Biology subjects

Shea, E.

Publications and source records attributed to Shea, E..

5 recordsLinked to original sources

bioRxiv2025

Machine learning behavioral analysis reveals cervical instability as an early biomarker of Amyotrophic Lateral Sclerosis

Early detection of neuromuscular disorders is a major clinical challenge, with most diagnoses only occurring after considerable motor neuron degeneration has already taken place. The central problem for early diagnosis of neuromuscular diseases is the subtlety of early symptoms and where to look for them. Without defined behavioral markers, the earliest stages of disease go undetected, delaying intervention and limiting neuroprotective therapeutic evaluation. Here, we present a machine learning (ML) based framework that identifies subtle postural alterations in freely behaving animals. Using longitudinal pose data from SOD1G93A mice, a widely used Amyotrophic Lateral Sclerosis (ALS) mouse model, we focused on postural states in idle periods, behavioral states usually overlooked in disease monitoring. Our analyses revealed consistent deviations in posture and a feature analysis pinpointed cervical instability during adolescence as a key distinguishing feature. We validated these findings through two independent behavioral assays engaging cervical musculature: rearing and wet-dog shakes, both of which showed significant impairments in male SOD1G93A mice as early as 3 weeks of age, many weeks earlier than conventional muscle function assays. This approach establishes an unbiased, non-invasive, scalable strategy for detecting early-stage neuromuscular dysfunction, and provides a foundation both for clinical behavioral biomarker development in ALS and related disorders and will enable evaluation of early neuroprotective interventions.

neuroscience↗

bioRxiv2025

Injured SSTR2+ nociceptor axons in neuromas drive chronic spontaneous neuropathic pain

Spontaneous pain is a common but poorly understood consequence of peripheral nerve injury1-3, including injuries that lead to the formation of neuromas4,5. We developed a chronic neuroma model for measuring spontaneous pain-related behaviours in mice, which revealed that limb flicks - emerging predominantly 2 months post-injury - reflect spontaneous paroxysmal pain. Ectopic activity of injured dorsal root ganglia (DRG) sensory neurons whose axonal endings terminate within the neuroma drives this spontaneous pain. In vivo imaging showed that a subset of small-diameter DRG sensory neurons are the source of spontaneous neural signals emanating from the neuroma, and these spontaneously active neurons are distinct from the intact larger diameter sensory neurons that mediate stimulus-evoked mechanical allodynia from spared nerves. Cell-type-specific gain- and loss-of-function studies identified a genetically- and functionally-defined subtype of small-diameter C-fibre nociceptors whose injured axons in neuromas drive spontaneous limb flicks/neuropathic pain. These findings establish the neurobiological basis of spontaneous pain enabling targeted pain management strategies and define a cellular and mechanistic separation between spontaneous and evoked neuropathic pain.

neuroscience↗

bioRxiv2022

The effects of ethnoracial-related stressors during pregnancy on the developing offspring brain.

We are increasingly aware of the effects of ethnoracial stress on health, with emerging interest in the potential for intergenerational transmission before birth. Here, we investigate the effect of maternal prenatal discrimination and acculturation experiences on fetal growth, birth outcomes, and functional connectivity in the infant brain. In pregnant adolescent women, we collected self-report measures of acculturation (tailored to an adolescent and Latinx population), discrimination, and maternal distress (i.e., stress and depressive symptoms; n=165). Fetal growth were obtained via electronic health records (n=92), and infant amygdala seed connectivity was assessed using functional magnetic resonance imaging (n=38). We found that greater maternal prenatal assimilation to the host culture was associated with slower fetal growth, lower gestational age at birth, and weaker amygdala-fusiform connectivity. Maternal prenatal discrimination was associated with weaker amygdala-prefrontal connectivity. Together, these results suggest intergenerational effects of ethnoracial stressors on the growth and neural development of future generations.

neuroscience↗

bioRxiv2020

Massively parallel phenotyping of variant impact in cancer with Perturb-seq reveals a shift in the spectrum of cell states induced by somatic mutations

Genome sequencing studies have identified millions of somatic variants in cancer, but their phenotypic impact remains challenging to predict. Current experimental approaches to distinguish between functionally impactful and neutral variants require customized phenotypic assays that often report on average effects, and are not easily scaled. Here, we develop a generalizable, high-dimensional, and scalable approach to functionally assess variant impact in single cells by pooled Perturb-seq. Specifically, we assessed the impact of 200 TP53 and KRAS variants in >300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison to orthogonal assays. Surprisingly, KRAS variants did not merely fit into discrete functional categories, but rather spanned a continuum of gain-of-function phenotypes driven by quantitative shifts in cell composition at the single cell level. We further discovered novel gain-of-function KRAS variants whose impact could not have been predicted solely by their occurrence in patient samples. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, which can be applied in cancer and other diseases driven by somatic or germline coding mutations.

genomics↗

bioRxiv2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

Approaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

cancer biology↗