Biology subjects

Troyanskaya, O.

Publications and source records attributed to Troyanskaya, O..

8 recordsLinked to original sources

bioRxiv2025

CLONAL SELECTION SUPPORTED BY SINGLE CELL DNA SEQUENCING REVEALS HORMONAL ADAPTATION AND RESISTANCE IN LOCALLY ADVANCED BREAST CANCER DURING NEOADJUVANT AROMATASE INHIBITION

BackgroundThe aromatase inhibitors (AI) letrozole and exemestane, are often used in sequence in targeting ER+ breast cancers. However resistance to AI poses a major barrier to sustained clinical benefit, while the biological mechanisms underlying the phenomenon remain largely unknown. In this study, we build on our clinical NeoLetExe trial, with the aim to investigate the molecular basis of resistance to AI, by analysing subclonal evolutionary dynamics during sequential treatment. MethodsWe use whole-exome sequencing (WES) data from 11 ER+ breast cancer patients and 3 timepoints of the Neoletexe trial to reconstruct cancer cell fraction-based subclonal composition. Single-cell DNA sequencing from matched tumour samples is used for validating identified clones and variants. Subclonal variants were annotated to genes by integrating evidence from public data and ExpectoSc. Pathway enrichment analysis using Human Base was conducted. ResultsHigher cancer cell fraction clone trajectories were significantly associated with reduced treatment response (p = 0.023). Clones reconstructed by WES were validated at 81% using single-cell DNA sequencing. Clones resistant to both letrozole and exemestane demonstrated PIK3CA/AKT/mTOR signaling activation, KRAS pathway dysregulation, hedgehog signaling, and androgen receptor pathways, alongside extensive immune activation and metabolic reprogramming. Drug-specific resistance patterns showed exemestane-resistant clones enriched for epigenetic control and miRNA-mediated silencing, while letrozole-resistant clones displayed metabolic dysregulation but notably lacked immune pathway activation. In contrast, treatment-sensitive clones maintained coordinated cell cycle control, preserved DNA damage responses, and retained immune signaling capacity. Analysis of FDA-approved breast cancer targets identified actionable alterations in PIK3CA (4 patients) and AKT1 (1 patient) that persisted through AI treatment, with RNA expression analysis revealing 48 additional therapeutic targets spanning PI3K/AKT/mTOR, CDK4/6, DNA repair (BRCA1/2, ATM), and immune checkpoint pathways. ConclusionWES-based cancer cell fraction analysis successfully captured subclonal evolutionary trajectories during AI treatment, revealing drug-specific mechanisms and identifying key molecular players in endocrine therapy resistance. This work establishes a framework for precision oncology approaches by providing actionable therapeutic targets and advancing our understanding of resistance mechanisms to improve clinical outcomes in sequential AI therapy.

cancer biology↗

bioRxiv2024

Contrastive pre-training for sequence based genomics models

MotivationIn recent years deep learning has become one of the central approaches in a number of applications, including many tasks in genomics. However, as models grow in depth and complexity, they either require more data or a strategic initialization technique to improve performance. ResultsIn this project, we introduce cGen, a novel unsupervised, model-agnostic contrastive pretraining method for sequence-based models. cGen can be used before training to initialize weights, reducing the size of the dataset needed. It works through learning the intrinsic features of the reference genome and makes no assumptions on the underlying structure. We show that the embeddings produced by the unsupervised model are already informative for gene expression prediction and that the sequence features provide a meaningful clustering. We demonstrate that cGen improves model performance in various sequence-based deep learning applications, such as chromatin profiling prediction and gene expression. Our findings suggest that using cGen, particularly in areas constrained by data availability, could improve the performance of deep learning genomic models without the need to modify the model architecture. Contactogt@cs.princeton.edu Availability and implementationSource code available at github.com/ksenia007/cGen

genomics↗

bioRxiv2021

Neuron-Glia Signaling Regulates the Onset of the Antidepressant Response

Commonly prescribed antidepressants, such as selective serotonin reuptake inhibitors (SSRIs) take weeks to achieve therapeutic benefits1, 2. The underlying mechanisms of why antidepressants take weeks or months to reverse depressed mood are not understood. Using a single cell sequencing approach, we analyzed gene expression changes in mice subjected to stress-induced depression and determined their temporal response to antidepressant treatment in the cerebral cortex. We discovered that both glial and neuronal cell populations elicit gene expression changes in response to stress, and that these changes are reversed upon treatment with fluoxetine (Prozac), a widely prescribed selective serotonin reuptake inhibitor (SSRI). Upon reproducing the molecular signaling events regulated by fluoxetine3 in a cortical culture system, we found that these transcriptional changes are serotonin-dependent, require reciprocal neuron-glia communication, and involve temporally-specified sequences of autoregulation and cross-regulation between FGF2 and BDNF signaling pathways. Briefly, stimulation of Fgf2 synthesis and signaling directly regulates Bdnf synthesis and secretion cell-non-autonomously requiring neuron-glia interactions, which then activates neuronal BDNF-TrkB signaling to drive longer-term neuronal adaptations4-6 leading to improved mood. Our studies highlight temporal and cell type specific mechanisms promoting the onset of the antidepressant response, that we propose could offer novel avenues for mitigating delayed onset of antidepressant therapies.

neuroscience↗

bioRxiv2020

Specific viral RNA drives the SARS CoV-2 nucleocapsid to phase separate

A mechanistic understanding of the SARS-CoV-2 viral replication cycle is essential to develop new therapies for the COVID-19 global health crisis. In this study, we show that the SARS-CoV-2 nucleocapsid protein (N-protein) undergoes liquid-liquid phase separation (LLPS) with the viral genome, and propose a model of viral packaging through LLPS. N-protein condenses with specific RNA sequences in the first 1000 nts (5-End) under physiological conditions and is enhanced at human upper airway temperatures. N-protein condensates exclude non-packaged RNA sequences. We comprehensively map sites bound by N-protein in the 5-End and find preferences for single-stranded RNA flanked by stable structured elements. Liquid-like N-protein condensates form in mammalian cells in a concentration-dependent manner and can be altered by small molecules. Condensation of N-protein is sequence and structure specific, sensitive to human body temperature, and manipulatable with small molecules thus presenting screenable processes for identifying antiviral compounds effective against SARS-CoV-2.

biochemistry↗

bioRxiv2020

An analytical framework for interpretable and generalizable 'quasilinear' single-cell data analysis

Scaling single-cell data exploratory analysis with the rapidly growing diversity and quantity of single-cell omics datasets demands more interpretable and robust data representation that is generalizable across datasets. To address this challenge, here we developed a novel quasilinear framework that combines the interpretability and transferability of linear methods with the representational power of nonlinear methods. Within this framework, we introduce a data representation and visualization method, GraphDR, and a structure discovery method, StructDR, that unifies cluster, trajectory, and surface estimation and allows their confidence set inference. We applied both methods to diverse single-cell RNA-seq datasets from whole embryos and tissues. Unlike PCA and t-SNE, GraphDR and StructDR generated representations that both distinguished highly specific cell types and were comparable across datasets. In addition, GraphDR is at least an order of magnitude faster than commonly used nonlinear methods. Our visualizations of scRNA-seq data from developing zebrafish and Xenopus embryos revealed extruding branches of lineages from a continuum of cell states, suggesting that the current branch view of cell specification may be oversimplified. Moreover, StructDR identified a novel neuronal population using scRNA-seq data from mouse hippocampus. An open-source python library and a user-friendly graphical interface for 3D data visualization and analysis with these methods are available at https://github.com/jzthree/quasildr.

bioinformatics↗

bioRxiv2018

Selective neuronal vulnerability in Alzheimer’s disease: a network-based analysis

A major obstacle to treating Alzheimers disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, which is a key characteristic of the disease. Here we present a framework to integrate high-quality neuron-type specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for AD vulnerable and resistant neurons, identify specific genes and pathways associated with AD pathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by both amyloid accumulation and aging. Overall, our study provides a molecular framework for understanding the complex interplay between A{beta}, aging, and neurodegeneration within the most vulnerable neurons in AD.

neuroscience↗

bioRxiv2018

Minor isozymes tailor yeast metabolism to carbon availability

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time for reasons that can be unclear. We find that, in yeast, isozymes are strongly enriched in central carbon metabolism. Using a gene expression compendium, we find that many isozyme pairs show anticorrelated expression during the respirofermentative shift, suggesting roles in adapting to changing carbon availability. Building on this observation, we assign function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2s deletion is rescued by expressing the major pyruvate kinase, but only if that enzyme carries mutations mirroring PYK2s allosteric regulation. Thus, central carbon isozymes enable more precise tailoring of metabolism to changing nutrient availability.\n\nImportanceGene duplication is one of the main evolutionary drivers of new protein function. However, some gene duplicates have nevertheless persisted long-term without apparent divergence in biochemical function. Further, under standard lab conditions, many isozymes have subtle or no knockout phenotypes. These factors make it hard to assess the unique contributions of individual isozymes to fitness. We therefore developed a method to identify experimental perturbations that could expose such contributions, and applied it to yeast gene expression data, revealing a potential role for a set of yeast isozymes in adapting to changing carbon sources. Our experimental confirmation of distinct roles for two \"minor\" yeast isozymes, including one with no previously described knockout phenotype, highlight that even apparently redundant paralogs can have important and unique functions, with implications for genome-scale metabolic modeling and systems-level studies of quantitative genetics.

systems biology↗

bioRxiv2018

Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism

We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts specific regulatory effects and deleterious disease impact of genetic variants. Applying this framework to 1,790 Autism Spectrum Disorder (ASD) simplex families reveals autism disease causality of noncoding mutations by demonstrating that ASD probands harbor transcriptional (TRDs) and post-transcriptional (RRDs) regulation-disrupting mutations of significantly higher functional impact than unaffected siblings. Importantly, we detect this significant noncoding contribution at each level, transcriptional and post-transcriptional, independently and after multiple hypothesis correction. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development, and reveals a convergent genetic landscape of coding and noncoding (TRD and RRD) de novo mutations in ASD. We demonstrate that sequences carrying prioritized proband de novo mutations possess transcriptional regulatory activity and drive expression differentially, and highlight a link between noncoding mutations and IQ heterogeneity in ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD, prioritizes high impact transcriptional and post-transcriptional regulatory mutations for further study, and is broadly applicable to complex human diseases.

genomics↗