SynBioFromLeukipposInstitute

For Lucas Farnung, there is no question more fascinating than how a single fertilized egg develops into a fully-functioning human. As a structural biologist, he is studying this process on the smallest scale: the trillions of atoms that must synchronize their work to make it happen.

Researchers analysed thousands of laboratory-made plasmids and discovered that nearly half of them had defects, raising questions of experimental reproducibility.

Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools. Single-cell, spatial and multi-omic profiling technologies generate large-scale data that reveal the output of genome-scale experiments across diverse cells, tissues and organisms. Cole Trapnell reviews the underlying core statistical challenges that need to be tackled to harness the power of these technologies and advance our understanding of gene function in health and disease.

Fractals are patterns that are self-similar across multiple length-scales1. Macroscopic fractals are common in nature2–4; however, so far, molecular assembly into fractals is restricted to synthetic systems5–12. Here we report the discovery of a natural protein, citrate synthase from the cyanobacterium Synechococcus elongatus, which self-assembles into Sierpiński triangles. Using cryo-electron microscopy, we reveal how the fractal assembles from a hexameric building block. Although different stimuli modulate the formation of fractal complexes and these complexes can regulate the enzymatic activity of citrate synthase in vitro, the fractal may not serve a physiological function in vivo. We use ancestral sequence reconstruction to retrace how the citrate synthase fractal evolved from non-fractal precursors, and the results suggest it may have emerged as a harmless evolutionary accident. Our findings expand the space of possible protein complexes and demonstrate that intricate and regulatable assemblies can evolve in a single substitution. Citrate synthase from the cyanobacterium Synechococcus elongatus is shown to self-assemble into Sierpiński triangles, a finding that opens up the possibility that other naturally occurring molecular-scale fractals exist.

Prime editing enables the precise modification of genomes through reverse transcription of template sequences appended to the 3' ends of CRISPR-Cas guide RNAs1. To identify cellular determinants of prime editing, we developed scalable prime editing reporters and performed genome-scale CRI …

Self-assembling DNA can process information, but the computations have been limited to digital algorithms. A self-assembling DNA system has now been designed to perform complex pattern recognition.

Possessing only essential genes, a minimal cell can reveal mechanisms and processes that are critical for the persistence and stability of life1,2. Here we report on how an engineered minimal cell3,4 contends with the forces of evolution compared with the Mycoplasma mycoides non-minimal cell from which it was synthetically derived. Mutation rates were the highest among all reported bacteria, but were not affected by genome minimization. Genome streamlining was costly, leading to a decrease in fitness of greater than 50%, but this deficit was regained during 2,000 generations of evolution. Despite selection acting on distinct genetic targets, increases in the maximum growth rate of the synthetic cells were comparable. Moreover, when performance was assessed by relative fitness, the minimal cell evolved 39% faster than the non-minimal cell. The only apparent constraint involved the evolution of cell size. The size of the non-minimal cell increased by 80%, whereas the minimal cell remained the same. This pattern reflected epistatic effects of mutations in ftsZ, which encodes a tubulin-homologue protein that regulates cell division and morphology5,6. Our findings demonstrate that natural selection can rapidly increase the fitness of one of the simplest autonomously growing organisms. Understanding how species with small genomes overcome evolutionary challenges provides critical insights into the persistence of host-associated endosymbionts, the stability of streamlined chassis for biotechnology and the targeted refinement of synthetically engineered cells2,7–9. An engineered minimal cell evolves to escape the negative consequences of genome streamlining.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision3 by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets. A context-aware, attention-based deep learning model pretrained on single-cell transcriptomes enables predictions in settings with limited data in network biology and could accelerate discovery of key network regulators and candidate therapeutic targets.

Microbiome research is now demonstrating a growing number of bacterial strains and genes that affect our health1. Although CRISPR-derived tools have shown great success in editing disease-driving genes in human cells2, we currently lack the tools to achieve comparable success for bacterial targets in situ. Here we engineer a phage-derived particle to deliver a base editor and modify Escherichia coli colonizing the mouse gut. Editing of a β-lactamase gene in a model E. coli strain resulted in a median editing efficiency of 93% of the target bacterial population with a single dose. Edited bacteria were stably maintained in the mouse gut for at least 42 days following treatment. This was achieved using a non-replicative DNA vector, preventing maintenance and dissemination of the payload. We then leveraged this approach to edit several genes of therapeutic relevance in E. coli and Klebsiella pneumoniae strains in vitro and demonstrate in situ editing of a gene involved in the production of curli in a pathogenic E. coli strain. Our work demonstrates the feasibility of modifying bacteria directly in the gut, offering a new avenue to investigate the function of bacterial genes and opening the door to the design of new microbiome-targeted therapies. Edited bacteria were stably maintained in mouse gut for at least 42 days following the delivery of a base editor using an engineered phage-derived particle to modify Escherichia coli colonizing the gut.

Recombinase enzymes that recognize DNA sequences using a ‘bridge’ RNA.

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Treatment with chimeric antigen receptor (CAR) T cells targeting the B-cell maturation antigen (BCMA) has shown remarkable results in patients with relapsed or refractory multiple myeloma (MM). However, most patients eventually relapse, underscoring the need for improved therapeutic approaches. Now, Díez-Alonso et al. have engineered T cells to secrete T cell–engaging (TCE) antibodies targeting BCMA on cancer cells and CD3 on T cells. In mouse models of MM, these TCE antibody secreting (STAb) T cells were more effective at eliminating target cells than traditional CAR-T cells......"

Artificial electron donors and acceptors expand researchers’ metabolic engineering options — if only cells would cooperate.

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a …

APPLIED SCIENCES AND ENGINEERING

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5–7 and guide protein engineering8–10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40–72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability. Large-scale assays using cDNA display proteolysis are used to measure the folding stabilities of protein domains, providing a method to quantify the effects of mutations on protein folding, with applications in protein design.

The paper-folding mechanism has been widely adopted in building of reconfigurable macroscale systems because of its unique capabilities and advantages in programming variable shapes and stiffness into a structure1–5. However, it has barely been exploited in the construction of molecular-level systems owing to the lack of a suitable design principle, even though various dynamic structures based on DNA self-assembly6–9 have been developed10–23. Here we propose a method to harness the paper-folding mechanism to create reconfigurable DNA origami structures. The main idea is to build a reference, planar wireframe structure24 whose edges follow a crease pattern in paper folding so that it can be folded into various target shapes. We realized several paper-like folding and unfolding patterns using DNA strand displacement25 with high yield. Orthogonal folding, repeatable folding and unfolding, folding-based microRNA detection and fluorescence signal control were demonstrated. Stimuli-responsive folding and unfolding triggered by pH or light-source change were also possible. Moreover, by employing hierarchical assembly26 we could expand the design space and complexity of the paper-folding mechanism in a highly programmable manner. Because of its high programmability and scalability, we expect that the proposed paper-folding-based reconfiguration method will advance the development of complex molecular systems. A method is presented to harness the paper-folding mechanism of reconfigurable macroscale systems to create reconfigurable DNA origami structures, in anticipation that it will advance the development of complex molecular systems.

Whole-genome synthesis provides a powerful approach for understanding and expanding organism function1–3. To build large genomes rapidly, scalably and in parallel, we need (1) methods for assembling megabases of DNA from shorter precursors and (2) strategies for rapidly and scalably replacing the genomic DNA of organisms with synthetic DNA. Here we develop bacterial artificial chromosome (BAC) stepwise insertion synthesis (BASIS)—a method for megabase-scale assembly of DNA in Escherichia coli episomes. We used BASIS to assemble 1.1 Mb of human DNA containing numerous exons, introns, repetitive sequences, G-quadruplexes, and long and short interspersed nuclear elements (LINEs and SINEs). BASIS provides a powerful platform for building synthetic genomes for diverse organisms. We also developed continuous genome synthesis (CGS)—a method for continuously replacing sequential 100 kb stretches of the E. coli genome with synthetic DNA; CGS minimizes crossovers1,4 between the synthetic DNA and the genome such that the output for each 100 kb replacement provides, without sequencing, the input for the next 100 kb replacement. Using CGS, we synthesized a 0.5 Mb section of the E. coli genome—a key intermediate in its total synthesis1—from five episomes in 10  days. By parallelizing CGS and combining it with rapid oligonucleotide synthesis and episome assembly5,6, along with rapid methods for compiling a single genome from strains bearing distinct synthetic genome sections1,7,8, we anticipate that it will be possible to synthesize entire E. coli genomes from functional designs in less than 2 months. BAC stepwise insertion synthesis (BASIS) can be used to build synthetic genomes for diverse organisms, and continuous genome synthesis (CGS) enables the rapid synthesis of entire Escherichia coli genomes from functional designs.

By modifying the blueprint of life, researchers are endowing proteins with chemistries they’ve never had before.