Biology subjects

Leach, S. M.

Publications and source records attributed to Leach, S. M..

7 recordsLinked to original sources

bioRxiv2026

Circulating and brain-resident memory CD8+ T cells seed distinct bystander TRM-like populations in glioblastoma

Across cancers, tumor-infiltrating CD8+ T cells expressing the tissue-resident memory T cell (TRM) markers CD69 and CD103 are strongly associated with favorable clinical outcomes. However, a substantial fraction of these cells in human tumors are not tumor-specific, but instead recognize unrelated viral antigens. These virus-specific bystander TRM-like cells are prevalent in tumors and retain functional potential, raising interest in strategies that leverage pre-existing antiviral immunity for cancer immunotherapy. Yet their origins and differentiation states remain poorly defined, limiting both the interpretation of residency-based tumor-infiltrating lymphocyte (TIL) phenotyping and efforts to rationally harness these TRM-like cells. Here, using mouse models of GBM and melanoma, we demonstrate that resting circulating memory T cells trafficked into tumors via GPCR-dependent signaling and rapidly adopted a tissue-resident phenotype, independent of cognate antigen. Strikingly, in GBM, but not melanoma, pre-existing brain TRM contributed substantially to the bystander TIL compartment and were the dominant source of CD69+/CD103+ bystander T cells, revealing a tumor- and tissue-specific origin for this subset. These findings were further supported by transcriptional analysis of T cell receptor clones present in both paired patient GBM tumor and peritumoral brain, which identified shared features with TRM-derived TILs in mouse GBM. Overall, this work provides new insight into tumor immunosurveillance, inform the interpretation of CD69+/CD103- and CD103+ TIL populations, and lay a foundation for immunotherapeutic strategies aimed at harnessing circulating and pre-existing virus-specific TRM populations in tumors.

immunology↗

bioRxiv2025

Monocyte Subset Predict Clinical Outcomes in Fibrotic Hypersensitivity Pneumonitis

RationaleHypersensitivity pneumonitis (HP) is an immunologically mediated form of lung disease resulting from inhalational exposure to various antigens. While there is growing understanding of the immune cells involved in HP, the relationship between clinical outcomes and circulating cell types in HP is not well established. MethodsSingle-cell RNA sequencing was performed on peripheral blood mononuclear cells (PBMCs) from 40 patients with fibrotic hypersensitivity pneumonitis (fHP) in a clinical trial of pirfenidone (NCT02958917). After quality control, we compared data from 33 fHP patients with 36 sex-matched healthy controls (GSE196735). Using Seurat v5, we harmonized the data, formed clusters, and reduced dimensionality through UMAP. Cell type identities were assigned based on a published reference (GSE271789), with clusters annotated for predominant cell type(s) present in at least 30% of the cells. We compared cell type proportions between fHP and HC using the chi-squared test and performed differential expression analysis with DESeq2 on pseudobulk counts, adjusting for sex. Genes significantly upregulated in fHP versus HC at an FDR-adjusted p-value of 0.01 were used to create a multivariate predictive signature for clinical outcomes in fHP participants. Performance was assessed through leave-one-out cross-validation for prediction accuracy. ResultsCompared to HC, fHP has an increased proportion of non-classical CD16+ monocytes (4.2% fHP vs. 1.5% HC), C14+ monocytes with a myeloid-derived dendritic phenotype (2.1% fHP vs. 0.1% HC), platelets (1.0% fHP vs. 0.3% HC), plasmacytoid dendritic cells (0.5% fHP vs. 0.1% HC) and predominantly classical CD14+ monocytes (29.5% fHP vs. 3.4% HC, adjusted-p<0.001). In patients with fHP, cell clusters were associated with the presence of baseline chest CT honeycombing, increased extent of CT lung fibrosis, and worse progression-free survival. CD14+ monocytes consistently demonstrated a high prediction accuracy (>0.90) for these clinical metrics. ConclusionsPeripheral monocyte clusters may be valuable prognostic markers, potentially helping identify at-risk fHP patients.

genetics↗

bioRxiv2025

Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis

BackgroundSarcoidosis is a granulomatous disease of unknown cause with a highly variable clinical course. The inability to predict progressive inflammation, fibrosis, or both underscores the limited understanding of the underlying molecular mechanisms. ObjectiveWe aimed to identify novel protein signatures associated with distinct pulmonary phenotypes of sarcoidosis, including progressive inflammation, progressive fibrosis, and disease resolution. MethodsWe performed the SomaScan 11K Assay to measure more than 10,000 unique human plasma proteins and compared protein expression between chest CT-defined phenotypes using principal component analysis, differential expression, correlation analysis, and gene set enrichment analysis. ResultsWe identified distinct proteomic signatures that differentiate progressive fibrosis from progressive nodular inflammation in sarcoidosis. Enrichment and differential expression analyses revealed that progressive fibrosis was associated with epithelial-mesenchymal transition pathways, while progressive nodular disease was linked to mTORC1 and MYC signaling, as well as metabolic activation. Additionally, expression of 44 proteins correlated moderately to strongly with thoracic lymph node enlargement, suggesting that lymph node- driven immune activity may be a major source of circulating proteomic signals. ConclusionsThis study leverages a unique longitudinal imaging approach to define extreme pulmonary phenotypes based on serial chest CT scoring, enabling the discovery of proteomic signals linked to distinct trajectories of sarcoidosis progression. Once validated, these findings could inform the development of blood-based biomarkers for disease stratification, monitoring, and therapeutic targeting in sarcoidosis.

immunology↗

bioRxiv2022

Creating an Ignorance-Base: Exploring Known Unknowns in the Scientific Literature

BackgroundScientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. ResultsWe present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development), which were buried among the many standard enriched concepts, by searching for concepts enriched in ignorance statements. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. ConclusionOur goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/519634v2_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1edb6e6org.highwire.dtl.DTLVardef@182bf3corg.highwire.dtl.DTLVardef@d90312org.highwire.dtl.DTLVardef@1589433_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIWe created the first ignorance-base (knowledge-base) to capture goals for scientific knowledge C_LIO_LIOur exploration methods provide analyses, summaries, and visualizations based on a query C_LIO_LIIgnorance enrichment provided fruitful avenues for future research C_LIO_LIExploration by topic in vitamin D found three avenues to explore C_LIO_LIExploration by experimental results for vitamin D and preterm birth found an emerging topic C_LI

bioinformatics↗

bioRxiv2021

Identifying and Classifying Goals For Scientific Knowledge

MotivationScience progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the questions stated in the biomedical literature. Formally, the task is to identify and characterize ignorance statements, statements where scientific knowledge is missing or incomplete. The creation of such technology could have many significant impacts, from the training of PhD students to ranking publications and prioritizing funding based on particular questions of interest. The work presented here is intended as the first step towards these goals. ResultsWe present a novel ignorance taxonomy driven by the role ignorance statements play in the research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10,000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AvailabilityCorpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python. ContactMayla.Boguslav@CUAnshcutz.edu

bioinformatics↗

bioRxiv2021

Analysis of SARS-CoV-2 Mutations Over Time Reveals Increasing Prevalence of Variants in the Spike Protein and RNA-Dependent RNA Polymerase

Amid the ongoing COVID-19 pandemic, it has become increasingly important to monitor the mutations that arise in the SARS-CoV-2 virus, to prepare public health strategies and guide the further development of vaccines and therapeutics. The spike (S) protein and the proteins comprising the RNA-Dependent RNA Polymerase (RdRP) are key vaccine and drug targets, respectively, making mutation surveillance of these proteins of great importance. Full protein sequences for the spike proteins and RNA-dependent RNA polymerase proteins were downloaded from the GISAID database, aligned, and the variants identified. Polymorphisms in the protein sequence were investigated at the protein structural level and examined longitudinally in order to identify sequence and strain variants that are emerging over time. Our analysis revealed a group of variants in the spike protein and the polymerase complex that appeared in August, and account for around five percent of the genomes analyzed up to the last week of October. A structural analysis also facilitated investigation of several unique variants in the receptor binding domain and the N-terminal domain of the spike protein, with high-frequency mutations occurring more commonly in these regions. The identification of new variants emphasizes the need for further study on the effects of these mutations and the implications of their increased prevalence, particularly as these mutations may impact vaccine or therapeutic efficacy.

bioinformatics↗

bioRxiv2020

A composite method to infer drug resistance with mixed genomic data

BackgroundThe increasing incidence of drug resistance in tuberculosis and other infectious diseases poses an escalating cause for concern, emphasizing the urgent need to devise robust computational and molecular methods identify drug resistant strains. Although machine learning-based approaches using whole-genome sequence data can facilitate the inference of drug resistance, current implementations do not optimally take advantage of information in public databases and are not robust for small sample sizes and mixed attribute types. ResultsIn this paper we introduce the Composite MetaDistance method, an approach for feature selection and classification of high-dimensional, unbalanced datasets with mixed attribute features from various data sources. We introduce a mixed-attribute, multi-view distance function to calculate distances between samples, with optimal handling of nominal features and different feature views. We also introduce a novel feature set for drug resistance prediction in Mycobacterium tuberculosis, using data from diverse sources. We compare the performance of Composite MetaDistance to multiple machine learning algorithms for Mycobacterium tuberculosis drug resistance prediction for three drugs. Composite MetaDistance consistently outperforms existing algorithms for small sample training sets, and performs as well as other algorithms for training sets with larger sample sizes. ConclusionThe feature set formulation introduced in this paper is utilizes mutational and publicly available information for each gene, and is much richer than ever devised previously. The prediction algorithm, Composite MetaDistance, is sample size agnostic and robust especially given small sample sizes. Proper handling of nominal features improves performance even with a very small number of nominal features. We expect Composite MetaDistance to be even more robust for datasets with a higher percentage of nominal features. The algorithm is application independent and can be used for any mixed attribute dataset.

bioinformatics↗