Biology subjects

Thompson, K. J.

Publications and source records attributed to Thompson, K. J..

9 recordsLinked to original sources

bioRxiv2025

Glial Contribution to the Pathogenesis of Post-Operative Delirium Revealed by Multi-omic Analysis of Brain Tissue from Neurosurgery Patients

Post-operative delirium (POD) is a common complication after surgery especially in elderly patients, characterized by acute disturbances in consciousness and cognition, which negatively impacts long-term outcomes. Effective treatments remain elusive due to the unclear pathophysiology of POD. To address the knowledge gap, we investigated DNA methylation profiles and gene expression changes in brain cells from POD and non-POD patients who underwent brain resection surgery for medication refractory epilepsy. DNA methylation analysis revealed alteration in epigenetic status of immune and inflammation-related genes. Single-nucleus RNA sequencing (snRNAseq) identified POD-specific glial cell alterations, particularly in microglia, where neuroinflammation was strongly enhanced, consistent with epigenetic findings. Astrocytes exhibited changes in synapse-related functions and migration. Furthermore, downstream analysis indicated similarities between POD-associated glial cell states and pathologies such as encephalitis and dementia. Overall, this study--the first multi-omics analysis of brain tissue from POD patients--provides direct evidence of glial cell contributions to POD pathogenesis, and highlights potential therapeutic targets.

neuroscience↗

bioRxiv2024

Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort

IntroductionAfrican Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimers disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. MethodsUntargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy. ResultsA plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94). DiscussionThis study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.

neuroscience↗

bioRxiv2024

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores-- 0.98{+/-}0.02 for lung cancer subtype differentiation, 0.83{+/-}0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient, and interpretable approach that contributes to a deeper understanding of disease mechanisms.

bioinformatics↗

bioRxiv2022

Integration of multi-omics data shows downregulation of mismatch repair, purin, and tublin pathways in AR-negative triple-negative chemotherapy-resistant breast tumors

Triple negative breast cancer (TNBC) patients who fail to achieve a pathological complete response to neoadjuvant chemotherapy (NAC) will likely experience recurrence of the disease within 3-4 years. Prognostic assessment of early recurrence could facilitate clinical decisions and impact disease survival. This study investigated pre- and post-NAC multi-omics data from both an in-house and a public clinical study to identify biomarkers of NAC response. We observed significant transcriptional differences associated with response in the residual disease (post-NAC biopsies), which did not exist in the pre-NAC biopsies. We further refined the post-NAC transcriptional changes to a 17-gene signature and machine learning models were applied to evaluate the signatures diagnostic potential (area under the curve [≥] 0.8). Interestingly, the signature was enriched with down-regulated immune genes and several of these genes demonstrated prognostic potential in basal TNBC tumors. Our 17-gene signature provides additional insight into TNBC recurrence, which warrants further investigation.

cancer biology↗

bioRxiv2022

Correlative cryogenic montage electron tomography for comprehensive in-situ whole-cell structural studies

Imaging large fields of view while preserving high-resolution structural information remains a challenge in low-dose cryo-electron tomography. Here, we present robust tools for montage electron tomography tailored for vitrified specimens. The integration of correlative cryo-fluorescence microscopy, focused-ion beam milling, and micropatterning produces contextual three-dimensional architecture of cells. Montage tilt series may be processed in their entirety or as individual tiles suitable for sub-tomogram averaging, enabling efficient data processing and analysis.

biophysics↗

bioRxiv2020

Low Cost Metabolic Fuel Sensor for On-Demand Personal Health and Fitness Tracking

Metabolic health in the general population has declined significantly in just one to two generations despite increased emphasis on dieting and exercise. A challenge in prescribing a healthy diet and exercise regimen is the variability individuals exhibit in response to particular foods, calorie restricted diets and exercise regimens. This paper describes a prototype metabolic fuel sensor designed for ease of use and personal tracking of metabolic energy expenditure and fuel substrate utilization. Examples of the sensor measurements and potential applications to weight management and tracking of chronically high blood glucose are described.

bioengineering↗

bioRxiv2019

Phylogenetic and structural diversity of aromatically dense pili from environmental metagenomes

Electroactive type IV pili, or e-pili, are used by some microbial species for extracellular electron transfer. Recent studies suggest that e-pili may be more phylogenetically and structurally diverse than previously assumed. Here, we used updated aromatic density thresholds ([≥]9.8% aromatic amino acids, [≤]22-aa aromatic gaps, and aromatic amino acids at residues 1, 24, 27, 50 and/or 51, and 32 and/or 57) to search for putative e-pilin genes in metagenomes from diverse ecosystems with active microbial metal cycling. Environmental putative e-pilins were diverse in length and phylogeny, and included truncated e-pilins in Geobacter spp., as well as longer putative e-pilins in Fe(II)-oxidizing Betaproteobacteria and Zetaproteobacteria.\n\nOriginality and SignificanceElectroactive pili (e-pili) are used by microorganisms to respire solid metals in their environment through extracellular electron transfer. Thus, e-pili enable microbes to occupy specific environmental niches. Additionally, e-pili have important potential for biotechnological applications. Currently the repertoire of known e-pili is small, and their environmental distribution is largely unknown. Using sequence analysis, we identified numerous genes encoding putative e-pili from diverse anoxic, metal-rich ecosystems. Our results expand the diversity of putative e-pili in environments where metal oxides may be important electron acceptors for microbial respiration.

microbiology↗

bioRxiv2018

The Metabolic Capability and Phylogenetic Diversity of Mono Lake During a Bloom of the Eukaryotic Phototroph Picocystis strain ML

Algal blooms in lakes are often associated with anthropogenic eutrophication; however, they can occur naturally. In Spring of 2016 Mono Lake, a hyperalkaline lake in California, was near the height of a rare bloom of the algae Picocystis strain ML and at the apex of a multi-year long drought. These conditions presented a unique sampling opportunity to investigate microbiological dynamics during an intense natural bloom. We conducted a comprehensive molecular analysis along a depth transect near the center of the lake from surface to 25 m depth during June 2016. Across sampled depths, rRNA gene sequencing revealed that Picocystis associated chloroplast were found at 40-50 % relative abundance, greater than values recorded previously. Despite the presence of the photosynthetic oxygenic algal genus Picocystis, oxygen declined below detectible limits below 15 m depth, corresponding with an increase in microorganisms known to be anaerobic. In contrast to previously sampled years, metagenomic and metatranscriptomic data suggested a loss of sulfate reducing microorganisms throughout the lakes water column. Gene transcripts associated with Photosystem I and II were expressed at both 2 m and 25 m, suggesting that limited oxygen production may occur at extremely low light levels at depth within the lake. Oxygenic photosynthesis under low light conditions, in the absence of potential grazing by the brine shrimp Artemia, may allow for a cryptic redox cycle to occur in an otherwise anoxic setting at depth in the lake with the following effects: enhanced productivity, reduced grazing pressure on Picocystis, and an exacerbation of bloom.\n\nIMPORTANCEMono Lake, California provides habitat to a unique ecological community that is heavily stressed due to recent human water diversions and a period of extended drought. To date, no baseline information exists about Mono Lake to understand how the microbial community responds to drought, bloom, and what genetic functions are lost in the water column. While previously identified anaerobic members of the microbial community disappear from the water column during drought and bloom, sediment samples suggest these microorganisms seek refuge at lake bottom or in the subsurface. Thus, the sediments may represent a type of seed bank which could restore the microbial community as a bloom subsides. Our work also sheds light on the activity of the halotolerant algae Picocystis strain ML during a bloom at Mono Lake, its ability to potentially produce oxygen via photosynthesis even under extreme low-light conditions, and how the remainder of the microbial community responds.

microbiology↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗