Biology subjects

Oakes, C. C.

Publications and source records attributed to Oakes, C. C..

7 recordsLinked to original sources

bioRxiv2026

CD200R1 Marks a Branchpoint During Human Natural Killer Cell Development in Tonsils

Human natural killer (NK) cells arise from common innate lymphoid cell (ILC) progenitor and precursor populations that are selectively enriched within secondary lymphoid tissues (SLTs) and that complete their terminal differentiation through stages of progressively lineage-restricted downstream developmental intermediates. We identify two subsets of tonsil CD94/NKG2A+NKp80- (stage 4A) NK-cell development intermediates that show differential expression of the inhibitory receptor, CD200R1. Ex vivo single-cell transcriptomic, DNA methylation, phenotypic, and functional analyses demonstrate that CD200R1 stage 4A cells represent an immature, oligopotent precursor population enriched for tissue-resident and cytokine-responsive programs. In contrast, CD200R1- stage 4A cells show more restricted NK-cell developmental potential concomitant with features of mature NK cells. These findings help to refine the human NK cell developmental pathway in tonsils and highlight CD200R1 as a useful marker delineating an uncommitted developmental state linking microenvironmental cues to NK-cell maturation and plasticity.

immunology↗

bioRxiv2026

Hierarchical classification of hematologic malignancies using epigenetic and genetic information

Molecular testing in hematology requires different assays for disease subgroup identification, risk stratification and selection of appropriate treatment regimens. Yet, molecular tests are not necessarily standardized between diagnostic laboratories, resulting in varying turnaround times and potentially divergent results. To resolve this issue and enable single-assay molecular testing, we have developed a hierarchical classification framework that combines epigenetic and genetic data from whole genome nanopore sequencing (WGNS) with machine learning to determine disease entities, epigenetic subgroups (epitypes) and genetic aberrations in hematopoietic neoplasms. We curated DNA methylation data from 5,420 samples and trained a classifier allowing entity-level diagnostics featuring 21 conditions, including healthy controls, acute and chronic myeloid and lymphoid neoplasms. This classifier was subsequently combined with entity-specific epitype classifiers predicting 44 therapeutically or prognostically relevant states, followed by integration of genetic data. Benchmarking of the combined (epi-)genetic testing strategy using WGNS confirmed high accuracy in the detection of diagnostic groups and risk stratification, and identified diagnosis-defining molecular alterations that were not reported by standard-of-care work-up.

cancer biology↗

bioRxiv2023

plasma: Partial LeAst Squares for Multiomics Analysis

Recent growth in the number and applications of high-throughput "omics" technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. We present a novel algorithm, plasma, to learn models to predict time-to-event outcomes from multiomics data sets. Plasma uses two layers of existing partial least squares algorithms to first select components that covary with the outcome and then construct a joint Cox proportional hazards model. We apply plasma to the stomach adenocarcinoma (STAD) data from The Cancer Genome Atlas. We validate the model both by splitting the STAD data into training and test sets and by applying it to the subset of esophageal cancer (ESCA) containing adenocarcinomas. We use the other half of the ESCA data, which contains squamous cell carcinomas dissimilar to STAD, as a negative control. Our model successfully separates both the STAD test set (p = 2.73 x 10-8) and the independent ESCA validation data (p = 0.025) into high risk and low risk patients. It does not separate the negative control data set (ESCA squamous cell carcinomas, p = 0.57). The performance of the joint multiomics model is superior to that of the individually trained models. It is also superior to the performance of an unsupervised method (Multi Omics Factor Analysis; MOFA) that finds latent factors to be used as putative predictors in a post-hoc survival analysis. Many of the factors that contribute strongly to the plasma model can be justified from the biological literature. SignificanceTo fill the unmet need for supervised multiomics methods, we introduce plasma, an algorithm based on partial least squares that integrates multiomics features into biologically relevant "components" that can predict patient outcomes. Availability and ImplementationThe plasma R package can be obtained from The Comprehensive R Archive Network (CRAN) athttps://CRAN.R-project.org/package=plasma. The latest version of the package can always be obtained from R-Forge at https://r-forge.r-project.org/R/?group_id=1746. Source code and data for the analysis presented here can be obtained from GitLab, at https://gitlab.com/krcoombes/plasma. ContactEmail: kcoombes@augusta.edu Supplementary InformationSupplementary material is available from the journal web site.

bioinformatics↗

bioRxiv2021

Uncontrolled CD21low age-associated and B1 B cell accumulation caused by failure of an EGR2/3 tolerance checkpoint

CD21low age-associated or atypical memory B cells, enriched for autoantibodies and poised for plasma cell differentiation, accumulate in large numbers in chronic infections, autoimmune disease and immunodeficiency, posing the question of what checkpoints normally oppose their excessive accumulation. Here, we reveal a critical role for the calcium-NFAT-regulated transcription factors EGR2 and EGR3. In the absence of EGR2 and EGR3 within B cells, CD21low and B1 B cells accumulate and circulate in young mice in numbers 10-20 times greater than normal, over-express a large set of EGR2 ChIP-seq target genes including known drivers of plasma cell differentiation and under-express drivers of follicular germinal centers. Most follicular B cells constitutively express Egr2 proportionally to surface IgM down-regulation by self-antigens, and EGR2/3 deficiency abolishes this characteristic anergy response. These results define a key transcriptional checkpoint repressing CD21low B cell formation and inform how NFATC1 or EGR2 mutations promote B1 cell-derived chronic lymphocytic leukemias.

immunology↗

bioRxiv2021

Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia

Despite the extensive catalogue of recurrent mutations in chronic lymphocytic leukaemia (CLL), the diverse molecular driving events and the resulting range of disease phenotypes remain incompletely understood. To study the molecular heterogeneity of CLL, we performed RNA-sequencing on 184 CLL patient samples. Unsupervised analysis revealed two major independent axes of gene expression variation: the first one aligned with the mutational status of the immunoglobulin heavy variable (IGHV) genes, and concomitantly, with the three-group stratification of CLL by global DNA methylation pattern, and affected biological functions including B- and T-cell receptor signaling. The second one aligned with trisomy 12 status and affected chemokine signaling. Furthermore, we searched for differentially expressed genes associated with gene mutations and copy-number aberrations and detected strong signatures for TP53, BRAF and SF3B1, as well as for del(11)(q22.3), del(17)(p13) and del(13)(q14) beyond the dosage effect. We discovered strong non-additive effects (i.e., genetic interactions, or epistasis) of IGHV mutation status and trisomy 12 on multiple phenotypes, including the expression of 893 genes. Multiple types of epistasis were observed, including synergy, buffering, suppression and inversion. Our study reveals previously underappreciated gene expression signatures for (epi)genomic variants in CLL and the presence of epistasis between them. The findings will serve as a reference for a functional resolution of CLL molecular heterogeneity.

genomics↗

bioRxiv2020

Human neonatal B cell immunity differs from the adult version by conserved Ig repertoires and rapid, but transient response dynamics

The human infant B cell system is considered premature or impaired. Here we show that most cord blood B cells are mature and functional as seen in adults, albeit with distinct transcriptional programs providing accelerated responsiveness to T cell-independent and T cell-dependent stimulation and facilitated IgA class switching. Stimulation drives extensive differentiation into antibody-secreting cells, thereby presumably limiting memory B cell formation. The neonatal Ig-repertoire is highly variable, but conserved, showing recurrent B cell receptor (BCR) clonotypes frequently shared between neonates. Our study demonstrates that cord blood B cells are not impaired but differ from their adult counterpart in a conserved BCR repertoire and rapid but transient response dynamics. These properties may account for the sensitivity of neonates to infections and limited effectivity of vaccination strategies. Humanized mice suggest that the distinctness of cord blood versus adult B cells is already reflected by the developmental program of hematopoietic precursors, arguing for a layered B-1/B-2 lineage system as in mice. Still, our findings reveal overall limited comparability of human cord blood B cells and murine B-1 cells. Significance StatementNeonates and infants suffer from enhanced susceptibility to infections. Our study contrasts with the current concept of a premature or impaired B cell system in neonates, by showing that most cord blood B cells are mature and functional. However, their responses are rapid but provide only short-term protection, which may help to improve infant vaccination strategies. We propose an altered perspective on the early human B cell system, which looks similar to but functions differently from the adult counterpart. Finally, our analysis indicates that cord blood- and adult B cell development occur layered as in mice, but certain mouse models still may offer a limited view on human neonatal B cell immunity.

immunology↗

bioRxiv2020

Methylome-based cell-of-origin modeling (Methyl-COOM) identifies aberrant expression of immune regulatory molecules in CLL

BackgroundIn cancer, normal epigenetic patterns are disturbed and contribute to gene expression changes, disease onset and progression. The cancer epigenome is composed of the epigenetic patterns present in the tumor-initiating cell at the time of transformation, and the tumor-specific epigenetic alterations that are acquired during tumor initiation and progression. The precise dissection of these two components of the tumor epigenome will facilitate a better understanding of the biological mechanisms underlying malignant transformation. Chronic lymphocytic leukemia (CLL) originates from differentiating B cells, which undergo extensive epigenetic programming. This poses the challenge to precisely determine the epigenomic ground-state of the cell-of-origin in order to identify CLL-specific epigenetic aberrations. MethodsWe developed a linear regression model, methylome-based cell-of-origin modeling (Methyl-COOM), to map the cell-of-origin for individual CLL patients based on the continuum of epigenomic changes during normal B cell differentiation. ResultsMethyl-COOM accurately maps the cell-of-origin of CLL and identifies CLL-specific aberrant DNA methylation events that are not confounded by physiologic epigenetic B cell programming. Furthermore, Methyl-COOM unmasks abnormal action of transcription factors, altered super-enhancer activities, and aberrant transcript expression in CLL. Among the aberrantly regulated transcripts were many genes that have previously been implicated in T cell biology. Flow cytometry analysis of these markers confirmed their aberrant expression on malignant B cells at the protein level. ConclusionsMethyl-COOM analysis of CLL identified disease-specific aberrant gene regulation. The aberrantly expressed genes identified in this study might play a role in immune-evasion in CLL and might serve as novel targets for immunotherapy approaches. In summary, we propose a novel framework for in silico modeling of reference DNA methylomes and for the identification of cancer-specific epigenetic changes, a concept that can be broadly applied to other human malignancies.

cancer biology↗