SEARCH · bioRxiv Science

Results for “molecular biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 613 records · Page 34Linked to original sources

bioRxiv2023

Evolutionary graph theory on rugged fitness landscapes

Spatially-resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Specifically, it is hard to link previous deme-based or lattice-based models with datasets exhibiting complex patterns of spatial organization and the role of heterogeneous population structure in shaping evolutionary dynamics is still poorly understood. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single mutation appearing in the population. Complex traits arise from interactions among multiple genes and these interaction can result in rugged fitness landscapes, where evolutionary dynamics can vastly differ from the dynamics of stepwise fixation. Here, we develop a unifying theory of how heterogenous population structure shapes evolutionary dynamics on rugged fitness landscapes. We show that even a simple extension to a two- mutational landscape can exhibit evolutionary dynamics not observed in deme-based models and that cannot be predicted using previous single-mutation results. We also show how to link these models to spatially-resolved datasets and build the networks of the stem cell niches of the bone marrow. We show that these cellular spatial architectures reduce the probability of neoplasm initiation across biologically relevant mutation rate and fitness distributions.

evolutionary biology↗

bioRxiv2023

Robust higher-order multiplexing in digital PCR by color-combination

There is a growing need in molecular biology to interrogate samples for higher number of biomarkers, beyond the 2 to 5 biomarkers typically addressable with standard PCR technologies. Here, we demonstrate a novel approach to increase the level of multiplexing in digital PCR up to 15-plex by detecting each target with 2 distinct fluorophores with a 6-color digital PCR system, a method called digital PCR by color combination. We provide a statistical framework to interpret digital PCR data by color combination, predicting that high-plexed assays by color combination can, in theory, have the same precision and sensitivity as corresponding single-plex assays. We verified the statistical framework and confirmed the sensitivity prediction experimentally on a 15-plex assay and an 11-plex assay. These results establish the technical framework for future high-plex assays addressing a variety of applications and utilities, such as infectious disease syndromic panels or tumor mutation profiling panels.

genomics↗

bioRxiv2023

Inference of annealed protein fitness landscapes with AnnealDCA

The design of proteins with specific tasks is a major challenge in molecular biology with important diagnostic and therapeutic applications. High-throughput screening methods have been developed to systematically evaluate protein activity, but only a small fraction of possible protein variants can be tested using these techniques. Computational models that explore the sequence space in-silico to identify the fittest molecules for a given function are needed to overcome this limitation. In this article, we propose AnnealDCA, a machine-learning framework to learn the protein fitness landscape from sequencing data derived from a broad range of experiments that use selection and sequencing to quantify protein activity. We demonstrate the effectiveness of our method by applying it to antibody Rep-Seq data of immunized mice and screening experiments, assessing the quality of the fitness landscape reconstructions. Our method can be applied to most experimental cases where a population of protein variants undergoes various rounds of selection and sequencing, without relying on the computation of variant enrichment ratios, and thus can be used even in cases of disjoint sequence samples.

biophysics↗

bioRxiv2023

Quantifying uncertainty in off-target activity for CRISPR guide RNAs

CRISPR-based genome editing technologies have revolutionised the field of molecular biology, offering unprecedented opportunities for precise genetic manipulation. However, off-target effects remain a significant challenge, potentially leading to unintended consequences and limiting the applicability of CRISPR-based genome editing technologies in clinical settings. Current literature predominantly focuses on point predictions for off-target activity, which may not fully capture the range of possible outcomes and associated risks. Here, we present crispAI, a neural network architecture-based approach for predicting uncertainty estimates for off-target cleavage activity, providing a more comprehensive risk assessment and facilitating improved decision-making in single guide RNA (sgRNA) design. Our approach makes use of the count noise model Zero Inflated Negative Binomial (ZINB) to model the uncertainty in the off-target cleavage activity data. In addition, we present the first-of-its-kind genome-wide sgRNA efficiency score, crispAI-aggregate, enabling prioritization among sgRNAs with similar point aggregate predictions by providing richer information compared to existing aggregate scores. We show that uncertainty estimates of our approach are calibrated and its predictive performance is superior to state-of-the-art in silico off-target cleavage activity prediction methods.

bioinformatics↗

bioRxiv2023

Capture Efficiency Of Long-Adapter Single-Strand Oligonucleotide Probe Libraries

High throughput techniques that can massively produce in parallel, longer DNA sequences of interest can accelerate the decoding of gene functions. LASSO probes are a molecular biology tool that can enrich for DNA targets in a genomic sample via a multiplexed, single-pot reaction for downstream sequencing and/or cloning. Here we have explored aspects of process development and the design of the probes that relate to binding thermodynamics to determine impact on cloned library sequences. Control of ligase concentration, polymerase type, and melting temperature of probe are critical when translating the use of LASSO probes for homogeneous and high fidelity DNA capture.

genomics↗

bioRxiv2023

Cotyledon Peeling Method for Passion Fruit Protoplasts: a versatile cell system for transient gene expression in passion fruit (Passiflora edulis)

Passion fruit (Passiflora edulis) is a perennial evergreen vine that grows mainly in tropical and subtropical regions due to its nutritional, medicinal and ornamental values. However, the molecular biology study of passion fruit is extremely hindered by the lack of an easy and efficient method for transformation. The protoplast transformation system plays a vital role in plant regeneration, gene function analysis and genome editing. Here, we present a new method ( Cotyledon Peeling Method) for simple and efficient passion fruit protoplast isolation using cotyledon as the source tissue. A high yield (2.3 x 107 protoplasts per gram of fresh tissues) and viability (76%) of protoplasts were obtained upon incubation in the enzyme solution [1% (w/v) cellulase R10, 0.25% (w/v) macerozyme R10, 0.4 M mannitol, 10 mM CaCl2, 20 mM KCl, 20 mM MES and 0.1% (w/v) BSA, pH 5.7] for 2 hours. In addition, we achieved high transfection efficiency of 83% via the polyethylene glycol (PEG)-mediated transformation with a green fluorescent protein (GFP)-tagged plasmid upon optimization. The crucial factors affecting transformation efficiency were optimized as follows: 3 g of plasmid DNA, 5 min transfection time, PEG concentration at 40% and protoplast density of 100 x 104 cells/ml. Furthermore, the established protoplast system was successfully applied for subcellular localization analysis of multiple fluorescent organelle markers and protein-protein interaction study. Taken together, we report a simple and efficient passion fruit protoplast isolation and transformation system, and demonstrate its usage in transient gene expression for the first time in passion fruit. The protoplast system would provide essential support for various passion fruit biology studies, including genome editing, gene function analysis and whole plant regeneration.

plant biology↗

bioRxiv2023

Labelizer: systematic selection of protein residues for covalent fluorophore labeling

An essential requirement for the use of fluorescent dyes in biomedicine, molecular biology, biochemistry, biophysics and optical imaging is their (covalent) attachment to biomolecules. There is, however, no systematic and automated approach for the selection of suitable labeling sites in macromolecules, which is particular problematic for proteins. Here, we present a general and quantitative strategy to identify optimal residues for protein labeling using a naive Bayes classifier. Based on a literature search and bioinformatics analysis of >100 proteins with ~400 successfully labeled residues, we identified four parameters, which we combined into a labeling score to rank residues for their suitability as a label-site. The utility of our approach for the systematic selection of single residues and of residue pairs for FRET experiments is supported by data from the literature and by new experiments on different proteins. To make the method available to a large community of researchers, we developed a python package called "labelizer", that performs an analysis of a pdb-structure (or structural models), label score calculation, and FRET assay scoring. We further provide a webserver (https://labelizer.bio.lmu.de/) to conveniently apply our approach and to build up a central open-access database of (non-)successfully labeled protein residues to continuously improve and refine the labelizer approach.

biophysics↗

bioRxiv2023

Manipulation of encapsulated artificial phospholipid membranes using sub-micellar lysolipid concentrations.

Droplet Interface Bilayers (DIBs) constitute a commonly used model of artificial membranes for molecular biology studies with applications in synthetic biology research. However, these model membranes have limited accessibility due to their requirement to be surrounded by an oil environment. Here, we demonstrate in-situ bilayer manipulation of submillimeter, free-standing, encapsulated droplet interface bilayers (eDIBs) in hydrogel capsules formed using dual-material, 3D-printed microfluidic devices. These microfluidic devices required no post-fabrication assembly, nor surface treatment to achieve the high-order emulsification, required for the formation of robust eDIBs. The eDIB capsules were exposed to various concentrations of lysophosphatidylcholine (LPC), in order to investigate the interaction of lysolipids with three-dimensional, encapsulated droplet bilayer networks. Micellar LPC concentrations trigger the bursting of the eDIB droplets, while at concentrations below the critical micelle concentration (CMC), the encapsulated aqueous inner droplet networks endure structural changes, precisely affecting the DIB contact angles and bilayer area. Manipulation of these enclosed, 3D-orchestrated membrane mimics facilitates the exploration of readily accessible compartmentalized artificial cellular machinery. Collectively, the multi-compartmentalized capsules and the lysolipid-mediated membrane modulation, introduce a chemical approach to control the properties and mechanics of artificial cellular membranes, toward responsive soft material developments.

synthetic biology↗

bioRxiv2023

Missing-link fold reveals the evolutionary pathway between RNA polymerase and ribosomal proteins

Numerous molecular machines are required to drive the central dogma of molecular biology. However, the means by which such numerous proteins emerged in the early evolutionary stage of life remains enigmatic. Many of them possess small {beta}-barrel folds with different topologies, represented by DPBB conserved in DNA and RNA polymerases, and RIFT, OB, and SH3 in ribosomal proteins. Here, we discovered that the previously reconstructed ancient DPBB sequence could also adopt a novel {beta}-barrel fold named DZBB, which shares similarities with RIFT and OB. Indeed, DZBB could be transformed into them through simple engineering experiments. Furthermore, the OB designs could be converted into SH3 by circular-permutation. These results indicate that these {beta}-barrels diversified quickly from a common ancestor at the beginning of the central dogma evolution.

biochemistry↗

bioRxiv2023

Single-Molecule RNA Sizing Enables Quantitative Analysis of Alternative Transcription Termination

Transcription, a critical process in molecular biology, has found many applications in RNA synthesis, including mRNA vaccines and RNA therapeutics. However, current RNA characterization technologies suffer from amplification and enzymatic biases that lead to loss of native information. Here, we introduce a strategy to quantitatively study both transcription and RNA polymerase behaviour by sizing RNA with RNA nanotechnology and nanopores. To begin, we utilized T7 RNA polymerase to transcribe linear DNA lacking termination sequences. Surprisingly, we discovered alternative transcription termination in the origin of replication sequence. Next, we employed circular DNA without transcription terminators to perform rolling circle transcription. This allowed us to gain valuable insights into the processivity and transcription behaviour of RNA polymerase at the single-molecule level. Our work demonstrates how RNA nanotechnology and nanopores may be used in tandem for the direct and quantitative analysis of RNA transcripts. This methodology provides a promising pathway for accurate RNA structural mapping by enabling the study of full-length RNA transcripts at the single-molecule level.

biophysics↗

bioRxiv2023

DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction

In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of growth or decline among specific risk groups can aid in optimizing interventions, particularly when resources are limited. Phylogenetic trees have been widely used in the detection of transmission chains and high-risk populations. Moreover, tree topology and the incorporation of population parameters (phylodynamics) can be useful to reconstruct the evolutionary dynamics of an epidemic across space and time among individuals. We now demonstrate the utility of phylodynamic trees for infection forecasting in addition to backtracking, developing a phylogeny-based deep learning system, called DeepDynaForecast. Our approach leverages a primal-dual graph learning structure with shortcut multi-layer aggregation, and it is suited for the early identification and prediction of transmission dynamics in emerging high-risk groups. We demonstrate the accuracy of DeepDynaForecast using simulated outbreak data and the utility of the learned model using empirical, large-scale data from the human immunodeficiency virus epidemic in Florida between 2012 and 2020. Our framework is available as open-source software (MIT license) at: https://github.com/lab-smile/DeepDynaForcast. Author SummaryDuring an outbreak or sustained epidemic, accurate prediction of patterns in transmission risk can reliably inform public health strategies. Projections indicating growth or decline of transmission for specific risk groups can significantly enhance the optimization of interventions, especially when resources are limited. To address this, we present DeepDynaForecast, a cutting-edge deep learning algorithm designed for forecasting pathogen transmission dynamics. Uniquely, DeepDynaForecast was trained on in-depth simulation data and used more information from the phylogenetic tree of pathogen sequence data than any other algorithm in the field to date, allowing classification of samples according to their dynamics (growth, static, or decline) with incredible accuracy. We evaluated the models performance using both simulated outbreak data and empirical, large-scale data from the HIV epidemic in Florida between 2012 and 2020. We conclude DeepDynaForecast represents a significant advancement in genomics-mediated pathogen transmission characterization and has the potential to catalyze new research directions within virology, molecular biology, and public health.

bioinformatics↗

bioRxiv2023

Targeted protein editing with an antibody-based system

The chemical modification of proteins is one of the major mechanisms used to regulate the properties and functions of these macromolecules in the cell. It is therefore of great interest to develop tools to exploit this type of modifications for applications in molecular biology, medicine and biotechnology. Here we present a method of using antibodies to perform post-translational covalent modifications of endogenous proteins in complex environments by exploiting proximity-driven chemistry. The method is based on the ability of antibodies to hold a weakly reactive group close to its intended site of reaction by binding the target protein on a nearby epitope. We characterise this approach by modifying the green fluorescent protein in increasingly complex environments, and illustrate its applicability by targeting the disease-associated protein beta-2 microglobulin.

biochemistry↗

bioRxiv2023

A data-driven evaluation of Arabidopsis-centric research and the model species concept

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.

plant biology↗

bioRxiv2023

In silico analyses identify sequence contamination thresholds for Nanopore-generated SARS-CoV-2 sequences

The SARS-CoV-2 pandemic has brought molecular biology and genomic sequencing into the public consciousness and lexicon. With an emphasis on rapid turnaround, genomic data has been used to inform both diagnostic and surveillance decisions for the current pa ndemic at a previously unheard-of scale. The surge in the submission of genomic data to publicly-available databases has proved essential as comparing different genome sequences offers a wealth of knowledge, including phylogenetic links, modes of transmission, rates of evolution, and the impact of mutations on infection and disease severity. However, the scale of the pandemic has meant that once sequencing runs are performed, they are rarely repeated due to limited sample material and/or the availability of sequencing resources, resulting in some imperfect runs being uploaded to public repositories. As a result, it is crucial to investigate the data obtained from these imperfect runs to determine whether the results are reliable. Numerous studies have identified a variety of sources of contamination in public next-generation sequencing (NGS) data as the number of NGS studies increases along with the diversity of sequencing technologies and procedures [1-3]. For this study, we conducted an in silico experiment with known SARS-CoV-2 sequences produced from Oxford Nanopore Technologies sequencing to investigate the effect of contamination on lineage calls and single nucleotide variations (SNVs). Through a series of analyses, we identified a contamination threshold below which runs are expected to generate accurate lineage calls and maintain genomic sequence integrity. Together, these findings provide a benchmark below which imperfect runs may be considered robust for reporting results to both stakeholders and public repositories and reduce the need for repeat or wasted runs. Author SummaryLarge-scale genomic comparisons provide a wealth of knowledge, including modes of transmission, rates of evolution, and the impact of mutations on infection, disease severity, and treatment effectiveness. As a result, the public release of genomic data has proven to be crucial. However, studies continue to show that some of the genomic data in public repositories are contaminated due to a variety of reasons. For instance, in the case of SARS-CoV-2 sequences, the pandemic prevented many sequencing runs from being repeated, resulting in some imperfect runs being uploaded to public repositories. It is of note that when genomic data is contaminated, both scientific decisions/studies and public health measures may be compromised. To identify genome contamination threshold(s) for SARS-CoV-2 sequences generated by Nanopore sequencing, computational biology techniques were utilized to generate artificially subsampled contaminated genomes. This is the first study of its kind and so our hope is that the results obtained provide a starting point for the investigation of reporting contamination of NGS data.

genomics↗

bioRxiv2023

Oral administration of a specific p300/CBP Lysine acetyltransferase activator induces synaptic plasticity and repairing spinal cord injury

TTK21 is a small molecule activator of p300/CBP acetyltransferase activity, which upon conjugation with glucose-derived carbon nanosphere (CSP) can efficiently cross the blood-brain barrier and activate histone acetylation in the brain. Its role in adult neurogenesis and retention of long-term spatial memory upon intraperitoneal (IP) administration is well established. In this study, we have successfully demonstrated that CSP-TTK21 can be effectively administered via oral gavage. Using a combination of molecular biology, microscopy, and electrophysiological techniques, we systematically investigated the comparative efficacy of oral administration of CSP and CSP-TTK21 in wild-type mice, evaluating their functional effects in comparison to intraperitoneal (IP) administration. Our findings indicate that CSP-TTK21, when administered orally, induces long-term potentiation in the hippocampus without significantly altering basal synaptic transmission, a response comparable to that achieved through IP injection. Remarkably, in a spinal cord injury model, oral administration of CSP-TTK21 exhibits equivalent efficacy to IP administration. Furthermore, our research demonstrates that oral delivery of CSP-TTK21 leads to improvements in motor functions, histone acetylation dynamics, and increased expression of regeneration associated genes (RAGs) in a spinal injury rat model, mirroring the effectiveness of IP administration. Collectively, these results underscore the potential utility of CSP as an oral drug delivery system, particularly for targeting the neural system.

neuroscience↗

bioRxiv2023

Arrayed in vivo barcoding for multiplexed sequence verification of plasmid DNA and demultiplexing of pooled libraries

Sequence verification of plasmid DNA is critical for many cloning and molecular biology workflows. To leverage high-throughput sequencing, several methods have been developed that add a unique DNA barcode to individual samples prior to pooling and sequencing. However, these methods require an individual plasmid extraction and/or in vitro barcoding reaction for each sample processed, limiting throughput and adding cost. Here, we develop an arrayed in vivo plasmid barcoding platform that enables pooled plasmid extraction and library preparation for Oxford Nanopore sequencing. This method has a high accuracy and recovery rate, and greatly increases throughput and reduces cost relative to other plasmid barcoding methods or Sanger sequencing. We use in vivo barcoding to sequence verify >45,000 plasmids and show that the method can be used to transform error-containing dispersed plasmid pools into sequence-perfect arrays or well-balanced pools. In vivo barcoding does not require any specialized equipment beyond a low-overhead Oxford Nanopore sequencer, enabling most labs to flexibly process hundreds to thousands of plasmids in parallel.

synthetic biology↗

bioRxiv2023

The Unexpected Functional Diversity of Photoexcited NAD

Despite the vital role of nicotinamide adenine dinucleotide (NAD) as a cofactor in all living organisms, the diversity of its functions is poorly understood. Particularly in interaction with ultraviolet (UV) light, a variety of photorelaxation channels can be accessed, which current models lack to explain. In this work, for the first time, we used picosecond UV pump, mid-infrared (mIR) probe spectroscopy and accurate quantum-chemical calculations to elucidate the ultrafast photodynamics of NAD+ and NADH to unify contradictory mechanisms from the past decades in the big picture. We found direct evidence for a long-lived ([~]900 ps) charge-separated state in NADH, which has been unobserved previously and results in the parallel population of a fluorescent state. The photochemical pathways demonstrated here open up functions of NAD in chemistry and molecular biology, such as an electron donor, as a FRET agent or as a redox pair switch, which have not been considered previously. TOC GRAPHICS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=194 SRC="FIGDIR/small/563164v1_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@740620org.highwire.dtl.DTLVardef@13e2c71org.highwire.dtl.DTLVardef@d7cb0corg.highwire.dtl.DTLVardef@153ab51_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

A systematic screen for co-option of transposable elements across the fungal kingdom

How novel protein functions are acquired is a central question in molecular biology. Key paths to novelty include gene duplications, recombination or horizontal acquisition. Transposable elements (TEs) are increasingly recognized as a major source of novel domain-encoding sequences. However, the impact of TE coding sequences on the evolution of the proteome remains understudied. Here, we analyzed 1,237 genomes spanning the phylogenetic breadth of the fungal kingdom. We scanned proteomes for evidence of co-occurrence of TE-derived domains along with other conventional protein functional domains. We detected more than 13,000 predicted proteins containing potentially TE-derived domain, of which 825 were identified in more than five genomes, indicating that many host-TE fusions may have persisted over long evolutionary time scales. We used the phylogenetic context to identify the origin and retention of individual TE-derived domains. The most common TE-derived domains are helicases derived from Academ, Kolobok or Helitron. We found putative TE co-options at a higher rate in genomes of the Saccharomycotina, providing an unexpected source of protein novelty in these generally TE depleted genomes. We investigated in detail a candidate host-TE fusion with a heterochromatic transcriptional silencing function that may play a role in TE and gene regulation in ascomycetes. The affected gene underwent multiple full or partial losses within the phylum. Overall, our work establishes a kingdom-wide view of how domains likely derived from TEs contribute to the evolution of protein functions.

evolutionary biology↗