experiments provide unique opportunities to study their wiring in a cell. Integrating this information with high-throughput functional genomics data can help identifying networks associated with complex diseases and phenotypes.\n\nHere we propose a unified statistical framework to test network properties of single and multiple genesets. We focused on testing whether a geneset exhibits network properties and if two genesets are strongly interacting with each other.\n\nWe then assessed power and false discovery rate of the proposed tests, showing that tests based on a probabilistic model of gene and protein interaction are the most robust.\n\nWe implemented our tests in an open-source framework, called Python Geneset Network Analysis (PyGNA), which provides an integrated environment for network studies. While most available tools are designed as web applications, we designed PyGNA to be easily integrated into existing high-performance data analysis pipelines.\n\nOur software is available on GitHub (http://github.com/stracquadaniolab/pygna) and can be easily installed from PyPi or anaconda.

bioinformatics↗

bioRxiv2019

Gene-level heritability analysis explains the polygenic architecture of cancer

Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with increased risk of cancer. However, the amount of heritable risk explained by these variants is limited, thus leaving most of cancer heritability unexplained. Recent studies have shown that genomic regions associated with specific biological functions explain a large proportion of the heritability of many traits. Since cancer is mostly triggered by aberrant genes function, we hypothesised that SNPs located in protein-coding genes could explain a significant proportion of cancer heritability. To perform this analysis, we developed a new method, called Bayesian Gene HERitability Analysis (BAGHERA), to estimate the heritability explained by all the genotyped SNPs and by those located in protein coding genes directly from GWAS summary statistics. By applying BAGHERA to the 38 cancers reported in the UK Biobank, we identified 1, 146 genes explaining a significant amount of cancer heritability. We found these genes to be tumour suppressors directly involved in the hallmark processes controlling the transformation from normal to cancer cell; moreover, these genes also harbour somatic driver mutation for many tumours, suggesting a two-hit model underpinning tumorigenesis. Our study provides new evidence for a functional role of SNPs in cancer and identifies new targets for risk assessment and patients stratification.

genetics↗

bioRxiv2018

Synthetic chromosome fusion: effects on genome structure and function

As part of the Synthetic Yeast 2.0 (Sc2.0) project, we designed and synthesized synthetic chromosome I. The total length of synI is [~]21.4% shorter than wild-type chromosome I, the smallest chromosome in Saccharomyces cerevisiae. SynI was designed for attachment to another synthetic chromosome due to concerns of potential instability and karyotype imbalance. We used a variation of a previously developed, robust CRISPR-Cas9 method to fuse chromosome I to other chromosome arms of varying length: chrIXR (84kb), chrIIIR (202kb) and chrIVR (1Mb). All fusion chromosome strains grew like wild-type so we decided to attach synI to synIII. Through the investigation of three-dimensional structures of fusion chromosome strains, unexpected loops and twisted structures were formed in chrIII-I and chrIX-III-I fusion chromosomes, which depend on silencing protein Sir3. These results suggest a previously unappreciated 3D interaction between HMR and the adjacent telomere. We used these fusion chromosomes to show that axial element Red1 binding in meiosis is not strictly chromosome size dependent even though Red1 binding is enriched on the three smallest chromosomes in wild-type yeast, and we discovered an unexpected role for centromeres in Red1 binding patterns.

synthetic biology↗