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Dalal, T.

Publications and source records attributed to Dalal, T..

5 recordsLinked to original sources

A Frontal-Sensory Cortex Circuit Encoding Context-Dependent Stimulus Statistics

Animals continuously estimate the statistics of sensory events in their environment to guide behavior. Yet, how the brain constructs internal models that represent the probability distributions of stimuli through learning, how these models are updated during rapid contextual shifts, and what brain regions integrate contextual and probabilistic information remains unclear. Here, we devised a behavioral task in which mice used rapidly changing contextual information to estimate the probability of upcoming odors. Recordings from the piriform and orbitofrontal cortices during learning revealed two distinct neuronal subpopulations that differentially encoded odor probabilities. One subpopulation exhibited ramping activity that scaled with the estimated probability of the behaviorally relevant odor and sharply dropped at its onset. In contrast, a second subpopulation became active at odor onset and was modulated by prediction. Notably, probability information emerged in the orbitofrontal cortex before the piriform cortex, and silencing the orbitofrontal cortex abolished the propagation of this information to the piriform cortex, impairing probability learning and retrieval but not context identification. These findings reveal a frontal-sensory circuit that facilitates the learning and representation of context-dependent, behaviorally relevant event probabilities, and suggest that learned state transitions, a core feature of a cognitive map, are propagated to the sensory cortex through learning.

neuroscience↗

mRNABench: A curated benchmark for mature mRNA property and function prediction

Messenger RNA (mRNA) is central in gene expression, and its half-life, localization, and translation efficiency drive phenotypic diversity in eukaryotic cells. While supervised learning has widely been used to study the mRNA regulatory code, self-supervised foundation models support a wider range of transfer learning tasks. However, the dearth and homogeneity of standardized benchmarks limit efforts to pinpoint the strengths of various models. Here, we present O_SCPLOWMC_SCPLOWRNABO_SCPLOWENCHC_SCPLOW, a comprehensive benchmarking suite for mature mRNA biology that evaluates the representational quality of mature mRNA embeddings from self-supervised nucleotide foundation models. We curate ten datasets and 59 prediction tasks that broadly capture salient properties of mature mRNA, and assess the performance of 18 families of nucleotide foundation models for a total of 135K experiments. Using these experiments, we study parameter scaling, compositional generalization from learned biological features, and correlations between sequence compressibility and performance. We identify synergies between two self-supervised learning objectives, and pre-train a new Mamba-based model that achieves state-of-the-art performance using 700x fewer parameters. O_SCPLOWMC_SCPLOWRNABO_SCPLOWENCHC_SCPLOW can be found at: https://github.com/morrislab/mRNABench.

genomics↗

Reconstructing the sequence specificities of RNA-binding proteins across eukaryotes

RNA-binding proteins (RBPs) are key regulators of gene expression. Here, we introduce EuPRI (Eukaryotic Protein-RNA Interactions) - a freely available resource of RNA motifs for 34,736 RBPs from 690 eukaryotes. EuPRI includes in vitro binding data for 504 RBPs, including newly collected RNAcompete data for 174 RBPs, along with thousands of reconstructed motifs. We reconstruct these motifs with a new computational platform -- Joint Protein-Ligand Embedding (JPLE) -- which can detect distant homology relationships and map specificity-determining peptides. EuPRI quadruples the number of known RBP motifs, expanding the motif repertoire across all major eukaryotic clades, and assigning motifs to the majority of human RBPs. EuPRI drastically improves knowledge of RBP motifs in flowering plants. For example, it increases the number of Arabidopsis thaliana RBP motifs 7-fold, from 14 to 105. EuPRI also has broad utility for inferring post-transcriptional function and evolutionary relationships. We demonstrate this by predicting a role for 12 Arabidopsis thaliana RBPs in RNA stability and identifying rapid and recent evolution of post-transcriptional regulatory networks in worms and plants. In contrast, the vertebrate RNA motif set has remained relatively stable after its drastic expansion between the metazoan and vertebrate ancestors. EuPRI represents a powerful resource for the study of gene regulation across eukaryotes.

genomics↗

Activity-dependent lateral inhibition enables ensemble synchronization of odor-activated neurons in the olfactory bulb

Information in the brain is represented by the activity of neuronal ensembles. These ensembles are adaptive and dynamic, formed and truncated based on the animals experience. One mechanism by which spatially distributed neurons form an ensemble is by synchronizing their spike times in response to a sensory event. In the olfactory bulb, odor stimulation evokes rhythmic gamma activity in spatially distributed mitral and tufted cells (MTCs). This rhythmic activity is thought to enhance the relay of odor information to the downstream olfactory targets. However, how specifically the odor-activated MTCs are synchronized is unknown. Here, we demonstrate that optogenetic activation of one set of MTCs can gamma-entrain the spiking activity of another set. This lateral synchronization was particularly effective when the recorded MTC fired at the gamma rhythm, facilitating the synchronization of only the substantially active MTCs. Furthermore, we show that lateral synchronization did not depend on the distance between the MTCs and is mediated by granule-cell layer neurons. In contrast, lateral inhibition between MTCs that reduced their firing rates was spatially restricted to adjacent MTCs and was not mediated by granule-cell layer neurons. This dissociation between these two interaction types suggests that they are mediated by different neural circuits. Our findings propose a simple yet robust mechanism by which spatially distributed neurons entrain each other spiking activity to form an ensemble. HighlightsO_LIMTC activation entrains the spike timing of other MTCs in an activity-dependent and distance-independent manner. C_LIO_LIMTC to MTC suppression is activity- and distance-dependent C_LIO_LISpatially distributed Granule cell layer neurons control MTCs spike timing, yet do not substantially affect their odor-evoked firing rate. C_LI

neuroscience↗

PYPE: A Python pipeline for phenome-wide association (PheWAS) and mendelian randomization in investigator-driven phenotypes and genotypes of biobank data

MotivationPhenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover new and unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian Randomization (MR) can be harnessed to make causal statements about a pair of phenotypes (e.g., does one phenotype cause the other?) by comparing the genetic architecture of the phenotypes in question. Thus, approaches that automate both PheWAS and MR can enhance biobank scale analyses, circumventing the need for multiple bespoke tools for each task by providing a comprehensive, end-to-end pipeline to drive scientific discovery. ResultsWe present PYPE, a Python pipeline for running, visualizing, and interpreting PheWAS. Our pipeline allows the researcher to input genotype or phenotype files from the UK Biobank (UKBB) and automatically estimate associations between the chosen independent variables and the phenotypes. PYPE also provides a variety of visualization options including Manhattan and volcano plots and can be used to identify nearby genes and functional consequences of the significant associations. PYPE additionally provides the user with the ability to run Mendelian Randomization (MR) under a variety of causal effect modeling scenarios (e.g., Inverse Variance Weighted Regression, Egger Regression, and Weighted Median Estimation) to identify possible causal relationships between phenotypes. Availability and ImplementationPYPE is a free, open-source project developed entirely in Python and can be found at https://github.com/TaykhoomDalal/pype. PYPE is published under the Apache 2.0 license and supporting documentation can be found at the aforementioned link. ContactChirag_Patel@hms.harvard.edu

bioinformatics↗