Biology subjects

Arvanitis, M.

Publications and source records attributed to Arvanitis, M..

4 recordsLinked to original sources

bioRxiv2025

CCRL2 promotes the interferon-γ signaling response in myeloid neoplasms with erythroid differentiation and mutated TP53

Patients with myeloid neoplasms with loss-of-function TP53 mutations and erythroid differentiation have poor outcomes, and a better understanding of disease biology is required. Upregulation of interferon-{gamma} (IFN-{gamma}) signaling has been associated with acute myeloid leukemia (AML) progression and chemotherapy resistance, but its drivers remain unclear. In this study, we found that the surface receptor C-C motif chemokine receptor-like 2 (CCRL2) is overexpressed in AML with erythroid differentiation and TP53 mutations compared to other AML subtypes and healthy hematopoietic cells. The knockout (KO) of CCRL2 suppressed erythroleukemia growth in vitro and in vivo. Further proteomics and transcriptomics analysis revealed IFN-{gamma} signaling response as the top CCRL2-regulated pathway in erythroleukemia. Our mechanistic studies support direct CCRL2 driven IFN-{gamma} signaling independent of exogenous IFN-{gamma}, through phosphorylation of STAT1, via JAK2-dependent and independent mechanisms. CCRL2/IFN-{gamma} signaling is upregulated in erythroid leukemias, and TP53 mutated AML without concurrent increase of IFN-{gamma} secretion in the bone marrow microenvironment and is directly induced by TP53 KO. Finally, CCRL2/IFN-{gamma} signaling is associated with the transformation of pre-leukemic single-hit TP53 clones to multi-hit TP53 mutated AML, increased resistance to venetoclax and worse survival in AML. Overall, our findings support that CCRL2 is an essential driver of cell-autonomous IFN-{gamma} signaling response in myeloid neoplasms with erythroid differentiation and TP53 mutations and highlight CCRL2 as a relevant novel target for these neoplasms. One Sentence SummaryCCRL2 is overexpressed in AML with loss-of-function TP53 mutations and erythroid differentiation and promotes IFN-{gamma} signaling response via a cell-intrinsic mechanism.

cancer biology↗

bioRxiv2024

Sparse matrix factorization of GWAS summary statistics robust to sample sharing improves detection and interpretation of factors with diverse genetic architectures

Complex trait-associated genetic variation is highly pleiotropic. This extensive pleiotropy implies that multi-phenotype analyses are informative for characterizing genetic associations, as they facilitate the discovery of trait-shared and trait-specific variants and pathways ("genetic factors"). Previous efforts have estimated genetic factors using matrix factorization (MF) applied to numerous GWAS. However, existing methods are susceptible to spurious factors arising from residual confounding due to sample-sharing in biobank GWAS. Furthermore, MF approaches have historically estimated dense factors, loaded on most traits and variants, that are challenging to map onto interpretable biological pathways. To address these shortcomings, we introduce "GWAS latent embeddings accounting for noise and regularization" (GLEANR), a MF method for detection of sparse genetic factors from summary statistics. GLEANR accounts for sample sharing between studies and uses regularization to estimate a data-driven number of interpretable factors. GLEANR is robust to confounding induced by shared samples and improves the replication of genetic factors derived from distinct biobanks. We used GLEANR to evaluate 137 diverse GWAS from the UK Biobank, identifying 58 factors that decompose the genetic architecture of input traits and have distinct signatures of negative selection and degrees of polygenicity. These sparse factors can be interpreted with respect to disease, cell-type, and pathway enrichment. We highlight three such factors capturing platelet measure phenotypes and enriched for disease-relevant markers corresponding to distinct stages of platelet differentiation. Overall, GLEANR is a powerful tool for discovering both trait-specific and trait-shared pathways underlying complex traits from GWAS summary statistics.

genomics↗

bioRxiv2022

The functional impact of rare variation across the regulatory cascade

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotypes ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimers disease.

genomics↗

bioRxiv2019

Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLs

BackgroundGenetic regulation of gene expression, revealed by expression quantitative trait loci (eQTLs), varies across tissues in complex patterns ranging from highly tissue-specific effects to effects shared across many or all tissues. Improved characterization of these patterns may allow us to better understand the biological mechanisms that underlie tissue-specific gene regulation and disease etiology.\n\nResultsWe develop a constrained matrix factorization model to learn patterns of tissue sharing and tissue specificity of eQTLs across 49 human tissues from the Genotype-Tissue Expression (GTEx) project. The learned factors include patterns reflecting tissues with known biological similarity or shared cell types, in addition to a dense factor representing a universal genetic effect across all tissues. To explore the regulatory mechanisms that generate tissue-specific patterns of expression, we evaluate chromatin state enrichment and identify specific transcription factors with binding sites enriched for eQTLs from each factor.\n\nConclusionsOur results demonstrate that matrix factorization can be applied to learn the tissue specificity pattern of eQTLs and that it exhibits better biological interpretability than heuristic methods. We present a framework to characterize the tissue specificity of eQTLs, and we identify examples of tissue-specific eQTLs that may be driven by tissue-specific transcription factor (TF) binding, with relevance to interpretation of disease association.

genomics↗