Npas4: Linking Neuronal Activity to Memory

Immediate-early genes (IEGs) are rapidly activated after sensory and behavioral experience and are believed to be crucial for converting experience into long-term memory. Neuronal PAS domain protein 4 (Npas4), a recently discovered IEG, has several characteristics that make it likely to be a particularly important molecular link between neuronal activity and memory: it is among the most rapidly induced IEGs, is expressed only in neurons, and is selectively induced by neuronal activity. By orchestrating distinct activity-dependent gene programs in different neuronal populations, Npas4 affects synaptic connections in excitatory and inhibitory neurons, neural circuit plasticity, and memory formation. It may also be involved in circuit homeostasis through negative feedback and psychiatric disorders. We summarize these findings and discuss their implications.National Institutes of Health (U.S.) (Grant MH091220-01

The draft genome of Ditylenchus dipsaci.

Ditylenchus dipsaci is a devastating pest to many crops worldwide. We present the first genome sequence for this species, produced with PacBio sequencing and assembled with CANU. Ditylenchus dipsaci is a devastating pest to many crops worldwide. We present the first genome sequence for this species, produced with PacBio sequencing and assembled with CANU

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Genome annotation. (XLSX 9 kb

The Genomic Impact of Selection for Virulence against Resistance in the Potato Cyst Nematode, <i>Globodera pallida</i>

This work was funded through an AHDB PhD award, the USDA GLOBAL Project and by the Rural and Environmental Science and Analytical Services division of the Scottish Government. Bioinformatics and computational analyses for the generation of the new G. pallida genome assembly were supported by the University of St Andrews Bioinformatics Unit, which is funded by a Wellcome Trust ISSF award (grant 105621/Z/14/Z).Although the use of natural resistance is the most effective management approach against the potato cyst nematode (PCN) Globodera pallida, the existence of pathotypes with different virulence characteristics constitutes a constraint towards this goal. Two resistance sources, GpaV (from Solanum vernei) and H3 from S. tuberosum ssp. andigena CPC2802 (from the Commonwealth Potato Collection) are widely used in potato breeding programmes in European potato industry. However, the use of resistant cultivars may drive strong selection towards virulence, which allows the increase in frequency of virulent alleles in the population and therefore, the emergence of highly virulent nematode lineages. This study aimed to identify Avirulence (Avr) genes in G. pallida populations selected for virulence on the above resistance sources, and the genomic impact of selection processes on the nematode. The selection drive in the populations was found to be specific to their genetic background. At the genomic level, 11 genes were found that represent candidate Avr genes. Most of the variant calls determining selection were associated with H3-selected populations, while many of them seem to be organised in genomic islands facilitating selection evolution. These phenotypic and genomic findings combined with histological studies performed revealed potential mechanisms underlying selection in G. pallida.Publisher PDFPeer reviewe

Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode

The soybean cyst nematode (SCN) [Heterodera glycines Ichinohe] is a devastating pathogen of soybean [Glycine max (L.) Merr.] that is rapidly becoming a global economic issue. Two loci conferring SCN resistance have been identified in soybean, Rhg1 and Rhg4; however, they offer declining protection. Therefore, it is imperative that we identify additional mechanisms for SCN resistance. In this paper, we develop a bioinformatics pipeline to identify protein–protein interactions related to SCN resistance by data mining massive-scale datasets. The pipeline combines two leading sequence-based protein–protein interaction predictors, the Protein–protein Interaction Prediction Engine (PIPE), PIPE4, and Scoring PRotein INTeractions (SPRINT) to predict high-confidence interactomes. First, we predicted the top soy interacting protein partners of the Rhg1 and Rhg4 proteins. Both PIPE4 and SPRINT overlap in their predictions with 58 soybean interacting partners, 19 of which had GO terms related to defense. Beginning with the top predicted interactors of Rhg1 and Rhg4, we implement a “guilt by association” in silico proteome-wide approach to identify novel soybean genes that may be involved in SCN resistance. This pipeline identified 1,082 candidate genes whose local interactomes overlap significantly with the Rhg1 and Rhg4 interactomes. Using GO enrichment tools, we highlighted many important genes including five genes with GO terms related to response to the nematode (GO:0009624), namely, Glyma.18G029000, Glyma.11G228300, Glyma.08G120500, Glyma.17G152300, and Glyma.08G265700. This study is the first of its kind to predict interacting partners of known resistance proteins Rhg1 and Rhg4, forming an analysis pipeline that enables researchers to focus their search on high-confidence targets to identify novel SCN resistance genes in soybean

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

The genome of the yellow potato cyst nematode, Globodera rostochiensis, reveals insights into the basis of parasitism and virulence

Background: The yellow potato cyst nematode, Globodera rostochiensis, is a devastating plant pathogen of global economic importance. This biotrophic parasite secretes effectors from pharyngeal glands, some of which were acquired by horizontal gene transfer, to manipulate host processes and promote parasitism. G. rostochiensis is classified into pathotypes with different plant resistance-breaking phenotypes. Results: We generate a high quality genome assembly for G. rostochiensis pathotype Ro1, identify putative effectors and horizontal gene transfer events, map gene expression through the life cycle focusing on key parasitic transitions and sequence the genomes of eight populations including four additional pathotypes to identify variation. Horizontal gene transfer contributes 3.5 % of the predicted genes, of which approximately 8.5 % are deployed as effectors. Over one-third of all effector genes are clustered in 21 putative ‘effector islands’ in the genome. We identify a dorsal gland promoter element motif (termed DOG Box) present upstream in representatives from 26 out of 28 dorsal gland effector families, and predict a putative effector superset associated with this motif. We validate gland cell expression in two novel genes by in situ hybridisation and catalogue dorsal gland promoter element-containing effectors from available cyst nematode genomes. Comparison of effector diversity between pathotypes highlights correlation with plant resistance-breaking. Conclusions: These G. rostochiensis genome resources will facilitate major advances in understanding nematode plant-parasitism. Dorsal gland promoter element-containing effectors are at the front line of the evolutionary arms race between plant and parasite and the ability to predict gland cell expression a priori promises rapid advances in understanding their roles and mechanisms of action