Biology subjects

Torres-Espin, A.

Publications and source records attributed to Torres-Espin, A..

7 recordsLinked to original sources

bioRxiv2026

Polytraumatic SCI worsens maladaptive plasticity in spinal motor systems

Spinal cord injury (SCI) results in dysfunction of both motor and sensory systems, which can be characterized by neuropathic pain, hypersensitivity, muscular spasticity and rigidity. Most SCIs result from incidents such as vehicle accidents or falls, resulting in polytraumatic SCI that includes peripheral injuries in addition to direct CNS damage. Recent findings suggest that spinal cord synaptic plasticity plays a crucial role in neuropathic pain pathophysiology, specifically in association with spinal sensitization and the consequent onset of AMPA-related maladaptive plasticity. Further findings have demonstrated that nociceptive peripheral stimulation in the acute phase of SCI results in maladaptive spinal synaptic plasticity by overdriving GluA2-lacking calcium-permeable AMPARs (CP-AMPARs). Here, we investigated the effect of a spared nerve injury (SNI) in conjunction with SCI to determine the effect of polytraumatic SCI on maladaptive plasticity in the spinal cord. Near-IR quantitative Western blot analysis demonstrated that SCI+SNI increases spinal GluA1 expression, but not GluA2. Patch-clamp confirmed that AMPAR currents in spinal motorneurons increase after SCI with SNI, and decrease after the administration of NASPM, a CP-AMPAR antagonist. Data-driven analysis using non-linear principal components analysis (NL-PCA) also demonstrated that SCI with SNI produces a multivariate signature of AMPAR plasticity that is observed in other forms of nociceptive peripheral input, indicating a general mechanism for maladaptive plasticity in spinal motor systems in response to polytraumatic SCI.

neuroscience↗

bioRxiv2025

Conditioning electrical stimulation enhances functional rewiring in a mouse model of nerve transfer to treat chronic spinal cord injury

Nerve transfer surgery is a state-of-the-art surgical approach to restore hand and arm function in individuals living with tetraplegia, significantly impacting daily life. While nearly a third of all individuals with chronic SCI may benefit from this intervention, variability in outcomes can limit the functional impact. A bedside-to-bench approach was taken to address the variable response of tetraplegic individuals to nerve transfer surgery. We used a hierarchical multiple factor analysis to evaluate the effects of conditioning electrical stimulation (CES) on outcomes in a mouse model of nerve transfer to treat chronic cervical spinal cord injury. We found that CES of donor nerves one week prior to nerve transfer surgery enhanced anatomical and functional measures of innervation of targeted muscles. Furthermore, CES increased the rate of recovery of naturalistic behavior. While the model has some limitations due to the small size of the rodent, our results support the use of CES as an effective approach to improve outcomes in clinical nerve repair settings.

neuroscience↗

bioRxiv2023

Improving rigor and reproducibility in western blot experiments with the blotRig analysis software

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

neuroscience↗

bioRxiv2023

Sexually dimorphic differences in angiogenesis markers predict brain aging trajectories

Aberrant angiogenesis could contribute to cognitive impairment, representing a therapeutic target for preventing dementia. However, most angiogenesis studies focus on model organisms. To test the relevance of angiogenesis to human cognitive aging, we evaluated associations of circulating blood markers of angiogenesis with brain aging trajectories in two deeply phenotyped human cohorts (n=435, age 74+9) with longitudinal cognitive assessments, biospecimens, structural brain imaging, and clinical data. Machine learning and traditional statistics revealed sex dimorphic associations of plasma angiogenic growth factors with brain aging outcomes. Specifically, angiogenesis is associated with higher executive function and less brain atrophy in younger women (not men), a directionality of association that reverses around age 75. Higher levels of basic fibroblast growth factor, known for pleiotropic effects on multiple cell types, predicted favorable cognitive trajectories. This work demonstrates the relevance of angiogenesis to brain aging with important therapeutic implications for vascular cognitive impairment and dementia.

neuroscience↗

bioRxiv2021

Expert-integrated automated machine learning uncovers hemodynamic predictors in spinal cord injury

Automated machine learning (AutoML) is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed to create prediction models. However, successful translation of ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards discovering reproducible clinical and biological inferences. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics for performance precision and feature instability. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication and identified a detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured evolving clinical practices such as faster time-to-surgery and blood pressure management that affected clinical model validation. Altogether, we illustrate how augmenting AutoML for inferential reproducibility empowers biomedical discovery and builds trust in AI processes towards effective clinical integration.

bioinformatics↗

bioRxiv2021

Open Data Commons for Preclinical Traumatic Brain Injury Research: Empowering Data Sharing and Big Data Analytics

Traumatic brain injury (TBI) is a major unsolved public health problem worldwide with considerable preclinical research dedicated to recapitulating clinical TBI, deciphering the underlying pathophysiology, and developing therapeutics. However, the heterogeneity of clinical TBI and correspondingly in preclinical studies have made translation from bench to bedside difficult. Here, we present the potential of data sharing, data aggregation, and multivariate analytics to integrate heterogeneity and empower researchers. We introduce the Open Data Commons for Traumatic Brain Injury (ODC-TBI.org) as a user-centered web platform and cloudbased repository focused on preclinical TBI research that enables data citation with persistent identifiers, promotes data element harmonization, and follows FAIR data sharing principles. Importantly, the ODC-TBI implements data sharing at the level of individual subjects, thus enabling data reuse for granular big data analytics and data-hungry machine learning approaches. We provide use cases applying descriptive analytics and unsupervised machine learning on pooled ODC-TBI data. Descriptive statistics included subject-level data for 11 published papers (N = 1250 subjects) representing six distinct TBI models across mice and rats (implementing controlled cortical impact, closed head injury, fluid percussion injury, and CHIMERA TBI modalities). We performed principal component analysis (PCA) on cohorts of animals combined through the ODC-TBI to identify persistent inflammatory patterns across different experimental designs. Our workflow ultimately improved the sensitivity of our analyses in uncovering patterns of pro- vs anti-inflammation and oxidative stress without the multiple testing problems of univariate analyses. As the practice of open data becomes increasingly required by the scientific community, ODC-TBI provides a foundation that creates new scientific opportunities for researchers and their work, facilitates multi-dataset and multidimensional analytics, and drives collaboration across molecular and computational biologists to bridge preclinical research to the clinic.

neuroscience↗

bioRxiv2020

Blood RNA Profiles are Diagnostic for Severity in Human Acute Spinal Cord Injury

Biomarkers of spinal cord injury (SCI) could help determine the severity of the injury and facilitate early critical care decision making. We analyzed global gene expression in peripheral white blood cells during the acute injury phase and identified 197 genes whose expression changed after SCI compared to healthy and trauma controls and in direct relation to SCI severity. Unsupervised co-expression network analysis identified several gene modules that predicted injury severity (AIS grades) with an overall accuracy of 72.7% and included signatures of immune cell subtypes. Our findings indicate that global transcriptomic changes in peripheral blood cells have diagnostic and potentially prognostic value for SCI severity.

neuroscience↗