Miracle-dz/UniFAR-FTUs huggingface dataset:
Current population structure and pathogenicity patterns of Ascochyta rabiei in Australia

Ascochyta blight disease, caused by the necrotrophic fungus Ascochyta rabiei, is a major biotic constraint to chickpea production in Australia and worldwide. Detailed knowledge of the structure of the pathogen population and its potential to adapt to our farming practices is key to informing optimal management of the disease. This includes understanding the molecular diversity among isolates and the frequency and distribution of the isolates that have adapted to overcome host resistance across agro-geographically distinct regions. Thanks to continuous monitoring efforts over the past six years, a comprehensive collection of A. rabiei isolates was collated from the major Australian production regions. To determine the molecular structure of the entire population, representative isolates from each collection year and growing region have been genetically characterised using a DArTseq™ genotyping-by-sequencing approach. The genotyped isolates were further phenotyped to determine their pathogenicity levels against a differential set of chickpea cultivars and genotype-phenotype associations were inferred. Overall, the Australian A. rabiei population displayed a far lower genetic diversity (average Nei’s gene diversity of 0.047) than detected in other populations worldwide. This may be explained by the presence of a single mating-type in Australia, MAT1-2, limiting its reproduction to a clonal mode. Despite the low detected molecular diversity, clonal selection appears to have given rise to a subset of adapted isolates that are highly pathogenic on commonly employed resistance sources, and that are occurring at an increasing frequency. To better understand the mechanisms and patterns of the pathogen adaptation, multi-locus genotype analysis was performed and two hypotheses were proposed on how new genotypes emerge. These were: 1) In a local, within-region evolutionary pathway; or 2) Through inter-region dispersal, most likely due to human activities. Furthermore, a cluster of genetically similar isolates was identified, with a higher proportion of highly aggressive isolates than in the general population, indicating the adaptive evolution of a sub-set of isolates that pose a greater risk to the chickpea industry. The discovery of distinct genetic clusters associated with high and low isolate pathogenicity forms the foundation for the development of a molecular pathotyping tool for the Australian A. rabiei population. Application of such a tool, along with continuous monitoring of the genetic structure of the population will provide crucial information for the screening of breeding material and integrated disease management packages. Data Summary An online dataset containing all supporting genotyping and phenotyping data and the code required to reproduce the results, summary tables and plots found in this publication, is publicly available at Zenodo via the following links: https://zenodo.org/record/4311477; DOI: 10.5281/zenodo.4311477 (1).
Cerebral μ-opioid and CB1-receptor systems have distinct roles in human feeding behavior

Eating behavior varies greatly between healthy individuals, but the neurobiological basis of these trait-like differences in feeding remains unknown. Central μ-opioid receptors (MOR) and cannabinoid CB1-receptors (CB1R) regulate energy balance via multiple neural pathways, promoting food intake and reward. Because obesity and eating disorders have been associated with alterations in brain’s opioid and endocannabinoid signaling, the variation in MOR and CB1R systems could potentially underlie distinct eating behavior phenotypes, also in non-obese population. In this retrospective positron emission tomography (PET) study, we analyzed [11C]carfentanil PET scans of MORs from 92 healthy subjects (70 males and 22 females), and [18F]FMPEP-d2 scans of CB1Rs from 35 subjects (all males, all also included in the [11C]carfentanil sample). Eating styles were measured with the Dutch Eating Behavior Questionnaire (DEBQ). We found that lower cerebral MOR availability was associated with increase in external eating – individuals with low MORs reported being more likely to eat in response to environment’s palatable food cues. CB1R availability was negatively associated with multiple eating behavior traits. We conclude that although MORs and CB1Rs overlap anatomically and functionally in the brain, they have distinct roles in mediating individual feeding patterns.
Deep and accurate detection of m6A RNA modifications using miCLIP2 and m6Aboost machine learning

N6-methyladenosine (m6A) is the most abundant internal RNA modification in eukaryotic mRNAs and influences many aspects of RNA processing. miCLIP (m6A individual-nucleotide resolution UV crosslinking and immunoprecipitation) is an antibody-based approach to map m6A sites with single-nucleotide resolution. However, due to broad antibody reactivity, reliable identification of m6A sites from miCLIP data remains challenging. Here, we present miCLIP2 in combination with machine learning to significantly improve m6A detection. The optimised miCLIP2 results in high-complexity libraries from less input material. Importantly, we established a robust computational pipeline to tackle the inherent issue of false positives in antibody-based m6A detection. The analyses are calibrated with Mettl3 knockout cells to learn the characteristics of m6A deposition, including m6A sites outside of DRACH motifs. To make our results universally applicable, we trained a machine learning model, m6Aboost, based on the experimental and RNA sequence features. Importantly, m6Aboost allows prediction of genuine m6A sites in miCLIP2 data without filtering for DRACH motifs or the need for Mettl3 depletion. Using m6Aboost, we identify thousands of high-confidence m6A sites in different murine and human cell lines, which provide a rich resource for future analysis. Collectively, our combined experimental and computational methodology greatly improves m6A identification. Highlights miCLIP2 produces complex libraries to map m6A RNA modifications Mettl3 KO miCLIP2 allows to identify Mettl3-dependent RNA modification sites Machine learning predicts genuine m6A sites from human and mouse miCLIP2 data without Mettl3 KO m6A modifications occur outside of DRACH motifs and associate with alternative splicing
Chromosome-scale genome assembly of Japanese pear (Pyrus pyrifolia) variety ‘Nijisseiki’

Aim The Japanese pear (P. pyrifolia) variety ‘Nijisseiki’ is valued for its superior flesh texture, which has led to its use as a breeding parent for most Japanese pear cultivars. However, in the absence of genomic resources for Japanese pear, the parents of the ‘Nijisseiki’ cultivar remain unknown, as does the genetic basis of its favorable texture. The genomes of pear and related species are complex due to ancestral whole genome duplication and high heterozygosity, and long-sequencing technology was used to address this. Methods and Results De novo assembly of long sequence reads covered 136× of the Japanese pear genome and generated 503.9 Mb contigs consisting of 114 sequences with an N50 value of 7.6□Mb. Contigs were assigned to Japanese pear genetic maps to establish 17 chromosome-scale sequences. In total, 44,876 protein-encoding genes were predicted, 84.3% of which were supported by predicted genes and transcriptome data from Japanese pear relatives. As expected, evidence of whole genome duplication was observed, consistent with related species. Conclusion and Perspective This is the first genome sequence analysis reported for Japanese pear, and this resource will support breeding programs and provide new insights into the physiology and evolutionary history of Japanese pear.
Multiple infiltration and cross-species transmission of foamy viruses across Paleozoic to Cenozoic era

Foamy viruses (FVs) are complex retroviruses that can infect humans and other animals. In this study, by integrating transcriptomic and genomic data, we discovered 412 FVs from 6 lineages in amphibians, which significantly increased the known set of FVs in amphibians. Among these lineages, salamander FVs maintained a co-evolutionary pattern with their hosts that could be dated back to the Paleozoic era, while, on the contrary, frog FVs were much more likely acquired from cross-species (class level) transmission in the Cenozoic era. In addition, we found three distinct FV lineages had integrated into the genome of a salamander. Unexpectedly, we identified a potential exogenous form of FV circulated in caecilian, demonstrating the existence of exogenous form of FV besides mammals. Our discovery of rare phenomena in amphibian FVs has overturned our collective understanding of the macroevolution of the complex retrovirus. Importance Foamy viruses (FVs) represent, more so than other viruses, the best model of co-evolution between a virus and a host. This study represents so far, the largest investigation of amphibian FVs and revealed 412 FVs of 6 distinct lineages from three major orders of amphibians. Besides co-evolutionary pattern, cross-species and repeated infection were also observed during evolution of amphibian FVs. Remarkably, expressed FVs including a potential exogenous form were discovered, suggesting live FVs could be underestimated in nature. These findings revealed the multiple origin and complex evolution of amphibian FVs started from the Paleozoic era.
Optimizing reduction of Western blotting analytical variations: use of replicate test samples, multiple normalization methods, and sample loading positions

Western blot (WB) analysis is widely used, but obtaining consistent results can be problematic, particularly if all the treatments cannot be placed on one gel. Sources of variation were investigated by analyzing a set of samples which should produce statistically identical results, an approach commonly applied to analytical instruments. Test samples were lysates from RAW 264.7 murine macrophages treated with LPS to activate MAPK and NF-kB signaling targets. The resulting WB were probed for p-ERK, ERK, IkBβ and non-target protein levels. The first test set (Set 1) was a pooled cell lysate and the second (Set 2) four independently prepared lysates. Analytical replicate data were generated by using multiple WB. Different normalization methods and sample groupings were applied to the density values and the resulting coefficients of variation (CV) and ratios of maximal to minimal values (Max/Min) were compared. Ideally, as the samples were identical (Set 1), or nearly so (Set 2), CVs would be 0 and the Max/Min 1. Results showed the raw density data variance was composed of variability between gel lanes and between gels; but only between gels after applying normalization calculations. CV and Max/Min were 30-70% and 1.5 for raw data, respectively. Common normalization methods, total lane protein, % Control, and p-ERK/ERK ratios, did not have the lowest CVs or Max/Min values. Some of these methods were counterproductive, greatly increasing CV and Max/Min. The best normalization methods resulted in CV and Max/Min value, as low as 5-10% and 1.1, respectively, but more typically 20-25% and 1.2. Application of these methods should allow reliable interpretation of complex experiments that require samples to be placed on multiple gels to provide results, even if fold changes in the target proteins are relatively modest.
Origin and Evolution of the Cannabinoid Oxidocyclase Gene Family

Cannabis is an ancient crop representing a rapidly increasing legal market, especially for medicinal purposes. Medicinal and psychoactive effects of Cannabis rely on specific terpenophenolic ligands named cannabinoids. Recent whole-genome sequencing efforts have uncovered variation in multiple genes encoding the final steps in cannabinoid biosynthesis. However, the origin, evolution, and phylogenetic relationships of these cannabinoid oxidocyclase genes remain unclear. To elucidate these aspects we performed comparative genomic analyses of Cannabis, related genera within the Cannabaceae family, and selected outgroup species. Results show that cannabinoid oxidocyclase genes originated in the Cannabis lineage from within a larger gene expansion in the Cannabaceae family. Localization and divergence of oxidocyclase genes in the Cannabis genome revealed two main syntenic blocks, each comprising tandemly repeated cannabinoid oxidocyclase genes. By comparing these blocks with those in genomes from closely related species we propose an evolutionary model for the origin, neofunctionalization, duplication, and diversification of cannabinoid oxidocycloase genes. Based on phylogenetic meta-analyses, we propose a comprehensive classification of three main clades and seven subclades that is intended to aid unequivocal referencing and identification of cannabinoid oxidocyclase genes. Our data suggest that cannabinoid oxidocyclase gene copy number variation may have less functional relevance than previously thought. Instead, we propose that cannabinoid phenotype is primarily determined by presence/absence of single-copy genes. Increased sampling across Cannabis’ native geographic range is likely to uncover additional cannabinoid oxidocyclase gene sequence variation. Significance statement Cannabis genome sequencing efforts have revealed extensive cannabinoid oxidocyclase gene variation. However, phylogenetic relationships and evolution of these genes remains unclear. Our meta analysis of currently available data reveals that these genes comprise three main clades and seven subclades that originated through Cannabis-specific gene duplication and divergence. Our new conceptual and evolutionary framework serves as a reference for future description and functional analyses of cannabinoid oxidocyclases.
What we can and what we cannot see with extracellular multielectrodes

Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. Author Summary Every set of measurements is a window into reality rendering its incomplete or distorted picture. It is often difficult to relate the obtained representation of the world to underlying ground truth. Here we show, for brain electrophysiology, for arbitrary experimental setup (distribution of electrodes), and arbitrary analytical setup (function space of current source densities), that one can identify distributions of current sources which can be recovered precisely, and those which are invisible in the system. This shows what is and what is not observable in the studied system for a given setup, allows to improve the analysis results by modifying analytical setup, and facilitates interpretation of the measured sets of LFP, ECoG and EEG recordings.
ICM cells can be partitioned robustly through transient synchronization using secreted FGF4

The differentiation of ICM cells into epiblast (EPI) and primitive endoderm (PE) is central in embryonic development. It is known that FGF4 signaling is important in this process, but it remains unclear how cells can be correctly partitioned. Here we model the NANOG-GATA6-FGF4 network, and test all 64 logical regulatory combinations for their ability to partition a group of cells. We found that nearly all the logic combinations allowed for correct partitioning, including a minimal network where self-activation of NANOG and GATA6 was inactivated. However such self-activation increased the robustness of the system. Furthermore, the model also captured the reported changes in cell proportions in response to FGF perturbations. This constrains the possible regulatory logic and predicts the presence of an “OR” gate in cell-cell communication. We repeatedly found that FGF4 coordinated the decision in two phases: A convergence and a bifurcation phase. First FGF4 negative feedback drives the cells to a balanced “battle” state where most cells have intermediate levels of both regulators, thus being double positive. Subsequent bifurcation happens at constant FGF4 level. Together our results suggest that the frequently observed state of multipotency during differentiation may be an emergent phenomenon resulting from inter-cellular negative feedbacks.
The dynamic landscape of parasitaemia dependent intestinal microbiota shifting at species level and the correlated gut transcriptome during Plasmodium yoelii infection

Background Malaria, caused by Plasmodium, is a global life-threatening infection disease especially during the COVID-19 pandemic. However, it is still unclear about the dynamic change and the interactions between intestinal microbiota and host immunity. Here, we investigated the change of intestinal microbiome and transcriptome during the whole Plasmodium infection process in mice to analyze the dynamic landscape of parasitaemia dependent intestinal microbiota shifting and related to host immunity. Results There were significant parasitaemia dependent changes of intestinal microbiota and transcriptome, and the microbiota was significantly correlated to the intestinal immunity. We found that (i) the diversity and composition of the intestinal microbiota represented a significant correlation along with the Plasmodium infection in family, genus and species level; (ii) the up-regulated genes from the intestinal transcriptome were mainly enriched in immune cell differentiation pathways along with the malaria development, particularly, naive CD4+ T cells differentiation; (iii) the abundance of the parasitaemia phase-specific microbiota represented a high correlation with the phase-specific immune cells development, particularly, Th1 cell with family Bacteroidales BS11 gut group, genera Prevotella 9, Ruminococcaceae UCG 008, Moryella and specie Sutterella*, Th2 cell with specie Sutterella*, Th17 cell with family Peptococcaceae, genus Lachnospiraceae FCS020 group and spices Ruminococcus 1*, Ruminococcus UGG 014* and Eubacterium plexicaudatum ASF492, Tfh and B cell with genera Moryella and species Erysipelotrichaceae bacterium canine oral taxon 255. Conclusion There was a remarkable dynamic landscape of the parasitaemia dependent shifting of intestinal microbiota and immunity, and a notable correlation between the abundance of intestinal microbiota.
Hippocampal neurons construct a map of an abstract value space

The hippocampus is thought to encode a ‘cognitive map’, a structural organization of knowledge about relationships in the world. Place cells, spatially selective hippocampal neurons that have been extensively studied in rodents, are one component of this map, describing the relative position of environmental features. However, whether this map extends to abstract, cognitive information remains unknown. Using the relative reward value of cues to define continuous ‘paths’ through an abstract value space, we show that single neurons in primate hippocampus encode this space through value place fields, much like a rodent’s place neurons encode paths through physical space. Value place fields remapped when cues changed, but also became increasingly correlated across contexts, allowing maps to become generalized. Our findings help explain the critical contribution of the hippocampus to value-based decision-making, providing a mechanism by which knowledge of relationships in the world can be incorporated into reward predictions for guiding decisions.
Auditory statistics development does not rely on vision, but the processing of sound local features is hampered by late-onset sight loss

The auditory system relies on both local and global representations to recognize different sounds. Because local features analysis is constrained by storage capacity, average statistics of these features are computed over time to generate compact representations at the expense of temporal details availability. It is unknown whether these processes are restricted to the auditory system or interact with other modalities, such as vision. To answer this question, we examined how statistical properties of sounds are affected by atypical development of the visual system. We tested sighted and blind participants in two auditory experiments designed to target different strategies for analyzing sounds: one experiment relied on the availability of local features at specific time points; the other one benefitted from global statistical averaging over longer durations. Three groups of participants were recruited: sighted controls (SC), congenitally blind (CB), and late-onset blind (LB) individuals. SC performed as expected: when sound duration increased, local analysis gave way to global analysis. CB individuals showed almost identical patterns of behavior, revealing that auditory processing of local features, as well as statistical averaging of those features, are not altered by the complete absence of vision since birth. Surprisingly, we found that interactions between auditory computations and sight emerge later in development: LB individuals performed more poorly than those who were blind since birth. Crucially, performance was selectively degraded only for the use of local features, with no impact on statistical averaging. Taken together, these results suggested that early development is characterized by a segregation between basic auditory computations and sight, while at later developmental phases a functional interplay between the access to local auditory details and availability of visual input is involved. This interaction appears to be highly selective, as it does not affect other aspects of auditory processing, such as temporal averaging of sound statistics.
Microparticle-based Biochemical Sensing Using Optical Coherence Tomography and Deep Learning

Advancing continuous health monitoring beyond vital signs to biochemistry will revolutionize personalized medicine. Herein, we report a novel platform to achieve remote biochemical monitoring using microparticle-based biosensors and optical coherence tomography (OCT). Stimuli-responsive, polymeric microparticles were designed to serve as freely-dispersible biorecognition units, wherein binding with a target biochemical induces volumetric changes of the microparticle. Analytical approaches to detect these sub-micron changes in 3D using OCT were devised by modeling the microparticle as an optical cavity, enabling estimations far below the resolution of the OCT system. As a proof of concept, we demonstrated the 3D spatiotemporal monitoring of glucose-responsive microparticles distributed throughout a tissue-mimic in response to dynamically-fluctuating levels of glucose. Deep learning was further implemented using 3D convolutional neural networks to automate the vast processing of the continuous stream of three-dimensional time series data, resulting in a robust end-to-end pipeline with immense potential for continuous in vivo biochemical monitoring.