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Morphological diversity of single neurons in molecularly defined cell types.
Get PDFDendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits
An ethylene biosynthesis enzyme controls quantitative variation in maize ear length and kernel yield.
Get PDFMaize ear size and kernel number differ among lines, however, little is known about the molecular basis of ear length and its impact on kernel number. Here, we characterize a quantitative trait locus, qEL7, to identify a maize gene controlling ear length, flower number and fertility. qEL7 encodes 1-aminocyclopropane-1- carboxylate oxidase2 (ACO2), a gene that functions in the final step of ethylene biosynthesis and is expressed in specific domains in developing inflorescences. Confirmation of qEL7 by gene editing of ZmACO2 leads to a reduction in ethylene production in developing ears, and promotes meristem and flower development, resulting in a ~13.4% increase in grain yield per ear in hybrids lines. Our findings suggest that ethylene serves as a key signal in inflorescence development, affecting spikelet number, floral fertility, ear length and kernel number, and also provide a tool to improve grain productivity by optimizing ethylene levels in maize or in other cereals
Local adaptation and archaic introgression shape global diversity at human structural variant loci.
Get PDFLarge genomic insertions and deletions are a potent source of functional variation, but are challenging to resolve with short-read sequencing, limiting knowledge of the role of such structural variants (SVs) in human evolution. Here, we used a graph-based method to genotype long-read-discovered SVs in short-read data from diverse human genomes. We then applied an admixture-aware method to identify 220 SVs exhibiting extreme patterns of frequency differentiation - a signature of local adaptation. The top two variants traced to the immunoglobulin heavy chain locus, tagging a haplotype that swept to near fixation in certain southeast Asian populations, but is rare in other global populations. Further investigation revealed evidence that the haplotype traces to gene flow from Neanderthals, corroborating the role of immune-related genes as prominent targets of adaptive introgression. Our study demonstrates how recent technical advances can help resolve signatures of key evolutionary events that remained obscured within technically challenging regions of the genome
Lapses in perceptual decisions reflect exploration.
Get PDFPerceptual decision-makers often display a constant rate of errors independent of evidence strength. These 'lapses' are treated as a nuisance arising from noise tangential to the decision, e.g. inattention or motor errors. Here, we use a multisensory decision task in rats to demonstrate that these explanations cannot account for lapses' stimulus dependence. We propose a novel explanation: lapses reflect a strategic trade-off between exploiting known rewarding actions and exploring uncertain ones. We tested this model's predictions by selectively manipulating one action's reward magnitude or probability. As uniquely predicted by this model, changes were restricted to lapses associated with that action. Finally, we show that lapses are a powerful tool for assigning decision-related computations to neural structures based on disruption experiments (here, posterior striatum and secondary motor cortex). These results suggest that lapses reflect an integral component of decision-making and are informative about action values in normal and disrupted brain states
Enhancing grain-yield-related traits by CRISPR-Cas9 promoter editing of maize CLE genes.
No full textSeveral yield-related traits selected during crop domestication and improvement1,2 are associated with increases in meristem size3, which is controlled by CLE peptide signals in the CLAVATA-WUSCHEL pathway4-13. Here, we engineered quantitative variation for yield-related traits in maize by making weak promoter alleles of CLE genes, and a null allele of a newly identified partially redundant compensating CLE gene, using CRISPR-Cas9 genome editing. These strategies increased multiple maize grain-yield-related traits, supporting the enormous potential for genomic editing in crop enhancement
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks.
Get PDFDeep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k-mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias
Exploring the Three-Dimensional Architectures of Two Families of Large Pore Channels (CALHM1,2 and Pannexin1)
No full textReconsidering Dexamethasone for Antiemesis when Combining Chemotherapy and Immunotherapy
No full textWhether the immune suppressive action of glucocorticoid steroids, such as dexamethasone, might reduce the benefits of cancer immunotherapy has long been a concern. Observations that established tumor regressions in response to immune checkpoint inhibitors (ICIs) often persist, despite the use of steroids to mitigate ICI-related autoimmune breakthrough, are not sufficiently reassuring, because these observations do not address the potential blunting of immune priming at the initiation of ICI therapy. With increasing indications for ICI in combination with chemotherapy, this issue merits reconsideration. Professional society guidance advises that dexamethasone should be used as first-line prophylaxis for nausea and vomiting in patients receiving ICI and highly emetogenic chemotherapy combination regimens. Here, we review the availability of data on this subject and propose an alternative approach focused on the adoption of steroid minimization or sparing for prophylaxis of nausea until the underlying immune biology is better understood
BOSC 2021, the 22nd Annual Bioinformatics Open Source Conference
Get PDFThe 22nd annual Bioinformatics Open Source Conference (BOSC 2021, open-bio.org/events/bosc-2021/) was held online as a track of the 2021 Intelligent Systems for Molecular Biology / European Conference on Computational Biology (ISMB/ECCB) conference. Launched in 2000 and held every year since, BOSC is the premier meeting covering topics related to open source software and open science in bioinformatics. In 2020, BOSC partnered with the Galaxy Community Conference to form the Bioinformatics Community Conference (BCC2020); that was the first BOSC to be held online. This year, BOSC returned to its roots as part of ISMB/ECCB 2021. As in 2020, the Covid-19 pandemic made it impossible to hold the conference in person, so ISMB/ECCB 2021 took place as an online meeting attended by over 2000 people from 79 countries. Nearly 200 people participated in BOSC sessions, which included 27 talks reviewed and selected from submitted abstracts, and three invited keynote talks representing a range of global perspectives on the role of open science and open source in driving research and inclusivity in the biosciences, one of which was presented in French with English subtitles
