Biology subjects

Lindner, A. B.

Publications and source records attributed to Lindner, A. B..

16 recordsLinked to original sources

bioRxiv2025

A synthetic cell phage cycle

Viral infection of living cells, exemplified by bacteriophage interaction with bacteria, is fundamental to biology and universal across living systems. Here, we establish an all-cell-free viral cycle where T7 phages infect synthetic cells, equipped with lipopolysaccharides on the outer leaflet of the lipid membrane while encapsulating a cell-free gene expression system. We track each cycle step to demonstrate T7 phage-specific adsorption onto the liposomes, genome ejection, replication, expression, and assembly of new infectious virions within the synthetic cells. We quantify key characteristics of the cycle, including the multiplicity of infection, replication efficiency, liposome size constraints, and phage rebinding dynamics. This work establishes a versatile, fully defined in vitro platform for reconstructing and investigating viral infections from individual molecular components.

synthetic biology↗

bioRxiv2025

Generalizable prediction of liquid-liquid phase separation from protein sequence

Liquid-liquid phase separation (LLPS) is emerging as a fundamental process supporting multiple facets of biological systems. This phenomenon enables the dynamic compartmentalization of biomolecules contributing to a wide range of cellular functions, though in many instances its precise role and evolution remain unclear. Protein phase separation naturally occurs within cells and is prevalent across all species. Despite a recent surge in protein LLPS discovery, current predictive models lack generalizability and fail to identify the full spectrum of phase-separating proteins. To address this shortcoming, we developed Phaseek, a hybrid model integrating contextual sequence encoding with statistical graph representations to score LLPS propensity of amino acid sequences. Phaseek accurately identifies phase-separating proteins across diverse biological contexts, predicting key functional regions and the effects of point mutations. Proteome-wide predictions for 18 species highlight important physicochemical features. Gene Ontology enrichments recapitulate known processes (e.g., nucleic acid binding, nuclear localization, chromatin organization) and suggest novel areas of investigation. Phylogenetic analysis of orthologs further suggests that LLPS is evolutionarily conserved beyond sequence similarity. In addition, we used Phaseek to design de novo phase-separating peptides and achieved a 70% success rate in vivo. Provided with a user-friendly implementation, Phaseek serves as a multipurpose LLPS predictor for advancing both fundamental and applied LLPS research.

molecular biology↗

bioRxiv2024

qByte: Open-source isothermal fluorimeter for democratizing analysis of nucleic acids, proteins and cells

Access to affordable and reliable scientific instrumentation remains a significant barrier to the democratization of healthcare and scientific research. In the field of biotechnology, in particular, the complexity, cost, and infrastructure requirements of many instruments continue to limit their accessibility, especially in resource-limited environments. Despite the recent increase in the development of open-source tools, driven by advances in digital fabrication and electronic prototyping, few of these projects have reached large-scale implementation or validation in real-world settings. Here, we present qByte, an open-source, 8-tube isothermal fluorimeter designed to overcome these barriers by offering a cost-effective ($60) yet production-ready solution. qByte leverages standard digital manufacturing and Printed Circuit Board (PCB) assembly techniques and is designed to be portable, making it ideal for both laboratory and field use. The device has been benchmarked against commercial real-time thermocyclers and spectrophotometers, showing comparable results across four key applications: nucleic acid amplification and detection, analysis of protein activity and stability, genetic construct characterization, and bacterial viability tests. Further testing including the on-site diagnosis of human parasites in Kumasi, Ghana, and field work in Patagonia, Chile, validated qBytes reliability in real-world conditions. Taken together, our results proved qByte as flexible and reliable equipment for a variety of biological tests and applications, while its affordability and open-source design simplify further development and allow adaptation to the needs of future users.

bioengineering↗

bioRxiv2024

CodonTransformer: a multispecies codon optimizer using context-aware neural networks

The genetic code is degenerate allowing a multitude of possible DNA sequences to encode the same protein. This degeneracy impacts the efficiency of heterologous protein production due to the codon usage preferences of each organism. The process of tailoring organism-specific synonymous codons, known as codon optimization, must respect local sequence patterns that go beyond global codon preferences. As a result, the search space faces a combinatorial explosion that makes exhaustive exploration impossible. Nevertheless, throughout the diverse life on Earth, natural selection has already optimized the sequences, thereby providing a rich source of data allowing machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all kingdoms of life. The model demonstrates context-awareness thanks to the attention mechanism and bidirectionality of the Transformers we used, and to a novel sequence representation that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with negative cis-regulatory elements. This work introduces a novel strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a state-of-the-art codon optimization framework with a customizable open-access model and a user-friendly interface.

synthetic biology↗

bioRxiv2024

Open Educational Resources for distributed hands-on teaching in molecular biology

The urgent need to develop a more equitable bioeconomy has positioned biotechnology capacity building at the forefront of international priorities. However, in many educational institutions, particularly in low-and middle-income countries, this remains a major challenge due to limited access to reagents, equipment, and technical documentation. In this work, we describe Open Educational Resources (OER) composed of locally produced biological reagents, open source hardware and free software to teach fundamental techniques in biotechnology such as LAMP DNA amplification, RT-PCR RNA detection, enzyme kinetics and fluorescence imaging. The use of locally produced reagents and devices reduces costs by up to one order of magnitude. During the pandemic lockdowns, these tools were distributed nationwide to students homes as a lab-in-a-box for remote teaching of molecular biology. To test their performance, a total of 93 undergraduate students tested these resources during a biochemistry practical course. 27 out of 31 groups ([~]87%) successfully achieved the objectives of the PCR activity, while 28 out of 31 groups ([~]90%) correctly identified the target using LAMP reactions. To assess the potential application in secondary school, we organized three workshops for high school teachers from different institutions across Chile and performed an anonymous questionnaire, obtaining a strong agreement on how these OER broaden teachers perspectives on the techniques and facilitate the teaching of molecular biology topics. This effort was made possible through a close collaboration with open source technology advocates and members of DIYbio communities, whose work is paving the way for low-cost training in biology. All the protocols and design files are available under open source licenses.

molecular biology↗

bioRxiv2023

PHEIGES, all-cell-free phage synthesis and selection from engineered genomes

Bacteriophages constitute an invaluable biological reservoir for biotechnology and medicine. The ability to exploit such vast resources is hampered by the lack of methods to rapidly engineer, assemble, package genomes, and select phages. Cell-free transcription-translation (TXTL) offers experimental settings to address such a limitation. Here, we describe PHage Engineering by In vitro Gene Expression and Selection (PHEIGES) using T7 phage genome and Escherichia coli TXTL. Phage genomes are assembled in vitro from PCR-amplified fragments and directly expressed in batch TXTL reactions to produce up to 1011 PFU/ml engineered phages within one day. We further demonstrate a significant genotype-phenotype linkage of phage assembly in bulk TXTL. This enables rapid selection of phages with altered rough lipopolysaccharides specificity from phage genomes incorporating tail fiber mutant libraries. We establish the scalability of PHEIGES by one pot assembly of such mutants with fluorescent gene integration and 10% length-reduced genome.

synthetic biology↗

bioRxiv2023

Demonstration of Bioplastic Production from CO2 and Formate using the Reductive Glycine Pathway in E. coli

There is a strong need to develop technologies that reduce anthropogenic pollution and the dependence on nonrenewable Earth resources. One way of doing so is by harnessing biological systems for replacing the production of fossil-fuel based goods with low-environmental-impact alternatives. Recently, progress was made in engineering the model organism E. coli to grow using CO2 and formate as its only carbon and energy sources using the reductive glycine pathway (rGlyP). Here, we use this engineered strain of E. coli as a host system for the production of polyhydroxybutyrate (PHB), a biologically derived and biodegradable plastic. We confirmed the production of PHB in this strain using Nile red fluorescent microscopy, transmission electron microscopy, and GC measurements. Since formate can be efficiently generated from CO2 by electrochemical reduction using renewable energy sources, this study serves as a proof of concept for the emerging field of electro-bioproduction.

synthetic biology↗

bioRxiv2022

Damage dynamics in single E. coli and the role of chance in the timing of cell death

Genetically identical cells in the same stressful condition die at different times. The origin of this stochasticity is unclear; it may arise from different initial conditions that affect the time of demise, or from a stochastic damage accumulation mechanism that erases the initial conditions and instead amplifies noise to generate different lifespans. To address this requires measuring damage dynamics in individual cells over the lifespan, but this has rarely been achieved. Here, we used a microfluidic device to measure membrane damage in 648 carbon-starved E. coli cells at high temporal resolution. We find that initial conditions of damage, size or cell-cycle phase do not explain most of the lifespan variation. Instead, the data points to a stochastic mechanism in which noise is amplified by a rising production of damage that saturates its own removal. Surprisingly, the relative variation in damage drops with age: cells become more similar to each other in terms of relative damage, indicating increasing determinism with age. Thus, chance erases initial conditions and then gives way to increasingly deterministic dynamics that dominate the lifespan distribution.

systems biology↗

bioRxiv2021

Low-cost drug discovery with engineered E. coli reveals an anti-mycobacterial activity of benazepril

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogens slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (ALR) revealed benazepril as a targeted inhibitor. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical ALR inhibitors. This is the first report of an antimicrobial activity in an approved Angiotensin Converting Enzyme (ACE) inhibitor and may explain clinical data associating use of ACE inhibitors with reduced Mtb infection risk. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets. SIGNIFICANCE STATEMENTThe challenge of discovering new antibiotics is both scientific and economic. No simple test can determine if a given molecule will be safe and effective in real human patients. Many drug candidates must therefore be advanced for each new antibiotic that reaches the market - a risky and expensive process. In this work we use synthetic biology to engineer the common laboratory model bacterium E. coli as a tool for early stage antibiotic discovery. As a proof of concept we expressed a known tuberculosis drug target and found a novel inhibitor: benazepril. Many other drug targets could be screened similarly using the system that we describe. Because E. coli can be grown safely and cheaply, this approach may help to reduce costs and make drug discovery more accessible.

synthetic biology↗

bioRxiv2020

Remote Digital Psychiatry: MindLogger for Mobile Mental Health Assessment and Therapy

BackgroundUniversal access to assessment and treatment of mental health and learning disorders remains a significant and unmet need. There is a vast number of people without access to care because of economic, geographic, and cultural barriers as well as limited availability of clinical experts who could help advance our understanding of mental health. ObjectiveTo create an open, configurable software platform to build clinical measures, mobile assessments, tasks, and interventions without programming expertise. Specifically, our primary requirements include: an administrator interface for creating and scheduling recurring and customized questionnaires where end users receive and respond to scheduled notifications via an iOS or Android app on a mobile device. Such a platform would help relieve overwhelmed health systems, and empower remote and disadvantaged subgroups in need of accurate and effective information, assessment, and care. This platform has potential to advance scientific research by supporting the collection of data with instruments tailored to specific scientific questions from large, distributed, and diverse populations. MethodsWe conducted a search for tools that satisfy the above requirements. We designed and developed a new software platform called "MindLogger" that exceeds the above requirements. To demonstrate the tools configurability, we built multiple "applets" (collections of activities) within the MindLogger mobile application and deployed several, including a comprehensive set of assessments underway in a large-scale, longitudinal, mental health study. ResultsOf the hundreds of products we researched, we found 10 that met our primary requirements above with 4 that support end-to-end encryption, 2 that enable restricted access to individual users data, 1 that provides open source software, and none that satisfy all three. We compared features related to information presentation and data capture capabilities, privacy and security, and access to the product, code, and data. We successfully built MindLogger mobile and web applications, as well as web browser-based tools for building and editing new applets and for administering them to end users. MindLogger has end-to-end encryption, enables restricted access, is open source, and supports a variety of data collection features. One applet is currently collecting data from children and adolescents in our mental health study, and other applets are in different stages of testing and deployment for use in clinical and research settings. ConclusionsWe have demonstrated the flexibility and applicability of the MindLogger platform through its deployment in a large-scale, longitudinal, mobile mental health study, and by building a variety of other mental health-related applets. With this release, we encourage a broad range of users to apply the MindLogger platform to create and test applets to advance health care and scientific research. We hope that increasing availability of applets designed to assess and administer interventions will facilitate access to health care in the general population.

neuroscience↗

bioRxiv2020

Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing

RNA-based regulation offers a promising alternative of protein-based transcriptional networks. However, designing synthetic riboregulators with desirable functionalities using arbitrary sequences remains challenging, due in part to insufficient exploration of RNA sequence-to-function landscapes. Here we report that CRISPR-Csy4 mediates a nearly all-or-none processing of precursor CRISPR RNAs (pre-crRNAs), by profiling Csy4 binding sites flanked by > 1 million random sequences. This represents an ideal sequence-to-function space for universal riboregulator designs. Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score {approx} 0.93). Inspired by exhaustive probing of palindromic flanking sequences, we designed anti-CRISPR RNAs (acrRNAs) that suppress processing of pre-crRNAs via stem stacking. We validated machine-learning-guided designs with >30 functional pairs of acrRNAs and pre-crRNAs to achieve switch-like properties. This opens a wide range of plug-and-play applications tailored through pre-crRNA designs, and represents a programmable alternative to protein-based anti-CRISPRs.

synthetic biology↗

bioRxiv2020

TEARS: Spatial engineering of E. coli with addressable phase-separated RNAs

Biochemical processes often require spatial regulation and specific microenvironments. The general lack of organelles in bacteria limits the potential of bioengineering complex intracellular reactions. Here we demonstrate Transcriptionally Engineered Addressable RNA Solvent droplets (TEARS) as synthetic microdomains within the Escherichia coli. TEARS are assembled from RNA-binding protein recruitment domains fused to poly-CAG repeats that spontaneously drive liquid-liquid phase separation from the bulk cytoplasm. Targeting TEARS with fluorescent proteins revealed multilayered structures and a non-equilibrium mechanism controlling their composition and reaction robustness. We show that TEARS provide organelle-like bioprocess isolation for sequestering biochemical pathways, controlling metabolic branch points, buffering mRNA translation rates and scaffolding protein-protein interactions. TEARS are a simple and versatile tool for spatially controlling E. coli biochemistry.

synthetic biology↗

bioRxiv2019

Seq2DFunc: 2-dimensional convolutional neural network on graph representation of synthetic sequences from massive-throughput assay

In recent years, a pipeline of massively parallel reporter assay (MPRA), and next-generation sequencing (NGS) provided large-scale datasets to investigate biological mechanisms in detail. However, bigger data often leads to larger complexity. As a result, theories derived from low-throughput experiments lose explanatory power, requiring new methods to create predictive models. Here we focus on modeling functions of nucleic acid sequences, as a study case of massive-throughput assays. We report a deep learning approach, training a two-dimensional convolutional neural network (CNN) on an ordered graph representation of nucleic acid sequences to predict their functions (Seq2DFunc). To compare the performance of Seq2DFunc with conventional methods, we obtained customized database on a CRISPR RNA processing assay. For this specific assay, analyses of sequence and RNA structure determinants failed to explain the results regardless of dataset size. 1-dimensional CNN of raw sequences generate generally failed to converge at < 10,000 or fewer sequences. By contrast, Seq2DFunc trained on [~] 7,000 sequences still provided 86% accuracy. Given a sufficient dataset ([~] 120,000 sequences) for training, Seq2DFunc (96% accuracy, 0.93 f1-score) still outperformed the best 1D CNN (92% accuracy, 0.83 f1-score). We anticipate Seq2DFunc can be a versatile downstream tool for deciphering massive-throughput assays for many fundamental studies. In addition, the use of smaller dataset is especially beneficial to reduce the experiment budget or required sequencing depth.

synthetic biology↗

bioRxiv2019

Engineering gene overlaps to sustain genetic constructs in vivo

Evolution is often an obstacle to the engineering of stable biological systems due to the selection of mutations inactivating costly gene circuits. Gene overlaps induce important constraints on sequences and their evolution. We show that these constraints can be harnessed to increase the stability of synthetic circuits by purging loss-of-function mutations. We combine computational and synthetic biology approaches to rationally design an overlapping reading frame expressing an essential gene within an existing gene to protect. Our algorithm succeeded in creating overlapping reading frames in 80% of E. coli genes. Experimentally, scoring mutations in both genes of such overlapping construct, we found that a significant fraction of mutations impacting the gene to protect have a deleterious effect on the essential gene. Such an overlap thus protects a costly gene from removal by natural selection by associating the benefit of this removal with a larger or even lethal cost. In our synthetic constructs, the overlap converts many of the possible mutants into evolutionary dead-ends, effectively changing the fitness landscape and reducing the evolutionary potential of the system.

synthetic biology↗

bioRxiv2018

Temporal scaling of ageing as an adaptive strategy of Escherichia coli

Natural selection has long been hypothesised to shape ageing patterns, but whether and how ageing contributes to life-history evolution remains elusive. The complexity of various ageing-associated molecular mechanisms and their inherent stochasticity hinder reductionist approaches to the understanding of functional senescence, i.e. reduced fecundity and increased mortality. Recent bio-demographic work demonstrated that high-precision statistics of life-history traits such as mortality rates could be used phenomenologically to understand the ageing process. We adopted this approach to study cellular senescence in growth-arrested E. coli cells, where damages to functional macromolecules are no longer diluted by fast de novo biosynthesis. We acquired high-quality longitudinal physiological and life history data of large environmentally controlled clonal E. coli populations at single-cell resolution, using custom-designed microfluidic devices coupled to time-lapse microscopy. We show that E. coli lifespan distributions follow the Gompertz law of mortality, a century-old actuarial observation of human populations, despite developmental, cellular and genetic differences between bacteria and metazoan organisms. Measuring the shape of the hazard functions allowed us to disentangle quantitatively the demographic effects of ageing, which accumulate with time, from age-independent genetic longevity-modulating interventions. A pathway controlling cellular maintenance, the general stress response, not only promotes longevity but also temporally scales the whole distribution by reducing ageing rate. We further show that E. coli, constrained by the amount of total biosynthesis, adapt to their natural feast-or-famine lifestyle by modulating the amount of maintenance investment, rendering ageing rate a highly evolvable life-history trait.

evolutionary biology↗

bioRxiv2018

Systematic detection of amino acid substitutions in proteome reveals a mechanistic basis of ribosome errors

Translation errors limit the accuracy of information transmission from DNA to proteins. Selective pressures shape the way cells produce their proteins: the translation machinery and the mRNA sequences it decodes co-evolved to ensure that translation proceeds fast and accurately in a wide range of environmental conditions. Our understanding of the causes of amino acid misincorporations and of their effect on the evolution of protein sequences is largely hindered by the lack of experimental methods to observe errors at the full proteome level. Here, we systematically detect and quantify errors in entire proteomes from mass spectrometry data. Following HPLC MS-MS data acquisition, we identify E. coli and S. cerevisiae peptides whose mass and fragment ion spectrum are consistent with that of a peptide bearing a single amino acid substitution, and verify that such spectrum cannot result from a post-translational modification. Our analyses confirm that most substitutions occur due to codon-to-anticodon mispairing within the ribosome. Patterns of errors due to mispairing were similar in bacteria and yeast, suggesting that the error spectrum is chemically constrained. Treating E. coli cells with a drug known to affect ribosomal proofreading increased the error rates due to mispairing at the wobble codon position. Starving bacteria for serine resulted in specific patterns of substitutions reflecting the amino acid deficiency. Overall, translation errors tend to occur at positions that are less evolutionarily conserved, and that minimally affect protein energetic stability, indicating that they are selected against. Genome wide ribosome density data suggest that errors occur at sites where ribosome velocity is relatively high, supporting the notion of a trade-off between speed and accuracy as predicted by proofreading theories. Together our results reveal a mechanistic basis for ribosome errors in translation.

systems biology↗