Biology subjects

Morales, D.

Publications and source records attributed to Morales, D..

10 recordsLinked to original sources

bioRxiv2025

Basigin Links Altered Skeletal Stem Cell Lineage Dynamics with Glucocorticoid-induced Bone Loss and Impaired Angiogenesis

Glucocorticoid (GC) induced osteoporosis (GIOP) and osteonecrosis remain a significant health issue with few approved therapies that can treat the bone loss and dysfunction of skeletal vasculature. Therefore, we aimed to investigate the cellular and molecular processes by which GCs affect osteogenesis and angiogenesis, as well as how treatment with parathyroid hormone (hPTH 1-34) modifies these effects in a mouse model of GIOP. GC treatment reduced bone mass through decreased bone formation by skeletal stem cells (SSCs) while also increasing osteoclast mediated resorption. Concomitantly, endothelial cells were increased in numbers but displayed distorted phenotypical features. However, hPTH treatment reversed GC induced changes in osteogenesis and angiogenesis to control levels. Transplantation studies of SSCs combined with molecular analysis by single cell RNA-sequencing and functional testing of primary human cells tied GC-induced skeletal changes to altered stem and progenitor cell differentiation dynamics. This in turn perpetuated reduced osteogenesis and vascular malformation through direct SSC-endothelial crosstalk mediated at least in part by Basigin. Intriguingly, antibody-mediated blockade of Basigin during GC treatment prevented detrimental bone loss. In addition, when administered to aged mice, anti-Basigin therapy reinstated bone remodeling to significantly improve bone mass independent of sex. These findings, while helping to explain the cellular and molecular basis of how hPTH treatment can mitigate GC induced bone loss, provide new therapeutic vantage points for GIOP and other conditions associated with bone loss.

cell biology↗

bioRxiv2024

Characterizing the Responses of sorghum and Camelina to Environmental Stress through a Multi-Modal Approach

Due to their sessile nature, plants are unable to escape environmental factors that negatively impact health, resulting in losses to agricultural productivity. Rapid, non-invasive tools to detect plant stress response are essential for optimizing resource efficiency and mitigating the effects of extreme environmental pressures. However, many existing methods are either invasive, incompatible with other measurement techniques, or have not been applied to a wide range of varying environmental factors. In this study, we assess the physiological responses of four week old camelina (Camelina sativa) and sorghum (Sorghum bicolor) to chitosan, cold, drought, and both acute and chronic salt stress. Several plant characteristics were measured in parallel during stress exposure, including fluorescence and gas exchange parameters (MultispeQ and LI-6800), tissue electrical impedance with wearable biosensors (Multi-PIP), and biochemical properties via Fourier-transform infrared (FTIR) spectroscopy. We compiled unique profiles for whole plant physiological changes in response to environmental stress, demonstrating that certain aspects of plant health and makeup underwent alterations on differing temporal scales. This finding emphasizes the need for a comprehensive multi-modal approach to rapidly and accurately perform remote sensing of plant health in the field. Physiological parameters such as leaf impedance were also observed to rapidly change in response to treatment and can be leveraged to detect very early signs of plant perturbation. This research establishes the utility of a holistic phenotyping approach to inform agricultural strategies aimed at enhancing crop resilience under changing environmental conditions.

plant biology↗

bioRxiv2024

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

Extracting the relationship between high-dimensional recordings of neural activity and complex behav- ior is a ubiquitous problem in systems neuroscience. Toward this goal, encoding and decoding models attempt to infer the conditional distribution of neural activity given behavior and vice versa, while dimensionality reduc- tion techniques aim to extract interpretable low-dimensional representations. Variational autoencoders (VAEs) are flexible deep-learning models commonly used to infer low-dimensional embeddings of neural or behavioral data. However, it is challenging for VAEs to accurately model arbitrary conditional distributions, such as those encountered in neural encoding and decoding, and even more so simultaneously. Here, we present a VAE-based approach for accurately calculating such conditional distributions. We validate our approach on a task with known ground truth and demonstrate the applicability to high-dimensional behavioral time series by retrieving the condi- tional distributions over masked body parts of walking flies. Finally, we probabilistically decode motor trajectories from neural population activity in a monkey reach task and query the same VAE for the encoding distribution of neural activity given behavior. Our approach provides a unifying perspective on joint dimensionality reduction and learning conditional distributions of neural and behavioral data, which will allow for scaling common analyses in neuroscience to todays high-dimensional multi-modal datasets.

neuroscience↗

bioRxiv2023

Factors and mediators impacting the number of undergraduate research mentees at a research-intensive Hispanic-serving institution

Engaging in undergraduate research experiences is known to have broad and positive impacts on college students. Despite the benefits, achieving faculty buy-in and support can be challenging even when faculty have strong research funding. In order to understand how to better support undergraduate research programs, we applied quantitative models to explore how the impact of research funding is mediated by faculty beliefs about undergraduate research mentoring in STEM disciplines. The results indicate that faculty characteristics and beliefs about the benefits and barriers of mentoring undergraduate students in research impact the number of students mentored even when accounting for research funding levels of the individual faculty. Practical recommendations are presented based on the models from this research project in order to provide insight into how centers or units on a campus may work with faculty to support and encourage mentoring of undergraduates in research in the biomedical sciences.

scientific communication and education↗

bioRxiv2021

The Effect of A1/A2 Reactive Astrocyte Expression on Hydrocephalus Shunt Failure

Understanding the composition characteristics of the glial scar contributing to the high failure rate of neuroprosthetic devices implanted in the brain has been limited, to date, with the evaluation of cells, tissue, and biomarkers obstructing the implant. However, there remains a critical knowledge gap in gene expression profiles of the obstructing cells. This first-time study investigates the phenotypic expression specific to astrocyte scarring from those cells on hydrocephalus shunt surfaces at the time of failure, aimed at the development of therapeutic approaches to target reactive astrocytes for improved functional outcome. Recent evidence has indicated that the tissue obstructing shunts is over 80% inflammatory, with a more exaggerated astrocytic response. To understand how to mitigate the astrocyte immune response to shunts, we performed gene expression profiling of the C3 and EMP1 genes to quantify if astrocytes were classically activated and pro-inflammatory (A1) or alternatively activated and anti-inflammatory (A2), respectively. Shunt catheters were removed from patients at the time of failure and categorized by obstructed vs non-obstructed shunts. RNAscope fluorescent in situ hybridization and quantitative PCR analysis of the C3 and EMP1 expressed genes revealed that a heterogeneous mixed population of both the A1 and A2 reactive phenotype exist on the shunt surface. However, the number of A2 reactive astrocytes are significantly higher on obstructed shunts compared to A1 reactive astrocytes. ELISA data also confirmed higher levels of IL-6 for obstructed shunts involved in A2 reactive astrocyte proliferation and glial scar formation on the shunt surface. Since TNF- and IL-1{beta} propel resting astrocytes into an A2 reactive state, by simply blocking the secretion or action of these cytokines, astrocyte activation and attachment on obstructing shunts could be inhibited.

bioengineering↗

bioRxiv2021

Distinguishing dopamine and calcium responses using XNA-nanotube sensors for improved neurochemical sensing

To date, the engineering of single-stranded DNA-SWCNT (DNA-SWCNT) optical biosensors have largely focused on creating sensors for new applications with little focus on optimising existing sensors for in vitro and in vivo conditions. Recent studies have shown that nanotube fluorescence can be severely impacted by changes in local cation concentrations. This is particularly problematic for neurotransmitter sensing applications as spatial and temporal fluctuations in the concentration of cations, such as Na+, K+, or Ca2+, play a central role in neuromodulation. This can lead to inaccuracies in the determination of neurotransmitter concentrations using DNA-SWCNT sensors, which limits their use for detecting and treating neurological diseases. Herein, we present new approaches using locked nucleic acid (LNA) to engineer SWCNT sensors with improved stability towards cation-induced fluorescence changes. By incorporating LNA bases into the (GT)15-DNA sequence, we create sensors that are not only more resistant towards undesirable fluorescence modulation in the presence of Ca2+ but that also retain their capabilities for the label-free detection of dopamine. The synthetic biology approach presented in this work therefore serves as a complementary means for enhancing nanotube optoelectronic behavior, unlocking previously unexplored possibilities for developing nano-bioengineered sensors with augmented capabilities.

bioengineering↗

bioRxiv2020

LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals

Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here, we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3Ds versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals.

animal behavior and cognition↗

bioRxiv2019

DeepFly3D: A deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. However, the unique challenges associated with transforming these 2D measurements into reliable and precise 3D poses have not been addressed for small animals including the fly, Drosophila melanogaster. Here we present DeepFly3D, a software that infers the 3D pose of tethered, adult Drosophila--or other animals--using multiple camera images. DeepFly3D does not require manual calibration, uses pictorial structures to automatically detect and correct pose estimation errors, and uses active learning to iteratively improve performance. We demonstrate more accurate unsupervised behavioral embedding using 3D joint angles rather than commonly used 2D pose data. Thus, DeepFly3D enables the automated acquisition of behavioral measurements at an unprecedented level of resolution for a variety of biological applications.

animal behavior and cognition↗

bioRxiv2019

Mouse Model Systems of Autism Spectrum Disorder: Replicability and Informatics Signature

3.BackgroundPhenotyping mouse model systems of human disease has proven to be a difficult task, with frequent poor inter- and intra-laboratory replicability and translatability, particularly in behavioral domains such as social and verbal function. However, establishing robust animal model systems with strong construct validity is of fundamental importance as they are central tools for understanding disease pathophysiology and developing therapeutics. To complete our studies of mouse model systems relevant to autism spectrum disorder (ASD), we present a replication of the main findings from our two published studies comprising five genetic mouse model systems of ASD. MethodsTo assess the robustness of our previous results, we chose the two model systems that showed the greatest phenotypic differences, the Shank3/F and Cntnap2, and repeated assessments of general health, activity, and social behavior. We additionally explored all five model systems in the same framework, comparing all results obtained in this three-yearlong effort using informatics techniques to look for commonalities and differences. ResultsResults in the current study were very similar to our previously published results. The informatics signatures of the two model systems chosen for the replication showed that they were most distinguished by activity levels. Although the two model systems were opposite in this regard, those aspects of their social behavior not confounded by activity (vocalizations) were similar. ConclusionsOur results showed high intra-laboratory replicability of results, even for those with effect sizes that were not particularly large, suggesting that discrepancies in the literature may be dependent on subtle differences in testing conditions, housing enrichment, or background strains and not so much on the variability of the behavioral phenotypes. The overall informatics analysis suggests two main classes of model systems that in some aspects lie on opposite ends of the behavioral spectrum, supporting the view that autism is not a unitary concept.

animal behavior and cognition↗

bioRxiv2018

The endosomal sorting adaptor HD-PTP is required for ephrin-B:EphB signalling in cell collapse and motor axon guidance

The signalling output of many transmembrane receptors that mediate cell-cell communication is restricted by the endosomal sorting complex required for transport (ESCRT), but the impact of this machinery on Eph tyrosine kinase receptor function is unknown. We identified the ESCRT-associated adaptor protein HD-PTP as part of an EphB2 BioID interactome, and confirmed this association using co-immunoprecipitation. Although HD-PTP loss does not change EphB2 expression, it attenuates the ephrin-B2:EphB2 signalling-induced collapse of cultured cells and axonal growth cones, and results in aberrant guidance of chick spinal motor neuron axons in vivo HD-PTP depletion abrogates ligand-induced EphB2 clustering, and EphB2 and Src family kinase activation. HD-PTP deficiency also accelerates ligand-induced EphB2 degradation, contrasting the phenotypes reported for other cell surface receptors. Our results link Eph signalling to the ESCRT machinery and demonstrate a role for HD-PTP in the earliest steps of ephrin-B:EphB signalling, as well as in obstructing premature receptor depletion.

cell biology↗