Biology subjects

Geier, A.

Publications and source records attributed to Geier, A..

3 recordsLinked to original sources

bioRxiv2025

Immunological liver-skin axis: a systemic Vγ4+ γδT17 cell responselinks psoriasis-like inflammation and MASLD

Metabolic dysfunction-associated steatotic liver disease (MASLD) and psoriasis frequently co-occur and share interleukin-17 (IL-17)-mediated inflammatory mechanisms. Using a murine model combining Western-diet-induced early MASLD with chronic imiquimod-triggered psoriasis-like dermatitis, we observed exacerbated skin inflammation with increased cellularity and systemic IL-17-driven immune activation. In mice with psoriasis-like skin inflammation, hepatic {gamma}{delta}T cells shifted from IFN-{gamma}-producing {gamma}{delta}T1 toward IL-17-secreting {gamma}{delta}T17 cells, accompanied by an acute-phase response and induction of hepatic lipogenic gene expression. Single-cell RNA sequencing revealed an expansion of V{gamma}4 {gamma}{delta}T17 cells engaged in IL-17 and TGF-{beta} signaling, consistent with early fibrogenic responses. Together, these findings identify V{gamma}4 {gamma}{delta}T17 cells as a mechanistic link between psoriasis and MASLD, promoting systemic inflammation and early hepatic remodeling. Consistently, in a clinical cohort of patients with moderate-to-severe psoriasis and co-morbid MASLD, IL-17-targeted therapy improved hepatic steatosis, underscoring the translational relevance of this immunological axis.

immunology↗

bioRxiv2022

Development and validation of an AI/ML platform for the discovery of splice-switching oligonucleotide targets

This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of novel and verifiable splice-switching oligonucleotide (SSO) targets in-silico. SSOs are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). To leverage the potential of AS research for therapeutic development, we created SpliceLearn, an AI/ML algorithm for the identification of modulatory SSO binding sites on pre-mRNA. SpliceLearn also predicts the identity of specific splicing factors whose binding to pre-mRNA is blocked by SSOs, adding considerable transparency to AI/ML-driven drug discovery and informing biological insights useful in further validation steps. Here we predicted NEDD4L exon 13 (NEDD4Le13) as a novel target in triple negative breast cancer (TNBC) and computationally designed an SSO to modulate NEDD4Le13. Targeting NEDD4Le13 with this SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGF{beta} pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data. SpliceLearn is part of the SpliceCore(R) platform, an AI/ML predictive ensemble for AS-based drug target discovery.

bioinformatics↗

bioRxiv2019

Differential functions of splicing factors in breast-cancer initiation and metastasis

Misregulation of alternative splicing is a hallmark of human tumors; yet to what extent and how it contributes to malignancy are only beginning to be unraveled. Here, we define which members of the splicing factor SR and SR-like families contribute to breast cancer, and uncover differences and redundancies in their targets and biological functions. We first identify splicing factors frequently altered in human breast tumors, and then assay their oncogenic functions using breast organoid models. Importantly we demonstrate that not all splicing factors affect mammary tumorigenesis. Specifically, upregulation of either SRSF4, SRSF6 or TRA2{beta} promotes cell transformation and invasion. By characterizing the targets of theses oncogenic factors, we identify a shared set of spliced genes associated with well-established cancer hallmarks. Finally, we demonstrate that the splicing factor TRA2{beta} is regulated by the MYC oncogene, plays a role in metastasis maintenance in vivo, and its levels correlate with breast-cancer-patient survival.

cancer biology↗