Biology subjects

Burgess, J. L.

Publications and source records attributed to Burgess, J. L..

3 recordsLinked to original sources

bioRxiv2025

Multi-omics identification of activated T cells and spatial PD-1/PD-L1 signaling as biomarkers of diabetic foot ulcer healing

Diabetic foot ulcers (DFUs) are a common and debilitating complication of diabetes, and amputations from non-healing ulcers carry high morbidity and mortality. A critical need exists for biomarkers that can identify healing potential early and guide targeted interventions. To address this, we applied an integrated multi-omics approach across four patient cohorts comprising 51 DFUs (29 Healing, 22 Non-healing). Bulk RNA-sequencing revealed marked activation of Th1 and Th2 pathways (activation z-score +4.8, p = 3.8x10-{superscript 1}), and immune cell deconvolution predicted higher proportions of T cell populations in Healers. Spatial proteomics in a second cohort identified elevated CD3 T cell density and selective enrichment of PD-1 and PD-L1 expression in vascular niches of the papillary dermis in Healers (p < 0.001). Flow cytometry in a third cohort further demonstrated higher proportions of CD3PD-1 and CD3PD-L1 T cells in Healers compared with Non-healers. Single-cell RNA-sequencing from a fourth cohort showed upregulation of PD-1 and PD-L1 within CD4 T cells from Healers. Complementary immunofluorescence and serological profiling confirmed that both PD-1 and PD-L1 are elevated in tissue and circulating serum of healing DFUs, supporting their potential use as systemic biomarkers. Taken together, vascular-enriched PD-1/PD-L1 signaling and T cell activation were observed in association with healing DFUs, supporting PD-1/PD-L1 as candidate biomarkers in both tissue and blood with potential translational relevance for predicting DFU outcomes and informing precision therapies.

immunology↗

bioRxiv2021

Development and Application of Multidimensional Lipid Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity

Lipids play many biological roles including membrane formation, protection, insulation, energy storage, and cell division. These functions have brought great interest to lipidomic studies for understanding their dysregulation in toxic exposure, inflammation, and diseases. However, lipids have shown to be analytically challenging due to their highly isomeric nature and vast concentration ranges in biological matrices. Therefore, powerful multidimensional techniques such as those integrating liquid chromatography, ion mobility spectrometry, collision induced dissociation, and mass spectrometry (LC-IMS-CID-MS) have recently been implemented to separate lipid isomers as well as provide structural information and increased feature identification confidence. These multidimensional datasets are however extremely large and highly complex, resulting in challenges in data processing and annotation. Here, we have overcome these challenges by developing sample-specific multidimensional libraries using the freely available software Skyline. Specifically, the human plasma library developed for this work contains over 500 unique, experimentally validated lipids, which is combined with adapted Skyline functions for highly confident lipid annotations such as indexed retention time (iRT) for retention time prediction and IMS drift time filtering for increased sensitivity and selectivity. For broad comparison with other lipidomic studies, this human plasma database was initially used to annotate LC-IMS-CID-MS data from a NIST SRM 1950 extract, giving comparable results to previous studies. This workflow was then utilized to assess matched plasma and bronchoalveolar lavage fluid (BALF) samples from patients with varying degrees of smoke inhalation injury to identify potential lipid-based patient prognostic and diagnostic markers. O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

molecular biology↗

bioRxiv2017

DLPFC Transcriptome Defines Two Molecular Subtypes of Schizophrenia

The Clinical Brain Disorders Branch of the Intramural Research Program at the National Institutes of Health assembled a large collection of frozen post-mortem human brains from individuals diagnosed with schizophrenia and other psychiatric diagnoses, as well as matched control individuals. Illumina HumanHT-12 v4 expression array data was collected from dorsolateral prefrontal cortex (DLPFC) and the data deposited at dbGaP (Study ID: phs000979). We report an analysis of the data from the 189 adult schizophrenics and 206 adult controls in that cohort.\n\nTranscripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to the controls at levels of statistical significance which survive Bonferroni correction. Seventeen of those genes are differentially expressed at a level of statistical significance less than 10-8 after Bonferroni correction.\n\nWeighted Gene Co-expression Network Analysis (WGCNA) of the schizophrenic subjects using differentially expressed gene module eigengenes divides them into two groups, \"type 1\" and \"type 2.\" There are 2,635 genes differentially expressed in the DLPFC of type 2 schizophrenics at a level of statistical significance which survives Bonferroni correction. Of them 221 have P-values less than 10-15 after Bonferroni correction. In the type 1 schizophrenics however, only 6 genes are differentially expressed in the DLPFC at a level of statistical significance which survives Bonferroni correction. This striking difference in their DLPFC transcriptomes emphasizes the fundamental biologic difference between these two groups of patients.

neuroscience↗