Stojanovic, L. | Search bioRxiv

bioRxiv2024

ZNFX1 is a Novel Master Regulator in Epigenetically-induced Pathogen Mimicry and Inflammasome Signaling in Cancer

DNA methyltransferase and poly(ADP-ribose) polymerase inhibitors (DNMTis, PARPis) induce a stimulator of interferon (IFN) genes (STING)-dependent pathogen mimicry response (PMR) in ovarian (OC) and other cancers. We now show that combining DNMTis and PARPis upregulates expression of a little-studied nucleic-acid sensor, NFX1-type zinc finger-containing 1 protein (ZNFX1). We demonstrate that ZNFX1 is a novel master regulator for PMR induction in mitochondria, serving as a gateway for STING-dependent PMR. In patient OC databases, high ZNFX1 expression levels correlate with advanced stage disease. ZNFX1 expression alone significantly correlates with an increase in overall survival in a phase 3 trial for therapy-resistant OC patients receiving bevacizumab in combination with chemotherapy. In correlative RNA-seq data, inflammasome signaling through ZNFX1 correlates with abnormal vasculogenesis. ZNFX1 controls PMR signaling through the mitochondria and may serve as a biomarker to facilitate offering personalized therapy in OC patients, highlighting the strong translational significance of our findings. Significance statementDNA methyltransferase and poly(ADP-ribose) polymerase inhibitors upregulate expression of a novel nucleic-acid sensor, ZNFX1 that serves as a mitochondrial gateway to STING-dependent interferon/inflammasome signaling with tumor suppressor properties in ovarian cancer.

cancer biology↗

bioRxiv2023

Unlocking de novo antibody design with generative artificial intelligence

Generative AI has the potential to redefine the process of therapeutic antibody discovery. In this report, we describe and validate deep generative models for the de novo design of antibodies against human epidermal growth factor receptor (HER2) without additional optimization. The models enabled an efficient workflow that combined in silico design methods with high-throughput experimental techniques to rapidly identify binders from a library of [~]106 heavy chain complementarity-determining region (HCDR) variants. We demonstrated that the workflow achieves binding rates of 10.6% for HCDR3 and 1.8% for HCDR123 designs and is statistically superior to baselines. We further characterized 421 diverse binders using surface plasmon resonance (SPR), finding 71 with low nanomolar affinity similar to the therapeutic anti-HER2 antibody trastuzumab. A selected subset of 11 diverse high-affinity binders were functionally equivalent or superior to trastuzumab, with most demonstrating suitable developability features. We designed one binder with [~]3x higher cell-based potency compared to trastuzumab and another with improved cross-species reactivity1. Our generative AI approach unlocks an accelerated path to designing therapeutic antibodies against diverse targets.

synthetic biology↗