Biology subjects

Zangene, E.

Publications and source records attributed to Zangene, E..

4 recordsLinked to original sources

bioRxiv2025

Missing Values Are Valuable: Shifting Focus from Amount to Form of Missing Data

Missing data is often treated as a nuisance, routinely imputed or excluded from statistical analyses, especially in nominal datasets where its structure cannot be easily modeled. However, the form of missingness itself can reveal hidden relationships, substructures, and biological or operational constraints within a dataset. In this study, we present a graph-theoretic approach that reinterprets missing values not as gaps to be filled, but as informative signals. By representing nominal variables as nodes and encoding observed or missing associations as edges, we construct both weighted and unweighted bipartite graphs to analyze modularity, nestedness, and projection-based similarities. This framework enables downstream clustering and structural characterization of nominal data based on the topology of observed and missing associations; edge prediction via multiple imputation strategies is included as an optional downstream analysis to evaluate how well inferred values preserve the structure identified in the non-missing data. Across a series of biological, ecological, and social case studies, including proteomics data, the BeatAML drug screening dataset, ecological pollination networks, and HR analytics, we demonstrate that the structure of missing values can be highly informative. These configurations often reflect meaningful constraints and latent substructures, providing signals that help distinguish between data missing at random and not at random. When analyzed with appropriate graph-based tools, these patterns can be leveraged to improve the structural understanding of data and provide complementary signals for downstream tasks such as clustering and similarity analysis. Our findings support a conceptual shift: missing values are not merely analytical obstacles but valuable sources of insight that, when properly modeled, can enrich our understanding of complex nominal systems across domains. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/670516v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@99c5eaorg.highwire.dtl.DTLVardef@1909d8corg.highwire.dtl.DTLVardef@1578c93org.highwire.dtl.DTLVardef@ce2e90_HPS_FORMAT_FIGEXP M_FIG C_FIG Shiny app address https://ehsan-zangene.shinyapps.io/nimaa_app/

bioinformatics↗

bioRxiv2024

Targeting AML Resistance with LY3009120-Sapanisertib and Ruxolitinib-Ulixertinib Combinations Demonstrate Superior Efficacy in FLT3, TP53, and MUC4 mutations

Acute myeloid leukemia (AML) is a genetically heterogeneous malignancy characterized by the clonal expansion of myeloid precursor cells. Despite the advent of venetoclax-based regimens, resistance mechanisms remain a major clinical challenge, particularly in patients with high-risk mutations such as TP53, MUC4, HLA-B and FLT3. This study aims to investigate the efficacy of drug combinations for the treatment of AML using both, AML cell lines and zebrafish embryo xenograft model. Specifically, we focus on two drug combinations; the pan-RAF inhibitor LY3009120 combined with the mTOR inhibitor sapanisertib (designated as LS), and the JAK1/2 inhibitor ruxolitinib combined with the ERK inhibitor ulixertinib (designated as RU). The study integrates real-time cell viability assays, xenograft imaging, and genomic analyses to assess drug efficacy and explore correlations between treatment responses and mutational profiles, particularly TP53, MUC4, HLA-B and FLT3 mutations. Both combinations demonstrated superior efficacy compared to venetoclax-based therapy, with LS notably reducing viability in MOLM-16 and SKM-1 cells, and RU showing comparable efficacy with a favorable safety profile. In zebrafish embryos, LS combination effectively inhibited the proliferation of xenografted human AML cells, as evidenced by decreased fluorescence signals, indicating cell death. The RU combination also disrupted survival of cancer cells, showing promise as a therapeutic strategy. Furthermore, a correlation was identified between drug response and mutational profiles, with TP53, MUC4, HLA-B and FLT3 mutations significantly influencing sensitivity to the LS and RU combinations. These findings support the further development of LS and RU as effective alternatives to current clinical regimens, with implications for personalized AML treatment.

cancer biology↗

bioRxiv2024

Exploring Mechanisms of Action in Combinatorial Therapy through Stability/Solubility Alterations: Advancing AML Treatment

Acute myeloid leukemia (AML) remains a formidable clinical challenge due to genetic heterogeneity, high relapse rates, and toxicities associated with conventional chemotherapies. Rationally designed drug combinations offer improved efficacy, yet their selection is often empirical and lacks molecular mechanistic understanding. Here, we present CoPISA workflow (Proteome Integral Solubility/Stability Alteration Analysis for Combinations), a high-throughput proteomics workflow that captures protein solubility/stability alterations unique to combinatorial drug treatments, revealing mechanisms unattainable through single-drug analyses. Applying CoPISA to two rationally designed AML drug pairs, LY3009120-sapanisertib (LS) and ruxolitinib-ulixertinib (RU), we mapped primary (lysate) and secondary (living cell) protein target landscapes. Notably, our analysis uncovered an emergent mechanistic principle, "conjunctional targeting" (i.e., conjunctional inhibition), wherein cooperative drug actions induce treatment-specific targets not achievable individually, analogous to an AND-gate logic model. LS-specific AND-gate proteins converged on SUMOylation, chromatin condensation, and VEGF-linked adhesion, while RU-specific targets disrupted DNA-damage checkpoints, mitochondrial bioenergetics, and RNA-splicing machinery, collectively implicating synthetic-lethal vulnerabilities. Additionally, the post-translational modifications (PTMs) profiling of differential soluble proteins confirms several combination-induced modifications (e.g., acetylation, dimethylation, phosphorylation) on key AML proteins, such as NPM1. Network interrogation of AML-associated proteins showed that a high percentage of targeted proteins are unique to the combinations, including frequently mutated drivers DNMT3A, NPM1, and TP53. CoPISA exposes how drug pairs enact multi-axis pressure on AML cells through conjunctional targeting, a mechanistic layer beyond classical synergy. By pinpointing combination-exclusive protein targets and signaling pathways, CoPISA provides a blueprint for precision-guided regimen design in AML and other heterogeneous cancers. Data are available via ProteomeXchange with identifier PXD066812.

biochemistry↗

bioRxiv2023

DORSSAA: Drug-target interactOmics Resource based on Stability/Solubility Alteration Assay

Advancements in high-throughput techniques such as Thermal Proteome Profiling (TPP) and the high-throughput Proteome Integral Solubility Alteration (PISA) assay have revolutionized our understanding of drug-protein interactions. Despite these innovations, the absence of an integrative platform for cross-study analysis of stability and solubility alteration data represents a significant bottleneck. To address this gap, we introduce DORSSAA (Drug-target interactOmics Resource based on Stability/Solubility Alteration Assay), an interactive and expandable web-based platform for the systematic analysis and visualization of proteome stability and solubility alteration assay datasets. Currently, DORSSAA features 1,135,985 records spanning 38 cell lines and organisms, 135 compounds, and 480,456 potential protein targets. Through its user-friendly interface, the resource supports comparative drug-protein interaction analysis and facilitates the discovery of actionable therapeutic targets. Through two case studies; methotrexate target profiling in A549 cells and combinatorial-therapy drug-target interactions in leukemia cell lines, we demonstrate DORSSAAs utility for identifying protein-drug interactions across diverse experimental contexts. This resource empowers researchers to accelerate drug discovery and enhance our understanding of protein behavior. Unlike data repositories and interaction knowledgebases, DORSSAA provides assay-native, protein-level MoA evidence with rigorous per-study FDR control, enabling context-specific target nomination, off-target deconvolution, and combination-aware discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=172 SRC="FIGDIR/small/573639v4_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@2ba68dorg.highwire.dtl.DTLVardef@1c39c19org.highwire.dtl.DTLVardef@13d5f73org.highwire.dtl.DTLVardef@45b119_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗