Allelic variants confer Arabidopsis adaptation to small regional environmental differences.

Natural populations of Arabidopsis thaliana provide powerful systems to study the 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 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 but it is also sufficiently rich to elucidate the genetic and environmental factors underlying natural plant adaptation.

Adaptation of Fusarium Head Blight Pathogens to Changes in Agricultural Practices and Human Migration.

Fusarium head blight (FHB) is one of the most destructive wheat diseases worldwide. To understand the impact of human migration and changes in agricultural practices on crop pathogens, here population genomic analysis with 245 representative strains from a collection of 4,427 field isolates of Fusarium asiaticum, the causal agent of FHB in Southern China is conducted. Three populations with distinct evolution trajectories are identifies over the last 10,000 years that can be correlated with historically documented changes in agricultural practices due to human migration caused by the Southern Expeditions during the Jin Dynasty. The gradual decrease of 3ADON-producing isolates from north to south along with the population structure and spore dispersal patterns shows the long-distance (>250 km) dispersal of F. asiaticum. These insights into population dynamics and evolutionary history of FHB pathogens are corroborated by a genome-wide analysis with strains originating from Japan, South America, and the USA, confirming the adaptation of FHB pathogens to cropping systems and human migration.

A catalogue of recombination coldspots in interspecific tomato hybrids.

Increasing natural resistance and resilience in plants is key for ensuring food security within a changing climate. Breeders improve these traits by crossing cultivars with their wild relatives and introgressing specific alleles through meiotic recombination. However, some genomic regions are devoid of recombination especially in crosses between divergent genomes, limiting the combinations of desirable alleles. Here, we used pooled-pollen sequencing to build a map of recombinant and non-recombinant regions between tomato and five wild relatives commonly used for introgressive tomato breeding. We detected hybrid-specific recombination coldspots that underscore the role of structural variations in modifying recombination patterns and maintaining genetic linkage in interspecific crosses. Crossover regions and coldspots show strong association with specific TE superfamilies exhibiting differentially accessible chromatin between somatic and meiotic cells. About two-thirds of the genome are conserved coldspots, located mostly in the pericentromeres and enriched with retrotransposons. The coldspots also harbor genes associated with agronomic traits and stress resistance, revealing undesired consequences of linkage drag and possible barriers to breeding. We presented examples of linkage drag that can potentially be resolved by pairing tomato with other wild species. Overall, this catalogue will help breeders better understand crossover localization and make informed decisions on generating new tomato varieties.

Nanopores Reveal the Stoichiometry of Single Oligoadenylates 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 and confirmed the results by liquid chromatography-mass spectroscopy (LC-MS). Interestingly, both variants produce cOAs of nearly identical composition (within experimental uncertainties), 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.

Oomycete Metabolism Is Highly Dynamic and Reflects Lifestyle Adaptations.

The selective pressure of pathogen-host symbiosis drives adaptations. How these interactions shape the metabolism of pathogens is largely unknown. Here, we use comparative genomics to systematically analyze the metabolic networks of oomycetes, a diverse group of eukaryotes that includes saprotrophs as well as animal and plant pathogens, with the latter causing devastating diseases with significant economic and/or ecological impacts. In our analyses of 44 oomycete species, we uncover considerable variation in metabolism that can be linked to lifestyle differences. Comparisons of metabolic gene content reveal that plant pathogenic oomycetes have a bipartite metabolism consisting of a conserved core and an accessory set. The accessory set can be associated with the degradation of defense compounds produced by plants when challenged by pathogens. Obligate biotrophic oomycetes have smaller metabolic networks, and taxonomically distantly related biotrophic lineages display convergent evolution by repeated gene losses in both the conserved as well as the accessory set of metabolisms. When investigating to what extent the metabolic networks in obligate biotrophs differ from those in hemibiotrophic plant pathogens, we observe that the losses of metabolic enzymes in obligate biotrophs are not random and that gene losses predominantly influence the terminal branches of the metabolic networks. Our analyses represent the first metabolism-focused comparison of oomycetes at this scale and will contribute to a better understanding of the evolution of oomycete metabolism in relation to lifestyle adaptation. Numerous oomycete species are devastating plant pathogens that cause major damage in crops and natural ecosystems. Their interactions with hosts are shaped by strong selection, but how selection affects adaptation of the primary metabolism to a pathogenic lifestyle is not yet well established. By pan-genome and metabolic network analyses of distantly related oomycete pathogens and their nonpathogenic relatives, we reveal considerable lifestyle- and lineage-specific adaptations. This study contributes to a better understanding of metabolic adaptations in pathogenic oomycetes in relation to lifestyle, host, and environment, and the findings will help in pinpointing potential targets for disease control. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Full-length single-molecule protein fingerprinting.

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis.

Protein interaction mapping reveals widespread targeting of development-related host transcription factors by phytoplasma effectors.

Phytoplasmas are pathogenic bacteria that reprogram plant host development for their own benefit. Previous studies have characterized a few different phytoplasma effector proteins that destabilize specific plant transcription factors. However, these are only a small fraction of the potential effectors used by phytoplasmas; therefore, the molecular mechanisms through which phytoplasmas modulate their hosts require further investigation. To obtain further insights into the phytoplasma infection mechanisms, we generated a protein-protein interaction network between a broad set of phytoplasma effectors and a large, unbiased collection of Arabidopsis thaliana transcription factors and transcriptional regulators. We found widespread, but specific, interactions between phytoplasma effectors and host transcription factors, especially those related to host developmental processes. In particular, many unrelated effectors target specific sets of TCP transcription factors, which regulate plant development and immunity. Comparison with other host-pathogen protein interaction networks shows that phytoplasma effectors have unusual targets, indicating that phytoplasmas have evolved a unique and unusual infection strategy. This study contributes a rich and solid data source that guides further investigations of the functions of individual effectors, as demonstrated for some herein. Moreover, the dataset provides insights into the underlying molecular mechanisms of phytoplasma infection.

The use of proton transfer reaction mass spectrometry for high throughput screening of terpene synthases.

In this work, we introduce the application of proton transfer reaction mass spectrometry (PTR-MS) for the selection of improved terpene synthase mutants. In comparison with gas chromatography mass spectrometry (GC-MS)-based methods, PTR-MS could offer advantages by reduction of sample preparation steps and analysis time. The method we propose here allows for minimal sample preparation and analysis time and provides a promising platform for the high throughput screening (HTS) of large enzyme mutant libraries. To investigate the feasibility of a PTR-MS-based screening method, we employed a small library of Callitropsis nootkatensis valencene synthase (CnVS) mutants. Bacterial cultures expressing enzyme mutants were subjected to different growth formats, and headspace terpenes concentrations measured by PTR-Qi-ToF-MS were compared with GC-MS, to rank the activity of the enzyme mutants. For all cultivation formats, including 96 deep well plates, PTR-Qi-ToF-MS resulted in the same ranking of the enzyme variants, compared with the canonical format using 100 mL flasks and GC-MS analysis. This study provides a first basis for the application of rapid PTR-Qi-ToF-MS detection, in combination with multi-well formats, in HTS screening methods for the selection of highly productive terpene synthases.

baseLess: lightweight detection of sequences in raw MinION data.

Summary: With 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 mid-tier 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 monitoring or species identification, 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 20 bacterial species, on an inexpensive single-board computer. Availability and implementation: baseLess and all code used in data preparation and validation are 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. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

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

It has been 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 generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data were 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 that only relies on the sequence information of the first eight codons. In this region, close to the translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI) is the key determinant of protein production. This study clearly demonstrates the key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

Beyond sequence: Structure-based machine learning.

Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.

The genome sequence of Hirschfeldia incana, a new Brassicaceae model to improve photosynthetic light-use efficiency.

Photosynthesis is a key process in sustaining plant and human life. Improving the photosynthetic capacity of agricultural crops is an attractive means to increase their yields. While the core mechanisms of photosynthesis are highly conserved in C3 plants, these mechanisms are very flexible, allowing considerable diversity in photosynthetic properties. Among this diversity is the maintenance of high photosynthetic light-use efficiency at high irradiance as identified in a small number of exceptional C3 species. Hirschfeldia incana, a member of the Brassicaceae family, is such an exceptional species, and because it is easy to grow, it is an excellent model for studying the genetic and physiological basis of this trait. Here, we present a reference genome of H. incana and confirm its high photosynthetic light-use efficiency. While H. incana has the highest photosynthetic rates found so far in the Brassicaceae, the light-saturated assimilation rates of closely related Brassica rapa and Brassica nigra are also high. The H. incana genome has extensively diversified from that of B. rapa and B. nigra through large chromosomal rearrangements, species-specific transposon activity, and differential retention of duplicated genes. Duplicated genes in H. incana, B. rapa, and B. nigra that are involved in photosynthesis and/or photoprotection show a positive correlation between copy number and gene expression, providing leads into the mechanisms underlying the high photosynthetic efficiency of these species. Our work demonstrates that the H. incana genome serves as a valuable resource for studying the evolution of high photosynthetic light-use efficiency and enhancing photosynthetic rates in crop species.

Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS.

Expression quantitative trait locus mapping has been widely used to study the genetic regulation of gene expression in Arabidopsis thaliana. As a result, a large amount of expression quantitative trait locus data has been generated for this model plant; however, only a few causal expression quantitative trait locus genes have been identified, and experimental validation is costly and laborious. A prioritization method could help speed up the identification of causal expression quantitative trait locus genes. This study extends the machine-learning-based QTG-Finder2 method for prioritizing candidate causal genes in phenotype quantitative trait loci to be used for expression quantitative trait loci by adding gene structure, protein interaction, and gene expression. Independent validation shows that the new algorithm can prioritize 16 out of 25 potential expression quantitative trait locus causal genes within the top 20% rank. Several new features are important in prioritizing causal expression quantitative trait locus 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 expression quantitative trait locus 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.

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories.

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 test 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.

PanTools v3: functional annotation, classification and phylogenomics.

SUMMARY: The ever-increasing number of sequenced genomes necessitates the development of pangenomic approaches for comparative genomics. Introduced in 2016, PanTools is a platform that allows pangenome construction, homology grouping and pangenomic read mapping. The use of graph database technology makes PanTools versatile, applicable from small viral genomes like SARS-CoV-2 up to large plant or animal genomes like tomato or human. Here, we present our third major update to PanTools that enables the integration of functional annotations and provides both gene-level analyses and phylogenetics. AVAILABILITY AND IMPLEMENTATION: PanTools is implemented in Java 8 and released under the GNU GPLv3 license. Software and documentation are available at https://git.wur.nl/bioinformatics/pantools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic prediction in plants: opportunities for ensemble machine learning based approaches.

Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing kernel Hilbert space regression (RKHS), BayesA and BayesB. To explore problem characteristics, we use simulated and real plant traits under different genetic complexity levels determined by the number of Quantitative Trait Loci (QTLs), heritability ( h 2 and h 2 e ), population structure and linkage disequilibrium between causal nucleotides and other SNPs. Results: Decision tree based ensemble ML methods are a better choice for nonlinear phenotypes and are comparable to Bayesian methods for linear phenotypes in the case of large effect Quantitative Trait Nucleotides (QTNs). Furthermore, we find that ML methods are susceptible to confounding due to population structure but less sensitive to low linkage disequilibrium than linear parametric methods. Conclusions: Overall, this provides insights into the role of ML in GP as well as guidelines for practitioners.

Domestication Shapes Recombination Patterns in Tomato.

Meiotic recombination is a biological process of key importance in breeding, to generate genetic diversity and develop novel or agronomically relevant haplotypes. In crop tomato, recombination is curtailed as manifested by linkage disequilibrium decay over a longer distance and reduced diversity compared with wild relatives. Here, we compared domesticated and wild populations of tomato and found an overall conserved recombination landscape, with local changes in effective recombination rate in specific genomic regions. We also studied the dynamics of recombination hotspots resulting from domestication and found that loss of such hotspots is associated with selective sweeps, most notably in the pericentromeric heterochromatin. We detected footprints of genetic changes and structural variants, among them associated with transposable elements, linked with hotspot divergence during domestication, likely causing fine-scale alterations to recombination patterns and resulting in linkage drag.

The miR-200c/141-ZEB2-TGFß axis is aberrant in human T-cell prolymphocytic leukemia.

T-cell prolymphocytic leukemia (T-PLL) is mostly characterized by aberrant expansion of small- to medium-sized prolymphocytes with a mature post-thymic phenotype, high aggressiveness of the disease and poor prognosis. However, T-PLL is more heterogeneous with a wide range of clinical, morphological, and molecular features, which occasionally impedes the diagnosis. We hypothesized that T-PLL consists of phenotypic and/or genotypic subgroups that may explain the heterogeneity of the disease. Multi-dimensional immuno-phenotyping and gene expression profiling did not reveal clear T-PLL subgroups, and no clear T-cell receptor a or ß CDR3 skewing was observed between different T-PLL cases. We revealed that the expression of microRNA (miRNA) is aberrant and often heterogeneous in T-PLL. We identified 35 miRNA that were aberrantly expressed in T-PLL with miR-200c/141 as the most differentially expressed cluster. High miR- 200c/141 and miR-181a/181b expression was significantly correlated with increased white blood cell counts and poor survival. Furthermore, we found that overexpression of miR-200c/141 correlated with downregulation of their targets ZEB2 and TGFßR3 and aberrant TGFß1- induced phosphorylated SMAD2 (p-SMAD2) and p-SMAD3, indicating that the TGFß pathway is affected in T-PLL. Our results thus highlight the potential role for aberrantly expressed oncogenic miRNA in T-PLL and pave the way for new therapeutic targets in this disease.