Biology subjects

Koyyalagunta, D.

Publications and source records attributed to Koyyalagunta, D..

3 recordsLinked to original sources

bioRxiv2026

Time-resolved lineage recording reveals a pre-existing, heritable cell state underlying metastatic potential

Metastasis causes most cancer deaths1,2, yet no recurrent mutation specifically drives it3,4, raising the possibility that metastatic potential is a non-genetic yet heritable cell state. Classic experiments established that metastatically predisposed subclones pre-exist within a tumor and that these predispositions are inherited over many cell divisions5, but what molecular states or factors underlie this predisposition remain unknown. While previous lineage recording studies6,7 mapped how tumors disseminate, their recording sites saturate too quickly to resolve when a lineage branched, or to attribute a state to its founder. Here we show, using a DNA Typewriter lineage recorder8 with nearly 1,000 recording sites in lung cancer cells, that metastatic potential is already present before dissemination, with colonization predicted by a pre-existing glycolytic state and further spread by expression of ENO1, a glycolytic enzyme that also moonlights as a cell-surface plasminogen receptor9. Profiling the pre-transplant cells and the post-transplantation tumors for both their transcriptomes and their lineage recordings, we reconstructed time-resolved lineage trees across three orthotopically transplanted mice. These trees trace each liver metastasis to a single founder of known pre-transplant state, dating each dissemination event from the primary lung. When every clone was scored before transplant against 349 genes recurrently heritable in vitro, both that set and the glycolytic state independently shifted a clones odds of colonizing the lung. At the gene level, sixteen genes were both heritable and predictive of colonization, and ENO1 alone also predicted which established clones spread further. Hypoxia, the program most strongly associated with phylogenetic fitness within the metastases, did not predict colonization when scored before transplant, separating niche-selected traits from the inherited cell state. Metastatic potential in this system is therefore transmitted along the lineage rather than acquired after seeding. Looking forward, we anticipate that time-resolved lineage recorders will enable the separation of the heritable and acquired components of the cellular heterogeneity seen in single-cell studies of tumor progression and drug tolerance.

cancer biology↗

bioRxiv2025

mRNABench: A curated benchmark for mature mRNA property and function prediction

Messenger RNA (mRNA) is central in gene expression, and its half-life, localization, and translation efficiency drive phenotypic diversity in eukaryotic cells. While supervised learning has widely been used to study the mRNA regulatory code, self-supervised foundation models support a wider range of transfer learning tasks. However, the dearth and homogeneity of standardized benchmarks limit efforts to pinpoint the strengths of various models. Here, we present O_SCPLOWMC_SCPLOWRNABO_SCPLOWENCHC_SCPLOW, a comprehensive benchmarking suite for mature mRNA biology that evaluates the representational quality of mature mRNA embeddings from self-supervised nucleotide foundation models. We curate ten datasets and 59 prediction tasks that broadly capture salient properties of mature mRNA, and assess the performance of 18 families of nucleotide foundation models for a total of 135K experiments. Using these experiments, we study parameter scaling, compositional generalization from learned biological features, and correlations between sequence compressibility and performance. We identify synergies between two self-supervised learning objectives, and pre-train a new Mamba-based model that achieves state-of-the-art performance using 700x fewer parameters. O_SCPLOWMC_SCPLOWRNABO_SCPLOWENCHC_SCPLOW can be found at: https://github.com/morrislab/mRNABench.

genomics↗

bioRxiv2024

Inferring cancer type-specific patterns of metastatic spread

Cancers differ in how they establish metastases. These differences can be studied by reconstructing the metastatic spread of a cancer from sequencing data of multiple tumors. Current methods to do so are limited by computational scalability and rely on technical assumptions that do not reflect current clinical knowledge. Metient overcomes these limitations using gradient-based, multi-objective optimization to generate multiple hypotheses of metastatic spread and rescores these hypotheses using independent data on genetic distance and organotropism. Unlike current methods, Metient can be used with both clinical sequencing data and barcode-based lineage tracing in preclinical models, enhancing its translatability across systems. In a reanalysis of metastasis in 169 patients and 490 tumors, Metient automatically identifies cancer type-specific trends of metastatic dissemination in melanoma, high-risk neuroblastoma, and non-small cell lung cancer. Its reconstructions often align with expert analyses but frequently reveal more plausible migration histories, including those with more metastasis-to-metastasis seeding and higher polyclonal seeding, offering new avenues for targeting metastatic cells. Metients findings challenge existing assumptions about metastatic spread, enhance our understanding of cancer type-specific metastasis, and offer insights that inform future clinical treatment strategies of metastasis.

cancer biology↗