Search bioRxiv⌕ Search

Biology subjects

de Ridder, D.

Publications and source records attributed to de Ridder, D..

At least 37 records · Page 2Linked to original sources
bioRxiv2023

Local adaptation of Arabidopsis thaliana in a small geographic region with mild environmental clines

Natural populations of Arabidopsis thaliana provide powerful systems to study adaptation of wild plant species. Previous research has predominantly focused on global populations or accessions collected from regions with diverse climates. However, little is known about the genetics underlying adaptation in regions with mild environmental clines. We have examined a diversity panel consisting of 192 A. thaliana accessions collected from the Netherlands, a region with limited climatic variation. Despite the relatively uniform climate, we identified compelling evidence of local adaptation within this population. Notably, semidwarf accessions, due to mutation of the GIBBERELLIC ACID REQUIRING 5 (GA5) gene, occur at a relatively high frequency near the coast and these displayed enhanced tolerance to high wind velocities. Additionally, we evaluated the performance of the population under iron deficiency conditions and found that allelic variation in the FE SUPEROXIDE DISMUTASE 3 (FSD3) gene affects tolerance to low iron levels. Moreover, we explored patterns of local adaptation to environmental clines in temperature and precipitation, observing that allelic variation at LA RELATED PROTEIN 1C (LARP1c) likely affects drought tolerance. Not only is the genetic variation observed in a diversity panel of A. thaliana collected in a region with mild environmental clines comparable to that in collections sampled over larger geographic ranges, it is also sufficiently rich to elucidate the genetic and environmental factors underlying natural plant adaptation.

plant biology↗
bioRxiv2023

Nanopores reveal the stoichiometry of single oligo-adenylates produced by type III CRISPR-Cas

Cyclic oligoadenylates (cOAs) are small second messenger molecules produced by the type III CRISPR-Cas system as part of the prokaryotic immune response. The role of cOAs is to allosterically activate downstream effector proteins that induce dormancy or cell death, and thus abort viral spread through the population. Interestingly, different type III systems have been reported to utilize different cOA stoichiometries (with 3 to 6 adenylate monophosphates). However, so far, their characterization has only been possible in bulk and with sophisticated equipment, while a portable assay with single-molecule resolution has been lacking. Here, we demonstrate the label-free detection of single cOA molecules using a simple protein nanopore assay. It sensitively identifies the stoichiometry of individual cOA molecules and their mixtures from synthetic and enzymatic origin. To achieve this, we trained a convolutional neural network (CNN) and validated it with a series of experiments on mono- and polydisperse cOA samples. Ultimately, we determined the stoichiometric composition of cOAs produced enzymatically by the CRISPR type III-A and III-B variants of Thermus thermophilus. Interestingly, both variants produce cOAs of nearly identical composition, and we discuss the biological implications of this finding. The presented nanopore-CNN workflow with single-cOA resolution can be adapted to many other signaling molecules (including eukaryotic ones), and it may be integrated into portable handheld devices with potential point-of-care applications.

biophysics↗
bioRxiv2022

Microbiome, resistome and mobilome of chlorine-free drinking water treatment systems

Drinking water treatment plants (DWTPs) are designed to remove physical, chemical, and biological contaminants. However, until recently, the role of DWTPs in minimizing the cycling of antibiotic resistance determinants has got limited attention. In particular, the risk of selecting antibiotic-resistant bacteria (ARB) is largely overlooked in chlorine-free DWTPs where biological processes are applied. Here, we combined high-throughput quantitative PCR and metagenomics to analyze the abundance and dynamics of microbial communities, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs) across the treatment trains of two chlorine-free DWTPs involving dune-based and reservoir-based systems. The microbial diversity of the water being treated increased after all biological unit operations, namely rapid and slow sand filtration (SSF), and granular activated carbon filtration. Both DWTPs reduced the concentration of ARGs and MGEs in the water by about 2.5 log gene copies mL-1, despite their relative increase in the disinfection sub-units (SSF in dune-based and UV treatment in reservoir-based DWTPs). The total microbial concentration was also reduced (2.5 log units), and none of the DWTPs were enriched for antibiotic resistant bacteria. Our findings highlight the effectiveness of chlorine-free DWTPs in supplying safe drinking water while reducing the concentration of antibiotic resistance determinants. To the best of our knowledge, this is the first study that monitors the presence and dynamics of antibiotic resistance determinants in chlorine-free DWTPs.

microbiology↗
bioRxiv2022

baseLess: Lightweight detection of sequences in raw MinION data

AO_SCPLOWBSTRACTC_SCPLOWWith its candybar form factor and low initial investment cost, the MinION brought affordable portable nucleic acid analysis within reach. However, translating the electrical signal it outputs into a sequence of bases still requires high-end computer hardware, which remains a caveat when aiming for deployment of many devices at once or usage in remote areas. For applications focusing on detection of a target sequence, such as infectious disease or GMO monitoring, the computational cost of analysis may be reduced by directly detecting the target sequence in the electrical signal instead. Here we present baseLess, a computational tool that enables such target-detection-only analysis. BaseLess makes use of an array of small neural networks, each of which efficiently detects a fixed-size subsequence of the target sequence directly from the electrical signal. We show that baseLess can accurately determine the identity of reads between three closely related fish species and can classify sequences in mixtures of twenty bacterial species, on an inexpensive single-board computer. AvailabilitybaseLess and all code used in data preparation and validation is available on Github at https://github.com/cvdelannoy/baseLess, under an MIT license. Used validation data and scripts can be found at https://doi.org/10.4121/20261392, under an MIT license.

bioinformatics↗
bioRxiv2022

Pollen sequencing reveals barriers and aberrant patterns of recombination in interspecific tomato hybrids

Tomato is the most consumed vegetable in the world. Increasing its natural resistance and resilience is key for ensuring food security within a changing climate. Plant breeders improve those traits by generating crosses of cultivated tomatoes with their wild relatives. Specific allele introgression relying on meiotic recombination, is hampered by structural divergence between parental genomes. However, previous studies of interspecific tomato hybridization focused in single cross or lacked resolution due to prohibitive sequencing costs of large segregating populations. Here, we used pooled-pollen sequencing to reveal unprecedented details of recombination patterns in five interspecific tomato hybrids. We detected hybrid-specific recombination coldspots that underscore the influence of structural divergence in shaping recombination landscape. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. We also found gene complexes associated with metabolic processes, stress resistance and domestication syndrome traits, revealing undesired consequences of recombination suppression to phenotypes. Finally, we demonstrate that by using resequencing data of wild and domesticated tomato populations, we can screen for alternative parental genomes to overcome recombination barriers. Overall, our results will allow breeders better informed decisions on generating disease-resistant and climate-resilient tomato.

genomics↗
bioRxiv2022

Prioritizing Candidate eQTL Causal Genes in Arabidopsis using Random Forests

Expression quantitative trait locus (eQTL) mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of eQTL data has been generated for this model plant; however, only a few causal eQTL genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal eQTL genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype QTLs to be used for eQTLs by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize sixteen out of twenty-five potential eQTL causal genes within the 20% rank percentile. Several new features are important in prioritizing causal eQTL genes, including the number of protein-protein interactions, unique domains, and introns. Overall, this study provides a foundation for developing computational methods to prioritize candidate eQTL causal genes. The prediction of all genes is available in the AraQTL workbench (https://www.bioinformatics.nl/AraQTL/) to support the identification of gene expression regulators in Arabidopsis.

plant biology↗
bioRxiv2022

Revealing determinants of translation efficiency via whole-gene codon randomisation and machine learning

Codon usage refers to the occurrence of synonymous codons in protein-coding genes. It is known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we developed a method to generate a large, synonymous codon library of the gene encoding the red fluorescent protein (RFP). After expression in Escherichia coli, 1459 clones of this library were selected of which we measured protein production levels and determined the full coding sequences. Using different machine learning approaches, this data was used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model, which only relies on the sequence information for the first 8 codons. This study clearly demonstrated the key role of codons at the start of the coding sequence. As such, it provides not only important fundamental insights on the influence of codon usage on protein production but also relevant clues on optimising the design of efficiently translated synthetic genes.

molecular biology↗
bioRxiv2022

The genome sequence of Hirschfeldia incana, a species with high photosynthetic light-use efficiency

Photosynthesis is a biophysical and biochemical process that plays a key role in sustaining plant and human life, being the first step in the production of energy-rich molecules and oxygen in the biosphere. Improving the photosynthetic capacity of agricultural crops is highly desirable to increase their yields. While the core mechanisms of photosynthesis are highly conserved, certainly in higher plants, plants that can maintain a high photosynthetic light-use efficiency at high irradiance are exceptional and may be useful to understand and improve high irradiance photosynthesis of crops. One such exceptional species is Hirschfeldia incana, a member of the well-studied Brassicaceae family that is easy to grow under standard laboratory conditions, providing an excellent resource for studying the genetic and physiological basis of this trait. Here, we present a reference assembly of H. incana and affirm its high photosynthetic efficiency relative to the Brassicaceae species Brassica rapa, Brassica nigra, and Arabidopsis thaliana. We estimate that it diverged from B. rapa and B. nigra 10-11 million years ago and that its genome has diversified from that of the latter two species through large chromosomal rearrangements, species-specific transposon activity, and differential retention of duplicated genes. Genes present at copy numbers different from B. rapa and B. nigra include those involved in photosynthesis and/or abiotic stress, which may mediate the high photosynthetic efficiency of H. incana. We expect the reference assembly of H. incana to be a valuable genomic resource for identifying ways to enhance photosynthetic rates in crop species.

bioinformatics↗
bioRxiv2022

OptiDiff: structural variation detection from single optical mapping reads

Detecting structural variation (SV) in eukaryotic genomes is of broad interest due to its often dramatic phenotypic effects, but remains a major, costly challenge based on DNA sequencing data. A cost-effective alternative in detecting large-scale SV has become available with advances in optical mapping technology. However, the algorithmic approaches to identifying SVs from optical mapping data are limited. Here, we propose a novel, open-source SV detection tool, OptiDiff, which employs a single molecule based approach to detect and classify homozygous and heterozygous SVs at coverages as low as 20x, showing better performance than the state of the art.

bioinformatics↗
bioRxiv2021

Inferring kinetic rate constants from single-molecule FRET trajectories - a blind benchmark of kinetic analysis tools

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We tested them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

biophysics↗
bioRxiv2021

Evaluation of FRET X for Single-Molecule Protein Fingerprinting

Single-molecule protein identification is a novel, as of yet unrealized concept with potentially groundbreaking applications in biological research. We propose a method called FRET X (Forster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands, and accumulated FRET efficiency values constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on a system of model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

biophysics↗
bioRxiv2021

Signal-based optical map alignment

In genomics, optical mapping technology provides long-range contiguity information to improve genome sequence assemblies and detect structural variation. Originally a laborious manual process, Bionano Genomics platforms now offer high-throughput, automated optical mapping based on chips packed with nanochannels through which unwound DNA is guided and the fluorescent DNA backbone and specific restriction sites are recorded. Although the raw image data obtained is of high quality, the processing and assembly software accompanying the platforms is closed source and does not seem to make full use of data, labeling approximately half of the measured signals as unusable. Here we introduce two new software tools, independent of Bionano Genomics software, to extract and process molecules from raw images (OptiScan) and to perform molecule-to-molecule and molecule-to-reference alignments using a novel signal-based approach (OptiMap). We demonstrate that the molecules detected by OptiScan can yield better assemblies, and that the approach taken by OptiMap results in higher use of molecules from the raw data. These tools lay the foundation for a suite of open-source methods to process and analyze high-throughput optical mapping data. The Python implementations of the OptiTools are publicly available through http://www.bif.wur.nl/.

bioinformatics↗
bioRxiv2021

Chop-n-drop: in silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. However whether such fragmentation patterns are sufficiently characteristic of proteins to identify them in complex samples remained unclear. In the simulation study presented here, we show that 97.9% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 78.8% can still be identified if the resolution is twice as low as currently attainable and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof-of-concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.

bioinformatics↗
bioRxiv2021

Fast and adaptive protein structure representations for machine learning

The growing prevalence and popularity of protein structure data, both experimental and computationally modelled, necessitates fast tools and algorithms to enable exploratory and interpretable structure-based machine learning. Alignment-free approaches have been developed for divergent proteins, but proteins sharing func-tional and structural similarity are often better understood via structural alignment, which has typically been too computationally expensive for larger datasets. Here, we introduce the concept of rotation-invariant shape-mers to multiple structure alignment, creating a structure aligner that scales well with the number of proteins and allows for aligning over a thousand structures in 20 minutes. We demonstrate how alignment-free shape-mer counts and aligned structural features, when used in machine learning tasks, can adapt to different levels of functional hierarchy in protein kinases, pinpointing residues and structural fragments that play a role in catalytic activity.

bioinformatics↗
bioRxiv2020

Geometricus Represents Protein Structures as Shape-mers Derived from Moment Invariants

MotivationAs the number of experimentally solved protein structures rises, it becomes increasingly appealing to use structural information for predictive tasks involving proteins. Due to the large variation in protein sizes, folds, and topologies, an attractive approach is to embed protein structures into fixed-length vectors, which can be used in machine learning algorithms aimed at predicting and understanding functional and physical properties. Many existing embedding approaches are alignment-based, which is both time-consuming and ineffective for distantly related proteins. On the other hand, library- or model-based approaches depend on a small library of fragments or require the use of a trained model, both of which may not generalize well. ResultsWe present Geometricus, a novel and universally applicable approach to embedding proteins in a fixed-dimensional space. The approach is fast, accurate, and interpretable. Geometricus uses a set of 3D moment invariants to discretize fragments of protein structures into shape-mers, which are then counted to describe the full structure as a vector of counts. We demonstrate the applicability of this approach in various tasks, ranging from fast structure similarity search, unsupervised clustering, and structure classification across proteins from different superfamilies as well as within the same family. AvailabilityPython code available at https://git.wur.nl/durai001/geometricus Contactaaltjan.vandijk@wur.nl, janani.durairaj@wur.nl

bioinformatics↗
bioRxiv2020

FRETboard: semi-supervised classification of FRET traces

AO_SCPLOWBSTRACTC_SCPLOWForster resonance energy transfer (FRET) is a useful phenomenon in biomolecular investigations, as it can be leveraged for nano-scale measurements. The optical signals produced by such experiments can be analyzed by fitting a statistical model. Several software tools exist to fit such models in an unsupervised manner, but their operating system-dependent installation requirements and lack of flexibility impede wide-spread adoption. Here we propose to fit such models more efficiently and intuitively by adopting a semi-supervised approach, in which the user interactively guides the model to fit a given dataset, and introduce FRETboard, a web tool that allows users to provide such guidance. We show that our approach is able to closely reproduce ground truth FRET statistics in a wide range of simulated single-molecule scenarios, and correctly estimate parameters for up to eleven states. On in vitro data we retrieve parameters identical to those obtained by laborious manual classification in a fraction of the required time. Moreover, we designed FRETboard to be easily extendable to other models, allowing it to adapt to future developments in FRET measurement and analysis. Availabilitysource code is available at https://github.com/cvdelannoy/FRETboard. The FRETboard classification tool is also available as a browser application at https://www.bioinformatics.nl/FRETboard.

biophysics↗
bioRxiv2020

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

BackgroundGenome mining for Biosynthetic Gene Clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools suffer from a bottleneck caused by the expensive network-based approach used to group these BGCs into Gene Cluster Families (GCFs). ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes (MAGs) within ten days on a typical 36-cores CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a "query mode" that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration. ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global, searchable interconnected network of BGCs. As more genomes get sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.

bioinformatics↗
bioRxiv2020

Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia. Author summaryPredicting enzyme function is a popular problem in the bioinformatics field that grows more pressing with the increase in protein sequences, and more attainable with the increase in experimentally characterized enzymes. Terpenes and terpenoids form the largest classes of natural products and find use in many drugs, flavouring agents, and perfumes. Terpene synthases catalyze the biosynthesis of terpenes via multiple cyclizations and carbocation rearrangements, generating a vast array of product skeletons. In this work, we present a three-pronged computational approach to predict carbocation specificity in sesquiterpene synthases, a subset of terpene synthases with one of the highest diversities of products. Using homology modelling, machine learning and co-evolutionary analysis, our approach combines sparse structural data, large amounts of uncharacterized sequence data, and the current set of experimentally characterized enzymes to provide insight into residues and structural regions that likely play a role in determining product specifcity. Similar techniques can be repurposed for function prediction and enzyme engineering in many other classes of enzymes.

bioinformatics↗