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Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research
Get PDFNeuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain’s cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry
Cellular anatomy of the mouse primary motor cortex.
Get PDFAn essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture
WRKY transcription factors and ethylene signaling modify root growth during the shade avoidance response
No full textShade-intolerant plants rapidly elongate their stems, branches, and leaf stalks to compete with neighboring vegetation, maximizing sunlight capture for photosynthesis. This rapid growth adaptation, known as the shade avoidance response (SAR), comes at a cost: reduced biomass, crop yield, and root growth. Significant progress has been made on the mechanistic understanding of hypocotyl elongation during SAR; however, the molecular interpretation of root growth repression is not well understood. Here, we explore the mechanisms by which SAR induced by low red:far-red light restricts primary and lateral root (LR) growth. By analyzing the whole-genome transcriptome, we identified a core set of shade-induced genes in roots of Arabidopsis (Arabidopsis thaliana) and tomato (Solanum lycopersicum) seedlings grown in the shade. Abiotic and biotic stressors also induce many of these shade-induced genes and are predominantly regulated by WRKY transcription factors. Correspondingly, a majority of WRKY genes were among the shade-induced genes. Functional analysis using transgenics of these shade-induced WRKYs revealed that their role is essentially to restrict primary root and LR growth in the shade; captivatingly, they did not affect hypocotyl elongation. Similarly, we also found that ethylene hormone signaling is necessary for limiting root growth in the shade. We propose that during SAR, shade-induced WRKY26, 45, and 75, and ethylene reprogram gene expression in the root to restrict its growth and development
Connecting copper and cancer: from transition metal signalling to metalloplasia
No full textCopper is an essential nutrient whose redox properties make it both beneficial and toxic to the cell. Recent progress in studying transition metal signalling has forged new links between researchers of different disciplines that can help translate basic research in the chemistry and biology of copper into clinical therapies and diagnostics to exploit copper-dependent disease vulnerabilities. This concept is particularly relevant in cancer, as tumour growth and metastasis have a heightened requirement for this metal nutrient. Indeed, the traditional view of copper as solely an active site metabolic cofactor has been challenged by emerging evidence that copper is also a dynamic signalling metal and metalloallosteric regulator, such as for copper-dependent phosphodiesterase 3B (PDE3B) in lipolysis, mitogen-activated protein kinase kinase 1 (MEK1) and MEK2 in cell growth and proliferation and the kinases ULK1 and ULK2 in autophagy. In this Perspective, we summarize our current understanding of the connection between copper and cancer and explore how challenges in the field could be addressed by using the framework of cuproplasia, which is defined as regulated copper-dependent cell proliferation and is a representative example of a broad range of metalloplasias. Cuproplasia is linked to a diverse array of cellular processes, including mitochondrial respiration, antioxidant defence, redox signalling, kinase signalling, autophagy and protein quality control. Identifying and characterizing new modes of copper-dependent signalling offers translational opportunities that leverage disease vulnerabilities to this metal nutrient
Accelerated SuFEx Click Chemistry For Modular Synthesis
No full textSuFEx click chemistry is a powerful method designed for the selective, rapid and modular synthesis of functional molecules. Classical SuFEx reactions form stable S-O linkages upon exchange of S-F bonds with aryl silyl-ether substrates, and while near-perfect in their outcome, are sometimes disadvantaged by relatively high catalyst loadings and prolonged reaction times. We herein report the development of 'Accelerated SuFEx Click Chemistry' (ASCC), an improved SuFEx method for the efficient and catalytic coupling of aryl and alkyl alcohols with a range of SuFExable hubs. We demonstrate Barton's hindered guanidine base (2- tert -butyl-1,1,3,3-tetramethylguanidine; BTMG) as a superb SuFEx catalyst that, when used in synergy with silicon additive hexamethyldisilazane (HMDS), yields stable S-O bond linkages in a single step; often within minutes. The powerful combination of BTMG and HMDS reagents allows for catalyst loadings as low as 1.0 mol% and, in congruence with click-principles, provides a scalable method that is safe, efficient, and practical for modular synthesis. ASSC expands the number of accessible SuFEx products and will find significant application in organic synthesis, medicinal chemistry, chemical biology, and materials science
Sketching and sampling approaches for fast and accurate long read classification
Get PDFIn modern sequencing experiments, identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read.
Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a “screen”) of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read’s similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy.
The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching
Bottom-up inputs are required for establishment of top-down connectivity onto cortical layer 1 neurogliaform cells
No full textHigher-order projections to sensory cortical areas converge on layer 1 (L1), the primary site for integration of top-down information via the apical dendrites of pyramidal neurons and L1 GABAergic interneurons. Here we investigated the contribution of early thalamic inputs onto L1 interneurons for establishment of top-down connectivity in the primary visual cortex. We find that bottom-up thalamic inputs predominate during L1 development and preferentially target neurogliaform cells. We show that these projections are critical for the subsequent strengthening of top-down inputs from the anterior cingulate cortex onto L1 neurogliaform cells. Sensory deprivation or selective removal of thalamic afferents blocked this phenomenon. Although early activation of the anterior cingulate cortex resulted in premature strengthening of these top-down afferents, this was dependent on thalamic inputs. Our results demonstrate that proper establishment of top-down connectivity in the visual cortex depends critically on bottom-up inputs from the thalamus during postnatal development
PTP1B is an intracellular checkpoint that limits T cell and CAR T cell anti-tumor immunity.
No full textImmunotherapies aimed at alleviating the inhibitory constraints on Tcells have revolutionised cancer management. To date, these have focused on the blockade of cell surface checkpoints such as PD-1. Herein we identify protein-tyrosine-phosphatase-1B (PTP1B) as an intracellular checkpoint that is upregulated in T cells in tumors. We show that the increased PTP1B limits T cell expansion and cytotoxicity to contribute to tumor growth. T cell-specific PTP1B deletion increased STAT-5 signaling and this enhanced the antigen-induced expansion and cytotoxicity of CD8+ T cells to suppress tumor growth. The pharmacological inhibition of PTP1B recapitulated the T cell-mediated repression of tumor growth and enhanced the response to PD-1 blockade. Furthermore, the deletion or inhibition of PTP1B enhanced the efficacy of adoptively-transferred chimeric-antigen-receptor (CAR) T cells against solid tumors. Our findings identify PTP1B as an intracellular checkpoint whose inhibition can alleviate the inhibitory constraints on T cells and CAR T cells to combat cancer
Investigating rare pathogenic/likely pathogenic exonic variation in bipolar disorder.
Get PDFBipolar disorder (BD) is a serious mental illness with substantial common variant heritability. However, the role of rare coding variation in BD is not well established. We examined the protein-coding (exonic) sequences of 3,987 unrelated individuals with BD and 5,322 controls of predominantly European ancestry across four cohorts from the Bipolar Sequencing Consortium (BSC). We assessed the burden of rare, protein-altering, single nucleotide variants classified as pathogenic or likely pathogenic (P-LP) both exome-wide and within several groups of genes with phenotypic or biologic plausibility in BD. While we observed an increased burden of rare coding P-LP variants within 165 genes identified as BD GWAS regions in 3,987 BD cases (meta-analysis OR = 1.9, 95% CI = 1.3-2.8, one-sided p = 6.0 × 10-4), this enrichment did not replicate in an additional 9,929 BD cases and 14,018 controls (OR = 0.9, one-side p = 0.70). Although BD shares common variant heritability with schizophrenia, in the BSC sample we did not observe a significant enrichment of P-LP variants in SCZ GWAS genes, in two classes of neuronal synaptic genes (RBFOX2 and FMRP) associated with SCZ or in loss-of-function intolerant genes. In this study, the largest analysis of exonic variation in BD, individuals with BD do not carry a replicable enrichment of rare P-LP variants across the exome or in any of several groups of genes with biologic plausibility. Moreover, despite a strong shared susceptibility between BD and SCZ through common genetic variation, we do not observe an association between BD risk and rare P-LP coding variants in genes known to modulate risk for SCZ