Biology subjects

Kelley, E.

Publications and source records attributed to Kelley, E..

9 recordsLinked to original sources

bioRxiv2026

Proteome-wide discovery of novel diagnostic antigens for coccidioidomycosis

Coccidioidomycosis, commonly called Valley Fever (VF), caused by Coccidioides spp., is an understudied fungal disease that accounts for a substantial fraction of pneumonias in endemic areas. Diagnosis remains challenging because the mainstay of diagnosis is serology, and current assays have modest sensitivity in early infection. Discovery of new antigens could improve serologic detection early in disease. Here, we use PepSeq, a DNA-programmable serology platform, to represent the complete proteome of Coccidioides within a library of 244,000 DNA-barcoded peptides. By profiling antibody reactivity at the protein and epitope levels in VF patients, naturally infected pigtail macaques, and healthy controls, we identify numerous previously-unrecognized antigens and epitopes, including some shared between humans and macaque responses. When translated into a pooled peptide ELISA, we show that a subset of identified antigens can discriminate VF cases from controls in an independent cohort with high accuracy (AUC = 0.91). These findings establish high-dimensional peptide serology as a scalable approach for fungal antigen discovery and identify new targets for improved VF diagnosis.

immunology↗

bioRxiv2026

Sulforaphane targets Leishmania promastigotes to inhibit parasite motility and alters host autophagy to suppress parasite replication in macrophages

Visceral leishmaniasis (VL) is a neglected tropical disease caused by the protozoan parasites Leishmania infantum (LI) and Leishmania donovani (LD). VL disproportionately affects populations that live in extreme poverty and those affected by climate change. Treatment options are limited, fiscally prohibitive, and toxic to some that receive treatment. In cases where VL remains untreated, death is inevitable. Although disease prevalence is increasing in affluent countries with robust research capabilities, there remains little economic incentive to search for new, more accessible non-toxic treatments, emphasizing the neglected tropical aspect of this disease. Our previous results indicate that sulforaphane (SFN) inhibits VL promastigote and amastigote growth through an unknown mechanism. The current study extends the anti-parasitic effects of SFN establishing SFN's efficacy against multiple Leishmania parasites including L. tropica, L. mexicana, L. panamensis and L. amazonesis. Moreover, SFN was an effective therapy post-infection as stably infected macrophages treated with SFN (10 M for 48 hours) had a significant reduction in the number of infected cells and the number of amastigotes per cell against Leishmania species that cause both VL and cutaneous leishmaniasis (CL). Moreover, SFN led to a reduction of motility within 6 hours of treatment indicating a direct interaction with the parasite. Alkynyl SFN (A-SFN) was subsequently synthesized to determine the specific binding target of SFN. Interestingly, A-SFN was more effective than SFN in decreasing parasite viability and inhibiting motility against VL and CL parasite species at equivalent concentrations. Immunofluorescence analysis utilizing click chemistry indicated A-SFN binds to cytosolic promastigote components and colocalized only minimally with KMP-11. The binding of SFN to KMP-11 could not be confirmed in vitro. In terms of intracellular host effects, SFN treatment increased the induction of nonselective macroautophagy by increased lysosomal acidification as quantified by DALGreen and FITC-dextran staining in infected macrophages. The induction of autophagy occurred simultaneously with the upregulation of expression of cytoprotective, antioxidant and autophagy-related genes via the NRF2 transcription factor. These data provide robust evidence that SFN is an excellent lead compound against Leishmania that initiates multiple innate immune pathways that enable enhanced parasite killing. Future studies will focus on the in vivo efficacy of SFN against CL.

microbiology↗

bioRxiv2026

Charting developmental trajectories of dynamic brain networks during emotional face processing

Emotional face processing is a critical component in the development of social cognition through childhood. The neural mechanisms supporting this development can be understood by tracking age-related changes in brain activity. Prior work in this area with MEG relies on static, bandlimited, or region-specific measures, which do not capture the dynamic, distributed nature of brain activity. Here we used Dynamic Network Modes (DyNeMo) to analyze MEG data from a large cohort of typically developing individuals (N=224, ages 5-40) during an emotional faces vigilance task. DyNeMo is a data-driven, generative modelling approach which captures functional networks as a set of whole-brain spatiospectral "modes" whose relative mixture (i.e., activation levels) can change dynamically in response to stimuli. We inferred six modes from the MEG data and characterized developmental trajectories in task-related activation and mode connectivity. Across modes we observed distinct developmental trajectories in both measures. With respect to mode activation, a visual mode and a frontotemporal mode (whose labels reflect their respective spatial profiles) increased nonlinearly with age; meanwhile, activation in temporal and sensorimotor modes decreased linearly with age. Meanwhile, connectivity broadly increased with age in all modes, but with different degrees of nonlinearity. These results suggest developmental dissociations between different modes (e.g., visual versus sensorimotor), as well as within individual modes (task-related mode activation versus connectivity). These results provide a comprehensive and complex picture of functional network development underlying emotional face processing. Significance StatementBrain networks supporting social cognition undergo profound changes from childhood through adolescence to adulthood. However, current understanding of how these networks develop has been limited by conventional analyses of brain imaging data, which typically provide a static picture of brain activity. Here we leveraged a cutting-edge, data-driven modeling approach (DyNeMo) to characterize age-related changes in distributed and dynamic networks supporting emotional face processing, in a large cohort of children and young adults (N=224). We inferred six functional networks whose activation levels changed rapidly in response to emotional faces. Network activation and connectivity exhibited profound and distinct non-linear changes with age, indicating that emotional face processing is supported by complex interactions among multiple dynamic networks, each with different maturational trajectories.

neuroscience↗

bioRxiv2025

Functional organization of multisensory integration network in children and youth with neurodevelopmental disorders predicts clinical sensory issues

Differences in sensory processing in neurodevelopmental conditions, including Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD), cascade into downstream clinical symptomatology. This includes differences in combining sensory information from multiple modalities into a unified percept, known as multisensory integration. Little is known about the functional organization of multisensory network (MSN) in these groups, its relation to clinical sensory issues, or its interaction with other higher-order cortical networks. We examined resting-state fMRI data from 417 participants in the Province of Ontario Neurodevelopmental Network (ASD=174, ADHD=130, Typical Development=113; Mean age=11.96{+/-}4.10). Timeseries data were extracted from the MSN and seven additional resting-state cortical networks (RSNs). Undirected and directed functional connectivity (FC) metrics were computed within the MSN and between the MSN and other RSNs. FC was compared across diagnoses and related to clinical sensory characteristics. The thalamus emerged as a hub region in both undirected and directed FC within the MSN and between the MSN-RSNs. Some diagnosis-related differences were observed, with increased MSN-RSN FC particularly in ADHD; however, associations with sensory characteristics were stronger in both undirected and directed FC within the MSN and between the MSN-RSNs, regardless of diagnosis. Converging evidence was seen in data-driven clusters based on FC metrics, which did not align with diagnosis, but instead mapped on to the overall level of sensory issues reported. That the data-driven clusters sorted not by diagnosis but by sensory characteristics suggests that these sensory characteristics and their underlying neurobiology are transdiagnostic in nature as opposed to specific to ASD or ADHD.

neuroscience↗

bioRxiv2025

Decoding the neural basis of sensory phenotypes in Autism

BackgroundDifferences in sensory processing are a defining characteristic of autism, affecting up to 87% of autistic individuals. These differences cause widespread perceptual changes that can negatively impact cognition, development, and daily functioning. Recent research identified five sensory processing phenotypes with varied behavioural presentations; however, their neural basis remains unclear. This study aims to ground these sensory phenotypes in unique patterns of functional connectivity. MethodsWe analyzed data from 146 autistic participants in the Province of Ontario Neurodevelopmental Network. We classified participants into sensory phenotypes using k-means clustering of scores from the Short Sensory Profile. We then computed a connectivity matrix from 200 cortical and 32 subcortical regions and calculated graph-theoretic measures (betweenness centrality, strength, local efficiency, clustering coefficient) to assess information exchange between these regions. We then trained machine learning models to use these measures to classify between all pairs of sensory phenotypes. ResultsWe replicated that our sample of autistic participants was best categorized into five sensory phenotypes. The machine learning models distinguished 7/10 phenotype pairs using graph-theoretic measures (p < 0.005). Information exchange within and between the somatomotor network, orbitofrontal cortex, posterior parietal cortex, prefrontal cortex and subcortical areas were highly predictive of sensory phenotype. ConclusionsThis study shows that distinct sensory phenotypes in autism correspond with unique patterns of functional connectivity. Cortical, subcortical, and network-level connectivity all play a role in shaping distinct sensory processing styles in autism. These findings lay the groundwork for understanding these phenotypes and highlight opportunities to develop interventions in cases of maladaptive sensory processing.

neuroscience↗

bioRxiv2024

Preclinical Proof of Concept for SNAPTM-TIL: a personalized, next-generation tumor-infiltrating lymphocyte therapy platform

Tumor-infiltrating lymphocyte (TIL) therapy, which involves extracting, expanding, and reinfusing immune cells to target cancer cells, has shown promise in melanoma treatment, but requires optimization for broader efficacy. The success of TIL therapy depends on the recognition of tumor-associated antigens, but neoantigen-reactive T-cells are often rare and exhausted in less immunogenic malignancies. Isolating T cells enriched in neoantigen reactivity prior to in vitro expansion and reinfusion may improve the response rates. To this end, our proprietary Specific Neo-Antigen Peptides (SNAP) technology platform improves the accuracy of neoantigen prediction and validation by combining advanced computational modeling and PepSeq, a high-throughput screen for the physical credentialing of putative neoantigens based on their affinity to bind patient-specific HLA class II proteins. This approach allows for the education and enrichment of TILs (SNAP-TILs) with personalized, predefined, highly immunogenic neoantigens prior to expansion. Using the SNAP platform, we consistently achieved, on average, an SNAP-TIL product comprising 96% CD3+ cells, with a mixture of 75% effector and 23% central memory cells. SNAP-TILs exhibited greater efficacy and selectivity in immune infiltration than TIL, which was expanded by the rapid expansion protocol alone using ex vivo models. SNAP-TIL was also reactive in highly and poorly immunogenic tumors, with 70% and 50% tumor growth inhibition in melanoma and pancreatic patient-derived xenograft models, respectively. This study demonstrates the novel benefit of our Personalized Neoantigen Pipeline approach, potentially providing a durable antitumor immune response for a larger proportion of cancer patients.

immunology↗

bioRxiv2024

Using deep learning to predict internalizing problems from brain structure

Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the biological markers of internalizing problems are not well understood. Here, we used deep learning, a powerful tool for identifying complex and multi-dimensional brain-behaviour relationships, to predict cross-sectional and worsening longitudinal trajectories of internalizing problems. Data were extracted from four large-scale datasets: the Adolescent Brain Cognitive Development study, the Healthy Brain Network, the Human Connectome Project Development study, and the Province of Ontario Neurodevelopmental network. We developed deep learning models that used measures of brain structure (thickness, surface area, and volume) to (a) predict clinically significant internalizing problems cross-sectionally (N=14,523); and (b) predict subsequent worsening trajectories (using the reliable change index) of internalizing problems (N=10,540) longitudinally. A stratified cross-validation scheme was used to tune, train, and test the models, which were evaluated using the area under the receiving operating characteristic curve (AUC). The cross-sectional model performed well across the sample, reaching an AUC of 0.80[95% CI: 0.71,0.88]. For the longitudinal model, while performance was sub-optimal for predicting worsening trajectories in a sample of the general population (AUC=0.66[0.65,0.67]), good performance was achieved in a small, external test set of primarily ND conditions (AUC=0.80[0.78,0.81]), as well as across all ND conditions (AUC=0.73[0.70,0.76]). Deep learning with features of brain structure is a promising avenue for biomarkers of internalizing problems, particularly for individuals who have a higher likelihood of experiencing difficulties.

neuroscience↗

bioRxiv2024

Dataset factors influencing age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets, and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N=1,218) and function (resting-state functional connectivity strength; N=1,254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental network (POND) and the Healthy Brain Network (HBN). We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

neuroscience↗

bioRxiv2021

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders

BackgroundExternalizing and internalizing behaviors are common and contribute to impairment in children with neurodevelopmental disorders (NDDs). Associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in children with and without clinically significant internalizing/externalizing behaviors. This study examined whether such associations are present across children with different NDDs. MethodsMulti-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6-18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (n range=157-346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined. ResultsNo significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results. ConclusionsThe current study provides evidence in favour of the absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with various NDDs and TDC. Detecting shared brain-behavior relationships in children with NDDs may benefit from the use of different methodological approaches, including incorporation of multi-dimensional behavioral data (i.e. behavioral assessments, neurocognitive tasks, task-based fMRI) or clustering approaches to delineate whether subgroups of individuals with different brain-behavior profiles are present within heterogeneous cross-disorder samples.

neuroscience↗