Biology subjects

Dow, M.

Publications and source records attributed to Dow, M..

5 recordsLinked to original sources

bioRxiv2025

YAP/TEAD4-reagulated placental alkaline phosphatases ALPP and ALPPL2 are immunosuppressive ectonucleotidases modulated by MAPK inhibitors

Placental alkaline phosphatases ALPP and ALPPL2 (ALPP/L2) are promising clinical targets for therapies such as antibody-drug conjugates (ADCs). However, their regulation and biological functions remain unclear. Here, we identify the YAP/TEAD4 signaling axis as the primary regulator of ALPP/L2 expression in tumor cells. Importantly, we demonstrate that inhibitors targeting KRAS, MEK, and ERK upregulate ALPP/L2 expression in vitro and in vivo, thereby enhancing the efficacy of antibody-drug conjugates (ADCs) when used in combination. Moreover, our work reveals a novel immunomodulatory function of ALPP/L2 as cell surface ectonucleotidases that hydrolyze extracellular ATP, ADP, and AMP to adenosine, promoting regulatory T cell infiltration into the tumor microenvironment. Together, these findings reveal a biological function of ALPP/L2 and identify druggable regulators to enhance ALPP/L2-targeted therapies. SIGNIFICANCEThis study uncovers the YAP/TEAD pathway as a positive regulator of the clinically relevant tumor-associated antigens ALPP and ALPPL2. Kras, MEK, or ERK inhibitors enhance ALPP/L2 expression in tumor cells, thereby increasing the efficacy of ALPP/L2-targeted antibody-drug conjugates. These findings offer a promising combination strategy to overcome resistance to ALPP/L2-based therapies caused by low target expression.

cancer biology↗

bioRxiv2022

Morphological Subprofile Analysis for Bioactivity Annotation of Small Molecules

Fast prediction of mode of action for bioactive compounds would immensely foster bioactivity annotation in compound collections and may early on reveal off-targets in chemical biology research and drug discovery. A variety of target-based assays is available for addressing the modulation of druggable proteins. However, they cannot precisely predict how a compound would influence cellular processes due to polypharmacology. Furthermore, non-protein targets are often not considered. Morphological profiling, e.g., using the Cell Painting assay that monitors hundreds of morphological features upon compound perturbation and staining of cellular components, offers a fast, unbiased assessment of compound activity on various targets and cellular processes in one single experiment. However, due to incomplete bioactivity annotation and unknown activities of reference (landmark) compounds, prediction of bioactivity is not straightforward. Here we introduce the concept of subprofile analysis to map the mode of action for both reference and unexplored compounds. We defined mode-of-action clusters for a group of reference compounds and extracted cluster subprofiles that contain only a subset of morphological features (i.e., subprofiles) to represent a consensus profile. Subprofile analysis allows for assignment of compounds to, currently, ten different targets or modes of action in one single assay and bypasses the need of exploring all biosimilar reference compounds for the generation of target hypothesis. This approach will enable rapid bioactivity annotation of compound collections, particularly of uncharacterized small molecules, and will be extended to further bioactivity clusters in future. The data is public accessible via https://github.com/mpimp-comas/2022_pahl_ziegler_subprofiles and the web app tool http://cpcse.pythonanywhere.com/.

cell biology↗

bioRxiv2020

Discovering biomarkers of chemotherapy resistance using in vitro evolution in haploid human cells

BackgroundIn vitro evolution and whole genome analysis has proven to be a powerful method for studying the mechanism of action of small molecules in many haploid microbes but has generally not been applied to human cell lines in part because their diploid state complicates the identification of variants that confer drug resistance. To determine if haploid human cell could be used in MOA studies, we evolved resistance to five different anticancer drugs (doxorubicin, gemcitabine, etoposide, topotecan, and paclitaxel) using a near-haploid cell line (HAP1) and then analyzed the genomes of the drug resistant clones, developing a bioinformatic pipeline that involved filtering for high frequency alleles predicted to change protein sequence, or alleles which appeared in the same gene for multiple independent selections with the same compound. Applying the filter to sequences from 28 drug resistant clones identified a set of 21 genes which was strongly enriched for known resistance genes or known drug targets (TOP1, TOP2A, DCK, WDR33, SLCO3A1). In addition, some lines carried structural variants that encompassed additional known resistance genes (ABCB1, WWOX and RRM1). Gene expression knockdown and knockout experiments of 10 validation targets showed a high degree of specificity and accuracy in our calls and demonstrates that the same drug resistance mechanisms found in diverse clinical samples can be evolved, discovered and studied in an isogenic background.

cancer biology↗

bioRxiv2018

Creating a scalable deep learning based Named Entity Recognition Model for biomedical textual data by repurposing BioSample free-text annotations

High quality metadata annotations for data hosted in large public repositories are essential for research reproducibility, and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Informations (NCBIs) Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via Named Entity Recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic (AUROC) curve of 85.2% and 0.977 respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. AvailabilityAll the analyses, environments, and Jupyter notebooks pertaining to this manuscript are available on Github: https://github.com/cartercompbio/PredictMEE.

bioinformatics↗

bioRxiv2018

Extracting allelic read counts from 250,000 human sequencing runs in Sequence Read Archive

The Sequence Read Archive (SRA) contains over one million publicly available sequencing runs from various studies using a variety of sequencing library strategies. These data inherently contain information about underlying genomic sequence variants which we exploit to extract allelic read counts on an unprecedented scale. We reprocessed over 250,000 human sequencing runs (>1000 TB data worth of raw sequence data) into a single unified dataset of allelic read counts for nearly 300,000 variants of biomedical relevance curated by NCBI dbSNP, where germline variants were detected in a median of 912 sequencing runs, and somatic variants were detected in a median of 4,876 sequencing runs, suggesting that this dataset facilitates identification of sequencing runs that harbor variants of interest. Allelic read counts obtained using a targeted alignment were very similar to read counts obtained from whole genome alignment. Analyzing allelic read count data for matched DNA and RNA samples from tumors, we find that RNA-seq can also recover variants identified by WXS, suggesting that reprocessed allelic read counts can support variant detection across different library strategies in SRA. This study provides a rich database of known human variants across SRA samples that can support future meta-analyses of human sequence variation.

bioinformatics↗