Biology subjects

Ullrich, A.

Publications and source records attributed to Ullrich, A..

6 recordsLinked to original sources

bioRxiv2024

Ru(II)-Photoactive Agents for Targeting ER Stress and Immunogenic Cell Death

Immunotherapy has emerged as a promising avenue for cancer treatment by bolstering the immune systems ability to recognize and attack cancer cells. Photodynamic therapy shows potential in enhancing antitumor immunity, though the mechanisms behind its success are not fully understood. In this manuscript, we investigate two previously reported green light activated PCT/PDT agents where compound 2 - [Ru(tpy)(Me2bpy)(3)]2+, (tpy = 2,2:6,2- terpyridine, Me2bpy = 6,6-dimethyl-2,2-bipyridine, 3 = pyridyl-BODIPY-I2,) - shows remarkable photoselectivity in assays containing both 2D cancer cells and 3D cocultures containing BALB/c macrophages and 4T1 murine breast cancer cells. Through flow cytometry and protein analysis, we found complex 2 displays superior evidence of induced endoplasmic reticulum (ER) stress markers and indicators of immunogenic cell death (ICD) compared to its ligand 3, despite its weaker photoselectivity. Most importantly, these results were supported by in vivo studies where 2 produced anti-tumor immunity against the 4T1 tumor model in BALB/c mice. Complete tumor elimination was achieved in 2/8 mice, and these mice were both protected against a subsequent contralateral rechallenge and showed increased ex vivo peripheral tumor antigen-specific recall, suggesting memory T cells are induced by 2. Signatures of M1 macrophage polarization were also evident in tumor tissue from the remaining 6/8 mice treated with 2 compared to untreated tumors. These findings demonstrate Ru(II) complexation plays a critical role in ER targeting which triggers ICD, highlighting the potential of Ru(II) agents as future in situ tumor vaccines.

cancer biology↗

bioRxiv2023

Discovery of a functionally selective serotonin 5-HT1A receptor agonist for the treatment of pain

The G protein-coupled serotonin receptor 5-HT1AR mediates antinociception and may serve as a valuable target for the treatment of pain. Starting from a chemical library, ST171, a bitopic chemotype activating 5-HT1AR was evolved. In vitro pharmacological investigations of ST171 revealed potent and selective Gi activation (EC50 = 0.3 nM), with marginal Gs and {beta}-arrestin recruitment. Preclinical studies in mice showed that ST171 was effective in acute and chronic (inflammatory and neuropathic) pain models, without causing sedation. Comparison of cryo-EM structures of a 5-HT1AR-Gi complex bound to the functionally biased agonist ST171, with a structure bound to the functionally balanced agonist befiradol, showed that both ligands bind to the same orthosteric site, but address different exo-sites. The individual poses are associated with ligand-specific helical dispositions and rearrangements of microdomains. Complementation of these studies with molecular dynamics simulations allowed us to derive structural features associated with ST171s functional selectivity, a phenomenon that may be crucial to the discovery of more effective and safe GPCR drugs.

pharmacology and toxicology↗

bioRxiv2023

Host genetic background regulates the capacity for anti-tumor antibody-dependent phagocytosis

BackgroundAntitumor antibody, or targeted immunotherapy, has revolutionized cancer treatment and markedly improved patient outcomes. A prime example is the monoclonal antibody (mAb) trastuzumab, which targets human epidermal growth factor receptor 2 (HER2). However, like many targeted immunotherapies, only a subset of patients benefit from trastuzumab long-term. In addition to tumor-intrinsic factors, we hypothesize that host genetics may influence subsequent immune activation. MethodsTo model the human population, we produced F1 crosses of genetically heterogeneous Diversity Outbred (DO) mice with BALB/c mice (DOCF1). Distinct DOCF1 mice were orthotopically implanted with the BALB/c-syngeneic TUBO mammary tumor line, which expresses the HER2 ortholog rat neu. Treatment with anti-neu mAb clone 7.16.4 began once tumors reached [~]200 mm3. Genetic linkage and quantitative trait locus (QTL) effects analyses in R/qtl2 identified loci associated with tumor growth rates. Locus validation was performed with BALB/c F1 crosses with recombinant-inbred Collaborative Cross (CC) strains selected for therapy-associated driver genetics (CCxCF1). The respective roles of natural killer (NK) cells and macrophages were investigated by selective depletion in vivo. Ex vivo macrophage antibody-dependent phagocytosis (ADCP) assays were evaluated by confocal microscopy using 7.16.4-opsonized E2Crimson-expressing TUBO tumor cells. ResultsWe observed a divergent response to anti-tumor antibody therapy in DOCF1 mice. Genetic linkage analysis detected a locus on chromosome 10 that correlates to a robust response to therapy, which was validated in CCxCF1 models. Single-cell RNA sequencing of tumors from responder and non-responder models identified key differences in tumor immune infiltrate composition, particularly within macrophage (M{varphi}) subsets. This is further supported by ex vivo analysis showing M{varphi} ADCP capacity correlates to in vivo treatment outcomes in both DOCF1 and CCxCF1 models. ConclusionsHost genetics play a key regulatory role in targeted immunotherapy outcomes, and putative causal genes are identified in murine chromosome 10 which may govern M{varphi} function during ADCP.

cancer biology↗

bioRxiv2019

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical interaction effects of reported infections across time and space, which provides insight into the dynamics of the spread of infectious diseases. Testing the model on a one-week-ahead prediction task for campylobacteriosis and rotavirus infections across Germany, as well as Lyme borreliosis across the federal state of Bavaria, shows that the proposed model performs on-par with the state-of-the-art hhh4 model. However, it provides a full posterior distribution over parameters in addition to model predictions, which aides in the assessment of the model. The employed Bayesian Monte Carlo regression framework is easily extensible and allows for incorporating prior domain knowledge, which makes it suitable for use on limited, yet complex datasets as often encountered in epidemiology.\n\nAuthor summaryWhy was this study done?\n\nO_LIStatistical modeling is invaluable to public-health policy as it helps understand and anticipate the dynamics of the spread of infectious diseases. The available training data is often limited and reported with a low spatial and temporal resolution. This poses a challenge and makes it particularly important to incorporate domain knowledge and prior assumptions to guide the modeling process.\nC_LIO_LIIn order to evaluate the trustworthiness and reliability of a models predictions, it is crucial to be able to interpret the model and quantify the model uncertainty.\nC_LIO_LITo address this, we develop an interpretable model that uses Bayesian inference (rather than commonly used maximum likelihood estimation) and provides a probability distribution over inferred parameters.\nC_LI\n\nWhat did the researchers do and find?\n\nO_LIWe develop and test a single probabilistic model that learns to predict the number of weekly case counts for three different diseases (campylobacteriosis, rotaviral enteritis and Lyme borreliosis) at the county level one week ahead of time.\nC_LIO_LIWe employ a Bayesian Monte Carlo regression approach that provides an estimate of the full probability distribution over inferred parameters as well as model predictions.\nC_LIO_LIThe model learns an interpretable spatio-temporal kernel that captures typical interactions between infection cases of the tested diseases.\nC_LIO_LIThe predictive performance of our model compares favorably with a contemporary reference model for all diseases tested.\nC_LI\n\nWhat do these findings mean?\n\nO_LIInterpretable predictive models can be applied to surveillance data to gain insights into the dynamics of infectious diseases.\nC_LIO_LIProbabilistic modeling approaches provide a suitable framework for many challenges of working with epidemiological data.\nC_LI

epidemiology↗

bioRxiv2017

DNA-dependent protein synthesis exhibited by cancer shed particulates

Genetic heterogeneity in tumours is the bonafide hallmark applicable to all cancer types (Burrell et al, 2013). Furthermore, deregulated ribosome biogenesis and elevated protein biosynthesis have been consistently associated with multiple cancer types (Ruggero, 2012; Ruggero & Pandolfi, 2003). We observed that under cultivation conditions almost all cancer cell types actively shed significant amount of particulates as compared to non-malignant cell lines requiring frequent changing of cultivation media. We therefore asked if cancer cell shed particulates might still retain biological activity associated with protein biosynthesis. Here, we communicate our observations of DNA-dependent protein biosynthetic activity exhibited by the cell-free particulates shed by the cancer cell lines. Using pulsed isotope labelling approach we confirmed the cell-free protein translation activity exhibited by particulates shed by various cancer cell lines. Interestingly, the bioactivity was largely dependent on temperature, pH and on 3-DNA elements. Our results demonstrate that cancer shed particulates are biologically active and may potentially drive expression of tissue non-specific promoters in distant organs.

cancer biology↗

bioRxiv2017

Antibody-Mediated Depletion Of Protein Variant Expression In Living Cells (Protein Interference)

A significant development in the field of human biology is the revelation of millions of unannotated protein sequence variants emerging from the several human genotyping and genome sequencing initiatives. This presents unique opportunities as well as confounding challenges in our understanding of how molecular signalling outcomes vary among individuals in the general population. As a result the conventional one drug fits all lines of approach in the drug discovery process is becoming obsolete. However, an innovative genotype-specific approach targeting protein sequence variants instead of a reference protein target is currently lacking. In this short communication we report a remarkable observation of antibody-mediated knockdown of intracellular protein expression. This suggests allele-specific inhibition of protein-variant expression can be achieved by intracellular delivery of lipid conjugated linear epitope-specific monoclonal antibodies. The results presented here demonstrate novel opportunities for interrogating the protein coding variations in the human genomes and new therapeutic strategies for the inhibition of pathogenic protein variants in a genotype-centric manner.

genomics↗