Biology subjects

Allen, G. I.

Publications and source records attributed to Allen, G. I..

5 recordsLinked to original sources

bioRxiv2026

Maternal experience alters brain-wide representation of infant cries

One SentenceMaternal experience fundamentally reorganizes infant cue processing across the entire brain into a more efficient and specialized caregiving state. Pup calls signal the presence of a mouse infant in need of care (Bernd Haack et al., 1983; Ehret, 2005). Previous work demonstrated that experience-dependent plasticity within the left primary auditory cortex is oxytocin-facilitated and enhances neural and behavioral responses to these distress vocalizations (Marlin et al., 2015). However, whether maternal experience reshapes pup call processing beyond auditory regions remains unknown. Here, we show that both innate and learned maternal experience shape pup call-evoked behavior and brain-wide neural responses. Combining behavioral assays, whole-brain activity mapping, and oxytocinergic projection analyses, we find that pup calls recruit distinct large-scale neural networks in naive virgins, experienced virgins, and mothers. With more maternal experience we observe a distributed pup call response network that is sparser yet more strongly coactivated, consistent with a more efficient and specialized neural representation of infant cues. We identified an oxytocin-dense circuit that shows an experience-dependent pup call response pattern. Therefore, we propose that experience-dependent refinement of the brain-wide response to pup calls is facilitated by oxytocin. Taken together, our findings reveal that maternal experience refines brain-wide sensory processing to support adaptive caregiving behavior.

neuroscience↗

bioRxiv2023

SPA-STOCSY: An Automated Tool for Identification of Annotated and Non-Annotated Metabolites in High-Throughput NMR Spectra

Nuclear Magnetic Resonance (NMR) spectroscopy is widely used to analyze metabolites in biological samples, but the analysis can be cumbersome and inaccurate. Here, we present a powerful automated tool, SPA-STOCSY (Spatial Clustering Algorithm - Statistical Total Correlation Spectroscopy), which overcomes the challenges by identifying metabolites in each sample with high accuracy. As a data-driven method, SPA-STOCSY estimates all parameters from the input dataset, first investigating the covariance pattern and then calculating the optimal threshold with which to cluster data points belonging to the same structural unit, i.e. metabolite. The generated clusters are then automatically linked to a compound library to identify candidates. To assess SPA-STOCSYs efficiency and accuracy, we applied it to synthesized and real NMR data obtained from Drosophila melanogaster brains and human embryonic stem cells. In the synthesized spectra, SPA outperforms Statistical Recoupling of Variables, an existing method for clustering spectral peaks, by capturing a higher percentage of the signal regions and the close-to-zero noise regions. In the real spectra, SPA-STOCSY performs comparably to operator-based Chenomx analysis but avoids operator bias and performs the analyses in less than seven minutes of total computation time. Overall, SPA-STOCSY is a fast, accurate, and unbiased tool for untargeted analysis of metabolites in the NMR spectra. As such, it might accelerate the utilization of NMR for scientific discoveries, medical diagnostics, and patient-specific decision making.

bioinformatics↗

bioRxiv2022

Functional screening of lysosomal storage disorder genes identifies modifiers of alpha-synuclein mediated neurodegeneration

Heterozygous variants in the glucocerebrosidase (GBA) gene are common and potent risk factors for Parkinsons disease (PD). GBA also causes the autosomal recessive lysosomal storage disorder (LSD), Gaucher disease, and emerging evidence from human genetics implicates many other LSD genes in PD susceptibility. We have systemically tested 88 conserved fly homologs of 38 human LSD genes for requirements in the aging adult Drosophila brain and for potential genetic interactions with neurodegeneration caused by -synuclein (Syn), which forms Lewy body pathology in PD. Our screen identifies 15 genetic enhancers of Syn-induced progressive locomotor dysfunction, following knockdown of fly homologs of GBA and other LSD genes with independent support as PD susceptibility factors from human genetics (SCARB2, SMPD1, CTSD, GNPTAB, SLC17A5). For several genes, results from multiple alleles support dose-sensitivity and context-dependent pleiotropy in the presence or absence of Syn. Homologs of 2 genes causing cholesterol storage disorders, Npc1a / NPC1 and Lip4 / LIPA, were independently confirmed as loss-of-function enhancers of Syn-induced brain structural degeneration. The enzymes encoded by several modifier genes are upregulated in Syn transgenic flies, based on unbiased proteomics, revealing a possible compensatory response. Overall, our results reinforce the important role of lysosomal genes in brain health and PD pathogenesis, and implicate several metabolic pathways, including cholesterol homeostasis, in Syn-mediated neurodegeneration.

genetics↗

bioRxiv2021

Detection of Junctional Ectopic Tachycardia by Central Venous Pressure

Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for detecting arrhythmia and other cardiac events. In this paper, we develop a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It also corroborates and complements the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.

bioinformatics↗

bioRxiv2020

Correlation imputation in single cell RNA-seq using auxiliary information and ensemble learning

AO_SCPLOWBSTRACTC_SCPLOWSingle cell RNA sequencing is a powerful technique that measures the gene expression of individual cells in a high throughput fashion. However, due to sequencing inefficiency, the data is unreliable due to dropout events, or technical artifacts where genes erroneously appear to have zero expression. Many data imputation methods have been proposed to alleviate this issue. Yet, effective imputation can be difficult and biased because the data is sparse and high-dimensional, resulting in major distortions in downstream analyses. In this paper, we propose a completely novel approach that imputes the gene-by-gene correlations rather than the data itself. We call this method SCENA: Single cell RNA-seq Correlation completion by ENsemble learning and Auxiliary information. The SCENA gene-by-gene correlation matrix estimate is obtained by model stacking of multiple imputed correlation matrices based on known auxiliary information about gene connections. In an extensive simulation study based on real scRNA-seq data, we demonstrate that SCENA not only accurately imputes gene correlations but also outperforms existing imputation approaches in downstream analyses such as dimension reduction, cell clustering, graphical model estimation.

genomics↗