Biology subjects

Dede, M.

Publications and source records attributed to Dede, M..

10 recordsLinked to original sources

bioRxiv2026

TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition

T cell receptor recognition of peptide-MHC depends on sequence, interface chemistry, and three-dimensional geometry, but docking geometry is often summarized at the whole-receptor level, leaving CDR3-local pose difficult to compare across structures. We introduce TCR-FramePose, a local-frame descriptor set that represents each TCR-pMHC complex as three bodies -- whole TCR, CDR3, and CDR3{beta} -- measured relative to a pMHC groove frame. For each body, FramePose decomposes the native pose into reach, offset direction on S2, and orientation on SO(3); for tangent-space analyses, these components are mapped to six coordinates per body and 18 coordinates per complex. Applied to 378 curated {beta}TCR-pMHC crystal structures, FramePose recovers known class-associated receptor-placement differences and additionally resolved whole-TCR and CDR3{beta} orientation shifts that were not captured by crossing angle. The same orientation coordinates identified reverse-polarity and off-axis outliers as distinct modes. In cross-validated association analyses, FramePose added nonredundant BSA- and affinity-associated information beyond conventional descriptors, and the modest affinity gain was concentrated in CDR3 orientation blocks which were least recoverable from conventional descriptors. Biological grouping analyses showed that shared receptor pose over peptide-MHC was organized primarily by germline V-region framework. TCRs recognizing the same peptide-MHC target favors shared FramePose geometries rather than strong receptor-specific divergence, whereas CDR3 sequence did not detectably reposition the rigid-body pose after antigen context and germline framework were fixed. MHC allele and peptide length contributed smaller adjustments, localized mainly to CDR3{beta} and groove-normal orientation axes. Finally, interface analyses showed that affinity tracked interface burial, with CDR3{beta} reach linking FramePose geometry to binding through buried surface area. Within engineered panels, mutation-level effects were panel-specific, with CDR3{beta} remodeling localizing to a recurrent interface region but varying in direction across receptors. These properties enable FramePose to serve as a geometric filter for in silico TCR-pMHC models and as a feature layer for structure-guided TCR engineering. Together, TCR-FramePose provides a nonredundant geometric layer for structure-guided TCR-pMHC analysis, linking germline-scaffolded recognition, CDR3-local pose, and interface organization without replacing sequence, contact, or energetic descriptors.

bioinformatics↗

bioRxiv2025

Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous "virtual cells" created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.

bioinformatics↗

bioRxiv2024

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

bioinformatics↗

bioRxiv2024

Metabolic Reprogramming by Mutant GNAS Creates an Actionable Dependency in Intraductal Papillary Mucinous Neoplasms of the Pancreas

ObjectiveOncogenic "hotspot" mutations of KRAS and GNAS are two major driver alterations in Intraductal Papillary Mucinous Neoplasms (IPMNs), which are bona fide precursors to pancreatic ductal adenocarcinoma. We previously reported that pancreas-specific KrasG12D and GnasR201C co-expression in p48Cre; KrasLSL-G12D; Rosa26LSL-rtTA; Tg (TetO-GnasR201C) mice ("Kras;Gnas" mice) caused development of cystic lesions recapitulating IPMNs. Here, we aim to unveil the consequences of mutant GnasR201C expression on phenotype, transcriptomic profile, and genomic dependencies. DesignWe performed multimodal transcriptional profiling (bulk RNA sequencing, single cell RNA sequencing, and spatial transcriptomics) in the "Kras;Gnas" autochthonous model and tumor-derived cell lines (Kras;Gnas cells), where GnasR201C expression is inducible. A genome-wide CRISPR/Cas9 screen was conducted to identify potential vulnerabilities in KrasG12D;GnasR201Cco-expressing cells. ResultsInduction of GnasR201C - and resulting G(s)alpha signaling - leads to the emergence of a gene signature of gastric (pyloric type) metaplasia in pancreatic neoplastic epithelial cells. CRISPR screening identified the synthetic essentiality of glycolysis-related genes Gpi1 and Slc2a1 in KrasG12D;GnasR201C co-expressing cells. Real-time metabolic analyses in Kras;Gnas cells and autochthonous Kras;Gnas model confirmed enhanced glycolysis upon GnasR201C induction. Induction of GnasR201C made KrasG12D expressing cells more dependent on glycolysis for their survival. Protein kinase A-dependent phosphorylation of the glycolytic intermediate enzyme PFKFB3 was a driver of increased glycolysis upon GnasR201C induction. ConclusionMultiple orthogonal approaches demonstrate that KrasG12D and GnasR201C co-expression results in a gene signature of gastric pyloric metaplasia and glycolytic dependency during IPMN pathogenesis. The observed metabolic reprogramming may provide a potential target for therapeutics and interception of IPMNs. SUMMARYO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIActivating "hotspot" mutations of KRAS and GNAS are found in a majority of Intraductal Papillary Mucinous Neoplasms (IPMNs). C_LIO_LIExpression of mutant KRAS and GNAS drives development of IPMN-like cystic lesions in the murine pancreas that eventually progress to pancreatic ductal adenocarcinoma (PDAC). C_LI What this study addsO_LIMutant GNAS and the resulting aberrant G(s)alpha signaling drives a transcriptional signature of gastric (pyloric type) metaplasia in IPMNs with mucin production. C_LIO_LIAberrant G(s)alpha signaling enhances glycolysis via protein kinase A-dependent phosphorylation of the glycolytic enzyme PFKFB3. C_LIO_LIEnhanced glycolysis in KRAS;GNAS-mutated IPMN cells is validated via multiple orthogonal approaches in vitro and in vivo and represents an actionable metabolic vulnerability. C_LI How this study might affect research, practice or policyO_LIThe present study provides mechanistic insight into how aberrant G(s)alpha signaling alters the biology of Kras-mutant pancreatic epithelial neoplasia through metaplastic and metabolic reprogramming. C_LIO_LITargeting glycolysis in IPMNs may represent both a therapeutic avenue as well as an opportunity for intercepting progression to invasive cancer. C_LI

cancer biology↗

bioRxiv2022

Characterizing metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the TME (Tumor Microenvironment) is of emerging importance in ongoing cancer research and therapy development. Recent developments in mass spectrometry (MS)-based technologies allow simultaneous characterization of metabolic features of tumor, stroma, and immune cells in the TME. However, they only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and have recently been extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable the results are, particularly in the context of tissue TME characterization. To investigate this question and fill the analytical gaps, we developed a computational program METAFlux (METAbolic Flux balance analysis), which extends the FBA framework to infer metabolic fluxes from either bulk or single-cell transcriptomic TME data. We benchmarked the prediction accuracy of METAFlux using the exometabolomics data generated on the NCI-60 cell lines and observed significant improvement over existing approaches. We tested METAFlux in bulk RNA-seq data obtained from various tumor types including those in the TCGA. We validated previous knowledge, e.g., lung squamous cell carcinoma (LUSC) has higher glucose uptake than lung adenocarcinoma (LUAD). We also found a novel subset of LUAD samples with unique metabolic profiles and distinct survival outcome. We further examined METAFlux on scRNA-seq data obtained from coculturing tumor cells with CAR-NK cells and observed high consistency between the predicted and the experimental (i.e., Seahorse extracellular) flux measurements. Throughout our investigation, we discovered various modes of metabolic cooperation and competition between various cell-types in TMEs, which could lead to further target discovery and development.

bioinformatics↗

bioRxiv2022

Recovering false negatives in CRISPR fitness screens with JLOE

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a [~]20% false negative rate, in addition to library-specific false negatives. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues improves our understanding of core essential genes and suggest only a small number of lineage-specific essential genes, enriched for transcription factors that define pathways of tissue differentiation. To recover false negatives, we introduce a method, Joint Log Odds of Essentiality (JLOE), which builds on our prior work with BAGEL to selectively rescue the false negatives without an increased false discovery rate.

bioinformatics↗

bioRxiv2020

Discovery of novel tumor suppressors from CRISPR screens reveals lipid-sensitive subtype of AML

CRISPR knockout screens in hundreds of cancer cell lines have revealed a substantial number of context-specific essential genes that, when associated with a biomarker such as lineage or oncogenic mutation, offer candidate tumor-specific vulnerabilities for targeted therapies or novel drug development. Data-driven analysis of knockout fitness screens also yields many other functionally coherent modules that show emergent essentiality or, in rarer cases, the opposite phenotype of faster proliferation. We develop a systematic approach to classify these suppressors of proliferation, which are highly enriched for tumor suppressor genes, and define a network of 145 genes in 22 discrete modules. One surprising module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of AML lines, which we call Fatty Acid Synthesis/Tumor Suppressor (FASTS) cells. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe fitness phenotype for the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirmed biochemically. Overexpression of genes in this module is associated with a survival advantage in an age-matched cohort of AML patients, suggesting the gene cluster driving an in vitro phenotype may be associated with a novel, clinically relevant subtype.

genetics↗

bioRxiv2020

Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens

Major efforts on pooled library CRISPR knockout screening across hundreds of cell lines have identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens are very low compared to the number of constitutively expressed genes in a cell, raising the question of why there are so few essential genes. Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observed that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. We investigated paralog buffering through systematic dual-gene CRISPR knockout screening by testing algorithmically defined ~400 candidate paralog pairs with the enCas12a multiplex knockout system in three cell lines. We observed 24 synthetic lethal paralog pairs which have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions were present in at least two out of three cell lines and 14 of 24 (58%) were present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Together these observations strongly suggest that paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes due to genetic buffering in monogenic CRISPR-based mammalian functional genomics approaches.

systems biology↗

bioRxiv2020

Biases and Blind-Spots in Genome-Wide CRISPR Knockout Screens

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a [~]20% false negative rate, beyond library-specific false negatives previously described. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues suggest only a small number of lineage-specific essential genes and that these genes are highly enriched for transcription factors that define pathways of tissue differentiation. In addition, we show that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. Together these observations strongly suggest that functional buffering masks single knockout phenotypes for a substantial number of genes, describing a major blind spot in CRISPR-based mammalian functional genomics approaches.

systems biology↗

bioRxiv2018

Hierarchical organization of the human cell from a cancer coessentiality network

Genetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a \"coessentiality network,\" with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

systems biology↗